Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency

Üzeyir Fidan

doi:10.26466/opusjsr.1725180

EN TR

Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency

Öz

The increasing dependence on AI-supported services raises important questions about how positive beliefs about AI can turn into privacy risks. This study tests a gender-moderated mediation model of AI attitude, AI dependence, and online privacy concern (OPC) among Turkish university students. A cross-sectional survey conducted on 478 students using validated scales (AIAS-4, AI Dependency Scale, OPC Scale) was analyzed using structural equation modeling and the PROCESS Model 59. The measurement model demonstrated excellent fit (χ²/df = 1.01, CFI = 0.999, RMSEA = 0.005) and strong reliability-validity indicators. AI attitude significantly increased AI dependency (β = .50, p < .001), which in turn strengthened OPC (β = .77, p < .001). Gender moderates both relationships and reveals a significant moderator-mediation index (−.11; 95% CI [−.21, −.01]). Overall, the model explains 28% of the variance in OPC. The findings reveal a two-way effect of positive AI attitudes: while promoting beneficial participation, they also increase dependency-based privacy concerns, particularly among female users. Organizations should integrate privacy-aware AI literacy and gender-sensitive feedback mechanisms into digital platforms to mitigate risks while maintaining trust.

Anahtar Kelimeler

digitalization, AI attitude, AI dependence, online privacy concern

Dijital Çağda Bireysel Gizlilik Algısı: Yapay Zekâ Tutumu ve Bağımlılığının Etkileşimi

Öz

YZ destekli hizmetlere olan bağımlılığın artması, YZ hakkındaki olumlu inançların nasıl gizlilik risklerine dönüştüğü konusunda önemli sorular ortaya çıkarmaktadır. Bu çalışma, Türk üniversite öğrencileri arasında YZ tutumu, YZ bağımlılığı ve çevrimiçi gizlilik endişesi (OPC) arasında cinsiyete bağlı, ılımlı arabuluculuk modelini test etmektedir. 478 öğrenci üzerinde yapılan kesitsel bir anket, geçerliliği kanıtlanmış ölçekler (AIAS-4, YZ Bağımlılık Ölçeği, OPC ölçeği) kullanılarak gerçekleştirilmiş ve yapısal eşitlik modellemesi, PROCESS Model 59 ile analiz edilmiştir. Ölçüm modeli mükemmel uyum (χ²/df = 1,01, CFI = 0,999, RMSEA = 0,005) ve güçlü güvenilirlik-geçerlilik göstergeleri sergilemiştir. YZ tutumu, YZ bağımlılığını önemli ölçüde artırmakta (β = .50, p < .001), bu da OPC'yi güçlendirmektedir (β = .77, p < .001). Cinsiyet, her iki bağlantıyı da moderatör olarak etkilemekte ve önemli bir moderatör-aracılık indeksi (−.11; %95 CI [−.21, −.01]) ortaya çıkarmıştır. Genel olarak, model OPC varyansının %28'ini açıklamıştır. Bulgular, olumlu YZ tutumlarının iki yönlü bir etkisi olduğunu ortaya koymaktadır: faydalı katılımı teşvik ederken, özellikle kadın kullanıcılar arasında bağımlılığa dayalı gizlilik kaygılarını artırmaktadır. Kurumlar, güveni sürdürürken ortaya çıkan riskleri azaltmak için dijital platformlara gizlilik bilincine sahip YZ okuryazarlığı ve cinsiyete duyarlı geri bildirim mekanizmaları entegre etmelidir.

Anahtar Kelimeler

dijitalleşme, yapay zeka tutumu, yapay zeka bağımlılığı, çevrimiçi gizlilik endişesi

Kaynakça

Alakurt, T. (2017). Çevrimiçi mahremiyet kaygısı ölçeğinin Türk kültürüne uyarlanması. Pegem Eğitim ve Öğretim Dergisi, 7(4), 611-636.
Barnes, S. J., & Pressey, A. D. (2012). In search of the “privacy paradox”: Privacy concerns and willingness to disclose in online social networks. Journal of Business Research, 66(9), 1528–1535. https://doi.org/10.1016/j.jbus-res.2012.02.015
Bayor, L., Weinert, C., Maier, C., & Weitzel, T. (2025). Social-oriented communication with AI companions: Benefits, costs, and contextual patterns. Business & Information Systems Engineering, 67(4), 1–19. https://doi.org/10.1007/s12599-025-00955-1
Buchanan, T., Paine, C., Joinson, A. N., & Reips, U. D. (2007). Development of measures of online privacy concern and protection for use on the Internet. Journal of the American Society for Information Science and Technology, 58(2), 157–165. https://doi.org/-10.1002/asi.20459
Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and programming (1st ed.). New York, NY: Routledge. https://doi.org/10.4324/97802-03807644
Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling, 9(2), 233–255. http://doi.org/10.-1207/S15328007SEM0902_5
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Degeling, M., Lentzsch, C., Nolte, A., Herrmann, T., & Loser, K. U. (2016, November). Privacy by socio-technical design: A collaborative approach for privacy friendly system design. In 2016 IEEE 2nd International Conference on Collaboration and Internet Computing (CIC) (pp. 502-505). IEEE. https://doi.org/10.1109/CIC.2016.077
Elliott, D., & Soifer, E. (2022). AI technologies, privacy, and security. Frontiers in Artificial Intelligence, 5, 826737. https://doi.org/10.-3389/frai.2022.826737
Emon, M. M. H., Khan, T., Rahman, M. A., & Siam, S. A. J. (2024, September). Factors influencing the usage of artificial intelligence among Bangladeshi professionals: Mediating role of attitude towards the technology. In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS) (pp. 1-7). IEEE. https://doi.org/10.-1109/COMPAS60761.2024.10796110

