Entrepreneurial Disappointment: Let Down and Breaking Down, a Machine-Learning Study

Amanda Williamson, Andreana Drencheva and Martina Battisti

Entrepreneurship Theory and Practice, 2022, vol. 46, issue 6, 1500-1533

Abstract: Despite its importance, our understanding of what entrepreneurial disappointment is, its attributions, and how it relates to depression is limited. Drawing on a corpus of 27,906 semi-anonymous online posts, we identified entrepreneurial disappointment, inductively uncovered its attributions and examined how depression differs between attributions. We found that posts with internal, stable, and global disappointment attributions (e.g., not fitting entrepreneurial norms) are, on average, higher in depression symptoms than posts with external, unstable, and specific disappointment attributions (e.g., firm performance). Our findings offer novel theoretical and methodological avenues for future research on entrepreneurs’ affective experiences and mental health.

Keywords: emotions; psychology; depression; machine learning; artificial intelligence; uncertainty; entrepreneur (search for similar items in EconPapers)
JEL-codes: C63 D91 M13 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:sae:entthe:v:46:y:2022:i:6:p:1500-1533

DOI: 10.1177/1042258720964447

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