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CLASSIFICATION IN THE SPOTLIGHT: THE

IMPORTANCE OF CLASSIFICATION TO THE

PROGRESSION OF BUSINESS MODEL RESEARCH

Susan C Lambert

Email: [email protected]

Telephone: 618 82012766

Centre for Accounting, Governance and Sustainability

University of South Australia

PO Box 2471, Adelaide,

South Australia,

Australia 5000


ABSTRACT

Classification of research objects is critical to many management research studies with

taxonomies and typologies providing the means by which research objects are organized and

differentiated from one another. However, the nature and structure of the classifications are

frequently presupposed with little attention given to the objectives, functions and

characteristics of the classifications, not only to the immediate study but to future related

research. I bring to the fore the scientific significance of classification and its role in theory

development in organizational science and business model research. The alternative

philosophies of classification are described and their relevance to management research

proposed. Premised on the need for both deductive and inductive empirical research a case is

made for a general classification of business models that can progress the research towards

mid-range theory building

Keywords: classification, taxonomy, typology, mid-range theory, business model, research

schema


INTRODUCTION

In this paper the nascent field of business model research is used to illustrate the significance

of classifications in progressing research from conceptualizations to mid-range theorizing. A

business model depicts the value creation and transfer strategies of the firm including

relationships with other entities in the value domain.

Business model research is an emerging area of management research that suffers from a lack

of empirical research that can pave the way to mid-range theorising and is dominated by

studies that investigate and propose definitions, components and frameworks. However, ‘the

research community is yet to invent a common language, in terms not only of terminology

but basically in terms of conceptualization, for discussing and analyzing business models’

(Pateli & Giaglis, 2004, p.312). The conceptual nature of the research has resulted in there

being a myriad of concepts, ontologies and frameworks of business models all of which have

merit, but none of which have been universally accepted. The problem is not unique to

business model research. Hanks, Watson et al. (1993a, p.11) recognised a similar problem in

relation to research into organisational life cycle models concluding that due to ‘the absence

of careful empirical analysis, a plethora of conceptually based models have emerged’, and

that evaluation of the models could only be achieved through systematic empirical research.

Small enterprise and accounting researchers found themselves in a similar position in the late

twentieth century recognising that progress would only be made through exploratory,

inductive, empirical research (McMahon, 1998).

Existing business model research is predominately conceptualisation based research

producing metaphors, definitions, descriptions, simple classifications and explanations of

relationships between business model components aimed at producing a coherent unit of

analysis. The focus of the research is on the concept itself drawing from established research

areas including organisational theory, strategy theory and information systems theory.

Frameworks and ontologies of business models provide representations of relationships

between business model components some of which are causal models with the potential for

explanatory research (Gregor, 2006).

In parallel to the concept-focussed research, studies emerged that focus on the relationships

between the business model and other management phenomena such as change management

(Chaharbaghi, Fendt, & Willis, 2003; Samavi, Yu, & Topaloglou, 2009; Voelpel, Leibold,

Tekie, & Krogh, 2005), adoption factors (Afuah, 2004; Voelpel et al., 2005) and evaluation

(Mäkinen & Seppänen, 2007; Seppänen, 2008).

The existing research has progressed the unification of the business model conceptualisation

with several recurring themes emerging (Zott, Amit, & Massa, 2011) however because of the

highly specific context in which the research is couched it is severely limited in terms of

theory building. Findings that can be generalised to the whole population under any

circumstances represent the basis for grand theories which are rare (Gregor, 2006; Neuman,

2003). More commonly, generalisations relate to different classes of objects and hold true

under specific contexts. These context-bound generalisations form the basis of mid-range

theories which explain concepts in the context of their setting (Llewellyn, 2003). Mid-range

business model theories are proposed to hold true under particular circumstances and for


particular categories of business models rather than for all contexts or for all instances of

business models.

The need to understand what business models are being used in practice and the

characteristics that differentiate one from another is evident from the number of classification

schemes derived from or applied in the empirical studies. Numerous classification schemes

exist, each devised to meet the specific needs of the researcher however there is no exhaustive

classification of business models (Keen & Qureshi, 2006; Morris, Schindehutte, Richardson,

& Allen, 2006; Pateli & Giaglis, 2004). It is not possible for a single classification scheme

to serve all purposes although each well-conceived classification brings knowledge to the

area of research (Gilmour, 1951; Gilmour, 1940). Distinguishing one classification from

another, evaluating their utility for future research and understanding the underlying

decisions on which the classifications are based, is not always possible because to date, very

little consideration has been paid to taxonomical issues (Mäkinen & Seppänen, 2007; Morris

et al., 2006).

The paper proceeds with an explanation of the relationships between the purpose of a

classification and the necessary functions and characteristics of the classification. The

connection between the functions and characteristics, and the various philosophies of

classification is drawn followed by an overview of classification research in the

organisational sciences. The role of classification in business model research is described and

a case is made for a general classification of business models that can progress the research to

generalisations and mid-range theorising.

