EMERGENCE AND PERFORMANCE OF SOCIAL ENTREPRENEURSHIP:

A COMPLEXITY SCIENCE PERSPECTIVE

Mary Han Ted Rogers School of Business Management, Ryerson University

575 Bay Street, Toronto, Ontario, Canada M5G 2C5 Phone 416-979-5000 ext 6379 Fax: 416-979-5266 mhan@ryerson.ca

and

Bill McKelvey UCLAAnderson School of Management,

110 Westwood Plaza, Los Angeles, CA 90095-1481 Phone 310-825-7796 Fax 310-206-2002 mckelvey@anderson.ucla.edu

January 15, 2009

© Copyright. All rights reserved. Not to be quoted, paraphrased, copied, or distributed in any fashion.

ii

EMERGENCE AND PERFORMANCE OF SOCIAL ENTREPRENEURSHIP:

A COMPLEXITY SCIENCE PERSPECTIVE

Abstract

A dominant, but contested, view in the literature is that social entrepreneurship should be treated as “market failure,” with related performance measures. This paper suggests that the emergence of social entrepreneurship lies in adaptive tension created via societal disruptions and disequilibria brought on by greenhouse gases, dirty water, polluted air, etc., and that performance measures can range from ROI to performance relative to stakeholder interests. Drawing from complexity theory, and in particular the concept of adaptive tension as the basis of new socioeconomic order creation, we suggest an alternative explanation of how social entrepreneurial firms emerge. Seven 1st Principles of efficacious adaptation are presented. Scale-free theories and causes are reviewed for the purpose of outlining basic elements required for social entrepreneurial firms’ success. Testable propositions are included. Discussion of research and practitioner implications follows, as does a conclusion.

Keywords: emergence, performance, social entrepreneurship, complexity theory

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1 INTRODUCTION

Austin, Stevenson, and Wei-Skillern state that the “central driver for social entrepreneurship

is the social problem being addressed” (2006: 2; our italics). Alternatively, Tracey and Philips

(2007) argue that social entrepreneurs have to achieve both social and financial objectives. Oster,

Massarsky and Beinhacker (2004) call this a “double bottom line.” But the past centuries of

firms making money at the expense of air, land, and water, suggest that the double bottom line

may be oxymoronic. The challenge of making enough money to survive by doing both against

competition only aiming to make money is surely problematic. This suggests a follow-on

problem: What theory best explains how the seemingly oxymoronic, in fact, becomes possible?

To date, we don’t see any useful theory taking on the core problem: How to do both successfully

at the same time over the long haul?

Social entrepreneurship is no doubt an increasingly important subject. However, extant

literature focuses only on the definition, description or enumeration of social entrepreneurship

activities. True, the surge of recent literature on social entrepreneurship illustrates the increasing

attention to activities of social entrepreneurs, social enterprises, and collaborative movement of

government, private and public entities to improve social welfare. University courses on social

entrepreneurship, as well as academic conferences and commercial workshops, are growing in

number. However, the key issue of why the forces of social entrepreneurial activities are strong

even though they are nonprofit in nature and often run by volunteer organization, is not well

researched. Adding to the puzzle, many believe that social impact—as a result of these social

entrepreneurial activities—is difficult to measure and, thus, clearly places social and commercial

entrepreneurship into easily differentiated classes (Austin et al., 2006). However, is it true that

social and financial outcomes are all that difficult to measure? If we leave success measurement

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unresolved, will social entrepreneurship theory be even less useful than it already is?

Since theoretical explanation of social entrepreneurship is nascent, with minimal attention on

performance outcome measures, we focus our contribution on better explaining how/why

entrepreneurs can beat the challenge of the oxymoronic double bottom line. This necessarily

calls for explanations of how/why social entrepreneurs emerge in the first place and, then,

how/why they survive over the long term. Drawing from complexity theory, we challenge the

view of Austin et al. (2006) by offering an alternative explanation, first, about the emergence of

social entrepreneurship. We argue that social entrepreneurial activities emerge as a result of

adaptive tension created by broader societal disequilibria instead of simply economic “market

failure” (2006: 2), or institutional failure (Zahra et al., 2008). Second, we attempt to explain the

performance of social entrepreneurs over the long term. Instead of treating the performance

measure as simply “the differentiator between social and commercial entrepreneurship” (Austin

et al., 2006: 3), we take a proactive stance by arguing—from the perspective of order creation—

that double bottom line is also the critical factor determining the survival of the social

enterprises.

Based on complexity theory, we argue that the emergence of social entrepreneurship lies in

adaptive tension created via societal disequilibria. The key element lies in the chaos of social

disequilibria as it inevitably creates adaptive tension among parties involved. Further, we hold

that performance measures must be transparent and reasonable for constructive order to emerge.

We elaborate on this core issue of societal disequilibria from the perspective of complexity

theory to better theorize about and explain the emergence of social entrepreneurship. Though we

recognize that Austin et al. propose that “performance measurement of social impact will remain

a fundamental differentiator, complicating accountability and stakeholder relations” (2006: 3),

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we address the issues of accountability and stakeholder relations, using complexity lens to argue

that social impact maybe measured in the same method for-profit organization measure their

performance, for example based on ROI. Stakeholder relations are ever so critical as

performance measures in as much as the number of these relations directly affects the long term

performance of the social enterprise.

We aim to contribute to theory and practice in several ways. First, ours is one of the first

attempts to link complexity theory to the study of social entrepreneurship. Our is a first small

step towards adding a theoretical explanation to the body of literature for researchers on

emergence and performance of social entrepreneurship. Our model crystallizes the theory and

variables involved in the emergence process, its sustainability over time, and in measuring

performance. We believe that complexity theory offers relevant theory pertaining to the core

issues in studying social entrepreneurship and provides a well rounded view of the criticality to

the whole chain of the system. Second, we provide testable propositions for future researchers to

examine the robustness and rigor of our propositions. Future research following up with

empirical data will enrich our understanding of the importance of performance measure in social

entrepreneurial activities and entities.

Third, many social entrepreneurial entities follow the myth of managing organizations on a

“hand to mouth” strategy, therefore they focus on daily fund raising activities instead creating

social impact over the long term. Our understanding from the complexity theoretical model

unearth the key strategic goals to social entrepreneurs to realize that the long term self

sustainability, network building and transparency of their operation are critical, as they in turn

play a key role to their sustainability. Fourth, we provide a set of relevant strategic tools for

practitioners. Our model helps practitioners to understand the link between profit, growth, and

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value and the importance of constantly measuring their performance realistically. Finally, our

proposed strategic tools helps practitioners to compare and bench mark with other social

entrepreneurial activities, thereby improving their ability to obtain future funding support and

sustainability.

We review the extant literature on the social entrepreneurship, emergence and performance of

social entrepreneurship, then we introduce our developed theoretical model and propositions on

complexity science perspective of emergence and performance of social entrepreneurship. We

conclude with implications for researchers and practitioners.

2 BACKGROUND

Definition of social entrepreneurship

What is social entrepreneurship? Dees, Emerson, and Economy (2002), state that it is not

about starting a business or becoming more commercial, it is about finding new ways to create

social value. Social entrepreneurs aim to reconfigure resources in order to achieve social goals

and social transformation (Alvord, Brown & Letts, 2004). This emphasis on social value seems

to be the key to many social entrepreneurship scholars (Tracey & Jarvis, 2007; Austin, 2000;

Wallace, 1999). Most of the academic focus in social entrepreneurship research is on social

responsibility in the domain of non-profit firms (Wallace, 1999; Cornwall, 1998) where

entrepreneurs seek to satisfy social needs (Leadbeater, 1997), social change (Prabhu, 1998),

social rates of return (Canadian Centre for Social Entrepreneurship (CCSE), 2001), specific

disadvantaged groups (Hibbert et al., 2001), and social problems in general. The vehicle of such

activities often appears as new social organizations (Sullivan Mort et al., 2003; Thompson, 2002;

Shaw et al., 2002) or continued innovation via existing ones (Smallbone et al., 2001).

