Abstract
While primary data analysis has been popular in logistics and supply chain research, secondary data methods have been overlooked. These methods, however, have the potential to generate a variety of important opportunities to expand the horizons of logistics and supply chain research. In this article, we emphasize the use of secondary data analysis and how it can address contemporary challenges in logistics and supply chain research. Our review of the logistics and supply chain literature identifies six important methodologies that can be useful for secondary data generation and analysis. We discuss how these methods can help effectively address various logistics research questions.
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