Dear All,

You are cordially invited to an onsite academic seminar to be delivered by Prof. Gang Wang on November 1 (Friday). Details could be found below.

Seminar Information

Time and Date: 10:30 am - 12:00 pm, November 1, 2024 (Friday)

Venue: Room 316, Teaching A Building
Speaker: Prof. Gang Wang (The University of Delaware)

Topic: The Double-Edged Roles of Generative AI in the Creative Process: Experiments on Design Work

Zoom Access

Link: https://cuhk-edu-cn.zoom.us/j/3985407949?pwd=QnZJMHU3SDUwaFdtWTF6N3RWcGlMdz09

Meeting ID: 398 540 7949
Passcode: 779898

About the Speaker

Gang Wang is an associate professor of management information systems (MIS) and Joint Educational Institute (JEI) research fellow at the Lerner College of Business & Economics, the University of Delaware. He also serves as a co-director of the FinTech Innovation Hub, a university-level initiative. Dr. Wang received his Ph.D. degree in operations and information management from the University of Connecticut. His research interests include e-platforms, digital technology adoption and societal impact of technology. His research has been published in Management Science, MIS Quarterly, Information Systems Research, and other premier academic journals. He currently serves as an associate editor at Decision Support Systems and Decision Sciences.

Abstract

Generative Artificial Intelligence (GenAI) mimics human creativity by producing novel and complex content solutions across various creative domains, making it distinct from AI in prior studies. Our research is among the first to not only establish the overall impact of GenAI on creative work, but also delve into GenAI’s impact on the creative process, addressing a significant gap in the existing literature that largely views creativity as an end product rather than a cognitive process. Drawing on theories of creativity and cognitive fixation, we conceptualize creativity as a process that encompasses an initial ideation stage and a subsequent implementation stage, and we theorize the differing impacts of GenAI on these two stages, depending on the expertise level of the human creators. Findings from our field experiment (involving a real-world product design task) show that GenAI significantly improves the overall quality of creative work, especially among human creators with less expertise. Our lab experiment (involving a graphic design task) further demonstrates that GenAI tremendously boosts creative work by facilitating divergent thinking in the ideation stage. In the subsequent implementation stage, GenAI still benefits creators with less expertise. However, for high-expertise creators, GenAI considerably reduces their work efficiency without improving their design quality, suggesting that GenAI may disrupt the established work approaches of high-expertise creators to converge on a final design. Our work contributes to the burgeoning GenAI literature by elucidating the nuanced impact mechanism of GenAI in the creative process and the heterogeneity of such impact based on creators’ expertise levels. Our work also highlights the importance of recognizing the double-edged impacts of GenAI on creative work, as well as the need for its careful integration.