Logistics Theory Building
Gyöngyi Kovács* and Karen M Spens**
Building theory can be thought of as a never-ending journey. Theory building is particularly important to
disciplines that are emerging and growing. Compared to older and more established academic disciplines,
logistics does not have a rich heritage of theory development. This paper aims to construct a framework that
combines different research paradigms with research approaches for logistics theory building. This framework
can be used for positioning studies that aim at building and articulating core logistics theories. The framework
is illustrated by providing examples from logistics research adhering to different research paradigms (positivism,
scientific realism and interpretivism) and using different research approaches (deduction, induction, and
abduction). The paper discusses how these different research paradigms and research approaches contribute
to theory building in their own way.
Introduction
Logistics has neither a unified theory (Mentzer et al., 2004) nor a rich heritage of theory
development (Stock, 1997). Theory building, however, is argued to advance a discipline
and profession, and is thus relatively more important to an emerging and growing
discipline (Swanson, 2000). Logistics just like any other scientific discipline aims to
create new, or modify existing theories (Arlbjørn and Halldórsson, 2002). Theory
building can take different forms and follow different paths (Sebeok, 1983; and Nesher,
1999). As for logistics, borrowing theories from other disciplines has been advocated as
a viable option for advancing the discipline (Stock, 1997; and Arlbjørn and Halldórsson,
2002). Borrowing has the advantage of learning from others and avoiding the pitfall of
reinventing the wheel of building a theory.
At the same time, logistics research has often been discussed to be heavily footed in
positivism and predominantly employs a deductive research approach (Mentzer and
Kahn, 1995; Garver and Mentzer, 1999; Arlbjørn and Halldórsson, 2002; and Näslund,
2002). This has, however, its limitations for theory building, as the deductive derivation
of hypotheses can only modify existing theories (Gioia and Pitre, 1990; and Bjereld
et al., 2002). Yet, other research approaches also find their way to logistics research.
The use of the inductive research approach in logistics is gaining momentum (Kovács
and Spens, 2005). However, even this approach has been criticized by many, for instance
Peirce (1934) stated that “[it] never can originate any idea whatever. No more can
deduction”. Despite this criticism, we argue that the deductive and the inductive
research approaches are both relevant paths for advancing logistics knowledge. Kovács
and Spens (2005) also discuss the use of a third, the abductive, research approach in
* Assistant Professor (Acting), Swedish School of Economics and Business Administration (Hanken), Department
of Marketing, Helsinki, Finland; and the corresponding author. E-mail: [email protected]
* * Acting Professor, Swedish School of Economics and Business Administration (Hanken), Department of
Marketing, Helsinki, Finland. E-mail: [email protected]
Logistics
Theory
© 2007 The
Icfai Building
University Press. All Rights Reserved.
7
logistics research. Looking at different research paradigms and approaches, this paper
thereby advocates a paradigm-based approach to theory building. In this we adhere to
Gioia and Pitre (1990) who state that the grounding of theory in paradigm-appropriate
assumptions helps in avoiding the common tendency of trying to force-fit functionalist/
positivist theory building techniques as a universal approach. The aim of the paper is
to construct a framework that combines different research paradigms with research
approaches for logistics theory building. The framework can be used as a tool for
positioning studies that aim at building and articulating logistics core theories. We
exemplify the framework with articles using different research approaches in different
research paradigms in logistics.
The paper begins with a discussion on theory building and the views of different
research paradigms on theory. Thereafter, different research approaches are discussed for
building theory. Building on this, a framework is presented that combines different
research paradigms and research approaches for theory building in logistics. Next, this
framework is discussed through examples from articles in logistics journals. A summarizing
discussion of theory building in logistics concludes the paper.
Theories and Research Paradigms
Knowledge can be described as a multilevel abstraction of reality, and different layers of
knowledge can be distinguished (Grønhaug, 2002). Its base consists of facts,
observations, experiences and perceptions, which when recorded constitute the data.
The data, when organized tabulated and presented logically and meaningfully becomes
information. Information, when analyzed and processed with care can lead to inferences,
generalizations and insights. Insights based on empirical observations are derived
inductively, but insights can be arrived at deductively also (Ahmad, 2000). Insights can
lead to formulation of concepts that become the tools for thinking, analysis and
discussion. Concepts are then the basic elements for building theory. (Ahmad, 2000).
Figure 1 exhibits this sequence leading to theory building.
The top two boxes in Figure 1 indicate a separation between scientific theory and
managerial problem solving. The ultimate goal of scientific research is to create new
theories or test and/or modify existing theories (Arlbjørn and Halldórsson, 2002).
Logistics literature and research have so far focused on managerial problem-solving
mainly (Mentzer and Kahn, 1995). The body of knowledge for building theories exists,
even concepts are fairly well developed but the step of building scientific theories is not
yet accomplished.
But what is theory? The term ‘theory’ can mean different things, e.g., orderingframeworks used for predicting and explaining empirical events, conceptualizations or
theorizing, or even just hypotheses or explanations (Sayer, 1992). Vafidis (2002), in a
review of Nordic doctoral dissertations in logistics also concludes that theories
seemingly mean different things to different authors. Many of the theories referred to
and used as a basis in the dissertations were not (yet) mentioned in the Dictionary of
Theories (Bothamley, 2004) and even models, frameworks and concepts were labelled
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
Figure 1: Different Abstraction Levels
Managerial
Problem-Solving
-
Scientific Theory
Concepts
Knowledge
Insights
Information
Data
theories. If we adhere to the definition of theory by Sutherland (1975, as referenced in
Weick, 1989) which argues that theory is “an ordered set of assertions about a generic
behavior or structure assumed to hold throughout a significantly broad range of specific
instances”, then models and frameworks would not qualify as theory. The same goes for
references, data, variables, diagrams and hypotheses (Sutton and Staw, 1995). On the
other hand, Peter and Olson (1983) contend that “scientific ideas consist of invented
constructs and hypothesized relationships among them”. Similarly, Gioia and Pitre
(1990) provide a loose definition of theory as it is defined as “any coherent description
or explanation of observed or experienced phenomena”. They argue that this loose
definition of theory is needed because different paradigms offer a wide scope of
theoretical representations (Gioia and Pitre, 1990). Turning again to the issue of theory
building, the authors advocate what they call a paradigm-based approach.
They argue that the grounding of theory in paradigm-appropriate assumptions helps in
avoiding the common tendency of trying to force-fit functionalist/positivist theory
building techniques as a universal approach (Gioia and Pitre, 1990).
