Toward a Phylogenetic Reconstruction of Organizational Life

Journal of Bioeconomics (2005) 7:271–307 © Springer 2006
DOI 10.1007/s10818-005-5245-5

Toward a Phylogenetic Reconstruction
of Organizational Life
IAN PAUL McCARTHY
SFU Business, Simon Fraser University, 515 West Hastings Street, Vancouver, BC, V6B 5K3, CANADA
(imccarth@sfu.ca)

Synopsis: Classification is an important activity that facilitates theory development in many academic
disciplines. Scholars in fields such as organizational science, management science and economics and
have long recognized that classification offers an approach for ordering and understanding the diver-
sity of organizational taxa (groups of one or more similar organizational entities). However, even the
most prominent organizational classifications have limited utility, as they tend to be shaped by a spe-
cific research bias, inadequate units of analysis and a standard neoclassical economic view that does
not naturally accommodate the disequilibrium dynamics of modern competition. The result is a rela-
tively large number of individual and unconnected organizational classifications, which tend to ignore
the processes of change responsible for organizational diversity. Collectively they fail to provide any sort
of universal system for ordering, compiling and presenting knowledge on organizational diversity. This
paper has two purposes. First, it reviews the general status of the major theoretical approaches to bio-
logical and organizational classification and compares the methods and resulting classifications derived
from each approach. Definitions of key terms and a discussion on the three principal schools of biolog-
ical classification (evolutionary systematics, phenetics and cladistics) are included in this review. Second,
this paper aims to encourage critical thinking and debate about the use of the cladistic classification
approach for inferring and representing the historical relationships underpinning organizational diver-
sity. This involves examining the feasibility of applying the logic of common ancestry to populations of
organizations. Consequently, this paper is exploratory and preparatory in style, with illustrations and
assertions concerning the study and classification of organizational diversity.

Key words: cladistics, classification, configurations, diversity, evolution, organizations, phylogeny, tax-
onomy, typology

JEL classification: A1, L0, L2, L6, M1, N0

1. Introduction

Classification underlies language and cognition. For example, the nouns and verbs
of a language are used to label objects and activities, and this process of nam-
ing is a constant exercise in classification. It is both a process and a product, pro-
viding mental models for ordering, labeling, and articulating knowledge about the
world we live in. A classification ‘arranges materials in a way that tells us some-
thing about them: a mere list has no such character’ (Ghiselin 1997, p. 301) and
a good classification provides ‘a system which has high predictive value and will
allow maximum information retrieval’ (Mayr 1969, p. 54).

272 McCARTHY

This ability to order and represent differences has aided our philosophical and
scientific studies of biological, social, economic and technological entities, but it
is important to recognize that the cognitive models produced by any classification
are like the classifications themselves, incomplete, parsimonious and constantly
evolving. Consequently, a classification should permit continuous development and
refinement, whilst providing simple and powerful explanations of complex phe-
nomena (Schumacher & Czerwinski 1992). This intellectual and perspicacious
activity was discussed by Good (1965), who explained that classifications are
constructed for reasons that range from the need to conduct rigorous academic
research, to the desire to produce simple and fun check lists. Yet regardless of the
purpose, the value of any good classification is its ability to help organize and reg-
ulate data and thoughts about our reality and then develop and communicate asso-
ciated ideas.
In accord with the academic purpose of classification, scholars concerned with
the economic (Coase 1937, Williamson & Masten 1999), technological (Chan-
dler 1990) and behavioral (Cyert & March 1963, Wernerfelt 1984) views of the
firm, have long sought to understand organizational variety, change and survival.
To help study these issues, it has been necessary to develop appropriate frame-
works, essentially classifications, which characterize the interconnectivity between
the dimensions (managerial, technological, structural, market, etc.) that differenti-
ate organizations. Likewise classifications have been produced to map the develop-
ment and diffusion of different process and product technologies. As early as the
19th century Babbage (1835) sought to promote comprehension and adoption of
the various manufacturing processes that existed. His classification was based on
factors such as the newness of the technology, the type of power consumption,
the process control used, the transformational properties of the technology and
the utility of the technology. Although his ideas never developed into a universal
system of technological classification, they are consistent with the focus of mod-
ern classifications dealing with innovation. These include innovation versus inven-
tion and imitation (Schumpeter 1934), innovation as an output and process (Daft
1978), innovation newness (Dewar & Dutton 1986), and the adoption of innova-
tions (Subramanian 1996).
As a gesture to Good’s (1965) assertion that some people simply produce clas-
sifications for fun, it is worth mentioning an interesting and teasing classification
presented by Borges (1964, pp. 101–105). At first this classification appears to be
strange but genuine, but as no other record of the classification exists, it seems
that Borges fabricated it to amuse and demonstrate the role of perception in clas-
sification. He refers to a Chinese encyclopedia entitled, The Celestial Emporium
of Benevolent Knowledge, in which it is written that ‘animals are divided into: (a)
belonging to the Emperor, (b) embalmed, (c) tame, (d) sucking pigs, (e) sirens, (f)
fabulous, (g) stray dogs, (h) included in the present classification, (i) frenzied, (j)
innumerable, (k) drawn with a very fine camelhair brush, (1) et cetera, (m) having
just broken the water pitcher, (n) that from a long way off look like flies.’ Borges’

PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 273

classification illustrates how incomprehensible a classification can be to those who
are not familiar with the local context or rationales which govern the criteria for
differentiating. Thus, different societies can sometimes describe and classify things
that bewilder researchers in other societies.
This issue of perception and sense making of reality is central to the process
of classifying organizations, as different areas of organizational science and eco-
nomics will use different perspectives to recognize or ascertain what makes orga-
nizations different. Thus, when determining the unit of analysis for classification,
one must recognize the pitfalls of researcher bias, which can become amplified
through confusion and misuse of the various terms, methods and levels of analysis
involved with classification. Yet these problems are not unique to the classification
of organizations, as there is also a history of significant dispute concerning the unit
of analysis in biological classification literature. This is a problem which Keller
et al. (2003) call ‘semantic schizophrenia’, as many biological researchers appear to
have been largely unaware of the philosophical positions implied by their approach
to classification (de Queiroz 1994). For the study of organizational diversity to
advance, those involved in the discipline must recognize and address these system-
atic issues. Otherwise, they will continue to produce classifications that sometimes
reference each other, but rarely join with or expand on each other.
This situation is the impetus for this paper, which introduces and examines the
feasibility and value of using cladistic analysis to study and represent organiza-
tional genealogy. It argues that the cladistic focus on shared patterns of common
ancestry is an evolutionary logic compatible with the variation, selection and reten-
tion explanations for how and why new organizational taxa emerge. This paper
extends existing research on organizational systematics by (McKelvey 1975, 1978,
1982, Warriner 1979, Haas et al. 1966, Pugh et al. 1969, Rich 1992, Doty & Glick
1994, Bailey 1994) and advances more recent research that has developed ini-
tial and primitive cladistic analyses of organizations (McCarthy et al. 1997, 2000,
Leseure 2000), industries (Leask 2002, Andersen 2003) and organizational innova-
tion and industrial development (Baldwin et al. 2003).

2. A review of classification

The first formal classifications sought to make sense of the natural world and were
produced by philosophers and biologists. This intellectual combination led to the
development of a number of related and competing theoretical stances about how
to classify. As classification is now an established research process in the physical,
life and social sciences, the result is a diverse range of interpretations and fre-
quent misuse of classification terms, theories and methods. This has created seman-
tic barriers which affect how classifications are constructed and reported. With this
section of the paper, I hope to avoid similar misconceptions and provide a degree
of terminological clarity.

