Performance ranking for a set of comparable decision‐making units (DMUs) with multiple inputs and outputs is an important and often‐discussed topic in data envelopment analysis (DEA). Conventional DEA models distinguish efficient units from inefficient ones but cannot further discriminate the efficient units, which all have a 100% efficiency score. Another weakness of these models is that they cannot handle negative inputs and/or outputs. In this paper, a new modified slacks‐based measure is proposed that works in the presence of negative data and provides quantitative data that helps decision makers obtain a full ranking of DMUs in situations where other methods fail. In addition, the new method has the properties of unit invariance and translation invariance, and it can give targets for inefficient DMUs to guide them to achieve full efficiency. Two numerical examples are analysed to demonstrate the usefulness of the new method.
Show/webrooming refers to a consumer inspecting a product at a brick‐and‐mortar (BM)/online retailer before purchasing it from a competing online/BM retailer. Despite retailers adopting price‐matching and free‐shipping policies, show/webrooming remains prevalent. We model different product value information that consumers can learn by visiting a BM retailer and researching an online retailer, and studying consumers' show/webrooming behavior in a unified model. We confirm that consumers can engage in show/webrooming for informational reasons, but webrooming may also be driven by a noninformational reason. We find that show/webrooming may, respectively, benefit BM and online retailers; in particular, there exist win‐win‐win outcomes where both retailers and the consumers benefit from show/webrooming. We propose in‐store research assistance for BM retailers and free sampling for online retailers and show that these operational strategies may improve the retailers' competitiveness in the presence of show/webrooming. Our results are found to be robust in several model extensions.
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