Rigor in qualitative supply chain management research: lessons from applying repertory grid technique

Goffin, Keith; Raja, Jawwad; Claes, Björn; Szwejczewski, Marek and Martinez, Veronica (2012). Rigor in qualitative supply chain management research: lessons from applying repertory grid technique. International Journal of Physical Distribution & Logistics Management, 42(8/9) pp. 804–827.

DOI: https://doi.org/10.1108/09600031211269767


Purpose – The purpose of this article is to share our experiences of using the repertory grid technique in two supply chain management studies. We demonstrate how our two studies provided insights into how qualitative techniques such as the repertory grid can be made more rigorous than in the past, and how they can generate results that are inaccessible using quantitative methods.

Design/methodology/approach – This paper presents two studies undertaken using the repertory grid technique to illustrate its application in supply chain management research.

Findings – The paper presents insights into supply chain research that otherwise would not have emerged using traditional methods. Both studies derive a comprehensive list of empirical categories of constructs, many of which have not been identified in the extant literature. Moreover, the technique demonstrates that frequently mentioned constructs are not necessarily the most important.

Research limitations/implications – The paper demonstrates how quantitative calculations can strengthen qualitative research. Importantly, from our experience of using the technique we detail how to focus on demonstrating validity, reliability, and theoretical saturation.

Originality/value – It is our contention that the addition of the repertory grid technique to the toolset of methods used by logistics and supply chain management researchers can only enhance insights and the building of robust theories. Qualitative studies that adopt the technique cannot only provide rich insights but also counter the common criticism aimed at qualitative research that of failing to provide clear and transparent accounts of the analysis process and how findings are generated from the data set.

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