In this paper the paradigm-based approach is adopted, however, many different
categorizations exist for research paradigms. Arlbjørn and Halldórsson (2002)
distinguish between positivism, post-positivism and hermeneutics; Gioia and Pitre
(1990) adopt the framework distinguishing between interpretivism, radical humanism,
radical structuralism and functionalism; Hirschman (1986) contrasts the positivistic
metaphysic to the humanistic one; Mentzer and Kahn (1995) distinguish between
positivism and interpretivism; Solem (2003) between positivism, critical realism and
interpretivism, etc. Common to all these distinctions are their two extreme ends,
Logistics Theory Building
9
constituted of positivism at one end and interpretivism at the other (Mangan et al.,
2004). There is less agreement on what constitutes the ‘in-between’. While Arlbjørn
and Halldórsson (2002); and Solem (2003) refer to critical realism only, Muncy and
Fisk (1987) name many different approaches within scientific realism in this
‘in-between’. According to Hunt (1993), scientific realism is an umbrella term for all
these different versions of realism, ranging from social realism (closest to
interpretivism within scientific realism, Peter and Olson, 1983; and Sayer, 1992) to
critical realism (closest to positivism; Boyd, 1984; Leplin, 1984 and 1997; and Hunt,
1990, 1991 and 1993). Other authors again call scientific realism for relativism
(Muncy and Fisk, 1987).
The following discussion will start from the most common paradigm in logistics,
namely positivism (Mentzer and Kahn, 1995; Arlbjørn and Halldórsson, 2002; and
Näslund, 2002) and then proceed through scientific realism to interpretivism.
Positivism
Positivism is by far the most dominant research paradigm (Kirkeby, 1990; Alvesson and
Sköldberg, 1994; and Indick, 2002), thus it is not surprising that it also dominates in
logistics research (Garver and Mentzer, 1999). Modern logical positivists call themselves
empiricists (Boyd, 1984) to indicate the high standards they demand of empirical
research (Indick, 2002). Positivism strives to discover the true nature of reality,
believing in one universal absolute truth (Peter and Olson, 1983; and Mentzer and
Kahn, 1995). Research following this paradigm examines regularities and relationships
that lead to generalizations and ideally universal principles (Gioia and Pitre, 1990).
These universal principles are not only uncovered, but can be used for normative
predictions (Anderson, 1983). The universality claim is supported by a strong belief in
the possibility to conduct research truly objectively and value-free (Peter and Olson,
1983; and Hirschman, 1986). Objectivity is deemed possible because of the rationalityclaims of positivism that include the possibility to detach science from cultural, social,
political and economic factors, and to detach the measured subject from the
measurement process (Peter and Olson, 1983). Positivism can also be labelled
functionalism (Gioia and Pitre, 1990).
Theories in positivism are judged by their predictive capabilities and formal elegance.
Thus, the truth of a theory is judged by the truth of its predictions (Leplin, 1997).
Research in a positivist paradigm is corroborated with empirical observations that are
in line with its predictions, and falsified through any even minor deviating observation
(Popper, 1959). Positivism in fact relies on empirical testing as the sole means of theory
justification (Anderson, 1983). Any new hypothesis that is taken up and that endured
in empirical testing changes the original theory and thus creates a different theory
(Peter and Olson, 1983).
Scientific Realism
Even though scientific realism is used as an umbrella term for many different approaches
(Hunt, 1993; and Muncy and Fisk, 1987) that lie ‘in-between’ positivism and
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
interpretivism, there are in fact some commonalities to all these approaches. Scientific
realism does not, indeed, refute the ideas of truth and falsity (that would be scepticism),
just replaces the notion of “absolute truth” with that of a contextualized, ‘approximate’
or “relative truth” (Boyd, 1984; and Muncy and Fisk, 1987). The questioning of the
existence of an absolute truth even leads to the notion that science is seen as a process
without an ultimate goal (Anderson, 1983).
Common to scientific realists, however, is their shared belief that the world exists
independently of our knowledge of it (Leplin, 1984; and Sayer, 1992). Knowledge doesn’t
exist without any context, in fact all our knowledge products are theory-laden (Sayer,
1992) and cannot be detached from the approach that produced them (Boyd, 1984; and
Anderson Hudson and Ozanne, 1988). Scientific realists contextualize knowledge in
many ways, in essence data is created and interpreted in terms of a variety of theories
and theoretical commitments (Peter and Olson, 1983; and Boyd, 1984). Some social
realists see theories even as the outcome of a negotiation process between scientists in
a particular research community (Peter and Olson, 1983). A problematic notion could
be that what counts as scientific knowledge is determined by the community that
produces this knowledge (Anderson, 1983). In other terms, “a successful theory is one
which is treated seriously and studied by a significant portion of a research community”
(Peter and Olson, 1983, p. 112). One important notion of scientific realism is its
commitment to pluralism (Solem, 2003). No theory, research approach, methodology or
method is advocated as the sole path for science.
However, scientific realism should not be used as an excuse for ‘anything goes’ (Hunt,
1990). What makes an explanation, and a theory, right, is that it gives us what we need
to solve a problem in some context (Leplin, 1997). Not truth, but the pragmatic value
of a theory to explain and solve empirical problems should be the criterion for theories
to be appraised in scientific realism (Anderson, 1983; and Halldórsson and Aastrup,
2003). Nonetheless, scientific realism follows Popperian falsificationism in many ways.
The explanatory, predictive, and pragmatic success of a theory is the factor providing
evidence for the existence of its associated entities (Hunt, 1991).
Interpretivism
Interpretivism is that discipline of science that is concerned with the interpretation
of meaning (Sayer, 1992). The goal of theory building in the interpretive paradigm is
to generate descriptions, insights and explanations of events so that the system of
interpretations and meaning and the structuring and organizing processes are revealed
(Gioia and Pitre, 1990). In interpretivism, the researcher and phenomenon are
mutually interactive (Hirschman, 1986) and the researcher becomes part of the
evolving events studied (Gioia and Pitre, 1990). Interpretivism refutes the notion of
absolute truth due to refuting the possibility for truly objective ways of conducting
science (Peter and Olson, 1983). In fact, seeking objectivity is even deemed
undesirable in interpretivism (Hunt, 1993). Thus, interpretivism does not seek truth
but variety, i.e., wants to show the many versions of subjective impressions.
Logistics Theory Building
11
As Mentzer and Kahn (1995) put it, “interpretivism portrays reality as a collective of
multiple socially constructed realities”. In sociology, interpretivism is commonly
criticized for its subject-ridden level advocating that sensate experiences require the
presence of an actor (Bertilsson, 2004).
The interpretative researcher collects data that are relevant to the informants and
aims at preserving their unique representations. Analysis begins during data collection,
thereafter analysis, theory generation and further data collection go hand in hand which
means that the theory building process is iterative, cyclical and nonlinear. Through the
process, tentative speculations are confirmed or unconfirmed by consulting informants
(Gioia and Pitre, 1990). Academics coming from a positivist background tend to claim
that theories without a predictive function are pre-scientific (Cox, 1996). Interpretivists,
however, have no ambition to build theories for predictions (Mentzer and Kahn, 1995).
