realism rules ok: scientific paradigms in marketing ... - ANZMAC

Theme: Firms & Networks
REALISM RULES OK: SCIENTIFIC
PARADIGMS IN MARKETING RESEARCH
ABOUT NETWORKS
Address for all correspondence:
Assoc. Prof. Chad Perry
Department of Marketing
Faculty of Business
University of Southern Queensland
Toowoomba Q4350
Tel: 0746 311535
Facsimile: 0746 312811
e-mail: perry@usq.edu.au
Dr Chad Perry
Dr Andrew Riege
Mr Les Brown
Dr Andrew Riege
Department of Marketing and International Business
QUT
GPO Box 2434
Brisbane Qld 4001
Mr Les Brown
Department of Marketing
Faculty of Business
University of Southern Queensland
Toowoomba Q4350
Tel: 07 46 312849
Facsimile: 0746 312811
e-mail: brownl@usq.edu.au
1947

REALISM RULES OK: SCIENTIFIC
PARADIGMS IN MARKETING RESEARCH
ABOUT NETWORKS
ABSTRACT
This paper addresses the issue of scientific paradigms in marketing research about
networks. It begins with an overview of a ‘rapprochement’ model suggested for
network research by Borch and Arthur (1995) which attempts to integrate both
objective and subjective research. We argue that their two-dimensional approach could
be extended to include the realism paradigm that separates the objective, positivist
dimension and the subjective, interpretive dimension. Characteristics of that paradigm
are described and its appropriateness for case study research is established.
Key words: business networks, scientific paradigms, realism
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INTRODUCTION
In recent years, research about business and marketing strategy has been criticised for
not capturing real-world complexity (for example, Pettigrew 1987). Borch and Arthur
(1995) suggest that a mix of objectivist and subjectivist methodologies can help address
criticisms like these. We suggest that the mix involves more than methodological
considerations - it involves a reconsideration of all three aspects of scientific paradigms
used for marketing research.
This paper has three sections. In the first section, the Borch and Arthur (1995)
suggestion about methodologies is revisited. Then, four scientific paradigms are
discussed in terms of ontology and epistemology, as well as methodology. Reasons why
the realism paradigm is the most appropriate for some marketing research like that
about networks, are then addressed. Finally, the case study research methodology
within the realism paradigm is examined.
Borch and Arthur’s (1995) ‘Rapprochemont’ Methodology
The objectivist approach is predicated on explaining and predicting phenomena, while
the subjectivist approach emphasises describing and understanding phenomena. Borch
and Arthur (1995, p. 423) claim that both approaches should be used, arguing a mixed
methodology would “contribute to the richness of the new strategic management
models”. Their bipolar approach to research is common, for example, Easterby-Smith,
Thorpe and Lowe (1991) share it. In brief, their methodology aims to blend the rigour
of the scientific validity of objectivist research with the contextual elements and
insights of subjectivist research (Borch and Arthur 1995, p. 425).
We suggest that their blend of two approaches could be replaced with one approach, the
third approach of realism. This third approach involves a reconsideration of all three
elements of scientific paradigms - ontology, epistemology and methodology - and not
just methodology. This issue of scientific paradigms is addressed next.
The Four Scientific Paradigms
Researchers operate within a scientific paradigm that is either explicit or implicit. A
paradigm is an overall conceptual framework within which a researcher may work, that
is, a paradigm can be regarded as the “basic belief system or worldview that guides the
investigator” (Guba and Lincoln 1994, p. 105). Philosophical assumptions which
support the four different paradigms of social science relating to ontology,
epistemology and methodology are summarised in Table 1. In effect Table 1
summarises the following discussion.
