Date: 17^th May, 2019

Time: 10:00-11:30 a.m.

Venue: Room 1004, The Administration Building, Zhejiang University

【Speaker Introduction】:Ben Choi is an assistant professor in Information Technology and Operations Management at the Nanyang Technological University. He was previously a faculty at the University of New South Wales. Ben received his B Sci and PhD in Information Systems from National University of Singapore. He has been named the Reviewer of the Year 2016 at MIS Quarterly. He has published multiple papers in premier information systems journals including, Information Systems Research, Journal of Management Information Systems, and Journal of the Association for Information Systems. His research interests focus on mobile healthcare and analytics, information privacy, and social media.

【Seminar Abstract】：The growing availability of mobile health technologies, such as activity trackers and mobile apps, have given rise to cost-effective ways to promote regular physical activity. Despite the popularity, the effects of personal training app on improving fitness remains largely mixed. Extending the activity theory to the context of personal training app usage and integrating it with the self-quantification and self-presentation literature, we examine two popular app features, namely activity tracking and social sharing on fitness improvement over time. We also posit that the effects of these features on fitness improvement will interact with in-app achievement frequency, which is expected to reduce over time. To operationalize the study, we conducted a field experiment involving 647 subjects and observed their app usage as well as captured their fitness levels over an 18-month period. Results reveal that personal training app is generally helpful to improving fitness and social sharing enables greater fitness improvement compared with activity tracking. Furthermore, we found that in-app achievement frequency declines over time and this decline diminishes the effects of personal training app on fitness improvement in the long run. We discuss how these findings contribute to the self-quantification literature in information systems and self-presentation research in the human-computer interaction field. We also discuss implications for practice and provide suggestions for future works.