Human or Bot? How Tripartite Privacy Concerns Shape AI Service Adoption

Authors

La Toya M. Russell, John Carroll University
Neleen Leslie-Piper, John Carroll University

Document Type

Conference Proceeding

Publication Date

Spring 2026

Abstract

This study investigates how tripartite privacy concerns: online, offline, and AI-specific, shape consumer preferences for AI chatbots versus human representatives across five service sectors. Utilizing a large-scale U.S. sample, the research extends privacy calculus theory by conceptualizing privacy concern as a single trait.

Findings reveal that participants who prefer human interaction consistently report higher mean privacy concern scores than those who prefer AI. However, this “human preference” weakens as the sector's inherent risk increases. While significant differences exist in retail and travel, they disappear in high-stakes contexts like healthcare. The results suggest that while AI privacy concerns are unique, they are interrelated with traditional concerns, creating a “risk map” for firms. Ultimately, higher privacy concerns necessitate unique trade-offs depending on the service environment.

Recommended Citation

Russell, La Toya M. and Leslie-Piper, Neleen, "Human or Bot? How Tripartite Privacy Concerns Shape AI Service Adoption" (2026). AMTP Proceedings 2026. 63.
https://digitalcommons.georgiasouthern.edu/amtp-proceedings_2026/63