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Table 1 Basic belief systems of alternative inquiry paradigms
Paradigm
Item Positivism Realism Critical theory Constructivism
Ontology naïve critical realism: historical realism: critical
realism: reality is “real” but “virtual” reality relativism:
reality is real only imperfectly shaped by social, multiple local and
and
and
economic, ethnic, specific
apprehensible probabilistically political, cultural, “constructed”
apprehensible and and gender values, realities
so triangulation crystallised over
from many sources time
is required to try to
know it
Epistemology objectivist:
findings true modified
subjectivist: subjectivist:
objectivist: value mediated created findings
findings probably findings
true
Methodology experiments/ case
dialogic/dialectical: hermeneutical /
surveys: studies/convergent researcher is a dialectical:
verification interviewing: “transformative researcher is a
of
triangulation, intellectual” who “passionate
hypotheses: interpretation of changes the social participant”
chiefly research issues by world within which within the world
quantitative qualitative and participants live being investigated
methods quantitative
methods such as
structural equation
modeling
Note: Essentially, ontology is 'reality', epistemology is the relationship between that reality and
the researcher and methodology is the technique used by the researcher to discover that reality.
Source: Perry, Alizadeh and Riege, (1996, p. 547) based on Guba and Lincoln (1994).
Positivism. Positivists assume that natural and social sciences measure independent
facts about a single apprehensible reality composed of discrete elements whose nature
can be known and categorised (Guba and Lincoln 1994; Tsoukas 1989). The objectives
of the research inquiry often include the measurement and analysis of causal
relationships between variables that are consistent across time and context. The
primary data collection techniques include controlled experiments and sample surveys
which are outcome oriented and assume natural laws and mechanisms, with the primary
mode of the research inquiry being theory-testing or deduction. Data is usually
collected in a structured manner with the researcher not intervening in the phenomenon
of interest and seeking for theory testing in value-free or hopefully value-free
generalisations. In other words, the data and its analysis are value-free and data does
not change because they are being observed. That is, researchers view the world
through a ‘one way mirror’ (Guba and Lincoln 1994, p. 110). Even in nuclear physics
where particles cannot be directly observed, uncertainty in results is a methodological
issue and not an ontological one - different observers using the same methodology
should obtain similar results. All these assumptions of positivism are appropriate in a
natural science, for example, every zoologist in the world will count the same number
of bones in a dead kingfisher.
However, a positivist view is inappropriate when approaching a social science
phenomenon like networks which involves humans and their real-life experiences, for
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treating respondents as independent objects “ignores their ability to reflect on problem
situations, and act upon this” (Robson 1993, p. 60). That is, positivists separate
themselves from the world they study, while researchers within the three other
paradigms acknowledge that they have to participate in real-world life to some extent
so as to better understand and express its emergent properties and features (Denzin
1983; Gilmore and Carson 1996).
Furthermore, social science researchers should not seek to provide causal explanations
within a closed system as a positivist would. Instead, they should consider the complex
nature of reality and the research problem, reflecting, forming and revising meanings
and structures from managerial experiences and how these problems appear to
managers (Orlikowski and Baroudi 1991). To approach this task, social science
researchers could operate within the non-positivist scientific paradigms introduced next.
Critical theory and constructivism. The second paradigm, critical theory, assumes
apprehensive social realities, incorporating historically situated structures. Critical
theory researchers aim at critiquing and transforming social, political, cultural,
economic, ethnic and gender values. Examples of critical theory researchers are
Marxists, feminists and action researchers. Thus research inquiries are often long-term
ethnographic and historical studies of organisational processes and structures.
Assumptions are essentially subjective and hence knowledge is grounded in social and
historical routines and is therefore value-dependent and not value-free (Guba and
Lincoln 1994).
However, this paradigm is not appropriate for marketing research unless the researcher
aims to be a “transformative intellectual” who liberates people from their historical
mental, emotional and social structures (Guba and Lincoln 1994, p. 112). For example,
most business network research aims at understanding the actions of the decisionmakers
rather than changing them or their approaches to strategy formulation.
The third paradigm, constructivism, adopts a relativism ontology. An example of a
constructivist would be a psychologist or a researcher of organisational culture. From a
constructivist's perspective, truth is a construction which refers to a particular belief
system held in a particular context. Realities appear as multiple realities which are
socially and experientially based, intangible mental constructions of individual persons.