The purpose of an interpretivist theory can be descriptive or explanatory but not
predictive.
Theory Building through Different Research Approaches
Logistics research is largely following the hypothetico-deductive research approach
(Mentzer and Kahn, 1995; Arlbjørn and Halldórsson, 2002; and Näslund, 2002). Its
dominance is so striking that Kovács and Spens (2005) conclude that research
approaches are usually discussed in logistics research if deviating from deduction. In
fact, Cox (1996) even views any non-deductive study as pre-scientific, and Mentzer
and Kahn (1995) argue with the dominance of hypothetico-deductive research in
logistics for disregarding other research approaches when suggesting a framework for
logistics research.
In the following, we will discuss the three research approaches in the order of their
usage in logistics (Kovács and Spens, 2005), deduction, induction and abduction, before
presenting a framework of their application for theory building within different research
paradigms.
Deduction
The deductive research approach follows the path from theoretical advances to their
empirical testing. The process starts with an established theory or generalization, and
seeks to test whether the theory applies to specific instances (Hyde, 2000), thus
following the path from a general law to a specific case (Alvesson and Sköldberg, 1994;
and Johnson, 1996), in other words from rule to case to result (Kirkeby, 1990; and
Danemark, 2001). More specifically, deductive research starts with logical conclusions
that were derived from theory and are presented in the form of hypotheses or
propositions. Apart from logic, a good operationalization of the studied phenomena is
required for deduction (Bjereld et al., 2002). The hypotheses or propositions are
subsequently tested in an empirical setting, and general conclusions are presented based
on the corroboration or falsification of the prior self-generated hypotheses/propositions
(Kirkeby, 1990; and Arlbjørn and Halldórsson, 2002).
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
The deductive research approach has often been criticized for not being able to
generate new theories (Peirce, 1931, etc.) but just modify or refine theories (Gioia and
Pitre, 1990; and Bjereld et al., 2002). Nevertheless, deductive research contributes to
theory building in its own way. Gioia and Pitre (1990) see the tested hypotheses or
propositions along with their verifications or falsifications as revising or extending
the original theory. According to Peter and Olson (1983, p. 112), “any change in a
theory creates a modified product—i.e., a different theory”. Thus creating new
hypotheses that are logically deduced from a theory and testing them also qualifies as
theory building.
Even though the deductive approach is most commonly associated with a positivist
paradigm (Kirkeby, 1990; Alvesson and Sköldberg, 1994; Mentzer and Kahn, 1995;
Arlbjørn and Halldórsson, 2002; and Näslund, 2002), it finds its application in other
paradigms as well. Scientific realism uses the deductive research approach for refining
its theories as well, only the testing is not seen as asserting its hypotheses or
propositions absolute truth (Boyd, 1984; and Muncy and Fisk, 1987). This puts the
predictive force of deduction into perspective, as it is questionable how the outcome of
individual phenomena could be predicted based on universal facts without the question
of probabilities (Bertilsson, 2004). Thus, while the deductive research approach when
used in the positivist paradigm can be described as deterministic, it is only probabilistic
in scientific realism. The purpose of the deductive approach can be propositional or
predicative (Danemark, 2001). Propositions are related to one another with the
conjunctions ‘not’, ‘and’, ‘or’, ‘if…then’, while predicative logic also examines ‘all’ and
‘no’ junctions.
Research using deductive reasoning can be purely theoretical, thus skipping the
phase of empirical testing, however, the deductive research approach asks for a testing
phase. Different research methods can be used for this, ranging from (quantitative)
methods including simulations, model building and statistical surveys (Halldórsson and
Aastrup, 2003) to (qualitative) structured interviews (Hyde, 2000).
Induction
The inductive approach follows the path from empirical observations to theoretical
advances. The process starts with a specific empirical case or a collection of observations
to general law, i.e., from facts to theory (Alvesson and Sköldberg, 1994), in other words
from case to result to rule (Kirkeby, 1990; and Danemark, 2001). Previous theoretical
knowledge is not a necessity (Gioia and Pitre, 1990), and even if not ruled out, not
systematic (Bjereld et al., 2002). Instead, empirical observations lead to emerging
propositions and their generalization in a theoretical frame.
Generalization may, however, not be the purpose of all inductive research.
Interpretive theory building tends to be inductive in nature (Gioia and Pitre, 1990)
but does not aim at generalizations but to show subjective varieties (Hirschman,
1986). Thus the purpose of inductive research forms a continuum from subjective
variety in interpretivism towards categorizations and the development of taxonomies
Logistics Theory Building
13
to generalizations in scientific realism (Solem, 2003). Natural science research uses
the inductive approach also in positivism and even adheres to the ideas of
verification (Danemark, 2001). In business logistics research, however, induction
(and abduction) are seen as deviating from the dominating deductive positivism
(Mentzer and Kahn, 1995; and Kovács and Spens, 2005) and thus turned to when
leaving the positivist paradigm. The outcome of inductive research is a theory that
is tentatively accepted a priori and has been deemed as holding stand with a high
probability for all possible further experiences (Anderson, 1983). Nonetheless,
inductive research has also been criticized for not being able to originate radically
new theories (Peirce, 1934), because once rejecting a proposition, it lacks the power
of modifying action (Nesher, 1999).
Inductive research is often confused with qualitative research and vice versa.
While not all qualitative research is inductive (see, e.g., structured interviews in
deduction), inductive research can also encompass quantitative methods. This is the
case in data mining, where researchers engage in reinterpreting data from a new
perspective. The purpose of this type of inductive study is to analyze data from a
different angle than the original purpose of its collection, or recontextualize and
reinterpret this data. A reason for data mining can be if a previous quantitative study
is non-conclusive or shows very interesting outliers. While this delimits the role of
quantitative studies for the interpretivist paradigm, interpretivists claim that even
statistical data has to be interpreted when analyzed, and that in fact all research would
be interpretive (Gummesson, 2003).
Abduction
The abductive research approach was introduced by Peirce (1931). While some
scientific realists see abduction as rooted in empiricism (Boyd, 1984), Peirce himself
has been described as a scientific realist (Bertilsson, 2004). The outcome of the
abductive research process is a “relative truth” (Sebeok, 1983) like in scientific
realism (Boyd, 1984; and Muncy and Fisk, 1987), which can be seen in Peirce’s (1934,
p. 106, § 171) following description: “Deduction proves that something must be;
Induction shows that something ‘actually’ is operative; Abduction merely suggests
that something ‘may be’.”
There are two ways to initiate abductive research. In the first, the researcher makes
an empirical observation that deviates from a previously used theoretical framework
and thus challenges the usability of this framework. This empirical observation is
commonly called a “puzzling point” (Eco, 1983; Alvesson and Sköldberg, 1994; Dubois
and Gadde, 2002; and Kovács and Spens, 2005). In this case, the puzzling point
initializes a search for matching theories that can be used to explain the deviating
observation (Kirkeby, 1990; and Dubois and Gadde, 2002).
