Toward a Phylogenetic Reconstruction of Organizational Life


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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 diversity 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 specific 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 relatively 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 biological 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 biological 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 diversity. 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.

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Toward a Phylogenetic Reconstruction of Organizational Life

  1. 1. Journal of Bioeconomics (2005) 7:271–307 © Springer 2006DOI 10.1007/s10818-005-5245-5Toward a Phylogenetic Reconstructionof Organizational LifeIAN PAUL McCARTHYSFU Business, Simon Fraser University, 515 West Hastings Street, Vancouver, BC, V6B 5K3, CANADA( Classification is an important activity that facilitates theory development in many academicdisciplines. Scholars in fields such as organizational science, management science and economics andhave 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 themost 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 doesnot 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 ignorethe processes of change responsible for organizational diversity. Collectively they fail to provide any sortof universal system for ordering, compiling and presenting knowledge on organizational diversity. Thispaper 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 derivedfrom 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 classificationapproach 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 oforganizations. Consequently, this paper is exploratory and preparatory in style, with illustrations andassertions concerning the study and classification of organizational diversity.Key words: cladistics, classification, configurations, diversity, evolution, organizations, phylogeny, tax-onomy, typologyJEL classification: A1, L0, L2, L6, M1, N01. IntroductionClassification underlies language and cognition. For example, the nouns and verbsof 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 theworld 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) anda good classification provides ‘a system which has high predictive value and willallow maximum information retrieval’ (Mayr 1969, p. 54).
  2. 2. 272 McCARTHY This ability to order and represent differences has aided our philosophical andscientific studies of biological, social, economic and technological entities, but itis important to recognize that the cognitive models produced by any classificationare like the classifications themselves, incomplete, parsimonious and constantlyevolving. Consequently, a classification should permit continuous development andrefinement, whilst providing simple and powerful explanations of complex phe-nomena (Schumacher & Czerwinski 1992). This intellectual and perspicaciousactivity was discussed by Good (1965), who explained that classifications areconstructed for reasons that range from the need to conduct rigorous academicresearch, to the desire to produce simple and fun check lists. Yet regardless of thepurpose, 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 withthe economic (Coase 1937, Williamson & Masten 1999), technological (Chan-dler 1990) and behavioral (Cyert & March 1963, Wernerfelt 1984) views of thefirm, 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 betweenthe 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 the19th century Babbage (1835) sought to promote comprehension and adoption ofthe various manufacturing processes that existed. His classification was based onfactors such as the newness of the technology, the type of power consumption,the process control used, the transformational properties of the technology andthe utility of the technology. Although his ideas never developed into a universalsystem 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 (Daft1978), 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 classificationpresented by Borges (1964, pp. 101–105). At first this classification appears to bestrange but genuine, but as no other record of the classification exists, it seemsthat Borges fabricated it to amuse and demonstrate the role of perception in clas-sification. He refers to a Chinese encyclopedia entitled, The Celestial Emporiumof 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) havingjust broken the water pitcher, (n) that from a long way off look like flies.’ Borges’
  3. 3. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 273classification illustrates how incomprehensible a classification can be to those whoare not familiar with the local context or rationales which govern the criteria fordifferentiating. Thus, different societies can sometimes describe and classify thingsthat bewilder researchers in other societies. This issue of perception and sense making of reality is central to the processof 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 amplifiedthrough confusion and misuse of the various terms, methods and levels of analysisinvolved with classification. Yet these problems are not unique to the classificationof organizations, as there is also a history of significant dispute concerning the unitof analysis in biological classification literature. This is a problem which Kelleret al. (2003) call ‘semantic schizophrenia’, as many biological researchers appear tohave been largely unaware of the philosophical positions implied by their approachto classification (de Queiroz 1994). For the study of organizational diversity toadvance, those involved in the discipline must recognize and address these system-atic issues. Otherwise, they will continue to produce classifications that sometimesreference each other, but rarely join with or expand on each other. This situation is the impetus for this paper, which introduces and examines thefeasibility and value of using cladistic analysis to study and represent organiza-tional genealogy. It argues that the cladistic focus on shared patterns of commonancestry is an evolutionary logic compatible with the variation, selection and reten-tion explanations for how and why new organizational taxa emerge. This paperextends existing research on organizational systematics by (McKelvey 1975, 1978,1982, Warriner 1979, Haas et al. 1966, Pugh et al. 1969, Rich 1992, Doty & Glick1994, 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 classificationThe first formal classifications sought to make sense of the natural world and wereproduced by philosophers and biologists. This intellectual combination led to thedevelopment of a number of related and competing theoretical stances about howto 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 thissection of the paper, I hope to avoid similar misconceptions and provide a degreeof terminological clarity.
  4. 4. 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 thestudy 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 andschemes. Thus, constructing a classification is a taxonomic process with rules onhow to form and represent groups (taxa), which are then named (nomy). Withinbiology, 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: empiricaland theoretical. The principal difference between the two social science approachesis the stage at which a theory of differences is proposed and evidence then soughtto validate the theory (Warriner 1984, Rich 1992, Dotty & Glick 1994). Theoreti-cal classifications in the social sciences begin by developing a theory of differencesthat result in a classification of organizational types, known as a typology. Onlywhen the classification has been proposed, is a decision made as to where an entitybelongs 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) toproduce groups according to the measures of similarity and statistical techniquesused. Thus the overall aim is to use data to construct the classification, insteadof supporting it, but it should be noted that in practice data are seldom collectedwithout an expectation about what they will reveal or validate. It is also impor-tant to note, that most organizational classifications (theoretical and empirical) donot properly define the unit and level of analysis, and therefore misuse the termstaxon, group, class and type when presenting their classifications. This is probablythe 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 reflectthe 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 ifwe inspect its use across disciplines and relevant entries in dictionaries, there isa distinction between classification as a process (to classify) and classification asan output of the process (a classification). In the first instance, it represents thesorting and arrangement of information in a way that will inform (Ghiselin 1997).This definition partly relates to the mathematical and information theory conceptof classification, which assumes that given an equivalence relation for a subset ofa 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 thebiological 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
  5. 5. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 275identification (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 withhow groups and classes of entities will be arranged, in accord with the taxonomicapproach used (Mayr 1982, McKelvey 1982). It is a framework (e.g. a matrix, atable, a tree diagram, etc.) for ordering and representing, regardless of whether atheoretical 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 ofnomenclature, the Periodic Classification of chemical elements, the Dewey DecimalClassification System for organizing books and other bibliographic items, and theNorth 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 classificationTo understand the differences between the biological schools of classification, it ishelpful to have a basic appreciation of the history of classification philosophies,and in particular, the concepts of phylogeny and phenetics. This is because thethree schools vary in how (if at all) they represent phylogeny, the types of groupsthey 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 andmade-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 competingschools 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 worldby grouping organisms according to their size, structure, features, mode of repro-duction, and where they existed (location). This approach to classification can betraced back to Aristotelian essentialism, a philosophical belief that entities have aset 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 tohave 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 deniesthe existence of universals and therefore rejects the concepts of sets and groups.Nominalism believes that only individuals exist and that all proposed groupings ofentities are simply artifacts of the human mind. Not surprisingly, it does not fea-ture as a practicing taxonomic approach.