Meaning has more value than measurement, for perception itself is the most important
reality. Like critical theory, constructivism enquires about the ideologies and values
which lie behind a finding. Researching this created knowledge depends on the
interaction between interviewer and respondent, that is, the researcher has to be a
“passionate participant”' during his or her field work (Guba and Lincoln 1994, p. 112).
This constructivist approach may be suitable for some social science research like that
about religion, beauty or prejudice but it is rarely appropriate for business research
because the approach excludes concerns about the clearly real economic and
technological dimensions of business (Hunt 1991). For example, when BHP recently
closed a major plant, the decision was based not only on “beliefs” but included
constructs that were measurable such as present and past profit levels and plant
capacities. But these measurable constructs were difficult to measure and they were not
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the only considerations involved, and so positivism is not the appropriate paradigm to
investigate such phenomena either. That is, BHP had to forecast future levels of these
constructs based on the beliefs of many people and had to consider complex social
constructs like industrial relations and the impact of public opinion. What researchers
of phenomena like business network formulation need is a paradigm that is different
from but has some elements of both positivism and constructivism. That paradigm is
the realism paradigm (Hunt 1991), sometimes called the critical realism or
postpositivist paradigm (Guba and Lincoln 1994).
Realism. The fourth paradigm of realism is more appropriate for some marketing
research than the three above. This type of research is searching, albeit necessarily
imperfectly, towards an understanding of the common reality of an economic system in
which many people operate independently. That is, realists believe that there is a “real”
world to discover even if it is only imperfectly and probabilistically apprehensible
(Godfrey and Hill 1995; Guba and Lincoln 1994; Tsoukas 1989; Merriam 1988).
In other words, perception is not reality as constructivists and critical theorists might
aver, instead, a perception for realists is a window on to reality from which a picture of
reality can be triangulated with other perceptions. That is, realists acknowledge the
difference between the world and particular perceptions of it, and the pre-eminent
importance of that world. In brief, constructivists and critical theorists consider there
are many realities, while realists consider there is only one reality although several
perceptions of that reality must be triangulated to obtain a better picture of it.
Within the realism paradigm, the world can be distinguished as having the three
domains of reality of mechanisms, events, and experiences, as illustrated in Table 2
(Bhaskar 1978). In more detail, the three domains are the real domain, consisting of the
processes that generate events, in which generative mechanisms or causal powers exist
independently with a tendency to produce patterns of observable events under
contingent conditions; the actual domain in which patterns of events occur, whether
they are observed or not; and the empirical domain, in which experiences may be
obtained by direct observation (Tsoukas 1989: p. 553; Outhwaite 1983, p. 322; Bhaskar
1978, p.13). The discovery of these observable or non-observable structures and
mechanisms that underly events and experiences is the goal of realism research
(Tsoukas 1989). Given this complexity of their social science world, the knowledge that
realism researchers obtain “is considered real but fallible” (Wollin 1995, p. 80).
Table 2 Ontological assumptions of realism
Real domain Actual domain Empirical domain
Mechanisms
Events
Experiences
Source: Wollin (1995), adapted from Bhaskar 1978, p. 13
Summary and implications for researchers. Popper (Magee 1985, p. 61) summarises
the three ontological assumptions above in three “worlds”. World one is positivist and
consists of objective, material things. World two is related to critical theory and
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constructivism, and is the subjective world of minds. World three is related to realism
and consists of abstract things that are born of people’s minds but exist independently
of any one person … “the third world is largely autonomous, though created by us”. As
well, Figure 1 summarises ontological, epistemological and methodological aspects of
the above discussion, showing the three different levels of reality at the bottom and
middle of the figure and emphasizing the differences between theory-building,
analytical generalisation and theory-testing, statistical generalisation to a population at
the top of the figure.
