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See No Evil, Hear No Evil: How Users Blindly Overrely on Robots with Automation Bias

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  • Stock-Homburg, Ruth
  • Nguyen, Mai Anh

Abstract

Recent developments in generative artificial intelligence show how quickly users carelessly adhere to intelligent systems, ignoring systems' vulnerabilities and focusing on their superior capabilities. This is detrimental when system failures are ignored. This paper investigates this mindless overreliance on systems, defined as automation bias (AB), in human-robot interaction. We conducted two experimental studies (N1 = 210, N2 = 438) with social robots in a corporate setting to investigate psychological mechanisms and influencing factors of AB. Particularly, users experience perceptual and behavioral AB with the robot that is enhanced by robot competence depending on task complexity and is even stronger for emotional than analytical tasks. Surprisingly, robot reliability negatively affected AB. We also found a negative indirect-only mediation of AB on robot satisfaction. Finally, we provide implications for the appropriate use of robots to prevent employees from using them as a self-sufficient system instead of a supporting system.

Suggested Citation

  • Stock-Homburg, Ruth & Nguyen, Mai Anh, 2023. "See No Evil, Hear No Evil: How Users Blindly Overrely on Robots with Automation Bias," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 142980, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    • Handle: RePEc:dar:wpaper:142980
    Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/142980/
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