  6. 6. 276 McCARTHY Figure 1. Types of taxonomic characters and groups. Adapted from Ridley (1993, p. 366).Table 1. Differences between phenetic, cladistic and evolutionary classificationsClassification Characters used Groups recognised Homologies Monophyletic Paraphyletic Polyphyletic Analogies Ancestral DerivedPhenetic Yes Yes Yes Yes Yes YesPhylogenetic Yes No No No No YesEvolutionary 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 ratherthan classification (Schuh 2003). However, with the publication of The Origin ofthe Species, taxonomists were provided with an alternative to the essentialist and
  7. 7. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 277nominalist views of diversity. In very simple terms, the thesis was that naturalgroups did exist and that this is because members of a group have descended froma 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 ‘treeof life’, a branching diagram that illustrated his view of the evolution of life frombacteria to humans. Thus, phylogenetics, the evolutionary relationships betweenorganisms, 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’stheory of evolution and with advances in genetics this resulted in a resurgencefor evolutionary biology and systematics. The focus was on reconciling Darwin’stheory of evolution with genetics as the basis for biological inheritance and thusthis era gave rise to the evolutionary school of classification (Mayr 1942, Simp-son 1961). This school recognizes that evolution occurs and utilizes both pheneticand 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 withall descendants), thereby using derived and ancestral homologies, which are cor-respondingly characters with advanced or primitive states shared by two or moretaxa and present in their ancestor (see Figure 1 and Table 1). However, the mixingof phenetic and phylogenetic information, coupled with the uncertainty of delim-iting paraphyletic groups results in phylogenies that are difficult to translate intounequivocal classifications. The phenetic school of classification emerged in the late 1950s and early1960s (Michener & Sokal 1957, 1958, Sokal 1962) as an alternative and oppos-ing approach to the evolutionary school, but the origins of the basic pheneticapproach (observed similarities) can be traced back to the mid 1800s. Whewell(1840) and Mill (1843) suggested that the grouping of entities on the basis ofshared 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 tobe made regarding its constituent groups and which is therefore the most generallyuseful for the classification of living things.’ Thus, the phenetic approach placesemphasis on collecting and processing data to produce what it calls informationrich groups, rather than theory led groups. Phenetics flourished with the developments in computing technology in the1950s and the work on numerical taxonomy in the 1960s (Sokal & Sneath 1963).This was the period when phenetics became a recognized school of classificationconcerned with using a set of statistical methods (know as numerical taxonomy) togroup entities on the basis of observed similarities and according to certain mea-sures of similarity.
  8. 8. 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 inchemistry cannot be constructed phylogeneticaly, therefore suggesting that informa-tion rich groups do not have to evolve. Thus, phenetics discounts any theory thatmight explain differences, such as the theory of evolution for biological organismsand the theory of electron structures for chemical elements. It simply contends thatthe best measure of relatedness is overall similarity. The numerical taxonomy component of the phenetic school is mathematical indiscipline, but biological in application and some of the first applications of thesestatistical methods occurred in anthropology (Driver & Kroeber 1932) and psy-chology (Zubin 1938). As reported by Sokal & Sneath (1963), the early aims andassumptions of numerical taxonomy in biology revolved around: (1) the need forrepeatability and objectivity; (2) the use of quantitative measures of resemblancefrom numerous equally weighted characters; (3) the construction of taxa fromcharacter correlations leading to groups of high information content and (4) theseparation of phenetic and phylogenetic considerations. To address this last objec-tive, the unit of analysis, called the operational taxonomic unit (OTU), should beas theory and subject neutral as possible. The OTU is simply a group of entitiesthat 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 adendrogram as ‘. . . a diagrammatic illustration of relationships based on degreesof 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 fusionsor divisions of groups of entities made at each consecutive stage of the analysis.They are an expanding or hierarchical structure that continues until the initialgroup can no longer be sub-divided. The different types of agglomerative tech-niques arise from the various methods of establishing distance (the measure ofthe phenetic difference between two groups of entities) or similarity. Other namesfor 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 themethod 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 revolvearound the methodological assumptions and operational procedures they follow.For example, not all characters are equally important and phenetics does notoffer an objective way to select those that are. As a result, emphasis is placedon using all possible characters to avoid residual weighting. This raises questionsconcerning what characters are and how they should be determined. In a gen-eral and fundamental sense, characters are discernible features of an organism,
  9. 9. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 279 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 theseissues, Ghiselin (1997) and Inglis (1991) provide discussions concerning the phi-losophy and definition of characters for biological classification in general, whileSneath & Sokal (1973) present categories of inadmissible characters that do notcontribute to the mathematical tightness of a group in numerical taxonomy. A finaland 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 isnot possible to both classify and make theory-free observations. In summary, numerical taxonomy and phenetics have become synonymous, asthe former provides a method and rules that are appropriate to the observed andempirical nature of the latter. However, it is important to note that with modern
  10. 10. 280 McCARTHYday classification, phenetic, cladistic or otherwise, there is an obvious need to usenumerical methods to help process and order the data that constitute any classifi-cation. Numerical taxonomy has become an established methodological tool thatis 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) andeconomics (Wooldridge 2003, Bischi et al. 2003, Sellenthin & Hommen 2002). During the same period that phenetics and numerical taxonomy were coming toprominence, an alternative school began to emerge. The figurehead for this schoolwas the German entomologist Willi Hennig (1950), who believed that evolutionaryhistory should play a greater role in taxonomy. With early evolutionary taxonomythe aim was to produce classifications that reflected all aspects of phylogeny, butthis 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 largenumber of homologies, their classification within the same group cannot be conclu-sively assumed, as homologies can result from shared derived characters or sharedancestral characters (Figure 1). To ascribe evolutionary relationships, only sharedderived homologies (synapomorphies) should be taken into consideration. Hennig’swork was inspired partly by his desire to counter the German school of idealisticmorphological systematics (Schindewolf 1950), which was a fundamental form ofphenetics. He originally called his approach phylogenetic systematics, but his sup-porters and to a degree his opponents adopted the name cladistics from the GreekKλαδos for branch. Thus, cladistics is approximately equal to phylogenetic system-atics and originally meant the study of clades, which are ‘the individual branchesin the genealogical nexus’ (Ghiselin 1997, p. 306). The term cladism refers to themovement that supported Hennig’s approach to classification and the product ofa cladistic analysis is known as a cladogram (Figure 3). Cladograms are tree-likediagrams that depict the pattern of relationships among clades based upon sharedderived characters. The branches represent taxa, while the tips of the branches aregenerally species. Although Hennig explored mathematical set theory as an underlying reason andprinciple 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 classificationsprovide evidence to support a theory of evolution (Brower 2000). In support of theformer view, Wiley (1975, p. 234) interpreted and translated Hennig’s justificationof cladistic methods into three axioms: (i) evolution occurs; (ii) only one phylogenyof 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