274 McCARTHY

First, the overriding term that refers to the general study of diversity is sys-
tematics (Simpson 1961). It is viewed as an area of biology that deals with the
study of different types of organisms, their distinction, classification, and evolution
(Blackwelder & Boyden 1952). The term taxonomy refers to a branch of systemat-
ics concerned with the theory and practice of producing classification systems and
schemes. Thus, constructing a classification is a taxonomic process with rules on
how to form and represent groups (taxa), which are then named (nomy). Within
biology, three schools have dominated the recent history of classification: evolu-
tionary, phenetic and cladistic (these are discussed in next section of the paper),
while the social sciences have two general approaches to classification: empirical
and theoretical. The principal difference between the two social science approaches
is the stage at which a theory of differences is proposed and evidence then sought
to validate the theory (Warriner 1984, Rich 1992, Dotty & Glick 1994). Theoreti-
cal classifications in the social sciences begin by developing a theory of differences
that result in a classification of organizational types, known as a typology. Only
when the classification has been proposed, is a decision made as to where an entity
belongs in the classification. On the other hand, with the empirical approach,
social science classifications begin by gathering data about the entities under study.
The data are then processed using statistical methods (numerical taxonomy) to
produce groups according to the measures of similarity and statistical techniques
used. Thus the overall aim is to use data to construct the classification, instead
of supporting it, but it should be noted that in practice data are seldom collected
without an expectation about what they will reveal or validate. It is also impor-
tant to note, that most organizational classifications (theoretical and empirical) do
not properly define the unit and level of analysis, and therefore misuse the terms
taxon, group, class and type when presenting their classifications. This is probably
the main reason why most organizational classifications remain solitary, undevel-
oped and unconnected to other organizational classifications.
Although the term classification has been used throughout this paper to reflect
the topic of this paper and of this Special Issue of the Journal of Bioeconomics,
there is no agreement among biologists about the general use of the term. But if
we inspect its use across disciplines and relevant entries in dictionaries, there is
a distinction between classification as a process (to classify) and classification as
an output of the process (a classification). In the first instance, it represents the
sorting and arrangement of information in a way that will inform (Ghiselin 1997).
This definition partly relates to the mathematical and information theory concept
of classification, which assumes that given an equivalence relation for a subset of
a set of entities, there will be a partitioning of the set into a number of mutu-
ally disjoint equivalence classes (this use of the term class is not equivalent to the
biological taxonomic terms, classes or categories). Hence, classification as a pro-
cess should not be confused with categorical assignment (Scheffler 1967), deter-
mination (Radford et al. 1974), class identification (Capecchi & Moller 1968) and


identification (Capecchi 1964), which are concerned with determining where enti-
ties, taxa and classes should appear in a classification.
Classification as an output (a product of the process of classifying) deals with
how groups and classes of entities will be arranged, in accord with the taxonomic
approach used (Mayr 1982, McKelvey 1982). It is a framework (e.g. a matrix, a
table, a tree diagram, etc.) for ordering and representing, regardless of whether a
theoretical or empirical approach is used. The terms classification scheme and classi-
fication system are often used to distinguish and identify classification as an output
(Fox 1982). Examples of such schemes and systems include the Linnaean System of
nomenclature, the Periodic Classification of chemical elements, the Dewey Decimal
Classification System for organizing books and other bibliographic items, and the
North American Industrial Classification (NAIC) and Standard Industrial Classifi-
cation (SIC) systems for naming and organizing industry sectors.

2.1. The evolutionary, phenetic, and cladistic schools of classification

To understand the differences between the biological schools of classification, it is
helpful to have a basic appreciation of the history of classification philosophies,
and in particular, the concepts of phylogeny and phenetics. This is because the
three schools vary in how (if at all) they represent phylogeny, the types of groups
they recognize and the different types of characters they use to determine groups
(see Figure 1 and Table 1). As the history of classification is complicated and
made-up of a number interconnected areas and eras of thinking, I will simply sum-
marize some of the key issues. For more detailed accounts of how the competing
schools evolved, the reader is referred to Cain (1962), Mayr (1969), Hull (1988)
and Sneath (1995).
Prior to the publication of The Origin of Species (Darwin 1859, [1996 edition]),
the first formal classifications generally sought to make sense of the natural world
by grouping organisms according to their size, structure, features, mode of repro-
duction, and where they existed (location). This approach to classification can be
traced back to Aristotelian essentialism, a philosophical belief that entities have a
set of characteristics which make them what they are. The focus is on conceiv-
ing of groups according to their hidden reality and the resulting biological classi-
fications are known as typologies, because members of a group are considered to
have the same essence and are therefore the same type (Hull 1965, Mayr 1969).
This notion of classifying using observed features is also the basis of phenetics,
which classifies organisms based on similarities and differences in as many observ-
able characteristics as possible. There is also a doctrine (nominalism) that denies
the existence of universals and therefore rejects the concepts of sets and groups.
Nominalism believes that only individuals exist and that all proposed groupings of
entities are simply artifacts of the human mind. Not surprisingly, it does not fea-
ture as a practicing taxonomic approach.

276 McCARTHY

Figure 1. Types of taxonomic characters and groups. Adapted from Ridley (1993, p. 366).

Table 1. Differences between phenetic, cladistic and evolutionary classifications

Classification Characters used

Groups recognised Homologies
Monophyletic Paraphyletic Polyphyletic Analogies Ancestral Derived
Phenetic Yes Yes Yes Yes Yes Yes
Phylogenetic Yes No No No No Yes
Evolutionary Yes Yes No No Yes Yes

Source: Ridley (1993, p. 367).

With the development of the Linnaean system for assigning and naming spe-
cies, the essentialist approach had a convenient and stable information system,
motivating years of taxonomic activity, much of which was identification rather
than classification (Schuh 2003). However, with the publication of The Origin of
the Species, taxonomists were provided with an alternative to the essentialist and


nominalist views of diversity. In very simple terms, the thesis was that natural
groups did exist and that this is because members of a group have descended from
a recent and common ancestor. The term phylogeny was coined by Haeckel (1866),
a German biologist and philosopher, to indicate these ancestor-descendant rela-
tionships. He showed how these relationships could be represented with his ‘tree
of life’, a branching diagram that illustrated his view of the evolution of life from
bacteria to humans. Thus, phylogenetics, the evolutionary relationships between
organisms, became the central principle which differentiated the evolutionary, phe-
netic and cladistic schools of classification.
In the 1930s and 1940s biologists had accepted the broad premise of Darwin’s
theory of evolution and with advances in genetics this resulted in a resurgence
for evolutionary biology and systematics. The focus was on reconciling Darwin’s
theory of evolution with genetics as the basis for biological inheritance and thus
this era gave rise to the evolutionary school of classification (Mayr 1942, Simp-
son 1961). This school recognizes that evolution occurs and utilizes both phenetic
and phylogenetic relationships. It also recognizes and accepts paraphyletic groups
(a group containing the ancestor together with some, but not all of the descen-
dants) and monophyletic groups (a group containing the ancestor together with
all descendants), thereby using derived and ancestral homologies, which are cor-
respondingly characters with advanced or primitive states shared by two or more
taxa and present in their ancestor (see Figure 1 and Table 1). However, the mixing
of phenetic and phylogenetic information, coupled with the uncertainty of delim-
iting paraphyletic groups results in phylogenies that are difficult to translate into
unequivocal classifications.
The phenetic school of classification emerged in the late 1950s and early
1960s (Michener & Sokal 1957, 1958, Sokal 1962) as an alternative and oppos-
ing approach to the evolutionary school, but the origins of the basic phenetic
approach (observed similarities) can be traced back to the mid 1800s. Whewell
(1840) and Mill (1843) suggested that the grouping of entities on the basis of
shared properties could provide a system that minimizes information management,
while maximizing knowledge. Then Gilmour (1937, p. 1040) in support of the phe-
netic approach, argued that a classification should strive to provide ‘an arrange-
ment of living things which enables the greatest number of inductive statements to
be made regarding its constituent groups and which is therefore the most generally
useful for the classification of living things.’ Thus, the phenetic approach places
emphasis on collecting and processing data to produce what it calls information
rich groups, rather than theory led groups.
Phenetics flourished with the developments in computing technology in the
1950s and the work on numerical taxonomy in the 1960s (Sokal & Sneath 1963).
This was the period when phenetics became a recognized school of classification
concerned with using a set of statistical methods (know as numerical taxonomy) to
group entities on the basis of observed similarities and according to certain mea-
sures of similarity.

