Week 5 – Assess an Organization’s Structure and Propose Changes

Use SC Johnson A Family Company as organization

Week 5 Assignment:

A common approach for assessing an organization’s structure is examining organic and mechanistic characteristics. This assessment tool has been used by researchers and others who study organizational behavior. (Dust, Resick, & Mawritz, 2014). Thinking about your current organization or an organization familiar to you, assess its current structure based on the mechanistic and organic approach. Create an organizational diagram that accurately depicts the current organizational structure (using PowerPoint or another drawing tool). On a scale of 1 to 10, rate your perception of the following characteristics:

· Mechanistic vs. Organic

· Task Role: Rigid or Flexible

· Communication: Vertical or Multidirectional

· Decision Making: Centralized or Decentralized

· Sensitivity to Environment: Closed or Open

After completing your analysis, prepare a brief summary analyzing your results and determine whether you were surprised by your responses. Support your response with examples. Then, identify which aspects of your organization’s structure you would change. Create an updated organizational chart that accurately depicts the new organizational structure and explain why and how this new design reflects an improvement of the organization’s ability to meet its goals. End your paper by explaining how management might assess whether the proposed new organizational structure is working as intended after 6-12 months. What key performance indicators would you measure and why?

Support your assignment with at least three scholarly resources. In addition to these specified resources, other appropriate scholarly resources, including older articles, may be included.

Length: 4-5 pages, not including title and reference pages

Your assignment should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards.

References:

https://www.mckinsey.com/business-functions/organization/our-insights/getting-organizational-redesign-right

https://www.linkedin.com/pulse/come-hr-prove-me-wrong-2017-dave-millner

What Is The Right Organization Design For Your Corporation? And What Test To Use To Know If You've Got The Right One?

http://www.centerod.com/

Week 5 – Rubric

Criteria

Content (8 Points)

Points

The organization structure is rated and an analysis presented based on the following characteristics
·  Mechanistic vs. Organic
·  Task Role: Rigid or Flexible
·  Communication: Vertical or Multidirectional
·  Decision Making: Centralized or Decentralized
·  Sensitivity to Environment: Closed or Open

Prepared a brief summary analyzing your results and used relevant examples.

Current and new organizational charts are included along with an explanation of desired changes and how management may assess whether it has desired effects

Organization (2 Points)

Organized and presented in a clear manner. Included a minimum of Three (3) scholarly references, with appropriate APA formatting applied to citations and paraphrasing; 3-4 pages in length.

Total

Transformational leadership, psychological
empowerment, and the moderating role of
mechanistic–organic contexts

SCOTT B. DUST1*, CHRISTIAN J. RESICK2 AND MARY BARDES MAWRITZ2
1Department of Management, Marketing, and International Business, College of Business and Technology, Eastern
Kentucky University, Richmond, Kentucky, U.S.A.
2Department of Management, LeBow College of Business, Drexel University, Philadelphia, Pennsylvania 19104, U.S.A.

Summary The current study examines the empowering effects of transformational leaders and the extent to which these
effects differ across mechanistic–organic organizational contexts. Psychological empowerment is hypothesized
to provide a comprehensive motivational mechanism explaining the relationships between transformational lead-
ership and employee job-related behaviors. In addition, the relationships between transformational leadership,
employee psychological empowerment, and job-related behaviors are hypothesized to be stronger in organiza-
tions with more organic as opposed to mechanistic structures. Results based on a cross-organizational sample
of employees and their immediate supervisors provide support for the hypothesized relationships. Psychological
empowerment mediated relationships between transformational leadership and employee task performance and
organizational citizenship behaviors. The mediating role of psychological empowerment was then found to be
conditional upon mechanistic–organic contexts. More specifically, organic structures enhanced, whereas
mechanistic structures constrained, the empowering influence of transformational leaders. In highly mechanistic
contexts, the indirect effects were no longer statistically significant. Implications for theory, research, and orga-
nizational management are discussed. Copyright © 2013 John Wiley & Sons, Ltd.

Keywords: transformational leadership; psychological empowerment; organizational structure

Over the past 25 years, transformational leadership has emerged as a popular theoretical lens to examine the linkages
between leadership behaviors and leadership effectiveness (Hiller, DeChurch, Murase, & Doty, 2011). As a result, a
substantial body of empirical evidence that associates transformational leadership behaviors with the performance of
individuals, units, and organizations has accumulated (DeRue, Nahrgang, Wellman, & Humphrey, 2011; Judge &
Piccolo, 2004). Through creating and communicating a compelling vision, role modeling a commitment to that
vision, promoting outside-the-box thinking, and taking the wants, needs, and talents of their staff into consideration,
transformational leaders inspire their employees to put forth the effort needed to accomplish extraordinary goals
(Bass, 1985; Bass & Avolio, 1995). Motivation, therefore, is a key psychological mechanism through which trans-
formational leaders promote individual and unit effectiveness (Bass & Steidlmeier, 1999; Bono & Judge, 2003; Ilies,
Judge, & Wagner, 2006; Piccolo & Colquitt, 2006; Shamir, House, & Arthur, 1993). Surprisingly, however, rela-
tively few studies have examined employee motivation as a linking mechanism between transformational leadership
behaviors and employee outcomes (e.g., Bono & Judge, 2003; Piccolo & Colquitt, 2006).
As such, we investigate psychological empowerment as a mediator of the relationship between transformational lead-

ership and employee performance-related behaviors. Psychological empowerment is a comprehensive motivational pro-
cess that embodies a self-expressive and intrinsically motivated orientation toward work. Psychologically empowered
individuals are competent in their abilities, exhibit self-determined orientations, and view their work as having meaning
and impact (Conger & Kanungo, 1988; Spreitzer, 1995; Thomas & Velthouse, 1990). Employees who form a sense of

*Correspondence to: Scott B. Dust, Department of Management, Marketing, and International Business, College of Business and Technology,
Eastern Kentucky University, 521 Lancaster Ave., BTC 011, Richmond, Kentucky 40475, U.S.A. E-mail: scott.dust@eku.edu

Revised 15 May 2013, Accepted 30 September 2013

Journal of Organizational Behavior, J. Organiz. Behav. 35, 413–433 (2014)
Published online 25 October 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.1904

R
esearch

A
rticle

empowerment approach their work proactively and are resilient in their efforts (Spreitzer, 1995; Thomas & Velthouse,
1990) because they view their work as serving an important purpose (Blau, 1964; Conger & Kanungo, 1988).
Transformational leadership theory has long pointed to the empowering effects of transformational leaders. For

example, Burns (1978) noted that “the function of [transformational] leadership is to engage followers, not merely
to activate them” (p. 461). Transformational leaders engage and empower followers by promoting identification with
the organization’s goals, values, and members (Kark, Shamir, & Chen, 2003; Shamir et al., 1993) and by activating
intrinsic concerns regarding self-development, achievement, and fulfillment (Bass & Steidlmeier, 1999).
In addition to suggesting the motivational mechanism of psychological empowerment, extant research has suggested

that some organizational settings may enable or constrain the motivational influence of transformational leaders (Tosi,
1991; Yukl, 1999). For example, drawing on Mischel’s (1973) theory of situational strength, Shamir et al. (1993) argued
that the motivational effects of transformational leadership should be enhanced in organic organizations (e.g., fluid
structures that emphasize reciprocal communication and decentralized decision making) because the latitude for indi-
vidual expression is high and there are few formal rules to constrain behavior. In contrast, more mechanistic organiza-
tions (e.g., rigid and formally structured organizations with centralized decision making and authority) are likely to
constrain the motivational effects of transformational leaders because the latitude for individual expression is low
and role expectations are clear. However, surprisingly little empirical attention has been devoted to determining how
the motivational effects of transformational leaders vary across the mechanistic–organic continuum.
Thus, the purpose of the current study is to gain a more comprehensive understanding of the empowering influ-

ence of transformational leaders by determining how mechanistic–organic contexts play a role in shaping the rela-
tionships between transformational leadership, employee psychological empowerment, and performance-related
behaviors. To date, prior studies have independently examined the motivational effects of transformational leaders
and the contextualization of transformational leader influence. However, the extent to which organizational contexts
affect the motivational influence of transformational leaders remains unclear. The current study seeks to provide a
nuanced understanding of the motivational effects of transformational leaders by demonstrating the constraining
and enabling effects of organizational contexts. Likewise, the current study offers insights into why organizational
structures enhance or constrain the relationships between transformational leadership and employee behavior by
highlighting the mediating role of psychological empowerment. The current study also contributes to the psycholog-
ical empowerment literature by providing insights into the joint effects of leadership and structural characteristics in
working to promote or restrict employee psychological empowerment.

Transformational Leadership and Psychological Empowerment

Transformational leadership

Transformational leadership is generally conceptualized as a set of interrelated behaviors including idealized influ-
ence, inspirational motivation, intellectual stimulation, and individual consideration (Bass, 1985). By providing
idealized influence, leaders role model a commitment to high standards and achieving the organization’s vision.
Through inspirational motivation, leaders use emotional appeals to offer a compelling vision of the future and
inspire followers to commit to a shared vision. Leaders use intellectual stimulation to encourage followers to
challenge the status quo and to solve problems using novel ideas. Using individual consideration, leaders attend
to the individualized needs of followers by listening, mentoring, and giving feedback. By engaging in these actions,
transformational leaders motivate their staff to contribute extraordinary efforts and achieve extraordinary goals
(Avolio & Bass, 1988; Bass, 1985; Podsakoff, MacKenzie, Moorman, & Fetter, 1990).
Shamir et al. (1993) proposed that transformational leaders communicate salient messages to employees about the

meaningfulness and impact of the group’s mission and how employees’ efforts contribute to the group’s success. By
doing so, transformational leaders encourage their followers to internalize the group’s goals and make organizational
membership a salient aspect of their working self-concepts. As employees contribute efforts toward achieving the

414 S. B. DUST ET AL.

organization’s vision, they experience a heightened sense of self-worth through the organization’s successes (Liao &
Chuang, 2007). Thus, transformational leadership behaviors alter employees’ perceptions in ways that foster heightened
motivation, whereby employees feel a proactive orientation to perform well through participation in organizational goals.

Psychological empowerment

Building on prior empowerment theory (e.g., Conger & Kanungo, 1988; Thomas & Velthouse, 1990), Spreitzer (1995)
defined psychological empowerment as a form of intrinsic motivation that reflects a proactive orientation toward and sense
of control over work that is “manifested in four cognitions: meaning, competence, self-determination, and impact” (p. 1444).
Meaning represents the extent to which personal values and beliefs fit the demands of a job (Hackman & Oldham, 1980).
Impact is the degree to which an individual believes he or she can influence the strategic direction, operational processes,
and outcomes of the unit or organization (Ashforth, 1989). Self-determination involves a sense of autonomy and control over
the initiation, regulation, and continuance of work behaviors (Deci, Connell, & Ryan, 1989). Finally, competence refers to
beliefs about the extent to which one possesses the proficiencies needed to be successful at work (Bandura, 1989).
A recent meta-analysis found that, across studies, leadership had one of the strongest effect sizes (rc = .36) with

psychological empowerment (Seibert, Wang, & Courtright, 2011). The meta-analysis of Seibert et al. (2011) further
found that effect sizes for the relationships between psychological empowerment and task performance (rc = .36) and
organizational citizenship behavior (OCB; rc = .38) were consistently positive. From these findings and the theoret-
ical emphasis on the empowering effects of transformational leaders (e.g., Bass, 1985; Bass & Steidlmeier, 1999),
we suggest that psychological empowerment represents a comprehensive mechanism for understanding the motiva-
tional effects of transformational leaders and the linkages with followers’ job performance and citizenship behaviors.
Transformational leaders have the ability to influence their follower’s perceptions of their role within the organi-

zation (Piccolo & Colquitt, 2006; Wright, Moynihan, & Pandey, 2012) and therefore may have a particularly impor-
tant relationship with employee psychological empowerment (Spreitzer, 2008). To date, two studies provide some
empirical understanding of the empowering influence of transformational leaders. Kark et al. (2003), for one, found
that transformational leaders had an empowering effect on followers via followers’ social identification with the
group as opposed to personal identification with the leader. In addition, psychological empowerment has been found
to link transformational leadership with organizational commitment (Avolio, Zhu, Koh, & Bhatia, 2004). However,
the role of psychological empowerment as a motivational mechanism linking transformational leadership with
employee performance behaviors, such as task performance or citizenship, remains unclear.

Psychological empowerment as a linking mechanism

We draw upon the self-concept-based theory of transformational leadership (Shamir et al., 1993) and sensemaking
theory (Weick, 1993, 1995) to examine the linkages between transformational leadership, psychological empower-
ment, and job-related behaviors. The self-concept-based theory of transformational leadership helps explain the in-
fluence of transformational leaders on followers through altering employee self-concept (Shamir et al., 1993), which
is the collection of beliefs about oneself including self-image, self-worth, self-esteem, and the ideal self (Rogers,
1959). Transformational leaders are optimistic about the future and enthusiastic and confident in expressing what
needs to be accomplished (Avolio & Bass, 1988), which encourages employees to view the organization’s vision
as meaningful (Arnold, Arad, Rhoades, & Drasgow, 2000) and to perceive their own work as making a contribution
toward achieving the organization’s goals (Piccolo & Colquitt, 2006). In addition, by providing individual support,
spending time teaching and coaching, and developing employees’ strengths, transformational leaders help em-
ployees become self-aware of their full potential and encourage them to believe in their abilities (Dvir, Eden, Avolio,
& Shamir, 2002), which should enhance perceptions of competence. Also, through a transformational leader’s enthu-
siastic and optimistic expressions of confidence, employees feel competent in their ability to perform and master tasks

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 415

(Ashkanasy & Tse, 2000). Finally, transformational leaders encourage employees to reexamine critical assumptions,
challenge the status quo, and provide input in solving difficult problems (Jung, Chow, & Wu, 2003), which conveys
to followers that they have autonomy over their work and a legitimate impact in work initiatives and the direction of
the organization. Thus, transformational leadership behaviors alter an employee’s self-concept by fostering an em-
ployee’s psychological sense of the meaning and impact of their work and of their competence and self-determination
in completing their work, which encompass the four facets of psychological empowerment described earlier.
Transformational leaders also manage the meaning of their employees’ work by engaging in sensemaking (Weick,

1993, 1995). Sensemaking is a social construction process (Berger & Luckmann, 1966) through which individuals
interpret organizational situations and form perceptions of the meaning of an event (Weick, Sutcliffe, & Obstfeld,
2005). Leaders are critical to sensemaking processes because they monitor the environment, interpret issues and
events, and construct meaning, which influences employee interpretations “toward a preferred redefinition of orga-
nizational reality” (Gioia & Chittipeddi, 1991, p. 442). Leader narratives are an integral component of sensemaking
processes (Cunliffe & Coupland, 2012; Weick, 2012). Narratives that are conveyed in an animated manner help to
capture and sustain the attention of staff (Gioia & Chittipeddi, 1991; Maitlis, 2005; Zaccaro, Rittman, & Marks,
2001), whereas narrative content that is structured and organized enables staff to form a comprehensive understand-
ing of the meaning of events (Maitlis, 2005).
Transformational leaders engage in sensemaking by framing organizationally driven initiatives as necessary adap-

tations that lead to mutual benefits for employees and the organization (Herold, Fedor, Caldwell, & Liu, 2008;
Nemanich & Keller, 2007). By embracing challenges, remaining optimistic, and encouraging employees to look
for creative solutions to problems, transformational leaders help employees understand the impact and importance
of their work, thereby “managing the meaning” of the employees’ experience (Piccolo & Colquitt, 2006; Smircich
& Morgan, 1982). As transformational leaders discuss with employees the role they play in the organization’s suc-
cess, explanations are framed in an inspired and idealized way, influencing employees to view their work as more
meaningful and impactful (Bono & Judge, 2003; Piccolo & Colquitt, 2006; Purvanova, Bono, & Dzieweczynski,
2006; Sparks & Schenk, 2001). Transformational leaders also have a unique approach to making sense of work
obstacles. For one, transformational leaders frame work obstacles as opportunities for learning, development, and
growth (Sosik & Godshalk, 2000; Sosik, Godshalk, & Yammarino, 2004), which in turn, fosters feelings of em-
ployee competence. Second, transformational leaders frame the method in which employees overcome work obsta-
cles as being opportunities to express individual strengths and insight, thereby encouraging employees to approach
their work with self-determination. Hence, transformational leaders participate in sensemaking by framing an
employee’s work as meaningful and impactful and by framing work obstacles in ways that promote feelings of
competence and self-determinism, all of which are cognitive manifestations of psychological empowerment.
Understanding the empowering influence of transformational leaders is important for organizations because

psychological empowerment is related to increased employee task performance and citizenship behaviors. As
employees find meaning in their work and recognize that their work has a tangible impact on others, they have a
stronger internal drive to deliver a better work product (Grant, 2008). Also, when employees feel a sense of compe-
tence and self-determination, they pursue their work initiatives with confidence, and their work becomes intrinsically
important. Through these cognitive manifestations of psychological empowerment, employees become resilient in
their work tasks and feel comfortable taking initiative, which results in high levels of performance (Thomas &
Velthouse, 1990). Psychologically empowered employees also approach their work proactively, feel responsible
for constructive change, and exert extra effort toward organizational goals, resulting in citizenship behaviors
(Conger & Kanungo, 1988; Spreitzer, 1995).
Transformational leaders may be particularly apt at developing employee psychological empowerment. Through

their inspiring narratives of collective purpose and compelling vision of the future, employees manifest cognitions of
meaning and impact, and through their individualized coaching and promotion of creative problem solving, em-
ployees manifest cognitions of competence and self-determinism (Grant, 2012). Thus, it is through these cognitions
of psychological empowerment that transformational leaders motivate employees to higher levels of performance
and citizenship behaviors. Hence, we propose the following hypothesized relationships, which are depicted in Figure 1.

416 S. B. DUST ET AL.

Hypothesis 1: Psychological empowerment mediates the relationship between supervisor transformational leader-
ship and employee task performance.

Hypothesis 2: Psychological empowerment mediates the relationship between supervisor transformational leader-
ship and employee (a) interpersonal-focused organizational citizenship behavior (OCBI; H2a) and (b) organiza-
tion-focused organizational citizenship behavior (OCBO; H2b).

Organizational structure and the transformational leadership process

Leaders function within the context of their organizations (House, Rousseau, & Thomas-Hunt, 1995; Johns, 2006),
which may enhance or constrain their efforts to empower employees (Katz & Kahn, 1966). Structure is an organi-
zational contextual factor that signals the types of behaviors that are expected and the forms of leadership that are
endorsed or rejected (Ambrose & Schminke, 2003; Burns & Stalker, 1961; Dickson, Resick, & Hanges, 2006a).
Context also influences how much discretion employees believe they have in making decisions, forming plans,
and executing tasks (Hambrick & Finkelstein, 1987; Finkelstein & Hambrick, 1990).
Burns and Stalker (1961) offered an enduring and widely used perspective on organizational structures, proposing that

structures exist along a continuum ranging from mechanistic to organic forms. According to Burns and Stalker (1961),
mechanistic structures seek to gain operating efficiencies through role specialization and centralized decision making.
Employees have a clear understanding of the scope of their job responsibilities, and jobs are layered in a hierarchy that
offers a clear chain of command. Downward communication is the norm, whereas upward communication occurs less
frequently. Also, behaviors of organizational members are expected to follow specific guidelines, which are illustrated
through formally outlined policies, practices, and procedures. Hence, mechanistic structures are strong environments that
provide explicit cues regarding expected behavior (Mischel, 1973), which suppress managerial discretion (Hambrick &
Finkelstein, 1987) and provide limited latitude of action for leaders and employees (Shamir & Howell, 1999).
On the other end of the continuum, Burns and Stalker (1961) proposed that organizations with organic structures seek

to be adaptable to environmental conditions by creating roles that are more fluid and broadly defined than those in mech-
anistic organizations. Organic organizations are flat, with reciprocal communication patterns that flow upward as well as
downward. Behaviors are guided by shared values and goals, as opposed to specific policies or procedures. Thus, or-
ganic structures are weak environments that offer little guidance on specific role expectations (Mischel, 1973), provide
a high level of managerial discretion (Hambrick & Finkelstein, 1987), and offer latitude of action for both leaders and
employees (Shamir & Howell, 1999). As such, we expect that mechanistic and organic organizational contexts play a
role in shaping both the leader influence and individual motivational processes that occur within them.

Figure 1. Model of hypothesized relationships depicting the mediating role of psychological empowerment and the moderating
role of organizational structure

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 417

In organic, as opposed to mechanistic organizations, transformational leaders have a higher likelihood of success-
fully empowering employees because they have a high level of managerial discretion in their execution of transfor-
mational leadership behaviors (Fredrickson, 1986). Additionally, employees should be more likely to gain a sense of
psychological empowerment in response to the motivational efforts of transformational leaders because they see that
the environment tolerates and even encourages empowered behavior (Oldham & Hackman, 1981). The lack of ex-
plicit behavioral expectations as well as the communication patterns inherent in organic structures allows employees
to feel they can accept their leaders’ motivational influence and change their level of empowerment and subsequent
behaviors in accordance with this influence (Courtright, Fairhurst, & Rogers, 1989). In turn, empowered employees
are intrinsically motivated to take initiative, complete assignments with a sense of purpose, and be good organiza-
tional citizens. Therefore, we expect that the characteristics of organically structured organizations provide a context
that enhances transformational leaders’ efforts to psychologically empower their employees.
At the other end of the continuum, mechanistic organizations signal to members that roles are expected to be

performed according to set standards to promote consistency and efficiencies. Work within mechanistic organiza-
tions is highly specialized, and rewards are contingent on completing well-defined tasks (Burns & Stalker, 1961).
These organizational structures may limit errors and create efficiencies, but they reduce freedom in decision making
(Weber, 1947), leaving members with limited opportunities for self-expression in their work or latitude in deciding
how to approach work initiatives. As a result, leaders have a limited amount of managerial discretion and may see
fewer opportunities to empower employees or limited utility in doing so. Additionally, employees may be less likely
to form a sense of empowerment because the structure requires participation in narrowly defined tasks according to
specific standards. When work is guided by procedures and instructions, employees may be less likely to respond to
transformational leaders’ efforts at encouraging social identification or seeing the meaning or impact of their work
(Oldham & Hackman, 1981). As a result, when employees work for transformational leaders in more mechanistic
organizations, the environment is less tolerant of self-expressive behavior and employees are less likely to be psycho-
logically empowered, less motivated to take initiative or approach their work with meaning, and have fewer opportuni-
ties to engage in discretionary citizenship behaviors. Therefore, we expect that the characteristics of mechanistically
structured organizations provide a context that constrains the empowering influence of transformational leaders.

Hypothesis 3: Organizational structure moderates the mediated relationship between transformational leadership
and employee task performance such that relationships are stronger in organically structured organizations.

Hypothesis 4: Organizational structure moderates the mediated relationship between transformational leadership
and employee (a) interpersonal-focused organizational citizenship behavior (OCBI; H4a) and (b) organization-
focused organizational citizenship behavior (OCBO; H4b) such that relationships are stronger in organically
structured organizations.

Method

Sample and procedure

A total of 306 individuals participated in this study including 153 employee–supervisor matched pairs. Participants
represented different organizations from a variety of industries (27 percent service, 19 percent financial/insurance,
13 percent education, 12 percent retail, 10 percent health care, 8 percent manufacturing, and 11 percent other)
located throughout the Northeastern United States. Employee participants were predominantly female (53 percent)
with a mean age of 21.4 years and an average of 1.1 years of experience with their employing organization. Super-
visors were also predominantly female (54 percent) with a mean age of 42.3 years and a mean of 9.7 years of

418 S. B. DUST ET AL.

experience. Employees represented a variety of job titles (e.g., administrative assistant, assistant project manager,
communications associate, loan officer’s assistant, network engineer, pharmacy technician, software tester, and
teller) across a diverse set of functional areas including entry level (21 percent), analyst (18 percent), customer
service (10 percent), assistant level (9 percent), assistant management (8 percent), sales (7 percent), management
(6 percent), and non-specified (21 percent).
Participants were recruited from an upper-level, undergraduate organizational behavior course at a large

northeastern university in the United States. Potential participants had to have worked for their current organization
20 or more hours per week and with their current supervisor for a minimum of six consecutive months to be eligible
to participate. Eligible students who were interested in participating in the study were asked to invite their immediate
supervisor to also participate by completing a brief questionnaire. Participating students received course extra credit.
A total of 480 students were invited to participate in the research; 174 students participated in the study, and re-
sponses were received from 167 supervisors. Complete sets of responses were received from 153 pairs for a response
rate of 31.9 percent.
Employee participants were instructed to go to a secure website to complete the survey at a time and location of

their choice. Similar survey instructions for supervisors were relayed through employee participants. Employees
completed demographics, transformational leadership, and psychological empowerment measures. Supervisors
completed the organizational structure measures and rated employee task and citizenship performance. As a check
to determine if employee and supervisor surveys were completed by different people, we verified that IP addresses
were different for employee and corresponding supervisor surveys. In addition, participants were expressly warned
that falsifying employee or supervisor surveys would be a violation of the university’s academic honesty and code of
conduct policies. Finally, to ensure accurate responses and minimize socially desirable responding, statements
regarding confidentiality and anonymity were clearly and repeatedly stated. A number of researchers have used
similar approaches when collecting data (e.g., Grant & Mayer, 2009; Greenbaum, Mawritz, & Eissa, 2012; Mayer,
Kuenzi, Greenbaum, Bardes, & Salvador, 2009; Moore, Detert, Treviño, Baker, & Mayer, 2012; Morgeson &
Humphrey, 2006).

Measures

Transformational leadership
Employees rated their supervisors’ transformational leadership behaviors (α = .96) using 21 items from the Multifactor
Leadership Questionnaire 5X Short (Bass & Avolio, 1995). A sample item is “Talks optimistically about the future.”
Employees completed the transformational leadership and empowerment measures using a 7-point response scale,
ranging from 1 (strongly disagree) to 7 (strongly agree).

Psychological empowerment
Employees indicated their level of psychological empowerment (α = .87) by completing Spreitzer’s (1995) 12-item
scale. A sample item includes “I have considerable opportunity for independence and freedom in how I do my job.”

Organizational structure
Supervisors completed Khandwalla’s (1976/1977) seven-item organizational structure measure (α = .88) by indicat-
ing the degree to which one of two paired statements best describes their organization’s structure. Higher scores
indicated that the structure was perceived as more organic, whereas lower scores indicated that the structure was
perceived as more mechanistic. All items follow the following stem: “In general, the management philosophy in
my organization favors…” A sample item is “Tight formal control of most operations by means of sophisticated
control and information systems” versus “Informal control; heavy dependence on informal relationships and the
norm of cooperation for getting things done.”

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 419

Task performance
Supervisors rated employee task performance (α = .83) using William and Anderson’s (1991) seven-item measure. A
sample item is “Engages in activities that will directly affect his/her performance.” Supervisor completed both the task
performance and citizenship behavior measures using a 7-point response scale, ranging from 1 (strongly disagree) to 7
(strongly agree).

Organizational citizenship behavior
Supervisors rated employees’ citizenship behaviors using Lee and Allen’s (2002) OCB measure. Four items
assessed interpersonal-focused OCBs (α = .86). A sample item is “[The employee] helps others who have been
absent.” Four items assessed organization-focused OCBs (α = .90). A sample item is “[The employee] attends func-
tions that are not required but that help the organizational image.”

Controls
We controlled for employee age and gender because these variables may affect employee performance variables. In
particular, age and performance are thought to have an inverted-U-shaped relationship (Waldman & Avolio, 1986),
and gender may affect engagement in citizenship behaviors (Kidder, 2002). We also controlled for the number of
months the employee and supervisor had worked together because greater tenure may influence perceptions of rela-
tionship quality, which may inflate perceptions of psychological empowerment (Chen, Kirkman, Kanfer, Allen, &
Rosen, 2007; Wat & Shaffer, 2005). Finally, we controlled for organizational size as larger organizations may trend
toward mechanistic structures to build efficiencies (Child, 1972).

Analytical approach

To assess the indirect effect of transformational leadership on employee performance and OCBs through psycholog-
ical empowerment (Hypotheses 1 and 2), we used hierarchical multiple regression along with a bootstrap algorithm
procedure (Preacher & Hayes, 2008). Bootstrapping is a nonparametric approach, which imposes no assumptions
regarding distribution shape. The bootstrapping procedure assumes a full-mediation model and derives an empirical
sampling distribution to compute confidence intervals (CIs) of indirect effects that are considered to be more accu-
rate than coefficients derived from regression methods (Shrout & Bolger, 2002). We used macros created by
Preacher and Hayes (2008) and constructed bias-corrected CIs around the product coefficient of the mediated effect;
mediation is supported when the 95 percent CIs around the product coefficients do not include zero. Specific to our
analysis, 5000 iterations (with replacement) were used, and the indirect effect was computed and retained for each
sample. The point estimate of the indirect effect is the mean computed over the samples, and the estimated standard
error is the standard deviation of the estimates. To test the moderated-mediation model (Hypotheses 3 and 4), we
used Preacher, Rucker, and Hayes’ (2007) macro, which gives a statistical significance test of the conditional indi-
rect effect of transformational leadership on each of the dependent variables via psychological empowerment at var-
ious levels of the moderator.

Results

Prior to testing the hypothesized relationships, we first conducted a confirmatory factor analysis to test the expected
factor structure of the main study variables. The expected six-factor model fit the data well (χ2(89) = 148.09, p < .01, RMSEA = 0.06, CFI = 0.97) and offered a significant improvement in chi-squares over a five-factor model combin- ing interpersonal and organizational citizenship behaviors (χ2(94) = 200.64, p < .01, RMSEA = 0.09, CFI = 0.95, Δχ2(5) = 52.55, p < .01) or a four-factor model combining interpersonal and organizational citizenship behaviors and task performance (χ2(98) = 388.85, p < .01, RMSEA = 0.13, CFI = 0.85, Δχ2(9) = 240.76, p < .01). Next, we

420 S. B. DUST ET AL.

calculated the average variance extracted (AVE) for each latent construct following procedures set forth by Fornell
and Larcker (1981). The AVE estimates the variance explained by a latent construct; AVE values above 0.50 indi-
cate that the explained variance exceeds variance due to measurement error. The AVE values can then be used to
evaluate discriminant validity among the study variables. Sufficient discriminant validity is said to exist if the square
root of the AVE of each construct exceeds the zero-order correlations between the latent constructs (Gefen & Straub,
2005). As illustrated in Table 1, the AVE for each construct was greater than 0.50, and the square root of the AVE
values exceeded the zero-order correlations among constructs offering evidence of discriminant validity. Finally, be-
cause transformational leadership and empowerment ratings were provided by a single source, we checked for item
cross-loadings using an exploratory factor analysis including only the items from these two scales. As illustrated in
Appendix A, each item loaded onto the appropriate construct, and none of the items were found to cross-load onto
the other construct.
Table 1 summarizes the zero-order correlations for the entire sample, and Table 2 summarizes the zero-order cor-

relations separately for organizations with organic versus mechanistic structures. To test Hypotheses 1 and 2, we first
examined the relationships between transformational leadership and employee psychological empowerment, task
performance, and OCBs. Results indicated that transformational leadership was positively related to psychological
empowerment (β = .39, p < .01; ΔR2 = .19), task performance (β = .16, p < .01; ΔR2 = .03), OCBI (β = .28, p < .01; ΔR2 = .07), and OCBO (β = .40, p < .01; ΔR2 = .08). Next, we tested the relationship between employee psycholog- ical empowerment and task performance or OCBs while controlling for transformational leadership. Results indi- cated that psychological empowerment was positively related to task performance (β = .26, p < .01, ΔR2 = .05), OCBI (β = .29, p < .01, ΔR2 = .04), and OCBO (β = .38, p < .01, ΔR2 = .05). For task performance (Z = 3.17, p < .05) and OCBI (Z = 3.28, p < .05), Sobel tests indicated that transformational leadership had statistically significant indi- rect effects while the coefficient for the direct effect was no longer statistically significant, providing evidence of full mediation. For OCBO, transformational leadership had statistically significant indirect (Z = 3.56, p < .05) and direct effects, providing evidence of partial mediation. The results are summarized in Table 3. Next, we conducted bootstrapping analyses to further examine the indirect effects of transformational leadership

on task performance, OCBI, and OCBO, via psychological empowerment. The results are summarized in Table 4.
The confidence intervals for the indirect effect of transformational leadership on task performance (point estimate =
0.10, 95%CI [0.04, 0.18]), OCBI (point estimate = 0.11, 95%CI [0.03, 0.22]), and OCBO (point estimate = 0.15,
95%CI [0.05, 0.29]) through psychological empowerment did not include zero. Therefore, we concluded that the
pattern of results provided support for Hypotheses 1 and 2.
To test the moderated-mediation relationships proposed in Hypotheses 3 and 4, we first conducted a moderation

analysis to determine if organizational structure moderated the relationships between transformational leadership
and psychological empowerment. Variables were centered, and a hierarchical regression analysis was conducted
by entering the control variables in Step 1, transformational leadership and organizational structure in Step two,
and the Transformational Leadership × Organizational Structure interaction term in Step 3 (which was statistically
significant: β = .13, p < .01, ΔR2 = .04). We then plotted the interaction at one SD above and below the mean of organizational structure (Figure 2) and conducted simple slopes analyses to determine the magnitude of the relation- ships. Consistent with the hypothesized relationships, results indicated that transformational leadership was more strongly related to employee psychological empowerment in organizations with more organic (β = .20, t = 5.94, p < .01) as opposed to mechanistic structures (β = .10, t = 3.55, p < .01). Next, we examined the extent to which the indirect effects of transformational leadership on employee perfor-

mance and OCBs were conditional on organizational structure using the Preacher et al. (2007) macro. Results, which
are summarized in Table 5, indicate that the indirect effects of transformational leadership were stronger in organi-
zations with more organic (1 SD above the mean) as opposed to more mechanistic structures (1 SD below the mean)
for task performance (β = .17, p < .01 vs. β = .08, p < .05), OCBI (β = .19, p < .05 vs. β = .09, p < .05), and OCBO (β = .23, p < .05 vs. β = .11, p < .05). As a robustness check, we also tested the moderated-mediation hypotheses using Edwards and Lambert’s (2007) technique. Once again, the indirect effects of transformational leadership were stronger in organizations with more organic (1 SD above the mean) as opposed to more mechanistic structures (1 SD

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 421

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422 S. B. DUST ET AL.

below the mean) for task performance (β = .12, p < .01 vs. β = .05, p < .01), OCBI (β = .14, p < .01 vs. β = .06, p < .01), and OCBO (β = .20, p < .05 vs. β = .08, p < .05). Therefore, a similar pattern of results is found using either the Preacher et al. (2007) or Edwards and Lambert (2007) procedure. We also tested the conditional indirect effects at various values of the moderator, allowing for the identifi-

cation of organizational structure values for which the conditional indirect effect was statistically significant.
As shown in Table 5, the results indicated that the conditional indirect effect for task performance, OCBI,
and OCBO was statistically significant at p < .05 for any value of organizational structure greater than 2.5. The overall pattern of results suggests that the indirect effects of transformational leadership on employee task performance and citizenship were stronger in organizations with more organic as opposed to mechanistic struc- tures. Additionally, at high levels of mechanistic structure, the indirect effect of transformational leadership on employee performance and OCBs was not statistically significant. Therefore, we concluded that Hypotheses 3 and 4 were supported.

Discussion

Findings from the current study suggest that psychological empowerment is an important motivational mech-
anism linking transformational leadership with employee job-related behaviors. The findings further demon-
strate that these empowering effects are bounded by an organization’s structural characteristics. Therefore,
this research contributes a nuanced understanding of the motivational effects of transformational leaders by
demonstrating the constraining and enabling effects of organizational contexts. It appears that the fluid and
open nature of organically structured organizations enables transformational leaders to empower their
employees, who in turn are motivated to perform their jobs well and be good organizational citizens. In con-
trast, it appears that the rigid nature of mechanistically structured organizations constrains the empowering
effects of transformational leaders.

Table 2. Zero-order correlations and descriptive statistics for organic and mechanistic structures.

Variable 1 2 3 4 5 6 7 8 9

1. Employee age �.09 .20 �.03 .11 .17 �.05 .12 .05
2. Employee gender .04 �.03 .02 �.06 .01 .03 .19 .01
3. Experience with manager .01 �.19 �.10 �.11 .06 �.03 �.05 .16
4. Organization size �.05 �.17 �.13 .00 �.01 .09 .01 �.02
5. Transformational leadership �.03 .24* .04 �.09 .39** .13 .25** .24**
6. Empowerment .17 �.12 .16 �.11 .46** .37** .27** .27**
7. Task performance .00 .25* .13 .09 .26* .19 .48** .38
8. OCBI .00 .15 .14 .20 .32** .36** .48** .56**
9. OCBO .04 .12 .21 .11 .34** .38** .40** .80**

M (organic) 21.55 1.53 12.84 3.28 5.75 5.30 6.08 5.21 5.22
SD (organic) 1.34 .50 12.66 1.62 0.83 0.84 0.87 1.13 1.30
M (mechanistic) 21.51 1.44 12.17 9.08 5.50 5.03 6.02 5.20 4.98
SD (mechanistic) 3.24 .50 12.54 3.94 1.08 0.85 0.82 0.98 1.40

Note: Organic = structures rated at 4.00 or above (n = 80); correlations in more organic organizations are listed below the diagonal. Mechanistic =
structures rated at 3.99 or below (n = 73); correlations in more mechanistic organizations are listed above the diagonal. Gender (male = 1,
female = 2). Experience with manager = months employee has worked with manager; organizational size = number of individuals employed
within the organization (0.000); empowerment = psychological empowerment; OCBI = interpersonal-focused citizenship behaviors; OCBO =
organization-focused citizenship behaviors. N = 153.
*p < .05; **p < .01.

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 423

Table 4. Bootstrap analyses of the indirect effects of transformational leadership.

Model PEa Boot Bias SE

Bias-corrected
confidence intervalb

Lower Upper

Task performance
Transformational leadership–empowerment–task performance .10 .10 .00 .04 .04 .18

Interpersonal citizenship
Transformational leadership–empowerment–OCBI .11 .12 .01 .05 .03 .22

Organizational citizenship
Transformational leadership–empowerment–OCBO .15 .15 .00 .06 .05 .29

Note: N = 153. Empowerment = psychological empowerment; OCBI = interpersonal-focused citizenship behaviors; OCBO = organization-focused
citizenship behaviors.
aPoint estimate; 5000 bootstrap resamples.
bNinety-five percent level of confidence for confidence interval.

Table 3. Regression analyses—transformational leadership, psychological empowerment, employee performance, and
citizenship behaviors.

Empowerment

Task performance OCBI OCBO

Step 1 β Step 2 β Step 1 β Step 2 β Step 1 β Step 2 β Step 1 β Step 2 β

Step 1
Employee age .05 .04 �.01 �.02 .02 .01 .01 .00
Employee gender �.05 �.10 .25 .22 .37 .32 .26 .19
Experience with manager .01 .01 .01 .01 .01 .01 .02** .02**
Organization size .00 .00 .00 .00 .00 .00 .00 .00

Step 2
TFL .39** .16* .28** .40**

R2 .04 .23 .03 .07 .04 .11 .05 .13
F 1.82 9.81** 1.29 2.13 1.56 3.50** 1.76 4.35**
ΔR2 .19 .04 .07 .08
Finc 40.02** 5.37* 10.81** 14.10**

Step 1
Employee age �.02 �.10 .01 .00 .00 �.02
Employee gender .22 .15 .32 .35* .19 .23
Experience with manager .01 .07 .01 .01 .02** .02*
Organization size .00 .10 .00 .00 .00 .00
TFL .16* .07 .28** .17 .40** .26*

Step 2
Empowerment .26** .29** .38**

R2 .07 .12 .11 .15 .13 .17
F 2.13 3.33** 3.50** 4.28** 4.35** 5.14**
ΔR2 .05 .04 .05
Finc 8.74** 7.44** 8.06**

Note: Gender (male = 1, female = 2); experience with manager = months employee has worked with manager; TFL = transformational leadership;
empowerment = psychological empowerment; OCBI = interpersonal-focused citizenship behaviors; OCBO = organization-focused citizenship
behaviors. N = 153.
*p < .05; **p < .01.

424 S. B. DUST ET AL.

Table 5. Bootstrap analyses of the conditional indirect effects of transformational leadership.

Organizational
structure

Task performance OCBI OCBO

CIE SE z CIE SE z CIE SE z

�1 SD (mechanistic) 0.08 0.03 2.29 0.09 0.04 1.99 0.11 0.05 1.96
+1 SD (organic) 0.17 0.06 2.82 0.19 0.08 2.50 0.23 0.10 2.31

Range of values
1.0 0.03 0.05 0.57 0.03 0.04 0.62 0.03 0.04 0.61
1.3 0.04 0.05 0.86 0.04 0.04 0.90 0.04 0.04 0.90
1.6 0.05 0.04 1.16 0.05 0.04 1.21 0.05 0.04 1.22
1.9 0.06 0.04 1.46 0.06 0.04 1.54 0.06 0.04 1.56
2.2 0.07 0.04 1.74 0.07 0.04 1.86 0.07 0.04 1.88
2.5 0.09 0.04 1.98 0.08 0.04 2.15 0.08 0.03 2.18
2.8 0.10 0.05 2.17 0.09 0.04 2.40 0.09 0.04 2.43
3.1 0.11 0.05 2.31 0.10 0.04 2.59 0.10 0.04 2.62
3.4 0.12 0.05 2.40 0.11 0.04 2.72 0.11 0.04 2.75
3.7 0.13 0.05 2.45 0.12 0.04 2.80 0.12 0.04 2.82
4.0 0.15 0.06 2.48 0.13 0.04 2.84 0.13 0.04 2.86
4.3 0.16 0.06 2.49 0.14 0.05 2.86 0.14 0.05 2.87
4.6 0.17 0.07 2.49 0.15 0.05 2.85 0.15 0.05 2.86
4.9 0.18 0.07 2.48 0.16 0.06 2.84 0.16 0.06 2.84
5.2 0.19 0.08 2.46 0.17 0.06 2.81 0.17 0.06 2.82
5.5 0.20 0.08 2.44 0.18 0.06 2.78 0.18 0.06 2.79
5.8 0.21 0.09 2.42 0.19 0.07 2.75 0.19 0.07 2.76
6.1 0.23 0.09 2.40 0.20 0.07 2.72 0.20 0.07 2.72
6.4 0.24 0.10 2.38 0.21 0.08 2.69 0.21 0.08 2.69
6.7 0.25 0.11 2.36 0.22 0.08 2.66 0.22 0.08 2.66
7.0 0.26 0.11 2.34 0.23 0.09 2.63 0.23 0.09 2.63

Note: CIE= conditional indirect effect; SE= standard error; OCBI= interpersonal-focused organizational citizenship behaviors; OCBO = organization-
focused citizenship behaviors.

Figure 2. Relationship between transformational leadership and employee psychological empowerment

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 425

Theoretical implications

The findings from the current study have several implications for transformational leadership research. First, the
study provides evidence that employees who work for transformational leaders appear to put forth the efforts needed
to be high performers on their jobs and be good organizational citizens because of the influence transformational
leaders have on their employees’ psychological empowerment. Although the mediating role of several motiva-
tion-related mechanisms has been examined in prior studies (e.g., Bono & Judge, 2003; Hoffman, Bynum, Piccolo,
& Sutton, 2011), theory has argued that transformational leaders motivate followers to engage in extraordinary per-
formance and citizenship behaviors by enhancing their sense of psychological empowerment (Bass, 1985; Bass &
Steidlmeier, 1999). The current study is the first to provide empirical support for these assertions. By encompassing be-
liefs about the meaning and impact of one’s work as well as a sense of personal competence and self-determination
(Spreitzer, 1995), psychologically empowered individuals are proactive and self-expressive, approaching their work
with a sense of purpose and self-confidence (Conger & Kanungo, 1988; Spreitzer, 1995; Thomas & Velthouse,
1990). Psychological empowerment, therefore, provides a more comprehensive perspective on the motivational influ-
ence of transformational leaders, compared with prior studies that have focused on a single dimension of psychological
empowerment (e.g., Bono & Judge, 2003; Dvir et al., 2002; Piccolo & Colquitt, 2006; Walumbwa, Avolio, Gardner,
Wernsing, & Peterson, 2008). Psychological empowerment provides a unique perspective on intrinsic motivation be-
cause empowerment reflects an active orientation toward work, “in which an individual wishes and feels able to shape
his or her work role and context” (Spreitzer, 1995, p. 444). Thus, psychological empowerment entails a participative and
proactive approach to work, making it a construct that closely represents the self-expressive orientation toward work
that is central to self-concept-based theories of transformational leadership influence (Shamir et al., 1993).
Second, our findings suggest that the influence of transformational leaders on employee behaviors can be stronger

when embedded within an environment that fosters the empowering influence of leaders. It appears that organic or-
ganizational characteristics enable managers and employees to have more discretion and latitude in their behavior.
This discretion facilitates transformational leadership behaviors and employee receptivity to the empowering influ-
ence of transformational leaders. Transformational leadership theory has long suggested that organic organizational
structures may enhance the positive relationships between transformational leadership behaviors and employee be-
havioral outcomes (Shamir et al., 1993; Shamir & Howell, 1999; Tosi, 1991; Yukl, 1999). However, it was unclear
how and why the underlying motivational process of employees was altered in varying organizational contexts. Psy-
chological empowerment appears to provide a clearer understanding of the cognitive processes underlying these
transformational leadership contextualization propositions.
Third, our findings also suggest that organizational environments that thwart psychological empowerment may

diminish the transformational leader’s influence on employee behaviors. Transformational leadership may still be
important in mechanistic organizations. However, it appears that the lack of discretion inherent in highly mechanistic
organizational structures may impede empowering efforts.
Findings from the current study also have important implications for psychological empowerment theory. Leadership

and organizational structure have been identified as two significant social-structural antecedents of psychological em-
powerment (Maynard, Gilson, & Mathieu, 2012; Seibert et al., 2011). Psychological empowerment theory suggests that
leaders—transformational leaders in particular—are important promoters of employee empowerment given their ability
to influence employees’ subjective perceptions of their work (Spreitzer, 2008). Psychological empowerment theory also
suggests that organizations structured for high employee involvement, which is conceptually similar to organic organi-
zational structures, should lead to heightened levels of employee empowerment (Spreitzer, 1996). Our findings illustrate
that transformational leadership and organic structures interact in influencing empowerment, whereby increased effect
sizes result through the combination of both conditions. In addition, psychological empowerment theory typically looks
at the positive relationships between social-structural characteristics and psychological empowerment. Our findings
illustrate a scenario in which social-structural characteristics may be in conflict, whereby mechanistic structures have
detrimental characteristics that constrain the positive effects of transformational leadership on employee psychological
empowerment, task performance, and citizenship behaviors.