  11. 11. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 281 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 andsufficient for cladistics: (i) observed character differences among taxa provide theevidentiary basis; (ii) an irregular bifurcating hierarchy is a useful way to representrelationships among taxa; and (iii) parsimony is the guiding epistemological prin-ciple of the systematic approach (Platnick 1979, Nelson & Platnick 1981, Brower2000). 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 isgenerally accepted that cladistic analysis is valid for representing the patterns ofphylogenetic 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 thesingle character ‘the presence or absence of stripes’, the result is the logical, butridiculous observation, that tigers and zebras are held to be more closely relatedto one another than tigers are to lions. As with phenetics, the general rule is to
  12. 12. 282 McCARTHYuse as many characters as possible, so as to dilute the impact of any wronglyselected characters. Also, Kitching et al. (1998) report that continuous characterswith real number values (e.g. wing length) tend to produce cladograms with lowerlevels of confidence, compared to those produced using qualitative characters thatare 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 Hennigand 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 thefossil record. Yet cladistics has been used to classify a wide range of biologicalorganisms including bacteria, plants and animals and phylogeny has been used fornearly 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 forcladistic analysis does not have to be biological. What is required, are individualswhose evolutionary history can be inferred and represented as patterns of com-mon ancestry. This is the concept of species as individuals, as opposed to speciesas classes or kinds, both of which are abstract notions (Ghiselin 1966, 1974, Hull1976, 1978). Individuals, classes and kinds can each be described and differentiatedfrom 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 canchange. They are the unit of analysis for a cladistic classification. In summary, the evolutionary and phenetic schools believed that the empiricaland theoretical challenge of properly estimating homology and phylogeny was toodifficult. But it is now generally accepted that cladistic analysis provides an objec-tive and empirical method to assess and represent phylogeny and homology. Thisis because common ancestry is real i.e. a group of taxa either are, or are notrelated by ancestry, unlike perceived phenetic similarity which is inherently sub-jective. Also, the processing of character data using modern cladistic software isas analytical and repeatable as phenetic methods, but with the added value ofconveying significant information content in terms of character states and testablehypotheses about phylogenetic relationships. The output of a phenetic study leadsto mere associations.3. The classification of organizationsAlthough there is no established field of organizational systematics, researchershave long examined how organizations differ according to factors such as resourcerequirements (Penrose 1959, Barney 1991, Nelson & Winter, 1982), structural fea-tures (Chandler 1962, 1977), strategic behaviour (Miles & Snow 1978) and dynamiccapabilities and routines (Teece et al. 1997, Eisenhardt & Martin 2000, Winter2003). This interest in organizational diversity is best associated with the branch oforganizational science known as population ecology; an area of research primarily
  13. 13. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 283concerned with why there is a diversity of organizations and the reasons for thedifferences (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 anddespite the interest in organizational differences, there has not been a coordinatedeffort 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 managementscience have produced many classifications, but none however, that are adequatefor representing all potential organizational taxa. One approach to studying organizational form and diversity that does advocatea systematic view is configuration theory (Lawrence & Lorsch 1967, Miles & Snow1978, 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 isgenerally 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 amember of a group of similar organizations’ and McKelvey (1982, p. 196) refers toorganizational taxa as ‘a collectivity of the adaptive properties of all its includedorganizations.’ But configuration theorists also use terms and language that reflectthe 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) provideexplanations of configurations as gestalts which seem consistent with the notion oforganizational taxa as proper and incorporated individuals, but their suggestionsthat gestalt, configuration and archetypes are all synonyms, is not appropriate forclassifications 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 thatorganizational 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 individualwhole. Despite the fact that organizational and technological systems by and largeconform to these criteria, the majority of existing organizational classifications areunaware of the relevance and importance of the organizations as individuals thesisdespite different contexts and system perspectives (e.g. social, technological, legaland economic). There are of course differences in how this thesis relates to biological species andorganizational species and these will result in some deviation and points for dis-cussion. For example, once a biological organism is dead or a biological speciesis extinct, it currently remains that way. This is not necessarily the case for social,
  14. 14. 284 McCARTHYeconomic and technological entities, as information about their components, formand 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, considerthe Boneshaker bicycle, a technological system whose purpose is to provide groundtransportation by cycling. Historians and enthusiasts (Bijker 1995, Alderson 1972)would consider this technological entity to be a taxon of bicycles, while the bicyclewould be viewed as a class of transportation technologies. The Boneshaker has aproper name and a history, indicating that its existence was constrained spatiallyand temporally. That is, the Boneshaker emerged in certain regions in the 1870sand was descended from another group of bicycles, the Hobbyhorse. It is thereforepossible to infer phylogeny and observe shared innovations such as frame struc-ture, tire technology, wheel technology, drive chain technology and steering systemtechnology. When the Boneshaker began to disappear some twenty years later withthe advent of the Safety Bicycle, this technological extinction was not permanent.Today we have archives and manufacturing technologies that allow us to reproduceand 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 ordertypes of organizations by viewing them as social systems seeking to attain a spe-cific type of social goal. This fundamental typology differentiated organizationsaccording to four dimensions: (i) the value system which defines and legitimizesthe goals of the organization; (ii) the adaptive mechanisms which organize andoperate 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 manufacturingfirms according to the type and complexity of the production techniques used bythe organization. While Woodward’s classification has been widely accepted andverified 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, longevityand impetus for similar work. This includes classifications based on the coercive,remunerative, or normative power of the organization leaders (Etzioni 1964), thedifferentiation 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 (Child1973), 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 environmentalstability (Lawrence & Lorsch 1967), types based on bureaucracy, value rationalaction, rational-legal authority, or inner-worldly asceticism (Weber 1968) and clas-sifications based on the operative goals (Katz & Kahn 1966), and output goals,
