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When customer meets AI failure: expectation discrepancy perspective

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  • Chanil Boo

Abstract

Despite the rapid increase in the development and adoption of artificial intelligence (AI) in firms, its observed quality and performance frequently fall short of anticipated benchmarks. The inability of AI to meet customer expectations not only jeopardizes brand reputation but also impacts overall firm value. However, little is known about the impact and consequences of algorithmic failures. This study seeks to elucidate how customers respond to instances of AI errors, thereby expanding upon the expectation discrepancy perspective. Our hypothesis posits that AI failures, in comparison to human errors, prompt a more negative evaluation. Empirical evidence derived from an experimental study demonstrates that customers exhibit a pronounced adverse response to AI errors as opposed to managerial mistakes. This study contributes to the extant literature on expectation discrepancy theory and algorithmic marketing, offering managerial insights on the strategic utilization of AI systems.

Suggested Citation

  • Chanil Boo, 2025. "When customer meets AI failure: expectation discrepancy perspective," Applied Economics Letters, Taylor & Francis Journals, vol. 32(7), pages 1039-1043, April.
    • Handle: RePEc:taf:apeclt:v:32:y:2025:i:7:p:1039-1043
    DOI: 10.1080/13504851.2023.2300973
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