Trust and experience in online auctions

ABSTRACT

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first, to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust.

KEYWORDS:

• Online auction
• electronic markets
• trust behavior
• selling and bidding experience
• learning

Notes

¹ These results were duplicated with a second bidder analysis as well with similar results. As a check for robustness, similar results were obtained with no natural log transformation of dependent or independent variables, although the result was not as strong.

² Note that experiences levels are discrete integers. Although we tried to divide the bidders in Figure 2 into equal bins, 18,850 of the 24,579 bidders in our study had no selling experience. Hence, the first bar in Figure 2 contains the majority of bidders, and the remaining bars divide the remaining bidders more or less equally.

Additional information

Notes on contributors

Terence T. Ow

Dr. Terence T. Ow is an Associate Professor of Information Technology in the Management Department, College of Business Administration at Marquette University. He holds a Ph.D. in Business from the University of Wisconsin-Madison. His research focuses on the business value of IT, exploration of factors on online purchasing, bidding behavior in electronic market and participation behavior in online communities. He is also interested in decision support, the decision-making process and its implications in the development of business intelligence systems. His research has been published in information systems and management science journals including MIS Quarterly, Journal of Operations Management, Communications of the ACM, Decision Sciences, European Journal of Operational Research and other academic journals.  He serves as associate editor for Journal of Organization Computing and Electronic Commerce.

Brian I. Spaid

Brian I. Spaid is an Assistant Professor of Marketing at Marquette University. He holds a Ph.D. from the University of Tennessee. His research focuses on retail and services marketing with a special emphasis on the role of in-store and mobile technologies and their impacts on the brick-and-mortar and online retail realms. His work has been published in the Research Policy, Psychology & Marketing, Service Industries Journal, Journal of Consumer Marketing, Journal of Marketing Theory and Practice, and International Review of Retailing, Distribution and Consumer Research.

Charles A. Wood

Charles A. Wood has worked in the information security and IT infrastructure arena for a couple of decades. He holds a CISSP designation in information security, an undergraduate degree in computer sciences and corporate finance, and an MBA in Information and Decision Sciences. He holds a Ph.D. from University of Minnesota. He leads his own consulting firm, ShiftSecure, and has consulted for government and companies such as the State of Indiana and Comcast. Currently, Dr. Wood is serving in a principle data scientist role at Comcast, where he enjoys applying data science concepts to guide forecasting application development. He has received a patent on an information security procedure and has published in many prestigious books and journals, including Journal of Management Information Systems, Management Science, Journal of Marketing, Communications of the ACM, and European Journal of Operational Research, among others.

Sulin Ba

Sulin Ba is the Associate Dean of Academic and Research Support in the School of Business at the University of Connecticut. She is also the Treibick Family Endowed Chair Professor of Information Systems. She holds a Ph.D. from the University of Texas at Austin. Her current research interests include the design of crowdsourcing platforms, online Word-of-mouth, and digital health communities. Her work on the institutional setup to help small businesses grow in the digital economy has been used as the basis for testimony before the Congressional House Committee on Small Business. She has published in Management Science, Information Systems Research, Decision Support Systems, MIS Quarterly, Journal of Management Information Systems, Production and Operations Management, and other academic journals. She is a recipient of the prestigious Best Information Systems Publications Award (2010) (given by the Association for Information Systems and its Senior Scholars Consortium), Year 2000 MIS Quarterly Best Paper Award, UConn School of Business Research Excellence Award (2013), Best Paper Award (2009 and 2018), Undergraduate Teaching Award (2008), and Teaching Innovation Award (2007). She has been a senior editor for MIS Quarterly, Production and Operations Management, and Information Systems and e-Business Management. She also serves on the editorial board of Decision Support Systems.