278 McCARTHY

Phenetics ignores the evolutionary history of the entities under study and Sneath
(1988, 1995) attempts to justify this point by reasoning that the periodic table in
chemistry cannot be constructed phylogeneticaly, therefore suggesting that informa-
tion rich groups do not have to evolve. Thus, phenetics discounts any theory that
might explain differences, such as the theory of evolution for biological organisms
and the theory of electron structures for chemical elements. It simply contends that
the best measure of relatedness is overall similarity.
The numerical taxonomy component of the phenetic school is mathematical in
discipline, but biological in application and some of the first applications of these
statistical methods occurred in anthropology (Driver & Kroeber 1932) and psy-
chology (Zubin 1938). As reported by Sokal & Sneath (1963), the early aims and
assumptions of numerical taxonomy in biology revolved around: (1) the need for
repeatability and objectivity; (2) the use of quantitative measures of resemblance
from numerous equally weighted characters; (3) the construction of taxa from
character correlations leading to groups of high information content and (4) the
separation of phenetic and phylogenetic considerations. To address this last objec-
tive, the unit of analysis, called the operational taxonomic unit (OTU), should be
as theory and subject neutral as possible. The OTU is simply a group of entities
that is considered to be the ‘the lowest ranking taxa in a given study’ (Sneath &
Sokal 1973, p. 69).
The product of numerical taxonomy is a dendrogram, or tree diagram (Figure 2).
This term was first introduced by Mayr et al. (1953, pp. 575–578) who defined a
dendrogram as ‘. . . a diagrammatic illustration of relationships based on degrees
of similarity (morphological or otherwise). . . .’ Nearly two thirds of numeri-
cal taxonomy applications involve using the hierarchical agglomerative technique
(Blashfield & Aldenderfer 1978) to produce dendrograms that illustrate the fusions
or divisions of groups of entities made at each consecutive stage of the analysis.
They are an expanding or hierarchical structure that continues until the initial
group can no longer be sub-divided. The different types of agglomerative tech-
niques arise from the various methods of establishing distance (the measure of
the phenetic difference between two groups of entities) or similarity. Other names
for numerical taxonomy include mathematical taxonomy (Jardine & Simpson 1971),
numerical classification (Clifford and Stephenson 1975) and multivariate morpho-
metrics (Blackith & Reyment 1971), while the mathematical mechanics of the
method spawned a host of related techniques, including clustering or cluster analy-
sis (Everitt 1986), clumping (Needham 1965) and pattern recognition (Bezdek 1981).
The acknowledged limitations of phenetics and numerical taxonomy revolve
around the methodological assumptions and operational procedures they follow.
For example, not all characters are equally important and phenetics does not
offer an objective way to select those that are. As a result, emphasis is placed
on using all possible characters to avoid residual weighting. This raises questions
concerning what characters are and how they should be determined. In a gen-
eral and fundamental sense, characters are discernible features of an organism,


A’
Branch
Taxa A

Distance between Taxa A and
Node Taxa B = A’ + B’
Taxa B

B’

Taxa C

Taxa D

Taxa E

Branch Length

Taxa F

Figure 2. A dendrogram.

used to distinguish it from other organisms. But should such features be mor-
phological, physiological, ecological or behavioral? At what level (species, genus,
family, etc.) in a classification would a character be diagnostic? How are the char-
acter states determined? To help understand the complexity and relevance of these
issues, Ghiselin (1997) and Inglis (1991) provide discussions concerning the phi-
losophy and definition of characters for biological classification in general, while
Sneath & Sokal (1973) present categories of inadmissible characters that do not
contribute to the mathematical tightness of a group in numerical taxonomy. A final
and major criticism of the phenetic school of classification is that it does not pro-
vide an explanation of how researchers should actually define and collect the unbi-
ased and theory-free data that is central to its tenet. Thereby suggesting that it is
not possible to both classify and make theory-free observations.
In summary, numerical taxonomy and phenetics have become synonymous, as
the former provides a method and rules that are appropriate to the observed and
empirical nature of the latter. However, it is important to note that with modern

280 McCARTHY

day classification, phenetic, cladistic or otherwise, there is an obvious need to use
numerical methods to help process and order the data that constitute any classifi-
cation. Numerical taxonomy has become an established methodological tool that
is broader than phenetics and cladistics and is used by many disciplines includ-
ing organizational science (Goronzy 1969, Pinto & Pinder 1972, Hayes et al. 1983),
psychiatry (Pilowsky et al. 1969), medicine (Wastell & Gray 1987), market research
(Green et al. 1967), education (Aitken et al. 1981), archaeology (Hodson 1971) and
economics (Wooldridge 2003, Bischi et al. 2003, Sellenthin & Hommen 2002).
During the same period that phenetics and numerical taxonomy were coming to
prominence, an alternative school began to emerge. The figurehead for this school
was the German entomologist Willi Hennig (1950), who believed that evolutionary
history should play a greater role in taxonomy. With early evolutionary taxonomy
the aim was to produce classifications that reflected all aspects of phylogeny, but
this was problematic (Hull 1985). Hennig recommended that biological classifica-
tions should only focus on one aspect of phylogeny, the relative recency of com-
mon ancestry. In particular, Hennig explained that even if two taxa share a large
number of homologies, their classification within the same group cannot be conclu-
sively assumed, as homologies can result from shared derived characters or shared
ancestral characters (Figure 1). To ascribe evolutionary relationships, only shared
derived homologies (synapomorphies) should be taken into consideration. Hennig’s
work was inspired partly by his desire to counter the German school of idealistic
morphological systematics (Schindewolf 1950), which was a fundamental form of
phenetics. He originally called his approach phylogenetic systematics, but his sup-
porters and to a degree his opponents adopted the name cladistics from the Greek
Kλαδos for branch. Thus, cladistics is approximately equal to phylogenetic system-
atics and originally meant the study of clades, which are ‘the individual branches
in the genealogical nexus’ (Ghiselin 1997, p. 306). The term cladism refers to the
movement that supported Hennig’s approach to classification and the product of
a cladistic analysis is known as a cladogram (Figure 3). Cladograms are tree-like
diagrams that depict the pattern of relationships among clades based upon shared
derived characters. The branches represent taxa, while the tips of the branches are
generally species.
Although Hennig explored mathematical set theory as an underlying reason and
principle to rationalize the resulting hierarchical and nested sets of taxa in a clad-
ogram, his primary justification for grouping by synapomorphy was to try to pro-
duce natural and objective classifications based upon the process of evolution.
There is still significant debate as to whether a theory of evolution is a philosoph-
ical prerequisite for biological classification, or rather that biological classifications
provide evidence to support a theory of evolution (Brower 2000). In support of the
former view, Wiley (1975, p. 234) interpreted and translated Hennig’s justification
of cladistic methods into three axioms: (i) evolution occurs; (ii) only one phylogeny
of all living and extinct organisms exists, and this phylogeny is the result of genea-
logical descent; and (iii) characters may be passed from one generation to the next


node

branches

Ch 1

T1 T2 T3 T4

outgroup ingroup
T- Taxa, a named group of two or more entities.
Ch - Characters, an observable feature of entity, that can be used
to distinguish it from other entities.
Outgroup - The taxon used to help resolve the polarity of characters.
Ingroup - The group of interest.
Node - A point on a cladogram where three or more branches meet.
Branch - A line connecting to two nodes. Indicates taxa.
Figure 3. A cladogram.

