The effect of gender stereotypes on artificial intelligence recommendations

Jungyong Ahn, Jungwon Kim, Yongjun Sung

Research output: Contribution to journalArticlepeer-review

48 Citations (Scopus)


This study explores the effects of gender stereotypes on evaluating artificial intelligence (AI) recommendations. We predict that gender stereotypes will affect human-AI interactions, resulting in somewhat different persuasive effects of AI recommendations for utilitarian vs. hedonic products. We found that participants in the male AI agent condition gave higher competence scores than in the female AI agent condition. Contrariwise, perceived warmth was higher in the female AI agent condition than in the male condition. More importantly, a significant interaction effect between AI gender and product type was found, suggesting that participants showed more positive attitudes toward the AI recommendations when the male AI recommended a utilitarian (vs. hedonic) product. Conversely, a hedonic product was evaluated more positively when advised by the female (vs. male) AI agent.

Original languageEnglish
Pages (from-to)50-59
Number of pages10
JournalJournal of Business Research
Publication statusPublished - 2022 Mar

Bibliographical note

Funding Information:
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5B5A16075775).

Publisher Copyright:
© 2021 Elsevier Inc.


  • AI agent
  • AI recommendations
  • Artificial Intelligence (AI)
  • Gender stereotypes

ASJC Scopus subject areas

  • Marketing


Dive into the research topics of 'The effect of gender stereotypes on artificial intelligence recommendations'. Together they form a unique fingerprint.

Cite this