Selected Publications
Targeted Advertising as Implicit Recommendation: Strategic Mistargeting and Personal Data Opt-Out
( Z. Eddie Ning | Jiwoong Shin | Jungju Yu )MARKETING SCIENCEforthcoming
Abstract
We study an advertiser’s targeting strategy and its effects on consumer data privacy choices, both of which determine the advertiser’s targeting accuracy. Targeted ads, serving as implicit recommendations when consumer preferences are uncertain, not only influence the consumer’s beliefs and purchasing decisions but also amplify the advertiser’s temptation towards strategic mistargeting—sending ads to poorly matched consumers. Our analysis reveals that advertisers may, paradoxically, choose less precise targeting as accuracy improves. Even if prediction is perfect, the advertiser still targets the wrong consumers, leading to strategic mistargeting. Nev-ertheless, consumer surplus can remain positive due to improved identification of well-matched consumers, thereby reducing the incentive for consumers to withhold information. However, the scenario shifts with endogenous pricing; better prediction leads to more precise targeting, although mistargeting persists. To exploit the recommendation effect of advertising, the ad-vertiser raises prices instead of diluting recommendation power, lowering consumer welfare and prompting consumers to opt out of data collection. Furthermore, we investigate the impact of consumer data opt-out decisions under varying privacy policy defaults (opt-in vs. opt-out). These decisions significantly affect equilibrium outcomes, influencing both the advertiser’s tar-geting strategies and consumer welfare. Our findings highlight the complex relationship between targeting accuracy, privacy choices, and advertisers’ incentives.
Physical Friction and Digital Banking Adoption
( Choi, Hyun-Soo | Loh, Roger )MANAGEMENT SCIENCEforthcoming
Abstract
The behavioral literature suggests that minor frictions can elicit desirable behavior without obvious coercion. Using closures of ATMs in a densely populated city as an instrument for small frictions to physical banking access, we find that customers affected by ATM closures increase their usage of the bank’s digital platform. Other spillover effects of this adoption of financial technology include increases in point-of-sale transactions, electronic fund transfers, automatic bill payments, and savings, and a reduction in cash usage. Our results show that minor frictions can help overcome the status quo bias and facilitate significant behavior change
Leveraging the Digital Tracing Alert in Virus Fight: The Impact of COVID-19 Cell Broadcast on Population Movement
( Ghose, Anindya | Lee, Heeseung Andrew | Oh, Wonseok | Son, Yoonseock )INFORMATION SYSTEMS RESEARCHforthcoming
Abstract
Digital tracing alerts have emerged as an effective means to share information with agility in responding to disaster outbreaks. Governments are able to instantaneously coordinate the available information to provide information related to the disaster and promote preventive actions. However, despite the opportunities granted by these innovative technologies in managing disasters, privacy concerns can arise regarding how much of individuals' private information should be collected and disclosed. With these considerations, we examine the extent to which instant digital tracing alerts and the information included in the alerts affect people's actions toward disaster management in the context of South Korea. We leverage 4,029,696 subdistrict and hour level data set, including population movement and digital tracing alert transmission information. Our results show that digital tracing alerts are effective in inducing population movement out of the infected area and decreasing the population density. Specifically, instant messaging induces movement among 2.45% of an infected district's population to other administrative areas in a given hour and decreases population density by 3.68%. Furthermore, the effectiveness of digital tracing alerts hinges on the inclusion of different private information of individuals on case confirmation. We find the heterogeneous effect of digital alerts, with the effects being more pronounced among young and male individuals and in business-centric areas. Further analysis reveals that digital tracing alerts are particularly effective at the early stage of the disaster. In addition, sending more than three messages within a day has a valid counter-effect (i.e., fatigue effects), whereas messages sent when the cumulative number of confirmed cases is high exert a less positive effect than when the verified cases are low (i.e., desensitization effects). Our results provide policy makers and law enforcement with novel insights into whether and how the use of information technology can facilitate disaster management and to what extent they should collect and expose private information to effectively safeguards public health and safety during a crisis.
Agency Frictions and Procurement: New Evidence from U.S. Electricity Restructuring
( Abito, Jose Miguel | Han, Jin Soo | Houde, Jean-Francois | van Benthem, Arthur A. )JOURNAL OF INDUSTRIAL ECONOMICSforthcoming
Abstract
This article presents new quantitative evidence of the sources of efficiency benefits from deregulation. We estimate the heterogeneous effects of plant divestitures on fuel procurement costs during the restructuring of the U.S. electricity industry. Guided by economic theory, we focus on three mechanisms and find that restructuring reduced fuel procurement costs for firms that (i) were not subject to earlier incentive-regulation programs, (ii) had relatively strong bargaining power as coal purchasers after restructuring, and (iii) were locked in with disadvantaged coal contracts prior to restructuring.
Robust risk quantification via shock propagation in financial networks
( Ahn, Dohyun | Chen, Nan | Kim, Kyoung-Kuk )OPERATIONS RESEARCHforthcoming
Abstract
Given limited network information, we consider robust risk quantification under the Eisenberg-Noe model for financial networks. To be more specific, motivated by the fact that the structure of the interbank network is not completely known in practice, we propose a robust optimization approach to obtain worst-case default probabilities and associated capital requirements for a specific group of banks (e.g., systemically important financial institutions) under network information uncertainty. Using this tool, we analyze the effects of various incomplete network information structures on these worst-case quantities and provide regulatory insights into the collection of actionable network information. All claims are numerically illustrated using data from the European banking system.