generation, modified or unmodified, through genealogical descent. Meanwhile sup-
porters of the alternative view hold that the following axioms are necessary and
sufficient for cladistics: (i) observed character differences among taxa provide the
evidentiary basis; (ii) an irregular bifurcating hierarchy is a useful way to represent
relationships among taxa; and (iii) parsimony is the guiding epistemological prin-
ciple of the systematic approach (Platnick 1979, Nelson & Platnick 1981, Brower
2000). Regardless of whether you believe evolution provides a necessary and under-
lying ontological basis for cladistics, or if you assert that evolution is a relevant,
but methodologically redundant assumption for cladistics (Carpenter 1987), it is
generally accepted that cladistic analysis is valid for representing the patterns of
phylogenetic relationships.
As with phenetics, cladistics has limitations due to its assumptions and proce-
dures. For example, the task of choosing appropriate characters remains problem-
atic. If the cladistic method is applied to lions, tigers and zebras, using only the
single character ‘the presence or absence of stripes’, the result is the logical, but
ridiculous observation, that tigers and zebras are held to be more closely related
to one another than tigers are to lions. As with phenetics, the general rule is to

282 McCARTHY

use as many characters as possible, so as to dilute the impact of any wrongly
selected characters. Also, Kitching et al. (1998) report that continuous characters
with real number values (e.g. wing length) tend to produce cladograms with lower
levels of confidence, compared to those produced using qualitative characters that
are described with words (e.g. the presence or absence of wings). Another criti-
cism of cladistics concerns the type of material appropriate for analysis. As Hennig
and many of the early cladists were entomologists, there was a view that cladis-
tics was only appropriate for organisms whose characters could be found in the
fossil record. Yet cladistics has been used to classify a wide range of biological
organisms including bacteria, plants and animals and phylogeny has been used for
nearly two hundred years to represent the descent of language and manuscripts
(Zumpt 1831, Lachmann 1850, Platnick & Cameron 1977, Bateman et al. 1990,
Robinson & Robert O’Hara 1996). Thus, it is clear that the material suitable for
cladistic analysis does not have to be biological. What is required, are individuals
whose evolutionary history can be inferred and represented as patterns of com-
mon ancestry. This is the concept of species as individuals, as opposed to species
as classes or kinds, both of which are abstract notions (Ghiselin 1966, 1974, Hull
1976, 1978). Individuals, classes and kinds can each be described and differentiated
from other individuals, classes and kinds, but only individuals are real and con-
crete entities that are constrained in space and time, have proper names and can
change. They are the unit of analysis for a cladistic classification.
In summary, the evolutionary and phenetic schools believed that the empirical
and theoretical challenge of properly estimating homology and phylogeny was too
difficult. But it is now generally accepted that cladistic analysis provides an objec-
tive and empirical method to assess and represent phylogeny and homology. This
is because common ancestry is real i.e. a group of taxa either are, or are not
related by ancestry, unlike perceived phenetic similarity which is inherently sub-
jective. Also, the processing of character data using modern cladistic software is
as analytical and repeatable as phenetic methods, but with the added value of
conveying significant information content in terms of character states and testable
hypotheses about phylogenetic relationships. The output of a phenetic study leads
to mere associations.

3. The classification of organizations

Although there is no established field of organizational systematics, researchers
have long examined how organizations differ according to factors such as resource
requirements (Penrose 1959, Barney 1991, Nelson & Winter, 1982), structural fea-
tures (Chandler 1962, 1977), strategic behaviour (Miles & Snow 1978) and dynamic
capabilities and routines (Teece et al. 1997, Eisenhardt & Martin 2000, Winter
2003). This interest in organizational diversity is best associated with the branch of
organizational science known as population ecology; an area of research primarily


concerned with why there is a diversity of organizations and the reasons for the
differences (Hannan & Freeman 1977, 1989). It focuses on the development of the-
ories (which are evolutionary in origin) to explain organizational change and vari-
ety, but it is not overly concerned with classifying the diversity. Consequently and
despite the interest in organizational differences, there has not been a coordinated
effort to produce any form of universal classification suitable for all known organi-
zational taxa. That is not to say that there have been no attempts to classify orga-
nizations. On the contrary, the general areas of organizational and management
science have produced many classifications, but none however, that are adequate
for representing all potential organizational taxa.
One approach to studying organizational form and diversity that does advocate
a systematic view is configuration theory (Lawrence & Lorsch 1967, Miles & Snow
1978, Miller 1986, 1987, 1996, Meyer et al. 1993). It is concerned with explain-
ing the relationship between an organizational form (configuration) and the con-
ditions and demands of its environment; and the use of the term configuration is
generally comparable to the notion of organizational taxa. For example, Romanel-
li (1991, pp. 81–82) views organizational form as ‘those characteristics of an orga-
nization that identify it as a distinct entity and, at the same time, classify it as a
member of a group of similar organizations’ and McKelvey (1982, p. 196) refers to
organizational taxa as ‘a collectivity of the adaptive properties of all its included
organizations.’ But configuration theorists also use terms and language that reflect
the prevalent confusion about the unit of analysis and an ignorance of the species-
as-individuals thesis i.e. they do not recognize differences between entities, clas-
ses and types. For example, Meyer et al. (1993) and Dess et al. (1993) provide
explanations of configurations as gestalts which seem consistent with the notion of
organizational taxa as proper and incorporated individuals, but their suggestions
that gestalt, configuration and archetypes are all synonyms, is not appropriate for
classifications of entities. According to Ghiselin’s (1997) individual thesis, for orga-
nizations to be viewed as taxa, they should be real and not abstractions or types.
This notion of organizations as individuals is broader than the existence of indi-
vidual legally incorporated firms. It has a metaphysical context which implies that
organizational taxa are not classes of organizations, but rather uniform and con-
crete entities constrained in space and time, and that the components of an indi-
vidual are not members of the individual, but parts that help make the individual
whole. Despite the fact that organizational and technological systems by and large
conform to these criteria, the majority of existing organizational classifications are
unaware of the relevance and importance of the organizations as individuals thesis
despite different contexts and system perspectives (e.g. social, technological, legal
and economic).
There are of course differences in how this thesis relates to biological species and
organizational species and these will result in some deviation and points for dis-
cussion. For example, once a biological organism is dead or a biological species
is extinct, it currently remains that way. This is not necessarily the case for social,

284 McCARTHY

economic and technological entities, as information about their components, form
and operation can be recorded and stored in such a way that it is possible to recre-
ate them, if there is wish to and the environment allows. As an example, consider
the Boneshaker bicycle, a technological system whose purpose is to provide ground
transportation by cycling. Historians and enthusiasts (Bijker 1995, Alderson 1972)
would consider this technological entity to be a taxon of bicycles, while the bicycle
would be viewed as a class of transportation technologies. The Boneshaker has a
proper name and a history, indicating that its existence was constrained spatially
and temporally. That is, the Boneshaker emerged in certain regions in the 1870s
and was descended from another group of bicycles, the Hobbyhorse. It is therefore
possible to infer phylogeny and observe shared innovations such as frame struc-
ture, tire technology, wheel technology, drive chain technology and steering system
technology. When the Boneshaker began to disappear some twenty years later with
the advent of the Safety Bicycle, this technological extinction was not permanent.
Today we have archives and manufacturing technologies that allow us to reproduce
and use Boneshakers. As commented by Ghiselin (1997) at the end of his discus-
sion on what constitutes an individual, this ability to make extinct biological sys-
tems extant again only exists in the imagination of science fiction authors.
Prior to configuration theory, one of the earliest formal classifications of orga-
nizations is attributed to Parsons (1956) who attempted to identify and order
types of organizations by viewing them as social systems seeking to attain a spe-
cific type of social goal. This fundamental typology differentiated organizations
according to four dimensions: (i) the value system which defines and legitimizes
the goals of the organization; (ii) the adaptive mechanisms which organize and
operate the resources; (iii) the operative code for directly responding to goals;
and (iv) the integrating mechanisms. Following this work, Woodward (1958) pro-
duced an empirical classification of the functional behavior of manufacturing
firms according to the type and complexity of the production techniques used by
the organization. While Woodward’s classification has been widely accepted and
verified through subsequent studies, it has also been subject to criticism concern-
ing the simplicity and common sense nature of the findings (Clegg 1990). How-
ever, the value of her classification is acknowledged by its robustness, longevity
and impetus for similar work. This includes classifications based on the coercive,
remunerative, or normative power of the organization leaders (Etzioni 1964), the
differentiation of formal organizations according to who is the prime beneficiary
(Blau & Scott 1962), technology as a key determinant of organizational struc-
tures (Perrow 1967), organization size (Kimberly 1976), use of technology (Child
1973), strategies employed (Filley & Aldag 1978, Romanelli 1991), product service
(Fligstein 1985), control systems utilized (Etzioni 1964, Litz 1995), technology,
organization and control (Aldrich & Mueller 1982), the degree of environmental
stability (Lawrence & Lorsch 1967), types based on bureaucracy, value rational
action, rational-legal authority, or inner-worldly asceticism (Weber 1968) and clas-
sifications based on the operative goals (Katz & Kahn 1966), and output goals,