Based on the classification theory presented in the earlier sections of the paper a conceptual

framework (Classification Design Framework) is proposed that can guide researchers in

classification scheme design and articulation. The paper concludes with an appeal for explicit

and detailed consideration and articulation of classification design decisions that can

encourage cumulative research.

THE UNIVERSAL SIGNIFICANCE OF CLASSIFICATION

Classification is critical to the understanding of objective reality. It involves the ordering of

objects into groups or classes on the basis of their similarity and ordering of objects into

classes provides meaning to reality (Bailey, 1994; Bailey, 2005; Simpson, 1961).

Classification also aids our understanding of a knowledge domain as ‘we do not perceive,

remember and talk about each object and event as unique, but rather an instance of a class or

concept that we already know something about’ (Smith & Medin, 1981).

The action of putting things which are not identical into a group or class is so

familiar that we forget how sweeping it is. The action depends on recognizing a

set of things to be alike when they are not identical. We order them by what it is

that we think they have in common, which means by something that we feel to

be a likeness between them (Bronowski, 1951, p.21).

For centuries biological scientists have undertaken the theoretical study of classification,

taxonomy and, more recently, organizational and social scientists have recognized the

importance of classification to their research fields and adopted a more scientific approach to


classification. Classification is a form of analytic theory that contributes to the explanation of

simple relationships between objects and attributes of objects.

Embedded in numerous management research papers are classifications of research objects as

diverse as activities within the strategy process (Eppler & Platts, 2009) and reasons for

financial report restatement (Gertsen, van Riel, & Berens, 2006). Firms are classified

according to size (Brews & Purohit, 2007), entrepreneurial orientation (Jambulingam,

Kathuria, & Doucette, 2005), industry (Yip, Devinney, & Johnson, 2009) or business model

(Baden-Fuller & Morgan, 2010; Sabatier, Mangematin, & Rousselle, 2010). Classifications

play an important role in research making it possible to study and make generalizations about

discrete, homogeneous groups of objects and, ultimately, propose mid-range theories (Rich,

1992). Without some level of consensus on the classification of objects within a field of

research, knowledge accumulation and meta-analysis are impeded (Hollenbeck, Beersma, &

Schouten, 2012).

PURPOSES, FUNCTIONS AND CHARACTERISTICS OF CLASSIFICATIONS

Throughout the classification literature, authors allude to the purpose of classification by

listing desirable characteristics, objectives or functions often without distinguishing them.

These descriptors are interrelated and deserve to be distinguished from one another (Walker,

2003). The objective of classification is the same as the purpose of the classification as it is

what the classification will be used for and therefore relates to the user. The functions and

characteristics of a ‘good’ classification scheme depend largely on the purpose of the

classification; it is necessary to know the purpose of the classification before desirable

functions can be identified.

Classifications are often developed to serve a specific purpose such as to provide a means of

reference to the groups of objects through nomenclature (Bailey, 1994; Mäkinen & Seppänen,

2007; McKelvey, 1982; Simpson, 1961; Sokal & Sneath, 1963) or to facilitate information

storage and retrieval so that data can be accessed and used in future research (Chrisman,

Hofer, & Boulton, 1988; McKelvey, 1982). With reference to political strategy, Meznar

(2002) points out that a lack of coherence among studies is due, at least in part, to the

inability to store and retrieve related research due to the absence of a general classification

scheme. Classification provides a foundation for comparative studies, the best classification

being one that permits the most useful comparative investigations (Bock, 1973). Once

objects are classified, generalizations can be made about homogeneous groups of objects

rather than the whole population (Chrisman et al., 1988; Gilmour, 1951; Mäkinen &

Seppänen, 2007; Mayr, 1982; Meznar, Chrisman, & Carroll, 1990; Simpson, 1961). The

purposes are not necessarily mutually exclusive and a carefully designed classification may

serve multiple purposes.

The terms ‘function’ and ‘characteristic’ are used interchangeably throughout the

classification literature; however it is helpful to distinguish between these terms. The

propensity for a classification to meet its stated purpose depends on functionality and it is the

characteristics of the classification that enable this.


Some common functions of classification schemes include the ordering of objects (Bailey,

1994; McKelvey, 1982; Rich, 1992; Simpson, 1961; Sokal & Sneath, 1963), the provision of

an exhaustive and, perhaps, even definitive array of types or taxa (Bailey, 1994), to reduce

complexity and achieve parsimony (Bailey, 1994; Meznar et al., 1990) or, in contrast, to

provide versatility, i.e. suit many needs and display different aspects of the data (Bailey,

1994). Classifications can be designed to identify similarities between objects and facilitate

differentiation between objects so that homogeneous groups can be formed (Chrisman et al.,

1988). Classifications can also possess the function of identifying ideal types or archetypes

that can be used as criterion for measurement: one type can be used as the reference point and

others can be measured relative to that criterion (Baden-Fuller & Morgan, 2010; Bailey,

1994).

