Mindful matching: Ordinal versus nominal attributes
Peggy Liu – University of Pittsburg, United States
AI, Big Data, and Behavioral Science Workshop Series
The authors propose a new conceptual basis for predicting when and why consumers match others’ consumption choices. Specifically, they distinguish between ordinal (“ranked”) versus nominal (“unranked”) attributes and propose that consumers are more likely to match others on ordinal than on nominal attributes. Eleven studies involving a range of different ways of operationalizing ordinal versus nominal attributes collectively support this hypothesis. The authors’ conceptualization helps resolve divergent findings in prior literature and provides guidance to managers on how to leverage information about prior customers’ choices and employees’ recommendations to shape and predict future customers’ choices. Furthermore, the authors find process evidence that this effect is driven in part by consumers’ beliefs that a failure to match on ordinal (but not nominal) attributes will lead to social discomfort for one or both parties. Although the primary focus is on food choices, the effects are also demonstrated in other domains, extending the generalizability of the findings and implications for managerial practice and theory. Finally, the conceptual framework offers additional paths for future research.
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McGill Univeristy
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Canada