The second way to start an abductive process is more conscious. Here the
researcher applies a new theory, or a new framework, to already existing observations
(Kirkeby, 1990). The novelty of the theory does not need to be universal, the claim
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
is that it has not been used in this stream of research (e.g., introducing chaos theory
to logistics research). Borrowing theories from other disciplines thus can initiate an
abductive research process. However, doing so implies a belief in the possible
coexistence of theories, as in scientific realism (Anderson, 1983; and Leplin, 1997).
The aim of abductive reasoning is to understand the new phenomenon (Alvesson
and Sköldberg, 1994) and to suggest new theory (Kirkeby, 1990) in the form of new
hypotheses or propositions. The abductive research process closes with the application
of these hypotheses/propositions in an empirical setting (Alvesson and Sköldberg,
1994). This last step can also be characterized as a deductive part of the research.
While both induction and deduction have been largely criticized for their
restrictions in creating (radically) new theories (Peirce, 1934; Gioia and Pitre, 1990; and
Danemark, 2001), abduction is seen as being the most creative research approach
(Kirkeby, 1990) for theory building. With this creative power, abduction can introduce
radically new ideas and is thus best suited for the (in Kuhn’s, 1970, terms) revolutionary
phase of theory building. The abductive approach stems from a pragmatic view of science
(Nesher, 1999; and Bertilsson, 2004). Hence, it is most suitable for the scientific realist
paradigm that evaluates its theories in terms of their pragmatic applicability (Anderson,
1983) and usefulness (Hunt, 1991; and Leplin, 1997).
Theory Building in Logistics Research
The division into three research paradigms, positivism, scientific realism and
interpretivism, provides a good overview of the differences to theory building in general.
Turning to studies in logistics and Supply Chain Management (SCM), we can
distinguish different schools and research communities along the three main paradigms
or in Arbnor and Bjerke’s (1997) terms, three methodological approaches (Gammelgaard,
2004). These schools differ along their methodological, ontological and metaphysical
commitments (Anderson, 1983). As for methodological commitments, the positivist
paradigm largely corresponds with the analytical approach, the scientific realist
paradigm with the systems approach, and the interpretivist paradigm with the actors
approach (Arbnor and Bjerke, 1997; and Gammelgaard, 2004). The analytical approach
in unison with positivism emphasizes the need for objectivity in research (Arbnor and
Bjerke, 1997), while interpretivism specifically requires the presence of an actor
(Bertilsson, 2004) and advocates subjectivism.
These different paradigms and their respective theories should not be seen as
competing with each other, they rather fulfil complementary purposes (Solem,
2003). Even though, or maybe even because research in logistics is strongly footed
in the positivist paradigm (Mentzer and Kahn, 1995; Garver and Mentzer, 1999;
Arlbjørn and Halldórsson, 2002; and Näslund, 2002), logistics researchers have
often called for complementary approaches (Seaker et al., 1993; Arlbjørn and
Halldórsson, 2002; and Näslund, 2002). The different paradigms shed different
light on empirical phenomena and can help to solve different (managerial and
theoretical) problems.
Logistics Theory Building
15
Similarly, different research approaches contribute to theory building in different ways.
Even though the deductive research approach dominates in logistics research, in order to
build logistics theory, a combination of all three approaches would be needed, however,
this does not mean that they should all be used in one study. Instead, the trio of abduction,
deduction and induction complement each other (Sebeok, 1983; and Nesher, 1999).
Meredith (1993) describes the cycle of research to go from description to explanation to
testing. Different research approaches are suitable for different phases (Nesher, 1999) in
this cycle. Scientific revolutions (Kuhn, 1970) can be driven through the creative power
of abductive research, which introduces radically new ideas and theories (comp. Nesher,
1999). To fortify these yet rather loose theories (Lakatos, 1970; and Arlbjørn and
Halldórsson, 2002), induction can be used to further develop them, expanding existing
knowledge. Once these theories have been established, after the transition from loose to
solid theories (Arlbjørn and Halldórsson, 2002; and Lakatos, 1970), deductive research
helps to test them and further establish them. In other words, the abductive approach
discovers new concepts and rules, which the inductive approach can evaluate and that are
inferred in the deductive approach (Nesher, 2002). Nesher (2001) also distinguishes
between the logic of discovery (abduction), the logic of consequence (deduction) and the
logic of evaluation (induction). The scrutiny of falsification is needed in all phases
(Popper, 1959), however, in non-positivist paradigms bearing in mind the probabilities
associated with any suggested hypothesis or proposition.
Thus, the use of the three different research approaches adheres to different world
views, and different underlying research paradigms, are yielding different results in
Figure 2: Framework for Theory-Building in Logistics
Paradigm
Interpretivism
Scientific Realism
Positivism
Research
Approach
Process
Inductive
Method
Purpose
from empirical observations to theoretical propositions
grounded –––––– qualitative –––––––– non-structured
quantitative
variety ––––––––––––––––––––––––– generalization
descriptive/exploratory/explanatory ––––– prescriptive
Process
Deductive
Method
Not
applicable
Purpose
Process
Abductive
Method
Purpose
16
Not
applicable
Not
applicable
from theoretical hypotheses to empirical observations
semi-struct. ––– qualitative –––struct.
quantitative
probabilistic –––––– generalization ––––– deterministic
descriptive/explanatory/predictive
from emp.observations
to theor.propositions
to application
qualitative
quantitative
theory suggestion
descriptive/exploratory
/explanatory/predictive
in respective phases
Not
applicable
The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
building logistics theory. While following a deductive research approach in
positivism results in universal claims, the findings of deductive research in scientific
realism are deemed probable but not universal. Nonetheless, Nesher’s (2002) trio of
research approaches complement each other for the purposes of theory building.
Figure 2 combines the presented paradigms with research approaches in a framework
for logistics theory building. In doing so, representations of the typical research
processes, methods and purposes are indicated for each combination of research
paradigms and approaches.
For example, in the inductive research approach research processes in the
interpretivist and scientific realism paradigms proceed through empirical observations
to theoretical propositions. The methods used e.g., in the scientific realist paradigm can
range from quantitative to qualitative, purposes vary, however the ultimate goal, that
is the far end of the continuum, stresses generalization of the findings.
Illustrating the Framework in Logistics Research
Examples from current logistics research are used to illustrate the framework for theory
building for this particular discipline. In the following, we will discuss each of the
research approaches in the different research paradigms; starting with deduction and
induction in positivism; deduction, induction and abduction in scientific realism, and
induction in interpretivist logistics research.
