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Collecting (information) measures on each decision factor – phase 4


6.3. Theoretical model on Supply Chain Logistics Environmental Complexity

6.3.4. Collecting (information) measures on each decision factor – phase 4

Having had framed the construct into a hierarchy, which structured and demonstrated each decision factor relative to the construct, the study also moved a step forward in understanding the second aspect of RQ 2 i.e. (information) measures of these decision factors. For instance, it became easier to visualise the type of measures that provided information on each decision factor, and the place (level) that these would eventually occupy in the theoretical model. The process of discovering these (information) measures is described in a detailed manner by documenting each relevant aspect of data collection such as observation methods, sampling, phases and instrument in the following sections. Research method and sampling of the content

The empirical measures were identified using a comprehensive content analysis, representing the second round of in-depth content analysis in this dissertation, and one that the study referred to as the “CSCMP metrics analysis”. This is because it was performed on a leading practitioner publication within the field, namely the CSCMP Global Perspectives. According to Zikmund (2000), a content analysis is a research technique used for the purpose of

preparing a structured, systematic and (quantitative) description of the manifest content of

communication, and deals with the study of the message. A content analysis could then be employed within the exploratory stages of this study, and within the methodological confines of data collection using secondary (or historical) data.

CSCMP Global Perspectives is an alternating trade publication by the Council of Supply Chain Management Professionals (CSCMP) that takes an “an in-depth look at a particular country or region”30 in order to examine complexities facing global supply chain

management. For example, the publication examines environmental complexity by exploring macro-institutional and infrastructural factors that are essential to contend with while

operating in different environments viz. countries like China (Wang 2006), Italy (Borghesi and Signori 2006), Japan (Kitamura 2006) and Mexico (Torres 2007). There were compelling reasons for this obvious choice. Firstly, it may be evident through the meta-analysis

presented in Fig. 39 that the publication was an essential part of scarce domain literature that dealt with the subject area of this dissertation. Secondly, considering that this dissertation would lack empirical data in terms of managerial responses, this publication was very relevant from the point of view of installing a method and source triangulation by including managerial perspectives or impulses on what is important in measuring the construct.

Thirdly, CSCMP is a well-respected and representative association of the domain literature.

This analysis may be referred to as an “absolute” content analysis, because the entire population of the publication to date31 was examined in the analysis. In other words, no sampling was performed to sample a representative number of issues. The main reasons for this were that the publication is fairly young, and that no combination of samples was

envisaged to be uniform enough, as each issue focussed on a different (country) environment, and was authored by different types of stakeholders, each representing a different (business) context. Even though many authors (e.g. Bookbinder and Tan, 2003, Logistics Performance Index, 2007) seek to emphasise patterns of environmental differences by country

development status, the focus of each issue on a separate and distinct country environment was not the biggest concern. This is because environmental complexity, the object, had been conceptualised in this dissertation as arising precisely because of these differences. Besides, considering that each issue focussed on geographically disparate locations, this was

envisaged to provide a much richer picture in order for the construct to have global relevance.


31Until the year 2007

Since a lot of emphasis in this dissertation has been placed on making, and decision-making aspects related to the specific (managerial) contexts, it was therefore conceived that performing an absolute content analysis was not only necessary because of the above-mentioned reasons, but would also provide a more complete picture of the total range of decision factors and measures for operationalising the construct. Data collection

The content analysis involved three progressive phases and the entire process lasted about six months, a majority of which was conducted during the author’s stay at the George

Washington University in the United States. From here onwards, the reader is referred to Table CSCMP Total Measures (v.7) in Appendix B in order to relate to the following

description. The table presents the concentrated (data collection) work of these 3 progressive phases in the form of a sub-instrument that was used to identify measures of supply chain logistics environmental complexity, and was consequentially developed into a more full-fledged instrument for primary data collection. The first phase of the content analysis

identified empirical referents of the construct in each of the publication issues; and then used one-to-one mapping for linking each of the (21) decision factors to those empirical referents.

The purpose was to observe how each decision factor had been treated in every issue, and to observe patterns of measures that related to each factor in the different country environments.

For example, this aspect is represented in the Table by: a) linking a measure such as “Km seashore or coastline” to its corresponding decision factor Geographical Location; and b) developing country codes such as MX (Mexico), CN (China), IT (Italy), JP (Japan) and BR (Brazil) for the countries that each publication issue described. In this way, the coverage of each measure was observed in every issue. Data processing and analysis

The second phase involved developing a classification scheme that classified each measure according to its data type (source) e.g. objective data (also referred to as hard data) and perceptual data. In the Table [CSCMP Total Measures (v.7)] this aspect is represented by the classification of data sources depending on how measures were communicated in the

publication. The following data codes were thus developed:

– INST1 = hard data:

– INST2 = perceptions - survey based (P)

– INST3 = perceptions - author’s personal perceptions (P)

– INST4 = perceptions – data-based perceptions –DESCRIPTION (D)

Whereas the third phase firstly involved listing each measure together with its data type and compiling it together with its respective environmental complexity category. It secondly involved prioritising the measures in accordance to how consistently and frequently they had been considered, and then disposing of any spurious measures. Finally it also involved questioning the inclusion of those decision factors in the theoretical model in Fig. 40., that were found to have no measures relating to it in the content analysis e.g. Hub & spoke systems. Furthermore, it involved mainly identifying and promoting only those measures on which data availability was not a major issue. This was achieved by checking for data availability on each individual measure from three sources i.e.: two major electronic databases i.e. World Development Indicators (WDI database) and IMD’s World

Competitiveness Yearbook (WCY database)32 and the World Bank’s most recent publication in this field, the Logistics Performance Index (2007). The Table in Appendix B demonstrates all this in a self-explanatory manner. Findings of content analysis 2 (“CSCMP metrics analysis”) As a result, a total of 337 different types of “measures” that may be used to operationalise the construct of supply chain logistics environmental complexity, were identified. Though these measures were disproportionately aggregated, they were well-representative of their respective decision factors and environmental complexity categories. These measures now required meeting important validity concerns in order to be useful for any future

environmental complexity analyses. These validity concerns are dealt with in the next chapter.