The Stickiness of Category Labels: Audience Perception and Evaluation of Producer Repositioning in Creative Markets

Balázs Kovács (), Greta Hsu () and Amanda Sharkey ()
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Balázs Kovács: School of Management, Yale University, New Haven, Connecticut 06520
Greta Hsu: Graduate School of Management, University of California, Davis, Davis, California 95616
Amanda Sharkey: W.P. Carey School of Business, Arizona State University, Tempe, Arizona 85287

Management Science, 2024, vol. 70, issue 9, 6315-6335

Abstract: Market producers often seek to position themselves in different categories over time. Successful repositioning is difficult, however, as audiences often devalue offerings that depart from a producer’s past creations. Prior research suggests that this penalty arises as evaluators withhold opportunities for producers to reposition because of presumptions of a lack of competence in different categories. In this paper, we develop understanding of a novel evaluator-driven challenge to producers’ repositioning efforts: evaluators are prone to “categorical stickiness,” by which the categories they have come to associate with a producer through its prior offerings shape their perceptions of the producer’s subsequent offerings. The result is a systematic mismatch between what producers claim and what evaluators perceive when a producer repositions. We further propose that audience members who have the greatest prior experience with a producer are the least likely to recognize its repositioning efforts. We examine evidence for our theory using data from Goodreads.com on authors within the book publishing industry, 2007–2017. We first build a novel deep-learning framework to predict categorization of a given book based solely on an author’s description of its content. We then use data on how Goodreads users categorize and evaluate books as well as their past reading behavior to test for evidence of our proposed mechanism. Overall, our results extend understanding of the evaluative processes that generate categorical constraints and how these may differ among various types of audience members.

Keywords: repositioning; categorization; audiences; book publishing industry; reviews; deep learning; natural language processing (search for similar items in EconPapers)
Date: 2024
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