The characteristics of the classification enable its potential functions. The classification

systems might be hierarchically structured meaning that lower classes in the classification

system are nested within higher classes (Mayr & Bock, 2002). The classification may

generate mutually exclusive groups, present an exhaustive list of object attributes, be

conceptually elegant and use as few concepts as possible, and/or be intuitively sensible which

means the groups must reflect reality rather than simply be statistically valid (Scott, 1987).

Some functions and characteristics are broadly relevant to all classifications but the relevance

of others depends on the purpose of the classification. When designing a classification

scheme for a specific purpose, the functions and characteristics necessary for satisfying that

purpose must be incorporated into its design. Some functions and characteristics are

complementary while others conflict. For example, there is a trade-off between versatility

and parsimony. To be useful in multiple settings and for multiple purposes, many object

attributes need to be considered in the classification. However, this introduces complexity.

A parsimonious classification uses as few distinguishing attributes as possible but this limits

its utility. For example, a general purpose classification of objects that is intended for

multiple applications requires a large number, perhaps an exhaustive list, of attributes. On

the other hand, a classification that is intended for a specific, narrow purpose should, in the

interests of parsimony, use as few attributes as possible.

PHILOSOPHIES OF CLASSIFICATION

It is widely recognized that classification is a necessary step in understanding a research area,

however throughout history there has been continuous debate about the best way to classify

objects, what criteria to use, and what purpose the classification can serve. In this section, a

short historical overview of classification philosophies and principles are presented with a

view to explaining the issues associated with classification in general and then

contextualizing for management research.

Since ancient times, the natural historians worked to ‘bring order to the apparent chaos of the

natural world’ (Huxley, 2007, p.12) and for centuries, biologists have understood the

importance of classifying objects according to a general, widely accepted classification

scheme which facilitates the naming of objects and provides a common language within the

entire domain (Huxley, 2007). The importance of classification is not, however, peculiar to

biological science research. Researchers in the organizational sciences (Carper & Snizek,


1980; Chrisman et al., 1988; McKelvey, 1982; Scott, 1987; Sells, 1964), behavioral sciences

(Mezzich & Solomon, 1980), social sciences (Bailey, 1994), and information and computer

sciences (Fettke & Loos, 2003; Vessey, Ramesh, & Glass, 2005), also recognize the value of

an empirically derived, general classification scheme for their fields of research.

Three distinct theories of classification have emerged over two thousand years of

classification research in the natural sciences: essentialism, empiricism and ancestry. The

suitability of each theory depends on the purpose of the classification and subject matter. An

overview of each theory follows.

Essentialism

Essentialism stems from the Aristotelian (384-322 BC) view that there exists a few essential

attributes which define the essence of an organism and that, by identifying these attributes,

classes of organisms can be created. Classes based on a small number of attributes

considered essential to defining the essence of the group are called monothetic groups. For

objects to qualify for membership of the group they must possess the attribute(s) used to

define the group and possession of the attribute(s) is both sufficient and necessary for

membership of the group (Bailey, 1994; McKelvey, 1982). Classifications that are the

product of essentialist philosophy are called typologies.

Arguably the most well known typology was that developed by Carl Linnaeus in 1763.

Using a technique called ‘downward classification’ Linnaeus classified organisms firstly as

belonging to the plant or animal Kingdom, then to the relevant Phylum, Class, Order, Family,

Genus, and Species. Although this classification system has recognized shortcomingsi, it

provides a simple, practical classification system that serves the study of natural history. The

enduring contribution of Linnaeus’ classification scheme is the binomial naming system

(genus-species) for all living organisms that remains in use today (Huxley, 2007; Mayr,

1982).

Empiricism

In contrast to essentialism, empiricism entails upwards classification and is based on

observations of many attributes. The classification scheme is designed to be exhaustive and

free from researcher bias (failings of the Linnaeus hierarchy). Polythetic groups of objects

are formed. Polythetic groups ‘have the greatest number of shared character [attribute] states,

and no single state is either essential to group membership or sufficient to make an [object] a

member of the group’ (Sneath & Sokal, 1973, p.21). Classifications that are the product of

empiricist philosophy are called taxonomies.

Empirically derived classification is based on Adansonian principlesii which were largely

overlooked until the advent of computer processing in the middle twentieth century that

provided the means of handling the large amounts of data necessary to produce such

classifications. These neo-Adansonian classifications have come to be known as numerical

taxonomy (Sneath & Sokal, 1973). Numerical taxonomy is a form of classification that

groups taxonomic units into taxa based on their numerically derived similarity of attributes.

According to neo-Adansonian principles of classification that form the basis of numerical

taxonomy, taxonomy is an empirical science and the quality of the classification is directly


related to the number of attributes used to define the taxa and amount of information

contained in the taxa. A priori, all attributes have equal weighting and similarity between

entities is a function of the similarity between each of their many individual attributes. The

correlation of characters differs significantly from one taxa to another and classifications are

based on phenetic similarityiii

(Sneath & Sokal, 1973).