  15. 15. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 285adaptation goals, management goals, motivation goals, and positional goals (Gross1969). Many of these organizational classifications are considered to be typologies, asthey 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 smallnumber of organizational dimensions and have no common and proper definitionof the unit of analysis. Despite these limitations, the classifications still provide agood basis for understanding organizational form and diversity, but as remarkedby Meyer et al. (1993, p. 1182), ‘the allocation of organizations to types oftenis not clear cut. Because of their a priori nature and frequent lack of specifiedempirical 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 earlyorganizational classifications would not be appropriate for further analysis from ahistory of evolutionary relationships perspective. This is because the unit of anal-ysis in these classifications does not conform to the species-as-individuals thesisand the focus of differences is on functional concepts, rather than the adaptationof form and function. Evolutionary biologists contend that teleology alone is notadequate for historical explanation, while early organizational classifications werebased 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 unitand focus of a classification study, see Bigelow & Pargetter (1987), Amundson &Lauder (1994) and Ghiselin (1997). There are also a significant number of organizational classifications derivedusing numerical taxonomy methods (Haas et al. 1966, Goronzy 1969, Pughet al. 1969, Samuel & Mannheim 1970, Prien & Ronan 1971, Pinto & Pinder1972, 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 emphasizethe need for theory-free and quantitative data to ensure objectivity and repeat-ability, but they also failed to explain how researchers could design and conductstudies 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 classificationshave done. However, even this computational basis for objectivity is unsound, asresearchers using numerical taxonomy methods are still required to make intuitiveand ‘rule of thumb’ decisions concerning which method and parameters to use.Thus bias and approximations can easily appear in organizational classificationsderived using numerical taxonomy methods.
  16. 16. 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 helpexplain the causes of the diversity and similarity. To achieve this, Rich proposesthat the phylogenetic, population ecology and numerical perspectives be combinedto understand and explain the ‘blueprint’ of organizational forms. The result, hesuggests, will be a classification method that integrates a theory of differences withthe notion of fit and that uses numerical methods to build a hierarchical classifi-cation capable of representing the diversity of organizational life. Fourteen yearsearlier, McKelvey (1978) suggested that population ecology studies should use clas-sification methods to study organizational taxa and argued that the formulationof a classification is a prerequisite for the maturation of organization science. Heproposed that lessons could and should be learned from the area of biologicalsystematics 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 needfor organizational science to develop jointly a broad theory on how organiza-tional diversity is generated, along with a system of organizational classificationthat coincides with this theory.4. A cladistic analysis of organizational diversityThis paper argues that by using cladistic analysis to study and represent organiza-tional phylogeny, we can develop a theoretical context for organizational diversitythat permits interpretation of data and phenomena from an evolutionary point ofview. For this to be possible though, we must recognize that organizational taxa arerelated 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 takeplace in lineages over time. It is generally accepted that such conditions do exist fororganizations, but as is evidenced by the variety and number of redundant classifi-cations of organizations, there is limited agreement about how to conceptualize andcontinuously represent organizational diversity. So to proceed with this paper, it isnecessary to consider and present organizations as appropriate entities for cladisticanalysis and examine the assumption that they evolve from common ancestors. To understand how organizations are distinguished from other types of complexsystem and the problem of classification respective, I will refer to use four relatedand complementary organizational perspectives: the rational system view (Simon1945, Cyert & March 1963), the natural system view (Selznick 1957), the opensystem view (Boulding 1956, Katz & Kahn 1966) and the complex adaptive view(Anderson 1999, Dooley & Van de Ven 1999, Allen 2001, McCarthy 2004). The
  17. 17. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 287first three of these views are well established and are used to define organizationsand explain the history of organization studies (Scott 1987, Baum & Rowley 2002).The fourth is a relatively contemporary view and to an extent unifies and extendsthe first three. With the rational view, the assumption is that organizations are created for apurpose 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 assumesthat the purpose of organizations is simply survival and to achieve this, they mustexhibit 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 tangibleentities with ‘goal-directed, boundary maintaining, and socially constructed sys-tems of human activity’ (Aldrich 1999, p. 2), but with the open system view, thefocus is extended to include the connections and interdependences between anorganization and its environment. The open view recognizes that organizations aretransformation systems with internal and external interactions. They interact withother 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 tobe composed of levels of relatively autonomous sub-systems whose combined andemergent characteristics cannot easily be reduced to one level of description. Thus,the complex adaptive view defines organizations as open systems with agency, andthe ability to adapt, learn and create new rules, structures and behaviors at severalinterrelated 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 onpopulations 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. workgroups). Collectively these views and levels constitute a complex adaptive systemwhich has multiple complex adaptive systems hierarchically nested within. With the open system and complex adaptive view it is possible to recognize, analyzeand classify organizational entities and their histories from multiple levels of abstrac-tion. For example an economist might focus on sectors, an organizational scientist onoperational behavior and a business historian on companies. Each perspective couldpossibly 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 aclassification, the level and perspective of interest should be appropriately reasonedand explained. Otherwise, there is significant potential to focus on inappropriate unitsof analysis and then mistake a classification of classes or grades for a classification oftaxa (groups of one or more similar historical entities) and vice versa.