426 S. B. DUST ET AL.

Practical implications

The findings from the current research suggest that it is important for managers to understand the structural charac-
teristics of their organizations, and how these features may impact their attempts to psychologically empower em-
ployees within their unit. When managers reside within organizations where roles are well defined and rigidly
structured and decision making is highly centralized, their transformational behaviors are likely to have little to
no effect on the psychological empowerment that their employees experience. To circumvent the restricting effects
of mechanistic organizational structures, transformational leaders may want to consider creating a more fluid and
open environment within their unit. For example, restructuring the unit so that operating information is transparent,
using incentives to encourage employees to improvise or take initiative, and creating opportunities for capable em-
ployees to take on new roles should facilitate more organic organizational processes, which in turn should facilitate
the empowerment process.
The current study indicates that psychological empowerment is a key mechanism through which transformational

leaders motivate high levels of performance and citizenship among their employees. As such, the current study of-
fers additional support for the utility of training and development efforts designed to enhance transformational lead-
ership behaviors ( Barling, Weber, & Kelloway, 1996; Dvir et al., 2002). At the same time, managers should also be
encouraged to think about the influence of contextual factors. To gain the full benefits of transformational leadership,
managers must be aware of their organizational environment because these environments are likely to impact their
ability to empower and motivate members of their staff (Johns, 2006).

Strengths, limitations, and future research

As with all research, the current study has limitations that should be noted. First, the sample of focal employees,
which was drawn from adult students working on a full or part-time basis, may not be entirely representative of
the larger population of working adults. Although this type of sampling approach is common in organizational be-
havior research (e.g., Mawritz, Mayer, Hoobler, Wayne, & Marinova, 2012; Piccolo, Greenbaum, Den Hartog, &
Folger, 2010), participants tended to be relatively young (M = 21.1 years of age) and occupy positions with more
entry-level as opposed to advanced responsibilities. As such, caution should be used in generalizing findings to
the larger population of working adults. However, it should be noted that most of the students who were asked to
participate are part of a cooperative education program in which they complete approximately 18 months of full-time
work before graduating, giving them an opportunity to gain substantial work experiences. Nevertheless, perhaps the
results are most generalizable to employees who are in the early stages of their professional career. Future studies
should examine similar models within a broader sample to more fully understand the empowering influence of trans-
formational leaders.
Second, cross-sectional surveys are not an appropriate design for testing causal relationships owing to ambiguity

in the direction of the relationships. For example, employees who have a sense of confidence in their work, and see
their work as meaningful and impactful, may attribute their beliefs to the leadership provided by their supervisor. In
turn, they may rate their supervisor as highly transformational because they are experiencing psychological empow-
erment as opposed to being psychologically empowered because they work for a transformational leader. Therefore,
the results of the current study are correlational in nature, and caution should be used in drawing causal inferences
from the findings.
Third, employee participants provided ratings of both transformational leadership and psychological empower-

ment. As such, the relationships between these variables may be inflated because of common method bias
(CMB). However, our findings from the confirmatory factor analysis and AVE analysis suggest that the constructs
are distinct. Additionally, supervisors provided ratings of employee job-related behaviors, and the focus on the mod-
erating role of structure may mitigate some of these concerns, as interactions are unlikely to be influenced by CMB
(Siemsen, Roth, & Oliveira, 2009). A related concern is that the quality of the relationship between the supervisor

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 427

and the employee may influence employees’ ratings of their supervisor’s transformational leadership or their own
psychological empowerment. We did not measure or control for relationship quality. However, we did control for
the length of time that the employee and supervisor had worked together, which may serve as a partial proxy for
relationship quality, given that longer-term work relationships may involve higher levels of trust and more support-
ive behaviors than shorter-duration work relationship. In addition, although we took steps to check that the employee
and supervisor surveys were completed by individuals with different IP addresses, we have no way of knowing
whether the supervisor surveys were completed by the employees’ actual supervisors. Although we indicated to par-
ticipants that any fabrication of study data would be considered a violation of the university’s academic honesty and
code of conduct policies, the potential for inaccuracies to exist is unknown.
Fourth, our operationalization of the organizational structure construct may be problematic. We used a key infor-

mant approach to assess organization structure, which relies on the ratings of a single organizational member who
understands the nuances and features of the organization’s structure (Huber & Power, 1985). Because of their posi-
tion of authority within the organization’s formal or informal chain of command, supervisors as opposed to em-
ployee participants are likely to be more knowledgeable of and have a greater appreciation for the organization’s
channels of communication, formality/informality of policies and procedures, interdependencies among organiza-
tional members and units, and the boundaries of decision-making authority. However, different raters may be cog-
nizant of different stimuli within the environment, and aggregating multiple sources may lead to a more complete
picture of the overall phenomenon (Lance, Hoffman, Gentry, & Baranik, 2008). Although the key informant
approach has been widely used in organizational research (e.g., Covin & Slevin, 1989; Oldham & Hackman,
1981; Spell & Arnold, 2007; Terziovski, 2010; Wally & Baum, 1994), several other studies have also viewed per-
ceptions of mechanistic–organic organizational structure as a shared property representing a type of organizational
climate (Ambrose & Schminke, 2003; Dickson, Resick, & Hanges, 2006b). Future research should consider using
multisource ratings of organizational structure to provide a more comprehensive perspective on structural character-
istics or recruiting secondary informants to check the consistency of perspectives across individuals (Slevin &
Covin, 1997). We also recognize that perceptions of structure may not adequately reflect actual structural practices.
Future research could assess mechanistic–organic structural features using both perceptual indicators and evalua-
tions of organizational features such as number and pervasiveness of formal policies or number of layers in the
organizational hierarchy.
Finally, departments or functional areas embedded within an organization may differ in the level of organic versus

mechanistic practices (Williamson, 1975). Future research should examine how mechanistic and organic structural
characteristics operate across organizational levels to influence leadership and motivational processes. Additionally,
the centralization and formalization components of mechanistic and organizational structures may have a differential
moderating effect on the relationships between transformational leadership, psychological empowerment, and work
behaviors. For example, Walter and Bruch (2010) found that centralization constrained relationships between trans-
formational leadership climate and organizational energy, whereas formalization practices enhanced these relationships.
Future research could extend the findings of the current study by focusing on facets of organizational structure. Finally,
leadership is likely to have an influence on units as well as individuals within units (Seibert, Silver, & Randolph, 2004).
As our study focused exclusively on individual-level relationships, the linkages between unit transformational leader-
ship and team-level empowerment are unclear. Future research should examine the extent to which the empowering
influence of transformational leaders extends across individuals and across levels.

Conclusion

Findings from the current study provide a nuanced perspective on the relationship between transformational leadership
and employee motivation by demonstrating that psychological empowerment is an important motivational mechanism
linking transformational leadership with employee performance-related behaviors and that organizational structures
provide an important boundary condition on these relationships. It appears that organizations with organic structures

428 S. B. DUST ET AL.

provide a natural setting for transformational leaders to empower their employees and enable them to approach their
work with a sense of purpose, autonomy, and self-expression. By contrast, in mechanistically structured organizations,
it appears that transformational leaders have fewer opportunities to empower employees and employees are likely to be
less receptive to these efforts.

Author biographies

Scott B. Dust is an Assistant Professor at the College of Business and Technology at Eastern Kentucky University.
He received his PhD in Organizational Behavior from Drexel University. Scott’s research on leadership, work
design, and the work–home interface has appeared in journals such as Human Relations and Industrial and
Organizational Psychology: Perspectives on Science and Practice.
Christian Resick is an Associate Professor at the LeBow College of Business at Drexel University. He received his
PhD in Organizational Psychology from Wayne State University. Christian’s research on leadership, team effective-
ness, and organizational culture has appeared in leading journals such as the Journal of Applied Psychology and
Human Relations.
Mary Mawritz is an Assistant Professor in the LeBow College of Business at Drexel University. She holds a PhD in
Business Administration from the University of Central Florida. Her research interests include leadership, organiza-
tional justice, and counterproductive behavior. She has published research articles in journals such as the Journal of
Applied Psychology and the Journal of Management.

References
Ambrose, M. L., & Schminke, M. (2003). Organization structure as a moderator of the relationship between procedural justice,
interactional justice, perceived organizational support, and supervisory trust. Journal of Applied Psychology, 88, 295–305.

Arnold, J. A., Arad, S., Rhoades, J. A., & Drasgow, F. (2000). The empowering leadership questionnaire: The construction and
validation of new scale for measuring leader behaviors. Journal of Organizational Behavior, 21, 249–269.

Ashforth, B. E. (1989). The experience of powerlessness in organizations. Organizational Behavior and Human Decision
Processes, 43, 207–242.

Ashkanasy, N. M., & Tse, B. (2000). Transformational leadership as management of emotion: A conceptual review. In N. M.
Ashkanasy, C. E. J. Hartel, & W. J. Zerbe (Eds.), Emotions in the workplace: Theory, research and practice (pp. 221–235).
Westport, CT: Quorum Books.

Avolio, B. J., & Bass, B. M. (1988). Transformational leadership, charisma and beyond. In J. G. Hunt, H. R. Baliga, H. P.
Dachler, & C. A. Schriesheim (Eds.), Emerging leadership vistas (pp. 29–49). Lexington, MA: Heath.

Avolio, B. J., Zhu, W., Koh, W., & Bhatia, P. (2004). Transformational leadership and organizational commitment: Mediating
role of psychological empowerment and moderating role of structural distance. Journal of Organizational Behavior, 25,
951–968.

Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, September, 44(9), 1175–1184.
Barling, J., Weber, T., & Kelloway, E. K. (1996). Effects of transformational leadership training on attitudinal and financial
outcomes: A field experiment. Journal of Applied Psychology, 81, 827–832.

Bass, B. M. (1985). Leadership and performance beyond expectations. New York: Free Press.
Bass, B. M., & Avolio, B. J. (1995). MLQ Multifactor Leadership Questionnaire for research: Permission set. Redwood City,
CA: Mindgarden.

Bass, B. M., & Steidlmeier, P. (1999). Ethics, character, and authentic transformational leadership behavior. Leadership Quarterly,
10, 181–217.

Berger, P., & Luckmann, T. (1966). The social construction of reality. A treatise in the sociology of knowledge. London: Penguin.
Blau, P. M. (1964). Exchange and power in social life. New York: Wiley.
Bono, J. E., & Judge, T. A. (2003). Self-concordance at work: Toward understanding the motivational effects of transformational
leaders. Academy of Management Journal, 46, 554–571.

Burns, J. M. (1978). Leadership. New York: Harper & Row.
Burns, T., & Stalker, G. M. (1961). The management of innovation. London: Tavistock.

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 429

Chen, G., Kirkman, B. L., Kanfer, R., Allen, D., & Rosen, B. (2007). A multilevel study of leadership, empowerment, and
performance in teams. Journal of Applied Psychology, 92, 331–346.

Child, J. (1972). Organization structure and strategies of control—A replication of the Aston study. Administrative Science Quarterly,
17(2), 163–177.

Conger, J. A., & Kanungo, R. N. (1988). The empowerment process: Integrating theory and practice. Academy of Management
Review, 13, 471–482.

Courtright, J. A., Fairhurst, G. T., & Rogers, L. E. (1989). Interaction patterns in organic and mechanistic systems. Academy of
Management Journal, 32(4), 773–802.

Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Manage-
ment Journal, 10(1), 75–87.

Cunliffe, A., & Coupland, C. (2012). From hero to villain to hero: Making experience sensible through embodied narrative
sensemaking. Human Relations, 65, 63–88.

Deci, E. L., Connell, J. P., & Ryan, R. M. (1989). Self-determination in a work organization. Journal of Applied Psychology, 74,
580–590.

DeRue, D. S., Nahrgang, J. D., Wellman, N., & Humphrey, S. E. (2011). Trait and behavioral theories of leadership: A meta-
analytic test of their relative validity. Personnel Psychology, 64(1), 7–52.

Dickson, M. W., Resick, C. J., & Hanges, P. J. (2006a). Systematic variation in organizationally-shared cognitive prototypes of
effective leadership based on organizational form. Leadership Quarterly, 17, 487–505.

Dickson, M. W., Resick, C. J., & Hanges, P. J. (2006b). When organizational climate is unambiguous, it is also strong. Journal of
Applied Psychology, 91, 351–364.

Dvir, T., Eden, D., Avolio, B. J., & Shamir, B. (2002). Impact of transformational leadership on follower development and
performance: A field experiment. Academy of Management Journal, 45, 735–744.

Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework
using moderated path analysis. Psychological Methods, 12(1), 1–22.

Finkelstein, S., & Hambrick, D. C. (1990). Top management team tenure and organizational outcomes: The moderating role of
managerial discretion. Administrative Science Quarterly, 35, 484–503.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error.
Journal of Marketing Research, 18, 39–50.

Fredrickson, J. W. (1986). The strategic decision process and organizational structure. Academy of Management Review, 11(2),
280–297.

Gefen, D., & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: Tutorial and annotated example.
Communications of the Association for Information Systems, 16, 91–109.

Gioia, D. A., & Chittipeddi, K. (1991). Sensemaking and sensegiving in strategic change initiation. Strategic Management Journal,
12, 433–448.

Grant, A. M. (2008). The significance of task significance: Job performance effects, relational mechanisms, and boundary condi-
tions. Journal of Applied Psychology, 93, 108–124.

Grant, A. M. (2012). Leading with meaning: Beneficiary contact, prosocial impact, and the performance effects of transforma-
tional leadership. Academy of Management Journal, 55, 458–476.

Grant, A. M., & Mayer, D. M. (2009). Good soldiers and good actors: Prosocial and impression management motives as inter-
active predictors of affiliative citizenship behaviors. Journal of Applied Psychology, 94, 900–912.

Greenbaum, R. L., Mawritz, M. B., & Eissa, G. (2012). Bottom-line mentality as an antecedent of social undermining and the
moderating roles of core self evaluation, conscientiousness, and job performance. Journal of Applied Psychology, 97, 343–359.

Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley.
Hambrick, D. C., & Finkelstein, S. (1987). Managerial discretion: A bridge between the polar views of organizational outcomes. In B.
M. Staw, and L. L. Cummings (Eds.), Research in organization behavior (Vol. 4, pp. 369–406). Greenwich CT: JAI Press.

Herold, D. M., Fedor, D. B., Caldwell, S., & Liu, Y. (2008). The effects of transformational leadership and change leadership on
employees’ commitment to a change: A multilevel study. Journal of Applied Psychology, 93, 346–357.

Hiller, N. J., DeChurch, L. A., Murase, T., & Doty, D. (2011). Searching for outcomes of leadership: A 25-year review. Journal
of Management, 37(4), 1137–1177.

Hoffman, B. J., Bynum, B. H., Piccolo, R. F., & Sutton, A. W. (2011). Person–organization value congruence: How transforma-
tional leaders influence work group effectiveness. Academy of Management Journal, 54, 779–796.

House, R., Rousseau, D. M., & Thomas-Hunt, M. (1995). The meso-paradigm: A framework for the integration of micro and
macro organizational behavior. Research in Organizational Behavior, 17, 41–114.

Huber, G. P., & Power, D. J. (1985). Retrospective reports of strategic level managers: Guidelines for increasing their accuracy.
Strategic Management Journal, 6, 171–180.

Ilies, R., Judge, T., & Wagner, D. (2006). Making sense of motivational leadership: The trail from transformational leaders to
motivated followers. Journal of Leadership & Organizational Studies, 13(1), 1–22.

430 S. B. DUST ET AL.

Johns, G. (2006). The essential impact of context on organizational behavior. Academy of Management Review, 31, 386–408.
Judge, T. A., & Piccolo, R. F. (2004). Transformational and transactional leadership: A meta-analytic test of their relative validity.
Journal of Applied Psychology, 89, 755–768.

Jung, D. I., Chow, C., & Wu, A. (2003). The role of transformational leadership in enhancing organizational innovation: Hypotheses
and some preliminary findings. Leadership Quarterly, 14, 525–544.

Kark, R., Shamir, B., & Chen, G. (2003). The two faces of transformational leadership: Empowerment and dependency. Journal
of Applied Psychology, 88, 246–255.

Katz, D., & Kahn, R. L. (1966). The social psychology of organizations. New York: Wiley.
Khandwalla, P. N. (1976/1977). Some top management styles, their context and performance. Organization and Administrative
Sciences, 7(4), 21–51.

Kidder, D. L. (2002). The influence of gender on the performance of organizational citizenship behaviors. Journal of Manage-
ment, 28, 629–648.

Lance, C. E., Hoffman, B. J., Gentry, W. A., & Baranik, L. E. (2008). Rater source factors represent important subcomponents of
the criterion construct space, not rater bias. Human Resource Management Review, 18, 223–232

Lee, K., & Allen, N. J. (2002). Organizational citizenship behavior and workplace deviance: The role of affect and cognitions,
Journal of Applied Psychology, 87, 131–42.

Liao, H., & Chuang, A. (2007). Transforming service employees and climate: A multilevel, multisource examination of transfor-
mational leadership in building long-term service relationships. Journal of Applied Psychology, 92, 1006–1019.

Maitlis, S. (2005). The social processes of organizational sensemaking. Academy of Management Journal, 48, 21–49.
Mawritz, M. B., Mayer, D. M., Hoobler, J. M., Wayne, S. J., & Marinova, S. V. (2012). A trickle-down model of abusive super-
vision. Personnel Psychology, 65, 325–357.

Mayer, D. M., Kuenzi, M., Greenbaum, R., Bardes, M., & Salvador, R. (2009). How low does ethical leadership flow? Test of a
trickle-down model. Organizational Behavior and Human Decision Processes, 108, 1–13.

Maynard, M. T., Gilson, L. L., & Mathieu, J. E. (2012). Empowerment—fad or fab? A multilevel review of the past two decades
of research. Journal of Management, 38(4), 1231–1281.

Mischel, W. (1973). Toward a cognitive social learning reconceptualization of personality, Psychological Review, 80, 200–213.
Moore, C., Detert, J. R., Treviño, L. K., Baker, V. L., & Mayer, D. M. (2012). Why employees do bad things: Moral disengage-
ment and unethical organizational behavior. Personnel Psychology, 65, 1–48.

Morgeson, F. P., & Humphrey, S. E. (2006). The Work Design Questionnaire (WDQ): Developing and validating a comprehen-
sive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91, 1321–1339.

Nemanich, L. A., & Keller, R. T. (2007). Transformational leadership in an acquisition: A field study of employees. Leadership
Quarterly, 18, 4–68.

Oldham, G. R., & Hackman, J. R. (1981). Relationships between organizational structure and employee reactions: Comparing
alternative frameworks. Administrative Science Quarterly, 26, 66–83.

Piccolo, R. F., & Colquitt, J. A. (2006). Transformational leadership and job behaviors: The mediating role of core job charac-
teristics. Academy of Management Journal, 49, 327–340.

Piccolo, R. F., Greenbaum, R., Den Hartog, D. N., & Folger, R. (2010). The relationship between ethical leadership and core job
characteristics. Journal of Organizational Behavior, 31, 259–278.

Podsakoff, P. M., MacKenzie, S. B., Moorman, R. H., & Fetter, R. (1990). Transformational leader behaviors and their effects on
followers’ trust in leader, satisfaction, and organizational citizenship behaviors. Leadership Quarterly, 1, 107–142.

Preacher, K. J., & Hayes, A. F. (2008). Contemporary approaches to assessing mediation in communication research. In A. F.
Hayes, M. D. Slater, and L. B. Snyder (Eds.), The Sage sourcebook of advanced data analysis methods for communication
research (pp. 13–54.). Thousand Oaks, CA: Sage Publications.

Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Theory, method, and prescrip-
tions. Multivariate Behavioral Research, 42, 185–227.

Purvanova, R. K., Bono, J. E., & Dzieweczynski, J. (2006). Transformational leadership, job characteristics, and organizational
citizenship performance. Human Performance, 19: 1–22.

Rogers, C. R. (1959). A theory of therapy, personality, and interpersonal relationships, as developed in the client-centered frame-
work. In S. Koch (Ed.), Psychology: A study of a science (Vol. 3, pp. 184–256). New York: McGraw-Hill.

Seibert, S. E., Silver, S. R., & Randolph, W. A. (2004). Taking empowerment to the next level: A multilevel model of empow-
erment, performance, and satisfaction. Academy of Management Journal, 47, 332–349.

Seibert, S. E., Wang, G., & Courtright, S. H. (2011). Antecedents and consequences of psychological and team empowerment in
organizations: A meta-analytic review. Journal of Applied Psychology, 96, 981–1003.

Shamir, B., & Howell, J. M. (1999). Organizational and contextual influences on the emergence and effectiveness of charismatic
leadership. Leadership Quarterly, 10, 257–283.

Shamir, B., House, R. J., & Arthur, M. B. (1993). The motivational effects of charismatic leadership: A self-concept based theory.
Organization Science, 4(4), 577–594.

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 431

Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and non-experimental studies: New procedures and recommenda-
tions. Psychological Methods, 7, 422–445.

Siemsen, E., Roth, A., & Oliveira, P. (2009). Common method bias in regression models with linear, quadratic, and interaction
effects. Organizational Research Methods, 13, 456–467.

Slevin, D. P., & Covin, J. G. (1997). Strategy formation patterns, performance, and the significance of context. Journal of
Management, 23(2), 189–209.

Smircich, L., & Morgan, G. (1982). Leadership: The management of meaning. Journal of Applied Behavioral Science, 2(3), 257–273.
Sosik, J. J., & Godshalk, V. M. (2000). Leadership styles, mentoring functions received, and job-related stress: A conceptual
model and preliminary study. Journal of Organizational Behavior, 21, 365–390.

Sosik, J. J., Godshalk, V. M., & Yammarino, F. J. (2004). Transformational leadership, learning goal orientation, and expecta-
tions for career success in mentor–protégé relationships: A multiple levels of analysis perspective. Leadership Quarterly,
15, 241–261.

Sparks, J. R., & Schenk, J. A. (2001). Explaining the effects of transformational leadership: An investigation of the effects of
higher-order motives in multilevel marketing organizations. Journal of Organizational Behavior, 22, 849–869.

Spell, C. S., & Arnold, T. J. (2007). A multi-level analysis of organizational justice climate, structure, and employee mental
health. Journal of Management, 33(5), 724–751.

Spreitzer, G. M. (1995). Psychological empowerment in the workplace: Dimensions, measurement, and validation. Academy of
Management Journal, 38, 1442–1465.

Spreitzer, G. M. (1996). Social structural levers to individual empowerment in the workplace. Academy of Management Journal,
39, 483–504.

Spreitzer, G. M. (2008). Taking stock: A review of more than twenty years of research on empowerment at work. In J. Barling, &
C. L. Cooper (Eds.), Handbook of organizational behavior (pp. 54–72). Thousand Oaks, CA: Sage.

Terziovski, M. (2010). Innovation practice and its performance implications in small and medium enterprises (SMEs) in the
manufacturing sector a resource-based view. Strategic Management Journal, 31(8), 892–902.

Thomas, K. W., & Velthouse, B. A. (1990). Cognitive elements of empowerment. Academy of Management Review, 15,
666–681.

Tosi, H. L. (1991). The organization as a context for leadership theory: A multilevel approach. Leadership Quarterly, 2,
205–228.

Waldman, D. A., & Avolio, B. J. (1986). A meta-analysis of age differences in job performance. Journal of Applied Psychology,
71, 33–38.

Wally, S., & Baum, J. R. (1994). Personal and structural determinants of the pace of strategic decision making. Academy of
Management Journal, 37(4), 932–956.

Walter, F., & Bruch, H. (2010). Structural impacts on the occurrence and effectiveness of transformational leadership: An empirical
study at the organizational level of analysis. Leadership Quarterly, 21(5), 765–782.

Walumbwa, F. O., Avolio, B. J., Gardner, W. L., Wernsing, T. S., & Peterson, S. J. (2008). Authentic leadership: Development
and validation of a theory-based measure. Journal of Management, 34, 89–126.

Wat, D., & Shaffer, M. A. (2005). Equity and relationship quality influences on organizational citizenship behaviors: The
mediating role of trust in the supervisor and empowerment. Personnel Review, 34(4), 406–422.

Weber, M. (1947). The theory of social and economic organization. New York: Free Press.
Weick, K. E. (1993). The collapse of sensemaking in organizations: The Mann Gulch disaster. Administrative Science Quarterly,
38, 628–652.

Weick K. E. (1995). Sensemaking in organizations. Sage Publications, Thousand Oaks, CA.
Weick, K. E. (2012) Organized sensemaking: A commentary on processes of interpretive work. Human Relations, 65,
141–153.

Weick, K. E., Sutcliffe, K. M., & Obstfeld, D. (2005). Organizing and the process of sensemaking. Organization Science, 16,
409–421.

Williams, L. J., & Anderson, S. E. (1991). Job satisfaction and organizational commitment as predictors of organizational citizenship
behaviors. Journal of Management, 17, 601–617.

Williamson, O. E. (1975). Markets and hierarchies: Analysis and antitrust implications: A study in the economics of internal
organization. New York: Free Press.

Wright, B. E., Moynihan, D. P., & Pandey, S. K. (2012). Pulling the levers: Transformational leadership, public service motivation,
and mission valence. Public Administration Review, 72(2), 206–215.

Yukl, G. (1999). An evaluation of conceptual weakness in transformational and charismatic leadership theories. Leadership
Quarterly, 10, 285–305.

Zaccaro, S. J., Rittman, A. L., & Marks, M. A. (2001). Team leadership. The Leadership Quarterly, 12, 451–483.

432 S. B. DUST ET AL.

APPENDIX A

Exploratory Factor Analysis of Transformational Leadership and Psychological Empowerment

Item Item #

Factor

1 2

Reexamines critical assumptions to question whether they are appropriate TFL 1 0.75 0.19
Talks about his or her most important values and beliefs TFL 2 0.59 0.28
Seeks differing perspectives when solving problems TFL 3 0.71 0.13
Talks optimistically about the future TFL 4 0.73 0.07
Instills pride in me for being associated with him or her TFL 5 0.77 0.12
Talks enthusiastically about what needs to be accomplished TFL 6 0.74 0.17
Specifies the importance of having a strong sense of purpose TFL 7 0.79 0.24
Spends time teaching and coaching TFL 8 0.76 0.14
Makes clear what one can expect to receive when performance goals are achieved TFL 9 0.66 0.29
Goes beyond self-interest for the good of the group TFL 10 0.79 0.16
Treats me as an individual rather than just as a member of a group TFL 11 0.74 0.06
Acts in ways that build my respect TFL 12 0.82 0.10
Considers the moral and ethical consequences of decisions TFL 13 0.68 0.13
Displays a sense of power and confidence TFL 14 0.77 0.09
Articulates a compelling vision of the future TFL 15 0.76 0.20
Considers me as having different needs, abilities, and aspirations from others TFL 16 0.69 0.18
Gets me to look at problems from many different angles TFL 17 0.72 0.17
Helps me to develop my strengths TFL 18 0.82 0.17
Suggests new ways of looking at how to complete assignments TFL 19 0.78 0.16
Emphasizes the importance of having a collective sense of mission TFL 20 0.76 0.20
Expresses confidence that goals will be achieved TFL 21 0.74 0.20
The work I do is very important to me EMP 1 0.23 0.69
My job activities are personally meaningful to me EMP 2 0.17 0.75
The work I do is meaningful to me EMP 3 0.11 0.77
I am confident about my ability to do my job EMP 4 0.07 0.55
I am self-assured about my capabilities to perform my work activities EMP 5 0.06 0.58
I have mastered the skills necessary for my job EMP 6 0.10 0.37
I have significant autonomy in determining how I do my job EMP 7 0.07 0.60
I can decide on my own how to go about doing my work EMP 8 0.05 0.64
I have considerable opportunity for independence and freedom in how I do my job EMP 9 0.21 0.66
My impact on what happens in my department is large EMP 10 0.15 0.61
I have a great deal of control over what happens in my department EMP 11 0.20 0.58
I have significant influence over what happens in my department EMP 12 0.23 0.61

Note: TFL = transformational leadership; EMP = psychological empowerment.

TRANSFORMATIONAL LEADERSHIP AND EMPOWERMENT 433

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Optimal

Structure, Market Dynamism, and the Strategy of
Simple Rules

Citation Davis, Jason, Kathleen M. Eisenhardt and Christopher B.
Bingham “Optimal Structure, Market Dynamism, and the Strategy
of Simple Rules.” Administrative Science Quarterly, 54 (2009):
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Optimal Structure, Market Dynamism, and the Strategy of Simple Rules

Jason P. Davis

Massachusetts Institute of Technology

Kathleen M. Eisenhardt

Stanford University

Christopher B. Bingham

University of North Carolina

ABSTRACT

Using computational and mathematical modeling, this study explores the tension between too

little and too much structure that is shaped by the core tradeoff between efficiency and flexibility

in dynamic environments. Our aim is to develop a more precise theory of the fundamental

relationships among structure, performance, and environment. We find that the structure-

performance relationship is unexpectedly asymmetric, in that it is better to err on the side of too

much structure, and that different environmental dynamism dimensions (i.e., velocity,

complexity, ambiguity, and unpredictability) have unique effects on performance. Increasing

unpredictability decreases optimal structure and narrows its range from a wide to a narrow set of

effective strategies. We also find that a strategy of simple rules, which combines improvisation

with low-to-moderately structured rules to execute a variety of opportunities, is viable in many

environments but essential in some. This sharpens the boundary condition between the strategic

logics of positioning and opportunity. And juxtaposing the structural challenges of adaptation for

entrepreneurial vs. established organizations, we find that entrepreneurial organizations should

quickly add structure in all environments, while established organizations are better off seeking

stable environments unless they can devote sufficient attention to managing a dissipative

equilibrium of structure (i.e., edge of chaos) in unpredictable environments.•

A longstanding question in strategy and organization theory is how the amount of organizational

structure shapes performance in dynamic environments. Given its fundamental importance, this

question has been explored in a variety of research traditions, ranging from organizational

studies (Burns and Stalker, 1961; Hargadon and Sutton, 1997) and competitive strategy (Rindova

and Kotha, 2001; Rothaermel, Hitt, and Jobe, 2006) to network sociology (Uzzi, 1997; Owen-

Smith and Powell, 2003) and, more broadly, the complexity sciences (Kauffman, 1993;

Anderson, 1999). Although highly diverse, these literatures nonetheless highlight two

fundamental arguments.

The first argument is that a balance between too much and too little structure is critical to high

performance for organizations in dynamic environments. Organizations with too little structure

lack enough guidance to generate appropriate behaviors efficiently (Weick, 1993; Okhuysen and

Eisenhardt, 2002; Baker and Nelson, 2005), while organizations with too much structure are too

constrained and lack flexibility (Miller and Friesen, 1980; Siggelkow, 2001; Martin and

Eisenhardt, 2010). This tension produces a dilemma for organizations, as high performance in

dynamic environments demands both efficiency and flexibility. Research shows that high-

performing organizations resolve this tension using a moderate amount of structure to improvise

a variety of high-performing solutions (Brown and Eisenhardt, 1997, 1998). Overall, this

suggests an inverted U-shaped relationship between the amount of structure and performance, a

relationship often observed when tensions are at work.

The second argument is that achieving high performance with moderate structure is influenced

by the changing nature of environmental opportunities (Adler, Goldoftas, and Levine, 1999;

Rindova and Kotha, 2001). Highly dynamic environments require flexibility to cope with a flow

of opportunities that typically is faster, more complex, more ambiguous, and less predictable

than in less dynamic environments. Research shows that high-performing organizations cope

with dynamic environments with less structure (Eisenhardt and Martin, 2000; Rowley, Behrens,

and Krackhardt, 2000). Conversely, less dynamic environments favor efficiency, and so high-

performing organizations have more structure in these environments (Pisano, 1994; Rivkin and

Siggelkow, 2003). Overall, this suggests that the optimal amount of structure decreases with

increasing environmental dynamism, a consistent finding within multiple literatures.

Yet although these arguments are widely understood in general, unresolved issues remain. First,

the empirical evidence that supports an inverted-U shaped relationship is modest. It primarily

consists of qualitative case comparisons (Mintzberg and McHugh, 1985; Brown and Eisenhardt,

1997) and quantitative confirmations such as statistical tests of quadratic relationships and

interaction effects that are not sufficiently precise to identify a specific functional form (Bradach,

1997; Gibson and Birkinshaw, 2004; Rothaermel, Hitt, and Jobe, 2006), such as an inverted-U.

Rather, the evidence simply points to a unimodal shape for the relationship between structure

and performance that increases on one side and decreases on the other. So the evidence does not

rule out other shapes (e.g., broad plateau or inverted-V) and related functional forms. The shape

of the structure-performance relationship has consequential theoretical and managerial

implications. For instance, if the relationship is a broad plateau with a wide range of optimal

structures, then balancing between too much and too little structure is easy and unimportant. In

contrast, if the shape is an inverted-V, in which the optimal structure is a narrow peak,

sometimes called an “edge of chaos,” then balancing between too much and too little structure is

challenging and crucial.

Second, the theory that underlies the relationship between the amount of structure and

performance is incomplete. As sketched above, the basic theoretical argument is that

organizations with too much structure are too inflexible, while organizations with too little

structure are too inefficient. Although appealing, this argument neglects key factors such as

limited attention, time delays, and the fleeting and varied nature of opportunities that might

influence this tradeoff. So, for example, the theory does not consider that, although less structure

enables flexible improvisation, improvisation is an attention-consuming and mistake-prone

process (Hatch, 1998; Weick, 1998). As a result, the theory fails to clarify precisely how

structure influences efficiency and flexibility, and thus the exact nature of the efficiency-

flexibility tradeoff, including whether it is advantageous to err toward too much or too little

structure.

Third, the theory that underlies the argument that environmental dynamism influences the

optimal structure is imprecise. In particular, environmental dynamism is a multidimensional

construct (Dess and Beard, 1984), and yet the theory does not unpack how the dimensions of

dynamism operate. The empirical literature also reflects this imprecision, as studies often mingle

dimensions such as complexity, velocity, unpredictability, and ambiguity (Eisenhardt, 1989;

Pisano, 1994) that may have distinct effects. Understanding the influence of different dimensions

is important because they may have unexpected implications for theory and practice. For

example, it may be that only one or two dimensions shift optimal structure or that the structure-

performance relationship has distinct shapes in specific environments, such as highly ambiguous

nascent markets and high velocity “bubble” markets.

Overall, these unresolved issues suggest a lack of specific understanding in diverse literatures of

the fundamental relationships among structure, performance, and environment. This is the gap

that we address by exploring the relationship between structure and performance, the underlying

tradeoff between efficiency and flexibility, and the influence of environmental dynamism. There

are many definitions of structure, with varied attributes such as formalization (e.g., rules,

routines), centralization (e.g., hierarchy, use of authority, verticality), control systems (e.g., span

of control), coupling and structural embeddedness (e.g., tie strength, tie density), and

specialization (e.g., role clarity) (Weber, 1946; e.g., Burns and Stalker, 1961; Pugh et al., 1963;

Galbraith, 1973; Mintzberg, 1979; Granovetter, 1985; Scott, 2003). But although the definitions

include varied attributes, they all share an emphasis on shaping the actions of organizational

members. Entities are more structured when they shape more activities of their constituent

elements and thus constrain more action. Conversely, entities are less structured when their

constituent elements have more flexibility in their behavior. Thus we define structure broadly as

constraint on action.

We conducted this research using simulation methods, which are effective for research such as

ours in which the basic outline of the theory is understood, but its underlying theoretical logic is

limited (Davis, Eisenhardt, and Bingham, 2007). In this situation, there is enough theory to

develop a simulation model, yet the theory is also sufficiently incomplete that it warrants

examination of its internal validity (i.e., the correctness of its theoretical logic) and elaboration of

its propositions through experimentation, which are both strengths of simulation (Sastry, 1997;

Zott, 2003). Simulation is also a particularly useful method for research such as ours when the

focal phenomenon is nonlinear (Carroll and Burton, 2000; Rudolph and Repenning, 2002;

Lenox, Rockart, and Lewin, 2006). Though statistical and inductive methods may indicate the

presence of nonlinearities, they offer less precise identification, particularly of complex ones

such as tipping points and skews. Simulation is also a particularly useful method when empirical

data are challenging to obtain (Davis, Eisenhardt, and Bingham, 2007). For example, simulation

enables us to study mistakes that informants might be reluctant to reveal (Carroll and Burton,

2000; Finkelstein, 2003) and to unpack environmental dimensions that may be difficult to

disentangle in actual environments (Dess and Beard, 1984). Finally, simulation is especially

effective for research such as ours that involves longitudinal and process phenomena because

such phenomena can be studied over extended time periods that would be difficult to observe

with empirical data (March, 1991; Zott, 2003). Using these methods, we seek to understand the

effects of varying amounts of structure on performance in different

environments.

ORGANIZATIONAL STRUCTURE AND ENVIRONMENTAL DYNAMISM

Several research streams focus on the fundamental relationships among structure, performance,

and environment. One general argument is that organizations with too little structure are too

confused and lack efficiency, while organizations with too much structure are too constrained

and lack flexibility. By contrast, moderate structure balances between these two states and so is

likely to be high performing (Weick, 1976; Brown and Eisenhardt, 1997). Support for this

general argument emerges in several literatures. Studies in network sociology point to the

“paradox of embeddedness” wherein moderately connected actors outperform those who are

either less or more connected (Uzzi, 1997; Baum, Calabrese, and Silverman, 2000; Owen-Smith

and Powell, 2003). Uzzi (1997) found that firms in the garment industry that combined more and

less structured partnerships were more effective than those firms that used only one type.

Similarly, studies of partially connected technology standards (Garud and Jain, 1996) and

“leaky” networks in the Boston-area biotechnology field (Owen-Smith and Powell, 2003)

suggest that balancing too much and too little structure improves industry-level performance.

The argument for structural balance is also supported in areas of organizational studies in which

loose coupling, ambidexterity, and improvisation are key, including creativity (Amabile, 1996),

innovation (Davis, 2009), group problem solving (Bigley and Roberts, 2001; Okhuysen and

Eisenhardt, 2002), organizational change (Tushman and O’Reilly, 1996; Gilbert, 2005), and

organizational learning (Tripsas, 1997; Hansen, 1999). For example, Brown and Eisenhardt

(1997) found that high-tech firms with a moderate number of simple rules (i.e., semi-structure)

are more flexible and efficient—quickly creating high-quality, innovative products while

responding to market shifts—than firms with more or fewer rules.

In the strategy literature, there is also support for this argument in studies of vertical integration

(Schilling and Steensma, 2001; Rothaermel, Hitt, and Jobe, 2006), loose internal coupling

(Galunic and Eisenhardt, 2001; Williams and Mitchell, 2004; Martin and Eisenhardt, 2010),

innovation (Katila and Ahuja, 2002; Fleming, Sorenson, and Rivkin, 2006), and moderately

structured capabilities with simple rules (Burgelman, 1996; Bingham, Eisenhardt, and Furr,

2007). Rindova and Kotha (2001) found that Yahoo’s initially high performance in a dynamic

environment was partially due to its simple-rules structure for the critical process capabilities of

acquisitions and alliances.

More broadly, research in the complexity sciences also examines the tension between too much

and too little structure. A repeated finding is that moderately structured computational systems

evolve more effectively than systems with too little or too much structure (Kauffman, 1989;

Langton, 1992; Gell-Mann, 1994). A related finding is that systems tend to fall away from the

optimal “edge-of-chaos” amount of structure into catastrophes without constant intervention

(Anderson, 1999; Eisenhardt and Bhatia, 2001). In the language of nonlinear dynamics (Strogatz,

2001), the optimal structure is often an unstable or dissipative critical point that is difficult to

maintain. Overall, these literatures suggest the

following well-known proposition:

Proposition (P1): Performance has an inverted-U shaped relationship with the amount of

structure.

Several streams of research also focus on how environmental dynamism influences the

relationship between the amount of structure and performance. The general argument is that as

the environment becomes more dynamic, it becomes advantageous for the organization to be

more flexible and so less structured. Conversely, as the environment becomes less dynamic,

greater efficiency and so more structure are preferred. This general argument finds extensive

support in a number of literatures. Contingency theory (Lawrence and Lorsch, 1967; Thompson,

1967; Galbraith, 1973) is particularly prominent. In an early study, Burns and Stalker (1961)

found that a more structured mechanistic organization (e.g., role specialization, centralization,

and formalization) is high performing in stable environments because it is highly efficient in

these routine situations. In contrast, a less structured organic organization (e.g., decentralized

decision making, broader and more fluid roles, wider span of control) is high performing in

dynamic markets because it enables flexible action. Similarly, Eisenhardt and Tabrizi (1995)

found that more structure (e.g., planning, numerous and well-defined process steps,

specialization) is faster and more effective for innovation processes in the stable mainframe

computing industry, whereas less structure and more improvised action (e.g., prototyping) is

better in the dynamic personal computing industry. Pisano (1994) found a similar contrast for

new process development in the dynamic biotech industry vs. the stable chemical industry.

The argument is supported by strategy research that has found less structured emergent strategies

to be higher performing in dynamic environments, whereas more structured deliberate strategies

work better in stable ones (Mintzberg and McHugh, 1985). Similarly, network studies have

shown that that loosely coupled networks are more effective in highly dynamic industries

(Tushman and Katz, 1980; Uzzi, 1997; Ozcan and Eisenhardt, 2008). Rowley, Behrens, and

Krackhardt (2000) observed that the high-performing firms in the dynamic semiconductor

industry have loosely coupled alliance networks, whereas high-performing firms in the stable

steel industry have more structured dense networks. Overall, these literatures suggest the

following well-known proposition:

Proposition 2 (P2): As environmental dynamism increases, the optimal amount of structure

decreases.

Central to the underlying theory of these two propositions is the insight that the amount of

structure influences both efficiency and flexibility, but in opposite directions (Gibson and

Birkinshaw, 2004). By efficiency, we mean the rapid, less costly, mistake-free execution of

opportunities like new products, new market entry, or new acquisitions (Miller and Friesen,

1980; Adler, Goldoftas, and Levine, 1999). Structure creates the framework that enables reliable,

rapid, smooth execution in well-grooved routines that is efficient. In contrast, flexibility refers to

open, fluid execution of these opportunities (Weick, 1993; Sine, Mitsuhashi, and Kirsch, 2006).

Removing structure creates latitude for improvisation that is flexible. In dynamic environments,

high performance depends on balancing the tradeoff between flexibility and efficiency.

But though these general theoretical arguments are widely understood, unresolved issues remain.

First, the empirical evidence for an inverted-U shaped relationship is modest, consisting of

qualitative case comparisons (Brown and Eisenhardt, 1997; Gilbert, 2005), and quantitative

statistical tests of quadratic functions or interactions between efficiency and flexibility that are

not precise enough to determine that the relationship is, in fact, an inverted-U (Hansen, 1999;

Gibson and Birkinshaw, 2004; Rothaermel, Hitt, and Jobe, 2006). The evidence does not rule out

other shapes and functional forms that may have critical theoretical and practical consequences.

For example, if the shape is a broad plateau, such that there are a variety of high-performing

structures, then it is easy and unimportant to find the optimal structure. Conversely, if the shape

is an inverted-V, such that there are only a few high-performing structures, then the optimal

structure is challenging to find and crucial to maintain. An inverted-U relationship also requires

very specific functional forms, the simplest being that structure has linear relationships (and

opposite slopes) with efficiency and flexibility. But there is no clear theory for why these

relationships would be, for example, linear.

A second unresolved issue is that the theory underlying the relationship between structure and

performance is incomplete, particularly the theoretical logics tying structure with efficiency and

flexibility.1 Neglected considerations such as attention limits, mistakes, and the fleeting, varied

nature of opportunities suggest that these relationships are more complex than extant theory

indicates. For example, structure improves efficiency by constraining the behaviors of

organizational members within well-established guidelines determined by rules, roles, reporting

relationships, and other forms of structure (Feldman and Pentland, 2003; Rivkin and Siggelkow,

2003). Siggelkow’s (2001) study of Liz Claiborne provides an illustration. Here, executives

created organizational structures (e.g., hierarchies, rules, roles) to address a series of product

opportunities in the apparel industry. Rules were a particularly key form of structure that guided

basic decisions. For example, rules about apparel design stipulated that each season’s clothing

line comprise four to seven concept groups, sizes should be the same across styles, and colors

should not change across years. Together, these and other structures constrained organizational

actions and enabled Liz Claiborne to be highly efficient. Moreover, because Liz Claiborne

executives fit these structures to match specific environmental opportunities focused on a

growing number of professional women, the firm was able to execute a series of lucrative and

very related opportunities consistently, quickly, cheaply, and with few mistakes (Siggelkow,

2001).

Although greater structure improves efficiency, the rate of improvement often declines, and the

range of opportunities that can be captured narrows as well. So organizations may be able to

execute specific opportunities efficiently but not diverse or higher-payoff ones. Brown and

Eisenhardt (1997) described a highly structured product development process that could rapidly

and flawlessly capture similar product opportunities but could not flexibly adjust to capture

highly profitable, new product opportunities. Similarly, Gilbert (2005) described how highly

structured, traditional newspaper firms were too rigid to execute new Internet opportunities,

whereas more loosely coupled ones were more successful. The key point is that increasing

structure can trap organizations in a few or low-payoff opportunities with a declining rate of

efficiency improvements. Organizational action becomes frozen, approaching a non-adaptive

state that complexity theorists call a “complexity catastrophe” (Kauffman, 1993; Anderson,

1999).