Positivism in Logistics Research
Logistics literature often makes a link between the positivist paradigm and a ‘deductive
research approach’ (Mentzer and Kahn, 1995; Garver and Mentzer, 1999; Arlbjørn and
Halldórsson, 2002; and Näslund, 2002). Typically, quantitative methods are employed in
this category, mainly regression analysis (Keller, 2002; Richey et al., 2002; Bhatnagar
et al., 2003; and Zsidisin et al., 2003), or Structural Equation Modelling (SEM) (Autry
and Daugherty, 2003; Kent and Mentzer, 2003; and Wisner, 2003).
In natural sciences, the ‘inductive research approach’ adheres to the idea of
verification and can thus be used within a positivist paradigm (Danemark, 2001).
However, in logistics, the inductive research approach is mainly turned to when
purposefully leaving the positivist paradigm. Solem (2003) discusses inductive research
in scientific realism and interpretivism only. ‘Abduction’ is also used for leaving the
dominant research paradigm in logistics. Any theory-matching process implies a
pluralistic belief in the coexistence of theories, which can be found in scientific realism
(Anderson, 1983; and Leplin, 1997).
Scientific Realism in Logistics Research
Even though the ‘deductive research approach’ is often linked to positivism, it is not
confined to this research paradigm. Deductive hypothesis-testing in scientific realism,
however, asserts hypotheses only relative but not absolute truth (Boyd, 1984; and Muncy
and Fisk, 1987). This relativizes the generalizability or universality of the findings of
Logistics Theory Building
17
such research (Bertilsson, 2004). In other words, deduction in positivism is deterministic,
but the same research approach is only probabilistic in scientific realism.
This is probably most striking for the data analysis method of SEM. Most articles
that use SEM in logistics research (which currently dominates the publications of
the Journal of Business Logistics) reportedly use maximum likelihood in their
confirmatory factor analysis, which is presumably a sign for a positivist background.
At the same time, though, many articles using SEM in logistics test alternative
models as well and/or discuss the exploratory nature of their survey. Positivists
proclaiming absolute truth for one theory (Hirschman, 1986; and Gioia and Pitre,
1990) would, however, not be inclined to seek alternative models and hypotheses
to their theory, this is rather a sign of scientific realism (Solem, 2003). Alternative
model testing and exploratory surveys can therefore be considered as indicators for
a scientific realist paradigm.
Qualitative deductive studies adhering to scientific realism can also be found in
logistics. As an example, Harrison (1999) e.g., presents a very deductive, dual macromicro model when describing different logistics systems, which is then applied in his
study. According to his article, two quantitative studies precede the discussed study,
which is nonetheless a qualitative one, using semi-structured interviews as its data
collection technique. A predetermined set of codes is then used to categorize the
transcribed quotes of these interviews. As the data analysis strictly follows prior set
propositions and categories, this research is purely deductive. Harrison (1999) even
sets system boundaries for the data analysis and questions the possibility of absolute
generalization of his findings. This indicates that the study appertains to the scientific
realist paradigm.
Qualitative data collection can also precede a quantitative data analysis in
scientific realism. Moberg and Speh (2003) use structured interview guides for
telephone interviews to evaluate the strength of relationships in a supply chain, but
then use factor analysis to analyze this data. Zacharia and Mentzer (2004) even use
their data from “in-depth” interviews to develop a structural equation model for the
salience of logistics.
Inductive scientific realist research can also be found in logistics. According to
Solem (2003), the purpose of inductive research ranges from manifesting subjective
varieties in interpretivism to arriving at taxonomies and (relative) generalizations in
scientific realism. Miemczyk and Holweg (2004) e.g., explore the effects of customization
on inbound logistics in a study that uses semi-structured interviews and process
mapping alongside quantitative data about inventory levels prior to developing their
propositions. To be able to generalize their findings within particularly set system
boundaries, Miemczyk and Holweg (2004) triangulate their data from different data
collections. Hingley (2001) also uses a multi-case, multi-site approach to allow for the
generalization of his findings within its industrial and market context when examining
relationship management in the food industry. But while both these studies are
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
qualitative, there are also examples of quantitative inductive research in scientific
realism. Mathematical models are deductive if a model is only applied and refined in the
process, however, they are inductive if they are built from scratch e.g., in a case study
(de la Fuente and Lozano, 1998).
Some authors combine two studies in one article before presenting their supposedly
general conclusions. Dadzie (1998) e.g., sets survey categories deductively a priori to his
study but then goes on investigating inductively emerging clusters in his data analysis.
Svensson (2001) derives his propositions about disturbances in logistics flows from an
inductive (and in this case, qualitative) study before evaluating these in a deductive
survey. The combination of two such fundamentally different studies is only possible
within scientific realism that allows for pluralistic thinking.
The theory-matching element of abduction implies pluralistic thinking. Consciously
applying a new theoretical framework to investigate known empirical observations in
a field can be seen as borrowing theories. According to Stock (1997), theory borrowing
is common in logistics research. It even helps in avoiding the pitfall of “reinventing
the wheel”, i.e., having to develop these theories again even though they are already
known in other disciplines. This, however, is not the only reason abductive research
pertains to scientific realism. The aim of abduction to suggest theories instead of
arriving at generalizations and universal claims points at the relativistic view of this
research approach on its outcome. This resembles scientific realism (Sebeok, 1983;
Boyd, 1984; and Muncy and Fisk, 1987). This supports the proposition of our
framework that the abductive research approach adheres to the scientific realist
paradigm (Figure 2).
Examples for the abductive research approach in logistics originate from mathematical
modeling but also action, and constructive research. Westwood (1999) starts out with
the puzzling point of forecasting methods being insufficient and not applicable for a
particular case company. He then suggests a solution from this empirical background
while negotiating between theory and data from his case study. This solution is to
include inter-organizational transfers in forecasting. He then develops a mathematical
model to suggest his theory, and reapplies the model to the data from his case study to
evaluate its use. Campbell et al. (2001) also develop a mathematical model for a city as
their case study, before applying their findings to a larger geographical area and
presenting conclusions on the basis of this application. Holmström et al. (1999) study
process networks creating their own examples, i.e., companies are asked to implement
a process network design while the implementation process is studied. They negotiate
between different alternative theoretical frameworks before developing guidelines for
process networks that are subsequently implemented in their prior examples before
arriving at their conclusions.
Interpretivism in Logistics Research
Logistics researchers often make a link between interpretivism, inductive research, and
qualitative studies. More and more inductive research is indeed published in logistics
Logistics Theory Building
19
journals (Spens and Kovács, 2006). However, Hilmola et al. (2005) found that about half
of inductive case studies in the discipline use quantitative research methods. But while
not all inductive research is qualitative, and does not necessarily adhere to an
interpretivist paradigm, examples where this triple linkage holds stand can be found in
logistics literature.
In an article by Golicic et al. (2002), the authors set out to “examine the impact of
the dimensions of e-commerce on managing relationships in the supply chain” (Golicic
et al., 2002, p. 852). In order to do so, they interview employees of eight companies who
are responsible for activities related to the company’s supply chain. They stress the
importance of on-site, in-depth interviews to seek the perceptions of the interviewees
on e-commerce. Codes and categories for the collected data emerged during the
transcription of interviews, and the conceptualization of main themes in the research
is then purely based on the empirical data.