Numerical taxonomy requires objects to be grouped according to their observed similarity of

many attributes. The taxonomic groupings are polythetic, meaning that the members of the

group possess ‘a large number of common characters, that each character is possessed by

many [objects] in a group, but that no character is possessed by all the [objects] in the group’

(McKelvey, 1982, p.45). Numerical taxonomies evaluate affinity between objects

numerically (using multivariate techniques) and order the objects according to their degree of

affinity (McKelvey, 1982; Sokal & Sneath, 1963).

Ancestry

Since the middle nineteenth century, classifications in biological science have been phyletic

which means they are based on ancestry. Two types of phyletic classification exist, one

attributed to Charles Darwin, evolutionismiv and the other, a variation on evolutionism

attributed to Hennig (1950) called cladism (Mayr, 1982). According to evolutionism entities

are grouped based on their overall ancestral (genetic) affinities and under cladism entities are

grouped strictly according to lines of descent emphasizing the recency of branching points.

Group members have the same immediate ancestor species (McKelvey, 1982).

Ancestry relates only to living organisms and does not translate well to inanimate objects or

to objects that have no discernable ancestry and therefore has little relevance to management

research. McKelvey (1982) argues that organizations are social processes that have life-cycle

and evolutionary patterns and therefore are suitable for classification based on ancestry

although there is a dearth of support for this view. Essentialism and empiricism on the other

hand are appropriate for classification of inanimate objects and will therefore be explored and

contrasted further.

TAXONOMY V TYPOLOGY

Before proceeding further it is important to emphasize the dual meaning of taxonomy.

Taxonomical activities form part of the broader study of systematics which is ‘the scientific

study of the kinds of diversity of organisms and of any and all the relationships among them’

(Simpson, 1961, p.7). A taxonomy is an empirically derived classification of objects based

on the totality of their observable attributes. The term taxonomy is also used to refer to the

‘theoretical study of classification, including its bases, principles, procedures, and rules’

(Simpson, 1961, p.11).

This dual meaning of taxonomy can lead to confusion and misuse of the term. Researchers

who develop a classification schemes carry out taxonomic activity, yet their output, the actual

classification schemes, can be typologies or taxonomies.

Essentialist and empiricist theories of classification imply important differences in the

taxonomical approaches used to create a catalogue of objects and in the resulting catalogue

itself. The utility of those catalogues also differs. A typology is developed with a specific


purpose in mind, is based on only a few attributes and, therefore, has limited utility

(McKelvey, 1982). By contrast, taxonomies are the result of grouping objects based on the

totality of their observable attributes. Although many researchers use the terms

interchangeably it is important to distinguish between typologies and taxonomies since they

serve different purposes and have their own limitations and strengths. Table 1 summarizes

the differences between typologies and taxonomies.

-----------------------------------

Insert Table 1 about here

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Conceptually Derived Typologies

Typologies can take the form of traditional (commonsense) or theoretical classifications

(Rich, 1992; Warriner, 1984). Traditional classifications, ‘depend on implicit recognition of

the categories referred to, for there are no explicit classificatory criteria’ (Warriner, 1984,

p.134). The only function of traditional classifications is to identify and describe generic

kinds of objects that exist in the so-called ‘real world’.

Theoretical typologies are derived on the basis of a pre-existing theory such as economics,

management, strategy or entrepreneurship theory. The researcher conceptualizes and names

the ‘types’ that are relevant to the research and decides, a priori, the few attributes that

represent the essence of the object which in turn, depends on the intended purposes of the

classification. The result is a deductively-derived classification designed for a specific

purpose ‘but no matter how useful they may be in predicting certain features of special

interest to particular theories they have limited general utility’ (Warriner, 1984, p.135).

The purely theoretical-conceptual classification may have no empirical equivalents, and may

be ideal types or completely hypothetical (Bailey, 1994). Alternatively, theoretical

classifications can be populated with empirical cases what Bailey (1994) refers to as

conceptual-operational classifications: the categories have been conceptually conceived and

then empirical instances of them have been collected. All members of a category must

possess the characteristic(s) which define that category. Typologies are mostly generated

through qualitative classification rather than quantitative or statistical analysis although they

can be formed through conceptualizing types and then using cluster analysis to quantify the

empirical findings (Bailey, 1994).

As typologies categorize objects according to a limited number of attributes (often as few as

two) they are able to simplify complex concepts. However, their simplicity limits their power

to explain or predict phenomena (Hambrick, 1984). The criteria are determined by the

researcher based on their personal perspective and bias (Hambrick, 1984) making them

purpose-specific classifications. Classification can be specific in three dimensions. The

classification can be specific in terms of the taxonomic attributes being considered, the

environment or the situation to which it relates in terms of activity and timing. These

dimension are not mutually exclusive and the more specific the classification is along each of

these dimensions, the less generalisable it is (Chrisman et al., 1988).


A typology is well suited to a specific need, conceptualizing a small number of categories

based on a few attributes and collecting data based on those categories is a practical research

method. The problem is that any increase in the number of attributes considered will lead to

a disproportionate increase in the level of complexity of the task and in the ultimate result of

the research itself.

