ENTREPRENEURSHIP & REGIONAL DEVELOPMENT, 13 (2001) , 47± 63
Small enterprises as complex adaptive systems: a
methodological question?
TED FULLER* and PAUL MORAN**
*University of Durham Business School, Mill Hill Lane, Durham DH1 3LB,
UK, e-mail: [email protected]; and **Foundatio n for SME
Development, University of Durham, Mill Hill Lane, Durham DH1 3LB, UK
Complexity science constitutes an emerging post-positivist interdisciplinary ® eld of investigation
of dynamical systems in the natural and physical worlds. The central concept of complexity is
that interactions between parts of open systems create novel, unpredictable patterns, and that
while the history of the system is relevant in understanding its dynamic, the isolation of individual parts of the system (analysis) does not reveal the casual mechanisms in the system. It is
suggested that complexity science can inform our methodologies for investigating the social
sciences.
The paper explores whether complexity science oå ers ways of theory building that can take
account of pluralistic or interdisciplinary research in enterprise dynamics. The authors oå er a
model of six theorized ontological layers, derived from the canon of research literature within a
small enterprise domain, with boundaries at each end. It is suggested that dynamical concepts of
agency (adaption, evolution, ® tness, interdependence) coupled with the theory of evolutionary
autopoietic structures generate a plausible ® eld for the study of enterprise dynamics.
A focus on ontological and experimental adequacy is necessary to develop theory within this
framework. An appropriate methodology involves iterations between experimental forms of
scienti® c analysis and the grounding of emergent or evolving theories.
Keywords: complexity; complex adaptive systems; methodology; enterprise dynamics; small business; entrepreneurship; SMEs.
1. Introduction
In this paper, the authors articulate a number of reasons why complexity science has
value in small business and entrepreneurship research; it is developing as a postpositivistic approach and has considerable resonance with the substantive ® eld of
enterprise. The methodological possibilities for complexity are considered and it is
argued that there are at least two arenas in which it has value, as an interpretativist
lens and as an experimental medium. The authors outline how the analogies of
dynamical properties of open (complex adaptive) systems help to form useful research
methodologies for the study of entrepreneurshi p and small ® rms. In particular a focus
is provided for critiquing the apparent disjointed theory building in the entrepreneurship and small business domain. Issues of theoretical and ontological adequacy are
considered together with the continued need for grounding research in empirical
evidence, as well as existing knowledge. The authors tentatively suggest approaches
to this that places the entrepreneur at the centre of a research process.
This introduction continues with an overview of the complexity literature and its
apparent relevance to research in small business and entrepreneurship, and in
particular its methodological signi® cance. Next, complexity science is linked with
Entrepreneurshi p and Regional Development ISSN 0898± 5626 print/ISSN 1464± 5114 online # 2001 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
48
TED FULLER AND PAUL MORAN
organizationa l studies, both through the literature and by a discussion of dynamical
concepts that give rise to order and structure in open systems. It then moves on to
address the issue of small enterprises and introduces the idea of adaptive agents as a
model of entrepreneurial behaviour, which creates emergence. This analogy is shown
to be resonant with existing entrepreneurshi p literature, but takes us closer to a
methodology that appears to have the potential to address some of the ` garbage
can’ theoretical developments in incommensurate paradigms. The small enterprise
is modelled as existing in multiple ontologies simultaneously, and the signi® cant theoretical gaps become manifest as emergent properties, or symmetry-breakin g mechanisms, at each level. By adopting complexity dynamical concepts with these systemic
irreducible layers, one is able to construct a space for theoretical investigation. The
authors point to issues of theoretical and ontological adequacy in such an investigation, before giving two short examples of how this paradigm is reshaping their own
approaches to the analysis of empirical research.
Complexity science constitutes an emerging interdisciplinary ® eld of investigation
into the behaviour of a wide range of systems in the natural and physical worlds and
indeed in the silicon world inside computers (Casti 1997). The ® eld represents a
convergence (Pagels 1988) of several strands of scienti® c endeavour in diå erent
disciplines. It is strongly associated with the Santa Fe Institute in the USA
(Waldrop 1992, Lewin 1993) and the publication of a growing volume of accessible
works by scientists and science writers (Gell-Mann 1994, Goodwin 1994, Kelly 1994,
Coveney and High® eld 1995, Holland 1995b, 1998, Kauå man 1995), including those
that are somewhat sceptical or critical (Horgan 1996). This might be of passing
relevance only to small business and entrepreneurship researchers were it not the
case that complexity appears to have resonance with, and may have potential for
crossover to, the human social and organizationa l domains. Reasons for this are
given below.
Complexity science is a systematic paradigm, founded on observed similarities in
diverse dynamical systems. It illuminates meaning from dynamical perspective. The
essence of dynamical systems is that they are open and dissipative (Prigogine and
Stengers 1984), they do not follow the predictable entropic path of closed systems
tending to chaos, rather they move in patterns at the edge of order and chaos. This
state is far from equilibrium.
These properties are not con® ned to thermodynamic systems, but to many organic,
living systems. Similarly complex patterns can be observed everywhere in nature
(Gould 1989, Cohen and Stewart 1994, Coveney and High® eld 1995), and in social
systems (Luhmann 1986, Allen 1997). Complexity is not the study of chaos (which is a
result of entropy), nor is the study of randomness. It is a study of changing patterns
of order, self-organization or, (Wilden and Hammer 1987) constrained diversity.
The characteristics of complex adaptive systems are resonant with the observed
characteristics of small enterprises, at an organizationa l level and at other aggregate
levels, e.g. as a population. On the surface at least, the population of small enterprises
seems to resemble the characteristics that Holland (1995a: 2) ascribes to a complex
adaptive system, i.e.
(an) evolving perpetually novel world where there are many niches with no universal
optimum or competitor, [and where] anticipations change the course of the system, even
when they are not realised.
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
49
One important role of complexity science has been to act as a unifying language in a
wide range of substantive disciplines. Goodwin (1994: 158) for example refers to ` uni® cation’ of the sciences accelerated by complexity. This suggests that integrated,
interdisciplinary approaches become more important as science moves away from
reductionism to a concern with holism or contextualism (Cohen and Stewart 1994:
246). It is increasingly clear that the whole cannot be understood through atomization, by analysing down to the smallest component part, but must be understood as
substantially diå erent from the totality of the parts from which it is composed. For
example, the properties of water cannot be predicted from knowledge of the atoms of
which it is composed. Davies and Gribben (1991: 23) refer to this as a paradigm shift
as profound as any in scienti® c history. Wallerstein (1996) reports a similar call for
fundamental changes of perspective in the social sciences. This approach treats nature
and society as if they were ` onotologically open and historically constituted; hierarchy
structured, yet interactively complex; non-reductive and indeterminate’ (Reed and
Harvey 1992: 359). It is consistent with the obsolescence of positivism (Suppe 1977,
McKelvey 1999).
A number of authors (Reed and Harvey 1992, McKelvey 1999) have noted the
proximity of complexity to the epistemology of scienti® c realism (Suppe 1989,
Aronson et al. 1994), and in social sciences to critical realism (Bhaskar 1978,
Outhwaite 1987, Sayer 1992). Critical realism provides philosophical principles
upon which dynamical non-linear characteristics can be understood . These principles
allow knowledge that is socially constructed, and therefore fallible, that represents an
understanding of the world in which real mechanisms exist, but which can only be
sensed by their eå ects. For example, the appearance of novel structures and patterns
can be explained by a conception of contingent or latent powers inherent in the
interrelationships rather than by the external imposition of order. One epistemological
implication is that causality is not identi® ed from the observation of empirical regularities per se. Causality in a speci® c context may be traced by theory building using
concrete, intensive methods (Harrë 1979) but does not carry the same construct of
being generalizable that the notion of causality carries in social positivism.
This discourse of holism and anti-reductionism is not strongly articulated in
entrepreneurshi p and small business research. Critiques are limited, often characterized by frustration at a plethora of fragmented, over-simpli® ed and unconnected
® ndings (Gibb 1992, Harrison and Leitch 1994) or noted absences of an integrated
theoretical model or framework as a means of guiding grounded research (Low and
MacMillan 1988, Bygrave 1989). More caustically, the seemingly unconnected diversity of topic areas is characterized as a garbage can model (Ratnatung a and Romano
1997).
The theoretical framework that is emerging in complexity science, as a result of the
convergence of a large number of empirical studies and theorizing across a number of
disciplines, would appear to have relevance as an orienting device for entrepreneurship and small business research. In accepting this approach one is open to the possibility of a crossover of meaning between natural and human systems. This is plausible
if a social constructivist perspective of scienti® c theory is taken (Giddens 1984).
An approach that treats the dynamics of small enterprises as a complex adaptive
system may not necessarily lead to the establishment of a ` comprehensive theory’
(Gibb 1992). It may oå er an integrating framework that provides the potential
for interdisciplinary convergence (Ripsas 1998) and connection with other ® elds of
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TED FULLER AND PAUL MORAN
scienti® c endeavour concerned with similar phenomena e.g. how systems grow and
evolve, how order emerges from disorder. It oå ers new descriptive and modelling
possibilities, and paths for understanding dynamics in the ® eld of enterprise.
The ® nal reason for promulgating the idea of small enterprise dynamics as complex
adaptive systems is for the curiosity value. So little is understood of these dynamics
that investigation will uncover new knowledge. Whether the notion of complexity withstands the social evolutionary pressure of acceptable theory is a matter
for history.
The focus for this paper is on the dynamics of small enterprises. These enterprises
are characterized as small-scale, independent entities existing in relationship to and
dependent on other entities in the socio-economic sphere or business ecosystem
(including other small enterprises). The interconnected character of such enterprises
within complex economic webs (Lewin and Regine 1999), often of a very localized
nature, makes it diæ cult to isolate and measure them precisely. However, one of the
key indicators is their status as owner-managed businesses (i.e. ownership and management are vested in the same person or people), since this implies diå erentiation,
diversity, and some in¯ uence over direction (stemming from for example strategic
intent). In eå ect each enterprise is itself a complex adaptive system in¯ uenced, for
example, by the character and intentions of the key human agent in the system (the
owner-manager), but it can also be construed as an agent in a larger complex adaptive
system (i.e. the wider network or business ecosystem).
At this stage a distinction is not made between owner-manager and entrepreneur.
Although there have been attempts to typologize diå erences (Smith 1967, Dunkelberg
and Cooper 1982, McClelland 1987, Chell et al. 1991, Stewart et al. 1999), the present
concern is an explanation of small enterprise dynamics. It is proposed that entrepreneurial actions involve the creation of a new business entity of transformation of an
existing business entity. The eå ects of diå erences in behaviour (e.g. entrepreneurial
behaviour) identi® ed through a complexity approach may, of course, be substantive
and helpful in addressing the simplistic dichotomy between owner-manage r and
entrepreneur and the narrowly grounded typologies that currently exist.
The authors suggest most of the published work to date on complexity in the social
sciences has attempted to map complexity theory, grounded in natural sciences,
directly onto the empirical evidence and substantive practices in the social sciences.
Tests of relevance are therefore in the realm of ontological adequacy. That is, does it
map closely onto our observations and thereby help us to understand what exists? For
example, do the characteristics of an ant population or a thermodynamic chemical
reaction adequately correspond with our understanding of human social situations?;
or rather does the language used to describe these characteristics have adequacy of
meaning in the social realm? As suggested above, there have been some diæ culties
with this in practice.
Following McKelvey’ s ideas on model-centred science (McKelvey 2000), the
authors suggest that a more fruitful approach is to let complexity science inform
our methodologies for investigating the social sciences. The remainder of this paper
develops a tentative methodology explicitly constructed from the principles of complex adaptive systems. ` Methodology is about how we conceptualise, theorise and
abstract’ (Sayer 1992: 2) our models of explanation, understanding, research design
and methods of analysis.
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
51
2. Complexity in management and organization studies –
dynamical concepts
Complexity science is reaching the ® elds of organization and management studies. A
number of studies have explored the application of complexity models, metaphors and
methods to organizations and management (Wheatley 1992, McMaster 1995, Merry,
1995, Stacey 1996, Lissack 1997). The weakness of these texts from a perspective of
theoretical adequacy is that they are metaphorical. They use the metaphors drawn
from the language of complexity science and attempt to map these directly onto
management practice. As Rosenhead (1998: 10) points out there is no evidence that
these ` complexity theory-based prescriptions for management style, structure and process do produce the results claimed for them’ . Tsoukas (1998) argues that analogies
are socially constructed and that metaphors can cross discourses and legitimately take
on new meanings if they resonate with the actors. He points to the need for alternative
imagery and for narrative, unfolding descriptions to develop knowledge in organization studies. Thus from this methodological perspective, complexity provides patterns from which analogical reasoning can be constructed through the language of
metaphor. Metaphor can become meaning and so in¯ uence interpretation, behaviour
and intention (Fuller 1999).
Stevenson and Harmling (1990) spell out some of the post-positivis t issues in both
the study and practice of entrepreneurship, inspired by the notion of chaos theory.
Chaos theory is a mathematical and deterministic concept, and such is unlikely to
yield insights into dynamical open systems (Cohen and Stewart 1994). However, the
work with chaotic structures has contributed to the development of complexity
science.
Open systems are ones in which energy is transported in and entropy is transported
out. Unlike entropic, closed systems, dissipative structures do not necessarily become
more chaotic, but they dissipate entropy to outside the system. According to Harvey
and Reed (1996: 306), sustainable dissipative systems:
. convert free energy into more elaborate forms of internal construction;
. transport thermal disorder (positive entropy) out of the system (into the environment);
. resulting in net negative entropy giving rise to evolution; and
. far from equilibrium, i.e. constantly changing, never reaching equilibrium and
seldom static.
This idea of a system retaining energy through the formation of additional structure
resonates with Anderson’ s ideas of symmetry breaking (Anderson 1972). This implies
that dynamical systems do not become ever more complex, in a ¯ at sense of more
features, but create new structures, which behave in ways that are diå erent from the
ways that their generative components behave. These structures are therefore onotologically distinct from their causes.
Anderson (1972) suggests that each layer of reality is structurally unique, operates
according to its own laws and demands its own investigativ e protocols. Symmetrybreaking mechanisms provide the foundations for the emergence of new levels of
reality from established levels. Harvey and Reed (1996: 297) suggests that the resulting pyramiding complexities ` produced by the geometry of broken symmetries is the
source of our ever-deepening modern division of scienti® c labour’ . Each new layer
organizes itself around its own set of irreducible principles, and thus becomes a distinct
52
TED FULLER AND PAUL MORAN
ontology. This use of word ontology carries the meaning used by Burrell and Morgan
(1979), i.e. beliefs about the nature of reality, or a systematic account of existence, but
adopts a representational perspective. This is, an ontology is ` an explicit speci® cation
of a conceptualization’ (Gruber 1993: 199).
The creation of these structures is not imposed from outside the system, but is a
result of the dynamics of the system. The production of results from the Prigogine and
Lefever (Prigogine and Stengers 1984) experiments showed that non-linearity occurs
in a chemical reaction if a product catalyses its own production, a feedback process
known as autocatalysis . In living complex adaptive systems this self-ordering, or selforganization, is called autopoiesis (Maturana and Varela 1980, Zeleny 1981, Mingers
1995). Luhmann’ s work (1986) is seminal in linking autopoiesis to social systems.
The patterning concepts associated with self-organization are emergence, adaption
and evolution. The result of self-organization is emergent properties. For example, the
¯ ocking of birds, the shape of cities, the regular behaviour of ants and the polluting
qualities of consumption can be observed in a holistic sense as ordered patterns that
emerge from aggregate individual behaviour.
Evolution is a construct grounded in inter-generational diå erences, i.e. one generation evolves from the previous one, through some form of genetic crossover, or occasional mutation, creating a bifurcation in trajectories of the species (and thus subspecies). Evolution also implies selection; that those organisms unlucky enough to
evolve as less ® t to meet the demands of their environment are selected out.
Evolution is not linear. The Cambrian explosion seems to have been a peak in the
process of bifurcation of species and a kind of punctuation mark in otherwise wellde® ned steps in the evolution of individual species (Gould 1989). The implication is
that far from ever existing in a stable state (equilibrium), life organizes spontaneously
into a characteristic and much more precarious critical state (Bak 1996). Such emergence does not require external causes or catastrophes .
The concept of adaptation implies learning: where behaviour is learnt, often
through trial and error, and passed on in time through various forms (e.g. culture,
rules, social process, knowledge, etc.). Adaptation implies a more conscious or sentient
response to the environment, and memory. Whatever the mechanism of change, the
result is what Kauå man (1995: ch.8) calls a ® tness landscape. This is not a spatial
concept, although it is often mistaken for such, but a descriptive model of the relative
® tness of diå erent interconnected actors in a system.
Each of these dynamical notions of emergence, adaption and evolution have a
common characteristic, which is that they are irreversible ± the arrow of time is one
way. Unlike a linear Newtonian pendulum, the complex adaptive system does not
move back to an historic position. Its present position is however the result of its
history. Its history creates the conditions that project the system in a particular
trajectory. Thus our understanding of systems is an evolutionary tree of successive
structures (Allen 1997: 17).
3. Modelling complexity and small enterprise
The interpretation of complexity can be through many lenses. The discussion here
centres on modelling, not in a narrow sense of the mathematical model but in a wider
sense of representation. Models, images, symbols or metaphors are representations
that we make of the world. ` Contrary to idealist principles, our statements about
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
53
the world are not the world as such, the two must never be confused’ (Harvey and
Reed 1996: 301).
The central concept of complexity is that interactions between parts of system create
novel, unpredictable patterns, and that while the history of the system is relevant in
understanding its dynamic, the isolation of individual parts of the system (analysis)
does not reveal the causal mechanisms in the system. Representation, e.g. by modelling, typically objecti® es or constructs features in the system and describes the relationship of these objects in the system. The non-stati c nature of the relationships is
represented by the introduction of a motive force. The concept of agency is central
to such representation (Axelrod 1997, Casti 1997). Agents make up the population of
the system. Each agent is in receipt of local information, which means that it does not
share the precise information that all agents have in the space in which it exists, nor
does it have an overview of the whole of that space. Each agent is intelligent, i.e. uses
mechanisms to direct what it does (often called rules in this domain), and is adaptive,
i.e. able to change what it does and the rules it follows. The agent has become the
cornerstone of modelling and simulating evolutionary systems through inter-generational relationships. The above description of a system of interacting agents is resonant
with social theories of structure and agency (Giddens 1984).
The notion of the small enterprise, or alternatively the entrepreneur (Rydal 1996),
as an adaptive agent, is highly resonant with Schumpeterian notions of entrepreneurial innovation. Indeed, Schumpeter’ s work stimulated Nelson and Winter’ s (1982)
contribution to evolutionary economics. Methodologically , it seems prudent to treat
the notion of enterprise-as-agen t as a problematic (a questionable assumption), rather
than an axiom. The enterprise per se may not be an appropriate unit of analysis
because the ® rm is a multi-layered complex structure. For example, Gilles et al.
(1998) adopted the concept of the locus of expertise as a unit of analysis in research
on industrial co-operation between small enterprises.
3.1. The analogy with enterprise dynamics
The analogy between population ecologies and entrepreneurship is not new in the
entrepreneurshi p literature (Green® eld and Strickon 1986: 14, Low and MacMillian
1988: 144). Evolutionary and ecological metaphors of emergence, ® tness and mimicry
resonate with observations of the large number of smaller enterprises in the economy.
They vary considerably in size and sector activity, in their ownership, their location
and the markets served. Each business is diå erent. Each has its own initial conditions
and each incurs a number of accidents in their temporal path. Given that entrepreneurs
are innovative, then many businesses will operate with their own rules, as well as
complying (more or less) to more general rules. Business strategies explicitly operationalize the metaphor of niche specialization.
Each enterprise is part of bigger economic and social systems. All business interact
with key economic stakeholders, such as banks and government agencies. Businesses
operate in a regulated environment, providing at least some of the rules of behaviour.
The mimicry of doing business, i.e. copy-cat methods and the diå usion of information,
through benchmarking and best practice guides, is ubiquitous. Swarming is commonplace, for example physically in business districts and clusters (Gillies et al. 1998), or
the use of particular technologies (North et al. 1992). In addition energy, in the form of
54
TED FULLER AND PAUL MORAN
cash, ¯ ows within the system, with those enterprises that do not maintain cash¯ ow
ceasing to operate.
These apparent properties of small enterprises suggest that the analogy of a complex
adaptive system can provide a conceptual framework to understand or illuminate the
dynamics of small enterprises.
3.2 Towards a methodology
This necessarily brief overview of complexity leads to a consideration of methodological issues with respect to understanding enterprise dynamics. It has been suggested
that complexity science oå ers dynamical analogies, such as evolution and adaption,
that are already recognized in the organizationa l and enterprise literature. What the
authors now wish to explore is whether complexity science also oå ers ways of theory
building that can take account of pluralistic or interdisciplinary research. This will be
addressed in two ways. First, by examining the idea of hierarchical or nested structures in the enterprise domain revealed by pluralism in research. The second way is to
discuss the adequacy of meaning that could be generated by a modelling approach.
The strong ideas of symmetry-breakin g and the creation of novel ontological layers
provide a theoretical dimension to investigate multiple layers of enterprise characteristics and dynamics. Operationalizing this, the authors suggest that enterprise may be
understood to exist simultaneously at many layers, possibly unconnected, and each
having diå erent meaning and diå erent characteristics. This is partly why it is so
diæ cult to operationalize interdisciplinary research work; each area is concerned
with a diå erent ontology, not just diå erent perspectives on the same phenomenon,
as well as having possible incommensurate epistemological bases.
Figure 1 illustrates six theorized ontological layers, derived from the canon of
research literature within the small enterprise domain, and the boundaries at each
Figure 1. Posited ontological layers in the small Ž rm domain.
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
55
end. For small enterprises the relevant layers are posited to range from micro-economies to individual mental models and cognitions (i.e. of the entrepreneur or ownermanager).
The layers re¯ ect key areas of research and debate in the small business ® eld, i.e.
networks/clusters (Johannission 1987, 1995 Curran et al. 1992, Hansen 1995,
Chaston 1996); external relationships in the value chain (Larson 1992, Azzone
and Noci 1998, Lewis and Fuller 1998, Hall and Andriani 1999, Holmstrom
1999); business model/strategy/vision, etc. (Gibb and Scott 1985, Miller and
Toulouse 1986, Atherton and Hannon 1997); internal resourses/processes
(Hendry et al. 1995, Garnsey 1998); individual capabilities and motivations
(Carsrud et al. 1989, Harrison and Leitch 1994, Bellu and Sherman 1995, Miner
1997); individual cognitions, etc (McGaV ey and Christy 1975, Chell et al. 1991,
Gatewood et al. 1995, Moran 1998). Beyond the top boundary is where aggregations become superordinate structures such as the macro-economy or global economy. Below the bottom boundary is where physiology, biochemistry and so on
down to the quantum level in uence individual cognitions, mental models, etc.
The model in ® gure 1 implies a succession of systems, each one dependent upon the
lower one for its existence. At the same time, each system behaves diå erently from the
lower levels, and is understood by diå erent models or theories (hence the idea of
separate onotologies). In a complexity methodology, each layer might be understood
as an emergent property of the existence of the system or layer below. The authors
recognize this to be rather simplistic, in that other conditions may be necessary for
emergence, for example a legal system for recognizing small enterprises. Also, the
relative position of the ` layers’ is not sacrosanct at this stage of analysis, rather they
are stylized. However, to understand that these structures have emergent properties,
i.e. give rise to new structures, is to embrace a complexity methodology. This informs
us that the emergent systematic layer has a distinct ontology ± it cannot be understood
by atomization, by reduction to its components parts. The implications of this, for
example, is that one cannot understand the behaviour of networks by understanding
the behaviour of individual ® rms. However, one can seek to identify the causes of
emergence and also seek to synthesize the dynamical behaviour of systems of agents in
a particular ontology. This reinforces the importance of the bottom-up nature of
complexity science (Epstein and Axtell 1996).
The speculative model in ® gure 1 is drawn to explore emergent properties. What, if
any are the interactions between layers, or how do emergent properties arise from the
lower-level micro-state interactions? Focusing solely within one layer may result in a
limited view of the overall phenomenon and of how the reality of one layer is due to
behaviour or events at the layer below reaching some critical threshold (or phase
transition) suæ cient to create new, emergent structure or form. For instance, while
the ` business model’ within the small enterprises domain (layer 3) may be legitimately
studied in its own right, only a partial understanding will be achieved if the forces and
in¯ uences that give rise to it at lower ontological layers are ignored or assumed away
as not being germane.
One question that arises from the above is to what extent current theories in these
ontologies account for mechanisms with causal properties. Do these account for emergence? How might they be operationalized in experiments or studies?
The methodological issue for operationalizing complexity science in this respect is
the degree to which inter-agent (stakeholder) relationships can be modelled in a
meaningful way. Game-theory does give theoretical insights, but lacks ontological
56
TED FULLER AND PAUL MORAN
adequacy with respect to stakeholders theories of small enterprises. For example,
Fuller and Lewis (1999) identify diversity and asymmetry in the relationship
approaches of owner-managers.
This leads to a further problematic, which is the goal of the enterprise. Complexity
theory tends to use the concept of ® tness as synonymous with the ability to survive
over competitors. This is clearly resonant with notions of strategic competitiveness.
The issue of what constitutes ® tness in varying conditions, and how this is maintained,
is a central research question in many domains. In entrepreneurship the normative
question of how ® tness can be maintained is also a central issue.
Fitness may also be judged at an agent level or at a multi-agent systemic level.
Innovation and selection may be seen as inherently a good thing in which some
¯ ourish and others die. The diå ering perspective of economists seeking eæ cient
markets, bankers seeking to minimize risk, owner-managers trying not to lose
money or trying to achieve personal ambitions each provide diå erent and somewhat
judgemental interpretations of ® tness. Even at the individual ® rm level, behaviour
that maintains survival can have unwanted social consequences, e.g. unemployment of
staå . The ® tness of one agent in a system and the ® tness of the system overall may not
be connected.
The authors suggest that the above dynamical concepts of agency (adaption, evolution, ® tness, inter-dependence), coupled with the theory of evolutionary autopoietic
structures, provide a frame of reference for a study of enterprise dynamics.
. Adaption is an overarching concept of learning, memory and change over time.
. Evolution is an overarching concept of alternatives, bifurcation, diversity and
selectivity.
. Fitness is an overarching concept of goals and relativism.
. Inter-dependance is an overarching concept of relationships and co-evolution
between agents in the system.
Each concept is underpinned by temporal relativism± the arrow of time.
