Research revolves around theory. Hereby, the role of researchers is twofold: Researchers can either start with real-life observations and produce a set of propositions that summarize a new theory (inductive theory building), e.g., using grounded theory research, or start with an existing theory for formulating hypotheses and use data to test them (deductive theory testing), e.g., using structural equation modeling.
For an extensive investigation of this dual role see Colquitt and Zapata-Phelan (2007).
Colquitt, J. & Zapata-Phelan, C. (2007). Trends in theory building and theory testing: A five-decade study of the Adademy of Management Journal. Academy of Management Journal, 50 (6), 1281-1303 DOI: 10.5465/AMJ.2007.28165855
Qualitative research can be conducted to build theory from field data. The Discovery of Grounded Theory by Glaser and Strauss (1967) remains the fundamental handbook of this approach. SCM journals have recently seen a series of articles advocating for the use of grounded theory, e.g., Mello and Flint (2009, JBL) and Kaufmann and Denk (2011, JSCM). Therefore, I would like to draw attention to three helpful papers. First, Suddaby (2006), offers “a reasonable assessment of common errors researchers make in conducting and presenting grounded theory research”. Herein, he discusses six common misconceptions of what grounded theory is not. Second, O’Reilly et al. (2012) “demystify the key tenets of [grounded theory]”, “discuss the problematic impacts of adopting an a la carte approach to [grounded theory]”, “draw attention to [grounded theory] as a rigorous method”, and, again, “advocate for the increased use of [grounded theory]”. Third, Manuj and Pohlen (2012) “provide a framework to assist reviewers in evaluating grounded theory research”.
I like open access. The USF Tampa Library hosts a collection of open access textbooks. One of them, which might be useful for many SCM researchers, has been published by Anol Bhattacherjee, a professor of information systems. It is titled Social Science Research: Principles, Methods, and Practices. The book, which is succinct and compact, is about the entire research process and it is designed “to introduce doctoral and graduate students to the process of scientific research”. The initial chapters 1 to 4 give an introduction to research. This includes topics such as “thinking like a researcher” and “theories in scientific research”. The chapters 5 to 8 are about the basics of empirical research (i.e, research design, construct measurement, scale reliability/validity, and sampling). The chapters 9 to 12 are concerned with data collection (i.e., survey research, experimental research, case research, and interpretive research). Both qualitative and quantitative data analysis is explained in the chapters 13 to 15. The last chapter is about research ethics.
I just read an article by Vallet-Bellmunt et al. (2011): Supply Chain Management: A Multidisciplinary Content Analysis of Vertical Relations between Companies, 1997–2006. It gives a good overview of SCM research published in journals related to marketing, logistics, management, and marketing channels. The authors find that the work type of most research is empirical in nature, primary information is used more often than secondary information, the information type is mostly quantitative in nature, studies are mainly explanatory-predictive, the period of time in which the research is carried out is mostly cross-sectional, the geographical area is mainly national, and manufacturing samples are used most often. Particularly, the article confirms my suspicion that there is “shortage of studies conducted on the supply chain as a network of enterprises“. Instead, most research turns out to focus on a single enterprise or on the relationships of a single enterprise with its suppliers or customers.
In their interesting article, A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance, Richardson et al. (2009) define common-method variance (CMV) as “systematic error variance shared among variables measured with and introduced as a function of the same method and/or source”. Post-hoc techniques promise help, if the research design does not allow the independent and dependent variables to use data from different methods and sources. Richardson et al. evaluate (1) the correlational marker, (2) the confirmatory factor analysis (CFA) marker, and (3) the unmeasured latent method construct (ULMC) techniques. Interestingly, they find that only the CFA marker technique appears to have some practical value, but “recommend the CFA marker technique be used only as a means for providing evidence about the presence of CMV and only when researchers can be reasonably confident they have used an ideal marker”. A good description of the CFA marker technique can be found in an article by Williams et al. (2010).
Recently, the use of laboratory experiments has attracted attention in SCM research. The Journal of Supply Chain Management dedicated a discussion forum to that topic, which is introduced with an essay by Eckerd and Bendoly. In this forum, Bachrach and Bendoly “touch on some of the basic tenets of rigorous behavioral experimentation” and “promote such rigor in future SCM behavioral studies”. The commentary by Rungtusanatham et al. deals with scenario-based role-playing experiments and vignette design. The self-explanatory title of the article by Siemsen is The usefulness of behavioral laboratory experiments in supply chain management research. Finally, Stevens discusses questions to consider when selecting student samples. Similarly, Thomas has discussed this topic in his recent article When student samples make sense in logistics research, published in the Journal of Business Logistics. To sum up, experiments might become increasingly important as behavioral research plays a growing role in our field.
In 2010, I launched the Handbook of Management Scales. It is a collection of previously used multi-item measurement scales in empirical management research literature and contains numerous scales related to SCM research. It contains scales from high-ranked journals that are developed in a systematic scale development process and that are tested to measure a construct in terms of specification, dimensionality, reliability, and validity. For each scale at least objective items, source, and, if available, reliability (e.g. Cronbach’s alpha) are listed. Particularly, structural equation modeling might benefit from the Handbook of Management Scales. It is a wikibook and can be edited by anyone. Feel free to expand the Handbook of Management Scales by adding good scales. This can help to further develop the Handbook as a useful resource for empirical management research. Related handbooks are the Handbook of Metrics for Research in Operations Management and the Handbook of Marketing Scales.
Wieland, A. et al. (2010 ff.). Handbook of Management Scales. Wikibooks. Online: http://en.wikibooks.org/wiki/Handbook_of_Management_Scales
Performance measurement is difficult! Generations of empirical researchers have presented measures of organizational performance. In their article Measuring Organizational Performance: Towards Methodological Best Practice, Richard et al. (2009) have reviewed past studies. They reveal the multidimensional nature of this important construct. According to the authors, organizational performance encompasses three specific areas of firm outcomes: (a) financial performance (profits, return on assets, return on investment, etc.); (b) product market performance (sales, market share, etc.); and (c) shareholder return (total shareholder return, economic value added, etc.). Limited effectiveness of commonly accepted measures in tapping this multidimensionality is highlighted. The appendix of the article, which is published in the Journal of Management, includes many examples of research that includes organizational performance as a dependent, independent, or control variable. To my knowledge, no comparable review is available for supply chain performance. It would be interesting to know, how this construct can be measured properly.