Research
Research
Gratuities in a Digital Services Marketplace
Kim, Seung Hyun, On Amir, Kenneth C. Wilbur
Platforms increasingly request customers to consider leaving tips, which are defined as voluntary, post-transaction payments to service providers. We report the first large field experiments (310,737 transactions in total) that investigated how three types of tip request messages—Impersonal Reciprocity, Personalized Reciprocity, and injunctive Norms—influence tipping decisions relative to a control message. The empirical context is a purely-digital freelance marketplace with globally-distributed buyers and sellers. For first-time buyers, the Norms message increased tipping rates by 49% on the web (from 14.6% to 21.8%) and by 18% in the app (from 9.9% to 11.6%) relative to control, yet most transactions remained untipped. The Norms message was particularly effective in five-star transactions. Neither of the Reciprocity messages produced a statistically significant change. The data show no significant spillover effect of the Norms message on subsequent platform usage behavior for either buyers or sellers. Collectively, the results suggest that digital platforms can meaningfully increase tipping by adopting injunctive norm-based messages without harming platform engagement, offering actionable insights for platform design and theoretical support for tipping antecedents.
Did Early ChatGPT-4 Adopters Change Payments to Other Digital Services? Exploratory Evidence from a Consumer Spending Panel
Kim, Seung Hyun, Daniel McCarthy, Kenneth C. Wilbur
Management wisdom dictates that customers "hire" products and services to do "jobs." Does generative artificial intelligence threaten other digital services' "jobs"? We investigate consumer spending data to understand how early ChatGPT-4 subscribers changed their spending on other digital services. We employ Coarsened Exact Matching to balance early adopters with later adopters based on pre-ChatGPT-4 payment data. Then we use a triple-difference identification strategy to predict adopters' counterfactual payments to other digital services. We find two main patterns. First, early ChatGPT-4 adopters tended to also pay for other artificial intelligence services. Second, the estimates rule out market share declines larger than 2% for nearly all other digital service brands and categories. We interpret these non-findings as null effects, providing early evidence that initial generative artificial intelligence technologies did not seriously undermine other digital services' commercial potential.
Designing Distributed Ledger Technologies, Like Blockchain, for Advertising Markets
Mingyu Joo, Seung Hyun Kim, Anindya Ghose, Kenneth C. Wilbur
Published at IJRM (2023) Link
Distributed Ledger Technologies (DLTs), like Blockchain, could help improve brand safety, consumer privacy and transparency in digital advertising. However, paid advertisements transfer attention, money, and data between three parties: advertiser, consumer, and publisher. Therefore advertising-focused DLTs face more complex design considerations than currency-focused DLTs. We describe four key DLT characteristics: structure, participation/governance, transparency, and terms of exchange. We survey current advertising-focused DLTs and find they each serve only two of the three contracting parties in advertising transactions. We make design recommendations for future advertising-focused DLTs, including a goal of serving both consumers and publishers in addition to advertisers. We also recommend governance, transparency and terms-of-trade considerations. Advertising-focused DLTs have significant promise but also significant obstacles, including multi-sided “chicken-and-egg” problems in standards adoption.