For example, even if all dimensions [attributes] are dichotomous, the formula

for determining the number of cells [types] is 2M

, where M is the number of

dimensions. Thus for five dichotomous dimensions the typology will contain

only 25 or 32 cells, but for 12 dichotomous dimensions the number of cells is

212

or 4,096. (Bailey, 1994, p.4)

Typologies based on a few, carefully chosen attributes can serve a specific purpose well;

however as the number of attributes increases, the efficacy of the classification decreases.

Empirically Constructed Taxonomies

A taxonomy is derived empirically from inductive research (Sokal & Sneath, 1963) using

cluster or multivariate analysis (Hanks, Watson, Jansen, & Chandler, 1993b) to create taxa

(categories) based on a large number of variables. The purpose of taxonomy is to produce a

general classification of objects from which generalizations can be made, hypotheses

proposed and eventually mid-range theory generated. Eisenhart (1989, p.532) purports that

‘it is the intimate connection with empirical reality that permits the development of a testable,

relevant, and valid theory’. It is also argued that a general classification can take the place of

contingency theory because it can specify the class of objects to which the theory relates

(Carper & Snizek, 1980; McKelvey, 1975). By using a large number of variables the

researcher bias that is present in typologies is potentially reduced however there are still

many subjective decisions that need to be made. In research fields where little is known

about the object of classification and research is exploratory, the researcher must trawl the

data using as many variables as practical. The dangers with this approach are that key

variables may be overlooked and that irrelevant variables may dominate. The resultant

classification may be statistically valid but may not be intuitively sensible.

Where there is little domain knowledge, an alternative to the pure inductive method is to seek

expert opinion on variable selection, what Ketchen (2005) refers to as the cognitive approach.

In research areas that are more mature, with existing theories in relation to the object of

classification, the researcher can utilize that prior knowledge to minimize the chance of

irrelevant data obstructing the classification and to ensure all key variables are included. In

addition, where causal relationships are known, they need to be taken into account in order to

avoid overrepresentation of constructs (Ketchen, 2005). There must still be a large number of

variables; however utilizing existing theory to refine the variable set is recognized as

beneficial for classifications that are aimed at confirming existing theory.

Variables must be identified and measurement rules determined to allow data to be collected

and coded for cluster analysis. The data can be further analyzed using a range of multivariate

techniques. The aim is to minimize within-group variance and maximize inter-group

variance, thereby creating homogeneous groups. Once created, these homogeneous groups


can be used for a multitude of research applications, enabling the study of both within-group

behavior as well as inter-group behavior.

CLASSIFICATION IN THE ORGANIZATIONAL SCIENCES

During the mid twentieth century organizational science research became dominated by an

empiricist approach which Gergen and Thatchenkery (2004) refer to as the modernist era.

The modernist era was dominated by information technology advancement in the form of

computerized data processing that facilitated an empirical approach to research. The

emphasis was to determine what was actually taking place in the ‘real world’ or as Gergen

and Thatchenkery (2004, p.242) put it, researchers were essentially ‘polishers of mirrors’

striving to isolate variables, standardize measures and assess causal relationships. The

empirical, modernist emphasis of organizational science has more recently given way to post

modern organizational science where the objective is to ‘tell it as it might become’ (Gergen

& Thatchenkery, 2004, p.242) rather than how it is; to rely less on the observations of reality

and more on potential for change. Even though the emphasis of current organizational

research has moved away from empiricism, empiricism continues to play a role albeit a less

prevalent role in organizational research.

It is postulated here that research outputs from the empirical era facilitated the move towards

postmodern research and that this empirically derived knowledge was required prior to

developing the constructionist views. Before attempting to propose theories in relation to

nascent fields of research, it is necessary to ‘polish the mirror’ and see what is actually taking

place. A critical requirement of the empirical approach to research is the classification of the

objects of interest in order that generalizations can be made.

Hanks et al. (1993) claim that empirical research was required to subjugate an explosion of

conceptually based organizational life-cycle models and that systematic empirical research

was required to evaluate the models.

McKelvey (1982) summarizes the views of other organizational science scholars (Burns,

1967; Carper & Snizek, 1980; Haas, Hall, & Johnson, 1966; Hall, 1977; Pugh, Hickson, &

Hinnings, 1969; Pugh, Hickson, Hinnings, & Turner, 1968) who reviewed and analysed

typologies of organizations arguing that the typologies were of no use for empirical research

because they failed to capture important differences among organizations due to the fact that

they rely on only a few organizational attributes. McKelvey (1982) provided the theoretical

and methodological foundation for the development of taxonomies of organizations and these

methods were applied by several scholars (Rich, 1992). Rich (1992) observes that despite the

foregoing, research obstacles that inhibit the construction of a general classification of

organizations that can aid generalizations and theory building remain. These obstacles

include the development of a generic pool of variables to use in the classification process and

the measurement, weighting and methods of collecting the data.

The twentieth century organizational scientists analysed the biological science taxonomy

research in ways that can assist with the application of taxonomy to other management

research. The theory that was developed by the natural scientists to classify living organisms

was adapted to suit an abstract object, the organization.