adaptation goals, management goals, motivation goals, and positional goals (Gross
1969).
Many of these organizational classifications are considered to be typologies, as
they are based on a theoretical effort to explain differences, and are often gov-
erned by the ‘limitations, the biases, and/or the organizational frames of refer-
ence of those doing the classification’ (Baudhuin et al. 1985, p. 2). Consequently,
we have a collection of classifications that have been conceptualized using a small
number of organizational dimensions and have no common and proper definition
of the unit of analysis. Despite these limitations, the classifications still provide a
good basis for understanding organizational form and diversity, but as remarked
by Meyer et al. (1993, p. 1182), ‘the allocation of organizations to types often
is not clear cut. Because of their a priori nature and frequent lack of specified
empirical referents and cutoff points, typologies are difficult to use empirically’.
This is evidenced by the relatively low number of empirical studies to use the the-
oretical frameworks developed by these classifications. Also, some of these early
organizational classifications would not be appropriate for further analysis from a
history of evolutionary relationships perspective. This is because the unit of anal-
ysis in these classifications does not conform to the species-as-individuals thesis
and the focus of differences is on functional concepts, rather than the adaptation
of form and function. Evolutionary biologists contend that teleology alone is not
adequate for historical explanation, while early organizational classifications were
based on rational and natural views of organizations (as discussed in the next sec-
tion) and therefore conceptualized and classified organizations in terms of purpose.
For accounts of the role of form, function and adaptation in determining the unit
and focus of a classification study, see Bigelow & Pargetter (1987), Amundson &
Lauder (1994) and Ghiselin (1997).
There are also a significant number of organizational classifications derived
using numerical taxonomy methods (Haas et al. 1966, Goronzy 1969, Pugh
et al. 1969, Samuel & Mannheim 1970, Prien & Ronan 1971, Pinto & Pinder
1972, Reimann 1974, Galbraith & Schendel 1983, Hambrick 1983, Hayes et al.
1983, Hatten & Hatten 1985). In accordance with the phenetic school of biolog-
ical classification, many of these empirical organizational classifications emphasize
the need for theory-free and quantitative data to ensure objectivity and repeat-
ability, but they also failed to explain how researchers could design and conduct
studies using theory-free data. Instead the case for objectivity rests on the automa-
tion of the computations and the fact that these studies tend to collect and pro-
cess data on more organizations than studies based on theoretical classifications
have done. However, even this computational basis for objectivity is unsound, as
researchers using numerical taxonomy methods are still required to make intuitive
and ‘rule of thumb’ decisions concerning which method and parameters to use.
Thus bias and approximations can easily appear in organizational classifications
derived using numerical taxonomy methods.

286 McCARTHY

In a paper that recognizes both the benefits and limits of numerical taxonomy,
Rich (1992) presents a case for combining the empirical, theoretical and evolution-
ary perspectives of organizational diversity. He argues that organizational classifi-
cations should do more than simply present clusters of entities. They should help
explain the causes of the diversity and similarity. To achieve this, Rich proposes
that the phylogenetic, population ecology and numerical perspectives be combined
to understand and explain the ‘blueprint’ of organizational forms. The result, he
suggests, will be a classification method that integrates a theory of differences with
the notion of fit and that uses numerical methods to build a hierarchical classifi-
cation capable of representing the diversity of organizational life. Fourteen years
earlier, McKelvey (1978) suggested that population ecology studies should use clas-
sification methods to study organizational taxa and argued that the formulation
of a classification is a prerequisite for the maturation of organization science. He
proposed that lessons could and should be learned from the area of biological
systematics to classify organizations, and in his work Organizational Systematics
(McKelvey 1982) he discussed the merits of using phylogenetic relationships to rep-
resent organizational change and diversity. Both McKelvey’s and Rich’s proposi-
tions support the underlying tenet of this paper, which is, that there is a need
for organizational science to develop jointly a broad theory on how organiza-
tional diversity is generated, along with a system of organizational classification
that coincides with this theory.

4. A cladistic analysis of organizational diversity

This paper argues that by using cladistic analysis to study and represent organiza-
tional phylogeny, we can develop a theoretical context for organizational diversity
that permits interpretation of data and phenomena from an evolutionary point of
view. For this to be possible though, we must recognize that organizational taxa are
related by descent from a common ancestor, that there is a bifurcating or branch-
ing pattern of new clade development and that the changes in characteristics take
place in lineages over time. It is generally accepted that such conditions do exist for
organizations, but as is evidenced by the variety and number of redundant classifi-
cations of organizations, there is limited agreement about how to conceptualize and
continuously represent organizational diversity. So to proceed with this paper, it is
necessary to consider and present organizations as appropriate entities for cladistic
analysis and examine the assumption that they evolve from common ancestors.
To understand how organizations are distinguished from other types of complex
system and the problem of classification respective, I will refer to use four related
and complementary organizational perspectives: the rational system view (Simon
1945, Cyert & March 1963), the natural system view (Selznick 1957), the open
system view (Boulding 1956, Katz & Kahn 1966) and the complex adaptive view
(Anderson 1999, Dooley & Van de Ven 1999, Allen 2001, McCarthy 2004). The


first three of these views are well established and are used to define organizations
and explain the history of organization studies (Scott 1987, Baum & Rowley 2002).
The fourth is a relatively contemporary view and to an extent unifies and extends
the first three.
With the rational view, the assumption is that organizations are created for a
purpose and will therefore require appropriate capabilities and components (peo-
ple, technology, etc.) to achieve this purpose. They are viewed as machine-like sys-
tems with formal procedures and engineered structures. The natural view assumes
that the purpose of organizations is simply survival and to achieve this, they must
exhibit autonomous and adaptive properties. It moves from the machine metaphor,
to viewing organizations as organic and learning systems.
With both the rational and natural views, organizations are treated as tangible
entities with ‘goal-directed, boundary maintaining, and socially constructed sys-
tems of human activity’ (Aldrich 1999, p. 2), but with the open system view, the
focus is extended to include the connections and interdependences between an
organization and its environment. The open view recognizes that organizations are
transformation systems with internal and external interactions. They interact with
other systems to receive inputs such as energy, materials, information and routines,
and internally transform them into product and service offerings. These interac-
tions are the basis of the complex adaptive view, which considers organizations to
be composed of levels of relatively autonomous sub-systems whose combined and
emergent characteristics cannot easily be reduced to one level of description. Thus,
the complex adaptive view defines organizations as open systems with agency, and
the ability to adapt, learn and create new rules, structures and behaviors at several
interrelated levels (McCarthy 2004). With these multiple levels and interactions,
organizations are considered to be hierarchically arranged (Baum & Singh 1994),
which in turn leads to multiple levels of analysis. For example, we could focus on
populations of organizational entities based on sectoral differences (e.g. agriculture,
banking, electronics, biotechnology, etc.), or in terms of their operational activity
(e.g. retail, service, manufacturing, etc.), or study organizations at the firm level
(e.g. strategies and processes) or focus on intraorganizational activity (e.g. work
groups). Collectively these views and levels constitute a complex adaptive system
which has multiple complex adaptive systems hierarchically nested within.
With the open system and complex adaptive view it is possible to recognize, analyze
and classify organizational entities and their histories from multiple levels of abstrac-
tion. For example an economist might focus on sectors, an organizational scientist on
operational behavior and a business historian on companies. Each perspective could
possibly justify their unit of analysis to be real individuals with genealogy, while argu-
ing that the other perspectives are classes or sub-units. Therefore when producing a
classification, the level and perspective of interest should be appropriately reasoned
and explained. Otherwise, there is significant potential to focus on inappropriate units
of analysis and then mistake a classification of classes or grades for a classification of
taxa (groups of one or more similar historical entities) and vice versa.

