Building Theory about Evolution of Organizational Change Patterns
Abstract
This paper explores whether something can be said about the likely evolution of organizational
change patterns. It addresses such questions as (1) whether some patterns are more likely to
remain constant and others are more likely to evolve into different patterns and (2) whether some
patterns are likely to evolve more quickly than others. In other words, it considers whether some
patterns may be more (un)stable than others. If an organizational population performing in an
organizational pattern changes to a different pattern, this paper also explores the likely dynamics
at work, models what the changes might be and develops hypotheses about those changes.
Key words: innovation, organizational evolution, evolution of organizational change patterns,
organizational change process, organizational change hypotheses
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Introduction
The concept of patterns is used primarily in the study of complexity (Waldrop, 1992; Gell-Mann,
1995; Capra, 1996) and complex phenomena like organizations (Perrow, 1986; Rogers, et al.,
2005; Glor, 2007a). Some authors have suggested that nothing is predictable about
organizational patterns. Falconer (2002), for example, suggested that (1) Change is not managed,
(2) Change is not linear, (3) Change is not formalizable, and (4) Change is not discrete. He held
that “It is probably better to consider the epiphanies of change as singularities, as the appearance
of highly unstable states of order that are then as quickly lost” (Falconer, 2002: 126). He
recommended a “frame of complexity, openness, continuousness, and emergence” that can be
encapsulated in the concept of patterns, adding:
Pattern describes a knowledge metaphor, which encompasses instantiated artefacts, that
is as closely analogous as practicable to the thought patterns envisaged as tacit mental
metaphors….A business pattern essentially lays out a metaphorical device for the capture
and reuse of explicit organizational knowledge (Falconer, 2002: 127;Falconer, 2000).
Having defined a pattern as a way of thinking, he went on, however, to describe it as being “in
the domain of circumstances and behaviour that characterizes and defines a general business
milieu” (emphasis added). (Falconer, 2002: 127). This elevated consideration several conceptual
levels by referring to a pattern in a population, namely, businesses.
Falconer recommended “using patterns as the operational metaphor for observing,
understanding, and expressing [organizational] change, and using the pattern language that
would emerge both as the representation of the inherent meaning of the change under
consideration and, conceptually, as the environment in/with which it interacts” (2002: 128). He
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indicates that “This solution would exhibit the following characteristics: (1) Open, holistic,
continuous, nonlinear, flexible, and adaptable; (2) Having no implication of “management,”
structuredness, prescription, or methodology; (3) Not able to be modeled or described in its
essence; and (4) Driven by emergent phenomena within the change landscape” (Falconer, 2002:
128-129). While Falconer sees organizations as changing in patterns, his approach to
organizational change patterns would not permit much to be said about how patterns change.
Kauffman (1995: chapter 4), on the other hand, found patterns in change. He explained
biological evolution, a complex phenomenon, as having two sources of order, not just the one
that Charles Darwin identified. Darwin saw genes as the source of order and evolution as the
source of change in biological systems. Kauffman explained that genes emerged as a means of
creating order in higher order living beings but that the development of genes, and their guiding
relationship with living things, is a highly improbable development. Moreover, genes emerged
well after life evolved on Earth. An earlier source of order is needed to explain the emergence
and early evolution of life. He identified spontaneous (emergent or autocatalytic) order as this
other form. It emerges without intervention, “for free,” in sufficiently complex environments with
limits. He found that “astonishingly simple rules, or constraints, suffice to ensure that unexpected
and profound dynamical order emerges spontaneously” (Kauffman, 1995: 74). Kauffman
confirmed this natural emergence of order both analytically and through agent-based modelling.
The natural tendency of phenomena involving living beings, including organizations, is to grow
more complex; Kauffman demonstrated that order will emerge in such environments. Kauffman
also showed that autocatalytic evolution is more likely in more complex environments and less
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likely in less complex environments. Evolution requires heritable variations; evolution is
therefore supported by heterogeneity.
Rogers, et al. (2005: 20) supported Ross Ashby’s (1956) notion of a law of requisite variety
which “posits that system variation needs to match the corresponding features of environmental
demands if organization and collective action are to be effective” (Rogers et al., 2005: 20).
Kauffman (1995) suggested that evolution requires variation, the capacity for self-organized
emergence and selection. Organizations exhibit all of these characteristics. Glor explored these
three elements in organizations (2007a, b).
Like Falconer, Glor saw patterns as complex, open, continuous, and emergent, but she also saw
them as ways of functioning that organizations and organizational populations tend typically to
assume, as individual motivation, organizational culture and the challenge of implementation
interact and as the organization interacts with its environment. “Each interaction is unique, yet
the interactions tend to form into patterns, perhaps in a manner conceptually similar to those
produced by chaos theory” (Glor, 2001a). Glor agreed with Bandura that the inclination to adopt
innovations (a form of change) should be considered "in terms of controlling conditions rather
than in terms of types of people" (Bandura, 1977: 54). Glor found these elements formed eight
organizational change patterns. This inclination toward patterns of organizational behaviour has
an important impact on both organizational and organizational population behaviour and change.
Seeing patterns this way might allow some room for making statements about the evolution of
organizational and organizational population innovation and change patterns.
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This paper explores and links several issues related to the process of change (not the content)
(Barnett and Carroll, 1995) in organizations and organizational patterns: (1) Why do
organizations change little/considerably? (2) What are the patterns of organizational change? (3)
Why do organizational patterns change/ not change? (4) If patterns change, how might they
change? What dynamics could be at work? Five are discussed. How much would they change?
Are organizational patterns likely (eventually) to evolve into different patterns? If the answer is
“yes,” can we say anything about which patterns would change and which patterns they are
likely to evolve into? This might help illuminate whether organizational change patterns can be
expected to survive or whether some of them should be seen as short-term, transitional states.
The discussion addresses changes in organizational populations and their patterns more than
change in individual organizations. It takes a broad look at the patterns of change in
organizational populations. Organizational populations are such things as an industry, all the
newspapers in a country, all the members of an NPS that includes a population, and individual
governments. Built on Kauffman’s (1995) identification of the patterns of evolution of complex
systems, the paper uses the eight self-organized patterns of functioning of organizations and
populations that Glor identified (2001a, b) and her methods for studying them.
Using Glor’s scores for the complexity of the patterns (Glor, 2007b), the eight organizational
population change patterns are examined to see what would be required for the patterns to
change and whether the patterns can be distinguished in terms of their comparative likelihood
and rate of change. If they can be distinguished, something can be said about which patterns are
more likely to change and which are likely to change faster. The analysis has five aspects. The
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starting point is an examination of the direction in which change is likely to occur, followed by
consideration of the magnitude of change required to change to another pattern, and finally
basins of attraction, likely incidence of change, and the speed at which patterns are likely to
change. A new analysis considers where the patterns sit within their pattern complexity score
ranges—close to or far from other patterns: The eight patterns’ scores can exist in three ranges—
close to the next less complex pattern; in the middle of the range, where considerable change is
required to change patterns; or near the next most complex pattern. This describes the magnitude
of change required to change a pattern. As Kauffman indicated, change is more likely in the
more complex areas of the ranges. At the same time, there is also order in patterns. Because
Glor’s patterns describe organizational populations that are changing, it is self-evident that they
are capable of at least some change. People may be able to intervene in organizational patterns to
reduce change and complexity but constraint is also needed to produce evolution (Kauffman,
1995). The results of the analysis are then used to judge the overall effect of the several forces at
work, to follow how the organizational change patterns would likely evolve, and to develop
probabilistic hypotheses about how they would likely evolve.
Issues
Issue 1: Why do organizations change little/change considerably?
A model of organizational population evolution needs to account for both inertia and change in
organizations and organizational populations. There is literature on both topics.
Why do organizations stay the same?
