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

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  • Amanda Jasmine Williamson
  • Andreana Drencheva
  • Martina Battisti

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.

Suggested Citation

  • Amanda Jasmine Williamson & Andreana Drencheva & Martina Battisti, 2022. "Entrepreneurial Disappointment: Let Down and Breaking Down, a Machine-Learning Study," Entrepreneurship Theory and Practice, , vol. 46(6), pages 1500-1533, November.
    • Handle: RePEc:sae:entthe:v:46:y:2022:i:6:p:1500-1533
    DOI: 10.1177/1042258720964447
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    References listed on IDEAS

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • M13 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - New Firms; Startups
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

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