288 McCARTHY

The open and complex adaptive views have also helped existing and new evolu-
tionary research to blossom in economics (Schumpeter 1934, 1943, 1954, Alchian
1950, Nelson & Winter 1982), technology and innovation (Basalla 1988, Metcalfe
1998), evolutionary philosophy (Campbell 1960, 1969) and organization science
(Weick 1979, Aldrich 1979, McKelvey 1975, 1982). Schumpeter proposed that eco-
nomic change be viewed as an evolutionary process of incremental and bifurcat-
ing change. Nelson & Winter (1982) used evolutionary theory to develop models
of economic change, where routines are deemed to be the equivalent of organi-
zational genes. Routines are considered the norms, rules, procedures, conventions,
and technologies around which organizations are constructed and through which
they operate (Levitt & March 1988). New routines are first produced by innovating
organizations and then shared and retained by other organizations. Campbell was
the first to explain how this form of organizational evolution is governed by the
processes of variation, selection and retention, and Weick offered descriptions and
theories on how these processes relate to the decision making capabilities found in
organizations. These theories and concepts, combined with research on open sys-
tems thinking, influenced the work of Aldrich who explained how the processes
of variation, selection, retention and struggle govern the creation and adoption of
organizational routines.
Despite these significant advances in organizational science, our understanding
of evolution at work in organizations is limited. It is accepted that the process of
descent occurs in organizations and that characteristics such as routines are trans-
ferred from ancestors to descendants (Phillips 2002, Brittain & Freeman 1980, Astley
1985). But we only have a primitive understanding of the rate, direction and mecha-
nisms of change and of how these factors might correlate to the different types and
degrees of adaptive response exhibited by organizations. At present we do not have
an operational philosophy and framework that is capable of explaining these issues
and simultaneously representing the resulting organizational diversity.
Motivated by this need and by McKelvey’s (1982) work on organizational
systematics, preliminary cladistic analyses of organizations were produced by
McCarthy et al. (1997, 2000) and Leseure (2000). From a methodological stand-
point, these studies provide the first demonstrations of what a cladistic analysis
of organizations would involve and look like, and have led to further evaluation
and development by researchers in the social and economic sciences. For exam-
ple, at a recent conference organized by the Danish Research Unit for Indus-
trial Dynamics (DRUID) to celebrate the 20th anniversary of Nelson & Winter’s
work, a paper proposing the use of cladistics to examine evolutionary change in
the pharmaceutical industry was presented by Leask (2002). This was followed
by a paper that was originally delivered at the 2002 meeting of the Brisbane
Club; it modeled the cladogram produced by McCarthy et al. (2000) to exam-
ine the interdependence of the characters possessed by each taxa (Allen 2002,
Allen & Strathern 2003). Returning from the Brisbane Club meeting to DRUID,
Andersen (2003) used phylogeny to represent the evolutionary transformation of


industry sectors and to question existing classification systems used in indus-
trial statistics. Andersen’s approach to producing industry cladograms differs from
McCarthy et al.’s (2000) in that he aggregates organizational activities by using
complete input-output datasets for the whole industry, thus seeking differences
between industries over large and potentially inconsistent sets of characteristics.
Most recently, Baldwin et al. (2003) combine cladistics and evolutionary systems
modeling to address questions concerning how organizational diversity and flexi-
bility can be retained, how organizational and technological innovations interact
in transformations and how the timescales in these kinds of transformations can
be reduced.

5. The cladistic method

The following account of the cladistic method provides a primer to help research-
ers respond to the task of reconstructing the phylogeny of organizational diversity.
It was originally adapted from the work and methods described by Wiley et al.
(1991), Forey et al. (1992), Kitching et al. (1998), Lipscomb (1998) which in turn
were used to develop and explain the organizational classifications by McCarthy
et al. (1997, 2000) and Leseure (2000) and the method presented by Rakotobe-Joel
et al. (2002). The explanation given below follows and develops these accounts.

5.1. Select a clade

This step is concerned with selecting the taxa whose evolutionary history is of
interest and to a great extent is a form of pre-classification, since the selection
is based on some form of pre-existing knowledge and interest in the taxa. It is
necessary that the taxa constitute a clade i.e. a group which includes the most
recent common ancestor as well as all and only all of its descendants. For exam-
ple, if we consider the simple and illustrative cladogram shown in (Figure 4 and
Table 2), it is assumed that evolution occurs and characters may be passed modi-
fied or unmodified, through genealogical descent. Thus, the Craft taxon is the most
recent common ancestor and the other taxa (Standardized Craft, Modern Craft,
Mass and Just-in-Time) are assumed to be all known descendants. With this clad-
ogram the category or class of entity of interest is the manufacturing organiza-
tion, while the named taxa represent entities whose characteristics identify it as
a distinct entity and, at the same time, classify it as a member of a group con-
sisting of two or more similar entities. The taxa are individuals with geographical
and time constraints, and the ability to change through time and give rise to other
individuals. For instance, we find the genesis of Craft production in the European
Craft Guilds in the fifteenth and sixteenth centuries, Mass production appeared in

290 McCARTHY

Ch 1

Ch 2

Ch 3

Ch 4
Ch 5
Ch 6

Ch 7

T1 T2 T3 T4 T 5
Figure 4. Example cladogram of organizational (manufacturing) taxa.

Table 2. Example data set for organizational (manufacturing) taxa

Characters Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7
Production Standardized Standardized Automation Vertical assembly Pull
Technology parts processes integration line scheduling
of supply
chain

Taxa
T 1 – Craft 1 0 0 0 0 0 0
T 2 – Standardized 1 1 0 0 0 0 0
Craft
T 3 – Modern 1 1 1 0 0 0 0
Craft
T 4 – Mass 1 1 1 1 1 1 0
T 5 – 1 1 1 1 1 1 1
Just-in-Time

Sweden, France and England in the early 1800s, and Just-in-Time production orig-
inated in Japan in the 1950s. This descent from Craft production to Just-in-Time
production is well documented (Rae 1959, Hounshell 1984, Womack et al. 1990)
and shows technological, structural and behavioral features that are homologies.
Thus, with this most basic of examples, we observe the feasibility of reconstructing
organizational genealogies based on common ancestry.


5.2. Determine characters

The previous step of selecting the clade often reveals a number of different taxa
that appear to be a member of that clade. Initially the complete membership and
the diagnostic characteristics of the clade are not necessarily known, and for both
biological and non-biological systems the problem is determining those charac-
ters that are cladistically valuable from the set of all potential characters. For
example, with the organizational cladogram, evidence should be sought to main-
tain the assumption that the characters selected will infer and represent descent
from common ancestors. Consequently, the aim of this step is to review the his-
tory of the entities and to ﬁnd evidence that will represent the pattern of his-
torical relationships for the selected taxa. For social and technological entities,
this evidence tends to be in the form of published material or archives, which
can be systematically assembled to produce a data matrix. The matrix indicates
which characters have been selected and how they are coded for cladistic analy-
sis.
The data in Table 2 are deliberately basic to help explain the cladistic method.
They provide a simple and plausible illustration of the key innovations that have
been selected and retained by advanced taxa and are potentially shared derived
characters. An actual cladistic analysis of the entities would almost certainly
involve more taxa and more characters as well as some parallel evolution. This
form of evolution is determined by derived (apomorphic) characters which do not
have a mutual and unique evolutionary origin. A history of similar environmental
selection conditions on different taxa in different locations is a potential explana-
tion for this independent and parallel development.
Characters vary in the properties they represent and their information content.
They can be discrete, continuous, quantitative or qualitative in nature, but regard-
less, they should be easy to measure, unambiguous and the character states should
vary between taxa. This variation can be coded according to the presence or
absence of one character, as binary variables expressing different character states of
one character, or as multi-state characters. As discussed by Kitching et al. (1998)
even though candidate characters normally are ﬁltered to convert continuous and
quantitative characters into a discrete and qualitative form, this should not over-
ride the main issue which is to select characters that indicate common ances-
try.
Once a set of taxa and characters are selected, the initial pattern of relation-
ships will often contain one or more polytomies (a node with more than two
descendant branches) and if the data are completely unresolved the tree dia-
gram will appear as shown in Figure 5. Polytomies exist because the associa-
tions between the taxa have not yet been determined. This is the aim of the next
step.

292 McCARTHY

T1 T2 T3 T4 T1 T3 T4 T2
Polytomy Possible Phylogeny

Figure 5. Polytomy and phylogeny.

5.3. Character coding and polarization

To produce trees with phylogenetic order it is necessary to identify the existence
of shared derived characters. This involves understanding and coding the proper-
ties of the characters and character states. Figure 6 shows how the coding of char-
acter states can reveal three properties: direction, order and polarity (Swofford &
Maddison 1987). Ordering refers to the sequence of character state changes that
occur, whilst direction refers to the transition between character states. When the
direction and order of the character state changes have been determined, then the
character series is considered polarized, revealing whether the character or charac-
ter state is ancestral or derived.
Understanding the properties of characters is relatively straightforward, but
determining them is another matter. If we are fortunate to have a detailed and reli-
able record of the entities histories then this makes the task easier. This is espe-
cially the case, if the record contains information about the changes and dates for
when new taxa emerged, but still the transmission of characters in social and tech-
nological systems is not straightforward. They are complex adaptive systems with
multiple system levels and therefore any study with an inappropriate or poorly
deﬁned unit of analysis could easily confuse and mix multiple levels of selection
and descent. Also, characters can be inherited from a diverse range of sources, and
can be adapted as a consequence of intentional and blind variations. This is not
to say that social and technological evolution is more complicated than biological
evolution, because as Hull (1988) argues, social scientists may understand the com-
plexities of sociocultural transmission, but their limited understanding of biological
evolution often leads them to underestimate its complexities.
If appropriate historical records are not available, a method called polarization
or argumentation (Wiley et al. 1991, Hennig 1950, 1966) is used to determine
which characters are ancestral and which are derived. This method uses outgroup
comparison, where a taxon that is hypothesized to be less closely related to each
of the taxa under consideration than any are to each other is called the outgroup,


0
(a) 0 1
(c) (d) 0 1 2
(b) 0 1
1 2
(a) Un-polarized binary characters (c) Un-ordered transformation series of (d) The same transformation
(b) Polarized binary characters three characters series polarized

Figure 6. Character coding and properties.

and is used to help resolve the polarity of characters. The basic principle is that
for a given character with two or more states within a taxon, the state occurring
in the outgroup is assumed to be the ancestral state (Watrous & Wheeler 1981).

5.4. Constructing cladograms

At this stage we have chosen taxa that are evolutionarily related, selected and
coded characters for the taxa and where possible have determined the polarity of
the characters (ancestral or derived). With this step we begin assessing potential
cladograms by grouping taxa by shared derived characters, rather than by shared
ancestral characters or any shared derived characters that are the result of inde-
pendent evolutionary development.
There are a number of methods for constructing cladograms, including Hennigian
argumentation (Hennig 1950, 1966), Wagner (1961), Farris (1970) optimization,
Fitch (1971) optimization, Dollo optimization (Farris 1977), and Camin-Sokal
(1965) optimization. In general these construction and optimization methods dif-
fer in how they interpret and process the character information content and whether
or not the data have been polarized. For example, the Wagner and Fitch methods are
optimization procedures that seek to reconstruct the minimum number of character
state changes according to an optimality criterion. The Wagner method is used for
ordered characters and the Fitch for unordered characters. The Hennigian argumen-
tation method considers the information provided by each character, at each step of
the construction process. It follows the inclusion/exclusion rule where the informa-
tion available allows for either complete inclusion or complete exclusion of taxa, so
that a hypothesis of relationships can be generated.
Using the example data (Table 3) provided by Rakotobe-Joel et al. (2002) this
section will manually show how the Hennigian argumentation method is applied
to construct a cladogram (see Figure 7). The data represent six organizational taxa

294 McCARTHY

Table 3. Set of cladistic data

Characters Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10

Taxa
T 1 0 0 0 0 0 0 0 0 0 0
T 2 0 0 0 0 0 0 0 0 1 1
T 3 0 0 0 0 0 0 1 1 1 1
T 4 0 0 0 0 0 1 1 1 1 1
T 5 0 0 1 1 1 1 1 1 0 1
T 6 1 1 1 1 1 1 1 1 1 1

Data Source: Rakotobe-Joel et al. (2002, p. 340).

(T 1 to T 6) and ten organizational characters (Ch 1 to Ch 10) and are processed
as follows:

• The matrix (Table 3) contains character data for ten characters and six taxa,
one of which T 1 is considered the outgroup. At this stage the relationships
between the taxa have not been determined and the data are considered a poly-
tomy (Figure 7 – step 1).
• Characters Ch 1 and Ch 2 have uniquely derived states as they are found only
in taxon T 6 (Figure 7 – step 2).
• Characters Ch 3, Ch 4 and Ch 5 have shared derived states as they are shared
by and connect taxa T 5 and T 6 (Figure 7 – step 3).
• Character Ch 6 has a shared derived state as it is shared by and connects taxa
T 4, T 5 and T 6 (Figure 7 – step 4).
• Characters Ch 7 and Ch 8 have shared derived states as they are shared by and
connect taxa T 3, T 4, T 5 and T 6 (Figure 7 – step 5).
• Characters Ch 9 and Ch 10 have shared derived states as they are shared by and
connect taxa T 2, T 3, T 4 and T 6 (Figure 7 – step 6). Ch 10 is also present in T
5, but Ch 9 is not and this is indicated by Ch –9 (a character conﬂict) on T 5.

With only six taxa and ten characters this example data is relatively straightforward.
However, when the data set is larger and more complex, it is usually processed using
cladistic software, of which there are a number for building, comparing and analyz-
ing cladograms. The most widely used are PHYLIP (Felsenstein 1993) and PAUP
(Swofford 1998) for analyzing data sets and searching for cladograms, and MacClade
(Maddison & Maddison 1992) for analyzing cladograms and reconstructing ances-
tral states.


Figure 7. Constructing a cladogram. Rakotobe-Joel et al. (2002, p. 344).

5.5. Cladogram selection

It is important to note that a study involving signiﬁcantly more characters and
taxa is likely to exhibit numerous conﬂicts in the relationships. For instance, eco-
nomic, technological and organizational systems are likely to demonstrate parallel
evolution. This assumes that different taxa will develop the same innovations inde-
pendently at different places. Although our knowledge about evolutionary diffusion

