Unveiling the dynamic duo : How AI acts as the moderator between human intelligence and consumer buying behavior in automobile industry
The automobile industry stands at the forefront of technological innovation, where advancements in artificial intelligence (AI) have revolutionized the way consumers navigate the purchasing process. This study investigated the moderating effect of Artificial Intelligence (AI)between Human Intelligence (HI) and consumer buying behavior in the automobile industry. Results revealed significant enhancements of AI on human intelligence, with beta coefficients surpassing direct human intelligence effects. Both Human Intelligence and AI exhibit positive direct effects on consumer behavior (β = 0.54, p = 0.001 and β = 0.45, p = 0.001 respectively), alongside a substantial AI moderation effect (β = 0.621, p = 0.000). These findings underscored the significance of integrating AI-driven strategies alongside human intelligence for effective consumer engagement. Managerial implications suggested automobile dealers must adopt AI technologies to enhance customer experiences and tailor marketing strategies accordingly. Future research could delve into the long-term effects of AI integration and explore moderating effects of other variables across industries, contributing to a broader understanding of AI’s role in shaping consumer behavior and informing strategic decision-making. Additionally, the increase in R-square from 0.689 before moderation to 0.722 after moderation highlights the added explanatory power of the model, emphasizing the significance of AI’s moderating effect in enhancing our understanding of consumer behavior in the automobile industry.
Authors
- B. C. M. Patnaik (ORCID: https://orcid.org/0000-0002-5979-0989)
- Rashmi Gujrati (ORCID: https://orcid.org/0000-0002-1128-3742)
- Garima Vijh (ORCID: https://orcid.org/0000-0002-3361-4792)
- Cemalettin Hatipoğlu (ORCID: https://orcid.org/0000-0002-3129-9725)
- Ameet Sao (ORCID: https://orcid.org/0000-0002-0864-9336)
- Divya Pathak
Publication Details
- Journal
- Journal of Information and Optimization Sciences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.47974/jios-1953
- Citations
- 4
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 9.14