Data Privacy Protection, Perceived Financial Risk, and Trust in Artificial Intelligence as Determinants of Smart Device Brand Purchase Intention: The Moderating Role of Digital Literacy

Authors

DOI:

https://doi.org/10.51738/kpolisa.2026.2r.ssjza

Keywords:

Artificial Intelligence, Data Privacy Protection, Financial Risk, Digital Literacy, Smart Device Brands, Purchase Intention

Abstract

The increasing integration of artificial intelligence into smart devices offers numerous benefits to consumers while simultaneously emphasizing the importance of the legal protection of data privacy and the security of personal information. In this context, the aim of this study is to examine the effects of perceived data privacy protection, perceived financial risk, and trust in artificial intelligence on consumers' purchase intention toward smart device brands. Furthermore, digital literacy is examined as a moderating variable in the relationships between all independent variables and purchase intention. The empirical study was conducted in Serbia in early 2026 using a structured questionnaire administered to a sample of 242 respondents. The proposed conceptual model was tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) implemented in SmartPLS 4. The findings confirmed that perceived effective legal protection of data privacy and trust in artificial intelligence exert statistically significant positive effects on consumers' purchase intention toward smart device brands, whereas perceived financial risk has a statistically significant negative effect. In addition, digital literacy was found to significantly moderate the relationships between data privacy protection, perceived financial risk, and trust in artificial intelligence, on the one hand, and purchase intention toward smart device brands, on the other. This study contributes to the literature by integrating legal, economic, and technological determinants into a unified consumer behavior framework and demonstrates that the establishment of a reliable legal framework for data privacy protection represents one of the key prerequisites for fostering consumer acceptance of artificial intelligence-based products.

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References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Aleisa, N., & Renaud, K. (2020). The privacy paradox applies to IoT devices too: A Saudi Arabian study. Computers & Security, 96, 101882.

Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior: A systematic literature review. Telematics and Informatics, 34(7), 1038–1058.

Bjelajac, Ž., Filipović, A., & Stošić, L. (2022). Internet Addiction Disorder (IAD) as a Consequence of the Expansion of Information Technologies. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 10(3), 155–165.

Bjelajac, Ž., Filipović, A., & Stošić, L. (2023). Can AI be Evil: The Criminal Capacities of ANI. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), 11(3), 519–531.

Borgert, N., Hussey, I., Jansen, L., & Elson, M. (2025). Do I value my private data? Exploring modeldriven predictions on the willingness to use smart home devices. Media Psychology, 29(1), 142–170.

Cannizzaro, S., et al. (2020). Trust in the smart home: Findings from a nationally representative survey in the UK. PLoS ONE, 15(5), e0231615.

Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers' attitudes and behavioral intentions toward using artificial intelligence for organizational decision-making. Technovation, 106, 102312.

Chatterjee, S., Rana, N.P., Tamilmani, K. and Sharma, A. (2021) The Role of Artificial Intelligence in Reshaping Digital Marketing: A Review and Research Agenda. Journal of Business Research, 124, 202-217.

Choung, H., David, P., & Ross, A. (2023). Trust in AI and its role in the acceptance of AI technologies. International Journal of Human–Computer Interaction, 1-13.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., et al. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human–Computer Studies, 59(4), 451–474.

Fornell, C. and Larcker, D. F. (1981). Evaluating structural equation models with un-observable variables and measurement error. Journal of Marketing Research, Vol. 18 No. 1, pp. 39–50.

Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping. MIS Quarterly, 27(1), 51–90.

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169.

Hasan, R., Shams, R., & Rahman, M. (2021). Consumer trust and perceived risk for voice-controlled artificial intelligence: The case of Siri. Journal of Business Research, 131, 591–597.

Jaspers, E. D. T., & Pearson, E. (2022). Consumers' acceptance of domestic Internet-of-Things: The role of trust and privacy concerns. Journal of Business Research, 142, 255–265.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information Systems Research, 15(4), 336–355.

Mariani, M., Machado, I., Magrelli, V. & Dwivedi, Y. (2023). Artificial intelligence innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.

Nunnally, J.C. (1978). Introduction to Psychological Measurement. New York, NY: McGraw-Hill.

Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3), 101–134.

Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science. 48(1), 137–141.

Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human–Computer Studies, 146, 102551.

Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989–1015.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178.

Xu, H., Teo, H. H., Tan, B. C. Y., & Agarwal, R. (2012). Effects of individual self-protection, industry selfregulation, and government regulation on privacy concerns: A study of location-based services. Information Systems Research, 23(4), 1342–1363.

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Published

2026-10-03

How to Cite

Sinanović, V., Spalevic, Z., Jovanović, A., Zdravković, S., & Arsenijević, D. (2026). Data Privacy Protection, Perceived Financial Risk, and Trust in Artificial Intelligence as Determinants of Smart Device Brand Purchase Intention: The Moderating Role of Digital Literacy. KULTURA POLISA, 23(2), 1–15. https://doi.org/10.51738/kpolisa.2026.2r.ssjza

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