Fogel, J., & Nehmad, E. (2009). Internet social network communities: Risk taking, trust, and privacy concerns. Computers in Human Behavior, 25(1), 153–160. http://dx.doi.org/-10.1016/j.chb.2008.08.006
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
Fossa, F. (2025). Artificial intelligence and human autonomy: the case of driving automation. AI & Soc, 40, 1851–1862. https://doi.org/10.1007/s00146-024-01955-7
Golda, A., Mekonen, K., Pandey, A., Singh, A., Hassija, V., Chamola, V., & Sikdar, B. (2024). Privacy and security concerns in generative AI: a comprehensive survey. IEEE Access. https://doi.org/10.1109-/ACCESS.2024.3381611
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in psychology, 14, 1191628. https://doi.org/10.3389/fpsyg.-2023.1191628
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2020). Multivariate data analysis (9th ed.). Harlow, England: Pearson.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. http://doi.org/10.1007/s11747-014-0403-8
Herbert, F., Becker, S., Schaewitz, L., Hielscher, J., Kowalewski, M., Sasse, A., ... & Dürmuth, M. (2023, April). A world full of privacy and security (mis) conceptions? findings of a representative survey in 12 countries. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-23). https://doi.org/10.1145/3544548.358141
Herriger, C., Merlo, O., Eisingerich, A. B., & Arigayota, A. R. (2025). Context-Contingent Privacy Concerns and Exploration of the Privacy Paradox in the Age of AI, Augmented Reality, Big Data, and the Internet of Things: Systematic Review. Journal of Medical Internet Research, 27, e71951. https://doi.org/10.2196/71951
Hoy, M. G., & Milne, G. (2010). Gender differences in privacy-related measures for young adult Facebook users. Journal of interactive advertising, 10(2), 28-45. https://doi.org/10.-1080/15252019.2010.10722168
Hu, L.T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. http://doi.org/10.1080/10705519909540118
Hyra, A., & Premti, F. (2024). The Double-Edged Sword of AI in Cybersecurity: Boosting Security While Addressing Privacy Risks. Smart Cities and Regional Development (SCRD) Preprints, 1(1).
Ibrahim, F., Münscher, J. C., Daseking, M., & Telle, N. T. (2025). The technology acceptance model and adopter type analysis in the context of artificial intelligence. Frontiers in Artificial Intelligence, 7, 1496518. https://doi.org/10.3389/frai.2024.1496518
Kline, R. (2013). Exploratory and confirmatory factor analysis. In Applied quantitative analysis in education and the social sciences. 171-207. Routledge.
Kosinski, M., Khambatta, P., & Wang, Y. (2024). Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation.American Psychologist, 79(7), 942–955. https://doi.org/10.1037/amp-0001295
Li, X., & Zhang, T. (2017, April). An exploration on artificial intelligence application: From security, privacy and ethic perspective. In 2017 IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (ICCCBDA) (pp. 416-420). IEEE. https://doi.org/10.1109/ICCCBDA.2017.7951949
Little, R. J. A., & Rubin, D. B. (2019). Statistical analysis with missing data (3rd ed.). Hoboken, NJ: Wiley. https://doi.org/10.1002-/9781119482260
Maphosa, V. (2024). The rise of artificial intelligence and emerging ethical and social concerns. AI, Computer Science and Robotics Technology. https://doi.org/10.5772/acrt.-20240020
Menard, P., & Bott, G. J. (2025). Artificial intelligence misuse and concern for information privacy: New construct validation and future directions. Information Systems Journal, 35(1), 322-367. https://doi.org/10.1111-/isj.12544
Morales-García, W. C., Sairitupa-Sanchez, L. Z., Morales-García, S. B., & Morales-García, M. (2024, March). Development and validation of a scale for dependence on artificial intelligence in university students. In Frontiers in Education (Vol. 9, p. 1323898). Frontiers Media SA. https://doi.org/10.-3389/feduc.2024.1323898
Nigam, A., Pasricha, R., Singh, T., & Churi, P. (2021). A systematic review on AI-based proctoring systems: Past, present and future. Education and Information Technologies, 26(5), 6421-6445. https://doi.org/10.-1007/s10639-021-10597-x
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569. http://doi.org/10.1146/annurev-psych-120710-100452
Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. http://doi.org-/10.3758/BRM.40.3.879
Rohden, S. F., & Zeferino, D. G. (2023). Recommendation agents: an analysis of consumers’ risk perceptions toward artificial intelligence. Electronic Commerce Research, 23(4), 2035-2050. https://doi.org/10.1007/s10660-022-09626-9
Said, N., Potinteu, A. E., Brich, I., Buder, J., Schumm, H., & Huff, M. (2023). An artificial intelligence perspective: How knowledge and confidence shape risk and benefit perception. Computers in Human Behavior, 149, 107855. https://doi.org/10.1016-/j.chb.2023.107855
Satici, S. A., Okur, S., Yilmaz, F. B., & Grassini, S. (2025). Psychometric properties and Turkish adaptation of the artificial intelligence attitude scale (AIAS-4): evidence for construct validity. BMC Psychology, 13(1), 1-14. https://doi.org/10.1186/s40359-025-02505-6
Savaş, B. Ç. (2024). Yapay Zekâya Bağımlılık Ölçeğinin Türkçe’ye Uyarlanması: Geçerlik ve Güvenirlik Çalışması. Herkes için Spor ve Rekreasyon Dergisi, 6(3), 306-315. https://doi.org/10.56639/jsar.1509301
Shrestha, A. K., Barthwal, A., Campbell, M., Shouli, A., Syed, S., Joshi, S., & Vassileva, J. (2024). Navigating AI to unpack youth privacy concerns: An in-depth exploration and systematic review. arXiv preprint arXiv:2412.16369. https://doi.org/10.48550-/arXiv.2412.16369
Tifferet, S. (2019). Gender differences in privacy tendencies on social network sites: A meta-analysis. Computers in Human Behavior, 93, 1-12. https://doi.org/10.1016/j.chb-.2018.11.046
Vasconcelos, H., Jörke, M., Grunde-McLaughlin, M., Gerstenberg, T., Bernstein, M. S., & Krishna, R. (2023). Explanations can reduce over-reliance on AI systems during decision-making. Proceedings of the ACM on Human–Computer Interaction, 7(CSCW2), 1–38. https://doi.org/10.-1145/3579605
Yadrovskaia, M., Porksheyan, M., Petrova, A., Dudukalova, D., & Bulygin, Y. (2023). About the attitude towards artificial intelligence technologies. In E3S Web of Conferences (Vol. 376, p. 05025). EDP Sciences. https://doi.org/10.1051/e3sconf/202337605025
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11(28), 1–24. https://doi.org/10.1186/s40561-024-00316-7
Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behaviour. International Journal of Educational Technology in Higher Education, 21(34), 1–26. https://doi.org/10.1186/s41239-024-00467-0
Zhang, X., Hu, J., & Zhou, Y. (2025). The role of perceived utility and ethical concerns in the adoption of AI-based data analysis tools: A multi-group structural equation model analysis among academic researchers. Education and Information Technologies, 1-33. https://doi.org/10.1007/s10639-025-13535-3