Drawing on the previously discussed concepts of autopoiesis and symmetry breaking, conceptually it would be expected that the dynamics associated with complex
adaptive systems would be related to the linking of hierarchical (emergent) ontological structures. Thus one can generate a plausible ® eld of study by the simple crosstabulation of these two sets of characteristics, as shown in ® gure 2. To illustrate this,
the authors have generated examples of the type of question or issue germane to areas
of this space, using an arbitrary pattern (® gure 3). The range of research questions
generated in this conceptual space and their linkages require further work and are
outside the scope of this paper.
One diæ culty with historic or longitudinal empirical approaches to complexity
studies is that of accidental regularities, that is, observed events that are not the consistent
results of an identi® able force. In exemplifying this McKelvey (2000: 217) cites the
® nding, ` ® rms producing rent were market pioneers,’ identi® ed post hoc, is now shown
to have been an accidental regularity (Tellis and Golder 1996).
Longitudinal empirical observations can yield descriptive accounts of dynamics,
and of actors’ perspectives or constructions of dynamics. Descriptions of competitive
® tness, of the evolution of an organization or a sector, or of entrepreneurial adaptions
are manifold in the research literature. The ontological adequacy of such description is
often assessed by its instrumental reliability with actors, for example if it accords with
their perceptions or it helps to explain a sensed phenomenon. However, the identi® ca-
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
57
Figure 2. Ontological levels tabulated with complexity dynamical concepts (for
question reå ered to by numer, see Ž gure 3)
Figure 3. Some research questions relevant to the Ž eld of study generated
(refer to Ž gure 2)
58
TED FULLER AND PAUL MORAN
tion of casual mechanisms (Bhaskar 1978) and diå erentiating these from accidental
regularities is weak. The state of enterprise research is such that one does not see
explicit reference to intensive (Harrë 1979) analysis that seeks to reason about the
way in which things work in experimental and contextual conditions. It is such
insights into how things work, identifying salient features in systems, that are necessary
for theory building. Insight per se may be theoretically accidental, even if instrumentally adequate. The question is whether complexity theory provides ways to examine
the theoretical adequacy of such insights.
McKelvey (2000) argues that modelling provides a way of developing theory and
testing its adequacy through counterfactual experimentation by establishing a range
of alternative (if . . . then . . .) conditions to see whether the theory holds. Axelrod
(1997) and Casti (1997), amongst others, have established agent-based models as a
central method for experimenting with complex dynamical systems. Such an application would be novel in the ® eld of entrepreneurship. The issue that arises from the
analysis in this paper is whether we have the adequate descriptive power of initial
conditions and inter-agent relationships.
4. Possible applications: two examples
The authors brie¯ y give two examples of work in progress by them in the ® eld of
enterprise dynamics and how this is constructed within a complexity paradigm.
The notion of the ® rm as being part of a wider ecology or nexus of stakeholder
relationships and actions (Fuller 1997) is signi® cant in theorizing the small enterprise.
The nature of these relationships is not well articulated in the literature, although it is
experienced in everyday actions. Its representation in agency theory (Williamson
1991) as a nexus of contracts does not adequately take account of qualitative or
non-economic factors. The nature of the relationship between the environment and
the small ® rm or various aggregations of small ® rms is a complex issue and not
explained by any single substantive theory. Naman and Slevin (1993) identi® ed a
relationship between organizationa l complexity and environmental complexity. Gibb
(1993) provides a descriptive model of the ® rm as having various relationships with a
range of organizations. Mitchell and Agle (1995, 1997) set these relationships in a
theoretical framework as ` stakeholder relationships’ . Stakeholders include owners,
employees, customers, suppliers, investors and lenders. Larson (1992) identi® ed the
(often thwarted) long-term strategy that small enterprises have in key relationships.
The main systematic concept of small enterprises is however networks ( Johannisson
1987, Lorenzoni and Ornati 1988, Castells 1996, Jarillo 1998). These studies stress the
importance of both social and economic rationale for the relationships.
Fuller and Lewis (1999) have focused on the ways in which the everyday relationship between ® rms and their stakeholders, in particular their customers, are impacted
by their use of technology. The relationship strategies identi® ed give insights into the
diverse re¯ exive responses to the changing environment, and shifts in the ® tness landscape. As this line of research develops it can begin to provide answers to questions 3
and 4 in particular in ® gure 3.
From the perspective of a complexity methodology, the study suggests strategies of
the agent (i.e. diå erent relationship strategies adopted by the ® rm) as a casual
mechanism of aggregate patterns of technology adoption. Not that aggregate adoption
is the same as that of the individual ® rm, but that it is generated by these strategies.
SMALL ENTERPRISES AS COMPLEX ADAPTIVE SYSTEMS
59
The work to date had identi® ed diå erent types of relationship strategies. The strategies provide ontological adequacy in terms of accurate representation of experienced
behaviour. What is proposed is to abstract rules of inter-relational behaviour from
these strategies, which may provide adequacy for modelling systems of technology
adoption.
