Going beyond optimal distinctiveness: Strategic positioning for gaining an audience composition premium

Majid Majzoubi,Eric Yanfei Zhao

STRATEGIC MANAGEMENT JOURNAL(2023)

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摘要
Research Summary: A core question in strategy research is how firms should position themselves to gain favorable audience evaluations. Emphasizing the heterogeneity in audience predispositions, we propose that firms can gain an audience composition premium by strategically positioning themselves to gain more (less) attention from audiences with positive (negative) predispositions toward them. We argue that this approach to strategic positioning is more conducive for firms with high dispersion in their audience predispositions and that firms can increase their ability to gain an audience composition premium by engaging with audiences holding moderately diverse evaluative schemas. We employ recommender systems and topic modeling to analyze 152,312 firm-analyst-year observations from 1997 to 2018 and 297,931 earnings call transcripts of U.S. public firms and find strong support for our predictions. Managerial Summary: A key question managers encounter is how to increase their firms' evaluations from external evaluators such as security analysts. In this study, we show that firms can increase their aggregate analyst recommendations by influencing the composition of analysts who opt to cover them and gaining evaluations from analysts who have more favorable predispositions toward them (i.e., by gaining an audience composition premium). Our findings also suggest that gaining an audience composition premium is more important for enhancing a firm's aggregate analyst recommendations when there is a higher dispersion in analyst predispositions toward the firm. To increase its ability to gain an audience composition premium, the firm should engage with analysts who exhibit a moderate degree of heterogeneity in their evaluative schemas.
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关键词
audience heterogeneity, machine learning, optimal distinctiveness, recommender systems, strategic positioning
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