Similarly, the relationship between structure and flexibility is likely to be more complicated than

extant theory suggests. Decreasing structure increases flexibility because it gives executives

more degrees of freedom to operate (Weick, 1998; Gilbert, 2005). There is greater latitude of

action and thus a wider range of possible opportunities that can be addressed as managers

combine some structured actions and some actions improvised in real-time (Miner, Bassoff, and

Moorman, 2001; Davis, 2008). But in reality, improvised actions consume more attention than

rule-following actions because they require managers to figure out what actions to take (Hatch,

1998; Miner, Bassoff, and Moorman, 2001). Likely mistakes pose further demands on attention.

Because attention is constrained (March and Simon, 1958; Ocasio, 1997), it limits the number of

possible actions in a given time period. In other words, the benefits of flexibility depend on

having enough attention to figure out what to do (Weick, 1998; Okhuysen and Eisenhardt, 2002).

As an example, Brown and Eisenhardt (1997: 15) described a high-tech firm with few rules,

priorities, and formal roles that “reveled in the excitement of panicked product development” but

engendered “enormous time wasting” and many mistakes. Though some participants enjoyed the

“Silicon Valley organic management,” this firm ultimately generated too many ineffective

products that were behind schedule. Thus limits of attention complicate the structure-flexibility

relationship.

Similarly, the fleeting nature of opportunities complicates the structure-flexibility relationship.

Although organizations could take enough time to engage in extensive trial-and-error actions to

capture any opportunity, opportunities actually have limited time windows in which they are

viable (D’Aveni, 1994). Moreover, mistakes during improvisation introduce time delays that are

particularly damaging because opportunities are fleeting (Tyre and Orlikowski, 1994; Perlow,

Okhuysen, and Repenning, 2002). Figuring out successful improvised actions becomes

especially difficult with low structure because so much is changing that it is hard to get

everything right at once (Moorman and Miner, 1998; Bingham, Eisenhardt, and Davis, 2009). As

structure decreases, action becomes increasingly chaotic, approaching a non-adaptive state that

complexity theorists call an “error catastrophe,” in which organizations make too few correct

actions to succeed (Reynolds, 1987; Kauffman, 1993).

A third unresolved issue is that the theory underlying the argument that more environmental

dynamism lowers the optimal structure is imprecise. Specifically, environmental dynamism is a

multidimensional construct. For example, environmental dynamism includes velocity—the speed

or rate at which new opportunities emerge (Eisenhardt, 1989). The Internet bubble is a good

example of a high-velocity environment (Goldfarb, Kirsch, and Miller, 2007). But dynamism

also includes ambiguity—lack of clarity, such that it is difficult to interpret or distinguish

opportunities (March and Olsen, 1976). Nascent markets like nanotechnology are examples of

environments with high ambiguity (Santos and Eisenhardt, 2009). It also refers to

unpredictability—disorder or turbulence, such that there is no consistent pattern of

opportunities.

Growth markets such as Web 2.0 and wireless services often have unpredictable opportunities.

Environmental dynamism can also include complexity—the number of opportunity

contingencies that must addressed successfully. Opportunities within “green” power, for

example, involve many scientific, regulatory, safety, and commercial aspects and so are highly

complex (Sine, Haveman, and Tolbert, 2005).

Although environmental dynamism is multidimensional, existing theory does not unpack how

different dimensions operate. Empirical research reflects this imprecision. Some research focuses

on specific environmental features such as unpredictability (Lawrence and Lorsch, 1967) and

ambiguity (March and Olsen, 1976). Other research mixes several dimensions together, such as

ambiguity and complexity, to describe environmental dynamism in an industry (Pisano, 1994).

Still other research uses a single term such as velocity but then actually combines multiple

dimensions such as unpredictability, ambiguity, and velocity (Eisenhardt, 1989). Adding to the

imprecision, these dimensions are often correlated in many actual environments. For example,

high-velocity environments can be unpredictable (Eisenhardt, 1989), and complex environments

can involve multiple ambiguities (Gavetti, Levinthal, and Rivkin, 2005). Unpacking the

dimensions of environments, as we do in our simulation study, will provide a better

understanding of optimal structure in different environments.

METHODS

We used stochastic process modeling to study the structure-performance relationship in distinct

environments. This approach enables a custom design of the simulation because it is not

constrained by an explicit problem structure (e.g., cellular automata) (Davis, Eisenhardt, and

Bingham, 2007). Rather, it allows the researcher to piece together processes that closely mirror

the focal theoretical logic, bring in multiple sources of stochasticity (e.g., arrival rates of

opportunities), and characterize them with a variety of stochastic distributions (e.g., Poisson,

Gamma) (Law and Kelton, 1991).

Stochastic modeling is an effective choice for our research because the problem structure does

not fit well with any structured approach. This enables more accurate representation of our

phenomena rather than force-fitting them into an ill-suited structured approach.2 Further,

because our baseline theory is well established in the empirical literature, we enhance the

likelihood of realism by building a model from the ground up (Burton and Obel, 1995) and thus

mitigate a key criticism of simulation. This approach also enabled us to include several sources

of stochasticity that are theoretically important (e.g., improvisational action, opportunity flow)

and to experiment flexibly with theoretically relevant environmental dimensions (e.g., velocity,

ambiguity). Stochastic process modeling also has an influential tradition in our focal literatures,

such as the garbage can model (Cohen, March, and Olsen, 1972), dynamics of culture (Carroll

and Harrison, 1998), and exploration versus exploitation (March, 1991)3.

Modeling Organization Structure and Environment

Our simulation model includes two primary components: organization structure and

environment. We modeled organization structure as rules. Though we could have used other

types of structure (e.g., roles, networks) or other aspects of structure (e.g., centralization,

verticality), we chose rules in order to create a parsimonious model that captures the fundamental

features of structure. As Burton and Obel (1995) explained, effective simulation reveals the

minimal elements of the problem at hand and so uses the least complex conceptualization that

still captures the essence of the phenomenon. That is, the model’s purpose is to represent the core

features of the phenomenon (e.g., organization structure), not be a literal replication of the

phenomenon (Lave and March, 1975; Rivkin and Siggelkow, 2003). As described earlier, rules

are a particularly important type of structure in dynamic environments (Burgelman, 1994; Brown

and Eisenhardt, 1997; Rindova and Kotha, 2001; Zott, 2003). They also fit especially well with

our research because rules directly relate to how structure generates actions to execute (or fail to

execute) environmental opportunities (Bingham, Eisenhardt, and Furr, 2007; Bingham,

Eisenhardt, and Davis, 2009). Rules are also very commonly used to represent structure in

simulations (e.g., Baligh, 2006) because of their direct link to action (March, Schultz, and Zhou,

2000; Eisenhardt and Sull, 2001). Thus our study follows a long, influential tradition of simple

yet powerful computational models that rely on rules to represent structure (Nelson and Winter,

1982; March, 1991; Rivkin, 2000).

We modeled the environment as a flow of heterogeneous opportunities, consistent with our

earlier discussion that organizational structure constrains action in the capture and execution of

varying environmental opportunities (Burgelman, 1996; Eisenhardt and Martin, 2000; Miner,

Bassoff, and Moorman, 2001). Our focus on heterogeneous opportunities is also consistent with

the Austrian economics (Hayek, 1945; Kirzner, 1997) and entrepreneurship (Shane, 2000;

Schoonhoven and Romanelli, 2001) literatures in which environmental dynamism is also a core

interest. Conceptualizing the environment as a flow of heterogeneous opportunities also permits

a rich modeling of environmental dimensions. It enables us to unpack and explore environmental

dynamism more fully, a key theoretical aim of our research.

To capture heterogeneity, we modeled each opportunity as having 10 features that can be either 1

or 0 (e.g., 0101101101) and included four environmental dynamism dimensions, described

below. In contrast, many simulation models assume a fixed environment, a single environmental

jolt, or a single environmental dimension and so preclude the kind of rich exploration of

environmental dynamism that we seek. Although a parsimonious simulation is important (Burton

and Obel, 1995), the richness of the simulation should focus on the part of the model in which

the primary exploration will occur (Burton and Obel, 1995; Davis, Eisenhardt, and Bingham,

2007).

As in all research, we made several assumptions, some fundamental to our modeling. For

instance, we assumed that organizations take actions to capture opportunities, actions require

attention, and attention is limited (Ocasio, 1997). We also assumed that organizations use a

combination of rule-based and improvised actions and that improvised actions require more

attention than rule-based ones because they involve real-time sensemaking (Weick, 1993).These

assumptions are well grounded in field studies of improvisation (Brown and Eisenhardt, 1997;

Miner, Bassoff, and Moorman, 2001; Baker and Nelson, 2005).

Other assumptions are less essential to the theory simplify the model. For example, to focus on

the effects of structure on performance, not learning, we assumed that the rules have already

been learned and that adaptation to new opportunities occurs through improvised actions in real

time. This is consistent with empirical research showing that heuristics are learned quickly and

stabilize rapidly (Bingham, Eisenhardt, and Davis, 2009) and that real-time, improvisational

learning is often not retained in new heuristics (Weick, 1996; Moorman and Miner, 1998).

Similarly, to focus on effects of structure, we assumed that all rules are appropriate for at least

some opportunities. We also assumed that the effects of competitors are realized through the

flow of opportunities, an assumption that mirrors the Austrian economics argument that market

dynamism is endogeneously created through competitive interaction and technological

innovation (Kirzner, 1997).

In our model, the organization has a set of rules to capture opportunities in its environment. In

each time step, the organization takes a combination of rule-based and improvised actions to

attempt to execute a given opportunity. When enough of these actions match the opportunity, the

opportunity is captured, and firm performance increases by the value of the opportunity. Because

attention is limited, and actions (rule-based and improvised) consume attention, however, the

organization can take only a limited number of actions in each time step.

Environmental Dynamism

We modeled four environmental dynamism dimensions based on our review of the structure-

environment research in the organizational and strategy literatures: velocity, complexity,

ambiguity, and unpredictability (Burns and Stalker, 1961; Lawrence and Lorsch, 1967; March

and Olsen, 1976; D’Aveni, 1994; Eisenhardt and Tabrizi, 1995). These four dimensions are

important, frequently used, and distinct from each other, though some research uses alternative

terms for them. This is particularly true of unpredictability. For example, instead of

unpredictability, terms like uncertainty, turbulence, and volatility are also used to capture the

same notion of disorder or dissimilarity in the environment. Terms like turbulence and volatility

focus particularly on disorder, while terms like unpredictability and uncertainty focus more on

the lack of pattern that disorder implies. Finally, though there may be other dimensions of

environmental dynamism, these four are among the most important. A strength of our model is

its rich representation of the environment.

Velocity is the speed or rate at which new opportunities emerge. The Internet bubble is an

example of an environment with a high velocity of opportunities. We operationalized velocity as

the rate that new opportunities flow into the environment (Eisenhardt, 1989; Eisenhardt and

Tabrizi, 1995). We used a Poisson distribution to model the stochastic arrival time of

opportunities into the environment where velocity is lambda, λ. A Poisson distribution, p(k),

describes the probability of k opportunities arriving in t time steps and is determined by the

single rate parameter λ:

p(k) = (λt)e-λt / k! (1)

Poisson is a well-known probability distribution used to model arrival flow (Cinlar, 1975; Glynn

and Whitt, 1992). It is attractive here and in many simulations because it makes few assumptions

about the timing of opportunities (Law and Kelton, 1991). Although lambda can range from 0 to

infinity, we fixed an upper bound on the rate of execution because bounded rationality and

limited attention constrain the number of opportunities that can be addressed (March and Simon,

1958; Shane, 2000).

Complexity was operationalized as the number of features of an opportunity that must be

correctly executed to capture that opportunity. Complexity increases the difficulty of capturing

opportunities because organizations have less latitude for errors when there are numerous,

relevant contingencies (Gavetti, Levinthal, and Rivkin, 2005). Like computational complexity,

complexity can be conceptualized as the minimum number of correct steps that are needed to

execute a plan (Simon, 1962; Sipser, 1997). Biotechnology is an example of a high-complexity

environment because many features of the opportunity must be correct to achieve success (Hill

and Rothaermel, 2003). Complexity is an integer indicating the number of actions that must be

correct in order to execute an opportunity successfully. Because each opportunity has 10

features, complexity ranges from 0 to 10.

Ambiguity was defined as lack of clarity such that it is difficult to interpret or distinguish

opportunities. Because ambiguity makes the misperception of opportunities more likely (March

and Olsen, 1976), we operationalized environmental ambiguity as the proportion of perceived

opportunity features that differ from actual ones. Nascent markets like nanotechnology are

typically highly ambiguous (Santos and Eisenhardt, 2009). The actual features of an opportunity

are represented by a 10-element bit string (i.e., vector) of 1s and 0s—e.g., 0100100110. The

misperceived features of the same opportunity are also a 10-element bit string of 1s and 0s but

differ from the actual features by those features for which perception does not match reality—

e.g., 0110100110. Ambiguity was operationalized as the proportion of misperceived opportunity

features. For example, the actual and perceived features of the two bit strings above differ by one

element of 10, so the ambiguity = .1. This is an especially useful way to model ambiguity

because it allows us to capture the difficulty of interpretation that leads to misperception of

opportunities. Ambiguity

ranges from 0 to 1.

Unpredictability was defined as the amount of disorder or turbulence in the flow of opportunities

such that there is no consistent similarity or pattern. An implication of increasing unpredictability

is that managers are less able to adjust or “tune” their structures to the environment because there

is less pattern to match (Galbraith, 1973).4 We manipulated unpredictability by changing the

probability that any opportunity feature will be a 1 or a 0—i.e., p(1) and p(0). Opportunities with

features that have a higher probability of 1 or 0 are less unpredictable than opportunities with

features having an equal probability of 1 or 0. This approach has the advantage of stochastically

generating similar opportunities without the researcher’s bias as to what those patterns should be.

To have a monotonically increasing measure of unpredictability, we converted these probabilities

using a well-known disorder computation from mathematical information theory (Cover and

Thomas, 1991). Unpredictability, U, of a flow of opportunities depends on the probability, p, of a

feature being either a 1 or a 0 and is given by:

U = – ∑ p * log2(p) (2)

To illustrate, when p(1) = .7 and p(0) = .3, then unpredictability is relatively low. There is a

70/30 split of 1s and 0s in the features vector of each opportunity (making 1s more likely than

0s) such that U = –.7*log2(.7) + .3*log2(.3) = .88. By contrast, when unpredictability is high

[p(1) = p(0) = .5 and U = 1], the distribution of 1s and 0s in the opportunity features is random.

Both opportunities and rules have a 50/50 split of 1s and 0s (making 1s and 0s equally likely),

and there is no consistent similarity or pattern in the flow of opportunities. Unpredictability

ranges from 0 to 1.

Organizational Structure as Rules

We modeled structure as a set of rules for capturing opportunities, with each rule specifying

particular actions for executing opportunities. Rules as structure are common in our focal

literatures. For example, Galunic and Eisenhardt (2001) described rules for carrying out

“patching” opportunities in a high-performing, multi-business corporation, including that new

product-market charters should always be assigned to business units that (1) have relevant

product-market experience and (2) are currently assigned charters with shrinking markets or

fading profit margins. Similarly, Rindova and Kotha (2001) described rules for executing

alliance opportunities at Yahoo!, such as (1) making the basic service free and (2) having no

exclusive deals. Overall, rules specify actions for addressing opportunities and are central to

organizational processes and capabilities such as interfirm collaboration, product development,

and country entry (Burgelman, 1996; Eisenhardt and Sull, 2001; Rindova and Kotha, 2001;

Bingham, Eisenhardt, and Furr, 2007; Davis, 2008).

Rules were operationalized with a 10-element vector of 1s, 0s, and ?s (e.g., 0?1?10???0). When

an organization attempts to execute an opportunity with a rule, it generates 10 specific actions.

That is, each 1 or 0 generates a rule-based action in that position. The proportion of 1s and 0s in

a rule was set equal to the probability of 1s and 0s in the flow of opportunities. This captures the

insight noted earlier that organizations can adjust their structures to approximately match

patterns in the flow of opportunities if they exist (March and Simon, 1958). Additionally, for

each “?” the organization improvises either a 1 or 0 “improvised action” with a 50/50 likelihood.

For example, a combination of rule-based and improvised (underlined) actions using the rule

above could produce the vector 0111100110. The computer program then compares this set of 10

actions to the opportunity’s 10 features. If the number of actions (both rule-based and

improvised) that match the actual features of the opportunity equals or exceeds the value of the

environmental complexity parameter, then the opportunity is executed and the firm gains the

payoff value of that opportunity. For example, if complexity = 6 and the actions above—

0111100110—are compared to the opportunity 0110101010, then the opportunity is successfully

executed because 7 of the actions were correct. This operationalization captures the idea that

structure constrains some actions, while others are left open to improvisation (Brown and

Eisenhardt, 1997; Miner, Bassoff, and Moorman, 2001).

Amount of structure. We operationalized the amount of structure as simply the number of rule-

based actions specified by each rule (i.e., number of 1s and 0s). For example, the amount of

structure in the rule 01?0??011? is 6. Thus increasing the amount of structure for an

organization’s set of rules increases their constraint on action. For ease of exposition, we term

rules with little to moderate structure (i.e., 3 to 5) simple rules. This operationalization is

consistent with theoretical notions of structure such as Simon’s (1962) and Daft’s (1992), in

which the amount of structure is associated with the number of components. It is in contrast to

some prior research (Rivkin and Siggelkow, 2003) emphasizing the interactions among structural

features and putting less emphasis on the number of structural features. By emphasizing the

number of structural features, we focus on the amount of structure in rules. Structure, however,

constrains action in both. Thus, for the same research questions, the results should be

qualitatively consistent.

Performance. Each opportunity is associated with a randomly determined payoff value.

Performance was operationalized as the sum of all payoffs from every opportunity executed,

across all time steps. This is particularly appropriate for our research because it is consistent with

the empirical studies of dynamic environments indicating that performance is derived from a

series of temporary advantages and related payoffs (D’Aveni, 1994; Roberts, 1999; Rindova and

Kotha, 2001; Chen et al., 2009).

Simulating the Model

We implemented this model in Matlab software. The computer program flow is outlined below,

and the Technical Appendix provides more details. In the beginning, the organization’s structure

(i.e., its rules) and environment (i.e., flow of heterogeneous opportunities determined by the

velocity, complexity, ambiguity, and unpredictability parameters) are randomly initialized using

draws from probability distributions (Law and Kelton, 1991). In each time step, opportunities

flow into the environment at velocity lambda. When the organization tries to capture an

opportunity with a rule, the organization generates both rule-based and improvised actions.

When the number of these actions that match the opportunity is greater than the environmental

complexity, the opportunity is executed and performance increases by the payoff value.

As discussed above, the firm’s actions (both rule-based and improvised) require attention, which

is limited (Cyert and March, 1963; Ocasio, 1997), so the organization has a limited number of

actions that it can take in any time step. When attention runs out, the organization can take no

further actions. At the end of t = 200 time steps, the simulation run ends and performance is

computed. We chose this number of time steps because it is large enough to allow sufficient

opportunities to flow into the environment such that any initialization effects on the findings are

mitigated (Law and Kelton, 1991), but we also experimented with multiple values for the amount

of attention required for improvised action relative to rule-based action, as described further in

the Technical Appendix. We found no qualitative differences in the findings and so present the

results for this representative value.

Monte Carlo Simulation Experiments

We used Monte Carlo simulation techniques. In the Monte Carlo approach, an experiment is a

simulation with fixed parameter settings that is run multiple times (Law and Kelton, 1991). The

results are then averaged and confidence intervals calculated (Kalos and Whitlock, 1986). Thus

for any given experiment, the result is the mean performance (and confidence interval) over

multiple simulation runs, which better reflects the underlying processes under investigation than

those produced by a single simulation run.

Each experiment consists of 30 or 50 simulation runs. We selected n = 30 as the number of

simulation runs for all experiments, except those on the basic relationship between structure and

performance, because exploratory analyses revealed that values of n greater than 30 yielded

insignificantly small incremental gains on reliability. We used n = 50 for the basic relationship

between the amount of structure and performance because the larger range of structure values

adds precision to our illustration of this relationship. These results are representative of the

findings produced by other construct values during our exploration of the parameter space (see

the Technical Appendix for more details).

We ran experiments for a wide range of values for each environmental dimension (e.g., velocity).

Given space limitations, we report only relationships using representative low and high values

from those experiments. Specifically, we plotted the relationship between the amount of structure

and performance for these representative values of the environmental dimensions.5 Confidence

intervals in the form of error bars (i.e., the square root of the variance over the number of trials)

are included to enable more accurate statistical interpretation of the results, as is standard in

Monte Carlo experiments (Kalos and Whitlock, 1986).

RESULTS

Amount of Structure and Performance

[Figure 1 about here]

We begin by examining the two propositions that form the baseline theory. P1 proposed that the

amount of structure has an inverted U-shaped relationship with performance. Figure 1 plots the

relationship between performance and the amount of structure, with each point representing the

average over 50 simulations. The results show that organizations with low or high structure rules

perform worse than those with moderate structure (optimal structure at a value of 3). Optimal

structure exists, but unexpectedly, the curve is asymmetric. That is, the performance decline

from the left endpoint to the optimum is steeper than the performance decline from the right

endpoint to the optimum.6 Within the bounds of these simulation experiments, too much

structure produces a more gradual decline, while too little structure produces a steeper drop in

performance for all deviations from the optimum. Thus there is an asymmetric relationship,

which suggests a more complicated theoretical logic than a simple tension between too much and

too little structure.

Our model offers some insight into this logic. In particular, rule-based actions are relatively

automatic, and so they conserve attention. This enables more actions in a given time frame to

capture additional opportunities. So although more structure narrows the range of potential

opportunities that can be addressed, there is an “attention advantage” of added structure that

partially compensates. This advantage occurs at relatively high values of structure across a broad

range of environmental conditions and so favors erring on the side of structure in these

environments. This suggests the following

modified proposition:

Proposition 1a (P1a): Performance has a unimodal, asymmetric right relationship with the

amount of structure.

Unpacking the Dimensions of Environmental Dynamism

P2 proposed an environmental contingency that the optimal amount of structure decreases with

increasing environmental dynamism. We used four experiments to understand which dimensions

explain this shifting optimum: we examined P2 by comparing curves with high and low values of

each dimension of environmental dynamism (i.e., velocity, complexity, ambiguity, and

unpredictability) while holding the other three constant at moderate values.

Environmental velocity. Figure 2 depicts the effect of increasing environmental velocity (i.e.,

rate of opportunity flow) on performance by superimposing the resulting curves of two

representative values. That is, we plotted the results that correspond to low and high values of

velocity (λ = .6 and 1.4) to examine the effects of velocity. P1a is roughly supported in both

environments.

[Figure 2 about here]

In contrast, the results do not support P2. Within the precision of this simulation experiment, the

optimal amount of structure—i.e., the amount of structure producing the highest performance—

is the same for both high- and low-velocity environments. Further, although the optimal amount

of structure is the same in the two velocity conditions, their performance is not. For a given

amount of structure, firms in high-velocity environments have higher performance than those in

low-velocity ones. In fact, increasing velocity appears to amplify performance and shift the

entire curve upward. Overall, this suggests that the large number of opportunities that emerge in

high-velocity environments (e.g., Internet bubble, Web 2.0) yields better performance for all

levels of structure, other things being equal.

[Figure 3 about here]

Environmental complexity. Figure 3 depicts the effects of increasing environmental

complexity (i.e., the difficulty of capturing opportunities, given numerous relevant

contingencies) on performance by superimposing the results of representative low and high

values of complexity (4 and 8). P1a is roughly supported in both high- and low-complexity

environments by unimodal, asymmetric curves. P2 is again not supported. Within the precision

of this simulation experiment, the optimal amount of structure is the same for both high and low

environmental complexity. Performance at the optimal structure differs in the two environments,

however, with increasing complexity shifting the curve downward. Firms perform worse in high-

complexity environments in which opportunities involve many contingencies (e.g., “green”

power, biotechnology), in contrast to the velocity findings.

[Figure 4 about here]

Environmental ambiguity. Figure 4 shows the effect of increasing environmental ambiguity

(i.e., lack of clarity of opportunities) on performance by superimposing the results of the two

representative cases that correspond to low and high values of ambiguity (0 and 0.2). P1a is

again roughly supported in both environments: the curves have unimodal, asymmetric shapes.

P2 is again not supported. The optimal amount of structure is the same in both low- and high-

ambiguity environments within the precision of this simulation experiment. Yet both the range of

optimal structures and the peak performance at the optimal structure differ in the low- versus the

high-ambiguity environments. When ambiguity is low, there is a narrow range of optimal

structures and a higher level of peak performance. This suggests an environment in which it is

difficult for managers to find and maintain an optimal structure, but they will achieve

particularly high performance when they do. To the extent that skilled executives more easily

locate and maintain the optimal structure, this is consistent with a skill-dominated environment.

In contrast, when ambiguity is high, as in nascent markets, there is a wide range of optimal

structures and lower peak performance. This suggests an environment in which it is easy for

managers to find and maintain an optimal structure, but they will not achieve particularly high

performance. This suggests a chance-dominated environment.

[Figure 5 about here]

Environmental unpredictability. Figure 5 illustrates the effects of environmental

unpredictability (i.e., disorder in the flow of opportunities) on performance by superimposing the

results of two representative cases of low and high unpredictability (U = .72 and 1). Again, a

unimodal, asymmetric relationship, supporting P1a, is found in both environments. But unlike

the results for velocity, complexity, and ambiguity, we find a shifting optimum, as predicted by

P2, as the optimal amount of structure decreases with higher unpredictability. Thus

unpredictability is the environmental dimension that shifts the optimal amount of structure.

Moreover, our model offers insight into the logic: the optimal structure decreases with increasing

unpredictability because managers are less able to adjust structure to fit the environment when

the presence of consistent patterns in the opportunity flow declines. In these environments,

managers must rely more on real-time improvised actions and less on structure because there is

less pattern in the environment that can be mirrored in organizational structure. This suggests a

modified proposition:

Proposition 2a (P2a): As environmental unpredictability increases, the optimal amount of

structure decreases.

There are also unexpected findings related to the range of optimal structures. As figure 5 shows,

when environments have low unpredictability, the relationship between structure and

performance forms a broad plateau. This suggests a forgiving environment in which there is a

wide range of optimal structures with roughly the same performance outcomes. In contrast, when

environments have high unpredictability, there is an inverted-V relationship between structure

and performance. This suggests a punishing environment in which there is a narrow range of

optimal structures, such that it is challenging to find the optimal amount of structure, hard to

maintain the optimal structure even when perturbations of structure are small, and very low

performance when the optimal structure is not achieved. Even small changes in structure have

large effects on performance, consistent with an edge of chaos in which only a narrow range of

structures leads to superior performance. Thus, in contrast to forgiving low-unpredictability

environments, high-unpredictability environments are punishing, with a narrow range of optimal

structures.

Analyzing mistakes. Because mistakes are likely to be relevant in a more complete theoretical

logic linking structure, performance, and environment, we next examined mistakes. We define a

mistake as an application of any action (rule-based or improvised) to an opportunity feature that

does not match, and mistake size as the number of mistakes (i.e., count of mismatches of actions

with opportunity features) committed in an attempt to capture an opportunity.

We computed the frequency distributions of mistake size, focusing on unpredictability because of

its role in shifting the optimal structure. We ran the simulation at multiple unpredictability and

structure settings and then tabulated the number of attempts to capture an opportunity for each

mistake size. As shown in figure 6, we have nine values of structure, from low = 1 to high = 9,

down the rows (omitting the value of 10 because it produced undefined endpoint values), and

three values of unpredictability—high, low, and very low (U = 1, .72, and .47)—across the

columns. The sum of each distribution is normalized to 1 for easy comparison across

distributions. Each of the resulting 27 distributions is a mini-graph that plots the proportion of

attempts to capture opportunities at each mistake size for specific values of unpredictability and

structure.

[Insert Figure 6 about here]

The mistakes analysis sheds light on the theoretical logic for why the range of optimal structures

decreases (i.e., from a broad plateau to an inverted-V) as unpredictability increases. First, in low-

unpredictability environments (column 2 in figure 6), the analysis indicates that increasing

structure reduces the mean mistake size and eliminates large mistakes. These trends are

accentuated in environments with very low unpredictability (column 3 in figure 6). The

underlying reasoning is as follows. When unpredictability is low, opportunities are more

homogeneous and there are recognizable patterns occurring in the opportunities. This

predictability allows managers to adjust their structures to more closely fit the opportunities. So a

structured action is more likely to be successful for capturing an opportunity. This means that

although increasing structure narrows the range of opportunities that can be addressed, the

elimination of large mistakes and the drop in mean mistake size partially offset this

disadvantage, such that there is a “mistakes advantage” for structure in less unpredictable

environments. This suggests a relatively broad range of successful structures (i.e., plateau) in

low-unpredictable environments, as observed in figure 5.

In contrast, in high-unpredictability environments, the mistakes analysis indicates that

organizations at all levels of structure are likely to commit multiple mistakes of varying size,

including some very large mistakes (column 1 in figure 6). When unpredictability is high,

opportunities are very heterogeneous and there is very little pattern in the flow of opportunities.

Thus managers cannot adjust their structures to fit environmental opportunities because they do

not know what those opportunities will be. The result is mistakes of varying sizes (even large

ones) at all levels of structure, including the optimal structure. So there is no “mistakes

advantage” for structure that compensates for the loss of flexibility when structure is added.

Rather, there is a narrow range of optimal structures, making the tradeoff between efficiency and

flexibility more severe in highly unpredictable environments.

Modeling structure and performance. To gain added theoretical insights, we next created a

simple mathematical formalization. This model formulates the theoretical logics of efficiency,

flexibility, and unpredictability more precisely in terms of specific functional forms (Davis,

Eisenhardt, and Bingham, 2007).7

Let e(x) and f(x) represent efficiency and flexibility as functions of structure, respectively. Prior

researchers have argued that efficiency and flexibility have interdependent, non-substitutable

effects on how structure influences performance (Adler, Goldoftas, and Levine, 1999; Gibson

and Birkinshaw, 2004) in which the aggregate effect on performance, A(x), is a roughly inverted

U-shaped curve of the following form:

A(x) = e(x)*f(x). (3)

Yet not all e(x) and f(x) functions produce a unimodal A(x) curve and shift the optimal structure,

x’, as unpredictability increases. As shown in the Mathematical Appendix, the requirements for

such a curve and shifting optimum put strong constraints on the forms of e(x) and f(x).8

Consistent with our mistakes analysis and prior research, we assume that increasing structure

increases efficiency—i.e., e’ (x) > 0 (Brown and Eisenhardt, 1997; Siggelkow, 2001)—such that

more structure enables faster, more reliable execution of those opportunities for which the

structure is appropriate. But as structure increases, the number of opportunities that fit the

structure decreases, and the gains to efficiency of economizing on attention grow more slowly

(Donaldson, 2001). So there are likely to be decreasing efficiency returns for added increments

of structure that we capture with a logarithmic function of efficiency:

e(x) = ln(x) (4)

The logarithmic form of e(x) satisfies the important condition that e’(x) > 0 because e’(x) = 1/x >

0 for x > 0 and captures the intuition that efficiency increases, albeit at a declining rate, as

structure increases.

Conversely, the literature suggests that flexibility declines as structure increases—i.e., f’(x) < 0

(Brown and Eisenhardt, 1997; Miner, Bassoff, and Moorman, 2001). On the one hand, less

structure enables organizations to use improvised actions to address more different opportunities.

On the other hand, more structure constrains improvised actions, forces more rule-based actions,

and limits the heterogeneity of opportunities that can be addressed (Weick, 1993; Baker and

Nelson, 2005). Empirical studies of structural inertia have found that this decline in flexibility

occurs most dramatically at low levels of structure, at which even small additions of structure

can greatly constrain organizational actions (Greve, 1999). This is consistent with the argument

that the effect of incremental additions of structure is to eliminate successive fractions of

opportunities that could have been flexibly addressed by less structure. This implies that

flexibility is rapidly declining and inversely proportional to structure, a relationship that we

capture as follows:

f(x) = 1/x (5)

This function satisfies the important condition that f’(x) < 0 because f’(x) = –1/(x^2) < 0 for x >

0. As described in the Mathematical Appendix, this function is a particularly appropriate choice

because it captures the effect of eliminating successive fractions of opportunities with each

increment of structure. Finally, because efficiency and flexibility are interdependent and non-

substitutable (Gibson and Birkinshaw, 2004), aggregate performance is:

A(x) = ln(x)/x (6)

Though other functional forms for efficiency and flexibility may be possible, this A(x) produces

a unimodal, asymmetric right relationship between structure and performance that is consistent

with our simulation results and theory, as noted in the Mathematical Appendix.9

Next, we move to unpredictability. Though researchers have simply argued that flexibility

becomes more influential than efficiency as environmental dynamism increases, we show in the

Mathematical Appendix that simply increasing flexibility does not shift the optimal structure.

Instead, unpredictability, u, has two separate effects on performance that shift the optimum.

First, as unpredictability increases, the heterogeneity of opportunities increases. Organizations

with less structure can potentially capture at least some of these more varied opportunities

through improvisation. But executing these additional opportunities critically depends on having

the greater latitude of action (i.e., flexibility) that less structure provides and so is inversely

proportional to structure, 1/x. Also, though additional opportunities can be addressed, the number

of opportunities that can be captured grows increasingly slowly as unpredictability increases.

The reason is that less structure slows down improvisation and takes more attention because the

number of opportunity features that must be successfully improvised at once grows. So although

there are more opportunities available, the number of additional opportunities that can be

successfully captured increases at a decreasing rate. We represent this increasing difficulty with a

logarithmic function of unpredictability, ln(u). Thus we model the added performance

improvement that occurs with increasing unpredictability by ln(u)/x. Combining this effect with

A(x) changes performance to A(x) + ln(u)/x = ln(x)/x + ln(u)/x = ln(ux)/x.

Second, as unpredictability increases, it becomes more challenging to capture opportunities

regardless of whether improvised or rule-based actions are used. Adding structure is ineffective

in this environment because there is little predictable pattern in the flow of opportunities that

managers can use to adjust their organizational structures to the environment. Subtracting

structure is helpful, as noted above, in terms of adding opportunities that can potentially be

addressed. But it is also harmful because improvisation is more difficult. Improvisation demands

more attention, has more degrees of freedom, and generates many mistakes (including large

ones) and so becomes more challenging as unpredictability increases. We represent this overall

declining performance with a dampening parameter, 1/u. Adding this second effect of

unpredictability generates a performance function, P(x,u):

P(x,u) = 1/u [ln(ux)/x] = ln(ux)/ux (6)

As noted in the Mathematical Appendix, this function satisfies the conditions for P1a, generating

a unimodal, asymmetric right relationship between structure and performance. It also satisfies the

conditions for P2a that as unpredictability, u, increases, the optimal structure, x’ = e/u, decreases.

Overall, the mathematical model is consistent with our simulation results and theory.

This mathematical model offers several useful extensions. First, it clarifies the approximate

functional forms and rates of change of efficiency and flexibility that contribute to the

asymmetry between structure and performance. Performance is asymmetric because efficiency

and especially flexibility are changing more rapidly at low structure than at high. When structure

is low, even small increments in structure create large increases in efficiency, ln(x), and large

decreases in flexibility, 1/x. Thus there is a severe tradeoff between efficiency and flexibility. In

contrast, when structure is high, performance is much less sensitive to structure. Efficiency

improves very gradually with added structure. Flexibility is already so low that increases in

structure have little effect. Thus there is only a modest tradeoff between efficiency and

flexibility. Overall, having too little structure is particularly risky because efficiency and

flexibility are highly sensitive to even small changes in structure when structure is low.

Second, this model clarifies the inverted-V curve and related edge of chaos in highly

unpredictable environments. According to prior research, less structure is better in highly

dynamic environments because flexibility is more advantageous than efficiency. In contrast, a

core insight of our model is that neither efficiency nor flexibility works very well in highly

unpredictable environments. As expected, extensive structure and so efficiency are ineffective

because they are overly rigid. But unexpectedly, improvised actions and so flexibility are not

very effective either. With so little structure, improvisation consumes a lot of attention, is fraught

with mistakes, and is very slow. As a result, the organization can only capture a few

opportunities, and risks falling into an “error catastrophe,” in which it lacks enough traction to

improvise fast enough to capture opportunities before they disappear. So the optimal structure is

a narrow range (i.e., at the edge of chaos) of just enough structure to capture at least a few

opportunities.

DISCUSSION AND CONCLUSION

[TAB 1]

Using computational and mathematical modeling, we added to theory on the fundamental

relationships among structure, performance, and environment. As summarized in table 1, our

core contribution is a more precise theory of how the locus, asymmetry, and range of optimal

structures are grounded in the tradeoff between efficiency and flexibility in differing

environments. First, we clarify this tradeoff between flexibility and efficiency. Prior theory

focuses on balancing efficiency and flexibility (Tushman and O’Reilly, 1996; Brown and

Eisenhardt, 1997; Uzzi, 1997; Rowley, Behrens, and Krackhardt, 2000). In contrast, we find that

this tradeoff is more accurately the flexible capture of widely varying opportunities vs. efficient

execution of specific opportunities.10 Less structure opens up the organization to the possibility

of addressing a wider range of opportunities that serendipitously occur, but it also hinders the

rapid, mistake-free execution of those opportunities. Conversely, more structure enables the

efficient execution of particular opportunities that can be anticipated. But too much structure is

more than just too rigid. It also narrows the range of possible opportunities, suggesting that

structure is most valuable when many similar opportunities are available.

Second, the relationship between structure and performance is unexpectedly asymmetric:

performance gradually fades with too much structure but drops catastrophically with too little.

Thus structure and performance do not have an inverted-U relationship, as argued previously

(Brown and Eisenhardt, 1997; Gibson and Birkinshaw, 2004; Rothaermel, Hitt, and Jobe, 2006).

Rather, efficiency and flexibility are distinct functions that change increasingly slowly when

structure is high. In contrast, efficiency and especially flexibility are changing more rapidly

when structure is low, creating a more acute tradeoff between efficiency and flexibility. The

consequential implication is that it is safer to err on the side of too much structure (efficiency)

than on the side of too little (flexibility).

Third, our results show that simple rules and other semi-structures are surprisingly robust across

multiple environments, in contrast with research arguing that they are best only in highly

dynamic environments (Burns and Stalker, 1961; Rowley, Behrens, and Krackhardt, 2000;

Eisenhardt and Sull, 2001).11 In predictable environments, there is a broad plateau of optimal

structures, and so numerous high-performing structures exist. The tension between too much and

too little structure is easy to manage in this forgiving environment in which many structures are

roughly equivalent. So executives can rely on simple rules, loose coupling, and other semi-

structures that favor flexibility (albeit with more attention and mistakes) or elaborate structures

with tight coupling that favor efficiency (albeit with a narrower range of opportunities) without

sacrificing much performance. For example, executives who need to minimize mistakes (e.g.,

nuclear power plants, aircraft carriers) can design highly reliable organizations that utilize very

extensive structure (Perrow, 1984; Weick and Roberts, 1993) with little performance penalty.

In contrast, in unpredictable environments, there is an inverted-V relationship between structure

and performance with only a narrow band of optimal structures. Even minor perturbations in

structure can be catastrophic in these punishing environments in which performance is precarious

and mistakes can be many, large, and fatal. The tension between too much and too little structure

is challenging and crucial to manage. The mistakes advantage of structure vanishes, and

improvisation is difficult. Here, only simple rules are high performing. The overall implication is

that simple rules and other semi-structures are robust across diverse environments—i.e., they are

viable in predictable environments and essential in unpredictable ones.

Underlying the robustness of simple rules across environments are the dynamics of

unpredictability that shape the locus and range of optimal structure. Prior research has included

velocity (Eisenhardt, 1989), complexity (Gavetti, Levinthal, and Rivkin, 2005), and ambiguity

(March and Olsen, 1976; Rindova and Kotha, 2001) as major dimensions of environmental

dynamism. But though these dimensions have intriguing implications for strategy and

performance (see below), only unpredictability influences optimal structure. Underlying this

finding is the insight that structure is valuable when there are consistent patterns in the flow of

environmental opportunities and when managers have adjusted their structures to match these

patterns. But as our simulation suggests, these tuning adjustments need not be exactly accurate.

Rather, sometimes matches may occur by chance, and sometimes structure helps just by

diminishing the degrees of freedom in mistake-prone improvisation. The key implication is that

adding structure when unpredictability decreases can be valuable (or at least not harmful), even

when it is not completely clear what exactly that structure should be. Thus our results support a

structural explanation for Weick’s (1990) well-known observation of the success of a European

army in navigating the Alps based on a map of the Pyrenees (see also Gavetti, Levinthal, and

Rivkin, 2005).

A particularly intriguing optimum in the structure-performance-environment relationship is

simple rules in highly unpredictable environments. Prior researchers have argued that favoring

flexibility leads to high performance (Burns and Stalker, 1961; Brown and Eisenhardt, 1998).

But though we find that flexibility is helpful, this argument is too simplistic because neither

structure nor improvisation is very effective in these environments. As a result, the optimal

structure not only diminishes, but its range unexpectedly shrinks from a broad plateau to an

inverted-V (i.e., edge of chaos). And more unexpectedly, the number of opportunities that can be

successfully executed also drops as improvisation becomes more difficult. A consequential

implication is that the content of a high-performing simple-rules strategy will likely focus on

capturing a few, high-payoff opportunities – i.e., a small number of rules to quickly select a few

“home-run” opportunities and to quickly exit those opportunities when they do not pan out. This

implication also helps explain why heuristics that focus on prioritizing and exiting opportunities

are particularly high performing in highly dynamic environments (Bingham, Eisenhardt, and

Furr, 2007).

Finally, we contribute insights into the edge-of-chaos concept from the complexity sciences

(Kauffman, 1993; Carroll and Burton, 2000). Research has defined the edge-of-chaos as a phase

transition between order and disorder (Kauffman, 1993), and it is often described more colorfully

with phrases like “snooze, you lose” and “only the paranoid survive” (Brown and Eisenhardt,

1998; Burgelman, 2002). Our contribution is theoretical insights into this intriguing construct,

and its role within our elaborated theory of structure, performance, and the environment. First,

we identify where the edge of chaos is likely to occur: in highly unpredictable environments. In

these environments, the relationship between structure and performance is an inverted-V with

tipping points on both sides of the optimal structure, consistent with an edge-of-chaos. Second,

we explain why the edge of chaos occurs—when structure is low, rapidly changing efficiency

and flexibility with difficult improvisation create a thin range of optimal structures. Third, we

characterize the distribution of mistakes at the edge of chaos—many errors of widely varying

size and including some large errors. Managers are likely to experience both small oversights

and debilitating miscalculations. Note that we did not find an inverse power law distribution of

many small mistakes and few large ones (Bak, 1996). Rather, the distribution is roughly normal.

Finally, we provide insight into the energy required to maintain a position at the edge of chaos.

Researchers have argued that the edge of chaos is a dissipative equilibrium, an unstable critical

point that requires constant energy to maintain (Prigogine and Stengers, 1984). We extend this

notion to our focal literatures by clarifying that managerial energy at the edge of chaos centers on

real-time improvisation of opportunities, recovery from the inevitable mistakes that will occur,

and continuous monitoring of the amount of structure to avoid drift from the optimum.

Toward a Pluralistic View of Strategies

More broadly, our work also contributes to strategy and its mandate to develop theoretical logics

explaining variance in firm performance. First, we contribute to the strategic logic of opportunity

and the related strategy as simple rules (Eisenhardt and Martin, 2000; Eisenhardt and Sull, 2001).

According to the logic of opportunity, firms achieve high performance in dynamic markets by

using a few simple rules to guide the capture of opportunities (e.g., Gersick, 1994; Burgelman,

1996; Galunic and Eisenhardt, 2001; Miner, Bassoff, and Moorman, 2001; Rindova and Kotha,

2001; Bingham, Eisenhardt, and Furr, 2007). Our research extends this view with support and

insights into the core theoretical logic by clarifying the implications of limited attention,

mistakes, and the fleeting and varied nature of opportunities. These dynamics place a premium

on using increasingly simple rules to capture increasingly unpredictable opportunities. Thus, like

other simulations that provide internal validation of theory (e.g., Sastry, 1997), our simulation

helps to sharpen the theory that underlies the strategic logic of opportunity.

Second, we contribute insights into the boundary conditions of several strategic logics. In

positioning logic, executives achieve high performance by building tightly linked activity

systems in valuable strategic positions, such as low-cost or high-differentiation (Porter, 1985;

Rivkin, 2000). Our findings add to this view by clarifying that such high-structure strategies are

effective in predictable markets. Further, our findings contribute to a deeper understanding of

why tightly linked activity systems are high performing in such predictable markets—i.e., while

fewer opportunities may fit these highly structured strategies, their tightly linked activity systems

produce both few and small mistakes. Therefore they efficiently execute a flow of similar

opportunities. In addition, given that there are many possible high-performing structures in

predictable markets (i.e., a plateau relationship between structure and performance), our findings

indicate why executives can achieve good performance with many alternative strategies. These

numerous optimal strategic alternatives help to explain why multiple differentiated positions are

often viable in predictable markets (Porter, 1985). Finally, our findings clarify why, once

achieved, competitive advantage gained through positioning is relatively robust to environmental

and structural perturbations, creating a foundation for sustainable competitive advantage and

superior performance.

By contrast, in opportunity logic, executives achieve high performance by using a few simple

rules or heuristics to capture varied opportunities (Eisenhardt and Sull, 2001; Bingham and

Eisenhardt, 2008). Our findings contribute to this view by indicating that low-structure

opportunity logic is particularly essential in unpredictable markets, while positioning logic is

most effective in predictable markets, thereby sketching a boundary condition between these

strategic logics. Our findings further contribute a subtle insight into the precarious nature of

competitive advantage (D’Aveni, 1994; Lenox, Rockart, and Lewin, 2006). Though prior

researchers have argued that firms should seek a series of short-term, competitive advantages in

dynamic environments (Roberts and Amit, 2003; Chen et al., 2009), our results indicate that

competitive advantage in these environments is unstable and its duration unforeseeable (but not

necessarily short-term). Overall, this suggests that firms with a strategic logic of opportunity are

threatened by internal collapse—i.e., they can fail as a result of having too much or too little

structure and not just as a result of external competition. This potential for internal collapse

offers an alternative explanation of intraindustry performance heterogeneity that differs from

path dependent and competitive explanations (McGahan and Porter, 1997; Bowman and Helfat,

2001). Thus a key insight is that the managerial challenges of finding and maintaining optimal

structure at the edge of chaos may contribute to heterogeneous firm performance within dynamic

industries.

A Richer View of Environments

Our work also contributes to a better understanding of distinct environments. Prior research tends

to focus on single environmental dimensions or mix several dimensions together. The result is an

imprecise understanding of different environments. In contrast, we highlighted four distinct,

widely used environmental dynamism dimensions (i.e., velocity, complexity, ambiguity, and

unpredictability) and developed their unique implications for strategy and performance. We

covered unpredictability above and now turn to the remaining three dimensions.

High velocity environments are particularly attractive. Because they are opportunity-rich,

managers can be selective, and so choose many, high-payoff opportunities. In addition, this

finding offers further insight into why rapid executive actions and processes such as fast strategic

decision making (Eisenhardt, 1989) and fast product innovation (Eisenhardt and Tabrizi, 1995)

are so effective in high-velocity environments. In these opportunity-rich environments, there are

likely to be many high-payoff opportunities. By acting quickly, executives can secure a larger

number of these superior payoffs for a longer time and so achieve high performance. In contrast,

by acting slowly, executives are likely to secure fewer opportunities and to exploit them for less

time, leading to low performance. The attractiveness of high-velocity environments may also

explain why the Internet era (with its high velocity of opportunities) had a surprisingly low

failure rate. Although many firms died, the death rate was unusually low when compared with

the total number of foundings (Goldfarb, Kirsch, and Miller, 2007). Overall, we found that high-

velocity environments are attractive for achieving high performance.

In contrast, complex environments are particularly unattractive. In highly complex environments,

opportunities have many features that executives must execute correctly. Thus these

opportunities are challenging to capture, and performance is correspondingly low. This finding

extends prior research by helping to explain why firms in complex environments such as

biotechnology (Owen-Smith and Powell, 2003) and “green” power (Sine, Mitsuhashi, and

Kirsch, 2006) often perform poorly even when their executives have high domain expertise. In

these technically and institutionally complex environments, executives must achieve success in

many areas (e.g., technical, manufacturing, safety, regulatory, marketing) to capture an

opportunity. When organizations fail to capture some opportunities, attention is wasted that

could have been used to address other opportunities. Thus organizations in complex

environments can address relatively few opportunities and are likely to have a low probability of

success when they do. Overall, we find that high-complexity environments are unattractive for

gaining high performance.

Our findings for environmental ambiguity are especially intriguing. When ambiguity is high,

executives are unable to perceive opportunities accurately and have a wide range of reasonably

optimal structures that produce roughly equivalent, albeit mediocre, performance. By contrast,

when ambiguity is low, the range of optimal structures narrows and so favors executives who are

able to locate and maintain optimal structure. Thus performance at the optimal structure

improves because executives can more accurately perceive opportunities and so more precisely

match structure to them.

These insights contribute to understanding effective institutional entrepreneurship in nascent

markets. Research indicates that entrepreneurs in these highly ambiguous markets often excel

when they shape industry structure to their advantage (Rao, 1994; Rindova and Fombrun, 1999;

Santos and Eisenhardt, 2009). For example, entrepreneurs succeed when they form portfolios of

relationships that shape the industry structure to gain a central network position (Ozcan and

Eisenhardt, 2008) or when they use analogies to provide some unique insight into the

opportunity structure of these novel markets that improves opportunity capture (Gavetti,

Levinthal, and Rivkin, 2005).12 We add to institutional entrepreneurship by revealing that these

actions are successful attempts to reduce ambiguity and so increase the possibility of very high

performance. Thus successful entrepreneurs seek to change nascent markets from games of luck

with likely mediocre performance in which the optimal structure is easy to find (high ambiguity)

to games of skill with potentially high performance in which the optimal structure is challenging

to find (low ambiguity).

Adaptation in Entrepreneurial vs. Established Organizations

Finally, our work contributes to organization theory. At the heart of our research is the core

tradeoff between flexibility and efficiency in dynamic environments. Less structure enables the

flexible capture of serendipitous opportunities. But with too much improvisation, the

organization runs the risk of incoherence, confusion, and drift. More structure enables tight focus

on the efficient execution of expected opportunities. With too much structure, however, the

organization runs the risk of stagnation and misalignment with fresh opportunities. The essence

of flexibility is thus the messy capture of the unexpected, while the essence of efficiency is the

smooth execution of the anticipated.

Our contribution is the insight that this core efficiency-flexibility tradeoff affects types of

organizations differently. For entrepreneurial organizations that typically have little structure, the

challenge in any environment is the same: to gain enough structure before failure ensues.

Legitimation and competition, of course, affect performance. But the key insight here is that

sufficient structure is also essential. Without sufficient structure, it is impossible to improvise

effectively and so to capture opportunities. Thus the well-known liability of newness may mask a

liability of too little structure.

In contrast, for established organizations that often have extensive structure, such as roles, rules,

and linkages among units, the imperative varies in different environments. If the environment is

predictable, this structure can be high-performing because it can take advantage of consistent

patterns in the environment that can be mirrored in structure. The number and size of mistakes

decreases with more structure in predictable environments, and only modest executive attention

is needed to retain an optimal amount of structure. Organizations can gain a stable equilibrium

that is robust to structural and environmental changes.

But as the environment becomes unpredictable or executives diversify into unpredictable

environments, our findings indicate major challenges for established organizations. One is

obviously to decrease the amount of structure. But a second, subtler challenge is the need for a

dramatically altered mindset. This mindset entails vigilantly managing the amount of structure

(not just its content), improvising to capture fresh opportunities, and quickly rebounding from

mistakes – all at the edge of chaos, where firms can at best capture only a few opportunities and

gain an unstable or dissipative equilibrium. Simply put, managing in unpredictable environments

is different, harder, and more precarious than in predictable environments. Overall, the irony of

adaptation is that, as it becomes more crucial for organizations to adapt, it also becomes more

challenging to do so. Thus the well-known liability of senescence may be as much a cognitive

phenomenon as an age phenomenon.

We began by noting that diverse literatures emphasized that balancing between too much and too

little structure is essential for high performance in dynamic environments. This consonance led

us to explore the theoretical logic of efficiency versus flexibility underlying fundamental

relationships at the heart of the science of organization. By incorporating limits on attention, time

delays, the inevitability of mistakes, and the fleeting and heterogeneous nature of opportunities,

we construct a more precise theory that links structure, performance, and environment. This

theoretical framework reveals the surprisingly wide applicability of a simple-rules strategy and

semi-structures, an asymmetry that favors more structure, and demanding managerial challenges

at the edge-of-chaos. Overall, we spotlight a research agenda that places complexity sciences

reasoning at the nexus of organizational studies, network sociology, and competitive strategy.

REFERENCES

Adler, P. S., B. Goldoftas, and D. I. Levine

1999 “Flexibility versus efficiency? A case study of model changeovers in the Toyota

production system.” Organization Science, 10: 43-68.

Amabile, T.

1996 Creativity in Context. Boulder, CO: Westview

Press.

Anderson, P.

1999 “Complexity theory and organization science.” Organization Science, 10: 216-232.

Bak, P.

1996 How Nature Works: The Science of Self-organized Criticality. New York: Springer-

Verlag.

Baker, T., and R. E. Nelson

2005 “Creating something from nothing: Resource construction through entrepreneurial

bricolage.” Administrative Science Quarterly, 50: 329-366.

Baligh, H. H.

2006 Organization Structures: Theory and Design, Analysis and Prescription. New York:

Springer.

Baum, J. A. C., T. Calabrese, and B. R. Silverman

2000 “Don’t go it alone: Alliance network composition and startups’ performance in canadian

biotechnology.” Strategic Management Journal, 21: 267-294.

Bigley, G., and K. Roberts

2001 “Structuring temporary systems for high reliability.” Academy of Management Journal,

44: 1281-1300.

Bingham, C. B., and K. M. Eisenhardt

2008 “Position, leverage, and opportunity: A typology of strategic logics linking resources with

competitive advantage.” Managerial and Decision Economics, 29: 241-256.

Bingham, C. B., K. M. Eisenhardt, and J. P. Davis

2009 “Opening the black box: What firms explicitly learn from their process experiences.”

Working paper, University of North Carolina at Chapel Hill, Kenan-Flagler Business

School.

Bingham, C. B., K. M. Eisenhardt, and N. R. Furr

2007 “What makes a process a capability? Heuristics, strategy, and effective capture of

opportunities.” Strategic Entrepreneurship Journal, 1: 27-47.

Bowman, E., and C. E. Helfat

2001 “Does corporate strategy matter?” Strategic Management Journal, 22: 1-23.

Bradach, J. L.

1997 “Using the plural form in the management of restaurant chains.” Administrative Science

Quarterly, 42: 276-304.

Brown, S. L., and K. M. Eisenhardt

1997 “The art of continuous change: Linking complexity theory and time-paced evolution in

relentlessly shifting organizations.” Administrative Science Quarterly, 42: 1-34.

1998 Competing on the Edge: Strategy as Structured Chaos. Boston: Harvard Business School

Press.

Burgelman, R. A.

1994 “Fading memories: A process theory of strategic business exit in dynamic environments.”

Administrative Science Quarterly, 39: 24-56.

1996 “A process model of strategic business exit: Implications for an evolutionary theory of

strategy.” Strategic Management Journal, 17: 193-214.

2002 “Strategy as vector and the inertia of coevolutionary lock-in.” Administrative Science

Quarterly.

Burns, T., and G. M. Stalker

1961 The Management of Innovation. London: Tavistock.

Burton, R. M., and B. Obel

1995 “The validity of computational models in organization science: From model realism to

purpose of the model.” Computational and Mathematical Organization Theory, 1: 57-72.

Carroll, G. R., and J. R. Harrison

1998 “Organizational demography and culture: Insights from a formal model and simulation.”

Administrative Science Quarterly, 43: 637-667.

Carroll, T., and R. Burton

2000 “Organizations and complexity: Searching for the edge of chaos.” Computational and

Mathematical Organization Theory, 6: 319-337.

Chen, E. L., R. Katila, R. M. McDonald, and K. M. Eisenhardt

2009 “All the right moves: Competitive interaction, temporary advantage, and firm

performance.” Stanford Technology Ventures Program Working Paper.

Cinlar, E.

1975 Introduction to Stochastic Processes. Englewood Cliffs, NJ: Prentice-Hall.

Cohen, M. D., J. G. March, and J. P. Olsen

1972 “A garbage can model of organizational choice.” Administrative Science Quarterly, 17:

1-25.

Cover, T., and J. Thomas

1991 Elements of Information Theory. New York: Wiley.

Cyert, R. M., and J. G. March

1963 A Behavioral Theory of the Firm. Englewood Cliffs, NJ: Prentice-Hall.

D’Aveni, R. A.

1994 Hypercompetition: Managing the Dynamics of Strategic Maneuvering. New York: Free

Press.

Daft, R. L.

1992 Organization Theory and Design, 4th ed. St. Paul, MN: West Publishing.

Davis, J. P.

2008 “Rotating leadership and collaborative innovation: Relationship processes in the context

of collaborative innovation.” Working paper, MIT Sloan School of

Management.

2009 “Network dynamics of exploration and exploitation: Pruning and pairing processes in

innovative interorganizational relationships.” Working paper, MIT Sloan School of

Management.

Davis, J. P., K. M. Eisenhardt, and C. B. Bingham

2007 “Developing theory through simulation methods.” Academy of Management Review, 32:

480-499.

Dess, G., and D. Beard

1984 “Dimensions of organizational task environments.” Administrative Science Quarterly, 29:

52-73.

Donaldson, L.

2001 The Contingency Theory of Organization. Thousand Oaks, CA: Sage.

Eisenhardt, K. M.

1989 “Making fast strategic decisions in high-velocity environments.” Academy of

Management Journal, 32: 543-576.

Eisenhardt, K. M., and M. M. Bhatia

2001 “Organizational complexity and computation.” In J. A. C. Baum (ed.), Companion to

Organizations. Oxford: Blackwell.

Eisenhardt, K. M., and J. A. Martin

2000 “Dynamic capabilities: What are they?” Strategic Management Journal, 21: 1105-1121.

Eisenhardt, K. M., and D. Sull

2001 “Strategy as simple rules.” Harvard Business Review, 79 (January–February): 107-116.

Eisenhardt, K. M., and B. Tabrizi

1995 “Accelerating adaptive processes: Product innovation in the global computer industry.”

Administrative Science Quarterly, 40: 84-110.

Feldman, M. S., and B. T. Pentland

2003 “Reconceptualizing organizational routines as a source of flexibility and change.”

Administrative Science Quarterly, 48: 94-118.

Finkelstein, S.

2003 Why Smart Executives Fail. New York: Portfolio.

Fleming, L., O. Sorenson, and J. Rivkin

2006 “Complexity, networks, and knowledge flow.” Research Policy, 35: 994-1017.

Galbraith, J.

1973 Designing Complex Organizations. Reading, MA: Addison-Wesley.

Galunic, D. C., and K. M. Eisenhardt

2001 “Architectural innovation and modular corporate forms.” Academy of Management

Journal, 44: 1229-1250.

Garud, R., and S. Jain

1996 “The embeddedness of technology systems.” In J. A. C. Baum and J. E. Dutton (eds.),

Advances in Strategic Management, 13: 389-408. Greenwich, CT: JAI Press.

Gavetti, G., D. Levinthal, and J. W. Rivkin

2005 “Strategy making in novel and complex worlds: The power of analogy.” Strategic

Management Journal, 26: 691-712.

Gell-Mann, M.

1994 The Quark and the Jaguar: Adventures in the Simple and the Complex. New York: W.H.

Freeman.

Gersick, C. J. G.

1994 “Pacing strategic change: The case of a new venture.” Academy of Management Journal,

37: 9-45.

Gibson, C., and J. Birkinshaw

2004 “The antecedents, consequences and mediating role of organizational ambidexterity.”

Academy of Management Journal, 47: 209-226.

Gilbert, C.

2005 “Unbundling the structure of inertia: Resource vs routine rigidity.” Academy of

Management Journal, 48: 741-763.

Glynn, P., and W. Whitt

1992 “The asymptotic efficiency of simulation estimators.” Operations Research, 40: 505-520.

Goldfarb, B., D. A. Kirsch, and D. Miller

2007 “Was there too little entry during the dot com era.” Journal of Financial Economics, 86:

100-144.

Granovetter, M.

1985 “Economic action and social structure: The problem of embeddedness.” American

Journal of Sociology, 91: 481-510.

Greve, H. R.

1999 “The effect of core change on performance: Inertia and regression toward the mean.”

Administrative Science Quarterly, 44: 590-614.

Hansen, M. T.

1999 “The search-transfer problem: The role of weak ties in sharing knowledge across

organization subunits.” Administrative Science Quarterly, 44: 82-111.

Hargadon, A., and R. I. Sutton

1997 “Technology brokering and innovation in a product development firm.” Administrative

Science Quarterly, 42: 716-749.

Hatch, M. J.

1998 “Jazz as a metaphor for organizing in the 21st century.” Organization Science, 9: 556-

557.

Hayek, F. A.

1945 “The use of knowledge in society.” American Economic Review, 35: 519-530.

Hill, C. W. L., and F. T. Rothaermel

2003 “The performance of incumbent firms in the face of radical technological innovation.”

Academy of Management Review, 28: 257-274.

Kalos, M., and P. Whitlock

1986 Monte Carlo Methods, vol. 1: Basics. New York: Wiley-Interscience.

Katila, R., and G. Ahuja

2002 “Something old, something new: A longitudinal study of search behavior and new

product introduction.” Academy of Management Journal, 45: 1183-1194.

Kauffman, S.

1989 “Adaptation on rugged fitness landscapes.” In E. Stein (ed.), Lectures in the Science of

Complexity: 517-618. Reading, MA: Addison-Wesley.

1993 The Origins of Order. New

York: Oxford University Press.

Kirzner, I.

1997 “Entrepreneurial discovery and the competitive market process: An Austrian approach.”

Journal of Economic Literature, 35: 60-85.

Langton, C.

1992 “Life at the edge of chaos.” In C. Langton, J. Farmer, S. Rasmussen, and C. Taylor (eds.),

Artificial Life II: Sante Fe Institute Studies in the Sciences of Complexity, 10: 41-91.

Sante Fe: Addison-Wesley.

Lave, C., and J. G. March

1975 An Introduction to Models in the Social Sciences. New York: Harper and Row.

Law, A. M., and D. W. Kelton

1991 Simulation Modeling and Analysis, 2d ed. New York: McGraw-Hill.

Lawrence, P. R., and J. W. Lorsch

1967 Organization and Environment: Managing Differentiation and Integration. Boston:

Harvard University Press.

Lenox, M. J., S. Rockart, and A. Lewin

2006 “Interdependency, competition, and the distribution of firm and industry profits.”

Management Science, 52: 757-772.

March, J. G.

1991 “Exploration and exploitation in organizational learning.” Organization Science, 2: 71-87.

March, J. G., and J. P. Olsen

1976 Ambiguity and Choice in Organizations. Bergen: Universitetsforlaget.

March, J. G., M. Schultz, and X. Zhou

2000 Dynamics of Rules. Stanford, CA: Stanford University Press.

March, J. G., and H. Simon

1958 Organizations. New York: Wiley.

Martin, J. A., and K. Eisenhardt

2010 “Rewiring: Cross-business-unit collaborations and performance in multi-business

organizations.” Academy of Management Journal (forthcoming).

McGahan, A. M., and M. E. Porter

1997 “How much does industry matter, really?” Strategic Management Journal, 18: 15-30.

Miller, D., and P. H. Friesen

1980 “Momentum and revolution in organizational adaptation.” Academy of Management

Journal, 23: 591-614.

Miner, A. S., P. Bassoff, and C. Moorman

2001 “Organizational improvisation and learning: A field study.” Administrative Science

Quarterly, 46: 304-337.

Mintzberg, H.

1979 The Structuring of Organizations. Englewood Cliffs, NJ: Prentice-Hall.

Mintzberg, H., and A. McHugh

1985 “Strategy formation in an adhocracy.” Administrative Science Quarterly, 30: 160-197.

Moorman, C., and A. S. Miner

1998 “Organizational improvisation and organizational memory.” Academy of Management

Review, 23: 698-723.

Nelson, R. R., and S. G. Winter

1982 An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press of

Harvard University Press.

Ocasio, W.

1997 “Towards an attention-based view of the firm.” Strategic Management Journal, 18: 187-

206.

Okhuysen, G. A., and K. M. Eisenhardt

2002 “Integrating knowledge in groups: How formal interventions enable flexibility.”

Organization Science, 13: 370-386.

Owen-Smith, J., and W. W. Powell

2003 “Knowledge networks as channels and conduits: The effects of spillovers in the Boston

biotechnology community.” Organization Science, 15: 5-21.

Ozcan, C. P., and K. M. Eisenhardt

2009 “Origin of alliance portfolios: Entrepreneurs, network strategies, and firm performance.”

Academy of Management Journal, 52: 246-279.

Perlow, L. A., G. A. Okhuysen, and N. Repenning

2002 “The speed trap: Exploring the relationship between decision making and temporal

context.” Academy of Management Journal, 45: 931-955.

Perrow, C.

1984 Normal Accidents: Living with High-risk Technologies. New York: Basic Books.

Pisano, G. P.

1994 “Knowledge, integration, and the locus of learning: An empirical analysis of process

development.” Strategic Management Journal, 15: 85-100.

Porter, M. E.

1985 Competitive Advantage: Creating and Sustaining Superior Performance. New York: Free

Press.

Prigogine, I., and I. Stengers

1984 Order Out of Chaos: Man’s New Dialog with Nature. New York: Shambhala.

Pugh, D., D. Hickson, C. Hinings, K. Macdonald, C. Turner, and T. Lupton

1963 “A conceptual scheme for organizational analysis.” Administrative Science Quarterly, 8:

289-315.

Rao, H.

1994 “The social construction of reputation: Certification contests, legitimation, and the

survival of organizations in the American automobile industry: 1895-1912.” Strategic

Management Journal, 15: 29-44.

Reynolds, C. W.

1987 “Flocks, herds, and schools: A distributed behavioral model, in computer graphics.”

SIGGRAPH ’87, 21(4): 25-34.

Rindova, V., and C. Fombrun

1999 “Constructing competitive advantage: The role of firm-constituent interactions.” Strategic

Management Journal, 20: 691-710.

Rindova, V., and S. Kotha

2001 “Continuous morphing: Competing through dynamic capabilities, form, and function.”

Academy of Management Journal, 44: 1263-1280.

Rivkin, J., W.

2000 “Imitation of complex strategies.” Management Science, 46: 824-844.

Rivkin, J. W., and N. Siggelkow

2003 “Balancing search and stability: Interdependencies among elements of organizational

design.” Management Science, 49: 290-311.

Roberts, P. W.

1999 “Product innovation, product-market competition and persistent profitability in the U.S.

pharmaceutical industry.” Strategic Management Journal, 20: 655-670.

Roberts, P. W., and R. Amit

2003 “The dynamics of capability development: The case of Australian retail banking.”

Organization Science, 14: 107-122.

Rothaermel, F. T., M. Hitt, and L. Jobe

2006 “Balancing vertical integration and strategic outsourcing: Effects on product portfolios,

new product success, and firm performance.” Strategic Management Journal, 27: 1033-

1056.

Rowley, T. J., D. Behrens, and D. Krackhardt

2000 “Redundant governance structures: An analysis of structural and relational embeddedness

in the steel and semiconductor industries.” Strategic Management Journal, 21: 369-386.

Rudolph, J., and N. Repenning

2002 “Disaster dynamics: Understanding the role of quantity in organizational collapse.”

Administrative Science Quarterly, 47: 1-30.

Santos, F. M., and K. M. Eisenhardt

2009 “Constructing markets and shaping boundaries: Entrepreneurial power in nascent fields.”

Academy of Management Journal, 52:643-671.

Sastry, M. A.

1997 “Problems and paradoxes in a model of punctuated organizational change.”

Administrative Science Quarterly, 42: 237-275.

Schilling, M. A., and H. K. Steensma

2001 “The use of modular organizational forms: An industry-level analysis.” Academy of

Management Journal, 44: 1149-1168.

Schoonhoven, C. B., and E. Romanelli, eds.

2001 The Entrepreneurship Dynamic: Origins of Entrepreneurship and the Evolution of

Industries. Stanford, CA: Stanford University Press.

Scott, W. R.

2003 Organizations: Rational, Natural and Open Systems, 5th ed. Upper Saddle River, NJ:

Prentice Hall.

Shane, S.

2000 “Prior knowledge and the discovery of entrepreneurial opportunities.” Organization

Science, 11: 448-469.

Siggelkow, N.

2001 “Change in the presence of fit: The rise, the fall, and the renascence of Liz Claiborne.”

Academy of Management Journal, 44: 838-857.

Simon, H. A.

1962 “The architecture of complexity.” Proceedings of the American Philosophical Society,

106: 467-482.

Sine, W. D., H. A. Haveman, and P. S. Tolbert

2005 “Risky business? Entrepreneurship in the new independent-power sector.” Administrative

Science Quarterly, 50: 200-232.

Sine, W. D., H. Mitsuhashi, and D. A. Kirsch

2006 “Revisiting Burns and Stalker: Formal structure and new venture performance in

emerging economic sectors.” Academy of Management Journal, 49: 121-132.

Sipser, M.

1997 Introduction to the Theory of Computation. Boston: PWS Publishing.

Strogatz, S.

2001 Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and

Engineering. Cambridge, MA: Perseus Books Group.

Thompson, J. D.

1967 Organizations in Action. New York: McGraw-Hill.

Tripsas, M.

1997 “Surviving radical technological change through dynamic capability: Evidence form the

typesetter industry.” Industrial and Corporate Change, 6: 341-377.

Tushman, M., and R. Katz

1980 “External communication and project performance: An investigation into the role of

gatekeepers.” Management Science, 26: 1071-1085.

Tushman, M., and C. A. O’Reilly, III

1996 “Ambidextrous organizations: Managing evolutionary and revolutionary change.”

California Management Review, 38 (summer): 8-30.

Tyre, M. J., and W. J. Orlikowski

1994 “Windows of opportunity: Temporal patterns of technological adaptation in

organizations.” Organization Science, 5: 98-118.

Uzzi, B.

1997 “Social structure and competition in interfirm networks: The paradox of embeddedness.”

Administrative Science Quarterly, 42: 36-67.

Weber, M.

1946 From Max Weber: Essays in Sociology. H. H. Gerth and C. W. Mills, eds. and trans. New

York: Oxford University Press.

Weick, K. E.

1976 “Educational organizations as loosely coupled systems.” Administrative Science

Quarterly, 21: 1-19.

1990 “Catastrophic myths in organizations.” In A. S. Huff (ed.), Mapping Strategic Thought:

1-10. New York: Wiley.

1993 “The collapse of sensemaking in organizations: The Mann Gulch disaster.”

Administrative Science Quarterly, 38: 628-652.

1996 “Drop your tools: An allegory for organizational studies.” Administrative Science

Quarterly, 41: 301-313.

1998 “Improvisation as a mindset.” Organization Science, 9: 543-555.

Weick, K. E., and K. H. Roberts

1993 “Collective minds in organizations: Heedful interrelating on flight decks.” Administrative

Science Quarterly, 38: 357-381.

Williams, C., and W. Mitchell

2004 “Focusing firm evolution: The impact of information infrastructure on market entry by

U.S. telecommunications companies, 1984–1998.” Management Science, 50: 1561-1575.

Zott, C.

2003 “Dynamic capabilities and the emergence of intra-industry differential firm performance:

Insights from a simulation study.” Strategic Management Journal, 24: 97-125.

TECHNICAL APPENDIX: Operationalization and Initialization of Opportunities

Each opportunity is composed of a 10-element vector of perceived features composed of either

1s or 0s (i.e., a bit string), a 10-element actual features bit string, and a randomly selected payoff

value. The feature vectors are produced by an algorithm that randomly assigns each element

either a 1 or 0. The probability of selecting a 1 or 0 is determined by the unpredictability

parameter. The perceived features vector differs from the actual features vector by a proportion

of elements as set by the environmental ambiguity parameter. The exact elements that differ are

randomly chosen. The payoff is drawn from a normal distribution with m = 30 and v = 5,

although sensitivity analyses showed that the results do not depend on these values. Moreover,

we assume that unexecuted opportunities stay in the environment for a random amount of time

drawn from a normal distribution with m = 20 and v = 5; sensitivity analyses showed that the

results do not depend on these values either.

Operationalization, Initialization, and Use of Rules

We initialized the rule structure in the computer program in a similar way as for the

opportunities. The rules are initialized as 10-element vectors but with ?s (elements that can be

improvised) scattered throughout a string of 1s and 0s. Thus the amount of structure is

operationalized by the number of 1s and 0s. Similar to the structure of opportunities, the

probability of selecting a 1 or 0 is determined by the unpredictability parameter. Thus our

computational model reflects that managers can adjust their structures to fit consistent patterns in

the flow of opportunities if such patterns exist, consistent with empirical evidence. Also, as in

actual organizations, there is typically an approximate fit but often not an exact one. Thus the

probability of getting a 1 or 0 is the same in both the rules and opportunities and is determined

by the unpredictability parameter. This assumption could be relaxed in future work to explore the

impact of misfit between environmental unpredictability and organizational structure, for

example, in attempting to understand better the role of learning to fit structure to environmental

patterns.

The exact placement of 1s, 0s, and ?s is randomly assigned. For example, if a rule’s amount of

structure is set to 6, then 0?0?1?01?0 or any other permutation could result as long as four ?s

were assigned. After initialization of both rules and opportunities, all available opportunities

(both those that recently flowed into the environment and those not yet captured but still in the

pool of opportunities) can be captured in each time step.

Rules are used to capture opportunities by combining rule-based and improvised (described

below) actions that produce a 10-element bit string (e.g., 0111100110). These bits are compared

with each opportunity bit string (e.g., 0110101010). An opportunity is captured and its payoff is

gained when the number of actions that correctly match the opportunity’s features is greater than

the value of environmental complexity.

Improvisation and Attention

A key feature of our model is the improvisation of action. Some actions are rule-based and some

are improvised. When a rule (e.g., 0?1?10???0) is applied to a given opportunity, the

organization follows the rule for each element as specified by a 0 or a 1. These are the rule-based

actions. In addition, the organization randomly improvises a 0 or 1 action for each ? placeholder

with a p = .5 likelihood of each outcome. Overall, this process produces a set of actions (e.g.,

0111100110 in which the 2nd, 4th, 7th, 8th, and 9th actions are improvised) that can be

compared with a given opportunity (e.g., 0110101010). When enough of the actions match the

opportunity features as specified by the environmental complexity parameter, the opportunity is

captured and the organization gains the opportunity’s payoff. When an insufficient number of

actions match the opportunity features, the opportunity remains in the environment to be

potentially captured using other actions. Depending on the attention available (see below), the

organization continues to try to capture an opportunity using improvisation again and in future

time steps until it disappears from the environment at a randomly determined time, as described

above.

In general, our operationalization of improvisation is consistent with existing research showing

that improvisation involves real-time action and that improvised action is not always correct

(Weick, 1993, 1998; Miner, Bassoff, and Moorman, 2001). As in actual organizations, only some

improvised actions are correct. We also found that different amounts of attention and ratios of

rule-based to improvised attention did not qualitatively change the results. As a manipulation

check, we also checked that the total number of mistakes decreased with increasing structure as

expected. We confirmed that, because decreasing structure increases the organization’s capacity

to improvise flexibly with a larger number of opportunities that potentially fit, there are more

mistakes. This result is similar for different rates of improvisation. We also conducted an

analysis of mistakes in figure 6 that normalizes the total number of mistakes to compare these

distributions across the mini-graphs, as described in the text. Overall, our approach is a

conservative one that nonetheless captures the fundamental features of improvisation—i.e.,

improvising requires more attention than following rules and is not always accurate. Our

modeling of improvisation thus offers a reasonable abstraction of the actual process that is

appropriate for our research question and the objectives of simulation models (Burton and Obel,

1995).

Another key feature of the model is attention. As in actual organizations, we assumed that the

organization has a finite amount of attention. In particular, the organization has a fixed attention

budget. In each time step, the attention budget is decremented for each application of rules to

opportunities, each rule-based action, and each improvised action. Consistent with research on

improvisation (Weick, 1993; Miner, Bassoff, and Moorman, 2001) and the use of rules (Cyert

and March, 1963), we assumed that an improvised action takes more attention than simply

checking whether a rule matches an opportunity or a rule-based action, because improvisation

has enhanced demands for real-time sensemaking and the convergence of figuring out actions

and executing them (Weick, 1993; Miner, Bassoff, and Moorman, 2001). Thus we set the

attention required to check the match of a rule with an opportunity or take a rule-based action at

1 unit of attention and each improvised action at 10 units of attention. Though we chose 10 as a

representative value, our sensitivity analyses indicated that the findings are robust to a broad

range of variations in the amount of attention that an improvised action requires. In general, the

robustness of our findings to a broad range of variations in attention suggests that a more

discriminate improvisation process (i.e., one requiring more attention or more improvisational

skill) is likely to yield qualitatively similar results. Similarly, sensitivity analysis indicated that

our findings are qualitatively robust to different orderings for addressing opportunities. So

although we address opportunities by their performance payoffs, other orders (such as random)

qualitatively produce the same results. In any given time step, the attention budget is

decremented until the attention budget is depleted or the time step ends. Action stops if the

attention budget is completely depleted. It is then replenished at the beginning of each new time

step. We set the attention budget to 2800 attention units. Sensitivity analyses that varied the

attention budget showed that increasing this budget increases the number of opportunities that

can be executed in a given time step, as expected, but that these variations (above a minimal

threshold) do not produce qualitatively different findings. Therefore we chose this representative

value for our simulation runs. Finally, in any given time step, rules are checked against

opportunities for a match, and rule-based and improvised actions are taken as long as attention is

still available.

Performance and Error Constructs in Monte Carlo Experiments

We used standard Monte Carlo techniques (Law and Kelton, 1991). Each experiment consists of

30 or 50 simulation runs. We selected n = 30 as the number of simulation runs for all

experiments, except those on the basic relationship between structure and performance, because

exploratory analyses revealed that values of n greater than 30 yielded insignificantly small

incremental gains on reliability. We used n = 50 for the basic relationship between the amount of

structure and performance because the larger range of structure values adds precision to our

illustration of this relationship. The results of these simulation experiments are graphed

consistently across figures 1-5: each point represents the results for one simulation experiment,

including the mean performance (Y-axis) computed across all simulation runs for a given

amount of structure (X-axis). A curve is then interpolated between the mean performance values

by connecting the points with a straight line.

As in all stochastic processes and related phenomena (regardless of whether empirical or

simulated), the results of experiments may typically vary across simulation runs even when the

construct parameter values are fixed (Law and Kelton, 1991). Therefore we computed not only

the mean performance for a given experiment but also its variability in terms of error variances.

We then plotted both a performance mean for each value of the amount of structure and

associated “error bar” confidence intervals, which indicate the variability of each result, a

standard graphical method used in Monte Carlo outputs (Kalos and Whitlock, 1986). We

computed the length of the error bar as the square root of the error variance of each experiment

over the number of trials (i.e., simulation runs) of these experiments. These error bars provide an

intuitive and visual display of the confidence intervals surrounding a result. As a rule of thumb,

if the mean of one result is contained within the error bars of another result, then the two are not

significantly different. For example, this implies that the peak performance can be generated by a

range of optimal structural values. These structural values can be characterized by their own

range intervals (e.g., 3–6) and medians (e.g., 4.5).

Comparing medians is necessary when optimal structure is a range of values. For instance, to

assess the shifting optimum in P2, we compared median structures when the optimum was a

range of values. P2 is confirmed when these median optimal structures differ. In addition, to

assess asymmetry, we compared the slope of the line from the median optimal structure to the

endpoint on the left side to the slope of the line from optimal structure to the endpoint on the

right side. Curves are asymmetric right when the absolute value of the left slope is higher than

the right slope.

Sensitivity Analyses

We performed extensive sensitivity analyses for all of the structure/performance relationships

reported in the Results section, thoroughly exploring the parameter space to discover if a given

finding remained when construct values (i.e., parameters) were varied. To ensure the robustness

of the results, we not only varied the amount of structure measure, but also secondary constructs

such as the environmental dynamism dimensions. We chose the specific values for presentation

because they represent extreme values of a parameter or the midpoint values between already

tested values, as appropriate. Thus we explored the parameter space in a very fine-grained way.

We paid special attention to exploring the full range of the environmental dimension values—

velocity, complexity, ambiguity, and unpredictability. Because velocity (λ) is unbounded in a

Poisson distribution, but actual organizations are both cognitively and resource bounded, we

placed an upper bound on λ at the value for which the number of opportunities is an order of

magnitude greater than the organization could capture in any time step. We then thoroughly

explored velocity at a variety of parameter values, including 0, .4, .6, .8, 1.2, 1.4, 1.6, 1.8, 2.0,

2.2, 2.4, 2.6, 2.8, 3, 4, and 5. All results are consistent with figure 2. We also explored

complexity, which ranges from 0 to 1, with a variety of parameter values, including 0, .2, .3, .4,

.5, .6, .7, .8, .85, and .9. All results are consistent with those in figure 3. We tested ambiguity,

which ranges from 0 to 1, with a variety of parameter values, including 0, .1, .2, .25, .3, .4, .6, .8,

and 1.0. All results are consistent with those in figure 4. Unpredictability ranges from 0 to1 in

our tests. We tested the sensitivity of the unpredictability results with a variety of parameter

values for the proportion of 1s, including 0, .1, .2, .3, .4, .5, .6, .7, .8, .9, and 1.0. All results are

consistent with those in figure 5.

MATHEMATICAL APPENDIX

The mathematical formalization that we constructed sheds light on the logic underlying P1a, P2a,

and the varying range of optimal structures from our simulation experiments. In this appendix,

we perform some of the mathematical operations that underlie this logic. We are especially

grateful to an anonymous reviewer who encouraged our building this interpretive model and

developing this line of thinking.

Though the literature is mostly silent about the specific functional forms underlying the

relationship between structure and performance, there is consensus that flexibility and efficiency

are inversely interdependent and have non-substitutable effects on how structure influences

performance (e.g., Gibson and Birkinshaw, 2004). Let x be the amount of organizational

structure. We begin by representing the aggregate effect of structure on performance by A(x) =

f(x)*e(x), where f(x) and e(x) are the non-negative functions of flexibility and efficiency.

Broadly, the literature suggests that efficiency increases and flexibility decreases as the amount

of structure increases, respectively: f’(x) < 0, e’(x) > 0.

This representation allows us to demonstrate that not all flexibility and efficiency functions

generate a unimodal curve, as predicted in P1a. Specifically, for a unimodal curve to exist, we

require that [A’(x) > 0 for x < x’] and [A’(x) < 0 for x > x’], where x’ is the optimal amount of

structure (i.e., at the performance “peak”).

Applying the chain rule [A’(x) = (e(x)*f’(x)) + (e’(x)*f(x))] and the absolute value equation [–

f’(x) = |f’(x)| when f’(x) < 0] yields these two important conditions for unimodal functions of the

type A(x) = f(x)*e(x):

|f’(x)| < f(x)*e’(x)/e(x) for x < x’ (1) and

e’(x) < |f’(x)|*e(x)/f(x) for x > x’ (2).

These constraints on the two underlying functions, f(x) and e(x), are necessary to predict a

unimodal relationship.

In addition, we can show that the argument that the shifting optimum predicted in P2a is

generated because of the increasing importance of flexibility is not correct. Let a > 0 represent

the importance of flexibility in A(x) = a*f(x)*e(x). Then, applying the chain rule again yields

A’(x) = a[f(x)*e’(x) + f’(x)*e(x)]. Inspecting this A’(x) reveals that simply increasing the

importance of flexibility by increasing the coefficient a does not affect the position of the

optimum given that the critical point of A’(x) is independent of a.

Instead, logical argument and empirical literature suggest functional forms that do satisfy the

conditions underlying P1a and P2a. For instance, the literature suggests an increasing function of

structure for efficiency such that e’(x) > 0. Examining the impact of adding a marginal amount of

structure sheds further light on the shape of e(x). One possibility is that each incremental

application of structure generates a constant improvement, e’(x) = c, where c is a constant. But a

constant improvement is unlikely over the full range of x. Instead, it is more likely that

increasing structure has a diminishing marginal effect on efficiency. A marginal improvement in

efficiency, de, is derived from a smaller set of opportunities and a smaller efficiency gain from

economizing on attention. Thus the marginal improvement in efficiency, de , derived from

applying a marginal amount of structure dx is inversely dependent on the base level of structure,

x, suggesting an inversely proportional relationship: de ∝ dx/x where x > 0. Integrating yields a

logarithmic efficiency function:

e(x) = ln(x)

which satisfies e’(x) > 0 as e’(x) = 1/x > 0 when x > 0. Moreover, this logarithmic efficiency

function has the important property of being unbounded —increasing structure always increases

efficiency, although at a diminishing rate.

By contrast, empirical literature and logical argument suggest decreasing flexibility as a function

of structure such that f’(x) < 0. Flexibility involves using improvisation to capture a variety of

opportunities that could not be captured by structure-based actions alone. Logic suggests that

adding structure eliminates successive fractions of opportunities, and so the amount of structure

is inversely proportional to the fraction of opportunities that could have been captured with

improvisation. Thus it is most rapidly decreasing at low structure, an argument that is also

consistent with empirical evidence (Greve, 1999). This suggests the following function:

f(x) = 1/x

which satisfies f’(x) < 0 as f’(x) = –1/(x^2) < 0 when x > 0. In our rule-based model, a simple

interpretation of the effect of increasing structure on flexible opportunity execution is to decrease

the pool of opportunities available to improvisational execution by successive fractions for each

addition of structure. That is, flexibility is the product of these fractional losses of opportunities

at each level of structure, n: f(x) = ∏ [1 – (1/n)] = ∏[(n – 1)/n] = (x – 1)!/x! = 1/x. A natural

interpretation, then, is that increasing structure quickly eliminates opportunities from the pool of

opportunities available for improvised actions. This modeling of flexibility also has the

important property of approaching a limit of 0 as structure increases.

Returning to an objective for this mathematical formalization, it can be shown that these

functional forms are consistent with P1a:

Let A(x) = f(x)*e(x) = ln(x)/x.

Recall that for A(x) to be unimodal, it is required that

|f’(x)| < f(x)*e’(x)/e(x) for x < x’ (1) and e’(x) < |f’(x)|*e(x)/f(x) for x > x’ (2).

Substituting f(x), e(x), f’(x), and e’(x) into A’(x) = [e(x)*f’(x)] + [e’(x)*f(x)] generates A’(x) =

(1 – ln(x))/(x^2), while letting A’(x) = 0 yields x’ = e as the optimum.

Substituting f(x) = 1/x and e(x) = ln(x) into the inequalities above also reveals that these

functions satisfy the conditions for P1a. For example, after reducing the equations, we find the

following true inequalities:

1 < ln(x) x < x’ (1) and

1 > ln(x) x > x’ (2).

These functions produce a unimodal, asymmetric right curve as predicted by P1a.

It can also be shown that these basic functional shapes are consistent with P2a as well. Consider

unpredictability, the key dimension of environmental dynamism underlying the logic in P2a. An

important insight is that unpredictability, u, shapes both flexibility and efficiency by affecting

how firms use structure to execute opportunities in two ways. One effect of increasing

unpredictability is that some additional opportunities can occasionally be captured in a more

unpredictable stream of heterogeneous opportunities. But this increment varies inversely with the

amount of structure—1/x—and grows increasingly slowly with increasing unpredictability –

ln(u)—because opportunity capture becomes increasingly difficult at lower levels of structure,

both of which we represent with ln(u)/x. Combining this with A(x) changes the performance

function: A(x) + ln(u)/x = ln(x)/x + ln(u)/x = ln(ux)/x. Another important effect of

unpredictability is to reduce the effectiveness of both structure and improvisation, which is

represented as a simple dampening parameter reducing the magnitude of performance, 1/u,

which can be applied to the performance equation above: (1/u)*ln(ux)/x. Although there are

potentially many ways to represent these effects, the resulting model is a simple one that

nonetheless captures the dual effects of unpredictability, u:

P(x,u) = ln(ux)/ux, where u > 0.

This modification of A(x) to include unpredictability, u, retains its key properties. For instance,

P(x,u) also satisfies the conditions for P1a:

Differentiating yields P’(x,u) = [1-ln(ux)]/ux^2 and setting P’(x,u) = 0 yields x’ = e/u.

Deriving the conditions again yields

1 > ln(ux) x < e/u (1) and

1 < ln(ux) x > e/u (2)

which are true for u > 0.

Turning back to P2a, this model is consistent with a shifting optimum because x’ = e/u depends

on u. Consistent with P2a, as u increases, x’ decreases. Moreover, this P(x,u) also shares other

important features of our simulation findings, such as the unimodal, asymmetric right shape and

the shift from a broad plateau to a sharp inverted-V edge of chaos as unpredictability increases.

Table 1: Comparison of Theoretical Frameworks for Structure-Environment-Performance
Relationships

Framework Feature Prior Revised
Core tradeoff Flexibility vs. Efficiency

Flexible capture of varying opportunities vs.

efficient execution of specific opportunities.

Relevance of limited attention, mistakes, time

delays, and fleeting and varied
opportunities.

Structure-
performance
relationship

Inverted-U

Flexibility and efficiency are

opposing, approximately linear
processes

Unimodal, asymmetric right.
Attention advantage of increasing structure.

Efficiency increases at a decreasing rate;

flexibility more rapidly decreases at
decreasing rate.

Major environmental
constructs

Environmental dynamism shifts locus
of optimal structure

Unpredictability shifts the locus and range of
optimal structure.

High velocity raises performance.
High complexity lowers performance.
High ambiguity lowers performance and

broadens the range of optimal structure.
Robustness of simple

rules
Necessary in highly dynamic

environments
Simple rules are robust across a wide range of

environments.

Viable in predictable environments.
Necessary in unpredictable environments.

Range of optimal
structures

Constant In predictable environments, plateau of many
optimal structures.

In unpredictable environments, inverted-V of
a few optimal structures, selection and exit
rules for opportunities.

Edge-of-chaos Highly dynamic environments

Inverse power law distribution of

mistakes

High managerial energy focused on

staying poised at the optimal
structure or edge-of-chaos

Highly unpredictable environments.

Many mistakes of varying sizes, including

large ones, roughly normal distribution.
Mistakes advantage of increasing structure in

less unpredictable environments.

High managerial energy focused on

improvisation, mistake recovery, and
staying poised at the optimal structure or
edge-of-chaos

Endnotes

• We appreciate the generous support of the National Science Foundation (IOC Award

#0323176), the Stanford Technology Ventures Program, and the MIT Sloan School of

Management. We also thank multiple individuals for their helpful comments, including Phil

Anderson, Steve Barley, Diane Burton, Tim Carroll, Rebecca Henderson, Pankaj Ghemawat,

Clark Gilbert, Riitta Katila, Bruce Kogut, Dan Levinthal, Tammy Madsen, Anne Miner, Woody

Powell, Jan Rivkin, Simon Rodan, Lori Rosenkopf, Nicolaj Siggelkow, Wesley Sine, Bob

Sutton, Brian Uzzi, Christoph Zott; participants at the Academy of Management Conference,

Atlanta Competitive Advantage Conference, West Coast Research Symposium on Technology

Entrepreneurship, Wharton Technology Conference, BYU/Utah Winter Strategy Conference;

and seminar participants at Stanford University, INSEAD, and the Harvard Business School. The

paper benefited greatly from the comments of Elaine Romanelli and three anonymous reviewers.

1 We appreciate the suggestion of an anonymous reviewer to focus on the relationships of

structure with efficiency and flexibility.

2 We develop a matching model whose fundamental feature is to allow for varying degrees of

match between opportunities and rules, something that is not present in other, more constrained

modeling approaches. We appreciate the comments of an anonymous reviewer in suggesting that

we make this point in explaining our use of stochastic process modeling.

3 Stochastic process modeling is more fully described in references such as Burton and Obel

(1995) and Davis, Eisenhardt, and Bingham (2007). Interested readers can also refer to the

exemplars cited in the text, such as March (1991) and Carroll and Harrison (1998). We

appreciate the comments of an anonymous reviewer that we provide more information about this

modeling approach.

4 We appreciate the insightful recommendation of an anonymous reviewer that we clarify the

meaning of unpredictability and its implications for whether there are patterns in the

environment that managers can use to adjust or “tune” their organizational structures to better

match the environment.

5 Additional results for other values of the environmental dimensions are available from the

authors.

6 To assess asymmetry, we compared the slope of the line from optimal structure to the endpoint

on the left side to the slope of the line from optimal structure to the endpoint on the right side.

Median values are used if optimal structure is a range of values. Curves are asymmetric right

when the absolute value of the left slope is higher than the right slope.

7 This mathematical formalization is not intended to be a formal derivation of our simulation

results. Rather, its aim is to build an interpretive model that increases understanding of the theory

and enhances confidence in the simulation results. We appreciate the encouragement and

guidance of an anonymous reviewer to add this formalization.

8 We thank an anonymous reviewer for this formulation and other helpful insights.

9 We tried other functional forms for efficiency and flexibility, including linear forms, which do

not reproduce these results. We chose these two functional forms because they also fit with

empirical literature and logical argument. More details are in the Mathematical Appendix.

10 We appreciate the advice of an anonymous reviewer to include this more nuanced

understanding of the core tradeoff between efficiency and flexibility.

11 We appreciate the suggestion of an anonymous reviewer to consider the robustness of a

simple-rules strategy.

12 We appreciate the observation of an anonymous reviewer that unique insight into the

opportunity structure can potentially provide large returns in highly ambiguous environments.

This observation suggests that these managers could use such insights (e.g., as derived from

analogies) to lower ambiguity. We use this interpretation as part of our explanation of the

behavior of successful executives in highly ambiguous markets, including nascent markets. In

addition, this reviewer also noted that such unique insights might also be effective in highly

unpredictable environments. Here, also, analogies may be a concrete example of the kind of

unique insights to which this reviewer referred.

Designing Organizations
for

Dynamic Capabilities

Teppo Felin
Thomas C. Powell

How can organizations put dynamic capabilities into practice? This article focuses on the power of organizational
design, showing how managers can harness new organizational forms to build a capacity for sensing, shaping and
seizing opportunities. Fast-moving environments favor open organization and self-organizing processes tha

quickly convert individual capabilities into actionable collective intellect. However, such self-organizing processes
require managers to design and execute them. Using Valve Corporation as a case example, this article shows
how new design principles—such as polyarchy, social proofs, and new forms of open organization—allow organ-
izations to build dynamic capabilities for sustained innovation in dynamic environments. (Keywords: Strategic
Management, Organizational Design, Innovation, Dynamic Capabilities, Sensing, Seizing, Crowds)

Valve Corporation was founded in 1996 by Gabe Newell and MikeHarrington, former Microsoft employees. Valve began as a videogame company, producing bestsellers such as Half Life and Portal.Later the company evolved into a digital distribution platform,
known for products such as Steam and SourceForge. Their self-reported revenues
per employee and profit per employee exceed those of Facebook and Google.

Valve has succeeded in a fast moving environment that requires constant
agility, strategic innovation, and market adaptation—precisely the kind of envi-
ronment that places dynamic capabilities at a premium. How did they develop
and harness these capabilities?

According to our interview with Valve, the secret to their success is organiza-
tional design, the principles of which are embedded in the company’s Handbook for
New Employees.1 Here are some quotes from the Handbook:

“The company is yours to steer—toward opportunities and away from risks. You
have the power to green-light projects. You have the power to ship products.”

“You were not hired to fill a specific job description. You were hired to constantly be
looking around for the most valuable work you could be doing.”

78 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU

“We’ve heard that other companies have people
allocate a percentage of their time to self-directed
projects. At Valve, that percentage is 100.”

We learned from our interview and other
sources that the company constantly reinforces
these principles by providing resources and support to put them into practice. For
example, employees are allowed to choose their own projects, recruit people to
those projects, and initiate new products or platforms without higher approval.

Valve, which we will examine in more detail, illustrates the core theme of
this article: that organizational design is the crucial enabler of dynamic capabilities.
Valve’s founders knew from experience at Microsoft, some of it hard-earned, that
the company could not succeed without staying at the forefront of market innova-
tion. They also knew that successful innovation depended on their capacity to har-
ness individual and team initiative, and that traditional forms of organizational
design—functional silos, top-down hierarchical structures, and routinized formal
processes—could stifle creativity, initiative, and market responsiveness. Hence,
they turned to alternative forms of organizational architecture—a unique blend
of polyarchy, social proofs, self-organizing teams, and open organization—to
release the creative power of teams and individuals. By attending to organizational
design and day-to-day execution, Valve built a capacity for sensing, shaping, and
seizing market opportunities.

Dynamic Capabilities

Many industries are subject to rapid technological change, market entry from
global innovators, and volatility in market demand. Companies that cannot antici-
pate or respond to external disruption are unlikely to survive. In volatile industries,
organizations need strategies, structures, and processes that enable agility and
responsiveness in a shifting competitive landscape.

The theory of dynamic capabilities came about as an attempt to explain com-
petitive advantage in volatile industries. As the 20th century came to a close, internet-
based technologies altered the competitive landscape across a broad range of market
sectors, raising new challenges to conventional views of competitive advantage. The
success of companies like Apple, Amazon, Google, and Facebook showed that a
capacity to sense, shape, and seize opportunities could revolutionize industries and
transform national and global economies.2

In volatile markets, the functional and operational routines that drive com-
petitive success in stable conditions—“baseline” capabilities such as supply chain
management and access to distribution channels—become subject to rapid obsoles-
cence. Even if a company’s advantages are inimitable due to experience or proprie-
tary knowledge, disruptive technologies and business models can undermine the
underlying drivers of industry advantage, making conventional advantages irrele-
vant or out of step with market conditions and customer requirements.

The dynamic capabilities view of competitive advantage argues that success
in volatile industries requires higher-order capabilities that enable companies to
anticipate, shape, and adapt to shifting competitive landscapes. The dynamic

Teppo Felin is Professor of Strategy at the
Saïd Business School, University of Oxford.

Thomas C. Powell is Professor of Strategy at
Saïd Business School, University of Oxford.

Designing Organizations for Dynamic Capabilities

CALIFORNIA MANAGEMENT REVIEW VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU 79

capabilities view accepts the importance of capabilities like product design and
manufacturing, but argues that success in volatile industries requires something
more than baseline capabilities: namely, adaptive processes and structures that
enable companies to change their baseline capabilities, anticipate shifts in market
demand, develop and integrate new technologies, learn from market events,
and foresee and capture new market opportunities.

The competitive landscape of the 21st centurymay or may not bemore turbu-
lent than theworld of the past—the point is debatable.3 However, it is certain that the
old tools of organizational design—hierarchy, chains of command, functional areas,
formal reporting, and long-term planning—are not well-suited to success in volatile
markets. Competitive advantage in these markets requires a higher-order capacity to
sense, shape, and seize new market opportunities.4 This means that continuous
improvement of existing capabilities is not enough, but that organizations need an
overarching capacity for developing new capabilities that anticipate and respond to
a turbulent marketplace. Perhaps it is not surprising that a few pioneering companies
have experimented with new organizational architectures, discovering new struc-
tures and processes suited to continuous innovation.

Individual and Collective Capabilities

Volatile environments place stringent demands on information processing
in organizations. For an organization to capture new market opportunities, it must
somehow obtain and process current and reliable information. As organizational
sociologist Arthur Stinchcombe wrote, “If organizations have to deal with uncertain-
ties, then someplace in the organization there have to be people who bring informa-
tion to bear on those uncertainties.”5

Who are those people? Who in an organization possesses enough informa-
tion, knowledge, and capability to cope with the uncertainties that arise in turbulent
competitive environments? Who fully grasps the latest changes in customer prefer-
ences, social media chatter, threats to the company’s supply chains, or innovations
of actual and potential competitors? Who understands the economic and social con-
sequences of national political debates, global military conflicts, demographic shifts,
government policies, digital technologies, global health crises, and climate change?

The short answer is: no one. Many chief executives keep a finger on the pulse
of political debates and general competitive dynamics, and this may be possible even
in volatile environments. However, CEOs are poorly placed to keep a close eye on
fast-moving developments in social media, product and process technologies, and
customer preferences while making unbiased inferences about their consequences.
Other people in (and beyond) the organization—scientists in the lab, salespeople
in the field, overseas manufacturing managers, customer service representatives
in branch offices, contract consultants, and customers themselves—have better infor-
mation and knowledge, more specialized training, and a finer-grained appreciation
of movements on the ground in their domains.

Therefore, the first challenge in designing organizations for dynamic capa-
bilities is to capture what is already there—information, knowledge, experience,
and capabilities—and to bring it all to bear on collective decisionmaking. As Kenneth

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80 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU

Arrow pointed out, an organization can “acquiremore information than any individ-
ual.”6 In organizational decision making, each individual has some unique informa-
tion but no individual has enough information to make collective decisions. The task
of the organizational designer, both in concept and practice, is to design structures
that put individuals in contact with their relevant environments, and to design pro-
cesses that facilitate learning, sharing, and aggregation of individual knowledge so
that the collective organization can make well-informed decisions.

This task presents special problems in volatile industries, with consequences
for the design of strategies, structures, and processes. The most common error com-
panies make is trying to have it both ways, giving lip service to innovation and deci-
sion autonomy, while retaining bureaucratic processes and reward systems that
perpetuate old ways of thinking. Sadly, most organizations talk a better game of
design innovation than they actually play, and few are willing to sacrifice the central-
ized controls that perpetuate cognitive and social inertia. This leads to misalignments
that deflate internal culture and stifle innovation—the well-known folly of reward-
ing A while hoping for B.7

At the other extreme, a company can go too far in flattening the organization
and giving full decision autonomy to unaccountable individuals at the organization’s
boundaries. A company that adopts this approach indiscriminately, without proper
systems for converting individual knowledge into collective intellect, is liable to spin
out of control.

Designing organizations for dynamic capabilities presents new challenges for
managers and entrepreneurs. Because these challenges arise from empirical events
in real-world environments, they are hands-on challenges, not theoretical ones.
Indeed, the theoretical problem was well-stated long ago by 20th century organiza-
tion theorists: How can we map the organization onto the full diversity of its envi-
ronment (“differentiation”), while employing structures, processes and systems
(“integrating mechanisms”) that prevent the organization from disintegrating into
chaos?8 As these theorists pointed out, there are many possible solutions to this
problem: mechanistic or “machine” organizations for stable environments, organic
forms or “adhocracies” for unstable environments, and mixed forms suitable for
other conditions.

These theories remain relevant and insightful. However, they do not give
managers the practical tools they need to design organizations for dynamic capa-
bilities.9 This is because many of the manufacturing and service industries that
formed the empirical base for these theories declined after the 1970s; and what
came after—the global, volatile, technology-driven information economy—gave
rise to new kinds of design problems. The forms of market volatility emerging in
recent years require organizations to differentiate their structures globally rather
than domestically, digitally as well as mechanically, virtually as well as physically,
and continuously rather than occasionally. This requires organizations to think
more cohesively about the links between strategy and structure, designing flexible
mechanisms for structural differentiation and integration that enable the develop-
ment of dynamic capabilities. It is no longer a matter of “structure follows strategy”
or “strategy follows structure” but of continuously orchestrating strategies and
structures that enable the sensing, shaping, and seizing of market opportunities.

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Design Tools for Dynamic Capabilities

Figure 1 illustrates the basic problem just described. New environments
require companies to find new solutions to the problem of market volatility. Com-
panies must establish information-absorbing teams and individual specialists at or
beyond the boundaries of the organization and give these people real autonomy
to solve problems and capture opportunities. A company that fails to differentiate
its internal structure will fall into traps of insularity or folly (see Figure 1), finding
it impossible to respond to fast-changing environments.

At the same time, companies must bring together the knowledge and capa-
bilities that sit with individuals and teams to achieve the shared purposes of the col-
lective enterprise. Companies that differentiate but do not integrate—perhaps in the
belief that autonomy is self-organizing—face the perils of organizational chaos.
These kinds of organizations can produce great ideas, but lack the means to imple-
ment or commercialize them in the marketplace.

This brings us back to our central questions:What are themechanisms of struc-
tural differentiation and integration that enable organizations to create dynamic capa-
bilities in volatile environments? How can companies increase collective intellect and

FIGURE 1. Chaos and Folly in Organizational Design

Integrative Mechanisms

R
es

po
ns

e
to

E
nv

ir
o

nm
en

Highly
Integrated

Minimally
Integrated

H
ig

hl
y

D
iff

er
en

tia
te

d
M

in
im

al
ly

iff
er

en
tia

te
d

CHAOS
(Misdirected

Innovation)

Optimal Design for
Dynamic Capabilities

FOLLY
(Stifled

Innovation)

INSULARITY
(Inadequate
Information)

Differentiation: The extent to which the organization maps onto the full diversity of its environment
by establishing specialized individuals or sub-units at its boundaries, and giving them autonomy to
solve problems and capture opportunities.
Integration: The extent to which the organization implements processes for converting distributed
information into collective intellect.

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82 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU

achieve shared goals without stifling the creative autonomy of empowered teams and
individuals?

To address these questions, we return to Valve Corporation and describe the
two primary mechanisms they employed—polyarchy and social proofs—to achieve
a balance of differentiation and integration in a dynamic environment.

Differentiation by Polyarchy

In political science, the term “polyarchy” refers to a system of government in
which power is widely distributed to many individuals. The opposite of polyarchy is
autocracy, with power residing in a single person; and there are many intermediate
forms, including representative democracy.10 In economics and organization the-
ory, scholars have discussed polyarchy in the context of flat or decentralized forms
of organization in which autonomous individuals are empowered to make signifi-
cant choices about the nature and scope of their own work.11

Like every form of governance, polyarchy has advantages and disadvantages,
and it is better suited to some conditions than others.12 As a guiding principle for the
internal organization of companies, it has the advantage of giving authority to those
who operate closest to the action.13 It gives autonomy to specialized individuals and
sub-units at the boundaries of an enterprise, thereby facilitating local creativity,
experimentation, and innovation, while minimizing bureaucratic impediments to
project approval and implementation. As such, polyarchy prepares the ground for
dynamic capabilities by enabling people to sense, shape, and seize new opportunities.

In traditional terms, polyarchy canbe seenas a formof radical decentralization—
not a mere flattening of organization structure, but bestowing full autonomy of
judgment, decision, and execution to decentralized individuals and subunits. In
recent years, many technology and internet-based companies have adopted ele-
ments of polyarchy, for example in software development (companies such as
Menlo Innovations and Basecamp) and online retail (the shoe retailer Zappos).
Even larger and more established companies have conducted partial experiments
with polyarchy, especially in R&D-intensive industries—for example, Google and
3Mgive employees a percentage of free time to develop their ownprojects. Limited
polyarchy can also be found in traditional industries such as foods and consumer
products (for example, Morning Star)14 and in knowledge-intensive industries
such as professional services and academia.15

At Valve Corporation, polyarchy is the driving principle of structural differ-
entiation. As part of its core values, the company deliberately seeks to build
dynamic capabilities for identifying and capturing new market opportunities. The
aim of structural differentiation at Valve is to go beyond traditional decentralization
and empowerment by giving full release to the market potential of talented specia-
lists in product research, design, and engineering. To this end, the company gives
individuals full autonomy to propose projects, recruit project teams, establish
budgets, set timelines, and ship products to customers.

A good example of this is the Valve product platform called Steam,
designed for digital distribution, digital rights management, broadcasting, and
social networking. The Steam project did not come from top-down processes such

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as competitive analysis, formal market research, and the capital investment and
budgeting cycle. Rather, a few creative individuals at the organization’s bound-
aries saw that their latest ideas about video software mapped onto a potential
market opportunity. They floated these ideas with potential users, pitched them
to other Valve employees, recruited talent onto a project team for design and
execution, and ultimately produced one of the industry’s most innovative and
successful platforms (75% of all games downloaded onto PCs are now sold
through Steam.).16 Compared with top-down R&D processes, Valve’s polyarchical
approach delivered improved consumer engagement, project team motivation, and
speed to market.

Polyarchy starts at the point of employee selection. Recruitment processes
at Valve do not focus on hiring into particular jobs with fixed job descriptions,
but on finding people with the capacity to create value in a marketplace of ideas.
The question is not whether the candidate can learn the rules, write reports, and
work within the requirements of a functional role, but how well they can thrive
when given the resources and freedom to identify and create a new market
opportunity. At Valve, the criteria for selection relate directly to the formation
of dynamic capabilities—that is, the company selects for the ability to identify and
capture new market opportunities.

The Handbook plays a crucial role in guiding people in the search for new
projects, and in deciding which project teams to join. Rather than telling people
what to do, the Handbook poses questions:

1. Of all the projects currently under way, what’s the most valuable thing I
can be working on?

2. Which project will have the highest direct impact on our customers? How
will the project benefit them?

3. Is Valve not doing something that it should be doing?

4. What’s interesting? What’s rewarding? What leverages my individual
strengths the most?

The responsibility for spotting and capturing opportunities does not rest with
the top management team, or with a marketing group, or with an R&D department,
but with all employees. As might be expected, individual rewards and incentives at
Valve Corporation are closely tied to idea generation, project success, and market
outcomes. A crucial element of remuneration and performance review is a system
of peer evaluation, inwhich people and project teams are ranked by their peers based
on measures of innovation, contribution, and value creation. Hence, the company is
giving more than lip service to the concept of empowerment. According to Valve’s
founders, skilled programmers and systems engineers are often undervalued in the
labor market, in part because somany companies in North America and Europe have
offshored core engineering activities, leaving capable engineers underemployed. The
founders sought talented engineers who could thrive when empowered to create
and implement new ideas, backed by a compensation and incentive system that
rewards individual initiative and market innovation.

Valve employs a number of mechanisms to encourage and guide the creative
forces unleashed by radical decentralization. However, these are not the traditional

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mechanisms of formal controls, operating procedures, plans, reporting, or even orga-
nization culture. Instead, Valve achieves structural integration through a set ofmech-
anisms that can be classified broadly as “social proofs.”

Integration by Social Proofs

In social psychology, a social proof is any mechanism of social influence that
tends to produce coordinated behavior among individuals. The nature of social
proofs is to induce a kind of social contagion in which beliefs, preferences, and prac-
tices disseminate through a population of individuals.17 There is no suggestion that
social proofs always produce positive outcomes, and indeed many mechanisms of
social influence are known to produce dysfunctional outcomes such as groupthink,
group polarization, conformity, herd behavior, and the madness of crowds.18

Nonetheless, companies like Valve have found that social proofs form an
effective counter-balance to the extreme differentiation of polyarchy. The strength
of polyarchy is its capacity to devolve decision authority to the individuals with the
most information, experience, expertise, and incentives to achieve. What it lacks is
coordination with colleagues or accountability to the company as a whole, and this
is what social proofs are designed to provide. A purposeful system of social proofs,
applied in a polyarchy of talented and well-resourced individuals, can provide an
effective counterweight to the chaotic tendencies of extreme differentiation.

The primary mechanism of social proofs is self-selection. At Valve Corpora-
tion, people choose their own projects and vote with their feet. They assess markets
for new opportunities, gather information about existing projects and teams, and
make their own judgments about whether to affiliate with existing teams or form
their own projects. This does not mean that everyone makes optimal choices, or that
social biases or politics are absent; indeed, people actively try to persuade others to
join their project teams. However, experience at Valve suggests that the aggregate
choices of individually empowered experts contain a powerful signal about the
future direction of the marketplace—a form of collective wisdom that serves as a
barometer and guide to the strategic direction of the enterprise.19 Even if the signal
contains social noise, Valve managers believe that self-selection yields more reliable
and lower cost information—and faster market responsiveness—than traditional
controls and incentives.

Self-selection empowers the right people to make decisions, but cannot alone
overcome the problems that arise in polyarchies in their purest form—for example,
cost inefficiencies and duplication of effort.20 Valve executives did not want to impair
individual initiative by requiring layers of project approval, but they had learned
from experience that one or two individuals, nomatter how talented, could not com-
mand enough information or resources to sense, shape and seize a large-scalemarket
opportunity.

To solve this problem, Valve’s founders devised a method of social conver-
gence they called the “rule of three,” a novel solution that harnessed individuals’
capacities to sense and seize opportunities while providing behavioral incentives
to coordinate activity and minimize inefficiency. According to this rule, one or
two people acting alone could not move a project forward, but a group of three

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could receive a green light. This allowed project teams to tap into company resour-
ces and the “wisdom of crowds” for designing and delivering significant products,
while giving decision makers a clear and implementable “tipping point” for project
investment decisions.21 As a principle of social convergence, the “rule of three”
offers a relatively light-touch intervention that allows the company both to stimu-
late innovation and to bring the chaos of polyarchy under control, a solution remi-
niscent of David Teece’s comment on innovation in Silicon Valley: “let chaos reign,
then rein in chaos.”22

New market innovations seldom present themselves as obvious opportuni-
ties for capital investment, and do not come with fully formed business models
for implementation. Design tools such as self-selection and the “rule of three”
incentivize people not only to sense new opportunities, but to shape those oppor-
tunities through social processes of bargaining, influence and recruitment. New
strategies and business models emerge and evolve as individuals compete for
resources and challenge each other over the definition, scale, scope, and imple-
mentation of proposed innovations. This is not the case when social proofs are
absent, especially in pure polyarchies where individuals are fully empowered to
act on perceivedmarket opportunities. Social proofs like self-selection and the “rule
of three” serve as valuable filtering and enabling devices that redefine and trans-
form new ideas, shaping market opportunities in the crucial period before the com-
pany makes significant strategic commitments.

Valve is not the only company to use polyarchy and social proofs to capture
the “wisdom of crowds.” For example, the Danish hearing aid manufacturer Oticon
experimented with a similar combination of polyarchy and social proofs, using
internal markets or “market-hierarchy hybrids” to produce the spontaneous order
of self-organized project teams. As Nicolai Foss pointed out from a transaction cost
perspective, the organizational designs of companies like Oticon replace the visible
hand of management with the invisible hand of social proofs, substituting social
markets for structural hierarchies.23

The combination of polyarchy and social proofs can also be found in creative
industries and project-based environments. In Hollywood, for example, movie
ideas and production processes are generally driven by flat, emergent structures.24

Unlike the studio culture of the 1930s—in which powerful studio heads exercised
firm control over people, contracts, and projects—modern film-making is a breed-
ing ground for polyarchy and social proofs, with diverse groups of talented people
empowered to develop new ideas, raise capital, and greenlight projects.25

Some of these innovations in organizational design have been adapted to
the contexts of larger organizations and more stable industries. Google’s uses of
flat organization and various aspects of polyarchy and social proofs are well
established.26 Michael Tushman and colleagues have documented how IBM
found ways to empower individuals to sense potential market opportunities,
linking individual and team initiative with corporate culture and reward sys-
tems. Though IBM did not achieve this through Valve-like social proofs (such
as the “rule of three”), managers linked the activities of specific functional areas
to the sensing of new opportunities and to the execution of strategies for capturing

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these opportunities. According to Tushman and colleagues, this approach transformed
IBM’s culture and practices, allowing the company to “sense changes in the
marketplace and to seize the opportunities by reconfiguring existing assets and
competencies.”27

For creating dynamic capabilities, the new market-hierarchy hybrids have a
number of advantages.28 A key advantage is that the ebb and flow of collective pref-
erences allows everyone in the enterprise, not only top managers, to monitor and
influence the strategic direction of the company—or, as Valve tells its employees,
“the company is yours to steer.” Because social convergence drives major investment
decisions, individuals track the internal grapevine closely and develop keen sensitiv-
ities to social thresholds. This has the effect of intensifying communication and
increasing overall strategic transparency. As evolved social actors, people seem to
know intuitively when an idea has reached the required threshold of social support,
or “tipping point.” When this happens in a polyarchy, the pace of events accelerates
and a critical mass of resources rapidly converges on the idea. Interestingly, this
heightened sensitivity to social thresholds is also a common feature of animal behav-
ior, where “quorum sensing” has been shown to produce better collective outcomes
than other decision processes (see Sidebar: Quorum Sensing).29

Sidebar. Quorum Sensing in Nature

Human beings are not alone in possessing highly-evolved intuitions for social
consensus. Many animals in nature use what is called “quorum sensing” as a
mechanism for harnessing collective wisdom and making crucial choices,
such as where to nest or where to hunt.
For example, ants do not choose nesting locations by sending a single scout
or small group on reconnaissance, but send roughly 30% of the population
to sense the environment and recruit others to the most promising sites.
The social threshold or tipping point for action—that is, when everyone
knows to move to the new location—occurs when a critical mass or
“quorum” of ants begins migrating to a preferred site. Mathematical models
show that a collective decision process based on quorum sensing produces
better decisions than other methods, showing the importance of social
information processing in the animal kingdom.
For an excellent review of the literature on social animals, decision making,
and behavior, see David Sumpter’s Collective Animal Behavior (Princeton,
NJ: Princeton University Press, 2010).

The combination of polyarchy and social proofs also has the useful property of
decoupling the sensing of market opportunities from the seizing of them. Research
on creativity suggests that discovery and idea-generation are better performed by
individuals than by groups, which use interactive processes such as brainstorming
that are subject to a host of behavioral and social biases (such as anchoring, status
relationships, formal authority, and groupthink). However, the evaluation and

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choice of ideas—the seizing of opportunities—is better accomplished when people
consider diverse perspectives, which is facilitated by social interaction and the mech-
anism of self-selection.30

Social proofs allow firms tomaximize individual and social cognition. At Valve
Corporation, the sensing of opportunities rests largely with expert individuals at the
interface of the organization and its potential markets; as we have seen, this is facili-
tated by polyarchy. The seizing of opportunities, however, depends on the ability of
one or more individuals to recruit people and resources by establishing the external
legitimacy of their ideas. This is the role of social proofs, and it involves sub-processes
such as pitching, recruiting, bargaining, and consensus building. These sub-processes
buffer the organization from poor investment decisions by providing a rigorous com-
petitive test of social legitimacy before resources are fully committed.

Social proofs also serve as partial countermeasures against individual cogni-
tive biases.31 Research in behavioral strategy shows that individuals exhibit a large
number of cognitive decision biases, such as overconfidence, wishful thinking, con-
firmation bias, and loss aversion.32 Most cognitive biases are hardwired, unconscious
and unintended, and resist remedies at the individual level regardless of knowledge
or awareness.33 However, as urged by Thaler and Sunstein and others, individual
biases can be mitigated by organizational designs and social architectures that con-
strain individual biases, or make them less harmful in their effects.34 For example,
organizations can design decision processes that require managers to consider a
range of strategic options rather than anchoring on a favored solution or require par-
ticipation according to expertise and experience rather than by rank and authority.

Organizational designs based on polyarchy and social proofs embed a number
of potential countermeasures to individual cognitive biases. For example, polyarchy
encourages companies to consider not only the favored options of influential top
managers, but a range of options derived from diverse sources. Social proofs encour-
age companies to consider ideas on their merits in a competitive marketplace of
ideas, not according to the biases of select individuals constrained by loss aversion,
confirmation bias, and inertia. The sub-processes that drive social proofs—pitching,
recruiting, bargaining, and consensus building—concentrate activity on project
advocacy by subject experts, rather than on the preferences of authority figures.
The sensitivity to social thresholds, or “quorum sensing,” directs attention to external
market signals and away from the predispositions of individuals. The involvement of
external stakeholders encourages an “outside view” of the organization as a counter-
balance to internal decision myopia.35 Although few remedies can overturn
hardwired executive biases or social biases deeply embedded in decision pro-
cesses, the new market-leaning structures promote a psychological architecture
that helps companies “nudge” individual and social cognition into more produc-
tive channels.

Open Sensing, Shaping, and

Seizing

In stable environments, executives rely on experience, routines, learning, and
scale effects to build baseline capabilities in functional areas like marketing and

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manufacturing. In volatile markets, however, organizational structures that protect
and exploit current strengths can foster strategic inertia, or lure the company into
“competency traps” in which they build increasing capabilities in things that no longer
matter.36 To develop the core processes that support dynamic capabilities—sensing,
shaping, and seizing—executives cannot manage solely by routines, systems, incen-
tives, and impersonal structural mechanisms. Instead, they must learn to rely on
the characteristics and judgments of people, as they work individually, in groups,
and toward the aims of the collective enterprise.37

Organizational architectures based on polyarchy and social proofs enable
individuals and groups to build higher-order capabilities for identifying and cap-
turing opportunities. Self-selected team members engage more directly with orga-
nizational projects, and social proofs incentivize communication and coordination.
The new designs give individuals the resources and incentives they need to build
individual and joint capabilities, not only in technical areas like programming,
computation, and systems engineering, but in softer skills such as communicating,
managing conflict, and making sound judgments under uncertainty.38 Sustained
attention to market opportunities maximizes individual and collective market
knowledge, while enhancing the capacity both to interpret that knowledge and
put it into action.

In the most effective polyarchies, the processes of sensing, shaping, and seiz-
ing do not stop at the boundaries of the organization. Instead of fine-tuning internal
functional capabilities, companies like Valve excel in discovering opportunities
that require the company to expand its repertoire of capabilities by opening up
to forces beyond its own formal boundaries. To achieve this, the organization main-
tains porous boundaries that facilitate the free flow of information to and from
the marketplace.

This means engaging with external stakeholders through open forms of inno-
vation that invite customers and other actual and potential stakeholders into the
company’s learning and innovation processes.39 Crowdsourcing, for example, allows
companies to engage external stakeholders in collaborative innovation.40 In its sim-
plest form, crowdsourcing involves soliciting ideas directly from customers, for
example by establishing online user communities in which customers critique exist-
ing products or contribute new product ideas.41 User communities engage external
stakeholders as partners in sensing and shaping newmarket opportunities. By giving
companies faster access to market data and new product ideas, crowdsourcing allows
companies to find new market niches and serve customers better, while reducing
search costs and time to market.42

Innovation contests provide another way of engaging external crowds.43 In
dynamic environments, companies often do not know whether their problems are
solvable or what kinds of expertise are required. Jeppesen and Lakhani argued that
innovation contests represent a form of “broadcast search” in which companies
pose problems for external constituents to solve.44 By broadcasting the problem
to targeted external crowds, the company gains access to a large and well-informed
population from which individuals, motivated by the prospect of a monetary prize,
can self-select to provide potential solutions.

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Some companies take crowdsourcing much further, inviting customers not
only to sense and shape opportunities, but to seize them. For example, Valve’s Steam
platform currently features more than 400 million pieces of user-generated content,
and serves as an iTunes-type platform on which geographically distributed users or
developers can sell or freely distribute products to consumers. For its role in manag-
ing this activity, Valve collects licensing and transaction fees. Valve also allows play-
ers to receive micropayments for adding new game levels or creating new in-game
products. In this way, Valve facilitates the co-creation of market innovations, while
extending its own dynamic capabilities for capturing opportunities.

Valve also brings stakeholders into the development process by acting as
curator for the preferences of its user communities. On the Steam platform, the
company facilitates discussions among the user community that determine which
games are developed and listed. Developers can post trial versions of new software
products, and users can vote and approve the ones they like (called Valve Green-
light). Valve also offers a tool called SteamWorkshop that facilitates match-making
and collaboration between game developers and consumers. Through such mech-
anisms, Valve continuously senses and seizes new market opportunities, collabo-
rating with stakeholders to build dynamic capabilities.

In some markets, crowdsourcing provides not only the ideas, but the
resources for implementing innovations. Many entrepreneurs have employed
crowdfunding to launch successful ventures, often using intermediaries like
Kickstarter, which connects entrepreneurs with potential investors. Crowdfund-
ing gives under-resourced entrepreneurs both a seizing mechanism for captur-
ing market opportunities and a form of external social proof for legitimizing
their market innovations.45

The rapid dissemination of crowdsourcing—from technology companies like
Valve to diversified consumer goods companies like PepsiCo, which used crowd-
sourcing to choose its Super Bowl ads—suggests that the range of organizations that
can benefit from open organization is broader than previously thought. At the same
time, this dissemination suggests that organizations cannot count on open organiza-
tion alone, any more than on traditional structures, to deliver sustained competitive
advantage. Organizational innovators are constantly developing new architectures
and dynamic capabilities, and they are combining them with new strategies and
technologies. In a connected world, every company faces potentially disruptive inno-
vations in its competitive markets, supply chains, or distribution channels. This
means that a company like PepsiCo can learn something about organizational archi-
tecture from a company like Valve.

Themarket conditions of 21st century competition demand less directive and
more open forms of organization. These are not the same organizational designs
proposed in theories of “organic” or “adhocratic” organization, but designs suited
to the current state of technology, global competition, and social demography.
Companies like Valve Corporation have led the way in developing these designs
and in exploring their consequences for market innovation. As companies continue
to co-evolve their strategies with new customers and technologies, they will face
ever-increasing pressures for innovation, not only in dynamic capabilities, but in
the organizational designs for putting them into practice.

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Conclusions

It would be wrong to suggest that a company like Valve Corporation has got
everything figured out. Valve competes in volatile industries and risky product seg-
ments, and nearly all of these markets experience high mortality rates. We can learn
from Valve, but not as a case study of invulnerability. The real learning takes place
when executives use examples like Valve to gain a newperspective on their own com-
panies, and to adopt strategies and organizational designs better suited to the realities
of their own environments (see Sidebar: Putting Dynamic Capabilities into Practice).

Sidebar. Putting Dynamic Capabilities into Practice

Does your company think beyond baseline capabilities and conventional
sources of competitive advantage? Does your organization structure facilitate
continuous innovation and the capacity to sense, shape, and seize new mar-
ket opportunities? Concepts like polyarchy and social proofs lend themselves
to launching a bold challenge to any company’s approach to strategy and
innovation. Below is a checklist for fundamentally rethinking a company’s
capabilities for sensing, shaping, and seizing opportunities.

Sensing

§ Where does knowledge about new technologies and mar-
ket opportunities reside in the organization? How do we
capture it?

§ What kinds of opportunities lend themselves to dispersed
sensing rather than top management opportunity search?
Do we make this distinction?

§ Who are our “sensers”? Who—inside or outside the orga-
nization—is best positioned to foresee new market oppor-
tunities?

§ What incentives and rewards would motivate sensers to
identify opportunities?

§ Howcanwe encourage people to sharenew ideaswith others?

§ How can the organization engage external customers (or
potential customers) in sensing new opportunities? Can
we “crowdsource” new ideas?

Shaping

§ Do we have enough knowledge and expertise to shape the
direction of technologies or product innovations? If so,
who has it? If not, can we get it?

§ How can we encourage our people to think boldly and cre-
atively about the future direction of the marketplace?

continued on next page

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CALIFORNIA MANAGEMENT REVIEW VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU 91

§ Does our culture induce people to produce the kinds of
new ideas that shape markets? If not, why not? If so,
how do we capture these ideas?

§ Do we have mechanisms for testing new ideas—e.g., pilot
tests, experiments?

§ Do we have processes to facilitate learning from the market?

§ How can we encourage people to capture the “wisdom of
the crowd”? How can we use external crowds to shape
new market opportunities?

Seizing

§ How many truly novel opportunities have we seized in the
past 18 months?

§ Who decides which opportunities we seize?

§ What rules do we use for project investment decisions? To
what extent do they capture the knowledge of the whole
organization?

§ Suppose we had a “rule of three.” How would people
respond? What problems would it create? How could we
harness the creativity?

§ Is there another rule that would suit our culture better yet
incentivize dispersed innovation and social convergence?

In volatile markets, the key function of senior managers is to
create a fertile environment for sensing, shaping, and seizing mar-
ket opportunities. The above questions form the basis for deeper
and more probing conversations about strategy, innovation, and
organizing for dynamic capabilities.

For most entrepreneurs and managers, we believe the lessons of contem-
porary organizational design are threefold. First, although open organizational
architectures may seem like a frightening prospect to many executives, they are
not impossible to achieve. Executives may think that open organization is risky,
like riding a bicycle down a mountain with no hands. However, executives at
companies like Valve would argue that the new architectures are less risky than
trying to control market responsiveness from the top. This, they might say, is like
using training wheels in an Olympic bicycle race. For most organizations, the solu-
tion lies somewhere in-between: companies that give new forms of empowerment
to their people can experience the rewards of innovation, but they must balance
empowerment with the guiding hand of social proofs and other forms of structural
integration—they must “let chaos reign” and “rein in chaos.”

The second lesson is that self-organizing processes are not self-organizing.
Just as there is nothing as rehearsed as skillful improvisation, there is nothing as
designed as an effective self-organizing process. Traditional theories of organization

Designing Organizations for Dynamic Capabilities

92 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU

have correctly insisted that a company must map its structural differentiation onto
the diversity of its external environments, and it must adopt mechanisms to inte-
grate the company into a cohesive whole. Dynamic environments magnify these
imperatives, requiring a continuous commitment to design, planning, and moni-
toring as well as continuous adaptation of organizational structures and processes.
Polyarchy without integration leads to chaos, and the new architectures fail when
managers leave structural integration to unconscious forces and self-organizing
processes.

Finally, we believe that the theory of dynamic capabilities gives support to
the notion that companies must align market strategies with internal structures.
The theory of dynamic capabilities is far from perfect, as many authors have
pointed out.46 However, the theory reminds us—in a tradition that stretches back
to Alfred Chandler, Peter Drucker, Igor Ansoff, and others—that competitive envi-
ronments are always changing, and that one of the most essential functions of
executive leadership is to align organizational capabilities with opportunities in
the marketplace. In a world of turbulent markets, this means creating dynamic
capabilities for sensing, shaping, and seizing new opportunities as well as creating
new structures matched to the realities of the global competitive landscape.

Notes

1. We gathered information about Valve Corporation by interviewing Greg Coomer, one of the
first employees of the company and the primary author of Valve: Handbook for New Employees.
See Valve: Handbook for New Employees, First Edition, March 2012. For press coverage about the
company, see “Game Maker Without a Rule Book,” New York Times, September 8, 2012, p.
BU1. For an employee account of working at Valve, see Michael Abrash, “Valve: How I Got
Here, What It’s Like, and What I’m Doing,” . For a recent presentation about the
structure of Valve, see Greg Coomer, “Welcome to Flatland,” Seattle Interactive Conference,
October 15-16, 2012, .

2. We build our arguments largely on the sensing, shaping and seizing framework developed by
Teece and colleagues. For example: D.J. Teece, G. Pisano, and A. Shuen, “Dynamic Capabilities
and Strategic Management,” Strategic Management Journal, 18/7 (August 1997): 509-533;
D.J. Teece, “Explicating Dynamic Capabilities: The Nature and Microfoundations of (Sustainable)
Enterprise Performance,” Strategic Management Journal, 28/13 (December 2007): 1319-1350;
D.J. Teece, Dynamic Capabilities and Strategic Management (Oxford: Oxford University Press,
2009); D.J. Teece, “The Foundations of Enterprise Performance: Dynamic and Ordinary
Capabilities in an (Economic) Theory of Firms,” Academy of Management Perspectives, 28/1
(November 2014): 328-352. For a review of definitions of capabilities, see I. Barreto, “Dynamic
Capabilities: A Review of Past Research and an Agenda for the Future,” Journal of Management,
36/1 (January 2010): 256-280.

3. G. McNamara, P.M. Vaaler, and C. Devers, “Same as it Ever Was: The Search for Evidence of
Increasing Hypercompetition,” Strategic Management Journal, 24/3 (March 2003): 261-278. Also
see H. Mintzberg, “That’s not ‘Turbulence,’ Chicken Little, It’s Really Opportunity,” Planning
Review, 22/6 (November/December 1994): 7-9.

4. We focus mainly on the capabilities of sensing, shaping, and seizing. There are other streams of
research on capabilities—for example, capabilities for product development. See: G. Verona and
D. Ravasi, “Unbundling Dynamic Capabilities: An Exploratory Study of Continuous Product Inno-
vation,” Industrial and Corporate Change, 12/3 (2003): 577-606; C. Salvato, “Capabilities Unveiled:
The Role of Ordinary Activities in the Evolution of Product Development Processes,” Organization
Science, 20/2 (2009): 384-409. For an article on the link between dynamic capabilities, resource
alteration, and cognition, see E. Danneels, “Trying to Become a Different Type of Company:
Dynamic Capability at Smith Corona,” Strategic Management Journal, 32/1 (January 2011): 1-31.

5. A. Stinchcombe, Information and Organizations (Berkeley, CA: University of California Press,
1990), p. 32.

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CALIFORNIA MANAGEMENT REVIEW VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU 93

6. K. Arrow, The Limits of Organization (New York, NY: W.W. Norton, 1974).
7. S. Kerr, “On the Folly of Rewarding A While Hoping for B,” Academy of Management Journal,

18/4 (December 1975): 769-783.
8. Throughout this article we use the term “differentiation” in the sense employed by organiza-

tion theorists such as Lawrence and Lorsch (1967), in reference to structural mechanisms
(such as roles, functional areas, and specialized project teams) designed to align the organiza-
tion’s structure with the degree of complexity and dynamism presented by its environment.
These mechanisms make the organization internally complex (“differentiated”), requiring
structural mechanisms (such as committees and formal planning) for coordinating complex
internal activities (“integration”). For further discussion, see: P. Lawrence and J. Lorsch, Orga-
nization and Environment: Managing Differentiation and Integration (Cambridge MA: Harvard Uni-
versity Press, 1967); T. Burns and G.M. Stalker, The Management of Innovation (London:
Tavistock, 1961); J.G. March and H.A. Simon, Organizations (New York, NY: Wiley, 1958);
R.E. Miles and C. Snow, Organizational Strategy, Structure, and Process (Stanford CA: Stanford
University Press, 2008). For applications of organization theory to strategies in high technology
industries, see: T.C Powell, “Firm-Specific Competitive Advantage in High-Technology Firms,”
Journal of High Technology Management Research, 4/2 (Autumn 1993): 197-209; D.J. Teece,
“Firm Organization, Industrial Structure, and Technological Innovation,” Journal of Economic
Behavior & Organization, 31/2 (November 1996): 193-224. Classic distinctions between hierar-
chy and bureaucracy and more decentralized forms of organization and governance go back to
the work of Max Weber and other organizational sociologists. A rich tradition also exists in
political science, contrasting decentralized and centralized forms. For example, see R. Dahl,
Polyarchy: Participation and Opposition (New Haven, CT: Yale University Press, 1971).

9. See Teece (2007), op. cit.
10. See R. Dahl, A Preface to Democratic Theory (Chicago, IL: University of Chicago Press, 1956).
11. R.K. Sah and J.E. Stiglitz, “The Architecture of Economic Systems: Hierarchies and Polyarchies,”

American Economic Review, 76/4 (September 1986): 716-727; T. Knudsen and D. Levinthal, “Two
Faces of Search: Alternative Generation and Alternative Evaluation,” Organization Science, 18/1
(January/February 2007): 39-54; F. Csaszar and J.P. Eggers, “Organizational Decision Making:
An Information Aggregation View,” Management Science, 59/10 (October 2013): 2257-2277.

12. Sah and Stiglitz, op. cit.; Knudsen and Levinthal, op. cit.; Csaszar and Eggers, op. cit.
13. Sah and Stiglitz (op. cit.) noted that polyarchies reduce Type I errors, in which valuable projects

are rejected; hierarchies reduce Type II errors, in which poor projects are initiated.
14. See T. Felin, T. Zenger, and J. Tomsik, “The Knowledge Economy: Emerging Organizational

Forms,MissingMicrofoundations, andKeyConsiderations forManagingHumanCapital,”Human
Resource Management, 48/4 (July/August 2009): 555-570; D.J. Teece, “Expert Talent and the
Design of (Professional Services) Firms,” Industrial and Corporate Change, 12/4 (2003): 895-916;
F.A. Csaszar, “Organizational Structure as a Determinant of Performance: Evidence fromMutual
Funds,” Strategic Management Journal, 33/6 (June 2012): 611-632; M. Reitzig and B. Maciejovsky,
“Corporate Hierarchy and Vertical Information Flow inside the Firm: A Behavioral View,” Strategic
Management Journal, 36/13 (December 2015): 1979-1999.

15. We thank an anonymous reviewer for making this point.
16. R. Smith, “Valve to Showcase SteamVR Hardware, Steam Machines and More at the GDC

2015,” Anandtech Hardware Magazine, Febuary 23, 2015; K. Boudreau and K.R. Lakhani, “Using
the Crowd as an Innovation Partner,” Harvard Business Review, 91/4 (April 2013): 61-69.

17. R.B. Cialdini, Influence: How and Why People Agree on Things (New York, NY: 1984).
18. C. Stangor, Social Groups in Action and Interaction (New York, NY: Psychology Press, 2004); G. Le

Bon, The Crowd: A Study of the Popular Mind (New York, NY: MacMillan, 1896); S. Freud, Group
Psychology and the Analysis of the Ego (London: International Psychoanalytic Press, 1922).

19. T. Felin and T. Zenger, “Information Aggregation, Matching and Radical Market-Hierarchy
Hybrids: Implications for the Theory of the Firm,” Strategic Organization, 9/2 (May 2011):
163-173; K. Croxson, “Information Markets for Decision Making: Performance and Feasibil-
ity,” Oxford University Working Paper.

20. “Pure form” polyarchies allow individuals to sense and seize their own opportunities. For fur-
ther discussion, see Knudsen and Levinthal (2007), op. cit.

21. T. Felin, “Cosmologies of Capability, Markets and Wisdom of Crowds,” Managerial and Decision
Economics, 33/5-6 (July-September 2012): 283-294; J. Surowiecki, Wisdom of Crowds (New York,
NY: Doubleday, 2004). For an excellent, recent managerial piece, see H. Courtney, D. Lovallo,
and C. Clarke, “Deciding How to Decide,” Harvard Business Review, 91/11 (November 2013): 62-70.

22. Teece (1996), op. cit.

Designing Organizations for Dynamic Capabilities

94 UNIVERSITY OF CALIFORNIA, BERKELEY VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU

23. N. Foss, “Selective Intervention and Internal Hybrids: Interpreting and Learning from the Rise
and Decline of the Oticon Spaghetti Organization,” Organization Science, 14/3 (May/June 2003):
331-349; T. Zenger and W.S. Hesterly, “The Disaggregation of Corporations: Selective Inter-
vention, High-Powered Incentives, and Molecular Units,” Organization Science, 8/3 (May/June
1997): 209-222.

24. R.R. Faulkner and A.B. Anderson, “Short-Term Projects and Emergent Careers: Evidence from
Hollywood,” American Journal of Sociology, 92/4 (January 1987): 879-909. Also see: J. Lampel
and J. Shamsie, “Capabilities in Motion: New Organizational Forms and the Reshaping of the
Hollywood Movie Industry,” Journal of Management Studies, 40/8 (December 2003): 2189-2210;
S. Deakin, A. Lourenco, and S. Pratten, “No ‘Third Way’ for Economic Organization? Networks
andQuasi-Markets in Broadcasting,” Industrial and Corporate Change, 18/1 (February 2009): 51-75.

25. D. Adam, “What Hollywood Can Teach Us about the Future of Work,” New York Times, May 5,
2015.

26. E. Schmidt, J. Rosenberg, and A. Eagle, How Google Works (New York, NY: Grand Central Pub-
lishing, 2014).

27. For example, see J.B. Harreld, C.A. O’Reilly, and M.L. Tushman, “Dynamic Capabilities at
IBM: Driving Strategy into Action,” California Management Review, 49/4 (Summer 2007): 21-43.

28. Foss (2003), op. cit.; Zenger and Hesterly (1997), op. cit.
29. D. Sumpter, Collective Animal Behavior (Princeton, NJ: Princeton University Press, 2010).
30. M. Diehl and W. Stroebe, “Productivity Loss in Brainstorming Groups: Toward the Solution of a

Riddle,” Journal of Personality and Social Psychology, 53/3 (September 1987): 497-509; S.E. Asch,
“Group Forces in the Modification and Distortion of Judgments,” in S.E. Asch, Social Psychology
(New York, NY: Prentice-Hall, 1952), pp. 450-501. For a more recent summary, see Stangor
(2004), op. cit.

31. When interviewing Valve, we asked if they were worried about introducing social biases into
their decision making. They recognized that social biases were possible. However, they argued
that the individuals they hire—talented and capable (and perhaps low self-monitors)—might
be less susceptible to unwarranted social influences, which is debatable but has some support
in psychological research. For example, see K.E. Stanovich, Rationality and the Reflective Mind
(New York, NY: Oxford University Press, 2011). For additional work on the relationship
between individual and group biases, see W.G. Luhan, M.G. Kocher, and M. Sutter, “Group
Polarization in the Team Dictator Game Reconsidered,” Experimental Economics, 12/1 (March
2009): 26-41. Also see M.G. Kocher and M. Sutter, “The Decision Maker Matters: Individual
Versus Group Behavior in Experimental Beauty Contest Games,” The Economic Journal,
115/500 (January 2005): 200-223. Thanks to an anonymous reviewer for pointing out this
literature.

32. T.C. Powell, D. Lovallo, and C.R. Fox, “Behavioral Strategy,” Strategic Management Journal,
32/13 (December 2011): 1369-1386; D. Lovallo and O. Sibony, “The Case for Behavioral Strat-
egy,” McKinsey Quarterly, Issue 2 (March 2010): 1-16.

33. T.C. Powell, “Neurostrategy,” Strategic Management Journal, 32/13 (December 2011): 1484-1499.
34. R. H. Thaler and C.R. Sunstein, Nudge: Improving Decisions About Health, Wealth, and Happiness

(New Haven, CT: Yale University Press, 2008); C. Heath, R.P. Larrick, and J. Klayman, “Cognitive
Repairs: HowOrganizations Compensate for the Shortcomings of Individual Learners,” Research in
Organizational Behavior, 20 (1998): 1-37; Courtney, Lovallo, and Clarke (2013), op. cit.

35. D. Kahneman and D. Lovallo, “Timid Choices and Bold Forecasts: A Cognitive Perspective on
Risk-Taking,” Management Science, 39/1 (January 1993): 17-31; D.A. Levinthal and J.G. March,
“The Myopia of Learning,” Strategic Management Journal, 14 (Winter 1993): 95-112.

36. B. Levitt and J.G.March, “Organizational Learning,” Annual Review of Sociology, 14 (1988): 319-340.
37. T.C. Powell, “Strategic Management and the Person,” Strategic Organization, 12/3 (August 2014):

200-207.
38. D.V. Budescu and E. Chen, “Identifying Expertise to Extract the Wisdom of Crowds,” Manage-

ment Science, 61/2 (February 2015): 267-280.
39. T. Felin and T.R. Zenger, “Closed or Open Innovation: Problem Solving and the Governance

Choice,” Research Policy, 43/5 (June 2014): 914-925; H.W. Chesbrough, Open Innovation: The
New Imperative for Creating and Profiting from Technology (Boston, MA: Harvard Business School
Press, 2003).

40. Boudreau and Lakhani (2013), op. cit.
41. L.B. Jeppesen and L. Frederiksen, “Why Do Users Contribute to Firm-Hosted User Communities:

The Case of Computer-Controlled Music Instruments,” Organization Science, 17/1 (January/
February 2006): 45-63; N. Franke and S. Shah, “HowCommunities Support Innovative Activities:

Designing Organizations for Dynamic Capabilities

CALIFORNIA MANAGEMENT REVIEW VOL. 58, NO. 4 SUMMER 2016 CMR.BERKELEY.EDU 95

AnExploration of Assistance and Sharing among EndUsers,” Research Policy, 32/1 (January 2003):
157-178.

42. L. Dahlander and H. Piezunka, “Open to Suggestions: How Organizations Elicit Suggestions
through Proactive and Reactive Attention,” Research Policy, 43/5 (June 2014): 812-827.

43. K. J. Boudreau, N. Lacetera, and K.R. Lakhani, “Incentives and Problem Uncertainty in Inno-
vation Contests: An Empirical Analysis,” Management Science, 57/5 (May 2011): 843-867.

44. L.B. Jeppesen and K.R. Lakhani, “Marginality and Problem-Solving Effectiveness in Broadcast
Search,” Organization Science, 21/5 (September/October 2010): 1016-1033.

45. P. Belleflamme, T. Lambert, and A. Schwienbacher, “Crowdfunding: Tapping the Right
Crowd,” Journal of Business Venturing, 29/5 (September 2014): 585-609.

46. For example, see: O.E. Williamson, “Strategy Research: Governance and Competence Perspec-
tives,” Strategic Management Journal, 20/12 (December 1999): 1087-1108; T.C. Powell, “Com-
petitive Advantage: Logical and Philosophical Considerations,” Strategic Management Journal,
22/9 (September 2001): 875-888; S.G. Winter, “Understanding Dynamic Capabilities,” Strategic
Management Journal, 24/10 (October 2003): 991-995; M. Peteraf, G. Di Stefano, and G. Verona,
“The Elephant in the Room of Dynamic Capabilities: Bringing Two Diverging Conversations
Together,” Strategic Management Journal, 34/12 (December 2013): 1389-1410; J. Denrell and
T.C. Powell, “Dynamic Capability as a Theory of Competitive Advantage: Contributions and
Scope Conditions,” in S. Leih and D. Teece, eds., Oxford Handbook of Dynamic Capabilities (forth-
coming 2016).

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Document 1 of 1

Measuring organic and mechanistic cultures

Reigle, Ronda F . Engineering Management Journal : EMJ; Huntsville Vol. 13, Iss. 4, (Dec 2001): 3-8.

ProQuest document link

ABSTRACT

Knowledge workers in today’s high-technology organizations require environments with organic characteristics. To

retain highly skilled knowledge workers, managers need to determine whether their organizations exhibit organic

or mechanistic cultures. Culture is an important factor in successful technology implementation, innovation,

mergers, acquisitions, job satisfaction, organizational success, and team effectiveness. This paper discusses

organic and mechanistic organizational cultures and the organizational culture assessment (OCA) tool. The tool

gives an organization’s culture type and a numerical score for each of 5 culture elements. Survey results indicate

that the OCA adequately measures organizational culture.

FULL TEXT

Headnote

Abstract

Headnote

Knowledge workers in today’s high-technology organizations require environments with organic characteristics. To
retain highly skilled knowledge workers, managers need to determine whether their organizations exhibit organic
or mechanistic cultures. Culture is an important factor in successful technology implementation, innovation,

mergers, acquisitions, job satisfaction, organizational success, and team effectiveness. This article discusses

organic and mechanistic organizational cultures and the organizational culture assessment (OCA) tool. The tool

gives an organization’s culture type and a numerical score for each of five culture elements. Survey results

indicate that the OCA adequately measures organizational culture.

Introduction

Some of the challenges that make organizational culture significant today include downsizing, budget cuts,

globalization, and the rapid rate of technological improvements. Kanter (1983) states that to manage change in an

organization requires that people find their stability and security in the culture and direction of the organization.

Studies show that the type of culture an organization exhibits affects employee retention. One study indicated that

new employees voluntarily stayed 14 months longer in cultures emphasizing interpersonal relationship values than

in cultures emphasizing work task values (Sheridan, 1992).

Denison performed a study indicating that companies with a participative culture reap a return on investment that

averages nearly twice as high as in firms with less efficient cultures (Denison, 1984). An effective organizational

culture is a key component influencing an organization’s ability to compete and to succeed long term (Morris,

1992). Lack of insight into culture leaves managers vulnerable to forces of evolution and change which they may

not understand and which they may have difficulty controlling (Schein, 1992).

Existing organizational culture measurement tools measure one element of culture or certain organizational

characteristics. However, they are not based on all culture elements as defined in engineering management

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literature.

The purpose of this study is to develop a tool to measure culture as defined by the leading authors in engineering

management literature. The OCA measures organizational culture and provides a five-dimensional score, one for

each of five culture elements.

Culture Definitions

Organizational culture is defined by numerous authors. Some of the significant definitions related to engineering

management are:

Ott (1989) and Westbrook (1993) define organizational culture as a function of language, artifacts and symbols,

patterns of behavior, basic underlying assumptions, and subcultures.

Culture is formally defined by Schein (1992, p. 12) as “a pattern of shared basic assumptions that the group

learned as it solved its problems of external adaptation and internal integration, that has worked well enough to be

considered valid and, therefore, to be taught to new members as the correct way to perceive, think, and feel in

relation to those problems.”

Ouchi (1981) states that organizational culture consists of a set of symbols, ceremonies, and myths that

communicate the underlying values and beliefs of an organization and its employees.

Rogers and Ferketish (1993) state that an organization’s culture is based on the shared values reflected in the

behaviors of leaders and employees at every level. The answer to the question “What’s important around here?”

provides positive or negative insight into what is valued in an organization.

Dyer (1985) describes organizational culture as artifacts, perspectives, values, and assumptions shared by

members of an organization. He defines artifacts as verbal (language, stories, myths), behavioral (rituals and

ceremonies), and physical (art, attire, layout, and technology). Perspectives are the socially shared rules and norms

applied in given situations. Values are the evaluations people make of situations, acts, objects, and people. They

represent the organization’s goals, ideals, and standards. Assumptions are the implied beliefs that underlie the

overt artifacts, perspectives, and values.

Blake and Mouton (1969) describe corporate culture as the routine ways of doing things that people accept and

live by. Organizations have norms and values that influence how members conduct themselves. These norms may

prevent members from applying a maximum effort or may encourage them to do so.

Culture Elements. Based on the culture definitions above, along with others, an author matrix of common culture

elements was developed. The author matrix yielded the following five culture elements:

Language-The language of an organization communicates its culture (Westbrook, 1993). Examples of how

language is used in organizations include jargon, metaphors, myths, slogans, ceremonies and celebrations, and

heroes and heroines (Deal, 1985).

Tangible artifacts and symbols-Artifacts and symbols provide tangible evidence of the culture (Westbrook, 1993).

Artifacts are objects manufactured by people to facilitate culturally expressive activities (Trice and Beyer, 1984).

Symbols or company logos often summarize or condense what a company stands for (Deal, 1985).

Patterns of behavior, rites and rituals, behavioral norms– Trice and Beyer (1984) define a ritual as a standardized,

detailed set of techniques and behaviors that manage anxieties but seldom produce intended, technical

consequences of practical importance.

Allen (1985) describes norms as encompassing all behavior that is expected, accepted, or supported by the group,

whether that behavior is stated or unstated. The norm is the sanctioned behavior, and people are rewarded and

encouraged when they follow the norms, and ostracized when they violate them. Schein (1992) defines group

norms as the implicit standards and values that evolve in working groups, such as the norm of “a fair day’s work

for a fair day’s pay” that evolved among workers in the bank wiring room in the Hawthorne studies.

Espoused values-Schein (1992) defines espoused values as the articulated, publicly announced principles and

values that the group claims to be trying to achieve, such as “product quality” or “price leadership.”

Beliefs and underlying assumptions-Basic underlying assumptions of an organization are revealed in management

decisions, policies, and procedures. McGregor’s Theory X and Theory Y describe management’s underlying

assumptions about employees. Theory X managers assume the existence of only the culture that exists in top-

management circles. They believe employees’ behavior must be directed, motivated, controlled, and modified by

management to prevent passivity. Theory Y managers recognize work group differences and understand that

different groups may have different subcultures. They believe their task lies in allowing employees to achieve their

own goals by directing their own best efforts toward organizational objectives (McGregor, 1960).

Organic and Mechanistic Characteristics. Burns and Stalker (1961) used the terms organic and mechanistic to

define organizational structure as shown in Exhibit 1.

Their studies identified that mechanistic structures are designed for stable environments while organic structures

are better suited for changing and innovative environments. Organic and Mechanistic Cultures. The terms organic

and mechanistic describe culture as well as organizational structure. Exhibit 2 shows organic and mechanistic

characteristics for each culture element.

Organizational Culture Assessment

The five culture elements and their definitions were used to develop the OCA. The OCA is a 45-question survey,

divided into five sections, one for each culture element. Each question in the survey is answered by marking one of

eight answers on a Likerttype scale with two possible answers in each of the following four categories: strongly

agree, somewhat agree, somewhat disagree, or strongly disagree.

The OCA score for an organization is determined by averaging the answers for all survey participants. The score

can range from 1.0 to 8.0. A score closer to 1.0 represents a mechanistic type of culture. A score closer to 8.0

represents an organic type of culture. A similar assessment can be made for each culture element.

Results

Thirty high-technology organizations were surveyed, with a total of 275 individual participants. Sixteen

organizations had sufficient data to perform analysis at the organizational level. Each survey participant

completed the OCA, the Likert Profile of Organizational Characteristics (POC-used for OCA validation), and a

demographics section. Exhibit 3 shows a graph of the OCA and Likert POC scores for the 16 organizations.

Enlarge this image.

Reliability

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A reliable survey is consistent in what it measures. The type of reliability used is internal consistency reliability.

Internal consistency uses a single survey to determine the degree to which the questions in the survey are

measuring the same thing. The methods used for measuring internal consistency are split-half reliability and

Cronbach’s alpha.

Validity

A valid survey measures what it is intended to measure (Litwin, 1995). Concurrent validity is used to analyze

validity. Analyses were conducted on survey participants’ opinion of the culture from the demographics form. An

examination ofthe organizations surveyed was also performed.

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Concurrent Validity. Concurrent validity requires a survey have empirical association with some criterion or “gold

standard” (DeVellis, 1991). This requires identification of an established, generally accepted test (Litwin, 1995). A

high correlation coefficient between the survey and the standard test suggests good concurrent validity.

Rensis Likert Associates, Inc. is no longer in business. However, permission to use the Likert POC was granted by

Rensis Likert’s daughter, Ms. Patricia Pohlman.

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Likert describes an organizational theory of a continuum of four management systems (Likert and Likert, 1976).

System I is an exploitive authoritative system. Managers use threats and punishment for motivation. In System 2,

the benevolent authoritative system, managers use rewards along with punishment to motivate. In System 3, the

consultative system, managers use less punishment and more rewards and involvement as motivating techniques.

In System 4, the participative group system, managers use high levels of group involvement and group-developed

reward systems to motivate.

The POC can determine where organizations fall in the Likert System 1 to 4 continuum. The version of the survey

used consists of 16 questions on an eight-point scale. The POC version used is divided into the following six

sections: leadership, motivation, communication, decisions, goals, and control. The POC measures slightly

different aspects of culture than the OCA, however, it has been extensively used to measure organizational

characteristics and was considered similar enough to the OCA to be of use.

The correlation between the OCA data and the Likert POC data is .95 (Exhibit 5). An analysis of variance produced

a significant F-value of .000, indicating that the OCA and the Likert POC are related. Analysis of the residuals

indicated the errors are normally distributed and that the order of the model is correct. The high correlation and the

ANOVA indicate that the OCA has high concurrent validity.

Participant Culture Opinion. An additional analysis was performed to further validate the OCA. The participants’

culture opinions of their organizations were collected in the demographics section of the survey. They were asked

whether their organization was viewed as very negative, negative, neutral, positive, or very positive. The

organizations surveyed were high technology, so a very negative culture opinion corresponds to a mechanistic

culture (low OCA score) and a very positive culture corresponds to an organic culture (high OCA score). The culture

opinion was converted to a five-point scale, where 1 represents a very negative culture opinion and 5 represents a

very positive culture opinion.

Exhibit 6 shows a comparison of the organization OCA scores to the culture opinions. The figure shows the OCA

scores resemble the culture opinions for each organization surveyed. The correlation between the OCA scores and

the culture opinion is .82. Therefore, it can be concluded that culture opinion is related to the OCA score and this

relationship further validates the OCA.

Organization Analysis. Another way to analyze the validity of the OCA is to examine the organizations surveyed.

The organizations with OCA results in the organic range also happen to have reputations for being innovative,

flexible, and having loose organizational structures. The management of one of the organic organizations used the

survey results to implement changes, improving an already organic score. The organization’s administrator

indicated management’s intent to use the survey throughout the organization to examine and improve the culture.

On the other hand, organizations scoring in the mechanistic range of the OCA have reputations for being

bureaucratic, rigid, and rules oriented. Many are government organizations and government support contractors.

No reports of the survey being used to study or improve these organizations’ cultures were received.

Culture Types

The OCA data were analyzed to determine whether it falls into groups. The data appeared to fall into four subsets

(Mechanistic, Mechanistic-Organic, Organic-Mechanistic, and Organic) as shown in Exhibit 7.

The boxplot for the four culture types is shown in Exhibit 8. The plot shows that the medians of the culture types

differ.

A one-way analysis of variance was performed to test the hypothesis that all the culture type means are equal. The

observed significance level is .000, so the null hypothesis that all the means are equal is rejected. Therefore, there

is a significant difference in the culture type means. A Levene test for homogeneity of variances was performed to

determine whether the group variances are equal. The significance level is .538. Since the significance level is

large, the null hypothesis that the variances are equal cannot be rejected. Therefore, the assumptions required for

the ANOVA have not been violated.

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TheANOVA does not indicate which means are significantly different from each other. Therefore, a Duncan’s

multiple range test was run to compare all possible pairs of means. The results of the Duncan’s multiple range test

with a significance level of .05 indicate that each pair of means differs significantly (Exhibit 9). Based on the test

results, it can be concluded that the culture type model is adequate.

Conclusions and Recommendations

The intent of this study was to develop an instrument to measure organizational culture. Data was collected from

275 participants from 30 organizations. The results of the analysis show evidence that (1) the OCA is a reliable

survey and is valid for measuring organizational culture, and (2) there are four types of organizational culture.

Implementing the OCA. Managers in high-technology organizations should perform investigations using the OCA

to determine whether their organizations have organic or mechanistic cultures. When the results are tabulated,

managers will have an overall OCA score for their organization, as well as a score for each of the five culture

elements. Managers should then decide whether the score is appropriate for their environment.

Culture Type Change Initiatives. If there is a need to change the culture type, responses to individual survey

questions can be examined and initiatives implemented in an attempt to improve the culture score. Although the

following ideas are not part of the research, some examples of change initiatives are shown below:

* Minimize the use of jargon

* Refrain from using derogatory terms for management, nonmanagement, and customers

* Praise employees for going to extremes to get the job done

* Managers should have an open-door policy

* Managers and non-managers should dress similarly

* Employees should have comfortable work areas with updated computer equipment

* Celebrate work accomplishments

* Managers should focus on work performance rather than office hours

* Managers should encourage employees to be open-minded and work together to solve problems

* Managers should focus on quality of work instead of quantity of work

* Managers should encourage innovative ideas and quickly adapt new technologies

* Managers should praise good performance and offer training opportunities

* Managers should provide support to employees to reach organizational goals

* Managers should assume that employees are responsible, capable, and vital organizational assets

* Decision-making should be pushed down to lower levels in the organization.

After initiatives have been implemented, then the OCA should be administered again. This post-initiative study can

be used to determine the impact of the change initiatives.

As technology continues to expand throughout the world, an organic organizational culture will become

increasingly important. In order for organizations to compete in the marketplace, a strong emphasis must be

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placed on developing a clear understanding of organizational culture and its impact on employees and

organizational success.

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References

https://search.proquest.comhttps://search.proquest.com/textgraphic/208951788/fulltextwithgraphics/2DF85C1A274DCFPQ/1/61?accountid=28180

https://search.proquest.comhttps://search.proquest.com/textgraphic/208951788/fulltextwithgraphics/2DF85C1A274DCFPQ/1/62?accountid=28180

Allen, Robert E, “Four Phases for Bringing about Cultural Change,” in Ralph H. Kilmann, Mary J. Saxton, Roy Serpa,

and Associates (eds.), Gaining Control of the Corporate Culture, Jossey-Bass (1985), pp. 332-350.

Blake, Robert R., and Jane S. Mouton, Building a Dynamic Corporation Through Grid Organizational Development,

Addison-Wesley (1969).

Burns, Tom, and George M. Stalker, The Management of Innovation, Tavistock Publications (1961).

Deal, Terrence E., “Cultural Change: Opportunity, Silent Killer, or Metamorphosis?” In Ralph H. Kilmann, Mary J.

Saxton, Roy

References

Serpa, and Associates (eds.), Gaining Control of the Corporate Culture, Jossey-Bass (1985), pp. 292-331.

Denison, Daniel R., “Bringing Corporate Culture to the Bottom Line,” Organizational Dynamics, 13:2 (Autumn 1984),

pp. 522.

DeVellis, Robert F., Scale Development: Theory and Applications, Applied Social Research Methods Series, Volume

26, Sage Publications (1991).

Dyer, W. Gibb, Jr., “The Cycle of Cultural Evolution in Organizations,” In Ralph H. Kilmann, Mary J. Saxton, Roy

Serpa, and Associates (eds.), Gaining Control of the Corporate Climate, Jossey-Bass (1985), pp. 200-229.

Kanter, Rosabeth M., The Change Masters, Simon and Schuster (1983).

References

Likert, Rensis, and Jane G Likert, New Ways of Managing Conflict, McGraw-Hill Book Company (1976).

Litwin, Mark S.,How to Measure Survey Reliability and Validity, Sage Publications (1995).

McGregor, Douglas M., The Human Side of Enterprise, McGrawHill Book Company (1960).

Morris, Richard M., III, “Effective Organizational Culture is Key to

References

a Company’s Long-Term Success,” Industrial Management, 34:2 (March/April 1992), pp. 28-29.

Ott, J. Steven, Organizational Culture and Perspective, Richard D. Irwin Company (1989).

Ouchi, William, Theory Z, Addison-Wesley (1981).

Rogers, Robert W., and B. Jean Ferketish, “Value-Driven Change Process,” Executive Excellence, 10:3 (March 1993),

pp. 5-6. Schein, Edgar H., Organizational Culture and Leadership, JosseyBass (1992), p. 12.

Sheridan, John E., “Organizational Culture and Employee Retention,” Academy ofManagement Journal 35:5

(December 1992), pp. 1036-1056.

StatSoft Electronic Textbook, Internet page at URL: www.statsoft.com/textbook/streliab.html (version current

June 12,2000).

Trice, Harrison M., and Janice M. Beyer, “Studying Organizational Cultures Through Rites and Ceremonials,”

Academy of Management Review, 94 (October 1984), pp. 653-669.

Westbrook, Jerry D., “Organizational Culture and Its Relationship to TQM,” Industrial Management, 35:1

(January/February 1993), pp. 1-3.

AuthorAffiliation

Ronda F Reigle, United States Army Aviation and Missile Command

AuthorAffiliation

About the Author

AuthorAffiliation

Ronda Reigle received her M.S. in industrial engineering from TexasA&M University. She holds a B.S. in mechanical

engineering from the University of Kentucky. She is currently a reliability engineer for the U.S. Army Aviation and

Missile Command. She is completing her dissertation for a Ph.D. in industrial and systems engineering and

engineering management at the University of Alabama in Huntsville.

Contact: Ronda Reigle, USA AMCOM, AMSAM-RD– SE-RA-MS, Redstone Arsenal, AL 35898; phone: 256-313– 6005;

ronda.reigle@rdec.redstone.army.mil

DETAILS

Subject: Studies; Corporate culture; Organizational structure; Measurement

Location: United States US

Classification: 9130: Experimental/theoretical; 2500: Organizational behavior; 9190: United States

Publication title: Engineering Management Journal: EMJ; Huntsville

Volume: 13

Issue: 4

Pages: 3-8

Number of pages: 6

Publication year: 2001

Publication date: Dec 2001

Publisher: Taylor &Francis Ltd.

Place of publication: Huntsville

Country of publication: United Kingdom

Publication subject: Engineering

ISSN: 10429247

CODEN: EMJOEH

Source type: Scholarly Journals

Language of publication: English

Document type: Feature

ProQuest document ID: 208951788

Document URL: http://search.proquest.com.proxy1.ncu.edu/docview/208951788?accountid=28180

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r Academy of Management Journal
2015, Vol. 58, No. 6, 1658–1685.
http://dx.doi.org/10.5465/amj.2013.0903

HARNESSING PRODUCTIVE TENSIONS IN HYBRID
ORGANIZATIONS: THE CASE OF WORK INTEGRATION

SOCIAL ENTERPRISES

JULIE BATTILANA
Harvard Business School

METIN SENGUL
Boston College

ANNE-CLAIRE PACHE
ESSEC Business School

JACOB MODEL
Stanford University

We examine the factors that influence the social performance of hybrid organizations
that pursue a social mission and sustain their operations through commercial activities
by studying work integration social enterprises (WISEs). We argue that both social im-
printing, defined as a founding team’s early emphasis on accomplishing the organiza-
tion’s social mission, and economic productivity are important drivers of a WISE’s
social performance. However, there is a paradox inherent in the social imprinting of
WISEs: Although social imprinting directly enhances a WISE’s social performance,
social imprinting also indirectly weakens social performance by negatively affecting
economic productivity. Results based on panel data of French WISEs gathered between
2003 and 2007 are congruent with our predictions. To understand how socially im-
printed WISEs may mitigate this negative relationship between social imprinting and
economic productivity, we also conduct a comparative analysis of case studies. We find
that one effective approach is to assign responsibility for social and economic activities
to distinct groups while creating “spaces of negotiation”—arenas of interaction that
allow members of each group to discuss the trade-offs that they face. We conclude by
highlighting the conditions under which spaces of negotiation can effectively be used to
maintain a productive tension in hybrid organizations.

Over the last 30 years, we have witnessed an un-
precedented increase in the number of organizations
that operate at the intersection of the social and
commercial sectors. These organizations, often called
“social enterprises,” primarily pursue a social mis-
sion while also engaging in commercial activities to

sustain their operations through sales of products
and/or services (see, e.g., Battilana & Dorado, 2010;
Galaskiewicz & Barringer, 2012; Hoffman, Gullo, &
Haigh, 2012; Pache & Santos, 2013). They straddle the
well-established categories of business and charity
(see Austin, Wei-Skillern, & Stevenson, 2006; Mair &

We are grateful to Michel Anteby, Rodrigo Canales, Tina
Dacin, Marie-Laure Djelic, Robin Ely, Mary Ann Glynn,
Boris Groysberg, Martine Haas, Johanna Mair, Joshua
Margolis, Charles Perrow, Woody Powell, Michael Pratt,
Michael Tushman, and three anonymous reviewers for
their detailed comments on earlier versions of this paper,
and to Stefan Dimitriadis, Ting Wang, and the Harvard
Business School European Research Center for research
assistance. This research also benefited from comments
and suggestions made by Steve Barley, Alnoor Ebrahim,
Javier Gimeno, Ranjay Gulati, Rebecca Henderson,

Matthew Lee, Christopher Marquis, Kathleen McGinn,
Rosabeth Moss Kanter, Tomasz Obloj, Jeff Polzer, Filipe
Santos, Sandra Waddock, and seminar participants at
Boston College, Cornell University, Harvard Business
School, INSEAD, London Business School, McGill Uni-
versity, Stanford University, University of California
Davis, University of Michigan, University of Pennsylvania,
and University of Toronto. Finally, we are indebted to
Jean-Marie Huguesat CNEIfor accessto theannualmember
survey data and to the members of the WISEs who shared
their time and experience with us.

1658

Copyright of the Academy of Management, all rights reserved. Contents may not be copied, emailed, posted to a listserv, or otherwise transmitted without the copyright holder’s express
written permission. Users may print, download, or email articles for individual use only.

http://dx.doi.org/10.5465/amj.2013.0903

Marti, 2006), and are thus “hybrid” organizations,
combining aspects of multiple organizational forms
(Battilana & Lee, 2014; Haveman & Rao, 2006; Padgett
& Powell, 2012). A large subset of these social enter-
prises face the challenge of serving two categories of
constituents: the customers of their commercial ac-
tivities and the beneficiaries of their social activities.

Organization theorists have long argued that or-
ganizations that serve multiple constituencies com-
plymore readily with demandsfrom the constituents
on whom they depend for access to key resources
(Oliver, 1991; Pfeffer & Salancik, 1978; Wry, Cobb, &
Aldrich, 2013). A direct implication of this pre-
diction is that social enterprises that serve distinct
groups of beneficiaries and customers are likely to
focus on their customers, on whom they depend for
financial resources, and potentially to neglect their
beneficiaries. Such neglect, however, would jeopar-
dize the ability of social enterprises to achieve their
social mission and call into question their raison
d’être. Consequently, these hybrid organizations (or
simply “hybrids”) pose an interesting question to
organizational theory: Can they sustainably achieve
their social mission in spite of the risk that they may
prioritize customers over beneficiaries?

In order to address this question, we study one type
of socialenterprisethatisprevalent aroundthe world:
the work integration social enterprise (WISE). Prom-
inent examples include the magazine The Big Issue in
the United Kingdom, the recycling network ENVIE in
France, and Goodwill stores in the United States. The
primary goal of these organizations is to help the long-
term unemployed to transition back into the labor
market. To accomplish this goal, WISEs hire un-
employedpeople,towhomwereferinthispaperasthe
“beneficiaries” of the WISEs’ social mission. These
beneficiaries work for WISEs to produce goods or ser-
vices, which are then sold on the commercial market.

These enterprises face a dilemma in deciding how
to allocate their resources. They need to provide
their beneficiaries with job training as well as in-
dividualized social counseling, which requires the
dedication of resources to social activities. Mean-
while, WISE customers expect goods and services at
a competitive price and quality, which requires the
dedication of resources to commercial activities. The
ensuing resource allocation is likely to prioritize
customers over beneficiaries because WISEs are de-
pendent on their customers for revenue generation.
However, this prioritization presents a challenge
because it diverts resources away from counseling
and other social support activities and thus puts the
fulfillment of a WISE’s social mission at risk. In

order to understand how WISEs may overcome
this challenge, we examine the factors that enable
them to achieve high levels of social performance.
In this context, “social performance” corresponds
to a WISE’s effectiveness in enhancing the job
market prospects of its beneficiaries.

Building on research on the enduring influence of
founders’ imprinting on organizations (for a review,
see Marquis & Tilcsik, 2013), we argue that social
imprinting—defined as the founding team’s early
emphasis on accomplishing the organization’s social
mission—plays a critical role in enabling these hy-
brids to maintain their focus on serving their bene-
ficiaries and achieving their social mission. Social
imprinting promotes the recruitment of permanent
staff with a background in social work as well as the
design of social-mission-oriented systems and pro-
cesses. In these ways, social imprinting helps hy-
brids to sustain their focus on their social mission
and offsets the risk of neglecting beneficiaries; hence,
it is positively associated with social performance.

However, a heightened social emphasis stemming
from social imprinting may come at a cost to eco-
nomic productivity (that is, the organization’s over-
allefficiencyinturninginputsintoeconomicoutputs).
This is because permanent staff with a social work
background may be less experienced in managing
commercial operations. Furthermore, their creation,
and subsequent institutionalization, of social-mission-
orientedorganizationalprocessesandsystemsmaylead
these staff to prioritize social activities over commercial
ones. Therefore, social imprinting is likely to be asso-
ciated with lower levels of economic productivity.
Lower economic productivity, in turn, decreases the
resources available with which to pursue the social
mission and may signal lower human capital to po-
tential employers of beneficiaries. This would reduce
beneficiaries’ employment opportunities and thereby
diminish a WISE’s social performance. We thus hy-
pothesize, echoing Smith and Lewis (2011) and Jay
(2013), that there is a paradox inherent in the social
imprinting of social enterprises that serve separate
groupsofcustomersandbeneficiaries:Althoughsocial
imprinting directly enhances a hybrid’s social perfor-
mance, social imprinting also indirectly weakens so-
cial performance by reducing economic productivity.

In this paper, we test our predictions regarding the
paradoxical nature of the relationship between so-
cial imprinting and social performance in WISEs
using data from a detailed survey administered an-
nually between 2003 and 2007, inclusive, to a large
panel of WISEs operating in France. The regression
results support our predictions and show that social

2015 1659Battilana, Sengul, Pache, and Model

imprinting and economic productivity are positively
associated with a WISE’s social performance. Also,
in line with our predictions, economic productivity
partially mediates the relationship between social
imprinting and social performance: Social imprint-
ing is negatively associated with economic pro-
ductivity and consequently has a negative indirect
effect on social performance. Our regression results
thus confirm the paradoxical relationship between
social imprinting and social performance.

Second, we extend our findings by exploring how
socially imprinted WISEs can resolve this paradox.
Todoso,weconductedanin-depthlongitudinalqual-
itative comparative case study of two WISEs: one that
successfully attenuated this relationship and one that
was unsuccessful in addressing the paradox. The
comparative analysis, based on data that we collected
from 35 interviews and a wide range of archival ma-
terial for each case, suggests that a structural differ-
entiation approach that assigns responsibility for
social and commercial activities to distinct groups
may allow hybrids to mitigate this relationship. Im-
portantly, in order to be successful, this approach
needs to be accompanied by “spaces of negotiation,”
which we define as arenas of interaction that allow all
staff members to discuss and agree on how to handle
the daily trade-offs that they face across social and
commercial activities. These spaces of negotiation
maintain a productive tension between the staff
members in charge of each of these activities.

WORK INTEGRATION SOCIAL ENTERPRISES

WISEs emerged in the late 1970s as an attempt to
address the rise of structural unemployment in de-
veloped countries (Bode, Evers, & Schulz, 2006).
Their founders recognized that a growing number of
people were out of work for long periods of time,
duringwhichtheirconfidenceandskillswereeroded.
These long-term unemployed people risked entering
a downward spiral of isolation, overindebtedness,
and substance abuse that would make it extremely
difficult for them to re-enter the job market. The
founders of WISEs developed a simple model to ad-
dress these issues: offering the long-term unemployed
work opportunities to help them to rebuild their hu-
man capital and ultimately to reintegrate into the job
market. Accordingly, WISEs hire the long-term un-
employed to produce goods or services in low-skilled
industries such as construction, catering, gardening,
or recycling, which are then sold at market prices.

WISEs can be found today around the world, in
Ireland, the United Kingdom, and the United States,

and as Beschäftigungsgesellschaften in Germany,
empresas de inserção in Portugal, and entreprises
d’insertion in France (Defourny & Kim, 2011; Spear &
Bidet, 2005). For instance, in the United States, the
Delancey Street Foundation developed a range of
WISEs, including a restaurant, a removals company,
andalandscapingcompany.InSwitzerland,Velosfür
Afrika recruits unemployed people to recycle old
bikes, thensells them inSwitzerland and West Africa.

The benefits of the WISE approach lie in its poten-
tial to provide the long-term unemployed with the
confidenceandskillsthattheyneedtoreintegrateinto
the workforce (Cooney, 2011). As they work on the
production line, workers acquire soft skills such as
attendance, workplace socialization, and discipline
as well as more job-specific skills such as dismantling
a refrigerator or building a wall. In addition to pro-
fessional training, WISEs also provide beneficiaries
with individualized social support to enhance their
job readiness. This includes counseling to help them
to address the personal issues (such as health or
housing) that are often significant barriers to em-
ployment. It also includes training to help beneficia-
ries to acquire basic aptitudes (such as literacy and
self-esteem) and job search skills (including writing
a resume or handling job interviews).

DRIVERS OF SOCIAL PERFORMANCE IN WISES

Social Imprinting

We argue that a WISE’s ability to achieve high
levels of social performance depends in part on its
social imprinting, which we define as the founding
team’s early emphasis on accomplishing the orga-
nization’s social mission. All WISEs, by definition,
are founded to pursue the social mission of helping
the long-term unemployed to reintegrate into the
workforce. However, some founders may emphasize
the importance of the social mission at the time of
founding while others may instead emphasize the
establishment of effective commercial operations in
order to ensure sustainable revenues. For instance,
in one of our interviews, the founder of a (socially
imprinted) WISE operating in the recycling industry
stated that the goal of his organization has always
been “work integration, clearly work integration: we
could be producing peas or whatever; it would make
no difference.” In contrast, the founder of another
(commercially imprinted) WISE operating in the
same industry explained that commercial activities
have taken precedence from the start: “A WISE that
focuses on its social mission against all odds is likely

1660 DecemberAcademy of Management Journal

to close down and, when it does, there is no social
impact anymore. That’s it. That’s why I always say:
first the business, then the social mission.”

Prior research has demonstrated that founding
conditions have enduring consequences for organiza-
tions. Most of the early work explored Stinchcombe’s
(1965) notion that environmental conditions during
founding affect organizational outcomes (such as sur-
vival) by creating structures and routines that are dif-
ficult to change (for a review, see Marquis & Tilcsik,
2013).Buildingonthisearlywork,amorerecentstream
of research has looked inside organizations and ex-
plored how founding teams’ early decisions have
effects that endure far beyond an organization’s found-
ing stage (see, e.g., Baron, Hannan, & Burton, 1999;
Beckman & Burton, 2008; Eisenhardt & Schoonhoven,
1990). In line with this more recent stream of research,
and echoing Whetten, Mackey, and Poly (2011), we
contend that social imprinting has a lasting influence on
the organization’s commitment to its social mission that
continues to affect a WISE’s social performance long af-
ter its founding.

This lasting influence works through two related
mechanisms. First, WISE founders who make a
strong commitment to the achievement of social wel-
fare goals early in the life of the organization are
likely to embed this commitment in the stated goals
and values of the organization (Ruef, Aldrich, &
Carter, 2003). They are further likely to consider
competencies related to accomplishing the organi-
zation’s social mission as critical when hiring full-
time staff. For example, in the socially imprinted
WISE mentioned earlier, production supervisors
were hired based on their relational and social work
skills. In contrast, founders who emphasize creating
effective economic operations are likely to consider
technical and business competencies to be the more
critical part of their hiring process for full-time staff.
For example, in the commercially imprinted WISE
mentioned earlier, industry experience was sought
when hiring permanent staff, “because [they] knew
the work [of repairing appliances], and that is what
mattered.” Homophilic processes, whereby organi-
zational members hire candidates that resemble
themselves, thus lead socially imprinted organiza-
tions to attract, select, and retain permanent staff
who adhere to similar social goals and values, and
who demonstrate mastery of similar skills (Burton &
Beckman, 2007). An important consequence of this
pattern is that the permanent staff in these organi-
zations are more likely to focus their attention on the
accomplishment of their social mission—that is, on
serving beneficiaries.

Second, WISE founders with a strong commitment
to social goals are likely to set up processes and
systems that are aligned with these goals. For in-
stance, they are more likely to go beyond training
beneficiaries only for their specific tasks at the WISE.
They are likely to adopt training policies designed
to enhance the general employability of their bene-
ficiaries, to design specific skills assessment pro-
cedures to help beneficiaries to market their skills
to prospective employers, or to organize meetings with
local employers to facilitate beneficiaries’ placement.
As these processes and systems are repeatedly enacted
by the staff, they become routines (Feldman, 2003) that
contribute to enhancing beneficiaries’ job prospects.
Therefore, we hypothesize that:

Hypothesis 1. In WISEs, social imprinting at
founding is positively associated with social
performance.

Economic Productivity

We argue that a WISE’s ability to achieve high
levels of social performance depends also in part on
its economic productivity, which we define, build-
ing on Caves, Christensen, and Diewert (1982), as
a WISE’s overall efficiency in turning inputs into
economic outputs. Economic productivity is likely
to be positively associated with the social perfor-
mance of WISEs in three main ways. First, a more
economically productive WISE, by definition, is able
to produce higher levels of output for any given level
of input than its less productive counterparts. As
a result, it will have relatively higher margins, prof-
itability, and capacity to innovate (Bourgeois, 1981).
The resulting slack reduces the pressure to achieve
sufficient revenues to ensure its survival and allows
a WISE to focus more on the attainment of social
objectives. For example, in a WISE with high eco-
nomic productivity, permanent staff can spend more
time training beneficiaries to help them to acquire
broader professional skills.

Second, an economically productive WISE is more
likely to be perceived as legitimate by key external
market constituents such as customers or investors.
In the eyes of these constituents, achieving high
economic productivity signals that the WISE is well
managed and has adopted business practices that are
taken for granted in competing corporations. Legiti-
macy increases the likelihood that these constituents
will support the organization (D’Aunno, Sutton, &
Price, 1991; Suchman, 1995). For example, a more
productive WISE may have an easier time attracting

2015 1661Battilana, Sengul, Pache, and Model

new contracts from customers who are satisfied with
its products or services. In turn, a more productive
WISE may be able to mobilize additional resources
for supporting its beneficiaries (for example, through
training or mentoring) and thereby better prepare
them to find a job.

Third, the benefits of legitimacy are likely to spill
over to a WISE’s beneficiaries as well. The level of
economic productivity of a WISE is likely to reflect the
degree to which the organization has been able to en-
hance the capacity of its beneficiaries to transform
inputs (such as raw material) into economic outputs.
Byimplication,beingamemberofanorganizationthat
has the reputation of being highly productive signals
higher human capital (Morrison & Wilhelm Jr, 2004).
As a result, a more productive WISE will be more
likely to be able to place its beneficiaries in jobs once
they leave the WISE. Therefore, we hypothesize that:

Hypothesis 2. In WISEs, economic productivity
is positively associated with social performance.

Relationship between Social Imprinting and
Economic Productivity

Adhering to the demands of both their customers
and beneficiaries is challenging for WISEs because
the activities that serve their beneficiaries are not
fully aligned with those that serve their customers.
With constrained resources, a decision to attend to
one constituency may carry a significant opportunity
cost. Because they depend on customers for their
survival, WISEs run the risk of placing too much fo-
cus on their customers at the cost of neglecting their
beneficiaries. One advantage of social imprinting, as
we highlight in the discussion of Hypothesis 1, is that
it counteracts this tendency through its influence on
workforce composition, organizational processes,
and systems. Yet, early social imprinting may also
affect how theWISE attendsto commercial activities.

Given the tendency of socially imprinted WISEs to
hire permanent staff with social work backgrounds,
their staff are likely to have skills and beliefs that fit
the demands of the social sector rather than those of
the commercial sector (Bourdieu, 1977). This is be-
cause these staff members have been socialized in
the social sector through their training and/or work
experience, and have thus become imbued with the
values and work practices associated with that sector
(Louis, 1980; Van Maanen & Schein, 1979). Their
prior socialization makes these staff likely to priori-
tize systems and processes that help beneficiaries
over systems and processes that improve economic

productivity. For example, they may introduce more
flexibility in production processes in order to allow
beneficiaries to attend individualized counseling
sessions, or they may be lenient vis-à-vis some ben-
eficiaries’ unprofessional behaviors that are harmful
to economic productivity, such as absenteeism or
lateness. Moreover, permanent staff members with
social work backgrounds are less likely to have de-
veloped skills and experience in establishing and
running commercial operations than those who have
been trained to work, and/or who have worked, in
commercial enterprises. For example, they are less
likely to be familiar with best practices associated
with operational efficiency, marketing, or account-
ing. Therefore, we hypothesize that:

Hypothesis 3. In WISEs, social imprinting is neg-
atively associated with economic productivity.

We summarize the relationships between social im-
printing, economic productivity, and social per-
formance in WISEs in Figure 1. According to our
hypotheses, both social imprinting and economic
productivity are positively associated with a WISE’s
social performance. However, socially imprinted
WISEs face a paradox because social imprinting also
indirectly weakens social performance through its
negative relationship with economic productivity.

METHODS

To fully understand the relationship between social
imprinting and social performance, we adopt a “mixed
methodology”approach(seeEdmondson&McManus,
2007). We first test the existence of the hypothesized
paradox by conducting regression analyses based on
our quantitative data. We then turn to our qualitative
data and conduct an in-depth comparative case anal-
ysis to explore how socially imprinted WISEs may be
able to resolve the paradox of social imprinting by
mitigating the negative relationship between their so-
cial imprinting and economic productivity.