Another example for interpretivist studies in logistics can be found in Pålsson
(2006), in which he describes the four phases he used in a participant observatory
study going from a preparation phase especially focusing on the finding of gatekeepers
to the empirical study, data collection through observations and interviews on site,
analysis and reflection emphasising the aspect of emerging interpretations of the data,
and finally, the phase of writing up the research. The study is about the
implementation of Radio Frequency Identification (RFID) technology in packaging and
food manufacturing companies in Sweden, apart from the technology provider. Not
even the goal of the RFID implementation is given a priori but determined through
interviews with the actors. Conclusions at the end of the research are presented as
in comparison with existing literature in the field. Pålsson (2006, p. 6) highlights an
important aspect of interpretivist studies when commenting on the resulting article
being his own “interpretation of many interpretations” by the interviewees.
Conclusion
The arena of theory building research can be thought of as a never-ending journey
(Swanson, 2000). Logistics has no unified theory (Mentzer et al., 2004). However,
looking at different ways to theory building we can contend that not one but many
possibilities exist to develop logistics theory, or indeed even theories. Theory building
is a very important discipline that is emerging and growing. Theory building in itself,
as important as it is, should not be taken lightly. The aim should always be to develop
a good theory. Theories are judged by different criteria depending on their underlying
research paradigms (Leplin, 1997). Good theory is a plausible theory, and it is judged
to be more plausible and of high quality if it is “interesting rather than obvious,
irrelevant or absurd, obvious in novel ways, a source of unexpected connections, high
in narrative rationality, aesthetically pleasing or correspondent with presumed
realities” (Weick 1989).
What makes an explanation, and a theory, right, is that it gives us what we need to
solve a problem in some context (Leplin, 1997). All judgments about theories are thus
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The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
context-dependent (Sayer, 1992). Different research paradigms and research approaches
contribute to theory building in their own (but very different) way.
In most fields, even natural science, many paradigms can coexist (Anderson,
1983). It is thus important to realize that different research communities coexist also
in logistics. While it is important to distinguish between these schools, they cannot
be evaluated as better or worse than the other. So far, no consensus in science exists
as to the nature or even the very existence of a unique scientific method (Anderson,
1983), paradigm or research approach. This even suggests that it is inappropriate to
seek a single best method (Anderson, 1983), paradigm or research approach for the
evaluation of logistics theory. Often many theories can come to the same prediction
about particularities of an observation (Leplin, 1997). Which theory is confirmed, or
corroborated through a particular observation is yet to be determined (Leplin).
Depending on its underlying paradigm, scientific theories can be evaluated in terms
of their truth content, or in terms of their usefulness (Anderson, 1983; and Peter and
Olson, 1983).
Although theory building can evolve from mainstream approaches to research,
rebellious behavior has and will continue to result in new insights and theories being
developed (Trim and Lee, 2004). The dominance of a research paradigm or research
approach in a discipline poses strong limitations on how theory in that discipline can
evolve. Meredith (1993) criticizes operations management research for skipping the
descriptive phase of research and thus becoming more and more unrealistic. If
management researchers are to produce new insight into complex management
problems they need to be innovative and have the confidence to question what
constitutes appropriate research (Trim and Lee, 2004). However, introducing radically
new theories, perspectives and research approaches can be compared to launching
discontinuous innovations (Peter and Olson, 1983). Theory building research often
receives rough treatment from journals and reviewers and has an uncomfortable
journey getting published (Swanson, 2000; and Halldórsson and Aastrup, 2003). Peter
and Olson (1983) conclude that new scientific streams have to be carefully ‘marketed’
before being introduced in their fields. This paper markets the paradigm-based
approach to theory building and presents the abductive approach in order to
complement the research approaches that have already established their existence in
the logistics literature. 
References
1. Ahmad Abad (2000), “Research with a Purpose”, Journal of Management Research,
Vol. 1, No. 1, pp. 5-11.
2. Alvesson Mats and Sköldberg Kaj (1994), Tolkning och reflektion, Vetenskapsfilosofi och
kvalitativ metod, Studentlitteratur, Lund.
3. Anderson Hudson Laurel and Ozanne Julie L (1988), “Alternative Ways of Seeking
Knowledge in Consumer Research”, Journal of Consumer Research, Vol. 14 (March),
pp. 508-521.
Logistics Theory Building
21
4. Anderson Paul F (1983), “Marketing, Scientific Progress, and Scientific Method”,
Journal of Marketing, Vol. 47 (fall), pp. 18-31.
5. Arbnor Ingeman and Bjerke Björn (1997), Methodology for Creating Business Knowledge,
Sage Publications, London.
6. Arlbjørn Jan Stentoft and Halldórsson Árni (2002), “Logistics Knowledge Creation:
Reflections on Content, Context and Processes”, International Journal of Physical
Distribution and Logistics Management, Vol. 32, No. 1, pp. 22-40.
7. Autry Chad W and Daugherty Patricia J (2003), “Warehouse Operations Employees:
Linking Person-Organization Fit, Job Satisfaction, and Coping Responses”, Journal of
Business Logistics, Vol. 24, No. 1, pp. 171-197.
8. Bertilsson Thora Margareta (2004), “The Elementary Forms of Pragmatism: On
Different Types of Abduction”, European Journal of Social Theory, Vol. 7, No. 3,
pp. 371-389.
9. Bhatnagar Rohig, Jayaram Jayanth and Phua Yue Cheng (2003), “Relative Importance
of Plant Location Factors: A Cross National Comparison Between Singapore and
Malaysia”, Journal of Business Logistics, Vol. 24, No. 1, pp. 147-170.
10. Bjereld Ulf; Demker Maria and Hinnfors Jonas (2002), Varför Vetenskap?,
Studentlitteratur, Lund.
11. Bothamley Jennifer (2004), Dictionary of Theories: More than 5000 Theories, Laws and
Hypotheses Described, Barnes and Noble, USA.
12. Boyd Richard N (1984), “The Current Status of Scientific Realism”, in Leplin Jarrett
(Ed.), Scientific Realism, University of California Press, pp. 41-82, Berkeley.
13. Campbell James F, Labelle Alain and Langevin Andre (2001), “A Hybrid Travel
Distance Approximation for a GIS-based Decision Support System”, Journal of
Business Logistics, Vol. 22, No. 2, pp. 165-181.
14. Cox Andrew (1996), “Relational Competence and Strategic Procurement Management,
Towards an Entrepreneurial and Contractual Theory of the Firm”, European Journal
of Purchasing and Supply Management, Vol. 2, No. 1, pp. 57-70.