This question is addressed in the institutional (isomorphism), complexity, organizational ecology
and complexity literatures. The institutional (e.g. Selznick, 1957; Perrow, 1986) and neo-
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institutional literature, with its link to structural-functionalism (e.g. DiMaggio and Powell, 1983;
Powell and DiMaggio, 1991; Frumkin and Galaskiewicz, 2004; Peters, 2005; Pursey, Heugens
and Lander, 2009) provide insight into organizational and population inertia. While this literature
does not deny any change, the main explanations for lack of change identified in it are the
stability of organizational arrangements, institutionalization and institutional pressures toward
the same organizational form, the organizational practice of isomorphism. Isomorphism involves
mitation and the tendency to create homogeneity with other organizations in the organizational
community and the organizational population (Greenwood and Hinings, 1996: 1023-25).
Greenwood and Hinings emphasized two major factors in organizational resistance to change:
first, their normative embeddedness within their institutional context, and second, structures of
the institutional sectors (1996: 1023). While this may require an initial change toward
homogeneity, once this change is achieved, according to the institutional literature, organizations
are not likely to change further in major (strategic) ways unless the population itself changes
again (Peters, 2005). This tendency towards isomorphism among organizations has been shown
to have positive implications by improving organizational legitimacy and reputation, and hence
access to resources (Deephouse, 1996; Frumkin and Galaskiewicz, 2004; Pursey, Heugens and
Lander, 2009). Isomorphism is probably a major factor in organizational constancy, and the
development of organizational and organizational population patterns of the fixed/frozen
(“fixed” will be used for conciseness) type.
The concept of isomorphism explains initial change followed by inertia. Isomorphism is a
particular change by many organizations in a population. As a group the organizations then
become fixed, with less change. Stuart Kauffman (1995: chapter 4), a complexity theorist, found
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that among three patterns of change, the fixed organizational pattern converges. Thus, both
(neo) institutionalists and complexity theorists see some complex phenomena/organizations
converging and growing more alike. The patterns presumably also decline in complexity, relative
to their environments. Once changed, the pattern of these populations (and organizations) is
likely either to remain constant or to decrease in complexity. This describes both Kauffman’s
fixed pattern and isomorphism. At the same time, isomorphism is known to enhance
organizational survival due to the legitimacy it attracts (Deephouse, 1996; Pursey, Heugens and
Lander, 2009). Burke also sees patterns, which he calls deep structures, as rarely changing (2002:
59).
The organizational ecologists (Hannan, 1988; Hannan and Freeman, 1984; Hannan et al., 2004)
support this idea:
(M)ajor innovations in organizational strategy and structure occur early in the life
histories of individual organizations and of organizational populations….change in core
features of organizational populations is more Darwinian than Lamarckian1….inertial
pressures prevent most organizations from radically changing strategies and structures.
Only the most concrete features of technique can be easily copied and inserted into
ongoing organizations (Hannan, 1988: 98).
A subfield of organizational ecology, structural inertia theory (Hannan and Freeman, 1977, 1984;
Baum, 1996) holds that organizations have trouble changing fast enough to keep up with their
environments. Because major innovations tend to occur early in the life of organizations and
populations, organizations are seen as relatively inert and inflexible. Hannan et al. suggest that
1
The distinction is explained later.
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architectural inertia has evolutionary consequences. Because selection favours architectural
inertia in organizational populations, the median level of inertia increases over time in a closed
population. This selection intensity increases in keeping with increases in the level “of intricacy
and structural opacity and decreases with cultural asperity” (2004: 213). All of these theories—
(neo)institutionalism, organizational adaptation, organizational ecology, and structural inertia
see organizations as not changing much.
Why do organizations change?
What causes individual organizations to change has also been of considerable interest. The
literature has suggested change can be evolutionary or revolutionary adaptation (both responsive
and proactive) (Greenwood and Hinings, 1996; Burke, 2002), occur through organizational
survival and death (organizational ecologists), and can be self-organized due to complexity
(Rogers et al., 2005). The neo-institutionalists see both the incidence and the pace of change as
varying within sectors due to differences in internal organizational dynamics in interplay with
the organizational context. The context is seen as primarily a function of the organization’s
normative embeddedness in its context and the organizational dynamics as primarily due to the
political dynamics of intraorganizational behaviour (Greenwood and Hines, 1996: 1023-24). The
organizational adaptation literature suggests changes occur in response to such factors as crises,
culture, leadership and changes in resource availability—such changes are seen as being due to
individual and social will. There is a large organizational adaptation literature promoting change
and innovation in organizations and suggesting how this might be accomplished. This literature
has tended to support specific approaches, whether bureaucracy, business process engineering,
or the New Public Management and privatization of public services. The impacts of such
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approaches have not been pursued much, however, especially at the organizational population
level.
The organizational evolution /ecology literature has illuminated organizational founding, change,
survival and mortality (the highest level of organizational pattern), but also community ecology,
and (at an even higher logical level) organizational population and sector patterns. Sectors are
considered to be the private, non-profit and public sectors (e.g. Baum, 1996). According to the
organizational evolutionists, change and variability occur primarily through organizations dying
and being replaced by new organizations in the population. This is similar to Darwinian
evolution, where change occurs primarily inter-generationally through genetic changes. Other
organizational observers take a more Lamarckian approach, focusing on variability within
organizations and populations as a source of pattern change (Rogers et al., 2005; Glor, 2007a, b).
Still other authors build on fitness-set theory (Levins, 1968), focusing on the environment as a
source of variability, both in terms of the variance of environmental fluctuations and variations
in grain. Grain can be fine (many small, periodic variations) or coarse (a few large, periodic
variations) (Hannan and Freeman, 1977).
Kauffman (1995) suggests that change evolves naturally, without will, out of complexity and
restraint, in all systems. He does not indicate resources are necessary for this change to occur.
Complex biological organisms and ecologies evolve naturally to even more complex forms
(Kauffman, 1995) and co-evolve with their environments. As they evolve, complex phenomena
self-organize into patterns, what Kauffman (1995, chapter 4) calls order for free. Like all
complex phenomena, organizations are inclined to evolve continually toward greater complexity.
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In complex systems the parts interact with each other and change emerges from this interaction.
As long as organizations retain complexity, they have the capacity to evolve. Kauffman sees lack
of complexity leading to less change and variety and more complex systems as having more
capacity to evolve than more simple ones.
Kauffman (1995: 86-92) identified three possible types of complex system (networks):
fixed/frozen, edge of chaos (EOC), and chaos. A fixed system is orderly. It has stability but lacks
flexibility. It is therefore less likely to change than his more complex systems. A system at EOC
is maintaining both stability and flexibility. It is in phase transition and generates surprises. At
the EOC the most complex behaviours can occur. A system in chaos lacks stability but has
enormous flexibility. Patterns change because they are drawn to state cycles/basins of attraction.
The effect of basins on organizational population pattern change is discussed later in the paper.
Some types of systems are much more sensitive to initial conditions than others: Ordered
systems are insensitive to initial conditions, while chaotic systems are sensitive to them
(Kauffman, 1995: chapter 4).
Even though the natural tendency is for organizations to become more complex over time,
human volition can interfere—it can reduce the complexity of organizations (e.g. through
reorganizations and reductions in resources), enforce isomorphism, and organize complexity into
patterns. This occurs, for example, through leadership, priority-setting, strategic planning,
budgeting systems, reorganizations, and auditing and accountability. Moreover, organizations
and organizational populations that change increase their risk of mortality. Once an organization
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has changed, it is more likely to change again than organizations that do not change (e.g. Singh,
House, and Tucker, 1986a).
A response to the first issue might be, then, that: (1) some organizations and organizational
populations do not change much, due to isomorphism, structural inertia, and low or decreased
complexity; and (2) organizations and organizational populations have a higher likelihood of
change if they are highly complex and if they have already changed.
Issue 2: What are the patterns of organizational change?
A number of researchers considered this question, often addressing only two ends of one
continuum e.g. evolutionary and revolutionary change (Tushman and Romanelli, 1990). This
paper explores patterns through Glor’s eight complex organizational and organizational
population patterns. She identified three complex organizational dynamics (factors)—individual
motivation, organizational culture, and magnitude of challenge, interacting to yield the eight
organizational innovation or change patterns (Glor, 2001a, b). Working initially from the
perspective of innovations, she identified the patterns by considering each multi-faceted factor to
have two poles. The patterns produced are imposed, reactive, active, buy-in, proactive,
necessary, transformational and continuous innovation/change. In the imposed innovation
pattern, for example, innovation occurs in an environment of extrinsic individual motivation, a
top-down social environment (including management style), and a major challenge to
accomplish the innovation. In the proactive pattern, on the other hand, innovation occurs in a
pattern of intrinsic motivation, a bottom-up social environment and a minor challenge. The eight
patterns can be applied to all or maybe most organizations and populations, not just innovative
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ones, and are useful in exploring change in organizations and organizational patterns. If
organizational patterns did change into other patterns, this would suggest that Glor’s eight
patterns might evolve into each other and possibly into more or fewer patterns over time.
Kauffman (1995) found in his agent-based modelling of complex systems that eight systems
eventually evolve into three. This possibility is discussed for Glor’s patterns later in the paper.
Issue 3: What causes organizational patterns to change? Is it Likely?
Organizational population patterns can be seen as changing by changing their complexity.
According to complexity theory, less complex organizations have less capacity for change and
evolution, while more complex organizations have more capacity for change and evolve faster
(Kauffman, 1995; Rogers, et al., 2005; Glor 2001b, 2007a, b). An understanding of what causes
organizational patterns to change thus requires an analysis of the complexity of the
organizational patterns. Glor (2007b) analyzed the complexity of the eight innovation patterns
and calculated a complexity score for each one (partially reproduced in Table 1).
An understanding of what causes organizational patterns to change also requires an
understanding of when, which, and how patterns change. Based upon agent-based modelling,
Kauffman (1995) found that complex systems grow continually more complex. In living
systems, this is not as likely since growth in complexity usually requires resources. Human
intervention in some natural systems for example (e.g.) has made them less complex. Consider
the effects of mono-agriculture and chemical use on soil: The soil loses many of its living
organisms and thus complexity. Likewise, some completely fished-out or low-oxygen areas (due
to hyper-algal growth) in the oceans now support fewer species of complex life (jellyfish). These
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are examples of complex ecologies being converted into simpler systems due to human
intervention. In becoming simpler, they have lost much of their capacity for change to emerge.
This is also probably true of organizations. One example of organizations becoming simpler is
ones going through downsizing—they are losing resources, becoming simpler, and responding to
fewer elements in the environment. Likewise, at least some organizations moving into
isomorphic structures become simpler. Consider when governments introduced the New Public
Management (NPM). They privatized substantial portions of government and retained only
portions of their original functions as they sought to “steer, not row”.
[Table 1 about here]
For the most part, environments evolve to become more complex over time but human effects
can reduce the complexity of environments. Human efforts are often also expended to see and
present issues less complex than they really are—this is a primary purpose of communications in
organizations. President Bush’s War on Terror, e.g., presented security as the primary issue in
the environment, amenable to intervention through both traditional and expanded military and
intelligence means, and the selection of priorities as a simple matter. Instead of controlling and
reducing terrorism, the complexity of the (security) environment increased substantially as more
individuals and organizations (including governments) entered the domain by adopting terror
tactics. Did the approach unwittingly facilitate an increase in the complexity of the security
issue? It probably did but the process led to the US government into deep debt and later reduced
resources for other programs (making them more simple) to pay for it. To take another e.g.,
President Obama began his attempts to secure action on gun control, by presenting the issue
through communications—political speeches, simplified for news stories and stories of
individuals, families and communities affected by mass murder with guns. While also relatively
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simple, stories have more potential to retain some of the complexity of realityPolitics is one of
the ways in which environments are brought to bear on organizations.
Some environmental factors identified in the literature that can affect the complexity of
organizations are outlined in Appendix 1. They relate to how internal unit environments,
organizational ecologies, top-down control and population ecologies evolve within and in
relation to their environments and affect organizational pattern change. For example, institutional
rules and challenges create an environment that affects individual motivation, the organizational
culture and how easy or difficult it is to deal with a challenge. Likewise, the history of changes in
an ecology affects and contributes to the patterns of functioning and the stories told in an
organization. They in turn affect the dynamics in an organizational population. Also, in a
community ecology, density dependence (DD) affects both the vital rates (founding and
mortality) of organizations and therefore competition for limited resources. DD can either
encourage organizational founding (if resources are plentiful) or mortalities (if resources are
scarce).
A response to the third question could, be that organizational patterns change because of changes
in complexity in organizations and environments.
Issue 4: If patterns change, what dynamics are likely to be at work?
Five possible dynamics affecting change are discussed: (1) direction of change, (2) magnitude
(quantity) of change in complexity required to cross a Glor pattern boundary, (3) basins of
attraction, (4) incidence of change, and (5) speed of change. Direction, magnitude and speed can
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be seen as push factors. They can be seen, for example, in a change in political ideology pushing
employees in a new direction. Basins of attraction and incidence are pull factors, drawing the
factors toward change of pattern.
1. In what direction will the organizational patterns move—toward less or more complexity,
toward less or more change?
Using an evolutionary approach, organizations have been defined as goal-directed, boundarymaintaining, socially constructed systems of human activities (Aldrich and Ruef, 2006: 4).
Because these three characteristics of organizations (and types of processes) interact with each
other, each of the three characteristics involves a number of aspects that also interact with each
other, organizations can be said to exhibit complex behaviour in interaction with their
environments. Complex factors interact with each other as well as affecting results simply and
directly. To be fit and to respond effectively to its environment, an organization must be able to
receive, recognize and act on information, allow change to emerge from complexity, and respond
to its environment in a way that matches the environment’s complexity through appropriate
orders of change (Michaels, 2000).
Organizations that are allowed to evolve and have sufficient resources to do so will move in a
more complex direction in step with their increasingly complex environment. If organizations are
not allowed to evolve or have insufficient or even fewer resources to keep up with the
environment, they will probably remain at the same level or even decline in complexity. Simple,
less expensive solutions will be sought and isomorphism adopted.
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More complex systems evolve faster, so they can be expected to be more innovative and to
change faster (Kauffman, 1995). Like other complex networks, organizational populations can be
expected to evolve most effectively in Kauffman’s EOC pattern and to evolve most and fastest in
his pattern of chaos. The most adaptable or fit state is at the EOC, that is, neither in a fixed state
nor in a state of chaos, but near a state of chaos. Kauffman hypothesized that complex adaptive
systems (CAS) evolve to the EOC (Kauffman, 1995: 91). The implications of CAS are (a) less
complex patterns are more likely to be homeostatic, and (b) more complex patterns are more
likely to become chaotic or appear to be chaotic (have large cycles). Accordingly, the EOC and
chaos are the conditions or patterns in which the most change or adaptation occurs. The change
itself is more adaptive at the EOC and hence organizations and organizational populations are
more fit than in chaos.
As mentioned earlier, Glor (2007a) developed a methodology for assessing the complexity and
innovativeness of the eight organizational innovation patterns. She measured the variety,
reactivity and capacity for emergence, the three characteristics of complexity identified by
Rogers et al. (2005), of each of the eight organizational change patterns and developed a
complexity score for each one. In keeping with Kauffman’s approach, fixed and chaotic patterns
were the least fit and organizational patterns at the EOC were considered the most fit (Glor,
2007b). Glor (2007b) went on to group the eight patterns into Kauffman’s three patterns, and
thereby to identify the direction of pattern movement, that is, whether the patterns were
converging, neither converging nor diverging, or diverging. Greenwood and Hinings (1996:
1024; 1026) see convergent change as fine tuning within an existing archetypal template while
other definitions emphasize convergence as approaching a limit or (in math) one point or
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approach (The Free Dictionary, 2013). Kauffman was likely thinking of the mathematical
definition as he is trained in mathematics. Using this scoring process, each pattern’s flexibility,
stability, and direction of movement were identified, notions Kauffman explored in his
modelling (1995: chapter 4).
A summary of Glor’s pattern scores is presented in Table 1, columns 1 to 3. The names of the
patterns are identified in column 1, the overall components of the patterns in column 2, and the
pattern complexity score in column 3. Each organizational pattern’s complexity score identifies
and thus its capacity for change. From these scores Glor developed a complexity or adaptability
ranking for each pattern (Glor, 2007a, Table 5): In Table 1, column 3, Kauffman’s patterns,
which he called categories of system flexibility, are assigned to Glor’s patterns. Glor’s
assignment of complexity ranges to Kauffman’s patterns is outlined in column 5. Her
identification of the direction in which the Glor and Kauffman patterns move is reproduced in
column 6 from Glor, 2007b, Table 2, column 9).
Three possible directions for organizational pattern movement were identified: converging,
neither converging nor diverging, and diverging. Converging patterns can be expected to move
very little; if they move, it will be toward more of the same, usually less complexity. At the same
time, fixed patterns that are close in complexity to that of other patterns will be drawn to them as
basins of attraction. Patterns may thus be drawn to contradictory attractions, experiencing
convergence or divergence as well as a basin of attraction created by a pattern. Isomorphism
could thus be caused both by convergence and by strong basins of attraction. Patterns that are
neither converging nor diverging can be expected to remain the same for awhile, but they are not
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stable like the converging patterns. When they change, most fixed patterns can be expected to
become less complex, and some may eventually die. The EOC chaos patterns are somewhat
unstable but some things may be able to be said about how they will change (see later). The
chaos pattern evolves quickly and according to Kauffman, can be expected to become more and
more complex and more and more chaotic, leading to crises. Other possibilities are discussed
later.
This analysis established the direction in which the patterns are likely to move, based upon the
likely direction of movement of complex systems. The boundaries of organizational pattern
complexity between fixed and EOC and between EOC and chaos were assigned by Glor (2007b)
using scores from the patterns (Glor, 2007a). A fixed pattern, in Glor’s (2007b) paper had a score
of 0 to 2.5, the EOC occurred between a score of 2.6 and 3.9, and chaos at 4.0 and higher. The
scores have been assigned somewhat differently in Table 2, to allow for organizational mortality
and to assign equal amounts of complexity to each pattern.
[Table 2 about here]
Glor has proposed that each of her organizational innovation patterns exist in one of the
Kauffman systems. Following an analysis of the complexity of her eight innovation patterns, she
arranged the patterns into a hierarchy of complexity and assigned the patterns to Kauffman’s
three systemic categories of complexity (Glor, 2007b) (reproduced in Table 1). The quantitative
complexity of the eight patterns and where they fit in the broader categories of complexity has
thus been suggested. Glor answered the question of what direction organizational patterns will
move: Fixed patterns become less complex in relation to their environments, EOC and chaotic
patterns move in the direction of more complexity unless affected by other factors. While
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Kauffman asserted that complex systems only become more complex, he identified a fixed
category that was converging. His patterns were not seen as having the capacity to become less
complex, but Glor suggested that the imposed, reactive, active and buy-in patterns will grow less
complex unless there is intervention in the system (typically increased resources). The necessary
and transformational patterns, on the other hand, based only on complexity, will continue to
grow more complex and will eventually evolve, as Kauffman suggested, into chaos. The
continuous pattern will continue to grow more complex and evolve at an accelerating pace. The
buy-in and proactive patterns may oscillate between the two patterns.
The direction of change, then, is largely affected by whether the patterns are inclined to converge
or diverge. Kauffman’s findings for networks are applied to organizational patterns.
(2) Magnitude of change required to change pattern
The magnitude of change is affected most by complexity. Based on modelling, Kauffman found
that systems in the fixed category changed complexity the least and were insensitive to initial
conditions. The EOC and chaos patterns changed the most and were the most sensitive to initial
conditions. If Kauffman is right, based only on a complexity score, and the knowledge that fixed
patterns are converging, we know that the converging patterns could decline in complexity
relative to their environment. Condiering only complexity, only the fixed (converging) patterns
could move in a less complex direction, while the EOC and chaos patterns would only add
complexity.
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Informed by direction, how would the patterns be likely to change? Would they move through
Glor’s patterns steadily, accumulating complexity and small changes until they reach a
Kauffman pattern boundary and change their behaviour substantially, or would they be more
likely to leapfrog patterns by suddenly losing or adding large amounts of complexity? Both are
possible but because adding a lot of complexity to an organization usually requires new
resources, leapfrogging would probably happen only rarely. Usually patterns would increase
their complexity steadily and leap-frog occasionally.
Magnitude of change needed to change a pattern or distance to the next pattern is assessed by the
difference between current complexity measure and distance to the next lowest or highest
complexity score that involves a pattern change. Table 2 provides a calculation of the distances
to a pattern change. In Table 2, column 7, the minimum complexity a pattern would need to
delete to move to a less complex pattern is calculated by subtracting each pattern’s score from
the score at the top of the next lower pattern range. The minimum complexity a pattern would
need to add to move to a more complex pattern is calculated in column 5 by subtracting the
pattern’s score from the score at the lowest edge of the next most complex pattern. This provides
an indication of the magnitude of change required to change pattern. These calculations provide
a comparable measure of how much complexity the pattern must delete or add to move into a
contiguous pattern.
[Figure 1 about here]
How far it is to an increase in pattern complexity is graphed in Figure 1. It presents column 5 of
Table 2, with the X axis being the range of complexity scores in the patterns and the Y axis being
Glor’s eight patterns, beginning at the top of the Table 2 column. Just as the distance to a change
20
in pattern could be ranked from closest to a new pattern (rank 1) to farthest from one (rank 5)
(Table 2, columns 6 & 8), it is clear that some patterns need to grow in complexity considerably
more (e.g. necessary/transformational) than others (e.g. imposed) before they could change to a
more complex pattern. Some patterns (imposed, continuous) are close to a Glor pattern and are
therefore more ripe for a change to a more complex pattern. The imposed pattern complexity
score is closer to the next higher boundary than the other patters, but it is still only middling
close. It has a low complexity score and the pattern is converging, so it may have little capacity
to move to a more complex pattern.
Mathematically, the distance from the complexity pattern score of each of Glor`s patterns to the
upper edge of the pattern to which a Glor pattern would need to decline to change pattern is
identified in Column 7 of Table 2 and is graphed in Figure 2. While Kauffman found in his
agent-based modelling that patterns continually grew more complex, even when constraints were
placed on them, he also found that some patterns were fixed and converging. In the world of
living creatures, humans sometimes make organizations less complex or cause them to converge
on one model. Where the Kauffman chaos pattern begins is somewhat uncertain in terms of
scoring and requires analysis of more population patterns. Given what we know, the bottom of
the Kauffman pattern of chaos is treated as the bottom of the Glor continuous pattern (it is the
only data we have).
[Figure 2 about here]
The reactive and active patterns need to lose the least complexity in order to change to a less
complex pattern. The continuous (by far) and proactive patterns would need to lose the most
complexity to change pattern. We know, from our analysis, that fixed patterns are losing
21
(relative) complexity, so moving to a less complex pattern seems plausible for the reactive and
active patterns. They would likely converge to an imposed pattern. Since the additional
complexity and change required to move into a more complex pattern is small for the continous
and proactive patterns, they would be more likely to move to a pattern at a higher level of
complexity—considering only complexity. The reactive and, active patterns would be the least
likely to move to a more complex pattern. For them, losing complexity is more likely than
adding it.
(3) Basins of attraction
As explained earlier, patterns whose complexity is proximate to a boundary (lower or upper, but
especially upper) are drawn to the basin of attraction of that pattern. Such patterns are therefore
more likely to move and will move more quickly into that pattern than patterns that sit in the
middle of a range. Fixed patterns tend toward isomorphism and organizations tend to coalesce
more with organizations and populations of similar or compatible pattern. Compatibility may be
influenced by the extent to which the patterns share factors - motivation, organizational culture
and challenges. Much of the initiative for change in the fixed patterns comes from inside the
organization—management motivating staff, a top-down organizational management
environment, and challenges addressed. The patterns become more complex as more selfgenerated motivation, social supports, and external resources are put into organizational change
and innovation and more substantial challenges are addressed. In other words, patterns act as
attractors for organizations and populations of organizations. The fixed patterns would likely
develop less and less complexity and the patterns would become less capable of addressing
challenges. Eventually organizational and organizational pattern death could occur.
