April 16, 1:00 to 2:00PM, SCIS conference room VC 11-217
Title. What Drives Users’ Website Registration? The Network Externalities versus Information Privacy Dilemma.
Abstract. User registration is an important prerequisite for the success of many websites that rely on network externalities for value creation, such as social networking and directory websites that need a large number of users to create network externalities. It is not always desirable for users, however, to register because they need to disclose their personal information. Based on the network externalities literature and the lens of the privacy calculus, we propose the network externalities-versus-information privacy dilemma to examine what drives user registration. Using a randomized field experiment combined with a survey on a real-life website that requires user registration to offer access to a directory of mobile numbers, we examine how the benefits from the website’s network externalities (greater access to mobile numbers) versus the risks from information privacy concerns (exposure of a user’s mobile number to other users) influence users’ intention to register and their actual registration behavior on the mobile directory website. Since external information stimuli are proposed to convey network externalities by building trust and alleviating privacy concerns, we manipulated the website’s popularity information (number of visitors, number of registered users, both number of visitors and registered users, none) and WOM (word-of-mouth) information (expert, user, none) in a 4 × 3 randomized field experiment. In addition, we examined the extent to which information sensitivity and website image influenced user registration by moderating the negative effect of information privacy concerns and the positive effect of trust on user registration using a survey of the same participants in the randomized field experiment. The results show the effectiveness of both popularity information and WOM information to increase registration through network externalities. Notably, when the popularity information of the registered users was highlighted along with expert WOM information, the website achieved over 7.5% higher user registration versus not showing any information. The results also show that while popularity information can boost registration by itself, the exact numbers displayed do matter. Interestingly, when the number of visitors and registered users are shown, a high number of registered users encourages, whereas a high number of visitors discourages, user registration. These finding suggest that the network externalities benefits that arise from displaying popularity and WOM information to prospective users outweigh the negative effects of information privacy concerns. We discuss the study’s theoretical and methodological contributions and implications, and we conclude with a set of concrete managerial recommendations for websites to increase user registration.
Bio. Dr. Ting Li is an Associate Professor at Rotterdam School of Management (RSM), Erasmus University in the Netherlands. Currently she is a visiting professor at Fox School of Business, Temple University. Her research interests include the strategic use of information technology, consumer decision making in online and mobile channels, business analytics, internet commerce, social media, and pricing and revenue management. Her work has been published in leading IS journals, including Information Systems Research, Decision Support Systems, European Journal of Information Systems, International Journal of Electronic Commerce, and many others. She was the runner-up for Prof. Aart Bosman Dissertation Award and Accenture-PIM Marketing Science Dissertation Award. Her interdisciplinary research has been sponsored by multiple grants from the Dutch National Science Foundation (NWO). Prior to joining academic, she worked for General Electric and IBM in the area of e-business in supply chains, web services, and grid computing. She obtained her Ph.D. in Management Science at the Erasmus University and MSc in Computational Science at the University of Amsterdam.