Digital Nudging for Online Social Sharing: Evidence from A Randomized Field Experiment

This study investigates the effectiveness of digital nudging for users’ social sharing of online platform content. In collaboration with a leading career and education online platform, we conducted a large-scale randomized experiment of digital nudging using website popups. Grounding on the Social Capital Theory and the individual motivation mechanism, we proposed and tested four kinds of nudging messages: simple request, monetary incentive, relational capital, and cognitive capital. We find that nudging messages with monetary incentive, relational and cognitive capital framings lead to increase in social sharing behavior, while nudging message with simple request decreases social sharing, comparing to the control group without nudging. This study contributes to the prior research on digital nudging by providing causal evidence of effective nudging for online social sharing behavior. The findings of this study also provide valuable guidelines for the optimal design of online platforms to effectively nudge/encourage social sharing in practice

Impacts of Live Chat on Refund Intention: Evidence from an Online Labor Market

Live chat plays a significant role in online labor markets, which mitigates the information asymmetry caused by the highly customized nature of service products. This study examines the impacts of live chat on refund intention in online labor markets and how these impacts are moderated by business familiarity. We collect unique archived data from a leading online labor market in Asia and hypothesize that reply speed has a negative effect on refund intention while both politeness intensity and sentiment intensity have a U-shaped effect on refund intention. In addition, these effects are proposed to be weakened by business familiarity formed by previous transaction experience. The study not only offers theoretical contributions to the online labor market literature by providing empirical insights on the impact of live chat on refund intention but also yields managerial implications for service providers and platform operators

Riding the Referral Express: Examining the Impact of Referral Reward Programs on Digital Transportation Platforms

This paper examines the effectiveness of referral reward programs (RRP) in sustaining network effects in digital transportation platforms (DTP). Using data of 119,130 users, the study assesses differences between referred and non-referred users in terms of revenue, retention, and engagement. Results show that referred users spend 5.77% more on average, have 11.66% less likelihood to defect and refer seven times more than non-referred users. However, the positive effects of RRPs do not significantly affect revenue of younger users and increases risk of defection in older generations over time. The study recommends firms to balance the referral and organic word-of-mouth effects of RRPs as one-size does not fit all

Social Distance and Information Avoidance in Public Security Events: A Dual Involvement Perspective

With the large spread of information thanks to ICT, public security events are increasingly focused on by the public. But meanwhile, the phenomenon of people’s information avoidance in these events still exists and even becomes more prominent. However, existing studies on information avoidance have ignored such an important context (i.e., public security event) and the influence of people’s perceptions of social relationship. To fill the gaps, we develop a model to explore the influence of social distance on information avoidance through two opposite mechanisms from a dual involvement perspective, perceived relevance and negative affect, in the context of public security events. We also consider self-efficacy’s moderating role to identify the boundary conditions. A scenario-based survey with college students was conducted to test the proposed research model. Finally, theoretical contributions and practical implications are discussed

Designing Promotion Incentive to Embrace Social Sharing: Evidence from Field and Lab Experiments

Despite the increasing connectivity between customers enabled by digital technologies, there is an absence of research investigating how firms should redesign the promotion incentives to engage customers as both ‘purchaser’ and ‘sharer’ in this social media era. In this study, we conduct a large-scale field experiment and two lab experiments to test the effectiveness of different incentive designs (varied by shareability and quantity of promo codes) in driving social sharing senders’ purchase and referrals. Providing senders with one non-shareable code significantly increases their purchase likelihood. In comparison, the senders who receive one shareable code are less likely to purchase themselves, but are much more likely to make successful referrals. We further conduct two lab experiments, which replicate the field experiment findings and explore the underlying mechanisms. We find that the exclusivity perception and social motive triggered by various incentive designs mediate and explain their effect on sender’s purchase and referrals. Our study extends prior IS literature on social sharing that has focused on sharing information to the domain of sharing incentives, providing implications to firms on how to design promotional incentive that accommodates the dual role of customers as purchasers and sharers and sheds light on the motives underlying social sharing

Taking Advantage of Algorithmic Preference to Reduce Product Returns in E-Commerce

