The Relationship between Privacy Invasion of Smart Services and Consumers' Intention to Use

Authors

  • Yiqin Wang

DOI:

https://doi.org/10.54097/nwtjx759

Keywords:

Smart Service; Privacy Invasion; Intention to Use.

Abstract

While the wide application of smart technology in services brings efficiency and convenience to consumers, it also can't avoid bringing some negative impacts. Nowadays, the problem of privacy invasion raises concerns about the issues of smart services and directly affects consumers' attitudes towards the use of smart services. This study aims to explore the impact of privacy invasion in smart services on consumers' intention to use them. The results of the study emphasize that privacy invasion has a significant negative impact on consumers' intention to use smart services. The conclusions of the study play a theoretical role for enterprises to grasp the psychology of smart service consumers and improve the mode and process of smart services.

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References

Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services, 66, 102900.

Pantano E, Pizzi G. Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis[J]. Journal of Retailing and Consumer Services, 2020, 55: 102096.

Malodia, S., Islam, N., Kaur, P., & Dhir, A. (2021). Why do people use Artificial Intelligence (AI)-enabled voice assistants?. IEEE Transactions on Engineering Management, 71, 491-505.

Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961-974.

Olson, C., & Kemery, K. (2019). 2019 Voice report: Consumer adoption of voice technology and digital assistants. Retrieved from https://about.ads.microsoft.com/enus/insights/2019-voice-report

Whitman, M. V., Halbesleben, J. R., & Holmes IV, O. (2014). Abusive supervision and feedback avoidance: The mediating role of emotional exhaustion. Journal of organizational behavior, 35(1), 38-53.

Freedy, J. R., & Hobfoll, S. E. (1994). Stress inoculation for reduction of burnout: A conservation of resources approach. Anxiety, Stress & Coping, 6(4), 311–325.

Chen, S., Westman, M., & Hobfoll, S. E. (2015). The commerce and crossover of resources: Resource conservation in the service of resilience. Stress and Health, 31(2), 95-105.

Menasco, M. B., & Hawkins, D. I. (1978). A field test of the relationship between cognitive dissonance and state anxiety. Journal of Marketing Research, 15(4), 650-655.

Haenlein, M., Huang, M. H., & Kaplan, A. (2022). Guest editorial: Business ethics in the era of artificial intelligence. Journal of Business Ethics, 178(4), 867-869.

Wang, L., Sun, Z., Dai, X., Zhang, Y., & Hu, H. H. (2019). Retaining users after privacy invasions: The roles of institutional privacy assurances and threat-coping appraisal in mitigating privacy concerns. Information Technology & People, 32(6), 1679-1703.

Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life.Palo Alto, CA: Stanford University Press.

Zarifis, A., Kawalek, P., & Azadegan, A. (2021). Evaluating if trust and personal information privacy concerns are barriers to using health insurance that explicitly utilizes AI. Journal of Internet Commerce, 20(1), 66-83.

Xu, Y., Zeng, Q., Wang, G., Zhang, C., Ren, J., & Zhang, Y. (2020). An efficient privacy‐enhanced attribute‐based access control mechanism. Concurrency and Computation: Practice and Experience, 32(5), e5556.

D'Souza, G., & Phelps, J. E. (2009). The privacy paradox: The case of secondary disclosure. Review of Marketing Science, 7(1).

Milne, G. R., & Culnan, M. J. (2004). Strategies for reducing online privacy risks: Why consumers read (or don't read) online privacy notices. Journal of interactive marketing, 18(3), 15-29.

Angst, C. M., & Agarwal, R. (2009). Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion. MIS quarterly, 339-370.

Eastlick, M. A., Lotz, S. L., & Warrington, P. (2006). Understanding online B-to-C relationships: An integrated model of privacy concerns, trust, and commitment. Journal of business research, 59(8), 877-886.

Wang, L., Sun, Z., Dai, X., Zhang, Y., & Hu, H. H. (2019). Retaining users after privacy invasions: The roles of institutional privacy assurances and threat-coping appraisal in mitigating privacy concerns. Information Technology & People, 32(6), 1679-1703.

Lee, D., & Park, N. (2021). Blockchain based privacy preserving multimedia intelligent video surveillance using secure Merkle tree. Multimedia Tools and Applications, 80(26), 34517-34534.

Aminizadeh, S., Heidari, A., Toumaj, S., Darbandi, M., Navimipour, N. J., Rezaei, M., ... & Unal, M. (2023). The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things. Computer methods and programs in biomedicine, 107745.

Willems, J., Schmid, M. J., Vanderelst, D., Vogel, D., & Ebinger, F. (2023). AI-driven public services and the privacy paradox: do citizens really care about their privacy?. Public Management Review, 25(11), 2116-2134.

Li, C., Dong, M., Xin, X., Li, J., Chen, X. B., & Ota, K. (2023). Efficient privacy-preserving in IoMT with blockchain and lightweight secret sharing. IEEE Internet of Things Journal.

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Published

21-03-2024

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Section

Articles

How to Cite

Wang, Y. (2024). The Relationship between Privacy Invasion of Smart Services and Consumers’ Intention to Use. Frontiers in Business, Economics and Management, 14(1), 314-316. https://doi.org/10.54097/nwtjx759