In brief, a researcher’s task is to discover, identify and then describe and analyse the
structures and generative mechanisms related to marketing. Obviously, using a
quantitative technique like regression analysis which assumes the dependent variable is
known with zero measurement error, is inappropriate. Instead, realism methodologies
include qualitative ones such as case studies or convergent interviews (Nair and Riege
1995) which are process-oriented and do not investigate cause and effect relations but
are rather concerned with underlying ‘causal tendencies’ or powers (Bhaskar 1978, p.
20). The data analysis is usually summarised in an interpretive, necessarily value-laden
way but with an awareness of the presence of those values. That is, the analysis is
usually non-statistical and uses qualitative techniques. Moreover, note that
constructivist tools like NUD*IST are not essential for realism research because realism
researchers do not need to map all the details of an interviewee’s subjective reality, they
merely look through some parts of that reality at an external reality and manual coding
of interview data can be adequate for this process.
In those research situations when complex phenomena have already been sufficiently
understood to warrant an attempt at generalisation to a population rather than to a
theory, then structural equation modeling may be the only appropriate quantitative
technique to use, for it has two attractive features for a realism researcher: it models
structures with complex interdependencies, and it explicitly allows for multi-item scales
and some measurement error in its constructs (Hair et al. 1995).
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Figure 1 Differences between analytical and statistical generalisation from
the point of view of realism
THEORY
analytical
generalisation
OBSERVABLE,
SURFACE
REALITY
= experiences
research
findings
ACTUAL REALITY
= experiences and
events
UNDERLYING REALITY
of mechanisms that
generate events and
experiences
Note: = insight-rich findings
X = findings of a survey of ‘observable’, independent phenomena
X
X
statistical
generalisation
X
theories about
how the world and its
complex interdependent
phenomena within which
nestle the new theories
from the research
X
X
X
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Case Study Methodology
Justification of the case study research methodology. Within the realism paradigm
discussed above, the case study research methodology appears to be especially
appropriate for marketing research about issues like networks. Note that we are
referring to the rigorously analytical method of case study research (Perry 1998; Yin
1994; Perry and Coote 1994) not the merely descriptive use of case studies that led
reviewers of many US PhD theses to comment that the best way of improving them
would be to ban the use of case study research (Adams and White 1994). This case
study research methodology is usually based on interviews within 4 to 14 cases
conducted using set questions in an interview protocol. The data is analysed using
structured steps outlined in Miles and Huberman (1994), Perry and Coote (1994) and
Perry (1998).
There are four major reasons for using this qualitative research methodology of case
studies for research about marketing issues like networks. The first reason relates to its
preparadigmatic stage (Borch and Arthur 1995). Qualitative methods such as case
studies address theory construction and theory building rather than theory testing and
theory verification (Tsoukas 1989; Lincoln and Guba 1985; Bonoma 1985). In the
early stages of theory development where phenomena are not well comprehended and
the relations between phenomena are not known, quantitative research methods can lead
to inconclusive findings (Parkhe 1993). In contrast, theory is built in case study and
related qualitative research by making comparisons, looking for similarities and
differences within the collected data, and for future questions to be examined (Neuman
1994, p. 405). That is, elements of the theory are being confirmed or disconfirmed,
rather than being tested for generalisability to a population. Furthermore, a qualitative
or exploratory method makes the research effort more flexible and allows data and
theory to interact (Neuman 1994, p. 322), at least in the early stages of the research
project.
In particular, this first justification for case study research methodology relates to
network research. Several authors have looked upon organisations as social
constructions whose strategies and policies change as a result of processes of human
reaction and they have advocated qualitative research such as in-depth case studies to
outline important dynamic dimensions of strategy development. Indeed, as Borch and
Arthur (1995, p. 436) themselves argued, “at the present state of knowledge within
strategic network research, theory creation should have priority over theory
verification”.
