Paper Number
ECIS2026-2432
Paper Type
CRP
Abstract
Personalization has been a key strategy for increasing user adoption of web-based applications. However, extant research examines personalization after users share their data. We study the new phenomenon of upfront personalization – individual-level personalization that occurs without user-shared data, in the user acquisition phase of a web-based application, where the user first interacts with and evaluates the application before adoption. Using an online experiment with 314 homeowners, we find that upfront personalization does not result in higher adoption, challenging established norms. Using the theoretical lens of privacy calculus, we use explanations on privacy risks and benefits to investigate how users respond to upfront personalization. We find that users’ privacy calculus strongly influences user adoption, and users’ individual predispositions play a significant role. This study contributes by introducing and examining a new phenomenon, expanding the boundary conditions for personalization and privacy calculus, and providing directions on future research on upfront personalization.
Recommended Citation
Bhargava, Abhay and PRAT, Nicolas, "Upfront Personalization In Web Applications: Analyzing Its Impact On User Adoption Through The Lens Of Privacy Calculus" (2026). ECIS 2026 Proceedings. 21.
https://aisel.aisnet.org/ecis2026/is_adopt/is_adopt/21
Upfront Personalization In Web Applications: Analyzing Its Impact On User Adoption Through The Lens Of Privacy Calculus
Personalization has been a key strategy for increasing user adoption of web-based applications. However, extant research examines personalization after users share their data. We study the new phenomenon of upfront personalization – individual-level personalization that occurs without user-shared data, in the user acquisition phase of a web-based application, where the user first interacts with and evaluates the application before adoption. Using an online experiment with 314 homeowners, we find that upfront personalization does not result in higher adoption, challenging established norms. Using the theoretical lens of privacy calculus, we use explanations on privacy risks and benefits to investigate how users respond to upfront personalization. We find that users’ privacy calculus strongly influences user adoption, and users’ individual predispositions play a significant role. This study contributes by introducing and examining a new phenomenon, expanding the boundary conditions for personalization and privacy calculus, and providing directions on future research on upfront personalization.
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