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

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  • 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 in terms of value creation. Using a real-life, major optical retail 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 a high-involvement AI purchase condition, music-recognition biometric-induced emotion mediates cognition and behavioral intentions. Both likability and the tempo of the 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 biometric systems in shaping customer behavior and it adds to the atmospheric literature. This is a significant contribution given the paucity of research in the context of the Chinese retail environment, which is now a significant retail market with global importance.

Suggested Citation

  • Rodgers, Waymond & Yeung, Fannie & Odindo, Christopher & Degbey, William Y., 2021. "Artificial intelligence-driven music biometrics influencing customers’ retail buying behavior," Journal of Business Research, Elsevier, vol. 126(C), pages 401-414.
    • Handle: RePEc:eee:jbrese:v:126:y:2021:i:c:p:401-414
    DOI: 10.1016/j.jbusres.2020.12.039
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    Citations

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    Cited by:

    1. Chueh, Hao-En & Huang, Duen-Huang, 2023. "Usage intention model of digital assessment systems," Journal of Business Research, Elsevier, vol. 156(C).
    2. Zahoor, Nadia & Zopiatis, Anastasios & Adomako, Samuel & Lamprinakos, Grigorios, 2023. "The micro-foundations of digitally transforming SMEs: How digital literacy and technology interact with managerial attributes," Journal of Business Research, Elsevier, vol. 159(C).
    3. Ghouri, Arsalan Mujahid & Mani, Venkatesh & Haq, Mirza Amin ul & Kamble, Sachin S., 2022. "The micro foundations of social media use: Artificial intelligence integrated routine model," Journal of Business Research, Elsevier, vol. 144(C), pages 80-92.
    4. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    5. Waymond Rodgers & Tam Nguyen, 2022. "Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways," Journal of Business Ethics, Springer, vol. 178(4), pages 1043-1061, July.
    6. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    7. Umesh Ramchandra Raut & Prafulla Arjun Pawar, 2023. "Analysis of Hedonic and Utilitarian Consumer Values Affecting the Retail Store Image in India," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 35(2), pages 129-143.
    8. Kim, Jaehwan & Kang, Moon Young, 2022. "Sustainable success in the music industry: Empirical analysis of music preferences," Journal of Business Research, Elsevier, vol. 142(C), pages 1068-1076.
    9. Wang, Cuicui & Li, Yiyang & Fu, Weizhong & Jin, Jia, 2023. "Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    10. Shuvam Chatterjee & Pawel Bryla, 2023. "Mapping consumers’ semi-conscious decisions with the use of ZMET in a retail market setup," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(2), pages 221-232, June.
    11. Badghish, Saeed & Shaik, Aqueeb Sohail & Sahore, Nidhi & Srivastava, Shalini & Masood, Ayesha, 2024. "Can transactional use of AI-controlled voice assistants for service delivery pickup pace in the near future? A social learning theory (SLT) perspective," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    12. Ullah, Subhan & Attah-Boakye, Rexford & Adams, Kweku & Zaefarian, Ghasem, 2022. "Assessing the influence of celebrity and government endorsements on bitcoin’s price volatility," Journal of Business Research, Elsevier, vol. 145(C), pages 228-239.
    13. Rodgers, Waymond & Degbey, William Y. & Söderbom, Arne & Leijon, Svante, 2022. "Leveraging international R&D teams of portfolio entrepreneurs and management controllers to innovate: Implications of algorithmic decision-making," Journal of Business Research, Elsevier, vol. 140(C), pages 232-244.

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