Theme:Consumer behavior and E-Business Decision-Making in Digital Economy

Session Chair

Minqiang Li
Professor
mqli@tju.edu.cn
College of Management and Economics, Tianjin University

 

 

 


Speech 1:Understanding Lenders’ Investment Behavior in Online Peer-to-Peer Lending: A Construal Level Theory Perspective

Yi Wu
Associate Professor
yiwu@tju.edu.cn
College of Management and Economics, Tianjin University

Abstract:Online Peer-to-Peer lending (i.e., P2P lending) has grown rapidly in recent years and is a new source of fixed income for investors. We empirically analyze how the interest rate offered, the borrower’s location, and education degree of a loan affect a lender’s investment, and also examine their interactions, based on the construal level theory. Using a rich data set from a popular online P2P platform in China and multiple identification strategies and estimation methods, we find that the interest rate and education distance between a lender and a borrower increase the lender’s biding value on a loan, whereas geography distance between a lender and a borrower decreases the lender’s biding value. Moreover, we further find that both geography distance and education distance strengthen the positive impact of interest rate on biding value. Our empirical findings provide important contributions to the literature on online financial markets and investment behaviors, and offer critical managerial implications to online fundraising, platform designers, and online investors.


Speech 2:Fairness or Pleasure? Understanding Consumer Behavior in Social Coupon Redemption

Cheng Luo
Assistant Professor
Cheng.luo@tju.edu.cn
College of Management and Economics, Tianjin University

Abstract:To attract consumers and drive redemption, social features are increasingly incorporated into digital coupon, which may induce social comparison among consumers. Such social comparison behavior may elicit fairness perceptions and emotions in consumers, and further influence consumers’ behavior in coupon redemption. To understand the role of social features in affecting consumers’ coupon redemption behavior, we develop a research model based on fairness heuristic theory and social comparison literature. We conduct a series of laboratory experiments to test our research model and report our research findings. Both contributions and implications are discussed.


Speech 3:Customer Unethical Behavior in the Sharing Economy:A View of  Socio-Technical Intervention Theory

Aihui Chen
Associate Professor
aihui@tju.edu.cn
College of Management and Economics, Tianjin University

Abstract:Customer unethical behavior in IT-enabled peer-to-peer sharing economy is alarmingly commonplace and causes severe consequences unethically. This study investigates how service providers’ social and technical intervention strategies attenuate customer unethical behavior in the context of sharing economy. The proposed theoretical model uses a tripartite data from 204 customers, their 152 matching providers and two professional observers, collected at three time points during the house sharing process, through a field study in China. The results show that social interaction intensifies the unethical behavior, and social support exerts no significant impact on customer unethical behavior. However, social trust, along with the functional perception and convenience perception of the technical system, can attenuate customer unethical behavior. Providers’ positive affection delivery and integrated information delivery can reduce customer unethical behavior both directly and indirectly through the mediation of customers’ perception of the social and technical systems. The authors address a gap in the literature on the customer ethical behavior in the sharing economy and challenge the consistently overstated positive role of social elements in the research on social commerce. Moreover, this study’s findings can guide service providers and platforms in the sharing economy in addressing customer unethical behavior.


Speech 4:Relevance or Profits? Cost-Regularized Recommender Systems Design in Digital Streaming Services

Haiyang Feng
Associate Professor
hyfeng@tju.edu.cn
College of Management and Economics, Tianjin University

Abstract:The most relevant recommendations do not always generate the highest profits for service providers. In recommender system design, the service provider can introduce the expected costs (or profits) of recommended items into the algorithm as a cost-regularization parameter, where a stronger cost-regularization effort will rank items with higher profits or lower costs higher at the cost of lower recommendation relevance. This study investigates the optimal cost-regularization effort for digital streaming platforms under a premium subscription model.