  18. 18. 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, Alchian1950, Nelson & Winter 1982), technology and innovation (Basalla 1988, Metcalfe1998), 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 modelsof 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 whichthey operate (Levitt & March 1988). New routines are first produced by innovatingorganizations and then shared and retained by other organizations. Campbell wasthe first to explain how this form of organizational evolution is governed by theprocesses of variation, selection and retention, and Weick offered descriptions andtheories on how these processes relate to the decision making capabilities found inorganizations. These theories and concepts, combined with research on open sys-tems thinking, influenced the work of Aldrich who explained how the processesof variation, selection, retention and struggle govern the creation and adoption oforganizational routines. Despite these significant advances in organizational science, our understandingof evolution at work in organizations is limited. It is accepted that the process ofdescent occurs in organizations and that characteristics such as routines are trans-ferred from ancestors to descendants (Phillips 2002, Brittain & Freeman 1980, Astley1985). 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 anddegrees of adaptive response exhibited by organizations. At present we do not havean operational philosophy and framework that is capable of explaining these issuesand simultaneously representing the resulting organizational diversity. Motivated by this need and by McKelvey’s (1982) work on organizationalsystematics, preliminary cladistic analyses of organizations were produced byMcCarthy et al. (1997, 2000) and Leseure (2000). From a methodological stand-point, these studies provide the first demonstrations of what a cladistic analysisof organizations would involve and look like, and have led to further evaluationand 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’swork, a paper proposing the use of cladistics to examine evolutionary change inthe pharmaceutical industry was presented by Leask (2002). This was followedby a paper that was originally delivered at the 2002 meeting of the BrisbaneClub; 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
  19. 19. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 289industry sectors and to question existing classification systems used in indus-trial statistics. Andersen’s approach to producing industry cladograms differs fromMcCarthy et al.’s (2000) in that he aggregates organizational activities by usingcomplete input-output datasets for the whole industry, thus seeking differencesbetween industries over large and potentially inconsistent sets of characteristics.Most recently, Baldwin et al. (2003) combine cladistics and evolutionary systemsmodeling to address questions concerning how organizational diversity and flexi-bility can be retained, how organizational and technological innovations interactin transformations and how the timescales in these kinds of transformations canbe reduced.5. The cladistic methodThe 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 turnwere used to develop and explain the organizational classifications by McCarthyet al. (1997, 2000) and Leseure (2000) and the method presented by Rakotobe-Joelet al. (2002). The explanation given below follows and develops these accounts.5.1. Select a cladeThis step is concerned with selecting the taxa whose evolutionary history is ofinterest and to a great extent is a form of pre-classification, since the selectionis based on some form of pre-existing knowledge and interest in the taxa. It isnecessary that the taxa constitute a clade i.e. a group which includes the mostrecent 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 andTable 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 mostrecent 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 asa 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 geographicaland time constraints, and the ability to change through time and give rise to otherindividuals. For instance, we find the genesis of Craft production in the EuropeanCraft Guilds in the fifteenth and sixteenth centuries, Mass production appeared in
  20. 20. 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) taxaCharacters 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 chainTaxaT 1 – Craft 1 0 0 0 0 0 0T 2 – Standardized 1 1 0 0 0 0 0 CraftT 3 – Modern 1 1 1 0 0 0 0CraftT 4 – Mass 1 1 1 1 1 1 0T 5 – 1 1 1 1 1 1 1 Just-in-TimeSweden, 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-Timeproduction 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 reconstructingorganizational genealogies based on common ancestry.
  21. 21. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 2915.2. Determine charactersThe previous step of selecting the clade often reveals a number of different taxathat appear to be a member of that clade. Initially the complete membership andthe diagnostic characteristics of the clade are not necessarily known, and for bothbiological and non-biological systems the problem is determining those charac-ters that are cladistically valuable from the set of all potential characters. Forexample, with the organizational cladogram, evidence should be sought to main-tain the assumption that the characters selected will infer and represent descentfrom 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, whichcan be systematically assembled to produce a data matrix. The matrix indicateswhich 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 havebeen selected and retained by advanced taxa and are potentially shared derivedcharacters. An actual cladistic analysis of the entities would almost certainlyinvolve more taxa and more characters as well as some parallel evolution. Thisform of evolution is determined by derived (apomorphic) characters which do nothave a mutual and unique evolutionary origin. A history of similar environmentalselection 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 shouldvary between taxa. This variation can be coded according to the presence orabsence of one character, as binary variables expressing different character states ofone character, or as multi-state characters. As discussed by Kitching et al. (1998)even though candidate characters normally are filtered to convert continuous andquantitative 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 twodescendant 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 nextstep.
  22. 22. 292 McCARTHY T1 T2 T3 T4 T1 T3 T4 T2 Polytomy Possible Phylogeny Figure 5. Polytomy and phylogeny.5.3. Character coding and polarizationTo produce trees with phylogenetic order it is necessary to identify the existenceof 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 thatoccur, whilst direction refers to the transition between character states. When thedirection and order of the character state changes have been determined, then thecharacter series is considered polarized, revealing whether the character or charac-ter state is ancestral or derived. Understanding the properties of characters is relatively straightforward, butdetermining 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 forwhen new taxa emerged, but still the transmission of characters in social and tech-nological systems is not straightforward. They are complex adaptive systems withmultiple system levels and therefore any study with an inappropriate or poorlydefined unit of analysis could easily confuse and mix multiple levels of selectionand descent. Also, characters can be inherited from a diverse range of sources, andcan be adapted as a consequence of intentional and blind variations. This is notto say that social and technological evolution is more complicated than biologicalevolution, because as Hull (1988) argues, social scientists may understand the com-plexities of sociocultural transmission, but their limited understanding of biologicalevolution often leads them to underestimate its complexities. If appropriate historical records are not available, a method called polarizationor argumentation (Wiley et al. 1991, Hennig 1950, 1966) is used to determinewhich characters are ancestral and which are derived. This method uses outgroupcomparison, where a taxon that is hypothesized to be less closely related to eachof the taxa under consideration than any are to each other is called the outgroup,
  23. 23. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 293 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 thatfor a given character with two or more states within a taxon, the state occurringin the outgroup is assumed to be the ancestral state (Watrous & Wheeler 1981).5.4. Constructing cladogramsAt this stage we have chosen taxa that are evolutionarily related, selected andcoded characters for the taxa and where possible have determined the polarity ofthe characters (ancestral or derived). With this step we begin assessing potentialcladograms by grouping taxa by shared derived characters, rather than by sharedancestral 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 Hennigianargumentation (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 whetheror not the data have been polarized. For example, the Wagner and Fitch methods areoptimization procedures that seek to reconstruct the minimum number of characterstate changes according to an optimality criterion. The Wagner method is used forordered characters and the Fitch for unordered characters. The Hennigian argumen-tation method considers the information provided by each character, at each step ofthe construction process. It follows the inclusion/exclusion rule where the informa-tion available allows for either complete inclusion or complete exclusion of taxa, sothat a hypothesis of relationships can be generated. Using the example data (Table 3) provided by Rakotobe-Joel et al. (2002) thissection will manually show how the Hennigian argumentation method is appliedto construct a cladogram (see Figure 7). The data represent six organizational taxa
  24. 24. 294 McCARTHYTable 3. Set of cladistic dataCharacters Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10TaxaT 1 0 0 0 0 0 0 0 0 0 0T 2 0 0 0 0 0 0 0 0 1 1T 3 0 0 0 0 0 0 1 1 1 1T 4 0 0 0 0 0 1 1 1 1 1T 5 0 0 1 1 1 1 1 1 0 1T 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 processedas 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 usingcladistic 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.