296 McCARTHY

versus parallel evolution in social and technological systems is limited, it is most
likely that both occur because of similar selection conditions, which equates to
what Darwin (1859 [1996 edition, p. 114]) referred to as fit: ‘those exquisite adap-
tations of one part of the organization to another part, and to the conditions of
life.’ Thus, with organizations demonstrating evolution which is partially rational
and intentional, and reinforced by environmental factors such as reputation, mar-
ket trends and fashion, the result can be competitive imitation or benchmarking
which is comparable to Campbell’s (1965) notion of cross-lineage borrowing.
With such factors, the data will produce a number of potential cladograms,
rather than just one. These candidate cladograms are assessed using optimization
methods (e.g. Wagner 1961, Fitch 1971, Camin & Sokal 1965) and descriptive sta-
tistics (tree length, consistency index and retention index) that together provide an
indication of the quality of the cladogram according to the principles of parsi-
mony and congruence. Another approach, the Bootstrapping method (Felsenstein
1985, Efron 1979, Efron & Gong 1983), assesses the reliability of the branches
in a cladogram by randomly replicating the real dataset several hundred times
and producing a new phylogeny for each new bootstrapped dataset. These boot-
strapped phylogenies have varying topologies, some with high levels of common-
ality and some with low levels. The overall degree of commonality is used to
estimate whether a cladogram is genuine.
The principle of congruence assumes that a cladogram will seek agreement
between the characters used, to produce a unique phylogenetic relationship. This is
because for any one set of taxa there will be one ‘best fit’ phylogeny, assuming that
the taxa are derived from a common ancestor. If analysis of three different sets of
data all show the same pattern for the different taxa, then it can be assumed that
the pattern represents a good and true approximation of relatedness (Forey et al.
1992). The principle of parsimony is derived from Ockham’s razor (Kluge 1984).
William Ockham (c.1280–1349) proposed that when alternative hypotheses exist,
the one requiring the least assumptions should be preferred. This general principle
has been used in cladistics to argue that a phylogeny is more plausible if it requires
less, rather than more changes in character states. Thus, from all of the theoreti-
cally possible cladograms a set of data may produce, the one with the least number
of steps is chosen.
The tree length descriptive indicates the total number of character state changes
necessary to support the relationships for the taxa shown in any cladogram. Thus,
the cladogram with the minimum length is considered to have fewer homoplasies
(when a character evolves more than once) and as a consequence is assumed to be
the best fit tree. Again using the example provided by Rakotobe-Joel et al. (2002)
it is possible to explain and compare the tree length descriptive for the final clad-
ogram shown in Figure 7, step 6 (now shown as Figure 8(a)), and another poten-
tial tree for the example data (Figure 8(b)). Figure 8(a) involves 11 character state
changes (tree length = 11), as characters 1 to 8 and 10 each change once, and char-
acter 9 changes twice. While Figure 8(b) involves 18 character state changes (tree


Figure 8. Tree length. Adapted from Rakotobe-Joel et al. (2002, p. 344).

length = 18) as characters 1 to 8 each change twice and characters 9 and 10 each
change once. Therefore the tree length descriptive would consider Figure 8(a) to be
more parsimonious than Figure 8(b).
The consistency index (CI) measures how well the character data ﬁts to a clad-
ogram and is given by:

CI = M/S (1)

298 McCARTHY

Table 4. Retention index calculation

Characters Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10

Taxa
T 1 0 0 0 0 0 0 0 0 0 0
T 2 0 0 0 0 0 0 0 0 1 1
T 3 0 0 0 0 0 0 1 1 1 1
T 4 0 0 0 0 0 1 1 1 1 1
T 5 0 0 1 1 1 1 1 1 0 1
T 6 1 1 1 1 1 1 1 1 1 1
Max Steps (g) 1 1 2 2 2 3 2 2 2 1
G= g = 18

Data Source: Rakotobe-Joel et al. (2002, p. 345)

where M is the minimum possible number of character changes and S is the
actual number of character changes (S). The consistency index can vary from
1 (no homoplasy) to 0 (a lot of homoplasy). For example, with the cladogram
in Figure 7, step 6, there are 10 characters each with two states and therefore
a possible minimum of 10 character changes (M = 10), while the tree length or
actual number of character changes is 11 (S = 11). Thus, the consistency index is
10/11 = 0.90.
The retention index (RI) is similar to the consistency index, but measures the
proportion of synapomorphy in a cladogram i.e. the degree of common ancestry
in a cladogram (Farris 1970). It is defined as:

RI = (G − S)/(G − M) (2)

where M and S are as per the consistency index and G is the total number of taxa
with state 1 or 0 (which ever is smaller). For example, if we use the data in Table
3 we find the total number of steps (G) to be 18 (Table 4), M to be 10, S to be
11 and therefore the RI is 0.875. The closer the RI is to 1 the better the tree is
considered to be.
With the example organizational cladogram in Figure 7, the descriptive statistics
produce near-perfect values, but this is only because the data in Table 3 are basic
and apposite. Actual studies would very likely result in data with a large number
of inconsistencies. This is not a negative result or a flaw in the logic of apply-
ing the cladistic method to non-biological entities. It is a reality of inferring and
representing the evolutionary relationships in social and technological entities. If
our current understanding of evolution in these entities is correct, then we should
expect organizational cladograms to have imperfect descriptive statistics.


6. Conclusions

Classifying organizational diversity and explaining the mechanisms that preside
over the differences are enduring research issues. This paper was motivated by
these and by the belief that we could better understand and advance existing
knowledge by using a system of organizational classification that coincides with
theories that explain organizational change and diversity.
Organizational scholars have developed a significant collection of classifications,
describing how factors such as structure, technology, processes and strategy define
organizational form. There is also a significant body of work that explains how
evolution occurs in organizations and how processes such as variation, selection
and retention govern the creation and adoption of innovations. Yet, despite this
research, our understanding of the genesis of organizational taxa in terms of his-
torical evolutionary relationships is limited. Not only do existing organizational
classifications avoid these issues, they also lack a universal and theoretically rel-
evant framework for ordering and continually developing a natural and objec-
tive system of organizational diversity. Thus, the related tasks of understanding
what produces organizational diversity and classifying the diversity are currently
research activities that are operationally separate. The result is a collection of
mostly speculative and unconnected classifications, where the combined informa-
tion management value is greatly reduced and the potential for theory and hypoth-
esis development diminished.
When it comes to the theory and practice of classification, biologists and philos-
ophers have been and still are, far ahead of the other sciences in the complexity
and rigor of their classification thinking and methods. Their debates about classi-
fication philosophy and logic resulted in competing schools which advanced and
established a relatively effective and organized body of systematic activity. A sim-
ple indicator of this, are the number of academic journals and societies dedicated
to systematics. The biological sciences have at least seven journals (Annual Review
of Ecology and Systematics, Cladistics, Integrative and Comparative Biology, System-
atic Biology, Systematic Botany, Taxon, and Molecular Phylogenetics and Evolution)
and approximately twenty societies; whereas the combined areas of organizational
science, management and economics have none.
To help develop a concerted field of organizational systematics, this paper
proposes that the cladistic school of classification is theoretically relevant to orga-
nizational diversity and methodologically richer than existing classifications of
organizations. This is not simply because cladistics is accepted by most biolo-
gists as the best method for comparative studies in biology. The basis for this
claim is that the concept of shared patterns of common ancestry is an evolu-
tionary logic compatible with existing theories on how and why new organiza-
tional taxa emerge. That is not to say that social, economic and technological
evolution is fully analogous to biological evolution, as it is well known that the
isolating mechanisms, adaptation processes and methods of new system creation

300 McCARTHY

have contextual differences. The fact is, social, economic and technological evo-
lution governs social, economic and technological diversity, and cladistics offers
a theory and methods for deducing and representing the evolutionary relation-
ships that accompany these developments. The reconstruction of organizational
phylogeny has the potential to produce classifications with objective and poten-
tially exhaustive groupings and as phylogeny is a property of any evolving sys-
tem, the classifications would provide a backcloth for contributions in other areas
such as ecological, institutional, transaction costs and resource based theories of
the firm. Also, the representation of a cladistic classification, the cladogram, pro-
vides an information management framework that is capable of developing with
new studies, new data and new organizational taxa. By using this hierarchical sys-
tem of representation we could avoid the relative taxonomic dormancy and redun-
dancy we have with existing matrix and table based classifications of organizations.
A cladogram offers a relatively transparent, accommodating and evolving informa-
tion system, which in turn, enables a more integrated and cumulative development
of organizational science.

Acknowledgements

I would like to thank Jane McCarthy and Brian Gordon for their insightful com-
ments on an earlier version of this paper. I also acknowledge the financial support
of the Social Sciences and Humanities Research Council and the Canada Research
Chair Program of Canada. Finally, I wish to thank an economist reviewer and a
special thanks to the Co-Editor, biologist Michael Ghiselin, for helpful suggestions
and guidance during the revision process.

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