The Role of User Empowerment, AI Hallucination, and Privacy Concerns in Continued Use and Premium Subscription Intentions: An Extended Technology Acceptance Model for Generative AI
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This study extends the Technology Acceptance Model for Generative AI (GAI) by incorporating perceived user empowerment (PUE) and two risk factors – privacy concerns and hallucination risks. A survey of 783 U.S. GAI users revealed that PUE directly influenced continuance intention (CI) and willingness to pay for premium service (PAY) and indirectly affected CI through attitude toward AI (ATTAI). Perceived ease of use was indirectly linked to CI via ATTAI. Privacy concerns negatively affected CI, while hallucination risks negatively influenced ATTAI, which reduced CI. The findings underscore the role of PUE and risk perceptions in CI and PAY for GAI.
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Perceived AI hallucination risk was assessed with three items on a five-point Likert scale adapted from Lim et al. (2025), with Cronbach's alpha of .82.
“Perceived AI hallucination risk was assessed using three items on a five-point Likert scale adapted from Lim et al. (2025). Respondents were asked how much they agreed with statements regarding the accuracy of AI-generated health information. A sample item includes, 'I am skeptical about whether Generative AI outputs are based on facts or are fabricated' (M = 3.33, SD = .78, α = .82).”
From Perceived Search Overload, Generative AI Credibility, and Comparative Usefulness: A Channel Complementarity Approach to Health Information Seeking (2025) · cites this paper as Lim et al. (2025)
Continuance intention was measured with three items on a five-point Likert scale adapted from Lim et al. (2025), with Cronbach's alpha of .87.
“Respondents were asked how likely they are to continue using AI for general health information searches using three items on a five-point Likert scale adapted from Lim et al. (2025). A sample item includes, 'I plan to continue using generative AI for health-related searches in the future.' (M = 3.55, SD = 1.10, α = .87).”
From Perceived Search Overload, Generative AI Credibility, and Comparative Usefulness: A Channel Complementarity Approach to Health Information Seeking (2025) · cites this paper as Lim et al. (2025)
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- Authors
- Lim, J. S., Shin, D., Lee, C., Kim, J., & Zhang, J.
- Affiliations
- Syracuse University (US)
- Texas Tech University (US)
- University of North Florida (US)
- Middle Tennessee State University (US)
- Corresponding author
- Lim, Joon Soo
- Venue
- Journal of Broadcasting & Electronic Media
- Publisher
- Informa UK Limited
- Published
- 2025-04-12
- Volume
- 69
- Issue
- 3
- Pages
- 183-199
- ISSN
- 0883-8151 (ISSN-L)
- 1550-6878
- OpenAlex
- W4409380988
- Cited by
- 32
- References
- 50
- Citations by year
- 2026: 31 · 2025: 1
- FWCI
- 31.86
- Type
- journal article
- Language
- en
- Update notices
- correction (2025-06-24)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Broadcasting & Electronic Media (publishedVersion, cc-by-nc-nd)
- Funders
- 2023 Page Legacy Scholar (2023DIG02)
- Arthur W. Page Center at the Penn State College of Communications
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Keywords
- generative AI, user empowerment, AI hallucination, privacy concerns, continuance intention, willingness to pay, premium subscription, attitudes toward AI, perceived ease of use, technology acceptance model, risk perception
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