BUSINESS MODELS AND CLASSIFICATIONS

Business model research finds itself in what Kuhn refers to as its ‘pre-paradigm’ period

indicating an immature science that lacks consensus and where ‘competing schools of

thought possess differing procedures, theories, even metaphysical presuppositions’ (Bird,

2008). Conceptual differences inhibit research that uses the business model as a vehicle to

collect information about other phenomena. Although some evidence of research aimed at

unifying the business model concept exists, it is underdeveloped and there is an absence of

order in the research field that obscures the path to business model theories.

The precursor to theorising is generalisation whereby the results of inductive empirical

research are used to infer general patterns of configurations and simple relationships between

variables can be hypothesised and tested. Generalisations are the product of empirical

research and ‘…it is the intimate connection with empirical reality that permits the

development of a testable, relevant, and valid theory’ (Eisenhardt, 1989 p.532).

In all forms of scientific research, including organisational science (McKelvey, 1982) and

behavioural science (Mezzich & Solomon, 1980), classification of objects within the research

domain is an important step towards other research. A good classification scheme forms the

foundation of theory development. To advance research towards theorising, it is necessary to

order the objects within the research domain since ‘theory cannot explain much if it is based

on an inadequate system of classification’ (Bailey, 1994, p.15). Classifications ‘…are

partway between a simple concept and a theory. They help to organise abstract, complex

concepts’ (Neuman, 2003 p.46). Business models are abstract, complex concepts, the

understanding of which can be enhanced through the development of a general classification

scheme.

Numerous business model classifications can be identified in the extant literature however

most treat as a ‘given’ the basis of the classification and the principles of the classification

system. That is to say, there is a lack of taxonomic research in relation to business models. It

has long been recognized that the business model literature lacks a systematic approach to the

development of taxonomies and that many of the so-called taxonomies of business models

are simply lists of existing business activities, or at best, typologies of generic kinds of

business models (Baden-Fuller & Morgan, 2010). The indiscriminate use of the terms

typology and taxonomy in the business model literature creates misunderstanding and

confusion for those attempting to understand and compare the various classification schemes.

The need for a general classification of business models remains unsatisfied.

Early business model classifications are simple traditional (otherwise known as

commonsense) classifications that ‘depend on implicit recognition of the categories referred

to, for there are no explicit classificatory criteria’ (Warriner, 1984, p.134). The only function

of the traditional classifications is to identify and describe business models that exist in the

so-called ‘real world’ and since the purpose of the classifications was to identify objects that

fit preconceived categories (for examples see Applegate, 2001; Bambury, 1998; Eisenmann,

2002; Laudon & Traver, 2003) the traditional typologies were useful. The traditional


typologies of business models use no explicit criteria for classification and produce generic

types or shorthand descriptions of existing business models (Baden-Fuller & Morgan, 2010)

which serves the limited purpose of identification and nothing more . As few as four types

and as many as fourteen types of business model are identified in the various traditional

classifications.

Advancing from the traditional typologies, a range of theoretical classifications were created

to enable comparisons between existing business models. Most of the theoretical

classifications use market related criteria such as customer profile (Bienstock, Gillenson, & T

Sanders, 2002; Leem, Suh, & Kim, 2004), pricing and profit factors (Afuah & Tucci, 2003;

Bienstock et al., 2002; Linder & Cantrell, 2000; Weill & Vitale, 2001), transaction factors

(Wang & Chan, 2003), market configuration factors (Tapscott, Ticoll, & Lowy, 2000;

Timmers, 1998; Wang & Chan, 2003) and marketing strategy (Weill & Vitale, 2001) to

categorise business models. Internal criteria including product related factors (Bienstock et

al., 2002; Rajala & Westerlund, 2007; Timmers, 1998), resource related factors (Betz, 2002;

Weill & Vitale, 2001) and operational issues (Betz, 2002; Rajala & Westerlund, 2007) also

feature in the theoretical typologies.

Some of the theoretically derived typologies are subsequently populated with empirical

instances of the types but there are few empirically derived taxonomies of business models

present in the literature. The Italian biotechnology industry is the subject of one series of

studies (Bigliardi, Nosella, & Verbano, 2005; Nosella, Petroni, & Verbano, 2005) and two

non-industry-specific studies involve United States based firms (Malone et al., 2006; Morris

et al., 2006).

Although many specific, arbitrary classifications exist in the extant literature (Mäkinen &

Seppänen, 2007; Malone et al., 2006; Morris et al., 2006) ‘the diversity in classification

schemes, suggest much more progress is needed. In particular, the ability to generalize a

given scheme to different types of industries and ventures is limited’ (Morris et al., 2006,

p.32). As yet there is no general classification of business models that can form the basis of

mid-range theory building.

