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Imitation: Mitigating AI backfire

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  • Zhang, Fan
  • Pan, Jieyi

Abstract

Previous research suggests that artificial intelligence (AI) influences organizational resources through automation and augmentation. We extend this perspective by identifying backfire effects, driven primarily by technological uncertainty in AI implementation. We propose that imitation strategies can help microenterprises mitigate these challenges. Using a simulation methodology, the findings indicate that imitation is more effective than non-imitation in addressing AI’s backfire effects on microenterprises. Specifically, imitating enterprises within the same size category yields greater advantages than imitating top entities across all categories, with the most effective strategy being to follow the leading entities of the same size. This study contributes to the literature on AI’s dark side and imitation strategies, providing a strategic direction for microenterprises to manage AI-related uncertainties.

Suggested Citation

  • Zhang, Fan & Pan, Jieyi, 2025. "Imitation: Mitigating AI backfire," Journal of Business Research, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001547
    DOI: 10.1016/j.jbusres.2025.115331
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