The construct of social entrepreneurship has been conceptualized in various ways. Most

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scholars perceive social entrepreneurship as a commitment to collective purpose that builds long

term relationships with minimal resources (Waddock & Post, 1991; Leadbeater, 1997), socially

oriented private sector activities, or services that the State does not provide (Smallbone et al.,

2001; CCSE, 2001), ethical (Shaw et al., 2002) and volunteer support that helps people in need

and is driven by social mission (Weerawardena and Mort, 2006; Thompson, 2002; Sullivan Mort

et al., 2003; Dees, 1998a,b), sources of innovation (Weerawardena & Mort, 2006; Borins, 2000),

and opportunity (Zahra et al., 2008).

It is clear that the social entrepreneurship literature is an aligned conceptualization. Indeed, it

remains fragmented with little coherent theory (Weerawardena & Mort, 2006). Recently, social

entrepreneurship scholars developed a theoretical model to depict the unique characteristics of

social entrepreneurs and the context of their operation (Weerawardena & Mort, 2006). The key

issue of the emergence of social entrepreneurial activities and why their performance measure is

critical to its sustainability is not yet articulated, however.

Duality of social entrepreneurship

The earliest literature documenting social entrepreneurial activities maybe in the Holy Bible

and in ancient China, India, Persia, and Egypt. To understand the academic meaning of the term,

however, it may be best to deconstruct the term and read them as, “social” matters related to the

collective concern in a society or “entrepreneurship” matters related to innovation and new

business activities that generate economic benefits. Taken together, they denote a special

meaning that combines value generated by business organization with value created by solving

solution onerous conditions confronting the target population. We extend the Han and McKelvey

complexity perspective—which holds that emergent technology-based new ventures “achieve

stability through instability” (2008: 4; our italics)—to the context of social entrepreneurship.

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Hence, we define social entrepreneurship as individuals and/or organizations (hereinafter called

agents) pursuing new order in the form of entrepreneurial activities seeking to reduce potentially

disastrous societal states to constructive (healthy) longer-term equilibrium conditions. Put more

succinctly, these are activities that create equilibrium through disequilibrium. In other words,

social entrepreneurial activities are new order-creation processes that reduce harmful states to

long-term equilibrium conditions offering improved social value.

The duality of social entrepreneurship is not from the term itself (social and

entrepreneurship), but from the method and result of its activities (commercial and social return).

Social entrepreneurship does not mean activities in the non-profit sector alone because non-profit

sectors often do not set income and profit generation as one of their priorities; social

entrepreneurs do (Tracey & Philips, 2007). In fact, social entrepreneurs have a double bottom

line, they produce both social and commercial objectives (CCSE, 2001; Hansmann, 1987; Oster

et al., 2004). These scholars take the view that social entrepreneurs may concurrently pursue

social goals as well as profitability—it is important to generate income so an enterprise can

reinvest in its social project and sustain the social mission over the long term. Ben & Jerry’s

“PartnerShop” initiative is an example where profit is reinvested back into social initiatives.

However, the nature of performance measures and why they are critically important to social

entrepreneurship’s sustainability has received little attention in literature.

Opportunity and challenges

Social entrepreneurship scholar agree in general that (1) social problems create opportunities

for social entrepreneurs; at the same time, (2) activities resulting from social entrepreneurship

create opportunities for people in need as well as people desiring to give (Neck, Brush, & Allen,

2009; Seelos & Mair, 2005; Predo & McLean, 2006). Harm-based problems and challenges—

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whether at the individual, ecological, or planet level—indeed, create opportunities for individuals

and organizations to develop innovative methods to overcome these challenges. For example, the

growing aging population leads to healthcare needs; the disadvantaged and/or orphans lead to the

need for housing (UdayanCare, 2008); rapid economic development and consumption in

developing regions such as India, China, Brazil, Russia and the Mid-East substantially increase

demands for food, energy, clean water, and construction. Concurrently, however, our planet

experiences shrinking natural resources (Neck et al., 2009), increased global warming, and

requirements for innovative new technologies. These are examples of problems, inequality,

shortages, and disruptions—what we term disequilibrium states in societies. But to entrepreneurs

they appear as golden opportunities calling for rapid responses with innovative solution (Yunus,

2008; Drayton, 2008).

What theory and practice in social entrepreneurship currently pay little attention to is the

challenge and importance of sustainability. Due to human nature, it may be rather easy to

participate in social causes given the urgency or sudden need of dramatic events such as South

Asian Tsunami of 2004. However, many disequilibria occur in societies over many years and,

therefore, require longer term care and more importantly scalability—from small initial goodwill

attempts to dramatic and lengthy responses—to truly combat the core issue. To achieve this, the

collective social enterprise needs to uphold a very clear and persistent vision and mission in both

social and economic goals for the successful development of social entrepreneurship. Therefore,

successfully meeting double bottom line or triple bottom line objectives (Neck et al., 2009; Dees,

1998; CCSE, 2001) in social entrepreneurial firms (SEFs) is a critical issue. In other words, the

traditional performance metrics of profit and growth are important and should be tightly

observed and employed (Skoll, 2004; Drayton, 2008). But, simultaneously, SEFs need to balance

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the tension of managing their social and commercial objectives (Tracey & Philips, 2007).

Stakeholders and donors of SEFs require a clear, transparent picture of the financial health and

future strategic plan of an SEF in order to be inspired to continue their support. For example,

UdayanCare, a social enterprise in India providing quality care to orphans and women, not only

provides annual reports to its donors and stakeholders, but on a monthly and quarterly basis, it

gives a “personal progress report” to its donors and stakeholders about each child’s academic and

personal advancement. The table below shows how the UdayanCare sets objectives and records

achievement by performance measures coupled with its financial reporting.

Objectives Achievement Reduce the financial burden of the HIV+ parents for better nutrition and education of their affected children along with counseling.

Induction of eight new HIV affected children.

Ensuring the children a positive future on the wings of education and good health.

Two more children had been shifted to English medium school from government schools.

Establishing a long-term relationship with the children through counseling and home visits.

Three more parents got employment during the year.

Providing the children an option of finding a Home in our Udayan Ghars in case of the unfortunate demise of their parent(s).

Wedding bells for our beneficiaries: Rekha Devi and Hari Shankar Singh had lost their spouses to the deadly disease and are now going to tie the knot with each other very soon.

Source: UdayanCare Annual Report 2007–2008 http://www.udayancare.org/

Moreover, UdayanCare also set up clear goals to be achieved by 2010 (see UdayanCare

Annual Report). This rigorous reporting method and clear strategic plan into the future is a key

ingredient needed for SEMs to be successful. Further defining comparable performance

objectives is an important challenge for social entrepreneurship theory and practice (Tracey and

Philips, 2007).

2.1 EXTANT LITERATURE ON EMERGENCE OF SOCIAL ENTREPRENEURSHIP

The extant literature pays little attention to the actual emergence of SEFs; rather most focus is

on the definition, description, or enumeration of the social entrepreneurial activities by existing

entities. Some scholars define social entrepreneurship as social focused commercial enterprises

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(Dees & Anderson, 2003); others treat social entrepreneurship by existing commercial

corporations or nonprofit organizations creating social value as social entrepreneurship. These

studies imply that social entrepreneurship mostly occurs when existing organizational entities

create novel social value using existing strategies, perhaps coupled with innovative methods.

Austin et al. (2006) propose that social entrepreneurship exists due to market failure. Therefore,

SEFs with a social purpose emerge only when there is a significant gap between demonstrable

Demand and lack of Supply. This Demand/Supply inequality may also occur when a public-in-

need cannot afford prices charged by the suppliers. For example, HIV patients in Africa cannot

pay for their medication at prices charged by firms located developed countries. Therefore,

groups of medical-based and pharmaceutical-based SEFs have collectively formed to overcome

this supply deficiency by providing medication at affordable prices to African with HIV.

However, this explains part of the rationale of SEF emergence.