22
Glor’s patterns with the lowest complexity scores (imposed, reactive, active and buy-in) fit
within Kauffman’s fixed/converging category. These low-complexity innovation patterns are
not, however, all equally fixed. Considering the complexity scores and ranking of closeness to
their boundaries, patterns are farther from and closer to neighbouring patterns. In Table 2,
columns 6 and 8 rank which patterns are most likely to change within the far/close continuum.
Some high-ranking (“1”) complexity patterns are closer to change than others, however.
(4) Incidence of Change
The incidence of change derives from the basins of attraction, whose analysis considered
complexity. Consider an example. The reactive and active patterns sit in a basin of attraction
(assuming it extends at least unit of measurement each side of a pattern boundary), drawing them
to the imposed pattern. They are the only patterns sitting in a basin. The reactive and active
patterns are therefore the most likely to experience an incidence of pattern change. All the other
patterns experience milder forces from the basins of attraction. The incidence likelihoods for all
patterns are outlined in Table 3.
[Table 3 about here]
It is worth noting the patterns that are experiencing milder but still some effect from the basins of
attraction (ranks 2, 3 in Table 2). They are also listed in the middle row of Table 3, but it should
be kept in mind that the proactive pattern only experiences a basin of attraction if there is such a
thing as a Buy-in+ pattern. It has not yet been found. These patterns are not likely to create
incidents on this basis alone, but perhaps they have an effect when combined with other forces.
23
It is also noticeable that the distances (complexity change needed) to change pattern are different
for increasing as opposed to decreasing complexity. In the analysis of incidence done for the
patterns in Table 3, it is more likely that patterns will decrease complexity than increase it
because most of the pattern scores are closer to their lower boundaries than to their upper
boundaries. This could be merely an artifact of the examples used to develop these patterns and
that other analyses would find patterns closer to their upper complexity limits. The issue is worth
watching, however.
5. Speed of evolution
Another important dynamic affects how quickly patterns will evolve, namely, the speed of
evolution. Speed is concerned with the rate of complexity change. According to Kauffman, fixed
patterns are unlikely to move quickly, ones at the EOC are likely to move more quickly and ones
in chaos are likely to move the fastest. If only the speed of Kauffman’s patterns is considered,
Glor’s imposed, reactive, active and buy-in patterns are likely to move slowly, the proactive,
necessary and transformational patterns are likely to move fast, and the continuous pattern is
likely to move fastest. The most complex patterns change the fastest. Speed probably interacts
with another factor, however. The speed of change of patterns close to Kauffman boundaries are
probably accelerating, due to the basins of attraction. The reactive and active patterns, for
example, would be expected be moving slowly. The basin of attraction of the reactive and
proactive patterns, however, probably causes them to accelerate their rate of change as they
move through the boundary between them and the imposed pattern. The buy-in, imposed and
other medium likelihood patterns are probably affected somewhat, but are unlikely to increase
24
the speed of their changes by much. We can nonetheless distinguish rates of pattern change
among patterns.
Where Change Could Lead
Organizational population change patterns are formed from what is happening in organizations in
their populations. Researchers have found patterns related to such structural inertiafactors as
organizational age and size (Hannan and Freeman, 1984; Singh, House and Tucker, 1986b;
Barnett and Amburgey, 1990; Barron et al., 1994), environmental factors such as time period,
resources and politics (Hannan, 1988: 101; Pfeffer and Salancik, 2003), and adaptation factors
such as leadership, networks and communities (Baum, 1996). The scoring process used to assess
complexity reflects some of these factors, such as top-down and bottom-up leadership and
individual motivation, which affect perceptions of legitimacy internal to organizations.
Challenges arise both internally and from the environment. All these factors are important to
pattern change, as are the five process dynamics discussed in this paper.
[Table 4 about here]
Without taking account of the program issues at work in organizational population patterns, but
merely the likely evolutionary process dynamics (Barnett and Carroll, 1995) , this analysis has
examined five dynamics that affect how patterns change—direction, complexity, basins of
attraction, incidence and speed of change. Direction describes whether patterns are converging or
diverging. Complexity affects patterns in three possible directions—no/little change in
complexity, leading to no change in pattern; reduced change; and increased change. With the
laying down of initial rules and conditions, Kauffman’s models only led to more complexity. In
human systems, however, with the expression of will through factors such as power, authority
25
and isomorphism, systems are capable of both increasing and decreasing in complexity. In the
analysis conducted, and especially with the continuous pattern sitting far from the
transformational/necessary EOC boundary, there was a great deal of movement to less
complexity. Other sets of assumptions and analyses of patterns could lead to increased
complexity. But this analysis found overall declines in innovation and change. Basins of
attraction affect patterns that are close to boundaries the most—there were only two. Incidence
of change is only likely for patterns in basins of attraction. Speed of change is affected by both
complexity (and which Kauffman category the pattern is in) and the pull of basins of attraction
(which are strongest at Glor pattern boundaries).
The important finding is that patterns can change pattern, becoming more or less complex as
they evolve. They can change direction. For example, patterns might become less complex in a
downsizing environment but change direction and become more complex if new resources are
added again. This happened in the population of the Government of Canada [GoC]) with its
major mid-1990s downsizing followed by economic expansion and increased tax revenues for
the federal government. There wasn’t the same dynamism in the government afterward,
however, compared to the period of the early 1990s when innovation was encouraged.
Moreover, to some extent the authoritarian pattern introduced to create the downsizing acted as
an initial condition and was more actively adopted by the newly hired employees. Top-down
patterns became more likely as a result.
The overall impact of these five dynamics on population patterns is not necessarily predictable.
At the same time, it is possible to identify the dynamics and the most likely changes in factors
and how they could be expected to act and interact. This provides a window on the future, even if
26
the window is translucent. Table 4 outlines possible effects of the five dynamics during one
round of evolution. . If a majority of the forces is pulling in the same direction, the pattern will
move in that direction. The changes have been assigned numerical values by the author – these
are based on judgements, not measures.
Using the results from Table 4 as the first round of change, Table 5 shows the first four likely
rounds of change (evolution). In this evolution, the patterns most likely to change do so.
Surprisingly, in round 1 the number of Glor patterns already declines, from eight to four. In
round 2, the number of patterns decreases to three. During round 3, the number of patterns
declines even more to two. When only these factors are considered, the number of patterns
remains at 2 in round 4. In this modelling, the Glor patterns decline from eight to two within four
rounds of evolution. They clearly change.
[Table 5 about here]
After four rounds, two patterns remain-- reactive and proactive (proactive is cycling with the
buy-in pattern). The most active patterns have either evolved into more complex patterns, at least
for now, or they have died. If the reactive pattern continues to lose complexity and moves into
imposed, it will probably disappear. Only the cycling buy-in and pro-active patterns would
remain, cycling between Kauffman’s fixed and EOC patterns. The continuous pattern, moving
the fastest, has been modelled as increasing in complexity and disappearing. Because it had so
far to decline to a less complex pattern, because it was sensitive to initial conditions (chaos), and
because it was diverging without a strong basin of attraction, the continuous pattern continued to
diverge beyond its capacity to deal with its complexity. At the same time as Glor’s patterns
disappeared, some of the hypothesized patterns appeared (in yellow in Table 4).
27
This quick decline in the number of patterns certainly surprised the author. It would appear that
organizational patterns may be isomorphic as well as organizations (which makes sense, since its
organizations are). Of course, new patterns could at the same time be in the process of being
created. Either way, there is far more organizational mortality and proactive patterning than
expected. This assessment could be correct, with changing organizational patterns with highly
responsive organizations being the ones that survive. Alternatively, there could be other,
unrecognized factors at work and other patterns as yet undiscovered that have basins of
attraction. Glor (2013) found a very low mortality rate per year among normal organizational
populations, so normal organizations are clearly resilient. This analysis adds credence to the idea
that changing organizations and populations are not as resilient nor as likely to survive. This
level of pattern change mortality, the pattern of evolution without human intervention, was not
expected, however. Some other factors that could be at work might include human will, a change
in patterns from the past to the future, unknown patterns, or a life factor(s) that enhances
organizational survival.