  25. 25. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 295 Figure 7. Constructing a cladogram. Rakotobe-Joel et al. (2002, p. 344).5.5. Cladogram selectionIt is important to note that a study involving significantly more characters andtaxa is likely to exhibit numerous conflicts in the relationships. For instance, eco-nomic, technological and organizational systems are likely to demonstrate parallelevolution. This assumes that different taxa will develop the same innovations inde-pendently at different places. Although our knowledge about evolutionary diffusion
  26. 26. 296 McCARTHYversus parallel evolution in social and technological systems is limited, it is mostlikely that both occur because of similar selection conditions, which equates towhat 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 oflife.’ Thus, with organizations demonstrating evolution which is partially rationaland intentional, and reinforced by environmental factors such as reputation, mar-ket trends and fashion, the result can be competitive imitation or benchmarkingwhich 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 optimizationmethods (e.g. Wagner 1961, Fitch 1971, Camin & Sokal 1965) and descriptive sta-tistics (tree length, consistency index and retention index) that together provide anindication of the quality of the cladogram according to the principles of parsi-mony and congruence. Another approach, the Bootstrapping method (Felsenstein1985, Efron 1979, Efron & Gong 1983), assesses the reliability of the branchesin a cladogram by randomly replicating the real dataset several hundred timesand 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 toestimate whether a cladogram is genuine. The principle of congruence assumes that a cladogram will seek agreementbetween the characters used, to produce a unique phylogenetic relationship. This isbecause for any one set of taxa there will be one ‘best fit’ phylogeny, assuming thatthe taxa are derived from a common ancestor. If analysis of three different sets ofdata all show the same pattern for the different taxa, then it can be assumed thatthe 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 principlehas been used in cladistics to argue that a phylogeny is more plausible if it requiresless, 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 numberof steps is chosen. The tree length descriptive indicates the total number of character state changesnecessary 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 bethe 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 statechanges (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
  27. 27. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 297 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 eachchange once. Therefore the tree length descriptive would consider Figure 8(a) to bemore 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)
  28. 28. 298 McCARTHYTable 4. Retention index calculationCharacters Ch 1 Ch 2 Ch 3 Ch 4 Ch 5 Ch 6 Ch 7 Ch 8 Ch 9 Ch 10TaxaT 1 0 0 0 0 0 0 0 0 0 0T 2 0 0 0 0 0 0 0 0 1 1T 3 0 0 0 0 0 0 1 1 1 1T 4 0 0 0 0 0 1 1 1 1 1T 5 0 0 1 1 1 1 1 1 0 1T 6 1 1 1 1 1 1 1 1 1 1Max 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 theactual number of character changes (S). The consistency index can vary from1 (no homoplasy) to 0 (a lot of homoplasy). For example, with the cladogramin Figure 7, step 6, there are 10 characters each with two states and thereforea possible minimum of 10 character changes (M = 10), while the tree length oractual number of character changes is 11 (S = 11). Thus, the consistency index is10/11 = 0.90. The retention index (RI) is similar to the consistency index, but measures theproportion of synapomorphy in a cladogram i.e. the degree of common ancestryin 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 taxawith state 1 or 0 (which ever is smaller). For example, if we use the data in Table3 we find the total number of steps (G) to be 18 (Table 4), M to be 10, S to be11 and therefore the RI is 0.875. The closer the RI is to 1 the better the tree isconsidered to be. With the example organizational cladogram in Figure 7, the descriptive statisticsproduce near-perfect values, but this is only because the data in Table 3 are basicand apposite. Actual studies would very likely result in data with a large numberof 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 andrepresenting the evolutionary relationships in social and technological entities. Ifour current understanding of evolution in these entities is correct, then we shouldexpect organizational cladograms to have imperfect descriptive statistics.
  29. 29. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 2996. ConclusionsClassifying organizational diversity and explaining the mechanisms that presideover the differences are enduring research issues. This paper was motivated bythese and by the belief that we could better understand and advance existingknowledge by using a system of organizational classification that coincides withtheories 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 defineorganizational form. There is also a significant body of work that explains howevolution occurs in organizations and how processes such as variation, selectionand retention govern the creation and adoption of innovations. Yet, despite thisresearch, our understanding of the genesis of organizational taxa in terms of his-torical evolutionary relationships is limited. Not only do existing organizationalclassifications 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 understandingwhat produces organizational diversity and classifying the diversity are currentlyresearch activities that are operationally separate. The result is a collection ofmostly 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 complexityand rigor of their classification thinking and methods. Their debates about classi-fication philosophy and logic resulted in competing schools which advanced andestablished a relatively effective and organized body of systematic activity. A sim-ple indicator of this, are the number of academic journals and societies dedicatedto systematics. The biological sciences have at least seven journals (Annual Reviewof 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 organizationalscience, management and economics have none. To help develop a concerted field of organizational systematics, this paperproposes that the cladistic school of classification is theoretically relevant to orga-nizational diversity and methodologically richer than existing classifications oforganizations. This is not simply because cladistics is accepted by most biolo-gists as the best method for comparative studies in biology. The basis for thisclaim 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 technologicalevolution is fully analogous to biological evolution, as it is well known that theisolating mechanisms, adaptation processes and methods of new system creation
  30. 30. 300 McCARTHYhave contextual differences. The fact is, social, economic and technological evo-lution governs social, economic and technological diversity, and cladistics offersa theory and methods for deducing and representing the evolutionary relation-ships that accompany these developments. The reconstruction of organizationalphylogeny 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 areassuch as ecological, institutional, transaction costs and resource based theories ofthe firm. Also, the representation of a cladistic classification, the cladogram, pro-vides an information management framework that is capable of developing withnew 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 developmentof organizational science.AcknowledgementsI 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 supportof the Social Sciences and Humanities Research Council and the Canada ResearchChair Program of Canada. Finally, I wish to thank an economist reviewer and aspecial thanks to the Co-Editor, biologist Michael Ghiselin, for helpful suggestionsand guidance during the revision process.References citedAitken, Murray, Dorothy Anderson & John Hind. 1981. Statistical modeling of data on teaching styles. Journal of Statistical Sociology 144:419–461.Alchian, Armen. A. 1950. Uncertainty evolution and economic theory. Journal of Political Economy 58:211–222.Alderson, Frederick. 1972. Bicycling: a history. Praeger Publishers, New York.Aldrich, Howard E. 1979. Organizations and environments. Prentice Hall, New York.Aldrich, Howard E. 1999. Organizations evolving. Sage Publications, London.Aldrich, Howard E. & Susan Mueller. 1982. The evolution of organizational forms: technology, coordi- nation and control. Pp. 33–87 in B. Staw & L.L. Cummings (ed.) Research in Organizational Behav- ior, JAI Press, New York.Allen, Peter M. 2001. A complex systems approach to learning in adaptive networks. International Jour- nal of Innovation Management 5(2):149–180.Allen, Peter M. 2002. The complexity of structure, strategy and decision making. Meeting of the Bris- bane Club, Manchester 5–7 July.