Ayrıntılar

Birincil Dil

İngilizce

Konular

Strateji, Yönetim ve Örgütsel Davranış (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Üzeyir Fidan ^*
0000-0003-3451-4344
Türkiye

Erken Görünüm Tarihi

28 Eylül 2025

Yayımlanma Tarihi

30 Eylül 2025

Gönderilme Tarihi

23 Haziran 2025

Kabul Tarihi

30 Ağustos 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 22 Sayı: 5

DOI

https://doi.org/10.26466/opusjsr.1725180

IZ

https://izlik.org/JA78HS76SX

APA

Fidan, Ü. (2025). Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency. OPUS Journal of Society Research, 22(5), 869-881. https://doi.org/10.26466/opusjsr.1725180

AMA

1.Fidan Ü. Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency. OPUS TAD. 2025;22(5):869-881. doi:10.26466/opusjsr.1725180

Chicago

Fidan, Üzeyir. 2025. “Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency”. OPUS Journal of Society Research 22 (5): 869-81. https://doi.org/10.26466/opusjsr.1725180.

EndNote

Fidan Ü (01 Eylül 2025) Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency. OPUS Journal of Society Research 22 5 869–881.

IEEE

[1]Ü. Fidan, “Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency”, OPUS TAD, c. 22, sy 5, ss. 869–881, Eyl. 2025, doi: 10.26466/opusjsr.1725180.

ISNAD

Fidan, Üzeyir. “Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency”. OPUS Journal of Society Research 22/5 (01 Eylül 2025): 869-881. https://doi.org/10.26466/opusjsr.1725180.

JAMA

1.Fidan Ü. Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency. OPUS TAD. 2025;22:869–881.

MLA

Fidan, Üzeyir. “Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency”. OPUS Journal of Society Research, c. 22, sy 5, Eylül 2025, ss. 869-81, doi:10.26466/opusjsr.1725180.

Vancouver

1.Üzeyir Fidan. Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency. OPUS TAD. 01 Eylül 2025;22(5):869-81. doi:10.26466/opusjsr.1725180

Individual Privacy Perception in the Digital Age: The Interaction of Artificial Intelligence Attitude and Dependency

Öz

Anahtar Kelimeler

Dijital Çağda Bireysel Gizlilik Algısı: Yapay Zekâ Tutumu ve Bağımlılığının Etkileşimi

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster