Sociological Investigation of Factors Influencing the Design and Support for AI Tracking and Monitoring Systems for Digital Camera Security and Theft Recovery

Mohammed D. Enaikele PhD.; Elvis E. Enaikele

doi:10.56201/ijemt.vol.12.no7.2026.pg434.444

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Sociological Investigation of Factors Influencing the Design and Support for AI Tracking and Monitoring Systems for Digital Camera Security and Theft Recovery

Mohammed D. Enaikele PhD., Elvis E. Enaikele

Abstract

The issue of digital camera theft has become increasingly problematic for photographers, photojournalists, and other users, bringing both professional and socio-economic difficulties. This research explored how often cameras are stolen, users’ perceptions of the reasons and effects of such thefts, their familiarity with Artificial Intelligence (AI)-based security options, and their readiness to use AI-driven tracking and monitoring technologies to safeguard their equipment and recover stolen devices. The study was framed by the Social Construction of Technology , Routine Activity Theory, and Rational Choice Theory. Data were collected through a quantitative cross-sectional survey of 200 respondents which include photographers, media workers, photography students, and camera retailers in Lagos State, Nigeria, using a structured questionnaire. Analysis involved descriptive statistics and Chi-square (χ2) tests. Findings showed that camera theft continues to be a significant occupational issue, with insufficient security, rising crime, and economic hardship identified as primary causes. Participants demonstrated strong awareness of AI security technologies and showed considerable support for AI-based camera tracking systems. The Chi-square analysis indicated a significant relationship between individuals with experience of camera theft and their support for AI-based security solutions (χ2 =23.47>3.84 at 0.05 significance level). The study concludes that AI-enhanced security approaches offer socially valuable means to address camera theft by improving protection, facilitating recovery, and increasing user confidence. It recommends integrating AI tracking capabilities into digital cameras and raising awareness about intelligent security technologies.

Keywords

Sociological InvestigationArtificial IntelligenceTracking and Monitoring SystemsDigital Camera SecurityTheft Recovery.

References

Bélissent, J. (2018). Smart cities and intelligent security systems. Forrester Research. Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44(4), 588– 608. https://doi.org/10.2307/2094589 Retrieved March 2026 Coleman, J. S. (1990). Foundations of social theory (p. 100). Belknap Press of Harvard University Press. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. Dwivedi, Y. K., Hughes, L., Ismagilova, E., et al. (2021). Artificial intelligence adoption and societal implications. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2020.102295 Retrieved March 2026 Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A survey on enabling technologies, protocols, and applications. IEEE Communications Surveys & Tutorials, 17(4), 2347–2376. https://doi.org/10.1109/COMST.2015.2444095 Retrieved March 2026 Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. Homans, G. C. (1961). Social behavior: Its elementary forms (p. 50). Harcourt, Brace & World. Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. Sage. Li, S., Xu, L. D., & Zhao, S. (2018). The Internet of Things: A survey. Information Systems Frontiers, 17(2), 243–259. https://doi.org/10.1007/s10796-014-9492-7 Retrieved March 2026 Merton, R. K. (1938). Social structure and anomie. American Sociological Review, 3(5), 672– 682. https://doi.org/10.2307/2084686 Retrieved March 2026ss Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance. British Journal of Management, 31(2), 272–293. https://doi.org/10.1111/1467-8551.12343 Retrieved March 2026 Pinch, T. J., & Bijker, W. E. (1984). The social construction of facts and artifacts: Or how the sociology of science and the sociology of technology might benefit each other. Social Studies of Science, 14(3), 399–441. https://doi.org/10.1177/030631284014003004 Retrieved March 2026 Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. Sarker, I. H. (2022). AI-based cybersecurity: Trends, challenges and future directions. Journal of Big Data, 9(1), Article 1–27. https://doi.org/10.1186/s40537-021-00543-4 Retrieved March 2026 Stallings, W. (2018). Effective cybersecurity: A guide to using best practices and standards. Addison-Wesley. Taddeo, M., & Floridi, L. (2018). Regulate artificial intelligence to avert cyber arms race. Nature, 556(7701), 296–298. https://doi.org/10.1038/d41586-018-04659-1 Retrieved March 2026 Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology. MIS Quarterly, 36(1), 157–178. West, D. M. (2018). The future of work: Robots, AI, and automation. Brookings Institution Press. Zuboff, S. (2019). The age of surveillance capitalism. Public Affairs.

Sociological Investigation of Factors Influencing the Design and Support for AI Tracking and Monitoring Systems for Digital Camera Security and Theft Recovery

Abstract

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