2.2 EXTANT LITERATURE ON PERFORMANCE OF SOCIAL ENTREPRENEURSHIP

The extant literature holds that the performance accountability of SEF’s activities carries

greater challenges than does more prosaic commercial entrepreneurship by existing entities,

whether commercial or nonprofit (Austin et. al., 2006). They argue that commercial

organizations can measure their performance using standard tangible and quantifiable measures

such as financial indicators (e.g., ROI or ROA) and market share. But, to make the issue more

complicated, the number of stakeholders at social entrepreneurial entities is much more diverse

and larger in number when compare to most commercial firms. Therefore, Austin et al. propose

that the challenge of measuring social change is complicated due to “nonquantifiability,

multicausality, temporal dimensions and perceptive differences of the social impact created”

(2006: 3). Alternatively, Dees et al. argue that values of the most non-profit organizations is

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defined by “measureable social impact” (2002: 162). It is increasingly important for social

enterprises to report their performance; absence of evidence is no longer acceptable.

3 THEORY DEVELOPMENT FOR SOCIAL ENTREPRENEURSHIP

By way of introduction, the existing literature on SEFs is fragmented; it begs for better

theories to explain the process, emergence and eventual performance of SEFs. We draw on

complexity theory—and in particular the concept of new order creation induced by adaptive

tensions (McKelvey, 2004b)—to articulate the causal relationship of emergence and

performance of SEFs. Figure 1, below, depicts the causal relationship, process, and direction of

the key variables in the process of achieving equilibrium from disequilibrium.

We propose that social disequilibrium emerges as a result of energy differentials (broadly

defined) within a societal population (for example oil-based wealth to politicians vs. an

impoverished population in Nigeria; clean water vs. contaminated water; disease outbreaks such

as cholera in Zimbabwe vs. health, etc.). Tensions stem from underlying disasters, disruptions—

and more broadly, disequilibria—that create adaptive tensions within a society and, then, give

rise to structures that dissipate the tensions: i.e., SEFs, and larger pockets of SEF-based new

order appear (e.g., groups of activities start to create tensions within a society). When the

dissipative energy (tension) reaches a specific level, a phase-transition process begins (changes

start to be made); this is when adaptive tension results. It may also happen that tension creates

positive feedback effects that further enhance the initial SEF activities). Adaptive tension

activates the emergent SEFs, which then leads to self-organization by various agents to create the

SEFs. But, this self-organization process cannot meaningfully turn disequilibrium into

equilibrium unless scalability dynamics are also set off. Therefore:

Our core proposition is: Societal disequilibria create adaptive tensions among disparate

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parties; this tension drives the emergence of SEFs, which in turn set in motion a trend toward

equilibrium that dissipates the social disequilibrium. Therefore, social entrepreneurship appears

as a new order-creation process.

Social Disequilibria

Adaptive Tension

Social Entrepreneurial

activities: Private, public, collaborative

Equilibrium

ProfitGrowth

Order creation process

P3P1 P2

Figure 1: Complexity model of creating equilibrium through disequilibrium

3.1 TOWARD A COMPLEXITY THEORY OF EMERGENCE OF SOCIAL ENTREPRENEURSHIP

Social entrepreneurship firms (SEFs) exist in dynamic, nonequilibrium, nonlinear

competitive environments —i.e., dynamical environments. Complexity scientists have

established that only complex adaptive systems (CAS) are able to adapt, survive, and grow under

these conditions (Kauffman, 1993; Holland, 1995; McKelvey, 2004c). SEFs have to transform

their firms into high performing CAS. To accomplish this, they have to:

• Fully address the adaptive challenges facing their firms in terms of the first principles of efficacious

adaptation (McKelvey, 2004a); this is the adaptive part of CAS;

• Make sure appropriate complexity dynamics operate at all levels, as their firms grow, differentiate, and

become multilevel, complex systems; this is the complexity part of CAS;

• Make sure scale-free dynamics foster emergent complexity dynamics at multiple levels; and

• Attain the state of self-organized criticality (Bak, 1996).

We begin by reviewing CAS theory and then set out our propositions regarding the adaptive

tension and emergence requirements for enabling CAS dynamics in SEFs. We separate

complexity science into three Phases, the first two of which we briefly describe next.

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Phase 1—Tension Dynamics. Emphasis focuses on critical values and dissipative structures.

It’s based on the works of Prigogine (1955, 1997), Haken (1977), Nicolis & Prigogine, 1989),

and Mainzer (1994/2007), among many others. It began with the Bénard (1901) process—an

energy differential is set up between warmer and cooler surfaces of a container (measured as

temperature, ΔT). In between the 1st and 2nd critical values (Rc1, Rc2), a region is created where

the system undergoes a dramatic phase transition in the nature of fluid flow. For example,

increasing the heat under water molecules in a vessel exposed to colder air above leads to

geometric patterns of hotter and colder water—the chef’s “rolling boil” emerges; new order

appears. The critical values define the “melting zone” (Stauffer, 1987; Kauffman, 1993), within

which new structures spontaneously emerge. Prigogine (1955) termed these “dissipative

structures” because they are pockets of new order—governed by the 1st Law of Thermodynamics

(the energy-conservation Law)—that speed up the dissipation of imposed energy differentials, by

creating new intra-system order (Swenson, 1989).

Phase 1 focuses on what sets order-creation dynamics in motion. It draws mostly from the

physical sciences and views imposing energy differentials leading to phase transitions as the

cause of order creation. A phase transition occurs because an imposing energy differential, what

McKelvey (2001, 2008) terms “adaptive tension” exceeds what is called the 1st critical value,

Rc1—which defines the lower bound of the region of emergent complexity.

Elsewhere, McKelvey (2004b) reviews the several theories about causes of emergent order in

physics and biology, some of which have been extended into the econosphere and social

behavior. Kelso, Ding, and Schöner (1992) offer the best synthesis of Phase 1:

Control parameters, Ri, externally influenced, create R > Rc1 with the result that a phase transition

(instability) approaches, degrees of freedom are enslaved, and order parameters appear, resulting

in similar patterns of order emerging even though underlying generative mechanisms show high

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variance.

Setting the stage for order-creation-based analysis of entrepreneurial start-ups, Schumpeter, in

1934, quite remarkably wrote about replacing evolution with phase transitions—well before

Prigogine (1955) and replacing gradualist evolution with punctuated equilibrium long before

Maruyama (1963) or Eldredge and Gould (1972)! Besanko, Dranove and Shanley (2000: 485)

summarize Schumpeter’s thesis as follows:

Schumpeter considered capitalism to be an evolutionary process that unfolded in a characteristic

pattern. Any market has periods of comparative quite, when firms that have developed superior

products, technologies, or organizational capabilities earn positive economic profits. These quiet

periods are punctuated by fundamental “shocks” or “discontinuities” that destroy old sources of

advantage and replace them with new ones. The entrepreneurs who exploit the opportunities these

shocks create achieve positive profits during the next period of comparative quiet. Schumpeter

called this evolutionary process creative destruction. (my italics and underlines)

For Prigogine, dissipative structures are pockets of order (governed by the 1st Law) that serve

the demands of the 2nd Law of Thermodynamics (the entropy-production Law) by dissipating the

difference between high-energy/high-order entities in the environment and the fundamental

equilibrium state of randomness or disorder. In short, dissipative structures are disequilibrium

states that emerge to speed up the production of equilibrium!, i.e., speed up entropy production.

For economists, firms are order creations that reduce the tension between Demand and Supply or

reduce differences in prices so as to produce market equilibrium. For Schumpeter, shocks are

disequilibria that give rise to entrepreneurial firms that, then, reduce the Supply/Demand or

old/new tension in the marketplace. For us, social entrepreneurs create firms to reduce the

Demand between green and clean vs. the Supply of environmentally and/or socially harmful

and/or destructive products.