A CLASSIFICATION DESIGN FRAMEWORK

In the interests of encouraging the application of theoretical rigor to the design of

classification schemes in management research and communicating their underlying structure

to potential users, a conceptual framework for the design of classification schemes is now

proposed. Conceptual frameworks guide decision making by providing a structure that

incorporates the broad principles of the relevant practice or process. The proposed

conceptual framework for classification development can guide the development of future

classifications and assist in articulating important information regarding the classification

scheme. Based on the classification theory presented earlier in this paper a series of decision

steps are identified that lead to a classification outcome.

For taxonomy (the process) to serve a practical purpose, it is necessary to match the theory to

the application. This requires the researcher to firstly specify the purpose of the

classification. The purpose might be specific or broad; the classification might be required to


serve several purposes or only one. Each purpose necessitates particular functions and

characteristics of the classification so the purpose must firstly be recognised.

Depending on the purpose and related characteristics and functions of the classification, the

relevant classification philosophy and related principles can be identified. For example, a

specific classification that aims only to identify types of organisational structure is consistent

with an essentialist philosophy of classification the principles of which include deriving the

types conceptually, using as few attributes as possible and forming monothetic groups.

Procedures and rules that are appropriate to the philosophy and principles must then be

selected. Continuing with the example of a specific classification, one procedure would be to

conceptualise the categories. An associated rule might be to specify the minimum and

maximum number of categories.

The steps required to design a classification scheme form a Classification Design Framework

(CDF) that is depicted in Figure 1. The first two steps in the CDF relate to the application of

the intended classification. Steps three to six equate to the definition of taxonomy proposed

by Simpson (1961); theory, principles, procedures and rules together form taxonomy, the

theoretical study of classification.

-----------------------------------

Insert Figure 1 about here

-------------------------------------

Step 1 State the purpose(s) of the classification: The three broad research purposes of a

classification are nomenclature, information storage and retrieval, and generalisations. These

purposes are not mutually exclusive and they can potentially be satisfied by a single

classification scheme although each may be better served by a specific classification designed

solely for that purpose. For example, a general classification of business models is required

to provide a foundation for comparative studies and to provide a means of reference to groups

of business models through nomenclature. Generalisations can then be made with respect to

homogeneous groups of business models as a precursor to mid-range theorising

Step 2 Identify the necessary functions and characteristics of the classification required

to satisfy the purpose(s): The relevant functions and characteristics become a checklist that

can assist in both building and evaluating the classification scheme.

The functions desirable in a general classification suitable for making generalisations and

comparative studies and providing a naming structure, include the ability to reliably and

consistently allocate business models to the appropriate category, to order the business

models, facilitate differentiation between business models and produce categories that are

collectively exhaustive and mutually exclusive and provide versatility in terms of purpose

and over time. The related functions are that it identifies similarities among business models,

produces mutually exclusive groups, presents an exhaustive list of key characteristics and it

reflect reality.

Step 3 Select the philosophical basis for the classification: The philosophical basis for the

classification must be consistent with the functions and characteristics of the classification.

For classifications of inanimate objects the choice is between essentialist and empiricist

philosophies. Essentialist philosophy of classification requires the researcher to identify the


characteristics of the object or concept under study that capture its ‘essence’. For the

essentialist philosophy of classification to be suitable for a general business model

classification there needs to be widespread agreement on what constitutes the essence of a

business model. Business model research has not reached a level of maturity for this to be

settled. The widely varying conceptualisations of business models indicate that the ‘essence’

of the business model is not a shared perception thereby providing no support for an

essentialist approach to developing a general classification of business models.

Empiricism, on the other hand, does not rely on agreement of the underlying essence of the

concept but instead is based on all identifiable key characteristics. An empirically derived

taxonomy groups objects based on overall similarity and, because it relies on observations to

form the categories, has potential to reflect reality. The categories are collectively exhaustive

and mutually exclusive. Based on the above, an empiricist philosophy would be required for

a general business model classification.

Step 4 Identify classification principles associated with the chosen philosophical basis:

The principles capture the fundamentals of the classification philosophy. These include the

number of attributes considered necessary to identify the object, the basis of similarity and

differentiation between objects and the choice of monothetic or polythetic groupings.

A general business model classification based on an empiricist philosophy creates

polymorphic groupings and a specific business model classification based on an essentialist

philosophy creates monothetic groupings.

Step 5 Choose a procedure consistent with the principles: The specific procedures are

selected according to the choices made in the first four steps of the classification building

process. Statistical programs are frequently used to analyse the results of conceptually

derived classifications that are based on essentialist philosophy although manual techniques

are also used. Modern empirical classification, numerical taxonomy, is principally carried

out using computerised statistics programs that perform cluster analysis to identify the taxa

based on the observed variables. Cluster analysis is considered appropriate when the research

is essentially exploratory (Huberty, Jordan, & Brandt, 2005; Timm, 2002) and ‘where the

number of groups, as well as their forms, may be unknown’ (Henry, Tolan, & Gorman-Smith,

2005, p.121).

Table 2 summarises the relationships between theories, principles and procedures and

illustrates the decisions in relation to essentialism and empiricism.

