Examining Hedonic and Utilitarian Bidding Motivations in Online Auctions: Impacts of Time Pressure and Competition

ABSTRACT

Hedonic and utilitarian bidding motivations have seldom been studied in the context of consumer-to-consumer (C2C) online auctions. Drawing on the stimulus-organism-response (S-O-R) framework, this study establishes a structural equation model to examine how competition and time pressure (stimulus) influence bidders’ hedonic and utilitarian motivations (organism) and how these motivations further affect impulse bidding and satisfaction with the bidding process (responses). Our results show that time pressure is a significant predictor of utilitarian motivation but is not significantly related to hedonic motivation. Moreover, hedonic motivation tends to increase impulse bidding, whereas utilitarian motivation discourages it. Finally, this study finds the relationship between impulse bidding and satisfaction to be significant. Our findings contribute to theory by describing the roles that bidders’ hedonic and utilitarian motivations play in C2C online auctions and elucidating the important implications for practice in attracting individuals to bid in such auctions.

KEY WORDS AND PHRASES:

• Auction satisfaction
• bidding motivations
• hedonic motivations
• impulse bidding
• online auctions
• utilitarian motivations

Additional information

Notes on contributors

Chia-Chi Chang

CHIA-CHI CHANG ([email protected]) is an associate professor of Management Science at National Chiao-Tung University, Taiwan. Her research interests include charitable giving, service failure and recovery, consumer behavior, selling and sales management, all in the context of e-commerce. Her work has appeared in Psychology and Marketing, European Journal of Marketing, Journal of Business to Business Marketing, and Cyberpsychology, Behavior, and Social Networking.

Chi-Wen Chen

CHI-WEN CHEN ([email protected], Corresponding author) is a Ph.D. candidate of information systems at the Zicklin School of Business, Baruch College, City University of New York. His research interests include data mining, sharing behavior in social media, user behavior in Web-based systems and human-computer interaction. His work has been published in European Journal of Information Systems, Internet Research, Cyberpsychology, Behavior, and Social Networking, and Information and Management Science.