For example, a technical or process innovation (Tushman & Anderson, 1986) can set up

disequilibrium between the entrepreneur and the current market; these innovations drive the self-

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organized emergence of new firms and new industries (Binks & Vale, 1990) and, for us, social

entrepreneurship. Slevin and Covin (1997: 56) describe this process by suggesting that

“successful entrepreneurial firms act as energy conversion systems.” In a broader managerial

context, Anderson (1999: 222) suggests that adaptive tension may be generated by managers:

Those with influence and/or authority turn the heat up…on an organization by recruiting new

sources of energy (e.g., members, suppliers, partners, and customers), by motivating stakeholders,

by shaking up the organization, and by providing new sets of challenges that cannot be mastered by

hewing to existing procedures.

The application of the 0th law in socioeconomics rests with Haken’s control parameters, the

first two words in the Kelso et al.’s statement. The Ri adaptive tensions (McKelvey 2001, 2008)

can appear in many different forms, from Jack Welch’s famous phrase, “Be #1 or 2 in your

industry [in better than average growth] or you will be fixed, sold, or closed” (Tichy & Sherman,

1994: 108; paraphrased), to narrower tension statements aimed at technology, market, cost, or

other adaptive inadequacies. Schumpeter observes (quote above) that entrepreneurs are

particularly apt at uncovering tensions in the marketplace. The applied implication of the 0th law

is that new order-creation activities are functions of (1) control parameters, (2) adaptive tension,

and (3) phase transitions motivating (4) agents’ self-organization. Take away any of these and

order creation stops.

Phase 2—emergent order via self-organization. Emphasizes agent1 self-organization absent

outside guidance or control (Holland, 1988). It consists largely of scholars associated with the

Santa Fe Institute (Pines, 1988; Cowan et al., 1994; Arthur et al., 1997). While Phase 1 focuses

mostly on dramatic phase transitions at Rc1,—the edge of order, Phase 2 complexity scientists

focus mostly on Rc2—the “edge of chaos” (Lewin, 1992/1999; Kauffman, 1993). The “edge of

1 “Agent” is a term broadly used to refer to cells, organisms, species, conversation elements, people, employees, groups, firms, industries, and societies, among many other more specific definitions.

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chaos,” long a Santa Fe reference point (Lewin, 1992), is now in disrepute, however (Horgan,

1996: 197)—it is better to just think of Rc2 as the 2nd critical value—the upper energy boundary

of the melting zone. Focusing on living systems (Gell-Mann, 2002), Phase 2 emphasizes the

spontaneous co-evolution of entities (i.e., the agents) in a CAS. Agents restructure themselves

continuously, leading to new forms of emergent order consisting of patterns of evolved agent

attributes and hierarchical structures displaying both upward and downward causal influences.

Considerable evidence of self-organized, emergent, coevolutionary behavior in organizations

exists. The earliest discoveries date back to Roethlisberger and Dixon (1939) and Homans

(1950)—both dealing with the mutual influence of agents (members of informal groups), the

subsequent development of groups, and the emergence of strong group norms that feed back to

sanction agent behavior (reviewed in Scott, 1998). Several of the articles in the Organization

Science special issue on coevolution (Lewin & Volberda, 1999) report out evidence of micro-

coevolutionary behavior in organizations. Finally, a number of recent studies of organization

change show much evidence of coevolution between organization and environment and within

organizations as well (Meyer & Gaba, 2002; Siggelkow, 2002; Murmann, 2004).

In his classic paper, Maruyama (1963) discusses mutual causal processes mostly with respect

to biological coevolution. He also distinguishes between the “deviation-counteracting” negative

feedback most familiar to general systems theorists (Buckley, 1968) and “deviation-amplifying”

positive feedback processes (Milsum, 1968). Boulding (1968) and Arthur (1990, 2000) focus on

“positive feedbacks” in economies. Negative feedback control systems such as thermostats are

most familiar to us. Positive feedback effects emerge when a microphone is placed near a

speaker, resulting in a high-pitched squeal. Mutual causal or coevolutionary processes are

inherently nonlinear—as noted by Maruyama (1963), Gleick (1987), and Ormerod (1998).

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Large-scale outcomes may be instigated by what Holland, 1995) calls “tags” (“very small

initiating events”). Lorenz (1972) referred to them when he asked “Does the Flap of a Butterfly’s

Wings in Brazil Set Off a Tornado in Texas?” We refer to them as butterfly events.

Bak (1996) extends this treatment in his discovery of “self-organized criticality”, a process in

which butterfly events can lead to complexity cascades of avalanche proportions best described

as an inverse power law (more on this later). The signature elements within the melting zone are

self-organization, emergence and nonlinearity. Kauffman’s “spontaneous order creation” begins

when three elements are present: (1) heterogeneous agents; (2) connections among them; and (3)

motives to connect —such as mating, improved fitness, performance, learning, etc. Remove any

one element and nothing happens. According to Holland (2002) we recognize emergent

phenomena as multiple level hierarchies, bottom-up and top-down causal effects, and

nonlinearities. Nonlinearity often stems from scalability reflected as power laws.

Both Phases are important. Phase transitions are often required to overcome the threshold-gate

effects characteristic of most human agents—so that there is a broader coevolutionary dynamic

set in motion once the tag occurs. This requires the adaptive tension driver to rise above Rc1.

Once the adaptive tension force is strong enough to overcome the threshold gates, and given that

a tag occurs, and assuming the other requirements are present (heterogeneous, adaptive learning

agents, and so forth), coevolution then starts. Neither R > Rc1 nor tag-plus-coevolution seems

both “necessary and sufficient” by itself, especially in social settings. This is why phase

transition and coevolution are “co-producers” (Churchman & Ackoff’s 1950 term). It seems

clear that (entrepreneurial) order-creation via adaptive tension, R > Rc1, and phase transition, as

Schumpeter figured out sixty years ago, fits with the Phase 1’s view of what causes self-

organization and subsequent nonlinearity. This approach, has been used to explain a broad range

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of organizational, sociological, and economic phenomena (e.g., Schieve & Allen, 1982; Smith &

Gemmill, 1991; Lesourne & Orlean, 1998; Lichtenstein, Dooley, and Lumpkin, 2006).

Proposition 1: Within the same community (society), if there is an energy differential (e.g.,

wealth or health differences), there will be social disequilibrium. This disequilibrium is likely to

lead to tensions (Prigogine, 1955) within the community.

Proposition 1a: When tension generated from social disequilibria increases within a

community (dissipative energy becomes strong), pockets of new order will form (i.e., dissipative

structures), changes will begin and phrase transitions emerge. This occurs when adaptive

tension results morphs into positive feedbacks of constructive forms, such as SEF activities

(McKelvey, 2004b,c).

Proposition 2: Adaptive tension activates the agents such that emergent self-organization of

individual entrepreneurs into SEFs appears. This positive energy/tension effect sets off the self-

organization process in the form of SEF formation(s).

3.2 1ST PRINCIPLES OF EFFICACIOUSLY ADAPTIVE SEF PERFORMANCE

We formulate our complexity-based theory explaining the emergence and performance of

SEFs from four concepts. We propose that vigorous provision creation of transparent and

responsible financial and social impact-based performance criteria/standards is critical to the

longevity and scalability of SEFs because: (1) positive feedback is provided and spirals up to

further stimulate strategic formulation of the kinds of strategies that are the determinants of long

term success (Burgelman & Grove, 2007); (2) positive-feedback effects form the basis of

scalability and transformation of the SEFs (Skoll, 2004); (3) results in the form of effective

performance reporting helps improve decision making and generate innovative solutions and

derivative stakeholder support (Levy, 1994); and finally, (4) even short-term performance

measures often lead to unpredictable long-term outcomes since SEFs are “living systems” (Gell-

Mann, 2002) existing in dynamical—i.e., unpredictable—CAS environments (Stacey, 1995).

Because they are living CASs, SEFs have to adapt to changing environments if they are to

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survive and grow, and/or they must co-evolve with competitors in stable niches (Kauffman,

1993; McKelvey, 1999). But what demands does adapting to changing environments impose on

an SEF? What does it actually have to do? How would one know whether an SEF has adaptive

structures and processes in place? In this section we briefly define seven ‘First Principles of

Efficacious Adaptation’ defined in the literature some 40 to 70 years ago (McKelvey, 2004a). A

first principle is defined as one logic step up from basic self-evident axioms, such as F = ma. In

this axiom, force, mass, and acceleration are self-evident—just stand in front of a bus or speeding

sports car and you will feel the effect of its truth! Each has been discussed in the literature for

decades and has survived without challenge. Collectively, they are multiplicative in that a system

that is ‘zero’ on any one of them won’t survive, whereas the effects of two or more have a

multiplicative adaptive effect.

