User Perceptions Of The Security Of Personal Health Data On Chatgpt In Indonesia
DOI:
https://doi.org/10.33751/jhss.v10i2.102Keywords:
ChatGPT, Data Privacy Calculus, Health Information, Willingness to ShareAbstract
This study aims to explore the relationship between perceptions of data security and privacy, awareness of cyberattacks, and other factors that influence users’ trust in ChatGPT and their willingness to provide personal information. Data was collected via a questionnaire and analyzed using SmartPLS. This study examines eight main variables, namely: Privacy Concern (PC), Perceived Effectiveness of Government Regulation (PEGR), Trust in ChatGPT (TR), Moral Motive (MM), Perceived Intelligence (PI), Perceived Risk (PR), Perceived Benefit (PB), and Sharing Willingness (SW). This study tested nine hypotheses regarding the influence of these variables. Data were collected via a questionnaire using a 1–5 Likert scale and analyzed using SmartPLS to test the relationships among constructs using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The analysis results indicate that four of the proposed hypotheses were supported, with p-values < 0.05, suggesting that the decision to share personal data is significantly influenced by perceived benefits, which are in turn influenced by perceptions of ChatGPT’s intelligence and trust in the platform. Perceived Risk does not have a significant impact on Sharing Willingness; however, privacy concerns influence users’ perceptions of risk. The results of this study indicate that the benefits of using ChatGPT, significantly influences their willingness to share personal health data. This study suggests that the sample size should be increased to conduct a more in-depth analysis of information-sharing decisions.
Keywords: ChatGPT, Data Privacy Calculus, Health Information, Willingness to Share
References
[1] M. Gupta, C. Akiri, K. Aryal, E. Parker, and L. Praharaj, “From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy,” 2023, Institute of Electrical and Electronics Engineers Inc. doi:
[2] Y. H. Yeo et al., “Assessing the performance of ChatGPT in answer- ing questions regarding cirrhosis and hepatocellu- lar carcinoma,” Clin. Mol. Hepatol., vol. 29, no. 3, pp. 721–732, Jul. 2023, doi: 10.3350/cmh.2023.0089.
[3] A. A. Jairoun, S. S. Al-Hemyari, M. Shahwan, G. R. Alnuaimi, N. Ibrahim, and A. A. S. Jaber, “Capturing pharmacists’ perspectives on the value, risks, and applications of ChatGPT in pharmacy practice: A qualitative study,” Exploratory Research in Clinical and Social Pharmacy, vol. 16, Dec. 2024, doi: 10.1016/j.rcsop.2024.100518.
[4] D. H. Hagos, R. Battle, and D. B. Rawat, “Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives,” IEEE Transactions on Artificial Intelligence, 2024, doi: 10.1109/TAI.2024.3444742.
[5] M. Charfeddine, H. M. Kammoun, B. Hamdaoui, and M. Guizani, “ChatGPT’s Security Risks and Benefits: Offensive and Defensive Use-Cases, Mitigation Measures, and Future Implications,” IEEE Access, vol. 12, pp. 30263–30310, 2024, doi: 10.1109/ACCESS.2024.3367792.
[6] J. woo Lee et al., “Development of AI-generated medical responses using the ChatGPT for cancer patients,” Comput. Methods Programs Biomed., vol. 254, Sep. 2024, doi: 10.1016/j.cmpb.2024.108302.
[7] U. Caglar et al., “Evaluating the performance of ChatGPT in answering questions related to pediatric urology,” Journal of Pediatric Urology (2024), vol. 20, Aug. 2023, doi: 10.1016/j.jpurol.2023.08.042.
[8] M. Gupta, C. Akiri, K. Aryal, E. Parker, and L. Praharaj, “From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy,” 2023, . doi: 10.1109/ACCESS.2023.3300381.
[9] A. Arista, L. Shuib, and M. A. Ismail, “A Glimpse of chatGPT: An Introduction of Features, Challenges, and Threads in Higher Education,” in 2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 694–698. doi: 10.1109/ICIMCIS60089.2023.10349057.
[10] D. H. Hagos, R. Battle, and D. B. Rawat, “Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives,” IEEE Transactions on Artificial Intelligence, 2024, doi: 10.1109/TAI.2024.3444742.
[11] G. Rani, J. Singh, and A. Khanna, “Comparative Analysis of Generative AI Models,” in 2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 760–765. doi: 10.1109/ICAICCIT60255.2023.10465941.
[12] M. Jozani, E. Ayaburi, M. Ko, and K. K. R. Choo, “Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective,” Comput. Human Behav., vol. 107, Jun. 2020, doi: 10.1016/j.chb.2020.106260.
[13] L. Baruh, E. Secinti, and Z. Cemalcilar, “Online Privacy Concerns and Privacy Management: A Meta-Analytical Review,” Journal of Communication, vol. 67, no. 1, pp. 26–53, Feb. 2017, doi: 10.1111/jcom.12276.
[14] E. Halim, A. F. Ikhsan, J. Suhartono, and M. Hebrard, “Gen-Z Awareness of Data Privacy Using Social Media,” in Proceedings of 2023 International Conference on Information Management and Technology, ICIMTech 2023, 2023, pp. 562–567. doi: 10.1109/ICIMTech59029.2023.10277920.
[15] J. Shi et al., “Factors Influencing the Sharing of Personal Health Data Based on the Integrated Theory of Privacy Calculus and Theory of Planned Behaviors Framework: Results of a Cross-Sectional Study of Chinese Patients in the Yangtze River Delta,” J. Med. Internet Res., vol. 25, 2023, doi: 10.2196/46562.
[16] D. Kim, K. Park, Y. Park, and J. H. Ahn, “Willingness to provide personal information: Perspective of privacy calculus in IoT services,” Comput. Human Behav., vol. 92, pp. 273–281, Mar. 2019, doi: 10.1016/j.chb.2018.11.022.
[17] K. Wach et al., “The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT,” Entrepreneurial Business and Economics Review, vol. 11, no. 2, pp. 7–30, Jun. 2023, doi: 10.15678/EBER.2023.110201.
[18] A. Almassaad, H. Alajlan, and R. Alebaikan, “Student Perceptions of Generative Artificial Intelligence: Investigating Utilization, Benefits, and Challenges in Higher Education,” Systems, vol. 12, no. 10, Oct. 2024, doi: 10.3390/systems12100385.
[19] J. Balakrishnan, S. S. Abed, and P. Jones, “The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?,” Technol. Forecast. Soc. Change, vol. 180, Jul. 2022, doi: 10.1016/j.techfore.2022.121692.
[20] C. Bartneck, D. Kulić, E. Croft, and S. Zoghbi, “Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots,” 2009, Kluwer Academic Publishers. doi: 10.1007/s12369-008-0001-3.
[21] J. W. Creswell and T. C. Guetterman, “Educational Research Planning, Conducting, and Evaluating Quantitative and Qualitative Research Sixth Edition,” 2019.
[22] E. Halim, A. F. Ikhsan, J. Suhartono, and M. Hebrard, “Gen-Z Awareness of Data Privacy Using Social Media,” in Proceedings of 2023 International Conference on Information Management and Technology, ICIMTech 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 562–567. doi: 10.1109/ICIMTech59029.2023.10277920.
