Latent variables in business logistics research: Scale development and validation

Journal of Business Logistics, 1994 by Dunn, Steven C, Seaker, Robert F, Waller, Matthew A

Logistics is confounded with an abundance of concepts that are not easily operationalized for scientific analysis. Variables such as customer service, partnerships, third parties, integration, and total quality management are unobservable and termed "latent." A latent variable (a.k.a., construct) "is an unobserved entity presumed to underlie observed variables. In science our real interest is more in the relations among latent variables than it is in the relations among observed variables because we seek to explain phenomena and their relations."(1)

Due to the difficulty of developing operational measures of latent variables, business logistics research that tests hypotheses or propositions involving such variables has been lacking. Conversely, research methods applied in logistics model building generally tend to be more rigorous, following well developed methodological standards common to other academic fields. The use of econometrics in transportation research, simulation modeling for distribution network analysis, and analytical models of inventory systems are all examples of logistics research that typically do not involve the measurement of latent variables.(2,3,4,5,6)

The majority of logistics research can be categorized into three distinct areas: (1) generalized descriptions of variables (e.g., case studies); (2) interpretation of informant perceptions (e.g., surveys, interviews, expert panels); and (3) artificial reconstruction of reality, such as model building.(7)

The strength of logistics research may lie in its diversity of approaches. What is missing, however, is a stronger methodological approach within the perceptive paradigm, especially when latent variables are involved. Surveys and structured interviews account for almost half of the research published in this category. Survey research in business logistics often does not use the data from the survey for hypothesis testing. Instead the data is used to make inferences that are not statistically verifiable. This is partially a result of the fact that latent variables have not been scientifically measured, a necessary step for testing theory.

The purpose of this paper is to suggest a logistics research methodology for the scientific analysis and testing of latent variables. The methodology is drawn from well established techniques utilized in other areas of business research. This paper is divided into three sections. The first provides an analysis of logistics research methods, accomplished through an extension of the review of the articles in five "top-tier" logistics research journals performed by Dunn et al.(8) The second section assesses the need for, and describes the arguments in favor of, scientific research within the framework of logistics. The concepts of theory-testing and the role of science are discussed. In section three, a detailed technical description of the methodology for construct measurement is presented as a guideline for interested logistics researchers. The conclusion calls for the application of this methodology in future logistics research.

LOGISTICS RESEARCH METHODS

According to La Londe and Dawson,(9) early references to logistics activity first appeared in the early twentieth century with the works of Shaw.(10) Cherington,(11) Beckman,(12) and Borosodi.(13) Logistics rapidly gained attention as an integrated concept during the early 1950s.(14) Since then, research methods used to study logistics have taken formats ranging from systems simulations and econometric analysis to case study development. Many of today's top logistics researchers started their careers focused on transportation economics. Consequently, these researchers may not have been well versed in construct measurement and scientific behavioral research.

These same researchers are not studying business logistics issues, which often involve latent variables. While they may be applying econometric techniques, they are still not adequately addressing the construct measurement issues. Econometric techniques would produce more meaningful results if rigorous construct measurement techniques were first applied.

It is now appropriate to analyze the contemporary research in business logistics. Using a framework for research paradigms developed by Meredith et al.,(15) Dunn et al.(16) classified logistics research articles for the years 1986-1990. Five journals were selected by the authors as a representative sample of logistics journals: International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Journal of Purchasing and Materials Management, Logistics and Transportation Review, and Transportation Journal. These journals were included in Allen and Vellenga's(17) assessment of top logistics journals. An explanation of the Meredith et al. framework is presented in Figure 1. (Figure 1 omitted) The Meredith model has two continuums that enable categorization of research based upon the underlying tenets of its methodology--the rational/existential (R/E) and the natural/artificial (N/A).