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The Impact of Personalization and Transparency on Trust in Generative AI: The Mediating Role of Data Control
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| 1.71 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
As generative AI systems such as ChatGPT become increasingly integrated into daily digital
interactions, users increasingly express concerns about their ability to manage privacy settings
in generative AI applications. As a result, ensuring user control over personal data and building
trust in AI interactions has become a critical challenge. Grounding on the Stimulus–Organism–
Response (SOR) model, this study investigates how personalization and privacy transparency
influence user trust and continuous intention to use generative AI, with a focus on the
mediating role of perceived data control. The findings demonstrate that privacy concerns
significantly strengthen the impact of transparency on perceived data control, and indirectly
on trust via perceived data control. Personalization was found to positively influence trust,
although it had no direct effect on users’ continuous intention to use generative AI. Trust, in
turn, emerged as a key predictor of continued use. Perceived benefits showed strong direct
effects on both trust and continuous use, and, unexpectedly, reduced the reliance on trust
when predicting long-term engagement. The model and its hypotheses were empirically
tested through a survey of 250 generative AI users residing in Europe.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems Management
Palavras-chave
Data Control Generative Artificial Intelligence Personalization Privacy Concerns Transparency Trust SDG 9 - Industry, innovation and infrastructure SDG 12 - Responsible production and consumption SDG 16 - Peace, justice and strong institutions