Higher privacy concerns were found to negatively influence both the frequency of ChatGPT use and the diversity of its applications, especially among users in organizations without GenAI policies.
Abstract
Generative Artificial Intelligence (GenAI) tools like ChatGPT, which can generate human-like responses from vast amounts of textual data, are increasingly transforming work routines across various fields, including education, healthcare, and IT. This integration, however, raises privacy concerns and questions the readiness of both environments and individuals. To investigate this issue, we conducted a user study with $N=224$ participants from a range of different employment sectors that have integrated ChatGPT into their work routines. We examined how proficiency in the utilization of ChatGPT, general privacy concerns, and organizational policies for GenAI usage impact users'actual ChatGPT usage and how these factors interact. Our findings reveal organizational policies are significantly positively associated with privacy-related ChatGPT proficiency, however, the overall proficiency is low. Higher privacy concerns were found to negatively influence both the frequency of ChatGPT use and the diversity of its applications, especially among users in organizations without GenAI policies.
As conversational artificial intelligence becomes increasingly embedded in digital service environments, understanding user resistance to chatbot-based information systems has become critical. While prior research has focused primarily on adoption drivers, less attention has been given to the mechanisms underlying resistance. Drawing on user resistance theory and human–computer interaction research, this study examines how intrapersonal factors (technological anxiety, perceived incompetence, and privacy concerns) and system-related factors (repetitiveness, impersonality, and irrelevance) jointly influence resistance and subsequent usage intention. Survey data from 400 chatbot users were analyzed using partial least squares structural equation modeling. The findings reveal that technological anxiety, perceived incompetence, impersonality, and irrelevance significantly increase resistance, which in turn reduces intention to continue use. By contrast, privacy concerns and repetitiveness show limited effects. By jointly modeling intrapersonal and system-related barriers within a single framework, the study clarifies their relative contribution to resistance and offers chatbot developers concrete guidance for reducing both psychological and interactional sources of user disengagement. The study advances understanding of resistance in artificial intelligence–based information systems and provides implications for improving interaction quality and sustaining user engagement.
Hyeon Jo, Jee-Sun Oh· Journal of information scien...· 0 citations
Investigating how individuals form a sense of identity around ChatGPT finds that regular interaction with ChatGPT is associated with the development of a distinct IT identity, and the presence of sufficient resources and organizational or contextual support significantly moderates this relationship, shaping user behaviors in meaningful ways.
Sofia Godinho, Tiago Oliveira, Catarina Neves· Journal of Enterprise Inform...· 0 citations
Background: Due to the increasing integration of chatbots into everyday digital systems, this study conducts a systematic literature review (SLR) to examine human–chatbot interactions. Purpose: The purpose of this study is to focus on the integration of artificial intelligence (AI) with human-computer interaction (HCI) principles to understand how chatbots are used across multiple contexts, how human factors and ethical considerations are addressed, and the impact of these interactions on UX and behaviour. Methods: The search query was applied across selected digital libraries to retrieve relevant studies. Results: From the reviewed studies, we found different types of chatbots used, the contexts of their use, how human and ethical factors are treated, user perceptions of the interactions, and broader implications for users’ lives. The results reveal that chatbots used across different domains with various purposes make user interaction vary significantly by the context of use, showing both positive and negative impacts and raising ethical concerns. Conclusion: Regarding open gaps, aspects such as the long-term impact of chatbots, the use of AI tools by people with physical disabilities, and non-generic UI/UX aspects were missing.
Unknown authors· Journal of Interactive Syste...· 0 citations
Chatbots powered by generative AI (e.g., OpenAI's ChatGPT and Google's Gemini) are increasingly being appropriated for emotional support and companionship. These tools offer a suite of security and privacy (S&P) controls, including model training opt-outs and memory toggles, yet how the presence of these controls influences users'attitudes toward emotionally sensitive disclosure remains understudied. We conducted a mixed-methods vignette study with 354 U.S. participants to examine how S&P controls influence users'willingness to engage with generative AI chatbots for emotional support, their perceptions of how protected they are when using these systems, and their perceptions of how effective the chatbots are for providing support. Controls enabling deletion of disclosures had the largest positive impact: these offerings outperformed technically sophisticated controls such as local-only processing and model training opt-outs, where participants expressed difficulty understanding the underlying mechanisms. Yet trust remains fragile, and participants often doubted S&P controls would function as promised. We conclude with actionable recommendations informed by our results to bridge users'comprehension gaps, build credible assurances, and properly calibrate barriers for users in distress.
Jabari Kwesi, Jiaxun Cao, Hailee Cunningham et al.· 0 citations
The release of ChatGPT, a generative artificial intelligence (AI) model, in November of 2022 marked a pivotal transition in how students use technology to learn. The ability of these models to process natural language inquiries and produce cogent, highly tailored answers has sparked serious concern among educators regarding its implications for academic integrity and the future of education. The current research surveyed (n = 845) college students in a cross-sectional design administered over the course of seven semesters and focused on students’ use of ChatGPT in the classroom and their views on the appropriateness of these actions. Results indicated that students’ awareness of ChatGPT rose rapidly following its release (Spring 2023: 58.5%; subsequent semesters: 81.8% to 100%). Cheating behavior (e.g., asking ChatGPT to write a final draft of a paper) increased over the course of data collection (r = 0.24, p < 0.001), with 33.9% of students reporting using the software sometimes to always to help with class assignments and 12.7% reporting using it to write essays/papers or parts of essays/papers due for class. The discussion focuses on key trends regarding student cheating behavior using ChatGPT over time as well as possible explanations for the changes in student usage.
Adelia C. Ehrlich, Christopher L. Groves, Luke J. Tacke et al.· Education sciences· 0 citations
This study investigated how college students’ use of ChatGPT has evolved from a tool for simply getting answers to one that supports motivation and learning engagement. Using a novel survey instrument based on Self-Determination Theory, we measured six types of motivation, three intrinsic, two extrinsic, and amotivation, across two cohorts of students (2023 and 2025). Results revealed statistically significant increases in both intrinsic and extrinsic motivation over time. While early use of ChatGPT focused on convenience and task completion, students increasingly reported using it to overcome mental blocks, build momentum, and stay engaged with their academic work. Follow-up analyses of repeat participants and comparisons by class standing suggest these trends reflect broader shifts in student engagement, not just cohort or experience differences. Cluster analysis revealed distinct motivational profiles, highlighting variation in how students incorporate ChatGPT into their academic routines. These findings suggest that as generative artificial intelligence becomes more familiar, accessible, and sophisticated, students are integrating it more deeply into their learning processes, not just for answers, but for sustained motivation and support.
Jayne Ann Harder, C. E. Klehm, A. Lang et al.· Artificial Intelligence and...· 0 citations