15. Dadzie Kofie Q (1998), “Management Education for Physical Distribution and
Logistics”, International Journal of Physical Distribution and Logistics Management, Vol. 28,
No. 4, pp. 259-271.
16. Danemark Berth (2001), Explaining Society: An Introduction to Critical Realism in the
Social Sciences, Routledge, Florence, KY.
17. de la Fuente David and Lozano Jesús (1998), “Determining Warehouse Number and
Location in Spain by Cluster Analysis”, International Journal of Physical Distribution and
Logistics Management, Vol. 28, No. 1, pp. 68-79.
18. Dubois Anna and Gadde Lars-Erik (2002), “Systematic Combining: An Abductive
Approach to Case Research”, Journal of Business Research, Vol. 55, Issue 7, pp. 553-560.
22
The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
19. Eco Umberto (1983), Horns, Hooves, Insteps. Some Hypotheses on Three Types of
Abduction, in Eco Umberto and Sebeok Thomas A (Eds.), The Sign of Three, Dupin,
Holmes, Peirce, Indiana University Press, pp. 198-220, Bloomington.
20. Gammelgaard Britta (2004), “Schools in Logistics Research? A Methodological
Framework for Analysis of the Discipline”, International Journal of Physical Distribution
and Logistics Management, Vol. 34, No. 6, pp. 479-491.
21. Garver Michael S and John T Mentzer (1999), “Logistics Research Methods:
Employing Structural Equation Modelling to Test for Construct Validity”, Journal of
Business Logistics, Vol. 20, No. 1, pp. 33-57.
22. Gioia Dennis A and Evelyn Pitre (1990), “Multiparadigm Perspectives on Theory
Building”, The Academy of Management Review, Vol. 15, No. 4, pp. 584-602.
23. Golicic Susan L, Davis Donna F, McCarthy Teresa M and Mentzer John T (2002),
“The Impact of E-commerce on Supply Chain Relationships”, International Journal of
Physical Distribution and Logistics Management, Vol. 32, No. 10, pp. 851-871.
24. Grønhaug Kjell (2002), “Is Marketing Knowledge Useful?”, European Journal of
Marketing, Vol. 36, No. 3, pp. 364-372.
25. Gummesson Evert (2003), “All Research is Interpretive!”, Journal of Business and
Industrial Marketing, Vol. 18, Nos. 6/7, pp. 482-492.
26. Halldórsson Árni and Aastrup Jesper (2003), “Quality Criteria for Qualitative
Inquiries in Logistics”, European Journal of Operational Research, Vol. 144, Issue 2,
pp. 321-332.
27. Harrison Alan (1999), “Horses for Courses: Human Aspects of Different
Logistics Systems”, International Journal of Logistics: Research & Applications, Vol. 2,
No. 3, pp. 247-261.
28. Hilmola Olli-Pekka, Hejazi Annukka and Ojala Lauri (2005), “Supply Chain
Management Research Using Case Study Analysis: A Literature Analysis”,
International Journal of Integrated Supply Management, Vol. 1, No. 3, pp. 294-311.
29. Hingley Martin (2001), “Relationship Management in the Supply Chain”, International
Journal of Logistics Management, Vol. 12, No. 2, pp. 57-71.
30. Hirschman Elizabeth C (1986), “Humanistic Inquiry in Marketing Research:
Philosophy, Method and Criteria”, Journal of Marketing Research, Vol. 23 (August),
pp. 237-249.
31. Holmström Jan, Hoover William E Jr., Eloranta Eero and Vasara Antti (1999), “Using
Value Reengineering to Implement Breakthrough Solutions for Customers”, International
Journal of Logistics Management, Vol. 10, No. 2, pp. 1-11.
32. Hunt Shelby D (1990), “Truth in Marketing Theory and Research”, Journal of
Marketing, Vol. 54, No. 3, pp. 1-15.
Logistics Theory Building
23
33. Hunt Shelby D (1991), “Positivism and Paradigm Dominance in Consumer Research:
Toward Critical Pluralism and Rapprochement”, Journal of Consumer Research, Vol. 18
(June), pp. 32-44.
34. Hunt Shelby D (1993), “Objectivity in Marketing Theory and Research”, Journal of
Marketing, Vol. 57, No. 2, pp. 76-91.
35. Hyde Kenneth F (2000), “Recognising Deductive Processes in Qualitative Research”,
Qualitative Market Research: An International Journal, Vol. 3, No. 2, pp. 82-90.
36. Indick William (2002), “Fight the Power: The Limits of Empiricism and the Costs
of Positivistic Rigor”, The Journal of Psychology, Vol. 136, No. 1, pp. 21-36.
37. Johnson Colleen (1996), “Deductive Versus Inductive Reasoning: A Closer Look at
Economics”, Social Science Journal, Vol. 33, No. 3, pp. 287-299.
38. Keller Scott B (2002), “Driver Relationships with Customers and Driver Turnover:
Key Mediating Variables Affecting Driver Performance in the Field”, Journal of
Business Logistics, Vol. 23, No. 1, pp. 39-64.
39. Kent John L and Mentzer John T (2003), “The Effect of Investment in
Interorganizational Information Technology in a Retail Supply Chain”, Journal of
Business Logistics, Vol. 24, No. 2, pp. 155-175.
40. Kirkeby Ole Fogh (1990), “Abduktion”, in Andersen Heine (Ed.), Vetenskapsteori och
metodlära, Introduktion, Translated from Danish by Carl G Liungman, 1994,
Studentlitteratur, Lund.
41. Kovács Gyöngyi and Karen M Spens (2005), “Abductive Reasoning in Logistics
Research”, International Journal of Physical Distribution and Logistics Management, Vol. 35,
No. 2, pp. 132-144.
42. Kuhn Thomas S (1970), The Structure of Scientific Revolutions, University of Chicago
Press, 2nd Edition, Chicago, IL.
43. Lakatos Imre (1970), “Falsification and the Methodology of Scientific Research
Programmes”, in Lakatos Imre and Musgrave Alan (Eds.), Criticism and the Growth of
Knowledge, Proceedings of the International Colloquium in the Philosophy of Science, 1965,
Vol. 4, pp. 91-196, London.
44. Leplin Jarrett (1984), “Introduction”, in Leplin Jarrett (Ed.), Scientific Realism,
University of California Press, pp. 1-7, Berkeley.
45. Leplin Jarrett (1997), A Novel Defense of Scientific Realism, Oxford University Press,
Oxford.
46. Mangan John, Lalwani Chandra and Gardner Bernard (2004), “Combining
Quantitative and Qualitative Methodologies in Logistics Research”, International
Journal of Physical Distribution and Logistics Management, Vol. 34, No. 7, pp. 565-578.
24
The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
47. Mentzer John T and Kahn Kenneth B (1995), “A Framework of Logistics Research”,
Journal of Business Logistics, Vol. 16, No. 1, pp. 231-250.