Each of these first principles is a generative force driving efficacious adaptation in organisms

and organizations. The seven principles are Adaptive Tension, Variation Rates, Requisite Variety,

Near Decomposability, Causal Complexity, Coevolution (scalability), and Causal Rhythms. We

hold that these seven generative forces drive SEF development. The ability of any SEF

entrepreneur to create viable emergent structures—emergent requisite complexity—capable of

efficacious adaptation in changing environments comprising scarce resources and aggressive

competitors is pursued within the confluence of these forces.

(1) Prigogine’s phase-transition theory. One of the origins of order creation is a

disequilibrium between two adjacent systems or ‘fields’, where one field enjoys a high

concentration of resources (e.g., information, knowledge, capital, market potential) compared to

an adjacent other field (Prigogine, 1997). The disequilibrium sets up an adaptive tension, defined

as a contextually imposed energy differential (McKelvey, 2001, 2008). The energy disparity

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causes a phase transition; that is, new order creation. Since tension seeks resolution, this energy

differential induces a creative response that generates new order within the system as a whole

(Barney & Arikan, 2001).

The concept of adaptive tension is particularly relevant in social entrepreneurship. This type

of “social” innovation largely results from an entrepreneur’s perception of disequilibrium relative

to a firm’s competitive environment (Eisenhardt & Schoonhoven, 1990). Such disequilibria drive

the process of opportunity identification and then innovation (Stevenson, 1999). This process

allows social entrepreneurs to identify new markets, industries, products, and services that can be

capitalized on via the value-creating activities within a new venture. Long ago Schumpeter

(1934) turned our attention to the critical effect that phase transitions and disequilibria have on

the creation of new economic order. He called it “creative destruction” as a result of

environmental shocks.

(2) Ashby’s law of requisite variety. In order to remain viable, a system needs to generate the

same degree of internal variety as the external variety it faces in the environment. Essentially,

external variety—including ‘disturbances’ or uncertainty—can be managed or ‘destroyed’ by

matching it with a similar degree of internal variety: ‘Only variety can destroy variety’. (Ashby,

1956, p. 207). McKelvey and Boisot (2008) update Ashby’s law to “requisite complexity.” But

there is a cost to matching internal and external variety. The more external complexity, the

higher the internal costs of adapting, in terms of time and capital.

This principle easily applies to SEF adaptation. Effective strategic management requires that a

firm’s structure, strategy, and mindset be aligned, both internally and externally (Eisenhardt &

Schoonhoven, 1990). Entrepreneurial and managerial cognition is bounded (March & Simon,

1958/1993); thus, when firms encounter new phenomena, they have to (1) expand requisite

20

variety as required (Wiklund, 1999); (2) adapt to environmental changes (Zajac & Kraatz, 1993);

(3) limit their perception of reality to foster effective schema development [what Gell-Mann

(1988, 2002) calls “effective complexity”] to fully appreciate and interpret the external

complexity—firms can’t respond to every degree of freedom in their environment (Boisot &

McKelvey, 2006); and/or (4) deny the conflicting information as irrelevant.

(3) Fisher’s change rate theorem. Fisher (1930) made the connection between variation and

adaptation, a link that is now all but axiomatic in the biological and social sciences. His basic

theorem states: “The rate of evolution of a character at any time is proportional to its additive

genetic variance at that time” (quoted in Depew & Weber, 1995: 251). In other words, adaptation

to a changing environment speeds up the rate that usable genetic variation becomes available.

Fisher’s theorem is especially relevant to research on innovation in high-velocity

environments where knowledge creation provides the key to ongoing variations within products

and product lines (Eisenhardt, 1989). As product life cycles shorten (Poza, 2004) and

hypercompetition increases (D’Aveni, 1994), rapidly creating new knowledge becomes a key

competitive advantage (Leonard-Barton, 1995). As Prusak (1996: 6) says:

The only thing that gives an organization a competitive edge—the only thing that is sustainable—is

what it knows, how it uses what it knows, and how fast it can know something new!

The speed of knowing something new is critical to SEFs, but the speed of shaping a new

entity such as an SEF is even more advantageous (Tushman & O’Reilly, 1997). The more

disequilibrium, the more phrase transition, and then the more new order creation. In the context

of SEFs, this means more changes and transitions and more opportunities to create even more

new SEF structures and processes.

(4) Simon’s principle of near decomposability. Simon’s (1962) classic essay on the

architecture of complexity articulates his design principle for modular systems. Complex systems

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consisting of nearly decomposable subunits (i.e., mostly independent from top-down control or

interdependencies with other subunits) tend to evolve faster and toward stable, self-generating

configurations. Simon’s idea re-emerged as Weick’s (1976) loose-coupling concept and more

recently as modular production and product design (Sanchez & Mahoney, 1996). Schilling

(2000) suggests that modularity is a continuum describing the degree to which a system's

components can be separated and recombined.

New venture creation is studied in terms of elemental behaviors or tasks that systemically

integrate to drive organizational emergence (Carter et al., 1996). The early development of

companies has long been framed in terms of increasing degrees and levels of structure and

control (Chandler & Hanks, 1994). The need to continuously increase internal levels of

differentiation and specialization has been see as a driving force in the evolution of new

organizational forms for decades (Lawrence & Lorsch, 1967; Miles et al., 1999).

(5) Lindblom’s principle of causal complexity. Lindblom (1959) introduced the processes of

parallel interaction, mutual adjustment, and coordination that characterize social units facing

complex and uncertain choice and action situations. Lindblom’s principle is the foundation of

Buchler’s (1966) “interactional complexity” that arises once near decomposability is achieved.

Groups of various kinds may be interconnected; each group has an agenda; each agenda pushes

policies and organizational action forward. In Lindblom’s work we have early, if not the first,

recognition of multiple causal influences in CASs.2

Cohen, March, and Olsen (1972) observe that emergent, differentiated, semi-autonomous

subunits generate the need to integrate “organized anarchy.” The latter leads to added

hierarchical levels and emergent integration processes among them (Galbraith, 1973). The task

22

here is to solve key problems that can limit order creation and emergence. The principle issue of

organized anarchy is one of top-down directing versus bottom-up emergent organizing—a causal

duality. If this problem isn’t solved, the unit loses its adaptive capability.

(6) Maruyama’s principle of coevolution via positive feedback. Mutual causality via

deviation amplification was initiated by Maruyama (1963). He suggests that a relationship that is

mutually causal and that amplifies an insignificant or accidental start is likely to spiral into a

different type of relationship depending on different (insignificant) initial conditions. Maruyama

anticipates the coining of the term coevolution by biologists Ehrlich and Raven (1964).

Kauffman (1993) states that organisms not only evolve, they coevolve. The more connections

among modules, the higher the possibility of mutual causal dynamics (Okes, 2003), which

magnify the emergence of order creation in biology (Goerner, 1994) and economics (Arthur,

1990; Ormerod, 1998).

In organizations, the defining issue is mutual learning by both the firm and the individuals

within it (March, 1991). A variety of organization scientists have studied coevolutionary

dynamics relevant to entrepreneurial settings (Meyer and Gaba, 2002; Siggelkow, 2002;

Murmann, 2004). In the context of SEFs, learning via coevolutionary processes comes at a price.

It is important for SEFs to coevolve with customer needs and competitor positioning. For

example, Microsoft tends to customer needs by developing user-friendly platforms; it also

connects with users by developing a system that locks users in such that their usage coevolves

with Microsoft’s products. In the context of technological competitiveness, users or products that

don’t coevolve will very likely lag behind and fail. Given limited time and capital resources, and

the fast rate at which market, customers, and competitors change, the optimal rate for SEF

2 This is one principle that does not originate in biology. In a personal note (August 8th, 2004), Salthe notes that several authors made forays into causal intricacy prior to his 1985 book, but they were not picked up by mainstream

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coevolution is necessarily faster.