-----------------------------------

Insert Table 2 about here

-------------------------------------

Step 6 Decide the rules to operationalise the procedure: The rules dictate how the

procedures are to be carried out. They include decisions regarding the definition of the

objects to be classified. In relation to classifications regarding tangible objects, this is a

relatively simple step, however, when the object is abstract and is the product of multiple

definitions, the step becomes more complex. Decision rules relating to the selection and


measurement of variables, choice of particular statistical techniques and decision rules

associated with those techniques should be made explicit. Some of the decisions that need to

be made to implement cluster analysis include; choice of the sampling unit, population and

sample size. Variables need to be selected, measured and coded, the clustering method

selected and the methods of analysis chosen.

The CDF provides a structure of decisions that need to be made to ensure the resultant

classification is consistent with the purpose of the classification. The CDF ensures that all

important issues have been considered and documented.

CONCLUSION

Classification is an integral part of business model and other management research

(Christensen & Carlile, 2009). To progress nascent management research fields such as

business model research towards mid-range theory building generalisations must first be

made and those generalisations must be based on a sound classification of business models.

It is also necessary to understand how existing classification schemes have been developed

and the decisions underlying their structure so they can be replicated and further developed.

A classification scheme, like a good theory, is seldom finished. It is only given

interim acceptance with the understanding that further studies will tend to elaborate

and refine it, or disconfirm it (McKelvey, 1982 p.30).

An overview of the theory of classification has been presented to bring to light the significant

differences between classification schemes and their relevance to research. The differences

between typologies and taxonomies have been highlighted in order to show how each serves

different research needs. Typologies produce specific classifications, derived either through

intuition (common sense classifications) or theoretically derived, some populated with ‘real

life’ instances of the types and others remaining purely theoretical, ideal types. Taxonomies

produce general classifications as a result of inductive, empirical research that takes into

account many variables and is based on analyzing many ‘real life’ instances of the object.

Thoughtful consideration of the purpose of the classification scheme that extends beyond the

immediate requirement has the potential to create bridges between current and future

research. An awareness of the implications of classification design criteria can guide

researchers in their choice of classification schemes and knowledge of the principles that

underlie existing classification schemes increases their potential for reuse. The CDF is

proposed to facilitate the classification decision process and to explicate the decision steps for

the benefit of potential users. The pervasiveness of classification throughout all fields of

research renders it an important construct worthy of careful and explicit consideration.


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TABLES

Typologies Taxonomies

The product of essentialist philosophy The product of empiricist philosophy

Categories (types) are conceptually derived Categories (taxa) are empirically derived

Few attributes considered Many attributes considered

Reasoning by deduction Reasoning by inference

Mostly qualitative classifications Quantitative classifications

Monothetic groupings Polythetic groupings

Specific classification General classification

Provides a basis for only limited generalizations Provides a basis for wider generalisation

Table 1

Summary of the Differences between Typologies and Taxonomies (adapted from Lambert, 2006))

Figure 1: Classification Design Framework (CDF)


Philosophy Essentialism Empiricism

Associated

Principles

Categories derived conceptually

Few characters

Monothetic groups

Categories derived through observation

Many characters

Polythetic groups

Related

Procedures Conceptualize the categories

Define the sampling unit and determine the

population

Identify objects that fit the categories

Analyse the results quantitatively and/or

qualitatively

Define the sampling unit and determine the

population

Discover and measure the variables

Code the variables

Form clusters

Analyse results quantitatively

Table 2

Principles and Procedures for the Development of an Essentialist and an Empiricist Classification Scheme


i Fails to classify all organisms and requires researcher discretion to allocate exceptions Mayr, E. 1982. The Growth of

Biological Thought: Diversity, Evolution, and Inheritance. Cambridge: Harvard University Press..

ii At about the same time that Linnaeus was proposing his classification scheme, Michel Adanson, a French botanist,

produced a ‘natural’ classification scheme designed to be exhaustive and to be free from researcher bias. This classification

scheme reflects the principles of John Ray’s ‘natural classification’ (proposed almost a century earlier) in that it is based on

observations of many attributes of the organism and it is a compositional (upward) classification Huxley, R. 2007. The Great

Naturalists. London: Thames & Hudson, Mayr, E. 1982. The Growth of Biological Thought: Diversity, Evolution, and

Inheritance. Cambridge: Harvard University Press..

iii Phenetics is a ‘system of classification based on similarities between organisms without regard to their evolutionary

relationships’ (Collins English Dictionary, 1991, p.1168). iv It should be noted that Mayr and Bock Mayr, E., & Bock, W. J. 2002. Classifications and other ordering systems. Journal

of Zoological Systematics & Evolutionary Research, 40(4): 169-194. argue strongly that Darwin’s classification of

organisms was based on two criteria, overall similarity (systematics) and descent however this is refuted by other respected

researchers Ghiselin, M. T. 2004. Mayr and Bock versus Darwin on genealogical classification. Journal of Zoological

Systematics & Evolutionary Research, 42: 165-169, Padian, K. 2004. For Darwin, 'genealogy alone' did give classification.

Journal of Zoological Systematics & Evolutionary Research, 42: 162-164.

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