Moran (1998) has focused on developing insight into the relationship between the
` intra-personal world’ of small business owner-managers and their ` extra-personal ’
business orientation and performance (layer 5 and 6 in ® gure 1). This has helped to
reveal some of the ` schemata’ that ` agents’ use to guide their actions in the business
sphere and how these relate their own propensities, values, goals, etc. Certain characteristics have been found to link quite strongly with diå erent levels of ` growth
orientation’ and these have been found in turn to link with actual performance
over time. This type of longitudinal study is very helpful in revealing the dynamics
of business development and change and how these are in¯ uenced by the choices,
preferences and orientation of the key agent in the system. In time, ` rules’ for adaption
and learning linked to the ® tness of the business entity (system) may be derived and
tested further through an interactive modelling and testing process. Derivation of such
models and insights may be helpful in enabling business owners and their advisers to
understand the nature and dynamics of business entities in a co-evolutionar y context
and structure their strategic behaviour and responses accordingly.
The key features of the research are that:
. it is longitudinal and can thus provide a descriptive sense of change in the case
of these businesses and owner-managers;
. it attempts to provide an objective assessment of ` initial conditions’ through the
employment of the GO criterion;
. it is concerned with the ` trajectory’ of development and how this is perceived
and in¯ uenced by the key agents in the system;
. it is concerned with the agents’ perspective of the ` system’ they operate in and
what extent this perspective in¯ uences or guides their ` strategy’ (adaptive moves);
and
. it attempts to uncover some of the ` intrapersonal’ in¯ uences on the dynamics
of small business development and how these inform the owner-manager’ s
perspectives and actions.
The practical output from this research could be particular ` sense-making’ tools,
which could be used by owner-managers themselves or their advisers to understand
their situation better and improve their ability to make better adaptive moves. There
might also be the opportunity aå orded by the building of dynamical models to explore
alternative ` trajectories’ of business growth/development at particular critical junctures in order to aid decision-making.
These examples follow a trend already apparent in entrepreneurship literature
(Ratnatung a and Romano 1997) of re¯ ective and grounded theory-building,
involving the actors themselves in the process of analysis and interpretation. The
methodological diå erence, informed by complexity, places the interpretation in a
dynamical self-organizing systemic framework. The examination of causal mechanisms through experimental simulation and ontological resonance is an aspiration of
this methodology.
60
TED FULLER AND PAUL MORAN
5. Conclusion and implications
Complexity is a new science that forms a nexus for exploratory scienti® c approaches,
such as open system agent-based modelling, and gives new life to ideas of social
systems as analogous with natural systems. It arises in a post-positivis t era and is
resonant with scienti® c and critical realism, philosophies that accept the existential
real and the fallibility of human knowledge relative to the real. Such thinking, it is
argued, should inform research in the ® eld of small business and entrepreneurship.
To date the application of complexity in the social sciences, including management,
has been in the form of metaphorical descriptions, which while evidently resonant
with observations lack theoretical adequacy. This has not stopped authors and practitioners from promulgating fashionable management practices in the name of complexity (or even chaos).
Methodologically , complexity is systemic in principle. Theorizing, conceptualizing
and abstractin g meaning is model-centred. Synthesis with dynamics, rather than
analysis is required as a methodology for generative, emergent and co-evolutionar y
phenomena, where historicity provides only one empirical set of data however diverse
its construction by diå erent observers. The reasons for the existence of these data are
perhaps better understood by contemplating multiple possible histories or futures.
Historic analysis and insightful heuristics gained from deep knowledge do provide
the frame of reference for multiple outcomes. Thus, the authors suggest that interpreting our knowledge of the small ® rms’ domain through a metaphorical language of
complexity may provide building blocks for explaining behaviour in terms of complex
adaptive systems and for modelling these to explore alternative and plausible future
behaviours. These metaphors are used analogically to produce a conceptual framework that links plural disciplined research with the dynamics of complex adaptive
systems.
In operationalizing this, a number of questions are raised that have no theoretical
by adequate answers in the current small ® rms research literature. The authors believe
that the enterprise of understanding the small ® rms’ domain can assimilate the conceptions of complex adaptive systems, and that if these evolve through critique and
testing, they will contribute to more sustainable small ® rms in society.
A cknowledgements
The authors acknowledge the ® nancial support of the Economic and Social Research
Council, project reference RO 22250070 (to Fuller), which contributed to this paper.
They would also like to thank the reviewers whose helpful comments helped to
improve the paper considerably.
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