How Do Past Privacy Choices Shape the Future?
Release time：27 April 2026
Apr
29
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Time & Date
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15:30 pm
-
17:00 pm,
April
29,
2026
(Wednesday)
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| TOPIC | How Do Past Privacy Choices Shape the Future? |
| TIME&DATE | 03:30 pm -05:00 pm, April 29, 2026 (Wednesday) |
| Venue | Room D504, Teaching Complex D Building |
| Speaker | Verina Que Nanyang Technological University |
| Abstract | Consumers face frequent and consecutive digital privacy choices, but each choice is not necessarily independent. This paper demonstrates that past privacy choices affect consumers’ current privacy choices. Such state-dependent choices suggest that privacy choices can have externalities within a platform in which one app's data requests can affect the ability of other apps to collect data. Specifically, I use an individual-level consumer panel to investigate data consent decisions by consumers on a major digital platform connecting users and third-party apps. Leveraging a natural experiment that encourages users to accept data requests, I find that the probability of rejecting the next request declines 15%. This effect decays over time; it is larger when preferences for the app at that moment are relatively weak, categorized by large language models (LLMs); and for users with weaker privacy preferences, including younger, less educated, and shorter-tenure platform users. The effect does not differ by whether the specific data requested in consecutive data consent decisions is the same. Overall, these results suggest that the externalities arising from state-dependent data consent choices are temporary and operate at the margin. Nevertheless, this temporary effect incentivizes platforms to encourage apps to provide consumer-friendly data request designs. |
| Biography | Verina Que is a tenure-track Assistant Professor at Nanyang Business School, Nanyang Technological University (NTU) in Singapore. Her research centers on the economics of digital privacy. Specifically, she focuses on how consumers make privacy decisions and how those decisions shape firm strategy in an increasingly AI- and data-driven economy. Firms face a fundamental tension between data access and privacy protection. Her recent works address it by developing an economic framework for the benefit-cost tradeoffs of dataflows, highlighting externalities in consumer privacy choices. The results inform firms to navigate data strategy tradeoffs and draw policy implications for regulators. She applies tools such as causal inference, field experiments, structural modeling, and LLMs. |