The patterns’ change tendencies can now be suggested. The imposed pattern would likely
continue to lose complexity and eventually most of the organizations in that pattern would
disappear. An example of this tendency is the ongoing and steady abolition and reduction in
funding for social programs and social security programs in the GoC. The US is now going
through a similar downsizing of the federal government and its social programs and has also
devolved some of its deficit to lower levels of government. Mortality is being seen most visibly
in U.S. municipalities going bankrupt: 13 in 2011, 12 in 2012, 5 in 2013 to July 19th, including
28
Detroit2. Depending on whether powerful management and elected officials perceived the change
as being necessary in order to deal with a specific problem or necessary to revitalize the
organization and therefore needing a strong employee contribution and commitment, the reactive
and active patterns would likely change into the imposed pattern as they decline in complexity.
The reactive and active patterns are dominated by extrinsic employee motivation, and therefore
do not have much potential to flip into intrinsic motivation and add complexity that way.
The buy-in pattern has less potential to move to the edge of chaos because it would need to add
so much complexity and is not in a basin of attraction. It might begin to evolve more quickly if,
for example, an external collaboration was created by employees and a bottom-up social
dynamic developed (especially if supported by management). It could then move into a proactive
pattern. The proactive pattern has the dilemma of some attraction toward greater complexity, but
a strong basin of attraction toward the buy-in pattern and less complexity. Moreover, it may have
initial conditions in a fixed category. This will matter in the EOC patterns. If the proactive
pattern did move into a buy-in pattern, we are positing the existence of a buy-in+ pattern
between the buy-in and proactive patterns, with higher complexity than the buy-in pattern. If
employees continue to be intrinsically motivated, and begin to take on larger challenges, a
necessary pattern could develop and continue to create a highly dynamic pattern. Even if
employees return to being extrinsically motivated, substantial evolution can occur. However,
despite their high complexity contributing to accelerated change, the necessary and
transformational patterns also have quite a strong basin of attraction toward the proactive pattern
2
Reuters. 2013, July 19. Factbox: Recent U.S. municipal bankruptcies, collected July 23, 2013 at:
http://www.reuters.com/article/2013/07/19/us-usa-detroit-cities-factbox-idUSBRE96H1BR20130719
29
and less complexity. As they continue to evolve, necessary and transformational patterns could
become less complex; alternatively, if a pattern emerges of addressing even greater challenges,
they could potentially change from a necessary or transformational into a continuous change
pattern, as the pattern passes over the Kauffman boundary into chaos.
What happens to the continuous pattern? This is unclear. Perhaps it will continue to evolve and
become even more complex, moving into an as yet unidentified chaotic pattern with greater
complexity than the transformational/necessary patterns. We identify this hypothesized EOC
pattern as Necessary or Transformational+. The continuous pattern has the potential to spin off
into dysfunctional complexity and die, because it lies far from the basin of attraction of the
necessary/transformational patterns. At the same time, if there is a continuous- (minus) pattern in
the 4.9 to5.4 complexity range, the continuous pattern would have some influence from its basin
of attraction. It such a case, it may have some potential to reduce its complexity and perhaps
(unlikely) move back eventually into an EOC pattern. Given its high complexity, high speed and
diverging character, the continuous pattern is more likely to continue to grow in complexity and
eventually die.
Some factors act against this possibility. As with all patterns, the people in the organizations in
the continuous pattern will be trying to find ways to help the organizations survive. If the
organizations are large, they will not tend to support either bottom-up management or intrinsic
motivation in their employees for long. Rather, both may tend to be transitional states, as
organizations deal with threatening environments. While the continuous pattern moves into the
30
EOC, sensitivity to initial conditions can drag the pattern back into chaos. If it does, its
sensitivity to its initial conditions in a continuous pattern may pull it back into it.
Consider an example of a continuous change pattern, the Government of Saskatchewan (a
Canadian province). Prior to 1944, the province elected conservative and liberal governments.
From 1944 to 1982, it elected many social democratic and one conservative Liberal government.
The social democratic government created continuous change while the Liberal government of
1962-71 probably functioned in a reactive pattern. This would seem to indicate that it is possible
for a chaotic pattern to flip into a fixed pattern, but it flipped back again in 1971 and has elected
alternating social democratic and extreme right-wing governments since then. The right-wing
conservative governments have consistently driven the province into deep debt and the social
democratic governments have been the ones working to balance the budget. The conservative
governments have been ideological while the social democratic ones have been less so. This has
been a switch from the earlier period of 1944 to 1982, when the social democratic government
was more ideological (but kept the budget in balance). The pattern has been one of continuous
change with a pattern of regular (eight year cycle) changes of government. The social democratic
party has moved from left of centre to right of centre while the conservative parties have moved
from right of centre to extreme right. The continuous change pattern has continued to exist, with
the economic environment, the roles of political parties, and political and public servant actors
changing (the right wing conservative government of 1982 fired and pushed out a large number
of public servants and replaced them with their supporters). This continuous change pattern
survived a long time in chaos (69 years).
31
Something can also be said about the likelihood of change. Based on the analyses in this paper,
only relative statements can be made. Kauffman discovered that the most likely patterns to
change are the most complex ones and the current analysis proposed that the ones that need to
add the least complexity would change first. Accordingly, the reactive, active and proactive
patterns, sitting on the boundary of complexity with the next less complex pattern and in its basin
of attraction, would be inclined to change—the reactive and active patterns to the imposed
pattern; the proactive pattern to the buy-in pattern. Likewise, the fixed buy-in+ pattern would be
likely to change to the EOC proactive pattern, because it sits in the basin of attraction for it. This
latter change may end up being an oscillation. These patterns are followed in likelihood of
change by the necessary, transformational and continuous patterns, which are changing fast.
They have conflicting tendencies, between being drawn back to the basins of attraction of a
pattern with closer complexity and their tendencies to grow continually more complex due to
already being very complex.
The incidence of two patterns being so close to the next lower complexity pattern that they are
very likely to move helps to explain the disappearance of patterns in Table 5. The three patterns
already the most complex—the continuous, transformational and necessary patterns—are
changing the fastest. It would therefore likely that the time before pattern changes would be
shortest in the continuous pattern but it has far to go. While the patterns needing to lose the least
complexity to change to a more complex pattern are the reactive, active and buy-in patterns, the
pattern changing fastest is the continuous pattern. Which patterns would change first is uncertain.
They are modelled with the reactive, active and continuous patterns changing in the first round.
32
While this analysis recognized that the continuous pattern is the most complex and the only one
in chaos, researchers should watch for whether there are other patterns in the chaos Kauffman
category into which the continuous pattern could evolve. If not, the fate of the continuous pattern
is uncertain. It dynamics suggest it would spin off into mortality, havingconsumed too many of
its resources, rather than diverging to EOC. On the other hand, that is what the example,
Saskatchewan, did, so it is possible. According to the modelling, chaos patterns would be
temporary patterns, as would the imposed, reactive, active and proactive patterns.
Hypotheses
On the basis of the preceding analysis, it is possible to assert a number of probabilistic
hypotheses about the direction, likelihood and speed of change of organizational pattern change.
These hypotheses are not all compatible.
Direction:

Hypothesis 1, H1: Given sufficient resources, organizational change patterns will tend to
become more complex.

H2: If an arrow of complexity is at work, as Kauffman found, and complex systems
(organizations) tend to become more complex over time, then organizations are unlikely
to move back to less complex states but rather they are more likely to move in the
direction of the next most complex pattern.

H3: If organizations tend to become continually more complex as Kauffman’s (1995)
modelling found, then, if the distance to the next pattern is a long distance, the
organization is likely to remain in the same pattern for a long time while it grows more
33
complex and enters a region of complexity suitable for a shift, which can be described as
an attractor.

H4: If organizations tend to match their complexity to that of their environment,
organizations will grow more complex as their environments grow more complex and
will decline in complexity as their environments decline in complexity.

H5: If a pattern is close to another pattern, it is more likely to change into that pattern
perhaps because it shares some of its characteristics including complexity, that act as an
attractor.

H6: Both converging and diverging organizational patterns are more likely to move to the
next less or more complex pattern than to a pattern that is very different in complexity.

H7: If there are two potential patterns (with the same complexity score) for a pattern to
move to, the pattern that is changing is most likely to move to the new pattern that is
more similar to its own pattern of functioning (e.g. motivation, social dynamic, challenge
of implementation).
Likelihood:

H8a.: If the added/reduced complexity and change required to move into a new pattern is
small, it is more likely to occur;
H8b: If the amount of change in complexity required to move into a new pattern is large,
it is less likely to occur.
H9: Patterns near other patterns will experience the other pattern as a basin of attraction.

H10: Organizations most likely to change pattern are those that require little change in
complexity, are close to a basin of attraction and are changing fast.
34

H11: The next most likely group of organizational patterns to change are the ones that are
more than half way to the next pattern and are changing fast.

H12: The least likely patterns to change are the ones in the fixed Kauffman pattern and
that are near the middle of a pattern range. They would need to lose or add considerably
more complexity before they would likely change pattern.
Speed:

H13: The more complex patterns are likely to change faster.

H 14: Patterns experiencing divergence and a basin of attraction are likely to change
fastest.
Conclusion
According to this analysis, Glor’s eight patterns are eventually likely to decline to two patterns.
This change in pattern structure will likely be induced as a result of five process forces—
convergence/divergence, complexity, basins of attraction, incidence and speed. Darwin
concluded that stability in natural systems cannot be imposed by natural selection—it must arise
as a condition of evolution itself (Kauffman, 1995: 80). In these patterns, stability did not
develop but cycling did. Systems with eight states (such as Glor’s), allowed to evolve and
change, settle down to three states, according to Kauffman (1995: 83). The analysis in this paper
showed how this might happen with organizational patterns. The organizational patterns are
likely to evolve into other patterns and new patterns may emerge. The three patterns that Glor’s
eight patterns evolved into were not, however, the three Kauffman patterns, only two of them
(fixed and EOC). Evolution occurred within and across Kauffman patterns. What Kaufman did
35
not predict was that fixed patterns would disappear, but they did here. This reduction in number
of patterns supports Kaufman’s prediction but also isomorphism, the idea that organizations
change in their youth but change less as they age, becoming fixed and unresponsive.
Should Glor’s population patterns evolve to different patterns, the patterns likely to change
soonest are the continuous pattern because it is changing so quickly, followed by the
reactive/active patterns, because they are sitting within a basin of attraction. In this modelling,
three fixed patterns and the chaotic pattern disappeared and the three EOC patterns moved to a
cycling proactive—buy-in+ pattern, a fixed—EOC pattern. Eventually organizational pattern
evolution seems to be headed toward only two change patterns. This could limit diversity and
possibly lead to fewer new ideas and possible innovations. In this sense it might reduce capacity
to evolve and rate of change. To create and maintain organizational vitality and resilience,
organizations need to support diversity, complexity, the capacity to change and complex basins
of attraction. While, according to Kauffman, the most unstable pattern was in chaos (continuous
innovation), in this modelling all the patterns changed and therefore turned out to be somewhat
unstable. They ended up in different states, however. The three least complex patterns died out,
the three patterns at the EOC ended up cycling between the fixed buy-in and the EOC proactive
patterns, and the continuous pattern ended up in EOC with an uncertain future direction.
Falconer’s description of organizational change patterns as “highly unstable states of order that
are then as quickly lost” (2002: 126) was closer to these results than expected.
36
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Appendix 1:
Some Environmental Inputs to Organizational Pattern Change
Inputs
Major Factors
Intervening
Factors
Organizational environment (Internal, Challenges, Organizational Level):
Institutional
Values & ethics
rules/constraints
Challenges =
environmental
challenges/changes/
inadequacies
Learning & innovation
Processes
--Exogenous legitimation
(Baum & Powell, 1995)
--size of
organization
(Hannan, 1988:
106)
Organizational Ecology (External, Organizational level):
--Changing
History & historical
-- Organizational agenda(s)
interests of
changes
--Presence & density of
institutional
other populations
controllers &
--Is the population
relevant
increasing or decreasing in
populations
density (Baum, 1996)
--Changing
--Organizational legitimacy
environment
(reputation). When density
is increasing, it increases
legitimacy; when density is
decreasing, it reduces
legitimacy (Baum, 1996)
Members/owners/elites
Centralization of power
Orgns : Interest
Control
groups, political
parties,
occupational
associations, unions
Population Ecology (population level):
Population of
Institutional processes
organizations
Social/historical
dynamics
Legitimation of forms
Population dynamics
(Hannan, 1988: 106)
Selection factors
Reductions/
increases in organ.
diversity
--Numbers &
distributions of
organizations
(Hannan, 1988:
104, 107)
--Forms (Hannan,
1988: 98)
--Social
networks***/
linkages (Miner et
al., 1990; Baum &
Oliver, 1991)
45
--federated organizational
populations (Hannan, 1988:
104)
Members/owners/elites
--At national & international
levels
--Evolution of size
distributions (Hannan, 1988:
104)
Competition
Cooperation
Foundings
--Nature of new
organizations being created
(Hannan, 1988: 98)
Mortality
--Nature of old
organizations that are
disappearing
--Structural embeddedness
that promotes social
Inputs
Major Factors
Learning and innovation
(Amburgey & Rao, 1996:
1276)
Community ecology*
(Hannan, 1988: 98)
Intervening
Factors
--Sources of
variability
--Sources of
homogeneity
--Tightness of
coupling (Hannan,
1988: 98)
Two interactions:
Processes
order/general disorder
(Granovetter, 1985)
Selection
Isomorphism
Competition/ cooperation
Selection
Mortality rates
Organizational influence
1. Density dependence in
vital rates (Hannan, 1988:
100)
Niche (Hannan 1988: 99)
--realized niche (Hannan,
1988: 100)
2. Competition for limited
resources
Selection
--Lamarckian evolution**
--Darwinian evolution
Competition
-Direct: limited resources
(membership, capital,
legitimacy)
--Diffuse resources
(Hannan, 1988: 101)
-- Mortality of populations
is a function of density
(Hannan, 1988: 101)
-- Founding of populations
is a function of density
(Mortality weaker link)
(Delacroix & Rao, 1994).
--Overlap & nonoverlap
density (Baum & Singh,
1994a, b)
--Mass (Hannan, 1988: 104;
Barnett & Amburgey, 1990)
Processes leading to success
(Hannan, 1988: 106)
--size distribution of
population (Hannan, 1988:
106)
Relational density (Baum
& Oliver, 1992)
*Two or more populations are interacting if the presence of one affects the growth rate of the other(s) (Hannan,
1988: 99-100)
** Lamarckian evolution is the idea that an organism or organization can pass on characteristics acquired during its
lifetime to its offspring. While this does not appear to be true of organisms, it is often true of humans and
organizations (through learning). Darwinian evolution occurs only through the passing on of genes, the maps of
organisms, to subsequent generations of organisms.