  31. 31. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 301Allen, Peter M. & Mark Strathern. 2003. Evolution, emergence, and learning in complex systems. Emer- gence 5(4):8–33.Amundson, Ron & George V. Lauder. 1994. Function without purpose: the use of causal role function in evolutionary biology. Biology and Philosophy 9:443–469.Andersen, Sloth E. 2003. The evolving tree of industrial life: an approach to the transformation of European industry. Paper for the 2nd Workshop on the Economic Transformation of Europe, Torino 31 Jan–2 Feb.Anderson, Philip. 1999. Complexity theory and organization science. Organization Science 10:216–232.Astley, Graham W. 1985. The two ecologies: population and community perspectives on organization evolution. Administrative Science Quarterly 30: 224–241.Babbage, Charles. 1835. The economy of machinery and manufactures. Charles Knight, London.Bailey, Kenneth D. 1994. Typologies and taxonomies: an introduction to classification techniques. Sage University Paper Series on Quantitative Applications in the Social Sciences, 102. Sage Publications, Thousand Oaks, USA.Baldwin, James S., Peter M. Allen, Belinda Winder & Keith Ridgway. 2003. Simulating the cladistic evo- lution of manufacturing. Innovation: Management, Policy and Practice 5:144–156.Barney, Jay B. 1991. Firm resources and sustained competitive advantage. Journal of Management 17:99–120.Basalla, George. 1988. The evolution of technology. Cambridge University Press, New York.Bateman, Richard, Ives Goddard, Richard T. O’Grady, Vicki A. Funk, Rich Mooi, W.J. Kress & Peter Cannell. 1990. Speaking of forked tongues: the feasibility of reconciling human phylogeny and the history of language. Current Anthropology 31:1–24.Baudhuin, Scott E., Robert. W. Swezey, G. D. Foster & Siegfried Streufert. 1985. An empirically derived taxonomy of organizational systems (ARI-TR-692). McLean, VA: Science Applications Inc. (DTIC Report No. AD-A173–440/XAG).Baum, Joel A. C. & Tim J. Rowley. 2002. Companion to organizations: an introduction. Pp. 1–34 in Baum, Joel A. C. Companion to Organizations. Oxford UK: Blackwell.Baum, Joel A. C. & Jitendra V. Singh. 1994. Organizational niches and the dynamics of organizational mortality. American Journal of Sociology 100:346–80.Bezdek, James C. 1981. Pattern recognition with fuzzy objective function Algorithms. Plenum Press, New York.Bigelow, John & Robert Pargetter. 1987. Functions. Journal of Philosophy 84:181–96.Bijker, Wiebe E. 1995. Of bicycles, bakelites and bulbs: toward a theory of sociotechnical change. The MIT Press, Cambridge.Bischi, Gian-Italo, Herbert Dawid & Michael Kopel. 2003. Spillover effects and the evolution of firm clusters. Journal of Economic Behavior and Organization 50:47–75.Blackith, Robert E. & Richard A. Reyment. 1971. Multivariate morphometrics. Academic Press, London and New York.Blackwelder, Richard E. & Alan Boyden. 1952. The nature of systematics. Systematic Zoology 1:26–33.Blashfield, Mark S. & Roger K. Aldenderfer. 1978. The literature on cluster analysis. Multivariate Behavioral Research 13:271–295.Blau, Peter M. & Richard W. Scott. 1962. Formal organizations. Chandler, San Francisco.Borges, Jorge Luis. 1964. The analytical language of John Wilkins. Pp. 101–105 in R. Simms & J. E. Irby (trans.) Other Inquisitions 1937–1952. University of Texas Press, Austin.Boulding, Kenneth E. 1956. General systems theory: the skeleton of science. Management Science 2:197–208.Brittain, Jack W. & John Freeman. 1980. Organizational proliferation and density dependent selection. Pp. 291–338 in J. R. Kimberly & R. H. Miles (ed.) The Organizational Life Cycle. Jossey-Bass, San Francisco.Brower, Andrew 2000. Evolution is not a necessary assumption of cladistics. Cladistics 16:143–154.Cain, Arthur J. 1962. The evolution of taxonomic principles. Pp. 1–13 in G. C. Ainsworth & P. H. A. Sneath (ed.) Microbial Classification. Cambridge University Press, New York.
