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The Systems approach: environmental complexity as a “decision-making” problem


2.2. Alternative research paradigms – “identifying the scientific domain”

2.2.2. The Systems approach: environmental complexity as a “decision-making” problem

“decision-making” problem

A presumption of “an objective (or at least objectively accessible) reality, consisting of wholes, the outstanding characteristic of which is synergy” (Arbnor & Bjerke, 1997, p.70), and one which treats human beings as intentional or deterministic creatures acting on the basis of reasons (Little, 1991), underlines the systems approach to problem solving.

Operationalising the construct from this point of view then entails visualising supply chain logistics environmental complexity as a “decision-making” problem, decision-making as an activity underlined by models based on e.g. rational choice theory (Little, 1991), and a systems paradigm that treats reality (or the problem) as a system of interdependent elements.

This implies that the construct has to be posited in a manner that intends to explain it from the point of the broad interrelationships it shares with other constructs and variables in its system of description, in order for it to make sense as a whole.

In the context of the present study, the problem owner would then be a priori presumptive about environmental complexity as affecting supply chain operations (e.g. in terms of

logistics strategy, or performance), for in order to solve decision-making problems related to strategy and choice. In other words, researchers would seek decision-making models and solutions that incorporate the total range of factors in order for the problem to make sense.

Examples of this type of research in the area of logistics/supply chain management include specifying supplier-selection models, site-location models and technology-selection models based on a range of variables that affect the choice. Here too, one may further distinguish between existing studies based on their end purposes (e.g. environmental scanning as the purpose; this aspect is also covered in Chapter 3). This is to say that even within this paradigm, methodological orientations may differ based on different intra-paradigmatic

divides i.e. quantitative –qualitative, inductive – deductive, formal – non-formal, content – process, prescriptive – descriptive, empirical – axiomatic etc. (see e.g. Mitroff et al. 1974;

Meredith et al. 1989; Bertrand & Fransoo, 2002) types of (decision-making) frameworks.

However, from the perspective of the present study, a divide within the systems paradigm in a manner that corresponds to the following classification was found to be most useful

because it fit Arbnor & Bjerke’s (1997) classification scheme. This divide was used to create a niche for this study, and is also documented further on in the dissertation (Chapter 3). The construct as a general (context free) “decision-making”


From this point of view, supply chain logistics environmental complexity causes a decision-making problem, regardless of the exact problem context. This may be likened to the

Meredith et al. (1989) logical positivist/empiricist approach for dealing with decision-making within the scope of the systems paradigm where, for example, a total reliance on “perceptions of object reality” is employed as the preferred methodological apparatus. Operationalising the construct from this point of view would entail prioritising all decision-making constraints related to environmental complexity equally, regardless of the specificity of the problem (e.g.

managerial) context. The application of such a paradigm would then either focus on the broad criteria that are required to operationalise the construct in order to seek a more standard solution to a standard problem by offering e.g. an absolute index of environmental

complexity related constraints, and performance rankings; the fundamental idea being that problems and/or solutions are generalisable to all/most (e.g. managerial) contexts. The better solution would try abstracting lesser and would seek to narrow the problem to a level that captures more of the context. The Logistics Performance Index (LPI) represents a good example of such a paradigm in the area of logistics/supply chain management. Other

examples of studies employing such a paradigm include Carter et al. (1997) and Menon et al.

(1998) and Bookbinder and Tan (2003). Applications corresponding to the use of such a paradigm, where a review of country economic/business indexes is presented, can also be found in Chapter 3. The construct as a specific (context dependent) “decision-making” problem

When viewing the problem from this perspective, supply chain logistics environmental complexity causes a decision-making problem for the problem owner from the perspective of specific decisional departure points, such as a site-location problem, a supplier selection

problem etc. The main difference between the previous approach and the present one is that managerial decision-making takes into account individual problem contexts (Zack, 2007) e.g.

a site-location problem where decision-making constraints are prioritised differently as per the overriding managerial problem context (see Bhatnagar et al., 2003). Availability of information and information systems then drives the paradigm in order to reach a justified decision based on the varying levels of artificial reconstructions of the object reality. As Zack (2007) proposes, decision support technologies are more appropriate in supporting decision-making under conditions of uncertainty and complexity, whereas under conditions of ambiguity or equivocality, human-centric approaches may be more appropriate. This said,

“information (or its absence) is central to decision making situations involving uncertainty and complexity”, (Zack 2007, p. 1664).

One may therefore argue that such an approach“is frequently used to construct models that are embodied in software for expert and decision support systems and in mathematical models of operational systems”, (Meredith et al., 1989, p. 314). They further state:

“These approaches recast the object reality, as originally determined from one of the above two categories (usually the researcher’s own belief concerning the object reality), into another form that is more appropriate for testing and experimentation, such as analytical models, computer simulations, or information constructs”, (p.


Additionally, based on Meredith et al. (1989), one could even further distinguish these systems based on the prescriptive/normative – descriptive divide, i.e. the level of rationality employed in the construction of these systems, whereby the more rationally presumptive would tend to be more prescriptive, as compared to those that are descriptive in terms of the decision-making process. For example, a common distinction between the Analytic

Hierarchy Process (AHP) and its well-accepted counterpart – the Multi-Attribute Utility Theory (MAUT) –, both well-accepted methodologies underlying decision systems and falling under this type of systems paradigm, is that the AHP is descriptive, whereas the MAUT is more normative (e.g. see Saaty, 1994a; Saaty, 1997).

The reader may gain further knowledge of this type of paradigm in Chapter 3, where conditions surrounding the analysis of environmental complexity using decision support systems (DSS) are cited. With respect to the present study, applying such a paradigm would then aim at posing its research objectives and questions as a decisional problem, in order to operationalise the construct of supply chain logistics environmental complexity. Some

examples of (issues related to environmental complexity) research in the area of logistics/supply chain management using this paradigm are: Kinra and Kotzab (2008a;

2008b), Bagchi (2001), Min (1994a; 1994b), Teng and Jaramillo (2006), and Min and Eom (1994).