OPEN ACCESS
PEER-REVIEWED
Comparative Study
| Published: October 19, 2025
Art in the Age of AI: Buying Behavior, Emotional Resonance, and Creative Engagement with AI-Generated Art vs. Handmade Art
DIP: 18.01.027.20251304
DOI: 10.25215/1304.027
ABSTRACT
As technology continues to advance and redefine artistic practices, the role of artificial intelligence (AI) in artistic production has raised important questions about authenticity, emotional resonance, and consumer perception. Although interest in AI-driven art continues to grow, research on audience emotional and behavioral responses to such pieces remains limited. In light of this gap in understanding, this study examines how audiences respond to AI-generated versus handmade art, leveraging a structured questionnaire to investigate the impact of these two types of art on audience engagement and purchasing intent. A randomized experimental design was conducted with 40 participants assigned to one of two groups; each group was exposed to a curated selection of either AI-generated or handmade artworks. Participants then completed the questionnaire, and the data was measured across the targeted domains. Results indicated that handmade art elicited significantly higher buying behavior and creative engagement, while emotional engagement did not differ significantly between the groups. These findings point to ongoing tensions between human and machine-made creativity, suggesting that while AI art introduces new avenues of innovation and can streamline creative processes, it falls short of matching the authenticity and personal impact of handmade work. As such, the results highlight the importance of emotional resonance in art, suggesting that while handmade works maintain a unique advantage, AI-driven industries can still adopt techniques that foster more meaningful viewer experiences. Ultimately, the study offers actionable insights for marketers, designers, and digital content creators seeking to balance innovation with emotional impact in a rapidly evolving creative landscape.
This is an Open Access Research distributed under the terms of the Creative Commons Attribution License (www.creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any Medium, provided the original work is properly cited.
© 2025, Jain Shah, R.
Received: August 14, 2025; Revision Received: October 15, 2025; Accepted: October 19, 2025
Article Overview
ISSN 2348-5396
ISSN 2349-3429
18.01.027.20251304
10.25215/1304.027
Download: 6
View: 494
Published in Volume 13, Issue 4, October- December, 2025