The second reason for using a qualitative method like case studies is the need to delve
deep to gain an understanding of the phenomenon. The primary objective of case study
and related qualitative research is to understand the phenomena under research and
interpret the respondent’s experiences and beliefs in their own terms (Gilmore and
Carson 1996). The depth and detail of qualitative data can be obtained only by getting
physically and psychologically closer to the phenomena through in-depth interviews:
“the closer the researcher gets to the phenomenon, the clearer it is understood” (Carson
and Coviello 1996, p. 55; Merriam 1988, p. 68). Thus “previously unknown
relationships … can be expected to emerge from case studies leading to a rethinking of
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the phenomenon being studied” (Stake 1981, p. 47).
The third reason for using the case study research methodology is related to the reasons
above and concerns the required classification into categories and the identification of
inter-relationships between those categories. As theory building in this area becomes
the researcher’s interest, so does the role of describing, classifying and comparing the
complexity of several organisational operations and managerial experiences (Bonoma
1985; Gilmore and Carson 1996). The goal of case study research can be to isolate and
define categories as precisely as possible and then to determine the relationship between
them (McCracken 1988). For this reason, details uncovered in a case can delve into the
complexities and processes of people and organisations, as network research often
requires.
Prior theory in case study research. The inductive nature of case study research has
been emphasised above, but some prior theory can have a pivotal function in the design
of the case study and analysis of its data. Pure induction might prevent the researcher
from benefiting from existing theory, just as pure deduction might prevent the
development of new and useful theory. Thus Parkhe (1993, pp. 252, 256) argues that
“both extremes are untenable and unnecessary” and that the process of ongoing theory
advancement requires “continuous interplay” between the two.
Figure 2 illustrates these differences between induction and deduction in case study
research, between the “indigenous concepts” of the data and the “sensitising concepts”
from prior theory which the analyst brings to the research (Patton 1990, p. 391). The
left-hand side of Figure 2 shows the more inductive or “exploratory” approach (Yin
1994, p. 5) to case study research. The first case on the left-hand side of the figure is
almost purely inductive, with very little prior theory. But data collection and analysis
of the following cases on the left-hand side are informed by preliminary concepts from
that first case and from prior theory in the literature which can “enfold” the data in these
exploratory case interviews (Eisenhardt 1989, p. 544). The disadvantage of this left
hand side's relatively inductive approach is that all cases cannot be compared with
others (because there are different interview questions for each case) and the researcher
runs the risk of “discovering” existing theory and so not making a contribution to his or
her body of knowledge.
The right hand side of Figure 2 shows our preferred position of “confirmatory” case
research (Miles and Huberman, 1994, p. 17; similar to Yin 1994, p. 18). The aim of this
type of research is to confirm or disconfirm elements of the prior theory, rather than to
develop theory as the left hand side aims to do. The prior theory is gained from the
literature or from some exploratory cases. Just one interview protocol is used for all
these right hand side cases, to facilitate cross-case analysis. Details of how to
implement both these approaches to case study research are provided in Perry (1998).
Validity and reliability. A final issue of case study research is criteria for judging its
quality. The issue of how validity and reliability are assessed in realism research has not
been finalised, and so that assessment still uses a blend of positivist and constructivist
approaches (Riege and Nair 1996). Of course, all reports of case study research should
conclude with the theory that has been built during the research project, together with
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some propositions that could be tested for statistical generalisability to a population in
later, survey research.
Figure 2 A comparison of two case study research positions: a purely
exploratory, inductive position (left hand side) and a preferred,
confirmatory/diconfirmatory position (right hand side).
high
Level of prior
theory
used in data
collection and
analysis
none
Source: Perry (1998).
CONCLUSION
Exploratory Confirmatory/disconfirmatory
0 Number of cases
In summary, the realism paradigm is appropriate for researching complex social
phenomena in marketing research about business networks and international marketing
strategy because of the nature of its reality, the required relationship between reality and
the researcher, and the related methodologies. Realism provides a coherent approach of
its own that is not a mere blend of other approaches. The application of rigor through
the adoption of case study research’s procedural approaches to data gathering and
analysis proposed in this paper enables the researcher to avoid the criticisms of
positivism and constructivism in marketing research about networks.
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