A Dynamic Model of User Behavior in a Social Network Site

• 영문명: A Dynamic Model of User Behavior in a Social Network Site
• 발행기관: 한국상품학회
• 저자명: Ahn, Dae-Yong Watson, Randal Lee, Jinyong
• 간행물 정보: 『상품학연구』제35권 제5호, 101~110쪽, 전체 10쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2017.10.30

영문 초록

This paper estimates a dynamic model of user behavior in a social network site using unique data on the daily login activity of a sample of members of MySpace.com. We view a social network as a stock of capital that yields a flow of utilities over time by creating interactions between the site user and her friends. This capital stock can be maintained and expanded by logging in to the site and communicating with other members, making the user s login decision a forward-looking one. We allow for two sources of persistence in members login decisions: state dependence and unobserved heterogeneity. Unlike many reduced-form models used in the studies of social network sites, our structural approach allows us to perform a series of counterfactual simulations, which can be used to assess the effects of new site features on site traffic. We found two distinct types of consumers as regards utility, cost, and state transition. Across types, real-time chat and messaging, features of MySpace.com, positively affect the usage decision. Our counterfactuals simulate the effect of preventing coordination failure between users and their friends in a social network site and the effect of raising switching costs to lock users in to the site. The results show that `the bigger network has double the effect of `the larger amount of chatting on daily site usage after one year, consistent with the lock-in effect of a bigger network through the flow-on effects on social interactions in future periods. These simulations demonstrate that social network sites may adopt or modify our modeling approach in designing new site features.

목차

I. Introduction

II. Model

III. Data

IV. Results

V. Policy Experiment

VI. Conclusion

키워드

dynamic discrete choice online social networks unobserved heterogeneity