  32. 32. 302 McCARTHYCamin, Joseph H. & Robert R. Sokal. 1965. A method for deducing branching sequences in phylogeny. Evolution 19:311–326.Campbell, Donald T. 1960. Blind variation and selective retention in creative thought as in other knowl- edge processes. Psychological Review 67:380–400.Campbell, Donald T. 1965. Variation and selective retention in socio-cultural evolution. Pp. 19–48 in H. R. Barringer, G. I. Blanksten & R. W. Mack (ed.) Social Change in Developing Areas: A Reinter- pretation of Evolutionary Theory. Schenkman, Cambridge MA.Campbell, Donald. T. 1969. Variation and selective retention in socio-cultural evolution. General Sys- tems 14:69–85.Capecchi, Vittorio. 1964. I modelli di classificazione e l’analisi della struttura latente. Quaderni di Soci- ologia XIII:289–340.Capecchi, Vittorio & Moeller, Frank. 1968. Some applications of entropy to the problems of classifica- tion. Quality & Quantity II 1–2:63–84.Carpenter, J. M. 1987. Cladistics of cladists. Cladistics 3:363–375.Chandler, Alfred D. 1962. Strategy and structure. Doubleday & Company Inc., New York.Chandler, Alfred D. 1977. The visible hand: the managerial revolution in American business. Belknap, Cambridge, MA.Chandler, Alfred D. 1990. Scale and scope: the dynamics of industrial capitalism. Belknap, Cambridge, MA.Child, John. 1973. Strategies of control and organizational behavior. Administrative Science Quarterly 18:1–17.Clegg, Stewart R. 1990. Modern organizations: organization studies in the postmodern world. Sage Publications, London.Clifford, Harold T. & William Stephenson. 1975. An introduction to numerical classification. Academic Press, New York.Coase, Ronald H. 1937. The nature of the firm. Economica 4:386–405.Cyert, Richard M. & James G. March. 1963. A behavioral theory of the firm. Englewood Cliffs, New Jersey.Daft, Richard L. 1978. A dual core model of organizational innovation. Academy of Management Jour- nal 21:193–210.Darwin, Charles [1859] 1996. The origin of species. J. W. Burrow (ed.). Pelican Classics, London.De Queiroz, Kevin. 1994. Replacement of an essentialist perspective on taxonomic definitions as exem- plified by the definition of ‘Mammalia’. Systematic Biology 43:497–510.Dess, Gregory G., Stephanie Newport & Abdul M. Rasheed. 1993. Configuration research in strategic management: key issues and suggestions. Journal of Management 19 4:775–795.Dewar, Robert D. & Jane E. Dutton. 1986. The adoption of radical and incremental innovations: an empirical analysis. Management Science 32:1422–1433.Dooley, Kevin & Andrew Van de Ven. 1999. Explaining complex organizational dynamics. Organization Science 10:358–372.Doty, Harold D. & William H. Glick. 1994. Typologies as a unique form of theory building: toward improved understanding and modeling. Academy of Management Review 19:230–251.Driver, Harold E. & Alfred L. Kroeber. 1932. Quantitative expression of cultural relationships. Univer- sity of California Publications in American Archaeology and Ethnology 31:211–256.Efron, Bradley 1979. Bootstrap methods: another look at the jackknife. Annals of Statistics 7:1–26.Efron, Bradley & Gail Gong. 1983. A leisurely look at the bootstrap, the jackknife and cross validation. American Statistician 37:36–48.Eisenhardt, Kathleen M. & Jeffrey A. Martin. 2000. Dynamic capabilities: what are they? Strategic Management Journal 21:1105–1121.Etzioni, Amitai. 1964. Modern organizations. Free Press, New York.Everitt, Brian S. 1986. Cluster analysis. 2nd edition. Gower Publishing, Aldershot.Farris, James S. 1970. Methods for computing Wagner trees. Systematic Zoology 19:83–92.
  33. 33. PHYLOGENETIC RECONSTRUCTION OF ORGANIZATIONAL LIFE 303Farris, James S. 1977. Phylogenetic analysis under Dollo’s Law. Systematic Zoology 26:77–88.Felsenstein, Joseph. 1985. Confidence limits on phylogenies: an approach using the bootstrap. Evolution 39:783–791.Felsenstein, Joseph. 1993. PHYLIP – phylogeny inference package, (version 3.5c). Distributed by the author, Department of Genetics, University of Washington, Seattle.Filley, Alan C. & Ramon J. Aldag. 1978. Characteristics and measurement of an organizational typol- ogy. Academy of Management Journal 21:578–591.Fitch, Walter M. 1971. Toward defining the course of evolution: minimal change for a specific tree topology. Systematic Zoology 20:406–416.Fligstein, Neil 1985. The spread of the multidivisional form. American Sociological Review 50:377–391.Forey, Peter L., Christopher. J. Humphries, Ian J. Kitching, Robert W. Scotland, Darrell J. Siebert & David M. Williams. 1992. Cladistics: a practical course in systematics. Clarendon Press, Oxford.Fox, John. 1982. Selective aspects of measuring resemblance for taxonomy. Pp. 127–151 in H. C. Hud- son (ed.) Classifying Social Data. Jossey-Bass, San Francisco.Galbraith, Craig & Dan Schendel. 1983. An empirical analysis of strategy types. Strategic Management Journal 4:153–174.Ghiselin, Michael T. 1966. On psychologism in the logic of taxonomic controversies. Systematic Zoology 15:207–215.Ghiselin, Michael T. 1974. A radical solution to the species problem. Systematic Zoology 23:536–544.Ghiselin, Michael T. 1997. Metaphysics and the Origin of Species. SUNY Press, Albany.Gilmour, John. S. L. 1937. A taxonomic problem. Nature 139:1040–1042.Good, Irving. J. 1965. Categorization of classification. Pp. 115–128 in Mathematics and Computer Sci- ence in Medicine and Biology. H.M.S.O, London.Goronzy, Freidhelm. 1969. A numerical taxonomy on business enterprises. Pp. 42–52 in A. J. Cole (ed.) Numerical Taxonomy. Proceedings of the Colloquium on Numerical Taxonomy. Academic Press, St Andrews University.Green, Paul E., Ronald E. Frank & Patrick J. Robinson. 1967. Cluster analysis in test market selection. Management Science l3:387–400.Gross, Edward. 1969. The definition of organizational goals. British Journal of Sociology 20:277–294.Haas, Eugene J., Richard H. Hall & Norman I. Johnson. 1966. Toward an empirically derived taxon- omy of organizations. Pp 157–180 in R. Bowers (ed.) Studies on Behavior in Organizations. Univer- sity of Georgia Press, Athens GA.Haeckel, Ernst. 1866. Generelle morphologie der organismen. Zweiter Band: Allgemeine Ent- wicklungsgeschichte der Organismen. Verlag Georg Reimer, Berlin.Hambrick, Donald C. 1983. An empirical typology of mature industrial product environment. Academy of Management Journal 26:213–230.Hannan, Michael T. & John H. Freeman. 1977. The population ecology of organizations. American Journal of Sociology 82:929–964.Hannan, Michael T. & John H. Freeman. 1989. Organizational ecology. Harvard University Press, Cam- bridge MA.Hatten, Kenneth J. & Mary L. Hatten. 1985. Some empirical insights for strategic marketers: the case of beer. Pp. 275–292 in H. Thomas & D. M. Gardner (ed.) Strategic Marketing and Management. Wiley, Chichester and New York.Hayes, Samuel L., A. Michael Spence & David Van Praag Marks. 1983. Competition in the investment banking industry. Harvard University Press, Cambridge MA.Hennig, Willi. 1950. Grundzuge einer theorie der phylogenetischen systematik. Deutscher Zentraverlag, Berlin.Hennig, Willi. 1966. Phylogenetic systematics. Urbana, University of Illinois Press.Hodson, Frank R. 1931. Numerical typology and prehistoric archaeology. Pp. 30–45 in F. R. Hodson, D. G. Kendall & .P. A. Tautu (ed.) Mathematics in the Archaeological and Historical Sciences. Uni- versity Press, Edinburgh.