Traditional methods of capturing and determining logistics attribute importance have serious research limitations. The purpose of this paper is to introduce maximum difference (MD) scaling as a new research methodology that will improve validity in measuring logistics attribute importance, overcoming many of the limitations associated with traditional methods. In addition, this new research method will allow logistics researchers to identify meaningful need‐based segments, an important goal of logistics research.
This paper provides an overview of MD scaling along with important research advantages, limitations, and practical applications. Additionally, a detailed research process is put forth so that this technique can be implemented by logistics researchers. Finally, an application of this technique is presented to illustrate the research method.
The importance of truck driver satisfaction attributes was analyzed using bivariate correlation analysis as well as MD scaling analysis. The two sets of results are compared and contrasted. The resulting rank order of attributes is very different and MD scaling results are shown to possess important advantages. As a result of this analysis, MD scaling analysis allows for meaningful, need‐based segmentation analysis, resulting in two unique need‐based driver segments.
From a practitioner viewpoint, knowing which attributes are most important will help in investing scarce resources to improve decision making and raise a firm's ROI. Although a number of relevant applications exist, the most important may include examining: the importance of customer service attributes; the importance of logistics service quality attributes; and the importance of customer satisfaction attributes.
MD scaling is a relatively new research technique, a technique that has yet to be utilized or even explored in existing logistics and supply chain literature. Yet, evidence is mounting in other fields that suggest this technique has many important and unique advantages. This paper is the first overview, discussion, and application of this technique for logistics and supply chain management and creates a strong foundation for implementing MD scaling in future logistics and supply chain management research.
Garver, M., Williams, Z. and LeMay, S. (2010), "Measuring the importance of attributes in logistics research", International Journal of Logistics Management, The, Vol. 21 No. 1, pp. 22-44. https://doi.org/10.1108/09574091011042160Download as .RIS
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