***Networks=micro-communities
46
Table 1:
Organizational Pattern Complexity and Direction of Movement
Glor’s
Organizational
Patterns
Ranked for
Innovativeness
Pattern Factors
Summed &
Ranked
Glor
Pattern
Complexity
Score
Glor’s
Glor’s Complexity
Patterns
Score Ranges
Assigned to
Assigned to
Kauffman’s
Kauffman’s System
Flexibility
Categories
Categories
Kauffman’s
Direction of
Movement of
organizational
patterns
Imposed
Extrinsic motivation
Top-down social
environment
Major challenge
1.2
Fixed/frozen
1.0-2.4
Converging
Reactive
Extrinsic motivation
Top-down social
Minor challenge
1.5
Fixed/frozen
1.0-2.4
Converging
Active
Extrinsic motivation
Bottom-up social
Minor challenge
1.5
Fixed/frozen
1.0-2.4
Converging
Buy-in
Intrinsic motivation
Top-down social
Minor challenge
2.2
Fixed/frozen
1.0-2.4
Converging
Proactive
Intrinsic motivation
Bottom-up social
Minor challenge
3.0
Edge of
chaos
2.5-4.4
Neither converging
nor diverging
Necessary
Extrinsic motivation
Bottom-up social
Major challenge
3.6
Edge of
chaos
2.5-4.4
Neither converging
nor diverging
Transformational
Intrinsic motivation
Top-down social
Major challenge
3.6
Edge of
chaos
2.5-4.4
Neither converging
nor diverging
Intrinsic motivation
Diverging*
Bottom-up social
5.6
Chaos
4.5-6.0+
Major challenge
Sources: Glor, 2001b; 2007a, b; Kauffman, 1995.
The social environment includes management style.
Convergence is movement toward similarity and less change and divergence is movement toward difference and
more change.
Continuous
47
Table 2:
Assessing Effects of Basins of Attraction
Difference
Rank,
Glor
Pattern Direction of
Difference
Rank,
between
Closeness
Pattern
Score Movement of
between
Closeness
Organization
Pattern Score to Less
Complexity
Organizational Pattern Score
to More
Patterns Ranked
& Top of
Complex
Range
Patterns
& Next
Complex
for Complexity
Next Lower Pattern
Higher Glor
Pattern
Glor Pattern
Range
Mortality
0 – 0.4
Downsizing?
0.5 – 0.9
Imposed
1.0-1.4
1.2
Converging
Fixed
0.3
1
-0.3
Reactive
1.5-1.9
1.5
Converging
Fixed
0.5
3
-0.1
Active
1.5-1.9
1.5
Converging
Fixed
0.5
3
Buy-in
2.0-2.4
2.2
Converging ?
Fixed
0.8
4
Buy-in+ pattern?
2.5-2.9
Proactive
3.0-3.4
3.0
EOC, Neither
converging nor
diverging
0.5
Necessary
3.5-3.9
3.6
EOC, Neither
converging nor
diverging
Transformational
3.5-3.9
3.6
Transformational/
necessary+
pattern+?
4.0-4.4
Continuous - -?
4.5-4.9
Continuous-?
5.0-5.4
Continuous
5.5-5.9
Continuous+
6.0 – 6.4?
5.6
3
1
-0.1
1
-0.3
3
3
-0.6
4
1.9
5
-0.2
2
EOC, Neither
converging nor
diverging
1.9
5
-0.2
2
Diverging
Chaos
0.4+
2
-1.7
5
Mortality
6.5+?
The Imposed pattern range starts at 1 rather than 0 because it is a changing pattern. There may be patterns that are
not changing with even lower scores that are on a path toward mortality.
The ranking (column 5) is from 1=closest to a pattern change to 5=farthest from a pattern change.
Note: The patterns in yellow highlighting have not been found, but are hypothesized.
48
Table 3:
Likely Incidence of Pattern Change – Based on Basins of Attraction
Likelihood:
Most Likely
Likely Incidence
Medium
Likelihood
Unlikely
Likely to Change Pattern by
Increasing Complexity (based on
Basins of Attraction)
None
0
Reactive, active
Buy-in, necessary, transformational
49
Likely to Change Pattern by
Reducing Complexity (based
on Basins of Attraction)
Reactive, active
2
Necessary, transformational ,
buy-in, imposed,, proactive
Continuous
Table 4:
Dynamics of Evolution of Organizations – Round One
Patte
rn
Rang
es
0-0.4
Pattern
Direction
Complexity
Strong
Basin of
Attraction?
Incident?
Speed
Immediate
Change?
Pattern after
First Round
Mortality
Converging
0.50.9
1.01.4
1.51.9
Downsizing
Converging
Imposed
1.1
Reactive
1.4
Converging
Low
Medium
No
Slow
No
Converging
Low
Yes
Slow
Yes
Active
1.4
Converging
Low
Yes
Slow
Yes
Imposed
1.4
2.02.4
2.52.9
Buy-in
2.1
Buy-in+
2.9
Converging
Low
Yes-very
strong to
less complex
Yes-very
strong to
less complex
No
Imposed
1.0
Imposed
1.4
1.51.9
No
Mediu
m
No
Buy-in
2.1
3.03.4
Proactive
2.9
Mediu
m
Yes- v.
strong less
complex
Yes
Mediu
m
Yes
Buy-in+
2.9
3.53.9
Necessary
3.9
Cycling
EOC &
Fixed
Cycling
between
EOC &
Fixed
Diverging
Mediu
m
Yes – strong
less
No
Fast
No –
3.53.9
Transf
3.9
Mediu
m
Yes – strong
less
No
Fast
No –
Necessary
Increasing
complexity
3.9
Transformational;
Increasing
complexity
3.9
No
Fastest
Yes, if
forces
align
Yes
Fastest
Diverging
4.04.4
4.54.9
5.05.4
Transf+
Diverging
Continuous- -
Diverging
Continuous-
Diverging
5.55.9
Continuous
5.6
Diverging
6.06.4
6.56.9
Total
Continuous+
Diverging
High
Yes, less
complex
Sensitive to
initial
condition
Increasing
Continuous+
6.0
Continuous+
6.1
Mortality
4 Glor patterns
8/16
50
Notes: Convergence means becoming less complex. Divergence means growing more complex.
+ means no pattern has been identified in the complexity range, so the putative pattern has been named for the next
lower pattern, designated with a plus; ++ or - - means second hypothesized pattern in a range. Hypothesized patterns
are in identified with yellow highlighter.
51
Table 5:
First Four Rounds of Pattern Evolution, Based on Likelihood
Pattern
Imposed
(slowest)
Reactive
Active
Buy-in
Proactive
Necessary
Transformational
Round 1
Less complex
Imposed
Less complex
Imposed
Less complex
Imposed
Less complex
Buy-in
Less complex
Buy-in plus
More complex
Necessary
More complex
Transf.
Round 2
Less complex
Dowsizing
Less complex
Imposed
Less complex
Imposed
Less complex
Reactive
More complex
Proactive
Cycling
More complex
Necessary+
More complex
Transf+
Round 3
Less complex
Downsizing
Less complex
Imposed
Less complex
Imposed
Less complex
Reactive
Less complex
Buy-in plus
More complex
Continuous - More complex
(basin)
Continuous - -
Round 4
Mortality
Downsizing
Downsizing
Less complex
Reactive
More complex
Proactive
More complex
Continuous - More complex
Continuous - -
More complex
More complex
Increasing faster
Mortality
ContinContinous+
Continous+
Mortality
uous
(fastest)
Total No.
Glor
4
3
2
2
Patterns
6
7
5
4
Total All
Patterns
Rules of evolution:
- The most complex patterns change fastest; least complex patterns change slowest
- Patterns closest to other patterns in complexity change first, drawn by the basin of
attraction of the closest pattern
- Buy-in+ has a higher complexity level than buy-in pattern.
Total all patterns refers to the sum of Glor & Hypothesized Patterns
52
Figure 1:
Difference between Each Glor Pattern Score and the Score at the Bottom of its Next Higher
Glor Pattern Range (i.e. to a pattern change)
Difference between Score & Bottom
of Next Higher Glor Pattern Range
2
1.5
1
0.5
0
53
Figure 2:
Difference between Each Glor Pattern Score and the Score at the Top of its Next Lower Glor
Pattern Range (i.e. a pattern change)
Difference between Score & Top of
Next Lower Glor Pattern Range
0
-0.2
-0.4
-0.6
Difference between Score
& Bottom of Glor Pattern
Range
-0.8
-1
-1.2
-1.4
-1.6
-1.8
54