(7) Dumont/Dupuy’s causal rhythms. The rhythm principle stems from Dumont’s (1966)

initiating study of Hindu society. He found that dominance oscillates between Brahmin and

Rajah (religion vs. secular forces) as the need for warfare comes and goes. In organizations, we

see dualities such as centralization-decentralization and exploitation-exploration (March, 1991).

Most academics propose balancing these; March (1999) says balance is impossible. Dupuy

(1992) adds the idea of fast or slow oscillation.

The dynamic rhythm idea, termed “circular organizing,” also appears in studies by Ackoff

(1981), Endenburg (1988), Nonaka, 1988; and Romme (1999). Interestingly, the biologist, Kelso

(1995) also writes about coordination rhythms across various biological levels of analysis, from

cells to fingers to brains. Further, he notes that these rhythms operate at different time scales

(1995: 247). Thomas, Kaminska-Labbé and McKelvey (2005) find that oscillation dynamics also

operate at different rhythms in firms, with profitability at stake. Thus:

Proposition 3: Double bottom line financial and social impact-based performance reporting

that is, valid, transparent, and responsible is critical to the longevity and scalability of SEFs.

Proposition 3a: Long term success is positively related to continuous reception of feedback

from performance reporting so that strategies can be modified. (Burgelman & Grove, 2007)

Proposition 3b: Innovative solutions are positively related to the continuous development of

valid and timely performance reporting so that improved decision processes can be generated

(Levy, 1994)

Proposition 3c: Short-term performance measures are positively related to long-term

outcomes (Stacey, 1995).

3.3 SCALE-FREE PERFORMANCE FOR SEFS: LESSONS FROM ECONOPHYSICS

For any commercial or social entity, stability often leads to organizational inertia and may

lead to termination by adaptive tensions imposed by market change or competitor dynamics.

biologists.

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Consequently, we argue that SEFs aimed translating disequilibrium-based tensions into

equilibrium conditions may require initiating scalability dynamics so as to achieve long-term

viability and success. We base our argument on the concept of multilevel scale-free regularities

and fractal dynamics—as described next. For example, scalability may be needed and employed

with respect to projects, locations, markets, target beneficiaries, caregivers, and methods of fund

raising. This multilevel focus of scalability opens up the possibility that these fractal dynamics

may often lead to significant positive energy and subsequent impact. For example, an initial idea

for a new SEF, focusing for example on better water management, that is first communicated to a

couple of friends, and then to a broader network of contacts, and eventually to an extensive

networks of possible stakeholders and other concerned non-government organizations, may then

blossom into a viable and significant SEF.

Introduction to Scale-free Theory

Phase 3, Econophysics, is the most recent development in complexity science. Its focus is on

how order creation actually unfolds once the forces of emergent order creation by self-organizing

agents—such as biomolecules, organisms, people, or social systems—are set in motion. Key

parts of this third phase are fractal structures, power laws, and scale-free theory. In his opening

remarks at the founding of the Santa Fe Institute, Gell-Mann (1988) emphasized the search for

scale-free theories—simple ideas that explain complex, multi-level phenomena. Brock (2000)

goes so far as to say that “scalability” is the core of the Santa Fe vision—no matter what the

scale of measurement, the phenomena appear the same and result from the same causal

dynamics. Gell-Mann (2002) concludes his chapter, “What is Complexity?” with a focus on

scalability and power laws.

Gell-Mann (2002) defines “effective complexity” as regularities or schema that are found or

25

judged to be useful—they are neither too simple nor overly complex. Like Goldilocks’ porridge,

they are “just right.” SEF entrepreneurs develop these schemas by separating the external

complexity dynamics they can cope with, from random noise. Gell-Mann notes that regularities

appear as equations (in physics), genotypes (in biology), laws and traditions (in cultures), and

best practices (in management). What is new is that he recognizes the chaos-derived regularities

in CASs, which he defines by separating out the pink, brown, and black portion of Schroeder’s

(1991) colored noise from white noise (see also Dooley and Van de Ven, 1999). In doing so, he

sets forth two regularities: Type 1: the old simplicity of reductionism, equations, linearity, and

predictions of classical physics; and Type 2: the new simplicity of “tiny initiating events”

(Holland, 2002)—what we call “butterfly-events”—that initiate causal dynamics leading to

nonlinearity, similar causal dynamics at multiple levels, power laws, and scale-free theory. We

describe Gell-Mann’s regularities in more detail below.

• Type 1 law-like regularities are the reductionist causal processes of normal science, which are

predictable and easily represented by equations. Their data and information are much preferred in

classical physics and neoclassical economics (Gell-Mann, 2002: 19). These regularities are the point

attractors of chaos theory—defined by forces, equilibrium, and energy conservation. These regularities

characterize existing empirical organization and management research. These may be confidently

described via Gaussian statistics and allow predictions that become the basis of schemata and

prescriptive solutions.

• Type 2 multilevel scale-free regularities are outcomes over time that result from an accumulation of

random tiny initiating events [Holland (1995) also calls them “tags”] that have lasting effects, are

compounded by positive feedback effects over time, and become ‘frozen accidents’ (Gell-Mann, 2002:

20). These are the strange attractors of chaos theory—never repeating, fostering indeterminacy,

offering a different kind of regularity. They are the bifurcation points of chaos theory. The butterfly-

events of chaotic histories are never repeated, are not predictable, and can produce significant

nonlinear outcomes that may become extreme events. Consequently, describing these systems is at

best problematic and easily outside the explanatory/scientific traditions of normal, reductionist

science. Gell-Mann concludes by noting that when butterfly-events spiral up such that their effects are

magnified at multiple levels, we see self-similarity, scalability, and power laws—all elements studied

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by econophysicists.

Fractals. Consider the cauliflower. Cut off a “floret”; cut a smaller floret from the first floret;

then an even smaller one; and then even another, and so on. Despite increasingly small size, each

lower-level component performs the same function and has essentially the same design as the

floret above and below it in size. This feature defines it as fractal. Fractals can result from

mathematical formulas—the very colorful ones figuring in Mandelbrot’s “Fractal Geometry”

(1982). We are more interested in fractal structures that stem from adaptive processes—like the

cauliflower—in biological and social contexts. In fractal structures the same adaptation dynamics

appear at multiple levels. McKelvey, Lichtenstein, and Andriani (2009) list 19 studies showing

fractal structures in predator/prey relationships.

Power laws. If plotted on a double-log graph, the Pareto-distributed progression of increasing

numbers of connections from, say, small airports to giant ones like Heathrow and Atlanta,

appears as a negatively-sloping straight line. This is the now famous power law “signature”

dating back to Auerbach (1913) and Zipf (1949). Formally, it appears thus: F ~ N –β, where F is

frequency, N is rank (the variable) and β, the exponent, is constant (in exponential functions the

exponent is the variable and N is constant). Stanley et al. (1996) find that manufacturing firms in

the U.S. show a fractal structure, as does Axtell (2001). Andriani and McKelvey (2007, 2009)

list over 120 kinds of power laws—which are good indicators of fractal geometry—in social, and

organizational phenomena. The econophysicist Barabási (2002) connects scalability, fractal

structure, and power law findings to social networks. He shows how networks in the physical,

biological and social worlds, are fractally structured such that there is a “rank/frequency”

effect—an underlying Pareto distribution showing many sparsely connected nodes at one end

and one very well connected node at the other. See also Newman (2005) and Newman, Barabási,

and Watts (2006).

