Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior

Rodgers, Waymond; Yeung, Fannie; Odindo, Christopher; Degbey, William Y.

Abstract

This study examines the digital transformation effects of artificial intelligence (AI)-based facial and music biometrics on customers' cognitive and emotional states and how these effects influence their behavioral responses on value creation. Using a real-life major optical retailer's store in China, 386 customers participated in a five-day experiment with different types of music (enhanced by music recognition biometrics). The findings show that for utilitarian type customers in an AI high-involvement purchase condition, music recognition biometric-induced emotion mediates cognition and behavioral intentions, while music does not influence emotion directly under this condition. Both likability and the tempo of music affect the impact of music on cognition. This study contributes to a better understanding of the relationship between cognition and emotion induced by AI-based facial and music biometrics systems in shaping customer behavior and adds to the atmospheric literature given the paucity of research in the context of the Chinese retail environment—now a significant retail market with global importance.

Citation

Rodgers, W., Yeung, F., Odindo, C., & Degbey, W. Y. (2021). Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior. Journal of business research, 126, 401-414. https://doi.org/10.1016/j.jbusres.2020.12.039

• Journal Article Type: Article
• Acceptance Date: Dec 15, 2020
• Online Publication Date: Jan 18, 2021
• Publication Date: 2021-03
• Deposit Date: Jan 21, 2021
• Publicly Available Date: Oct 27, 2022
• Journal: Journal of Business Research
• Print ISSN: 0148-2963
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 126
• Pages: 401-414
• DOI: https://doi.org/10.1016/j.jbusres.2020.12.039
• Keywords: Artificial intelligence; Atmospherics; Cognition; Emotion; Music; Retail
• Public URL: https://hull-repository.worktribe.com/output/3697479
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0148296320308687?via%3Dihub