Reimbursement of repair costs is a way to motivate customers to keep defective products instead of returning them. However, there is no research-based guidance on how retailers should frame repair costs reimbursement offers in terms of who decides on the size of the reimbursement and makes the offer—an employee or a machine. To guide further IS research and suggest ways that help e-commerce businesses to improve repair costs reimbursement effectiveness to decrease product return rates, the present research draws on literature on offer sources and on insights from a qualitative and an experimental study. We find that artificial intelligence-based (vs. human-based) repair costs reimbursement offers promote fairness perceptions, which, in turn, affect important customer outcomes—the likelihood to accept the offer and digital negative word of mouth. The results can guide e-commerce businesses’ returns-prevention efforts and IS research

Examining Exploitability Risk of Vulnerabilities: A Hazard Model

With the increasing number and severity of security incidents and exploits, information technology (IT) vendors, security managers, and consumers have begun to place more emphasis on security. Yet, fixing the sheer volume of vulnerabilities remains a challenge as IT vendors race against attackers to evaluate system vulnerabilities, prioritize them, and issue security patches before cybercriminals can exploit them. In this study, we posit that IT vendors can prioritize which vulnerabilities they should patch first by assessing their exploitability risk. Accordingly, we identified the vulnerabilities that cybercriminals will most likely exploit using vulnerability-related attributes and vulnerability types. To do so, we employed survival analysis and tested our models using historical data of vulnerabilities and exploits between 2007 and 2016. Our results indicate that IT vendors benefit the most from fixing remotely exploitable vulnerabilities; non-complex vulnerabilities; vulnerabilities that require no authentication; and vulnerabilities that affect confidentiality, integrity, and availability components. Furthermore, our findings suggest that IT vendors can mitigate the risk of exploit-related attacks by remedying code-injection vulnerabilities, buffer-overflow vulnerabilities, and numeric-error vulnerabilities

Medium Minimization Effect of Medium-type Reward in the Online Referral Reward Programs: A General Evaluability Perspective

Medium-type reward is a token that people receive as an immediate reward for their effort and can be traded for a desired outcome, and has been widely used in various promoting campaigns. However, our understanding of its impact remains limited. This research focuses on the effect of medium-type reward on individuals’ referral intention in online reward referral programs. Based on general evaluability theory, we propose the medium minimization effect, i.e., individuals have higher referral intention when the numerical value of medium-type reward is small (vs. large) and that the effect will be attenuated when the reward strategy does not care whether referral is successful or the actual reward is uncertain. Results of three experimental studies support our hypotheses. Findings put forward new insights into the medium effect, as well as its potential mechanism, and individuals’ referral behavior, and can help firms optimize the design of online reward referral program systems

Exploring the Mechanisms of Information Sharing

abstract: Online product ratings offer consumers information about products. In this dissertation, I explore how the design of the rating system impacts consumers’ sharing behavior and how different players are affected by rating mechanisms. The first two chapters investigate how consumers choose to share their experiences of different attributes, how their preferences are reflected in numerical ratings and textual reviews, whether and how multi-dimensional rating systems affect consumer satisfaction through product ratings, and whether and how multi-dimensional rating systems affect the interplay between numerical ratings and textual reviews. The identification strategy of the observational study hinges on a natural experiment on TripAdvisor when the website reengineered its rating system from single-dimensional to multi-dimensional in January 2009. Rating data on the same set of restaurants from Yelp, were used to identify the causal effect using a difference-in-difference approach. Text mining skills were deployed to identify potential topics from textual reviews when consumers didn’t provide dimensional ratings in both SD and MD systems. Results show that ratings in a single-dimensional rating system have a downward trend and a higher dispersion, whereas ratings in a multi-dimensional rating system are significantly higher and convergent. Textual reviews in MDR are in greater width and depth than textual reviews in SDR. The third chapter tries to uncover how the introduction of monetary incentives would influence different players in the online e-commerce market in the short term and in the long run. These three studies together contribute to the understanding of rating system/mechanism designs and different players in the online market.Dissertation/ThesisDoctoral Dissertation Business Administration 201