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Scale-free theories explain why fractals appear as they do and behave as they do. Though

scalability may have been at the core of the Santa Fe vision, scale-free theories have only

recently begun to be consolidated and featured collectively by the econophysicists (West &

Deering, 1995; Mantegna & Stanley, 2000; Newman, 2005). The key feature that sets scale-free

theories apart from most social science theories is that they use a single cause to explain fractal

dynamics at multiple levels. The earliest dates back to 1638—Galileo’s Square-Cube Law; the

cauliflower keeps subdividing to keep its surface area at a constant ratio to its growing volume;

Stephen (1983) applies this Law to organizations. Explanations for why some structures have

adaptive success while others do not, range from biology to social science. If the same theory or

principle applies to microbes and to organizations, it is assuredly scale-free. Andriani and

McKelvey (2009) describe 15 scale-free theories applying to firms.

How scale-free dynamics help Social Entrepreneurs satisfy the 1st Principles

The seven 1st Principles apply even to the smallest element of a system (even a few people

working in a garage—e.g., the emergence and growth of GE and Hewlett-Packard!)—they apply

to all levels of any firm, including SEFs. Likewise, scalability becomes increasingly important,

complex in their interaction, and difficult to enable as an SEF becomes larger with more people,

modules, and levels. Gell-Mann’s second regularity takes on an ever more crucial role as the

number of levels in the firm increases and it attempts to make more and more small-to-large

changes. Only in recently do we begin to see the relation between system change, scalability, and

power laws. In short, the adaptive dynamics defined by each principle have to be effectively

enabled at each level of a multilevel SEF if it is to survive and grow in a changing competitive

environment. Entrepreneurs who only focus on one level, or do not succeed in enabling scale-

free dynamics at multiple levels , will own SEFs with dubious survival capabilities.

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How to assure each principle operates at each level of an SEF? Our argument is very clear.

An SEF, as a CAS, needs to meet specific adaptive demands—which we have defined in terms of

the 1st Principles. SEFs also need to assure that they have adaptive CAS dynamics operating at

each level of their organizations. Otherwise there is an adaptive “scalability” gap, which acts as

a barrier to efficacious adaptation from top to bottom. It follows that an SEF entrepreneur must

assure that each of the following scale-free dynamics is operating in his/her firm at multiple

levels—with no level where the scalability dynamic is inactive—so as to assure the activation of

each 1st Principle. We now define seven scale-free theories from the longer list put together by

Andriani and McKelvey (2009), and show how they relate to specific first principles.

(1) Least effort. Zipf (1949) originally suggested that “least effort” best explained Zipf’s

Law. This is the power law of word usage known to apply to English, French, and Spanish.

Least-effort theory is now confirmed (Ferrer i Cancho & Solé, 2003); but it appears limited to

changing languages (Dahui, Menghui & Zengru, 2005). Applied to new venture creation, Zipf’s

law holds that efficient and effective interactive transactions are similar to conversation or

purchasing activities. Zipf’s least-effort theory now seems best applied to changing situations.

Thus, we now know that least effort theory applies to organizations (Andriani and McKelvey,

2009) and economies that are in transition (Podobnik et al., 2006). In management contexts,

Zipf’s law holds true in firms that are changing and have high growth rates (Ishikawa, 2006), but

does not hold true for large firms with slow growth rates (Dahui, Li, & Zengru, 2006).

(2) External tension. We have already shown that tension is essential for adaptation. Since

tension can apply to any and all levels, it is scale-free. A good example of this kind of tension in

the organizational world is Welch’s famous phrase to his division presidents, ‘Be #1 or #2 in

your industry…or you will be fixed, sold, or closed’ (Tichy & Sherman, 1994, p. 108,

29

paraphrased). See also Collins’s (2001) “face the brutal facts” element in his explanation of

“good to great” firms.

(3) Spontaneous order creation. In Kauffman’s (1993) biological theory, all that is needed to

stimulate emergent structure in the form of connections are heterogeneous agents (DNA, cells,

organisms, etc.) stimulated by the need to improve fitness of some kind—adaptive tension and

positive feedback are implicit. Carley has applied this basic idea to organizations, learning, and

cognition, and emergent structure over many years (Carley, 1999; Carley & Hill, 2001).

Kauffman’s approach is now widely applied to organizations; indeed, Maguire, McKelvey,

Mirabeau, and Öztas (2006) list over twenty applications.

(4) Contagion bursts. Incidents and theories about epidemics and pandemics have been with

us for years. What Watts (2003) and Andriani and McKelvey (2009) focus on is the reality that

random interactions among people, such as spreading a virus via coughing, are quickly speeded

up if the coughing occurs in a confined space or, as on an airplane, they are sitting near each

other for some length of time. Thus the change-rate advantages stemming from Fisher’s law

come when heterogeneous agents work in physical proximity or have easy networking

capabilities. Whereas lengthy contacts may lead to groupthink (Janis, 1972), the idea here is that

managers need to enable bursts of intense short-term tie development at moderate levels of

connection here-and-there and now-and-then in their firms. This is what leads to bursts of

communication, bursts of idea spreading, bursts of creativity, bursts of new product ideas, etc.

(5) Square-cube law. This law, dating back to Galileo, defines the nature of the cauliflower. It

holds that as the volume of an entity increases (dictated by survival advantages stemming from

increased size), its existing surfaces (means of absorbing energy) do not increase so as to

maintain the required ratio between energy use and energy absorption. Hence, fission takes

30

place—as with the cauliflower—to keep the surface area large enough to supply energy to the

increasing volume. This law has been applied to firms as far back as Haire (1959). More

recently, it has been used to describe the difference between surface employees (those who

directly bring in resources), and volume employees (everyone else in a firm) (Stephan, 1983).

Carneiro (1987) applies the law to explain the upper bounds of a village. The law limits a

village’s size unless it develops what he terms “structural complexity,” where complexity grows

at 2/3 power of the village population. Only by doing this does the village avoid splitting in two.

(6) Connection costs. Simon’s near decomposability principle is brought to life at any level

by balancing square-cube, fission-created modules with another scale-free theory—the cost of

inter-module connections. Absent anarchy, as modules (each having a few connections with

various other modules) increase in number, the number of their inter-module connections (and

connection time and energy costs) increase at an exponential rate (Bykoski, 2003). At some point

it is more efficient to recombine modules to lower communication costs.

(7) Preferential attachment. This scale-free theory rests on a positive feedback process in

which existing resources accrue even more resources—or as the saying goes, “the rich get

richer.” This theory underlies the logic of increasing returns to scale (Arthur, 1990), in which

firms making profits invest in other areas and thereby make even greater profits. It is obvious

that high-tech firms and firms that focus on standardized products often benefit from preferential

attachment. The key is to have the first significant means of requisite variety. Take the example

of Apple: A creative software engineer designed the iPod; the firm aligned its strategy with the

new competitive context (content fits with context—Stevenson, 1999); internal variety matched

external variety; and explosive and increasing returns were the end result.

Proposition 4: The activities of SEFs cannot meaningfully turn disequilibrium into

equilibrium unless scalability dynamics emerge and function at all hierarchical levels. In other

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words, an SEF’s social entrepreneurial activities have to show evidence that one or more of the

foregoing scale-free dynamics are active across several levels, and/or one scale-free dynamic(s)

spreads across a hierarchical dead spot in the range of some other scale-free dynamic(s).

Proposition 4a: Scalability and transformation of an SEF is positively related to measurable

impact (Skoll, 2004)

4 IMPLICATIONS

Implications for Research.

This paper contributes to the increasing body of literature and research drawing from

complexity science to examine managerial issues in several ways. First, our research is

analytically based and builds theory in the under-theorized social entrepreneurship literature. Our

premise forms a baseline for future research linking complexity theory and social

entrepreneurship. Understanding of societal disequilibrium and how it creates tension among

parties involved is an important new theoretical development. Many social issues are not

resolved because the root problem is not understood. It is not merely a market failure or a

system’s failure—these failures can be remedied. Nevertheless, tension stemming from

disequilibrium can be costly and impact a society explosively if not resolved early on. Further,

we clarify the link between adaptive tension and the emergence of social entrepreneurial

activities—specifically social entrepreneurial firms (SEFs). This key understanding forms the

baseline for future research in adaptive tension—the study of tensions that can create both

positive and negative outcomes. Research is especially useful if where negative outcomes

already exist or are more likely. If so, in what ways does do adaptive tensions of various kinds

cause further deterioration in societal disequilibrium.