FIGURE 1
Antecedents of Social Performance in WISEs

Social imprinting Social performance

Economic

productivity

–

1662 DecemberAcademy of Management Journal

Setting

We study WISEs operating in France (that is,
entreprises d’insertion).1 To operate as a WISE in
France, an organization is required to obtain accredi-
tation from the Ministry of Labor. This accreditation
entitles it to a public subsidy intended to offset the
opportunity cost of employing less-productive people
who require extra supervision and training. This is
a fixed amount per beneficiary (V9,681 annually in
2007) that is the same across all WISEs, and WISE
beneficiaries are limited by law to two years of em-
ployment under this program. A yearly evaluation
process allows the state to monitor this accreditation
and towithdraw itif evidence is found that a WISE has
abused the subsidy (that is, the WISE has received it
without complying with its workforce development
commitment). This subsidy, together with additional
grants, accounts on average for less than a quarter of
a WISE’s revenues, with the remaining revenues com-
ing from the sale of products and/or services.

The public accreditation also requires French
WISEs to hire their beneficiaries from a pool of in-
dividuals designated by Pole Emploi (the national
agency for employment) as “deserving” access to
work integration programs. These individuals have
all been unemployed for a prolonged period of time
(typically at least two years) and have been assessed
by Pole Emploi as “experiencing specific social and
professional difficulties which make it impossible
for them to access the regular job market” (DGEFP,
2003: 7). This requirement limits the pool of poten-
tial beneficiaries to individuals who face multiple
obstacles to work, including low qualifications, low
levels of self-confidence, and a lack of professional
skills. It further ensures that WISEs recruit those who
are really in need of support.

Despite the role of the state in providing accredita-
tion and subsidies, WISEs in France operate as private
entities. Half of them operate under a for-profit legal
status while the other half operate under a not-for-
profit legal status. However, given the commercial
character of their activity, not-for-profit WISEs cannot

benefit from the tax exemptions traditionally granted
to not-for-profits. This is because not-for-profits benefit
from these exemptions only if they do not compete in
commercial markets with similar products and prices
as for-profit companies. In turn, if for-profit WISEs
abandon their social mission and pursue profit gener-
ation as their only goal, they will have their accredi-
tation withdrawn by the state. Thus, regardless of the
legal status that they adopt, WISEs face similar con-
straints in the French context.

Entreprises d’insertion constitute an ideal setting
in which to test the arguments developed in this
paper because, like their counterparts around the
world, they are hybrid organizations caught between
the distinct and potentially competing demands of
their beneficiaries and their customers. Although
they pursue a social goal, commercial activities are
important to them because they depend on sales of
products and services to generate the majority of
their revenues. Furthermore, WISEs have a signifi-
cant and growing economic presence in France. First
established in the late 1970s, the number of French
WISEs increased significantly in the 2000s: At the
end of our observation period in 2007, there were
1,178 WISEs in France, employing more than 22,000
beneficiaries, and with a combined sales volume of
more than V800 million (US$1.17 billion).2

Quantitative Data

The quantitative data used in this study are from an
annual survey administered by the Comité National
des Entreprises d’Insertion (CNEI)—that is,the French
National Federation for Work Integration Social
Enterprises—to its members since 2003.3 The survey
collects detailed establishment-level information about
the WISEs, including their field of activity, sales,
wages, composition of human resources, and place-
ment of beneficiaries. The CNEI invests significant
resources in the administration of the survey in order

1 Although we focus exclusively on entreprises d’inser-
tion in this study, there are two additional organizational
forms, associations intermédiaires (AI) and associations
chantier d’insertion (ACI), which are part of the broader
workforce development sector in France. These organiza-
tions, however, operate outside of the commercial market
since they rely mainly on public support and benefit from
advantageous labor law exemptions. They therefore di-
verge from the hybrid organizational forms that are the
focus of this study.

2 The sales figure is the authors’ estimate based on data
from DARES, the research and statistics unit of the French
Ministry of Labor.

3 About half of French WISEs are affiliated with the
CNEI. Those that are not are either too small to be able to
pay the required membership fees or are affiliated with two
other social federations: Fédération Nationale des Asso-
ciations d’Accueil et de Réinsertion Sociale (FNARS), the
main federation for homeless shelters; or Fédération des
Comités et Organismes d’Aide aux Chômeurs par l’Emploi
(CORAACE), another federation for social service organi-
zations. The CNEI is the largest of these three federations
and is the only one exclusively open to WISEs.

2015 1663Battilana, Sengul, Pache, and Model

to use the data collected in its lobbying efforts
(Hugues, 2007). As a result, the response rate is ex-
tremely high (for example, 98% in 2007), covering
nearly the entire population of CNEI members.

Using this data source, we gathered a panel for
years between 2003 and 2007, inclusive. We then
imposed three criteria in the construction of our
working sample. First, we excluded from our ana-
lyses WISEs that had fewer than 10 employees (that
is, permanent staff and beneficiaries). Data from very
small WISEs are relatively less reliable and less sta-
ble than data from larger ones because small WISEs
are less likely to have the time and resources to col-
lect and provide accurate data for the CNEI survey
every year. Second, we focused exclusively on ob-
servations that covered a full year of data and hence
excluded the data from the founding year of WISEs
(if it was founded within our observation period).
Third, we excluded WISES operating in the tem-
porary work industry because they do not directly
hire beneficiaries on a full-time basis but operate
as intermediaries by placing beneficiaries in spe-
cific short-to-medium-term assignments with cli-
ent companies. As a result, beneficiaries have only
limited interactions with their temporary work
WISE.

Applying these criteria and eliminating observa-
tions with incomplete or missing information for
one or more variables of interest, we obtained 641
establishment-year observations with complete
information.4

Dependent Variable: Social Performance

Building on Scott (1977), we define social perfor-
mance as the degree to which an organization is ef-
fective at producing positive social outcomes. Within
the WISE field, a consensus has emerged that social
performance can be assessed by the proportion of
beneficiaries that are able to find regular jobs at the end
of their employment with the WISE (DGEFP, 2003).
This rate, known in the field as the “positive gradua-
tion rate,” is reported yearly by all WISEs to the state.
Accordingly, we measure a WISE’s social performance
as the percentage of beneficiaries completing their

term at the WISE in a given year who found a regular
job with a contract lasting more than six months.

Independent Variables

Social imprinting. We used the industry classifi-
cation of WISEs as a proxy for their social imprinting.
In France, at the time of incorporation, WISE founders
(like the founders of any organization) are required to
file a statement, called an objet social, describing their
organization’s purpose and its branch of activity. The
French National Institute of Statistics and Economic
Studies (INSEE) assigns this information to an Activité
Principale de l’Entreprise (APE) code, which corre-
sponds to the main activity of an organization. If
founders do not agree with their assigned APE code,
they can contest and ask INSEE for another. However,
such requests are very rare, as are changes requested
later in the life of the organization.5 Therefore APE
codes reflect the set of activities that the founder em-
phasized at the time of founding.

Qualitatively, a WISE founder can primarily de-
scribe his or her organization’s main activity in one
of two ways: either as performing a social activity
(such as social services) or as operating in a specific
industry (such as recycling, gardening, or catering).
Following the terminology used in the WISE field,
we refer to the former organizations as having a “so-
cial APE,” and we refer to the latter as having an
“industry-specific APE.” Two field experts whom
we interviewed strongly supported the notion that
APE codes reflect early imprinting. As one of these
experts explained:

It tells you something when [a WISE] has a [social
APE]: that [the founder] viewed his role as being
mainly social. For these guys, the commercial activity

4 Observations that were eliminated as a result of in-
complete or missing information were identical to those
kept in our final working sample in most attributes and
demonstrated negligible differences in others. The only
noticeable difference was a higher percentage of female
beneficiaries at 42%, compared to 33% in our final work-
ing sample.

5 Such code changes are very rare, mainly because APE
codes are assigned by INSEE only for statistical purposes
and do not have any fiscal, financial, or legal implications
at the time of founding or later (see Mercadal, Janin,
Charvériat, & Couret, 2004). Furthermore, once filed, an
objet social can be modified only by an extraordinary de-
cision of the board through a qualified majority. In our
sample, only two WISEs (corresponding to less than 1% of
unique WISEs) changed their APE codes between 2003 and
2007; both of these changes were from an industry-specific
APE to a social one in 2005 andback to the original industry-
specific APE in 2006. According to our best assessment and
cross-checks, these two cases reflect data entry errors in
2005. Even though the results were insensitive to inclusion
or exclusion of these two WISEs, we took a conservative
approach and excluded them from our analyses.

1664 DecemberAcademy of Management Journal

is only a medium for workforce development. Those
who [have] an industry-specific APE are in a different
state of mind. They know that they are here for the
social mission, but they think that their main role is to
develop their commercial activity and that the social
impact will follow from it.

Accordingly, we coded social imprinting as “1” if
the WISE had a social APE code and “0” if it had an
industry-specific APE code. The WISEs in our data-
set had 56 different APE codes. We classified the
following APE codes as social: 85.3H, aide par le
travail et ateliers protégés (sheltered workshops);
85.3K, autres formes d’action sociale (other types of
social service); and 91.3E, organizations associatives
n.c.a. (associations). In our sample, roughly 27% of
CNEI members had a social APE code. The rest were
industry-specific APE codes, such as 55.5D, traiteurs
et organization de receptions (catering services), or
52.7D, réparation d’appareils électroménagers (ap-
pliance repair).

Economic productivity. Following Huselid (1995)
and Rangan and Sengul (2009), we measured the
economic productivity of a WISE as the ratio of total
annual sales to the number of employees, including
both permanent staff and beneficiaries. To account
for productivity differences across sectors, we com-
puted standardized economic productivity scores
that capture by how many standard deviations the
economic productivity of each WISE lies above or
below the median score in its sector of activity. (As
we describe in “Control variables” below, in all
models we also included sector dummies.) We use
these median-standardized scores in all regres-
sions.6 The sales per employee measure is particu-
larly well suited to examining economic productivity
differences across organizations that operate in dif-
ferent sectors and, when controlling for mean differ-
ences across sectors, reflects a combination of the
managerial (sales, training, mentoring, etc.) capabil-
ity of permanent staff and production capability of
beneficiaries.

Control Variables

In terms of organizational characteristics, we
controlled for organization size (measured by the

log of total number of full-time equivalents, or FTEs,
working in the WISE) and organization age (mea-
sured by the number of years since its founding,
scaled by 1/10 to help the demonstration of co-
efficients). These factors might affect a WISE’s so-
cial performance because the extent of available
organizational resources may vary by organization
size (Huber, Sutcliffe, Miller, & Glick, 1993) and
because an organization’s ability to convert re-
sources to organizational outcomes is likely to vary
as a function of its age (Miller & Shamsie, 2001).
Another organizational characteristic of WISEs is
their legal status. As we noted earlier, WISEs face
similar constraints in the French context regardless
of their legal status. Still, we took a conservative
approach and included a control for the not-for-
profit legal status, coded as “1” if not-for-profit and
“0” otherwise.

In terms of human resources, we first controlled for
the level of supervision (measured by the log of the
ratio of the total number of permanent staff involved
in the mentoring and training of beneficiaries to the
total number of beneficiaries). Higher levels of su-
pervision may reflect higher levels of on-the-job
training and/or individualized support and may, in
turn, enhance beneficiaries’ employment prospects.
We also controlled for the demographic profile of
beneficiaries. Although WISEs hire all of their ben-
eficiaries from the pool of individuals listed by Pole
Emploi as experiencing social and professional dif-
ficulties, prior studies and anecdotal evidence have
shown that women and/or individuals who are
deemed to be too young (that is, too inexperienced)
or too old are particularly disadvantaged and find it
more challenging to find a job (D’Autume, Bethbèze,
& Hairault, 2006; Margaret, 2006). Accordingly, we
include the percentage of total beneficiaries who are
female, under 26 years old, and over 50 years old as
control variables.

In terms of financial structure, we first controlled
for subsidies received (measured by the ratio of the
total amount of all subsidies that the WISE received
in tens of thousands of euros to the number of its
employees). This variable captures the extent of or-
ganizational financial resources that do not directly
depend on economic productivity on a per employee
basis. Because effort allocation and marginal returns
to effort can be expected to be a function of costs
(Miller & Shamsie, 2001), we also controlled for av-
erage wage cost, which we measured by the log of the
ratio of total wage cost of the WISE to the number of
employees. Controlling for region and sector effects,
wage costs may account for some underlying quality

6 This measure is highly correlated with the raw sales
per employee values, as well as an alternative measure
standardizing at the sector mean (0.919 and 0.994, re-
spectively). Importantly, the results were qualitatively
identical when using raw, mean-standardized, or median-
standardized sales per employee values.

2015 1665Battilana, Sengul, Pache, and Model

of organizational members, which could affect the
WISE’s social performance.

Finally, we included founding period dummies to
control for the possible influence of environmental
imprints and historical conditions at the time of
founding (Stinchcombe, 1965). The WISE field in
France has evolved through four distinct stages: the
experimentation phase prior to 1985, the contesta-
tion phase between 1986 and 1990, the institution-
alization phase between 1991 and 1997, and the
professionalization phase starting in 1998 (see Ap-
pendix A). Accordingly, leaving founded prior to
1985 as the benchmark category, we include foun-
ded in period 1986–1990, founded in period 1991–
1997, and founded in period after 1997 dummies as
control variables.

We report the summary statistics and bivariate
zero-order correlations in Table 1. A prototypical
(that is, average) WISE establishment had about 28
permanent staff and beneficiaries. Legal statuses of
WISEs were roughly equally split, with half having
a not-for-profit status and half having a for-profit
status. Of beneficiaries, 18% were under 26 years
old, 12% were over 50 years old, and 33% were
female. Average sales were V24,270 per FTE, with
a corresponding average wage cost of V20,537. On
average, WISEs were able to place 38% of their
graduating beneficiaries in a job.

Estimation

In choosing our estimation method, we took into
consideration the cross-section and time-series
nature of our data and ran all regressions using
a generalized least squares (GLS) (random effects)
estimator. The random effects estimator assumes
that average values for each establishment differ
from the population average (that is, that there are
establishment-specific differences) and that differ-
ences across establishments have an underlying
distribution (that is, that each establishment is ran-
domly selected from a larger population of estab-
lishments). Accordingly, we report regressions with
random establishment effects.7 We also took into
account that factors unobservable to us (but observ-
able to WISEs) might affect the levels of both the
dependent variable and the right-hand-side vari-
ables. If this were the case, it would result in si-
multaneity: A shock that affects a WISE’s social
performance might also affect its size, wage costs,

and so on in the same year. To attenuate the potential
issue of simultaneity, and keeping with convention, we
lag all right-hand-side variables by one year. Addi-
tionally,toaccountforunobservedheterogeneityin the
data, we included year, region, and sector dummies
in all regressions. For regions, we used the official
French regional classification, which comprises 27
administrative regions. For sectors, using the clas-
sification in the CNEI dataset, we coded WISEs into
six sectors: business services (services aux entre-
prises); construction (bâtiment, travaux publics); en-
vironment (environnement, espaces verts); garbage
(déchets:collecte,tri,déconstruction,dépollution);recy-
cling (récuperation, recyclage et commerce d’occasion);
and others (all remaining sectors). We report robust (that
is, heteroskedasticity-corrected) standard errors clus-
tered by WISE for all models.

REGRESSION RESULTS

We hypothesized that both social imprinting (Hy-
pothesis 1) and economic productivity (Hypothesis
2) are positively associated with WISEs’ social per-
formance. The regression results reported in Table 2
strongly support these hypotheses: The coefficients
of social imprinting (Model 2) and economic pro-
ductivity (Model 3) are both positive and statisti-
cally significant, as predicted. The results are
substantively significant as well. Holding all other
variables at their mean values, organizations with
social imprinting have, on average, 9.9% (and
a 3.7 percentage-point) higher social performance
than those without it. Similarly, a 1SD increase in
economic productivity is associated with a 5.8%
(and a 2.2 percentage-point) increase in social
performance.8

Table 3 reports the regressions explaining the
drivers of economic productivity in WISEs. In line
with Hypothesis 3, the coefficient of social imprint-
ing is negative and statistically significant (Model 2).
Holding all other variables at their mean values,

7 Pooled ordinary least squares (OLS) yielded qualita-
tively identical results.

8 We also checked for a curvilinear relationship between
economic productivity and social performance. If achiev-
ing very high levels of economic productivity requires
WISEs to devote more and more resources to production
activities, this may result in diverting resources away from
activities that directly contribute to social performance.
Simply put, there might be an optimal level of economic
productivity. We did not find support for this argument
in supplementary regressions including a squared term
for economic productivity, which was insignificant in all
models.

1666 DecemberAcademy of Management Journal

organizations withsocialimprinting have, on average,
13.3% lower economic productivity (corresponding
to V3,342 lower sales per employee on an annual
basis) than those without it. It is important to note,
however, that economic productivity only par-
tially mediates the relationship between social
imprinting and social performance. Even though
social imprinting weakens social performance
through the negative relationship that it has with
economic productivity, it still has a positive and
significant net effect on social performance (see
Table 2, Models 2 and 4). To obtain more conclu-
sive evidence, we conducted Sobel, Aorian, and
Goodman tests of mediation, which enable an as-
sessment of indirect effects of the independent
variable on the dependent variable via the medi-
ating variable (see Iacobucci, 2008: ch. 2). The
results of these tests (which are all significant at
p , .05) indicate that social imprinting has a nega-
tive indirect (that is, partially mediated) effect on
social performance through its relationship with
economic productivity.

In supplementary regressions, we also explored
whether the observed relationships between social

imprinting and social performance, and between
social imprinting and economic productivity, persist
over time. To do so, we included an interaction
term between our measure of social imprinting and
organization age. The interaction term was negative
and statistically significant on both social perfor-
mance and economic productivity, indicating im-
print decay. We then calculated differences in
predicted values of social performance across so-
cially imprinted and commercially imprinted WISEs
over time. A year after its incorporation, a socially
imprinted WISE has an 8 percentage-point higher
positive graduation rate compared to a commercially
imprinted one. The difference goes down to a 5
percentage-point spread in the eighth year and is
halved, after a decade, to a 4.1 percentage-point dif-
ference. It is still roughly at 2 percentage points 15
years after incorporation. These results imply that the
relationship between social imprinting at founding
and organizational outcomes decreases in magnitude
over time but persists long after founding.

Lastly, some brief observations are in order re-
garding our control variables. First, organizational
characteristics—organization size and age—explain

TABLE 1
Means, Standard Deviations, and Bivariate Zero-Order Correlations

Variables Mean SD 1 2 3 4 5 6 7

1 Social performance 0.38 0.19
2 Social imprinting 0.27 0.45 0.07
3 Economic productivity 0.04 1.05 0.19 20.14
4 Organization size (log) 3.33 0.54 20.10 0.01 20.05
5 Organization age (410) 1.02 0.56 20.03 0.16 0.08 0.19
6 Not-for-profit legal status 0.54 0.50 20.08 0.44 20.29 20.11 0.36
7 Level of supervision (log) 20.99 0.77 0.17 20.05 0.40 0.05 0.13 20.18
8 Beneficiaries (%)—Female 0.33 0.30 20.08 20.04 20.28 0.06 20.02 0.10 20.16
9 Beneficiaries (%)—Under 26 years old 0.18 0.15 0.10 0.02 0.12 0.00 0.09 20.08 0.19
10 Beneficiaries (%)—Over 50 years old 0.12 0.10 20.01 0.09 20.04 20.04 0.02 0.16 20.13
11 Subsidies received 0.85 0.33 20.10 0.12 20.17 20.13 0.09 0.25 20.41
12 Average wage cost (log) 9.93 0.31 0.13 0.04 0.47 20.03 0.10 20.07 0.22
13 Founded in period: 1986–1990 0.12 0.32 20.08 20.06 0.03 0.20 0.38 20.02 0.18
14 Founded in period: 1991–1997 0.46 0.50 20.09 0.03 20.09 20.06 0.07 0.18 20.06
15 Founded in period: After 1997 0.33 0.47 0.05 20.12 0.02 20.10 20.74 20.34 20.09

Variables Mean SD 8 9 10 11 12 13 14

8 Beneficiaries (%)—Female 0.33 0.30
9 Beneficiaries (%)—Under 26 years old 0.18 0.15 20.09
10 Beneficiaries (%)—Over 50 years old 0.12 0.10 0.05 20.26
11 Subsidies received 0.85 0.33 0.02 20.11 0.06
12 Average wage cost (log) 9.93 0.31 20.19 0.15 20.04 0.12
13 Founded in period: 1986–1990 0.12 0.32 0.05 0.13 20.05 20.09 20.01
14 Founded in period: 1991–1997 0.46 0.50 20.13 20.14 0.04 0.09 20.07 20.33
15 Founded in period: After 1997 0.33 0.47 0.09 0.01 20.02 20.10 20.01 20.25 20.65

Note: n 5 641; all independent and control variables are one-year lagged.

2015 1667Battilana, Sengul, Pache, and Model

productivity differences across WISEs but not social
performance differences across them. Economic
productivity tends to be higher in WISEs that are
more experienced and relatively smaller.

Second, not-for-profit legal status does not have
a significant effect on social performance. Yet eco-
nomic productivity tends to be lower in not-for-
profit WISEs.

Third, supervision helps both economic pro-
ductivity and social performance. This reflects that
better trained and guided beneficiaries are more
productive, and that productive beneficiaries are
more likely to find jobs after their time at WISEs.

Fourth, the percentage of female beneficiaries is
negatively associated with economic productivity.

Fifth, average wage cost is positively associated
with economic productivity, in line with efficiency
wage models in labor economics (see, e.g., Yellen,
1984).

Sixth, WISEs founded in the 1986–1990 period
have lower social performance compared to WISEs
founded in other periods. This period was marked by
the withdrawal of state support from WISEs; hence,
the WISEs founded in this period faced significant
challenges in obtaining the required resources in
their early years (see Appendix A). This result, al-
though at a different level of analysis, resonates with
Tilcsik (2014), who showed that early career re-
source environment has a lasting influence on career
trajectories of individuals.

TABLE 2
Random-Effects Regressions Explaining the Social Performance of French Work Integration Social Enterprises, 2003–2007

Variables 1 2 3 4

Social imprinting 0.031† 0.037*
(0.022) (0.022)

Economic productivity 0.020* 0.022*
(0.011) (0.012)

Organization size 20.021 20.023 20.015 20.017
(0.019) (0.019) (0.019) (0.019)

Organization age 20.006 20.008 20.022 20.024
(0.047) (0.047) (0.047) (0.047)

Not-for-profit legal status 20.022 20.035 20.010 20.025
(0.021) (0.024) (0.021) (0.024)

Level of supervision 0.040** 0.040** 0.034* 0.034*
(0.014) (0.014) (0.014) (0.014)

Beneficiaries
Female 20.009 20.006 20.008 0.014

(0.037) (0.037) (0.038) (0.038)
Under 26 years old 20.005 20.007 20.006 20.008

(0.060) (0.059) (0.060) (0.059)
Over 50 years old 0.042 0.038 0.034 0.029

(0.075) (0.075) (0.075) (0.075)
Subsidies received 20.012 20.014 20.008 20.010

(0.023) (0.023) (0.023) (0.023)
Average wage cost 0.013 0.015 0.009 20.008

(0.024) (0.025) (0.026) (0.027)
Founded in period
1986–1990 20.083† 20.079† 20.083† 20.078†

(0.044) (0.043) (0.044) (0.043)
1991–1997 20.048 20.047 20.055 20.054

(0.058) (0.058) (0.058) (0.058)
After 1997 20.041 20.042 20.058 20.060

(0.087) (0.086) (0.087) (0.086)
R2 (overall) 0.180 0.184 0.187 0.191
Wald x2 99.24** 100.82** 107.04** 110.20**

Note: n 5 641; robust standard errors, clustered by WISE, in parentheses; constant, year, sector, and region dummies included in all models;
all independent and control variables are one-year lagged.

† p , .10
* p , .05

** p , .01 (two-tailed tests; one-tailed tests, when hypothesized)

1668 DecemberAcademy of Management Journal

Finally, unlike year and sector dummies, most re-
gion dummies (not reported) were statistically signifi-
cant, reflecting the influence of economic, social, and
demographic differences across regions on WISEs’
social performance.

Robustness Checks and Supplementary Analyses

We conducted a set of supplementary empirical
analyses using the available data to explore the ro-
bustness of the regression results to a number of po-
tential concerns. First, to verify the validity of APE
codes as a proxy for social imprinting, we collected
additional data and created two alternative measures
of social imprinting for the WISEs in our sample: one

based on our coding of original objet social state-
ments9 and another based on the assessment of
a former CNEI president.10 These two alternative
measures of social imprinting were highly similar to
the social vs. industry-specific APE codes that we
use: the former measure identical to APE codes in
91% of the cases and the latter, in 84%. When we re-
estimated our models using these two alternative
measures, social imprinting was still positively and
significantly associated with social performance,
and negatively and statistically associated with eco-
nomic productivity. Therefore, we are confident
that our measure (which is available for the full
sample) reliably captures social imprinting at
founding.

We also checked whether the measurement of so-
cial performance as a ratio bounded between 0 and 1
impose any bias in the reported results. Tobit is
the suggested estimation method for dependent

TABLE 3
Random-Effects Regressions Explaining the Economic

Productivity of French Work Integration Social
Enterprises, 2003–2007

Variable 1 2

Social imprinting 20.334**
(0.144)

Organization size 20.540** 20.526**
(0.093) (0.092)

Organization age 0.829** 0.833**
(0.317) (0.310)

Not-for-profit legal status 20.687** 20.541**
(0.106) (0.126)

Level of supervision 0.146** 0.149**
(0.060) (0.057)

Beneficiaries
Female 20.753** 20.793**

(0.184) (0.183)
Under 26 years old 0.121 0.122

(0.215) (0.216)
Over 50 years old 0.408 0.243

(0.264) (0.262)
Subsidies received 0.086 0.088

(0.139) (0.139)
Average wage cost 0.687** 0.686**

(0.182) (0.184)
Founded in period
1986–1990 0.056 0.019

(0.308) (0.308)
1991–1997 0.296 0.277

(0.381) (0.380)
After 1997 0.736 0.739

(0.559) (0.554)
R2 (overall) 0.408 0.416
Wald x2 246.67** 250.39**

Note: n 5 641; robust standard errors, clustered by WISE, in
parentheses; constant, year, sector, and region dummies included
in all models; all independent and control variables are one-year
lagged.

** p , .01 (two-tailed tests; one-tailed tests, when hypothesized)

9 These statements are kept at the local préfectures
(i.e., local government administrative centers) within
which each organization was originally incorporated.
Consequently, we filed official requests for each WISE that
we had in our final working sample to local préfectures.
Out of 230 individual requests that we made, we received
128 objet socialstatements inprint bymail at the end of this
process, corresponding to a total of 350 observations. Then,
two of the coauthors independently coded the social im-
printing of each WISE based on the information provided
in its objet social. Specifically, they coded whether the
social mission was emphasized at the time of founding. A
WISE with social imprint would, for example, have an
objet social such as “providing work for people facing so-
cial and professional challenges and offering them tailored
training in order to reintegrate them into the regular job
market,” and a WISE with an commercial imprint would,
for example, have an objet social such as “refurbishing
and recycling of used computer and electronic appli-
ances; sale of these products and associated services.”
Interrater reliability, as assessed by the k correlation co-
efficient, was 0.89, suggesting a high degree of agreement
between the two coders. The few remaining disagree-
ments were resolved through discussions until consensus
was reached.

10 We contacted a former CNEI president, who had wit-
nessed the rise of the field over 30 years, and asked him to
code the imprints of the organizations about which he felt
he had enough information to assess their early imprint.
We provided him the names and addresses of all of the
WISEs in our sample but did not share their APE codes or
objet social statements. He delivered his personal assess-
ment of whether a given WISE had an early social imprint
for 96 WISEs in our sample, corresponding to a total of 265
observations.

2015 1669Battilana, Sengul, Pache, and Model

variables that are bounded and have limit observa-
tions.(In our dataset, there were only a total of 46 limit
observations, with 26 occasions on which a WISE was
unable to place any of its participants in a job upon
graduation and 20 occasions on which it was able to
place all of them.) However, Tobit estimation is based
on an underlying latent variable, which is meaningful
when the latent variable could exist beyond the
limits—hence implying truncation—but not when
this is not feasible. When this is the case, frac-
tional logit, using generalized linear modeling
(GLM) or generalized estimating equations (GEE),
is the suggested estimation method (McDonald,
2009; Papke & Wooldridge, 1996). The results are
qualitatively identical across all different esti-
mations (that is, GLS, Tobit, GLM, and GEE), fur-
ther increasing our confidence in the reported
results.

Third, one might be concerned about a potential
survivorship bias if a nontrivial number of the WISEs
were to fail and those WISEs were to be qualitatively
different from our sample. To verify, we checked for
mortality in the CNEI dataset during our observation
period. In line with our knowledge of the field and
the information that we collected from field experts,
the mortality rate was low. Furthermore, the few
WISEs that ceased operations did not differ from
those in our working sample in observables, imply-
ing that there is no significant survival bias in the
reported results.

A fourth concern relates to the possibility that the
relationship between economic productivity and
social performance may be picking up the effect of
some of the causes of economic productivity and
hence may be spurious. To check, we obtained fitted
(that is, predicted) economic productivity values
and residuals from Model 2 of Table 3 (the fully
specified model on economic productivity); then,
as a second step, we re-estimated the models in
Table 2 (on social performance) using these fitted
values and residuals. In these regressions, the fitted
values and the residuals were both positive and
statistically significant, implying the presence of an
independent effect of economic productivity on
social performance in addition to those attributable
to potential predictors in common, such as quality
of management.

A fifth concern relates to the possibility that the
error terms may be correlated across the models
predicting social performance and economic pro-
ductivity. If that were the case and the correlation
were large, we could expect gains in efficiency
by estimating the equations together rather than

estimating each equation separately (Baltagi, 1998).
Accordingly, we reran all regressions using seem-
ingly unrelated regressions (SUR) and using struc-
tural equation modeling (SEM). The results of these
analyses (see Table 4) are highly similar to each
other, as well as to the GLS results that we reported in
Tables 2 and 3. Importantly, all hypothesized re-
lationships remain unchanged in terms of sign and
significance.

Finally, in terms of model specification, we also
checked the robustness of the results to serial corre-
lation and multicollinearity. To check the robust-
ness of the results to potential serial correlation, we
re-estimated all of the models using two separate
methods that account for first-order autocorrelation
in panel data: the Baltagi-Wu autoregressive esti-
mator and GEE regressions imposing a within-panel
correlation of the order AR(1). In both checks, social
imprinting and economic productivity were still
positively and (slightly more) significantly related to
social performance. Separately, to check the robust-
ness of the results to potential multicollinearity
(even though there are no critically collinear vari-
ables), we reran the regressions by dropping mod-
erately correlated control variables (such as average
wage cost, founding period dummies), both in-
dividually and in combinations. Regression results
were qualitatively insensitive to the inclusion or ex-
clusion of these variables (but exclusions typically
resulted in lower model fit).11

UNPACKING THE PARADOXICAL
RELATIONSHIP BETWEEN SOCIAL

IMPRINTING AND SOCIAL PERFORMANCE
IN WISES

The results of the quantitative analyses confirm
the paradoxical relationship between social im-
printing and social performance in WISEs: Although
social imprinting enhances a WISE’s social perfor-
mance by keeping it from neglecting its beneficiaries,
social imprinting also indirectly weakens social
performance through the negative relationship that
social imprinting has with economic productivity.
To unpack this paradox, we now examine the
mechanisms that underlie it and explore whether,
and how, socially imprinted WISEs can resolve it

11 We also calculated variance inflation factors (VIFs) on
both pooled and WISE-demeaned data. All calculated VIFs
were much lower than the critical value of 10, indicating
no serious multicollinearity.

1670 DecemberAcademy of Management Journal

through a longitudinal comparative analysis of two
WISEs, referred to as “ALPHA” and “BETA.”

Qualitative Data Collection and Analysis

Relying on a theoretical sampling approach
(Eisenhardt, 1989), we analyzed two cases of socially
imprinted WISEs operating in the recycling industry:
“ALPHA” and “BETA.” We selected these two or-
ganizations from our sample because they are both
socially imprinted WISEs (that is, they both have
a social APE code), yet achieved different levels

of economic productivity when compared to the
other socially imprinted WISEs operating in the
recycling sector: ALPHA’s economic productivity
was below average (at V17,460 of sales revenue per
employee) and BETA’s, above average (at V25,867),
in the period that followed an economic crisis that
both organizations coincidentally experienced in
2003.12 Beyond this stark difference in postcrisis

TABLE 4
Seemingly Unrelated Regressions (SUR) and Structural Equation Modeling (SEM) Regressions Explaining the Economic

Productivity and Social Performance of French Work Integration Social Enterprises, 2003–2007

SUR SEM

Economic productivity Social performance Economic productivity Social performance

Social imprinting 20.257** 0.038* 20.255** 0.039*
(0.085) (0.020) (0.085) (0.020)

Economic productivity 0.022** 0.023**
(0.009) (0.009)

Organization size 20.252** 20.016 20.257** 20.019
(0.066) (0.016) (0.066) (0.016)

Organization age 0.804** 20.018 0.802** 20.020
(0.175) (0.041) (0.175) (0.041)

Not-for-profit legal status 20.429** 20.026 20.435** 20.029
(0.082) (0.020) (0.082) (0.020)

Level of supervision 0.296** 0.033** 0.291** 0.030*
(0.051) (0.012) (0.050) (0.012)

Beneficiaries
Female 20.816** 0.011 20.813** 0.013

(0.135) (0.032) (0.135) (0.032)
Under 26 years old 0.125 0.006 0.117 0.002

(0.230) (0.053) (0.230) (0.053)
Over 50 years old 0.461 0.036 0.471 0.042

(0.328) (0.076) (0.329) (0.077)
Subsidies received 20.235* 20.008 20.237* 20.009

(0.117) (0.027) (0.117) (0.027)
Average wage cost 1.189** 20.005 1.184** 20.008

(0.115) (0.029) (0.115) (0.029)
Founded in period
1986–1990 20.032 20.075† 20.032 20.075†

(0.165) (0.038) (0.165) (0.038)
1991–1997 0.397† 20.045 0.393† 20.049

(0.212) (0.049) (0.212) (0.049)
After 1997 0.804** 20.049 0.881** 20.051

(0.309) (0.072) (0.309) (0.073)
R2 0.489 0.192 0.489 0.186
x
2 606.26** 152.23**

AIC 1258.15

Note: n 5 641; robust standard errors, clustered by WISE, in parentheses; constant, year, sector, and region dummies included in all models;
all independent and control variables are one-year lagged.
† p , .10
* p , .05
** p , .01 (two-tailed tests; one-tailed tests, when hypothesized)

12 In comparison, the average productivity of the socially
imprinted WISEs that operate in the recycling industry was
V23,153 in the same time period.

2015 1671Battilana, Sengul, Pache, and Model

economic productivity levels, ALPHA and BETA do
not demonstrate significant differences from each
other (with the exception of a relatively higher per-
centage of female employees at ALPHA) and are
similar to other WISEs in the recycling industry.
They are both midsized WISEs founded roughly
around the same time; they both operate in the
recycling sector under a not-for-profit legal status.
Therefore, the comparison between these two cases
allows us to gain valuable insights into the strategies
that socially imprinted organizations may use to re-
solve the social imprinting paradox.

We collected a broad range of data about both or-
ganizations. One of the authors conducted semi-
structured interviews with members of each WISE,
including founders, board members, executive di-
rectors, production supervisors, and social coun-
selors. A total of 20 interviews were conducted
onsite, each lasting between 30 minutes and 3 hours.
Four follow-up phone interviews were conducted
with case informants. In addition to case-level in-
terviews, 11 more interviews were conducted with
members of the broader WISE community (such as
CNEI executives, workforce development consul-
tants) in order to explore the field-level stakes and
trends. All interviews were conducted in French,
then later transcribed and translated to English.

During the case-level interviews, all informants
were asked about the founding and growth of their
WISE, its goals and values, and its interactions with its
environment, as well as their own perception of the
determinant of the WISE’s social performance. The
interview guide was adapted to the role of inter-
viewees (for example, executive director vs. social
counselor).Whileallinterviewsstartedwithquestions
related to the profile and background of interviewees,
founders were specifically asked about their motiva-
tion to create the WISE and about the process through
which they created, and later managed, it. Executive
directors were asked about the specifics of the WISE’s
functioning, including economic and social opera-
tions, organizational structure, governance, and hu-
man resources practices. Production supervisors and
social counselors were asked to describe in detail their
activities, as well as their interactions with the rest of
the organization. For eachcase, we asked interviewees
to provide us with archival material that they thought
might help us to understand the functioning of the
WISE. Through thisprocess, wecollected awiderange
of archival material, including by-laws, annual re-
ports, brochures, anniversary booklets, etc.

In analyzing the qualitative data, we adopted a
comparative case study design. Following Eisenhardt

(1989), we first focused on within-case analysis and
then turned to cross-case analysis. (See Appendix B
for a detailed description of the steps that we
followed in analyzing our qualitative data.) The
analyses that we conducted were longitudinal, ex-
amining the evolution of both organizations from
their founding until 2008.

We now present the results of these analyses in two
steps. We first analyze the evolution of both organi-
zations from their founding to the financial crisis that
both, coincidentally, experienced in 2003. We then
turn to the postcrisis period, during which BETA was
able to execute a successful turnaround whereas
ALPHA was not.

Social Imprinting at ALPHA and BETA (from
Founding to 2003)

Our within-case analyses suggest a general pat-
tern that supports our regression results and pro-
vides a more fine-grained understanding of the
mechanisms underlying the hypothesized relation-
ships that we tested in prior sections: In both ALPHA
and BETA, the founders’ goals and values had a
lasting effect on subsequent organizational prac-
tices and routines. The comparison of the lasting
influence of social imprinting on ALPHA and BETA,
which we present in more detail below, is summa-
rized in Table 5.

Founders’ imprints. A group of social workers
founded ALPHA in 1990. Drawing upon their expe-
rience of “sheltered workshops” for disabled
workers, its founders conceived ALPHA as a place
dedicated to teaching working skills to long-term
unemployed people. They chose recycling as their
commercial activity because it allowed them to cre-
ate an in-house workshop within which work could
be organized to fit the capacities of the long-term
unemployed. ALPHA’s founders defined the mis-
sion of the organization as “supporting individuals
with social and professional difficulties through
mentoring, tailored trainings and on-the-job practice
as a means to help them access or reintegrate into the
workforce.” The board of directors of ALPHA was
composed of a group of social workers that actively
promoted a focus on accomplishing ALPHA’s social
mission. Thus, ALPHA experienced a strong social
imprint from its founders, who emphasized from
the very beginning the achievement of the social
mission.

BETA was founded in 1993 by a former school-
teacher who was a board member of the community
center of his small town. BETA’s founder recalls:

1672 DecemberAcademy of Management Journal

After a few years, I realized that the workshops that we
organized in the community center were okay, but it
was not enough for the long-term unemployed . . . So I
got interested in the WISE model. When we decided to
create one, we were initially not sure what our com-
mercial activity was going to be.

Like ALPHA’s founders, he chose recycling as the
main activity of BETA because it was a simple ac-
tivity that beneficiaries without many skills could
learn. He translated his deep commitment to BETA’s
social mission into an organizational charter that
claimed to “fit the company to its beneficiaries,
rather than fit beneficiaries to the company,” and
emphasized values such as “humanism” and “em-
pathy.” He reinforced his commitment to the social
mission by recruiting board members from fellow
activists from the local community center. Like AL-
PHA, BETA’s founder stamped the new organization
with a strong social imprint.

Hiring patterns. At ALPHA, the first executive
director was one of the founding social workers and
the two subsequent executive directors also had
backgrounds in social work. Their profile had im-
portant consequences on the skills and values that
they brought to the organization. They hired staff
based on their care for beneficiaries and on their
knowledge of counseling. The first executive di-
rector created—and his successors perpetuated—a
permanent social counseling position, staffed by
a social worker. Three production supervisor posi-
tions were staffed either through the director’s social
work networks or by recruiting former beneficiaries.

Similar hiring patterns were found at BETA. The
strong social focus of the founder led him to recruit
an executive director with a social work background.
BETA’s founder explained: “Our core job is work-
force development. . . . So we needed to recruit an
executive director who could manage the workforce

TABLE 5
Social Imprinting at ALPHA and BETA (from Founding to 2003)

ALPHA BETA

Founders’ imprints 1990 1993
Motivation: ALPHA’s founders were a group of social
workers frustrated with the limited ability of social
work to address the needs of people with social and
professional difficulties. The founders created
ALPHA to provide them with mentoring and
on-the-job training to facilitate their (re)integration
into the workforce.

Motivation: The motivation of BETA’s founder,
a social activist involved in the local community
center, was to address the needs of the jobless
members of the community center on the board of
which he sat.

Activity emphasized: The founding team was
influenced by the notion of a “sheltered workshop,”
conceived as a place in which work would be
conducted in a way that protects fragile workers.
They chose recycling as their commercial activity
because it allowed them to create an in-house
workshop in which work could be organized to fit
the capacities of the long-term unemployed.

Activity emphasized: The focus of the founding team
was to create an organization that was able to meet
the needs of youth at risk. The commercial activity
(the management of waste reception centers) was
later chosen to provide development opportunities
to young people without qualification.

The lasting influence
of founders’ imprints

1990–2003 1993–2003

Hiring patterns: Recruitment of socially oriented
staff, including:

Three successive executive directors with social
work profile

Executive director with social work profile

Up to five production supervisors with social
work profile

Up to five production supervisors recruited among
beneficiaries

Up to three social counselors with social work
profile

Organizational features: Implementation of socially
oriented processes and systems, including:

Organizational features: Implementation of socially
oriented processes and systems

Creation of a training center Development of services in:
Creation of a housing shelter Psychological support

Social support
Job readiness support

Development of reporting tool (“work integration
passport”) to track beneficiaries’ progress

Development of reporting tool (“monitoring
scorecard”) to track beneficiaries’ progress

2015 1673Battilana, Sengul, Pache, and Model

development part of the job.” Subsequent recruitments
of permanent staff were also driven by a willingness to
help people in need. Overall, permanent staff at BETA
exhibited a combination of care and skills geared to-
ward the advancement of beneficiaries. In line with the
mechanisms that we presented in Hypothesis 1, our
qualitative analysis thus suggests that, in both organi-
zations, social imprinting led to the hiring of social-
mission-oriented staff members.

Organizational systems and processes. Social
imprinting also influenced the development of
activities that embedded social-mission-oriented
routines within the fabric of both organizations.
ALPHA’spermanent staff, for instance, createdan in-
house trainingcentertoprovidebeneficiarieswithjob
readiness and literacy training. They also created an
in-house shelter to provide accommodation for ben-
eficiaries who faced housing difficulties. In addition,
they designed a tool, which they called a “work in-
tegration passport,” to track beneficiaries’ perfor-
mance and help them to enhance their employability
during the course of their time at ALPHA.

In BETA, like ALPHA, the founder’s early emphasis
on the accomplishment of the social mission led the
organization to invest in the support of beneficiaries.
BETA developed specific processes in three distinct
areas: psychological support, social support, and job-
readiness support. On each of these themes, beneficia-
ries were offered specific counseling sessions that were
designed to help them to address various obstacles to
work. A scorecard, similar to ALPHA’s work in-
tegrationpassport,wascreatedtomonitorandsharethe
progress of beneficiaries in each of these dimensions.

Negative relationship between social imprint-
ing and economic productivity. In line with the
mechanisms that we presented in Hypothesis 3, our
qualitative analysis revealed that, in both organiza-
tions, staff members with a social work background
had developed skills and beliefs that fit the demands
of the social sector but did not fit with the com-
mercial sector. Between 1990 and 2003, ALPHA’s
successive executive directors, all with social
backgrounds, did not have experience and knowl-
edge about business and production processes in
the recycling industry. None of them had ever
worked in commercial operations, let alone in a for-
profit company, prior to joining ALPHA. Because
they had spent their entire career in social work
organizations, these former executive directors did
not have well-developed business skills and, as the
current executive director explained, “prioritized
beneficiaries’ needs over everything else.” Although
the issue of poor productivity had been lingering for

years, its persistence finally led to a major organiza-
tional crisis in a period of economic hardship in France
in the early 2000s.13 With debts increasing and in-
sufficient cash flow to pay staff salaries, ALPHA was in
a dire financial situation by 2003. The board of di-
rectors, still composed of social workers, was forced to
reconsider established organizational practices and
searched for alternative approaches.

Like ALPHA, BETA’s board and permanent staff
had a strong commitment and deep expertise in the
social realm but lacked business skills and experi-
ence. The current executive director described the
situation when she joined BETA in 2004, after it had
been managed for seven years by an executive di-
rector with a social work background and no prior
business experience, as follows: “Contracts were
developed orally, conventions were not signed.
Charges were not monitored and controlled, and as
a result, costs were inevitably too high.” Further-
more, because of the permanent staff’s extreme care
and empathy toward beneficiaries, they tended to be
lenient when beneficiaries were unprofessional. In
the current executive director’s words, “beneficia-
ries knew that they would never get in big trouble.”
As a result of this approach, BETA’s economic pro-
ductivity was recurrently low. Like ALPHA, BETA
also experienced a financial crisis in 2003, when
many years of poor productivity led to unsustainably
high levels of debt. This problematic situation forced
the board to embrace change and to take economic
demands more seriously into account.

After the Crisis: ALPHA and BETA’s Different
Courses of Action

The crises experienced by both ALPHA and BETA
played an important role in challenging the status
quo and unfreezing socially imprinted patterns in
both organizations. As a result, both organizations
changed their recruitment approach and hired an
executive director with a combination of social sec-
tor and business experience; both organizations also
relied on structural differentiation, with one group of
people (that is, production supervisors) focusing on
the needs of clients and another group (that is, social
counselors) focusing on the needs of beneficiaries.
However, ALPHA and BETA adopted different ap-
proaches to ensuring coordination between these two

13 Growth in gross domestic product (GDP) was only
0.9% in both 2002 and 2003—the lowest level in the five
decades (with the exceptions of 1975 and 1993) prior to the
2007–2008 global financial crisis (World Bank, 2013).

1674 DecemberAcademy of Management Journal

groups. Whereas, at ALPHA, the executive director
was the single “integrator” (Galbraith, 1973; Lawrence
& Lorsch, 1967), responsible for coordinating both
groups, BETA relied on “spaces of negotiation” that
engaged staff members throughout the organizational
ladder to ensure coordination among them. These
“spaces of negotiation” were arenas of interaction
that allowed staff members in charge of different
(that is, social vs. economic) activities to discuss and
agree on how to handle the trade-offs that they faced.