48. Mentzer John T, Soonhong Min and Michelle Bobbitt L (2004), “Toward a Unified
Theory of Logistics”, International Journal of Physical Distribution and Logistics
Management, Vol. 34, No. 8, pp. 606-627.
49. Meredith Jack (1993), “Theory Building Through Conceptual Methods”, International
Journal of Operation and Production Management, Vol. 13, No. 5, pp. 3-11.
50. Miemczyk Joe and Holweg Matthias (2004), “Building Cars to Customer Order—
What Does it Mean for Inbound Logistics Operations?”, Journal of Business Logistics,
Vol. 25, No. 2, pp. 171-198.
51. Moberg Christopher R and Speh Thomas W (2003), “Evaluating the Relationship
Between Questionable Business Practices and the Strength of Supply Chain
Relationships”, Journal of Business Logistics, Vol. 24, No. 2, pp. 1-19.
52. Muncy James A and Fisk Raymond P (1987), “Cognitive Relativism and the Practice
of Marketing Science”, Journal of Marketing, Vol. 51 (January), pp. 20-33.
53. Näslund Dag (2002), “Logistics Needs Qualitative Research—Especially Action
Research”, International Journal of Physical Distribution and Logistics Management, Vol. 32,
No. 5, pp. 321-338.
54. Nesher Dan (1999), “Peirce’s Theory of Signs and the Nature of Learning Theory”,
in Shapiro M (Ed.), The Peirce Seminar Papers, Berghahn Books, pp. 349-388, Oxford:
Berghahn Books,New York.
55. Nesher Dan (2001), “Peirce’s Essential Discovery: ‘Our Senses as Reasoning
Machines’ Can Quasi-Prove Our Perceptual Judgments”, Transactions of the Charles S
Peirce Society, Vol. 37, No. 1 (Winter), pp. 23-57.
56. Nesher Dan (2002), “Peircean Epistemology of Learning and the Function of
Abduction as the Logic of Discovery”, Transactions of the Charles S Peirce Society,
Vol. 38, Nos. 1/2 (Winter), pp. 175-206.
57. Pålsson Henrik (2006), “Participant Observation in Logistics Research: Experiences
from an RFID Implementation Study”, in Jahre Marianne (Ed.), Logistics in the New
Valley, CD-ROM Proceedings of the 18th Annual Conference for Nordic Researchers
in Logistics, NOFOMA 2006, Oslo, Norway.
58. Peirce Charles Sanders (Santiago) (1931), Collected Papers of Charles Sanders Peirce
Volume I: Principles of Philosophy, Hartshorne Charles and Weiss Paul (Eds.), Harvard
University Press, Cambridge, MA.
59. Peirce Charles Sanders (Santiago) (1934), Collected Papers of Charles Sanders Peirce
Volume V: Pragmatism and Pragmaticism Logic, Hartshorne Charles and Weiss Paul
(Eds.), Harvard University Press, Cambridge, MA.
Logistics Theory Building
25
60. Peter J Paul and Olson Jerry C (1983), “Is Science Marketing?”, Journal of Marketing,
Vol. 47 (Fall), pp. 111-125.
61. Popper Karl (1959), The Logic of Scientific Discovery, Routledge Classics (2002 Reprint),
London.
62. Richey R Glenn, Daugherty Patricia J, Genchev Stefan E and Autry Chad W (2004),
“Reverse Logistics: The Impact of Timing and Resources”, Journal of Business Logistics,
Vol. 25, No. 2, pp. 229-250.
63. Sayer Andrew (1992), Method in Social Science, A realist approach, Routledge, 2nd Edition,
London.
64. Seaker Robert F, Waller Matthew A and Dunn Steven C (1993), “A Note on Research
Methodology in Business Logistics”, Logistics and Transportation Review, Vol. 29, No. 4,
pp. 383-387.
65. Sebeok Thomas A (1983), “One, Two, Three Spells UBERTY”, in Eco Umberto and
Sebeok Thomas A (Eds.), The Sign of Three, Dupin, Holmes, Peirce, Indiana University
Press, pp. 1-10, Bloomington.
66. Solem Olav (2003), “Epistemology and Logistics: A Critical Overview”, Systemic
Practice and Action Research, Vol. 16, No. 6, pp. 437-453.
67. Spens Karen M and Kovács Gyöngyi (2006), “A Content Analysis of Research
Approaches in Logistics Research”, International Journal of Physical Distribution and
Logistics Management, Vol. 36, No. 5, pp. 374-390.
68. Stock James R (1997), “Applying Theories from Other Disciplines to Logistics”,
International Journal of Physical Distribution and Logistics Management, Vol. 27,
Nos. 9/10, pp. 515-539.
69. Sutton Robert I and Staw Barry M (1995), “What Theory is Not”, Administrative
Science Quarterly, Vol. 40, No. 3, pp. 371-384.
70. Svensson Göran (2001), “Firms’ Preventive Activities and the Occurrence of
Disturbances in the Inbound and Outbound Logistics Flows”, International Journal of
Logistics: Research and Applications, Vol. 4, No. 2, pp. 207-236.
71. Swanson Richard A (2000), “Theory and Other Irrelevant Matters”, Human Resource
Development International, Vol. 3, No. 3, pp. 273-278.
72. Trim Peter R J and Lee Yang-Im (2004), “A Reflection on Theory Building and the
Development of Management Knowledge”, Management Decision, Vol. 42, Nos. 3/4,
pp. 473-480.
73. Vafidis Dimitrios (2002), “Methodological Tendencies in Logistics Research—
Empirical Evidence form 25 Finnish and Swedish Doctoral Dissertations 19941998”, Publications of the Turku School of Economics and Business Administration,
Series D-1:2002, Turku.
26
The Icfai Journal of Supply Chain Management, Vol. IV, No. 4, 2007
74. Weick Karl E (1989), “Theory Construction as Disciplined Imagination”, Academy of
Management Review, Vol. 14, No. 4, pp. 516-531.
75. Westwood John B (1999), “Retail Inventory Movement—A Case Study in
Rationalisation”, International Journal of Physical Distribution and Logistics Management,
Vol. 29, Nos. 7/8, pp. 444-452.
76. Wisner Joel D (2003), “A Structural Equation Model of Supply Chain Management
Strategies and Firm Performance”, Journal of Business Logistics, Vol. 24, No. 1,
pp. 1-25.
77. Zacharia Zach G and Mentzer John T (2004), “Logistics Salience in a Changing
Environment”, Journal of Business Logistics, Vol. 25, No. 1, pp. 187-210.
78. Zsidisin George A, Ellram Lisa M and Ogden Jeffrey A (2003), “The Relationship
Between Purchasing and Supply Management’s Perceived Value and Participation in
Strategic Supplier Cost Management Activities”, Journal of Business Logistics, Vol. 24,
No. 2, pp. 129-154.
Reference # 34J-2007-12-01-01
Logistics Theory Building
27