Second, comparison may be made between adaptive tension in existing technology and new

technology and subsequent firm-level strategic actions towards success; adaptive tension

32

between societal disequilibrium and equilibrium, and the subsequent emerging SEF activities

leading toward new order creation. This carries important implications as it may demonstrate

increased need for taking social entrepreneurship to higher level of importance—even treating it

as a new discipline, field, or area of scholarship. Are we examining social issues, managerial

issues, or entrepreneurial issues, or all three? Or, if we are examining strategic issues, should we

place these studies under the overarching theme of strategy, or not?

Third, future research may also extend the order-creation process, the timing, and other

moderators of the process. Here, we treat social entrepreneurial activities as mediators of the

order-creation process, but interesting points may be drawn about when SEF activities are

subject to moderating effects by either government, individual, church missionary work, and/or

private or public collaboration. Comparison of the effects carried out by different stakeholders

may be interesting and effective for future policy makers in this area. Social entrepreneurship is

no longer a locally focused and bounded activity; SEFs often are internationally based, receive

funding from multiple sources around the globe. And, needless to say, the factors that moderate

performance in one country may not work the same way or be equally effective in another.

Fourth, understanding the critical importance of performance by existing SEFs, encourages

further research in how to best to measure short-term and long-term performance is one of the

contributions in this research. Although some of the current SEFs show rather transparent linking

of rewards to performance, and some literature has stress the issue of performance measures to

be applied to SEFs, it is nevertheless true that treating transparent performance measurement as

critically important to the sustainability of the social enterprise is not yet fully appreciated. Our

research stresses this stance and argues that (1) although measuring performance is difficult, (2)

given the multiple stakeholders with their diverse interests and that (3) rating the success of SEFs

33

is being done in a different ways so as to compare the success of SEF performance against the

performance of commercial entrepreneurship, even so it must be done and done right. If

performance is not properly measured and reported, a particular SEF itself may become another

disequilibrium, tension will arise, and replacement SEF action will be needed to taken to create

relevant new disequilibrium dissipative structures.

Finally, our testable propositions serve as guidelines for future research that may be

conducted to collect data-bases useful for longitudinal, international, and multiple units of

analysis, as well as bases for formulating more interview questionnaires to obtain more enriched

data. After the emergence of SEFs, the importance of community, social network, and resource

management emerges. Complexity science offers important theoretical implications for these

issues. SEF activities often are being conducted in either small or large communities or societies.

The success of managing a community-wide disruption—like cholera in Zimbabwe and given its

resource deficiencies—lies in the ability to find resources within other complex adaptive systems

and generating requisite scalability so appropriate resources become available. This calls for

future research of SEF resource-related issues to be conducted via the lens of complexity

science.

Implications for Practice.

Our theoretical approach and its overarching model have importance implications for practice

in several ways. Our model draws from the complexity science perspective to explain the

rationale of emergence and the criticality of performance measures relating to SEFs. Ours is a

first attempt in the literature of social entrepreneurship and the practice of social enterprise to

provide a meaningful explanation. Our model and theoretical explanation help to provide an

insightful, useful, and relevant framework that social entrepreneurs can use to achieve their

34

social goals, overcome social issues in innovative ways, and become more alert, accountable,

and efficient in managing their resources. Important implications from our application of

complexity theory lead to relevant social management approaches in several ways.

First, management by tension. That chaotic tensions exist within society due to energy,

health, and resource disruptions offers social entrepreneurs the opportunity to create SEFs as

dissipative structures create positive opportunities for social entrepreneurs. Entrepreneurs often

chase tensions instead of trying to escape from them since they perceive them as opportunities.

What is more interesting is the power of self-organization emerging in the form of social

entrepreneurship activities as pockets of new order form (groups or individual or firms working

on social issues). Thus, social disequilibrium can be an engine of innovation, creativity, and

newness. Disequilibria may initially create small perturbations, but they can spiral up via

positive feedback and other scalability dynamics so as to have highly consequential and positive

impacts on a system or society. We argue that scalability dynamics lie at the core of socially

relevant engines of exploration (March, 1991) if managed successfully.

Second, creating scalability conditions. SEFs need to put scalability dynamics into play so as

to meet performance measures in the form of traditional financial reporting as well as social-

impact measuring and reporting. More specifically, we suggest that SEFs need to take advantage

of scalability (often via positive feedback) by (1) maximizing scalability dynamics so as to

achieve their long-run viability and growth so as to sustain social value over the long term; (2)

realize the importance of effective management, network building, and transparency of

operations, as these feed back to enhance sustainability; (3) comparing strategic tools and bench

marks with comparable SEF activities and performance, thereby improving their ability to obtain

35

future funding support—which enhances sustainability, and (4) use a set of relevant strategic

tools to set goals and realistically measure their performance.

Finally, working toward multilevel scale-free regularities and fractal dynamics. Complexity

theorists demonstrate that the future is unknowable and that it is impossible to determine what

“will be relevant for more than a rather short time period” (Stacey, 1995: 492). We suggest that

SEFs focus on the constant scalability of the social entity that emerges from chaotic dynamics

and systemic feedback loops. This approach may also help SEFs to maximize already

constrained resources without wasting valuable resources simply to maintain the status quo. As

organizational inertia sets in, changes in the marketplace and rapid change in need-based

requirement will tile SEFs toward failure—thereby adding to disequilibrium and adaptive tension

rather than ameliorating it. Even tiny initiating events (e.g., providing training, employee

benefits, exchange programs for caregivers, key individuals in an SEF, updates of project

methods, and accomplishments, etc.) can enhance positive outcomes and significant impact to

the beneficiaries. These can help SEFs avoid the organizational and cognitive inertia that

develops when long-term stability creates homogeneity over time.

5 CONCLUSION

Drawing on complexity science, we develop a conceptual framework that links the concept of

energy differential, phrase transition, adaptive tension, self organization, and scale-free theory to

inform and explain SEF’s emergence and the criticality of their performance as it leads to their

scalability and sustainability. We argue, first, that social disequilibrium emerges from disruptive

energy differentials within a society (for example poor vs. rich; clean water vs. contaminated

water), and that this imposed energy creates disruption-based tensions within the community.

When the disruptive energy becomes strong enough (tension increases within the community),

36

phrase transition processes begin (changes starts to be made); this is when adaptive tension result

(tension turns into positive feedback in a constructive form such as social entrepreneurial

activities). At this point dissipative structures—i.e., pockets of new order—appear. Adaptive

tension causes the agents (i.e., individual, group, or organizational members of the community)

to self-organize so as to give rise to emergent (dissipative) structures, i.e., new social

entrepreneurship in the form of SEFs. Second, SEFs have to rigorously provide transparent,

valid, and responsible financial and social impact performance criteria, since these are essential

for the longevity and impact of any social enterprise. And third, SEFs aiming to translate

disruptive tensions into long-term equilibria need to be able to draw in scalability dynamics at all

organizational levels. The self-organization process cannot meaningfully translate disequilibrium

into equilibrium unless scalability spirals up in useful proportions and appears at all levels of an

organization.

The primary contribution of this article is to explicate the linking of social entrepreneurial

activities to complexity theory so as to achieve meaningful theoretical legitimacy in our

understanding of the emergence and performance issues. Social entrepreneurship is becoming

popular in the body of literature and in the international entrepreneurship movement.

Unfortunately, key theorists hold countervailing opinions of how social entrepreneurship works,

the literature is fragmented, and approaches toward measuring performance are unclear and

debated. It appears increasing important to understand the rationale of SEF emergence and the

criticality of their performance toward achieving sustainability over the long term. We hope our

work has established a baseline for further work applying complexity science to social

entrepreneurial theorizing and empirical research. Especially, research is required that examines

optimal strategies for achieving superior SEF performance in different cultures worldwide.

37

Theorizing on these new trends in the literature in ways that serve academic rigor and social

economic relevance is critical if forward progress is to be accomplished.

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