We now compare ALPHA and BETA’s approaches
after the crises that they both experienced.

Organizational structure and staffing. Follow-
ing the crisis, ALPHA’s board decided to change the
leadership of the organization and recruited—for the
first time—a new executive director with both social
sector and business experience. This director had
five years of experience in the banking industry and
had previously worked for two years at ALPHA as
part of his civic service. With this diverse experi-
ence, he brought to the organization a much better
understanding of business processes. The board
made him responsible for both economic produc-
tivity and social performance.

When he took over, there were five production
supervisors, who had been recruited on the basis of
their social work experience and were responsible
for supervising all production processes, and two
social counselors with social work background, who
were in charge of beneficiaries’ individualized social
support. Over the course of his first three years at
ALPHA, the new executive director brought in five
new production supervisors with industry (that is,
technical and commercial) experience to replace all
of the existing ones and made the new supervi-
sors responsible for customer satisfaction on a daily
basis. The executive director explained: “These
production supervisors are in direct contact with
clients and pressured to fill orders . . . So they are re-
ally focused on production.” In turn, social coun-
selors were in charge of managing social issues and
beneficiaries’ performance appraisal, which focused
mostly on their social skills. Thus, ALPHA adopted
a structurally differentiated approach, with social
counselors and production supervisors specializing,
respectively, in social or commercial activities in
which they had experience and/or training.

After the crisis that it experienced, BETA also en-
gaged in a series of changes. Like ALPHA, the board
of directors decided to hire a new executive director
with a background in both social work and business.
The new executive director had been trained as an
engineer, had worked in a multinational corporation

for six years, and had recently obtained a master’s
degree in social enterprise and community devel-
opment. Her personal values, which spurred her
career shift from the commercial to the social sector,
aligned her with the board of directors, whose main
concern, like ALPHA’s board, was to ensure that the
organization’s social goal would be achieved. Im-
portantly, for the first time in BETA’s history, the
board gave a clear mandate to the new executive di-
rector to improve economic productivity.

In a deliberate attempt to counterbalance her own
social focus, the new executive director created a dep-
uty director position to take responsibility for BETA’s
commercial activities and hired a seasoned profes-
sional with expertise in production. The executive di-
rector explained: “We work as a pair. I understand the
business, but I am more attuned to social issues, [the
deputy director] is more attuned to economic issues.
He is really committed to the business logic.” This
executive pair set up a differentiated structure similar
to ALPHA. Five production supervisors, who were
selected based on their technical and commercial
backgrounds, were in charge of activities and de-
cisions pertaining to customers’ orders and the day-
to-day organization of production, while three social
counselors with social work backgrounds were in
charge of activities and decisions pertaining to the
personal development plan of beneficiaries.

Managing tensions. In both organizations, struc-
tural differentiation led to internal tensions between
production supervisors and social counselors, who
quarreled over decisions such as the time spent by
beneficiaries in production vs. social activities. In-
terestingly, whereas both organizations experienced
tensions, they managed them in different ways. At
ALPHA, the executive director managed the ten-
sions between social and commercial activities as
they arose. For instance, he arbitrated when tensions
emerged regarding the need for beneficiaries to take
time off from the production line to attend counsel-
ing sessions or trainings. These ad hoc interventions
allowed him to relieve tensions temporarily and
provided fixes to immediate issues. However, they
did not equip staff members with the necessary tools
and skills to manage these tensions themselves. As
a result, tensions escalated into interpersonal con-
flicts between social counselors and production su-
pervisors, who were focused on the activities for
which they were responsible and remained blind to
others. Each group resented the other for keeping it
from achieving its own goal. This resentment accu-
mulated over time and ultimately led to ongoing in-
ternal conflicts. Despite the clear efforts made by

2015 1675Battilana, Sengul, Pache, and Model

ALPHA to enhance its economic productivity, it
remained below the average level of productivity of
socially imprinted WISEs in the recycling sector.

In contrast, BETA did not rely solely on the exec-
utive director to ensure coordination but instead
created “spaces of negotiation,” as we detail in the
next section. Similarly to ALPHA, roles and decision-
making responsibilities were clearly assigned, with
production supervisors in charge of technical training,
production,andorders, andsocial counselorsincharge
of the personal and professional development of ben-
eficiaries. In contrast to ALPHA, BETA set up spaces of
negotiation so that each group had to engage and con-
sult with the other group before making a decision.
This ensured that the entire staff was responsible for
handling the day-to-day trade-offs and remained at-
tentive to the demands of both social and commercial
activities. If tensions between the two groups could
not be resolved within the spaces of negotiation,
the executive director would ultimately make the
final decision. However, she and other staff mem-
bers reported that she very rarely had to intervene.

Understanding the Role of Spaces of Negotiation

As this analysis reveals, ALPHA was not able to
manage tensions between its social and economic
activities effectively, and continued to suffer from
below-average economic productivity during the
time frame of our study. In contrast, BETA was able
to manage these tensions effectively through the use
of spaces of negotiation that enabled sustained co-
ordination across structurally differentiated groups.
Table 6 summarizes the approaches adopted re-
spectively by ALPHA and BETA. Next, we examine
BETA’sapproach in detail to uncover the origin, role,
and the conditions that ensured the effective func-
tioning of spaces of negotiation.

Creating and delineating spaces of negotiation.
Spaces of negotiation at BETA were created through
a combination of mandatory meetings and formal
processes. Mandatory meetings were held to ensure
the coordination of social and commercial activities.
Every trimester, all social counselors and production
supervisors met together to jointly assess the prog-
ress of each beneficiary and to discuss any issues
related to the coordination of their respective activ-
ities. These meetings were internally referred to as
“regulation meetings,” because they were meant to
“regulate” tensions among staff by allowing each
group to listen to the concerns of the other. In be-
tween these regulation meetings, social counselors
met every week to discuss issues related to benefi-
ciaries. Similarly, production supervisors met every

week with the deputy director to organize production
matters. Both groups shared minutes of these meet-
ings with the executive director, keeping her in-
formed of issues being discussed.

In addition to these mandatory meetings, formal
processes were used to foster coordination between
social and commercial activities throughout the orga-
nizational ladder. In particular, work plan scheduling
and beneficiaries’ performance appraisal played a crit-
ical role. At the beginning of each month, the social
counselorspreparedareport about all ofthecounseling
activities organized for the following month. This re-
port was then circulated to production supervisors,
whowereaskedtoreportonpotentialclasheswiththeir
ownactivities.Discussionsensuedtoresolveconflicts,
such as the scheduling of training sessions during
a peak production period. Staff members had three
weeks within which to agree on a final schedule. The
executive director explained:

With this process, social counselors are able to inform
production supervisors about their own demands for
training or mentoring. In turn, production supervisors
are able to inform social counselors about production
peak periods. It took us hours to make [this planning
process] work,but itultimatelyreallyhelped. Once the
planning is agreed on, people have to work according
to it.

In addition to forcing production supervisors and
social counselors to coordinate with each other, this
process madeeach group publicly commit to specific
work-planning arrangements.

Beneficiaries’ performance appraisal was another
formal process that forced social counselors and
technical supervisors to interact regularly. Each
beneficiary met monthly with his or her assigned
production supervisor and social counselor to
jointly discuss his or her progress on technical,
behavioral, social, and professional dimensions.
Every meeting participant, including the benefi-
ciary, filled out a performance appraisal document
prior to the meeting. Assessments were compared
during the meeting and differences discussed. Par-
ticipants then jointly developed and committed to
a plan to ensure future progress. Performance ap-
praisal documents were then sent to the executive
director.

In summary, mandatory “regulation” meetings, com-
bined with the use of formal processes, created
spaces of negotiation at BETA in which both social
counselors and production supervisors had to engage
before making any of the decisions for which they were
responsible.Thearenasweredelineatedby:(a)theclear

1676 DecemberAcademy of Management Journal

definition and scope of the issues being addressed in
meetings; (b) decision-making rules that defined
who was responsible for what type of decision, who
should participate in that decision, and what would
happen if an agreement was not reached; and (c)
temporal patterns for meetings and clear deadlines
for decision making. Within these boundaries, the
use of formal processes (such as work plan schedules
and performance appraisal grids) helped members
with various profiles and priorities to express their
views and to develop a shared understanding of
a given issue.

Conditions enabling the effective functioning of
spaces of negotiation. The creation, and subsequent
use, of spaces of negotiation was associated with
higher economic productivity at BETA but did not
make the tensions between production supervisors
and social counselors disappear; rather, the inter-
actions between production supervisors and social
counselors came to be seen as “positive confronta-
tions.” The executive director explained:

It is important that [social counselors and production
supervisors] continue to voice their respective con-
cerns. All I ask them to do is to listen to each other and
understand each other. It means that they have to be
ready to defend their position while being able to
listen to the other group’s position. . . . This is all about
positive confrontation.

Similarly, reflecting on the instances of high ten-
sion between social counselors and production su-
pervisors, a social counselor reported that: “We never
avoid confrontation. When doubts are raised and the
tension is high, we just have to remind ourselves why
we are here. This helps us put things in perspective.”
Thus,theuseof spacesofnegotiation enabled BETA to
facilitate coordination between social counselors
and technical supervisors, while maintaining a “pro-
ductive tension” between them (Murray, 2010;
Stark, 2009). By empowering both social workers
and production supervisors, this approach enabled
BETA to maintain a social focus while better at-
tending to commercial imperatives.

TABLE 6
ALPHA and BETA’s Approaches to Managing Commercial and Social Activities after the Crisis

ALPHA BETA
Single integrator model Spaces of negotiation model

Spaces of negotiation

Note: Shaded areas highlight differences between ALPHA and BETA’s approaches after the crisis.

2015 1677Battilana, Sengul, Pache, and Model

Our analysis revealed two conditions that enabled the
effective functioning of spaces of negotiation at BETA.
First, organizational members across the organizational
ladder had a common understanding of their superor-
dinate goal. This common understanding facilitated
negotiation and thereby prevented tensions from esca-
lating into intractable conflicts (Fiol, Pratt, & O’Connor,
2009). Second, social counselors were aware that they
needed technical supervisors in order to achieve the
organization’s goal and vice versa. This mutual aware-
ness of interdependence fostered mutual respect, which
facilitated the management of tensions and trade-offs.
Because each group recognized the important contri-
bution of the other group to their common goal, they
were more likely to search for, and achieve, a common
ground. These two enabling conditions were fostered
and sustained in the organization by means of sociali-
zation processes (Louis, 1980; Van Maanen & Schein,
1979) that the executive director put in place, including
biannual retreats, the annual general assembly, internal
communication, training, and job shadowing.

Socialization processes. To ensure that all staff
membershadacommonunderstandingofBETA’sgoal
(that is, fostering professional integration of the long-
term unemployed), the executive director referred to it
on a daily basis as “the compass of all organizational
decisions.” Furthermore, all permanent staff members
got together twice a year for a retreat to nurture con-
viviality. These retreats were meant to allow them to
find joint solutions to a current organizational chal-
lenge (for example, how to improve mentoring). Staff
members were also asked to participate in the annual
general assembly at which all key stakeholders of
BETA met with the board to discuss the objectives of
the organization, how well the WISE did in the past
year, and what could be done to achieve its objectives
in the future. The executive director explained that she
used “the General Assembly as a key moment to re-
mind staff members of our mission and to celebrate it.”

To make production supervisors and social coun-
selors aware of their mutual interdependence, the ex-
ecutive director relied on communication, training, and
mandatory job shadowing. Highlighting the importance
of internal communication, she explained: “I always
remind staff members that we all depend on each other
to reach our goal. Social counselors and production
supervisors are in charge of different aspects of the
work that are equally important to our success.” In
addition, various training interventions were orga-
nized to increase everyone’s awareness of the im-
portance of each group’s contribution toward the
achievement of organizational objectives. For ex-
ample, all production supervisors attended external

training sessions designed to help them to better
understand the challenges faced by beneficiaries and
to appreciate the importance of social counseling in
helping beneficiaries to become ready to reintegrate
into the workforce. In turn, social counselors were
asked to attend meetings at the local chamber of
commerce so that they could become more aware of
the demands and norms of the corporate world.

Finally, and most importantly, the executive di-
rector made internal “job shadowing” mandatory.
Each social counselor was required to spend two
hours every week observing the work of production
supervisors to better understand its logic and con-
straints, and vice versa. As the executive director
explained: “Job shadowing made specialized groups
aware of their interdependence. It fostered mutual
understanding and respect across functional groups,
which was a necessary condition to maintain a pro-
ductive tension between them.”

Taken together, at BETA structural differentiation,
combined with spaces of negotiation, enabled staff
members in charge of economic and social activi-
ties, respectively, to jointly find solutions to the
trade-offs that they faced. Spaces of negotiation did
not make tensions disappear; rather, they trans-
formed potential internal conflict into a productive
tension between production supervisors and social
counselors. The intense socialization and ongoing
routines that shaped staff members’ understanding
of the organization’s superordinate goal, and of
their mutual interdependencies, ensured the effec-
tive functioning of spaces of negotiation.

DISCUSSION AND CONCLUSION

We hypothesized, and validated through regression
analyses, that the relationship between social im-
printing and social performance is paradoxical: While
social imprinting is positively associated with social
performance, social imprinting may also indirectly
weaken social performance through the negative as-
sociation that social imprinting has with economic
productivity. We also uncovered, through our lon-
gitudinal comparative case analysis, that socially
imprinted WISEs may resolve this paradox when: (a)
they face a situation (in our case, a severe financial
crisis) that enables organizational members to par-
tially unfreeze the organizations’ social imprint; and
(b) they introduce new structural and procedural ele-
ments that create spaces of negotiation, which facili-
tate coordination between structurally differentiated
groups of organizational members throughout the or-
ganizational ladder.

1678 DecemberAcademy of Management Journal

Contributions

Our study contributes to the growing body of work
onhybridorganizing(forareview,seeBattilana&Lee,
2014). In organization theory, multiple streams of re-
search have examined the nature and functioning of
hybrid organizations that combine dissimilar, and
potentially conflicting, aspects of different organiza-
tional forms (Haveman & Rao, 2006; Padgett & Powell,
2012). One stream of research has examined how
hybrids deal with the internal identity challenges that
they face (see, e.g., Albert & Whetten, 1985; Glynn,
2000; Golden-Biddle & Rao, 1997; Pratt & Foreman,
2000). Another stream of research has examined
how hybrids deal with the different and potentially
conflicting institutional demands that stem from
their environment, which prescribe how they should
operate (see, e.g., Greenwood, Raynard, Kodeih,
Micelotta, & Lounsbury, 2011; Kraatz & Block, 2008;
Pache & Santos, 2013; Smets, Jarzabkowski, Burke, &
Spee, 2015; Thornton, Ocasio, & Lounsbury, 2012).
While these studies have considerably enriched our
understanding of the internal and external chal-
lenges that hybrids face, they remain silent about the
factors that influence hybrids’ ability to achieve their
main goals. We address this gap by identifying and
providing the first (to our knowledge) quantitative
evidence for the factors that are associated with the
ability of one type of social enterprise (that is, the
WISE) to achieve high levels of social performance.

Second, our study uncovers spaces of negotiation
as important mechanisms that hybrids may use to
successfully coordinate structurally differentiated
staff with potentially competing interests. Spaces
ofnegotiationsharesimilaritieswith“relationalspaces”
(Kellogg, 2009) in that they both foster coordination
among diverse participants. However, they perform
different functions. Kellogg’s (2009) conceptualiza-
tion of relational spaces—like Mair and Hehenberger’s
(2014) extension of the concept to activists in organi-
zational fields—emphasizes the role that they play in
facilitating the activities of change agents. In the case of
our study, rather than facilitating change, spaces of
negotiation provide a mechanism to resolve the tension
that structurally differentiated staff with potentially
different interests will face. The combination of struc-
tural differentiation and spaces of negotiation fa-
cilitate ongoing coordination.

Third, and relatedly, our study contributes to a long
traditionoforganizationalscholarshipthathasstudied
the challenge of insuring coordination within organi-
zations (for a review, see Okhuysen & Bechky, 2009).
Of particular relevance to our study, research on

organizational ambidexterity has emphasized the role
of leaders in coordinating potentially contradictory
sets of activities in corporations (O’Reilly & Tushman,
2008; Tushman, Smith, Wood, Westerman, & O’Reilly,
2010). Going beyond the existing literature, our study
shows that coordination in hybrids may be better
achieved by means of formal structures and pro-
cesses that sustain spaces of negotiation, in which
staff members throughout the organization ladder
jointly find solutions to the trade-offs that they face,
instead of relying on a subset of managers or emer-
gent informal mechanisms to ensure coordination.

Additionally, we uncovered two critical conditions
that enable the effective functioning of spaces of nego-
tiation: (a) staff members’ common understanding of
the organization’s superordinate goal, and (b) aware-
ness of their mutual interdependencies. We also found
that creating and fostering such conditions requires
intense socialization processes, including internal
communication, training, and job shadowing. Overall,
our study suggests that the creation and maintenance of
spaces of negotiation, while consuming organizational
resources, may be necessary if hybrids are to sustain
a balance of focus while pursuing multiple objectives.

Finally, our study contributes to the imprinting
literature (for a review, see Marquis & Tilcsik,
2013)—especially to the study of the lasting influence
of founders (see, e.g., Almandoz, 2012). We argue, and
empirically show, that the relationship between so-
cial imprinting and social performance in hybrids
that serve distinct groups of beneficiaries, such as
WISEs, is made of contradictory yet interrelated effects:
Although social imprinting enhances a hybrid’s social
performance, social imprinting also indirectly weakens
social performance through the negative relationship
that social imprinting has with economic productivity.
Furthermore, our qualitative findings suggest that a
critical condition to overcome this paradox is the
partial unfreezing of the hybrid’s social imprint,
which opens the possibility for it to manage its im-
printing legacy (Cooper, Hinings, Greenwood, &
Brown, 1996; Johnson, 2007). While most of the
literature on imprinting views it as “an ecological
force devoid of agency” (Marquis & Tilcsik, 2013:
222), we show that, after founding, subsequent
generations of organizational members may be able
to depart from imprinted practices and introduce new
ones during sensitive periods of time, such as crises.

Future Research Directions

Future work should explore whether our results
hold in other contexts. Within our institutional

2015 1679Battilana, Sengul, Pache, and Model

context (France), our rich data enabled us to tease out
heterogeneity across organizations. Subsequent
studies of WISEs will need to explore the role of
public authorities across different contexts and,
more generally, the heterogeneity across institu-
tional settings. In our research setting, the state
played a role as a gatekeeper for the field by granting
WISEs the authorization to operate. In other contexts
with less active external monitoring and/or less sup-
port from publicauthorities,WISEsmaybemorelikely
to invest in economic productivity at the expense of
activities that enhance social performance.

Second, future research will need to explore in
more detail how hybrids like social enterprises,
which combine aspects of business and charity at
their core, can sustain a focus on both the accom-
plishment of their social mission and the establish-
ment of productive operations. In this paper, we
provide foundational work on the understanding of
how a subset of these hybrid organizations can suc-
cessfully achieve their social mission, even when
they serve distinct groups of beneficiaries and cus-
tomers. Much remains to be explored about the way
in which these organizations function. Social enter-
prises demand a deeper exploration and new theo-
rizing because they are neither typical businesses
nor typical charities; rather, they combine aspects of
both (Battilana & Lee, 2014; Besharov & Smith, 2014;
Dacin, Dacin, & Tracey, 2011).

Future research will also need to examine the in-
fluence of other factors on a hybrid’s social perfor-
mance. In particular, certain aspects of organizational
design—including allocation of decision rights, pro-
vision incentives, and financial structure—may affect
how hybrids manage intraorganizational negative spill-
overs (Sengul & Gimeno, 2013; Sengul, Gimeno, & Dial,
2012). In addition, aspects of organizational governance
(such as board composition) may also play an important
role (Ebrahim, Battilana, & Mair, 2014). Furthermore, as
governments are implementing new types of incorpo-
ration to better fit the needs of social enterprises, future
research will need to explore the influence of these new
legal statuses—such as the community interest com-
pany (CIC) in the United Kingdom, or benefit corpora-
tion in the United States—on the ability of hybrids to
achieve their objectives.

Finally, while our study focuses on one type of
hybrid organization, future research will need to
explore the extent to which our findings apply to
other kinds of hybrid, such as hospitals or universi-
ties. In particular, future research should explore the
generalizability of our findings on spaces of nego-
tiation and the conditions under which they can

effectively be used to maintain a productive tension
in hybrids. Our study shows that creating and
maintaining spaces of negotiation is an expensive
endeavor; ongoing socialization processes such as
job shadowing have significant opportunity costs for
organizations, and these costs may exceed their
benefits. More work should be done to understand
the relationship between the coordination benefits
achieved from various implementations of spaces of
negotiation and their relative costs to the organiza-
tion. For instance, we expect organizational size to be
a boundary condition because effectively engaging
staff throughout the organizational ladder in spaces
of negotiation is likely to become increasingly chal-
lenging and costly as the size increases. That being
said, larger hybrid organizations may still be able to
create, maintain, and effectively use spaces of ne-
gotiation in a more bounded way by engaging a sub-
set of their employees.

In conclusion, hybrids face the distinct challenge
of trying to optimize performance on multiple di-
mensions as they pursue multiple objectives at once.
Doing so requires distinct organizational arrange-
ments. Importantly, practices that may be perceived
as inefficient in the pursuit of a single objective may
be effective when it comes to the joint pursuit of
multiple organizational objectives. For example, al-
though spaces of negotiation may be costly to create
and maintain, in certain situations they may be
a necessary condition if hybrids are to achieve high
levels of performance in the objectives that they pur-
sue. Future research in this area will have profound
implications for the study not only of hybrids but also,
more broadly, of contemporary organizations that are
increasingly straddling the boundaries of multiple
sectors as they pursue multiple objectives.

REFERENCES

Albert, S., & Whetten, D. A. 1985. Organizational identity.
Research in Organizational Behavior, 7: 263–295.

Almandoz, J. 2012. Arriving at the starting line: The impact
of community and financial logics on new banking
ventures. Academy of Management Journal, 55:
1381–1406.

Austin, J., Wei-Skillern, J., & Stevenson, H. 2006. Social
and commercial entrepreneurship: Same, different, or
both? Entrepreneurship Theory and Practice, 30:
1–22.

Baltagi, B. H. 1998. Econometrics. Berlin: Springer-Verlag.

Baron, J. N., Hannan, M. T., & Burton, M. D. 1999. Building
the iron cage: Determinants of managerial intensity in

1680 DecemberAcademy of Management Journal

the early years of organizations. American Sociolog-
ical Review, 64: 527–547.

Battilana, J., & Dorado, S. 2010. Building sustainable hy-
brid organizations: The case of commercial micro-
finance organizations. Academy of Management
Journal, 53: 1419–1440.

Battilana, J., & Lee, M. 2014. Advancing research on hybrid
organizing: Insights from the study of social enterprises.
The Academy of Management Annals, 8: 397–441.

Beckman, C., & Burton, M. D. 2008. Founding the future:
The evolution of top management teams from found-
ing to IPO. Organization Science, 19: 3–24.

Besharov, M. L., & Smith, W. K. 2014. Multiple in-
stitutional logics in organizations: Explaining their
varied nature and implications. Academy of Man-
agement Review, 39: 364–381.

Bode, I., Evers, A., & Schulz, A. 2006. Work integration
social enterprises in Europe: Can hybridization be
sustainable? In M. Nyssens (Ed.), Social enterprise: At
the crossroads of market, public policies and civil
society: 237–258. London: Routledge.

Bourdieu, P. 1977. Outline of a theory of practice. Cam-
bridge: Cambridge University Press.

Bourgeois, L. J. 1981. On the measurement of organiza-
tionalslack.AcademyofManagementReview,6:29–39.

Burton, M. D., & Beckman, C. M. 2007. Leaving a legacy:
Position imprints and successor turnover in young
firms. American Sociological Review, 72: 239–266.

Caves, D. W., Christensen, L. R., & Diewert, W. E. 1982. The
economic theory of index numbers and the measure-
ment of input, output, and productivity. Econo-
metrica, 50: 1393–1414.

Cooney, K. 2011. An exploratory study of social purpose
business models in the United States. Nonprofit and
Voluntary Sector Quarterly, 40: 185–196.

Cooper, D. J., Hinings, B., Greenwood, R., & Brown, J. L.
1996. Sedimentation and transformation in organiza-
tional change: The case of Canadian law firms. Orga-
nization Studies, 17: 623–647.

D’Aunno, T., Sutton, R. I., & Price, R. H. 1991. Isomorphism
and external support in conflicting institutional en-
vironments: A study of drug abuse treatment units.
Academy of Management Journal, 34: 636–661.

D’Autume, A., Bethbèze, J. P., & Hairault, J. O. 2006.
L’emploi des séniors en France. Paris: Conseil
d’Analyse Economique.

Dacin, M. T., Dacin, P. A., & Tracey, P. 2011. Social en-
trepreneurship: A critique and future directions. Or-
ganization Science, 22: 1203–1213.

Defourny, J., & Kim, S. Y. 2011. Emerging models of social
enterprise in Eastern Asia: A cross-country analysis.
Social Enterprise Journal, 7: 86–111.

Délégation Générale à l’Emploi et à la Formation Pro-
fessionnelle (DGEFP). 2003. Circulaire n�2003-24 du 3
octobre 2003 relative à l’aménagementde la procédure
d’agrément par l’ANPE et au suivi des personnes
embauchées dans une structure d’insertion par l’activité
économique. Accessed online at https://www.cnle.
gouv.fr/IMG/pdf/Circulaire_dgefp-dgas_n2003-24_
3octobre2003 .

Ebrahim, A., Battilana, J., & Mair, J. 2014. The governance
of social enterprises: Mission drift and accountability
challenges in hybrid organizations. Research in Or-
ganizational Behavior, 34: 81–100.

Edmondson, A. C., & McManus, S. E. 2007. Methodological
fit in management field research. Academy of Man-
agement Review, 32: 1155–1179.

Eisenhardt, K. M. 1989. Building theories from case study
research. Academy of Management Review, 14: 532–
550.

Eisenhardt, K. M., & Graebner, M. E. 2007. Theory building
from cases: Opportunities and challenges. Academy
of Management Journal, 50: 25–32.

Eisenhardt, K. M., & Schoonhoven, C. B. 1990. Organiza-
tional growth: Linking founding team, strategy, envi-
ronment, and growth among U.S. semiconductor
ventures, 1978–1988. Administrative Science Quar-
terly, 35: 504–529.

Feldman, M. S. 2003. A performative perspective on sta-
bility and change in organizational routines. In-
dustrial and Corporate Change, 12: 727–752.

Fiol, C. M., Pratt, M. G., & O’Connor, E. J. 2009. Managing
intractable identity conflicts. Academy of Manage-
ment Review, 34: 32–55.

Galaskiewicz, J., & Barringer, S. N. 2012. Social enterprises
and social categories. In B. Gidron & Y. Hasenfeld
(Eds.), Social enterprises: An organizational perspec-
tive: 47–70. New York: Palgrave Macmillan.

Galbraith, J. R. 1973. Designing complex organizations.
Reading, MA: Addison-Wesley.

Glynn, M. A. 2000. When cymbals become symbols: Con-
flict over organizational identity within a symphony
orchestra. Organization Science, 11: 285–298.

Golden-Biddle, K., & Rao, H. 1997. Breaches in the board-
room: Organizational identity and conflicts of com-
mitment in a nonprofit organization. Organization
Science, 8: 593–611.

Greenwood, R., Raynard, M., Kodeih, F., Micelotta, E., &
Lounsbury, M. 2011. Institutional complexity & orga-
nizational responses. The Academy of Management
Annals, 5: 317–371.

Haveman, H., & Rao, H. 2006. Hybrid forms and the evo-
lution of thrifts. The American Behavioral Scientist,
49: 974–986.

2015 1681Battilana, Sengul, Pache, and Model

https://www.cnle.gouv.fr/IMG/pdf/Circulaire_dgefp-dgas_n2003-24_3octobre2003

Hoffman, A. J., Gullo, K., & Haigh, N. 2012. Hybrid orga-
nizations and positive social change: Bridging the for-
profit and non-profit domains. In K. Golden-Biddle &
J. E. Dutton (Eds.), Using a positive lens to explore
social change and organizations: Building a theoreti-
cal and research foundation: 131–150. New York:
Routledge.

Huber, P., Sutcliffe, K., Miller, C. C., & Glick, W. H. 1993.
Understanding and predicting organizational change. In
P.Huber&W.H.Glick(Eds.),Organizationalchangeand
redesign: 215–265. Oxford: Oxford University Press.

Hugues, J. M. 2007. Observatoire 2006 des entreprises
d’insertion. Paris: CNEI.

Huselid, M. A. 1995. The impact of human resource man-
agement practices on turnover, productivity, and
corporate financial performance. Academy of Man-
agement Journal, 38: 635–672.

Iacobucci, D. 2008. Mediation analysis. Los Angeles, CA:
Sage.

Jay, J. 2013. Navigating paradox as a mechanism of change
and innovation in hybrid organizations. Academy of
Management Journal, 56: 137–159.

Johnson, V. 2007. What is organizational imprinting?
Cultural entrepreneurship in the founding of the Paris
Opera. American Journal of Sociology, 113: 97–127.

Kellogg, K. C. 2009. Operating room: Relational spaces and
microinstitutional change in surgery. American
Journal of Sociology, 115: 657–711.

Kraatz, M. S., & Block, E. S. 2008. Organizational impli-
cations of institutional pluralism. In R. Greenwood,
C. Oliver, R. Suddaby & K. Sahlin-Andresson (Eds.),
The Sage handbook of organizational institutionalism:
243–275. London: Sage.

Lawrence, P. R., & Lorsch, J. W. 1967. Organization and
environment: Managing differentiation and inte-
gration. Boston, MA: Division of Research, Harvard
Business School.

Louis, M. R. 1980. Surprise and sense making: What
newcomers experience in entering unfamiliar orga-
nizational settings. Administrative Science Quar-
terly, 25: 226–251.

Mair, J., & Hehenberger, L. 2014. Front-stage and backstage
convening: The transition from opposition to mutu-
alistic coexistence in organizational philanthropy.
Academy of Management Journal, 57: 1174–1200.

Mair, J., & Marti, I. 2006. Social entrepreneurship research:
A source of explanation, prediction, and delight.
Journal of World Business, 41: 36–44.

Margaret, M. 2006. Travail et emploi des femmes. Paris:
La Découverte.

Marquis, C., & Tilcsik, A. 2013. Imprinting: Toward
a multilevel theory. The Academy of Management
Annals, 7: 193–243.

McDonald, J. 2009. Using least squares and Tobit in second
stage DEA efficiency analyses. European Journal of
Operational Research, 197: 792–798.

Mercadal, B., Janin, P., Charvériat, A., & Couret, A. 2004.
Droit des affaires: Sociétés commerciales, 2004.
Levallois: Éditions Francis Lefebvre.

Miles, M. B., & Huberman, A. M. 1994. Qualitative data
analysis: An expanded sourcebook. Thousand Oaks,
CA: Sage.

Miller, D., & Shamsie, J. 2001. Learning across the life cy-
cle: Experimentation and performance among the
Hollywood studio heads. Strategic Management
Journal, 22: 725–745.

Morrison, A. D., & Wilhelm, W. J., Jr. 2004. Partnership
firms, reputation, and human capital. The American
Economic Review, 94: 1682–1692.

Murray, F. 2010. The Oncomouse that roared: Hybrid
exchange strategies as a source of distinction at the
boundary of overlapping institutions. American
Journal of Sociology, 116: 341–388.

O’Reilly, C. A., & Tushman, M. L. 2008. Ambidexterity as
a dynamic capability: Resolving the innovator’s di-
lemma. Research in Organizational Behavior, 28:
185–206.

Okhuysen, G. A., & Bechky, B. A. 2009. Coordination in
organizations: An integrative perspective. The Acad-
emy of Management Annals, 3: 463–502.

Oliver, C. 1991. Strategic responses to institutional processes.
Academy of Management Review, 16: 145–179.

Pache, A. C., & Santos, F. 2013. Inside the hybrid organi-
zation: Selective coupling as a response to competing
institutional logics. Academy of Management Jour-
nal, 56: 972–1001.

Padgett, J. F., & Powell, W. W. 2012. The emergence of
organizations and markets. Princeton, NJ: Princeton
University Press.

Papke, L. E., & Wooldridge, J. M. 1996. Econometric
methods for fractional response variables with an ap-
plication to 401(k) plan participation rates. Journal of
Applied Econometrics, 11: 619–632.

Pfeffer, J., & Salancik, G. R. 1978. The external control of
organizations: A resource dependence perspective.
New York: Harper & Row.

Pratt, M. G., & Foreman, P. O. 2000. Classifying managerial
responses to multiple organizational identities.
Academy of Management Review, 25: 18–42.

Ragin, C. C. 1994. Introduction to qualitative comparative
analysis. In T. Janoski & A. Hicks (Eds.), The compar-
ative political economy of the welfare state: 299–319.
Cambridge: Cambridge University Press.

Rangan, S., & Sengul, M. 2009. The influence of macro
structure on the foreign market performance of

1682 DecemberAcademy of Management Journal

transnational firms: The value of IGO connections,
export dependence, and immigration links. Ad-
ministrative Science Quarterly, 54: 229–267.

Ruef, M., Aldrich, H. E., & Carter, N. M. 2003. The structure
of founding teams: Homophily, strong ties and iso-
lation among U.S. entrepreneurs. American Socio-
logical Review, 68: 195–222.

Scott, W. R. 1977. Effectiveness of organizational effec-
tiveness studies. In S. P. Goodman & J. Pennings (Eds.),
New perspectives on organizational effectiveness:
63–95. San Francisco, CA: Jossey-Bass.

Sengul, M., & Gimeno, J. 2013. Constrained delegation:
Limiting subsidiaries’ decision rights and resources in
firms that compete across multiple industries. Ad-
ministrative Science Quarterly, 58: 420–471.

Sengul, M., Gimeno, J., & Dial, J. 2012. Strategic delegation:
A review, theoretical integration, and research agenda.
Journal of Management, 38: 375–414.

Smets, M., Jarzabkowski, P., Burke, G., & Spee, P. 2015.
Reinsurance Trading in Lloyd’s of London: Balancing
conflicting-yet-complementary logics in practice.
Academy of Management Journal, 58: 932–970.

Smith, W. K., & Lewis, M. W. 2011. Toward a theory of
paradox: A dynamic equilibrium model of organizing.
Academy of Management Review, 36: 381–403.

Spear, R., & Bidet, E. 2005. Social enterprise for work
integration in 12 European countries: A descriptive
analysis. Annals of Public and Cooperative Eco-
nomics, 76: 195–231.

Stark, D. 2009. The sense of dissonance: Accounts of
worth in economic life. Princeton, NJ: Princeton
University Press.

Stinchcombe, A. L. 1965. Social structure and organiza-
tions. In J. G. March (Ed.), Handbook of organizations:
142–193. Chicago, IL: Rand–McNally.

Strauss, A., & Corbin, J. 1998. Basics of qualitative re-
search: Techniques and procedures for developing
grounded theory (2nd ed.). Thousand Oaks, CA: Sage.

Suchman, M. C. 1995. Managing legitimacy: Strategic and
institutional approaches. Academy of Management
Review, 20: 571–610.

Thornton, P. H., Ocasio, W., & Lounsbury, M. 2012. The
institutional logics perspective: A new approach to
culture, structure, and process. Oxford: Oxford Uni-
versity Press.

Tilcsik, A. 2014. Imprint–environment fit and perfor-
mance: How organizational munificence at the time
of hire affects subsequent job performance. Adminis-
trative Science Quarterly, 59: 639–668.

Tushman, M., Smith, W. K., Wood, R. C., Westerman, G., &
O’Reilly,C.2010.Organizational designs andinnovation

streams. Industrial and Corporate Change, 19:
1331–1366.

Van Maanen, J., & Schein, E. H. 1979. Toward a theory of
organizational socialization. Research in Organiza-
tional Behavior, 1: 209–264.

Whetten, D., Mackey, A., & Poly, C. 2011. Applying the
concept of organizational identity to the study of per-
sistently distinctive organizational practices. Working
Paper, Brigham Young University.

World Bank. 2013. World development indicators 2013.
Washington, DC: World Bank Publications.

Wry, T., Cobb, J. A., & Aldrich, H. E. 2013. More than
a metaphor: Assessing the historical legacy of resource
dependence and its contemporary promise as a theory
of environmental complexity. The Academy of Man-
agement Annals, 7: 441–488.

Yellen, J. L. 1984. Efficiency wage models of unemployment.
The American Economic Review, 74: 200–205.

Yin, R. K. 2003. Case study research: Design and methods.
Thousand Oaks, CA: Sage.

Julie Battilana (jbattilana@hbs.edu) is an associate pro-
fessor in the organizational behavior unit at Harvard
Business School. Her research examines the process by
which organizations or individuals initiate and imple-
ment changes that diverge from the taken-for-granted
practices in a field of activity. She is particularly in-
terested in hybrid organizations that diverge from the
archetypes of both typical corporations and typical
charities by combining aspects of both at their core. She
holds a joint Ph.D. in organizational behavior from INSEAD
and in management and economics from École Normale
Supérieure de Cachan.

Metin Sengul (metin.sengul@bc.edu) is an associate pro-
fessor at the Carroll School of Management at Boston Col-
lege. His research examines those internal organizational
choices that structure the organization, define the nature of
power and influence, and impact the firm’s competitive
stance. He received his Ph.D. in strategy from INSEAD.

Anne-Claire Pache (pache@essec.edu) is a professor of
social entrepreneurship at ESSEC Business School and
holder of the ESSEC Chair in Philanthropy. Her research
lies at the intersection of organizational theory and social
entrepreneurship, with a particular emphasis on pluralis-
tic environments, hybrid organizations, and scaling-up
processes in organizations. She received her doctorate in
organizational behavior from INSEAD.

Jacob Model (jmodel@stanford.edu) is a doctoral candi-
date in Organizational Behavior at the Stanford Gradu-
ate School of Business. His research includes work in

2015 1683Battilana, Sengul, Pache, and Model

mailto:jbattilana@hbs.edu

mailto:metin.sengul@bc.edu

mailto:pache@essec.edu

mailto:jmodel@stanford.edu

organizational theory, social movements and economic
sociology.

APPENDIX A

Phases of Development of the WISE Field in France

The experimentation phase (before 1986). Dur-
ing this phase, the first WISEs emerged, scattered across
France, invented by social workers who found typical
interventions inefficient in addressing structural un-
employment. They created WISEs to provide the un-
employed with work experience as a way in which they
could acquire the skills necessary for them to reintegrate
within traditional companies. Some government repre-
sentatives identified and recognized the contribution of
these initiatives to the fight against unemployment. Two
ministerial circulars, issued in 1979 and 1985, not only
recognized the value of these experiments but also pro-
vided them with some ad hoc financial support. Over 87%
of the WISEs created during this period were incorporated
as not-for-profits because it was the legal status most fa-
miliar to their founders, given their social work back-
grounds. The first regional union of WISEs was created in
Lyon in 1983 and soon replicated in other regions.

The contestation phase (1986–1990). During this
phase, the WISE model was challenged. A change in the
political balance at the National Assembly forced socialist
President Mitterrand to appoint a right-wing prime min-
ister, who formed a right-wing government. Its new Min-
ister of Employment interrupted all state support to WISEs.
The withdrawal of state financial support forced WISE
entrepreneurs to become financially self-sustainable. The
rate at which WISEs were being founded dropped during
this period, and the proportion of WISEs incorporated
as not-for-profits declined to 58%. The existing regional
unions federated into a National Union of WISEs—the
Comité National des Entreprises d’Insertion (CNEI) in
1988—to represent their vision more forcefully to politi-
cians and public officials. Over time, the CNEI also en-
gaged in building partnerships with other professional
organizations to enhance the visibility and performance of
WISEs and provided training and networking opportuni-
ties to its members.

The institutionalization phase (1991–1997). Dur-
ing this phase, political changes and the growing visibility
of the WISE movement led the new socialist government to
institutionalize the WISE model. In 1991, a law was passed
instituting WISEs as an important building block of em-
ployment policies; once accredited, WISEs were entitled to
systematicfinancialsupportfromthestatethatcompensated
for the “social service” that they delivered. They were,
however, required to comply fully with business and em-
ployment regulations, regardless of their legal status. This

official recognition created momentum for WISEs: The
number of incorporations increased. There was also a slight
increase in the proportion incorporated as not-for-profit (to
61%), as WISEs became more visible tools for social workers
in search of models with which to promote workforce
development.

The professionalization phase (after 1997). This
phase was characterized by the combined influence of the
state (which wanted to make WISEs more accountable) and
normative pressures from the CNEI (which encouraged
WISEs to operate as “real businesses” and to become more
“professional”). In 1998, the socialist government passed
a new law, which strongly encouraged the development of
WISEs and required beneficiaries first to be filtered by Pole
Emploi (the national agency for employment). It further
required WISEs to report to the state their positive gradu-
ation rate (that is, the proportion of beneficiaries able to
find regular jobs at the end of their employment at the
WISE) and also changed the administrative attachment of
WISEs from the Ministry of Social Affairs to the Ministry of
Labor, thus reinforcing their economic character. Also in
1998, following a resolution passed during its 1997 as-
sembly, the CNEI published a manual entitled Promoting
WISEs’ Social Mission with a Commercial Legal Status,
which was distributed through regional unions to potential
WISE founders. During that period, the number of WISEs
created rose. Yet, as a result of the strong normative pres-
sure imposed by the CNEI, the rate of incorporation as not-
for-profit dropped drastically to 30%.

APPENDIX B

Qualitative Data Analysis

Within-case analysis. Relying on interview and ar-
chival data on ALPHA and BETA, we first conducted
a within-case analysis, with the goal of allowing the unique
patterns of each case to emerge (Strauss & Corbin, 1998). To
this end, we developed a longitudinal case report of about
40 pages for each case, which synthesized and organized
the material collected. The case reports covered the evo-
lution of both organizations from their founding until 2008
and were organized around nine topics that emerged from
our reading of the interviews: founding and growth, goals
and values, people and human resources, organizational
structure and governance, business model and commercial
strategy, social integration strategy, performance, stake-
holders, and miscellaneous. For each topic, we triangulated
interview and archival data to ensure the reliability of our
accounts (Yin, 2003). We paid specific attention to tracking
the evolution of practices over time. These case reports
provided us with a comprehensive longitudinal under-
standing of the functioning of each WISE.

In the second step, we explored how each WISE had or-
ganized its operations over time to deal with the various
demands that it faced. To do so, we analyzed, for each case,
how responsibilities were assigned within the WISE and

1684 DecemberAcademy of Management Journal

how work was organized among organizational mem-
bers. We also analyzed the rules, processes, and routines
implemented. Finally, we analyzed the governance pro-
cesses at play in each WISE to understand how decisions
were made within the organization. We paid attention to
tracking major changes in these processes over time. We
developed tables and graphs for each case to describe
core features in a systematic fashion (Miles & Huberman,
1994). As we traced these different organizational fea-
tures, we were able to identify and describe the approach
designed by each WISE to satisfy customers’ and bene-
ficiaries’ demands.

Cross-case analysis. In the third step, we turned to
cross-case analysis, in which we compared the patterns
identified in one case with those from the other case
to identify consistent patterns, as well as differences
(Eisenhardt & Graebner, 2007). We systematically com-
pared the composition of workforce, human resources
policy, task design, and organizational design over time.

We paid particular attention to how social activities and
commercial activities were organized and coordinated.

A fourth step in our analysis involved follow-up in-
terviews with ALPHA and BETA’s executive directors to
confirm our preliminary findings. We described to them the
approach that we had identified and askedthem tocomment
on our description. Both confirmed our analysis, and com-
plemented it with details and anecdotes, which we used to
refine our final descriptions, analyses, and findings.

Finally, we compared the emergent findings of our data
analysis with the extant literature, thereby entering into
a dialogue between theory and data (Ragin, 1994). In par-
ticular, since our analysis revealed that coordination pro-
cesses played an important role in allowing BETA to
mitigate the negative relationship between social im-
printing and economic productivity, we leveraged the lit-
erature on coordination in organizations (for a review, see
Okhuysen & Bechky, 2009) to explore how it may illumi-
nate our understanding of the patterns uncovered.

2015 1685Battilana, Sengul, Pache, and Model

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Week 5: Mini Lecture

Dynamic Organizations

Today’s industries exist in a turbulent and competitive landscape resulting from rapid change and increasing international growth. Organizations need strategies, structures, and processes that are agile and responsive in their shifting competitive business environment. Every organization needs to be as dynamic as the business it is in.

According to Felin and Powell, it is certain that old tools of organizational design—hierarchies, chains of command, functional areas, formal reporting, and long-term planning—are not well-suited to success in today’s volatile markets. Instead, today’s dynamic organizations are designed based on rigorous leadership; knowledge management; continuous learning; flexibility; stakeholder and system integration; employee commitment; and change readiness.

A tool often used in organizational design that aids companies in making their design choices is the Galbraith’s Star Model framework. The five parts of the framework have a meaningful purpose: strategy determines direction, structure determines the location of decision-making power, processes center on the flow of information, rewards focus on influencing employee motivation, and human resources emphasize affecting and defining employees’ mind-sets and skills.

The Five Star Model adamantly encourages leaders to understand that organizational design is more than just the organizational chart putting the right people in the right positions. The model prevents overlooking important aspects of the organization, and it helps management to understand what in the organizational redesign and change process must be managed.

Included in the design of an organization is a focus on the process of constructing and adjusting an organization’s structure to achieve its goals. The organizational structure connects departments, jobs and tasks within the organization. It makes perfect sense that the structure and management of an organization are reflected in the conditions in which it must operate. Organizational structures fall on a spectrum, with “mechanistic” at one end and “organic” at the other. According to researchers Stalker and Burns, mechanistic organizations have a hierarchical, top-down structure, while organic structures use more flexible structures with less clear defined chains of command.

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