Determinants of Face Recognition Acceptance for Digital Attendance Systems in Indonesia
DOI:
https://doi.org/10.33096/jmb.v13i2.1761Keywords:
Consumer Behavior, Implementation Strategy, Operation Management, Face Recognition, Digital PresenceAbstract
Human resource management is increasingly complex with the widespread use of digital technologies. Facial recognition offers a potential solution for monitoring employee attendance and supporting performance management, yet limited research has examined user acceptance of this technology as an attendance system. This study evaluates user acceptance of facial recognition and identifies key factors influencing its adoption. Data were collected through an online survey conducted via Google Forms from January to June 2025. Of 304 responses received, 300 valid responses were analyzed using SmartPLS to assess the relationships among the proposed factors. The findings show that several factors significantly influence users’ acceptance and intention to use facial recognition technology. These factors should be prioritized to improve implementation effectiveness, perceived usefulness, and continued use. The results provide practical recommendations for developing a Digital Presence implementation strategy that supports effective human resource management and enhances the quality of organizational human resource development programs.
Downloads
References
Almaqtari, F. A., Yahya, A. T., Al-Maskari, N., Farhan, N. H. S., & Al-Aamri, A.-M. Y. Y. (2025). Assessing the Integrated Role of IT Governance, Fintech, and Blockchain in Enhancing Sustainability Performance and Mitigating Organizational Risk. Risks, 13(6), 105. https://doi.org/10.3390/risks13060105
Arcuri, E., Ardizzi, M., & Gallese, V. (2025). Reading out bodily cues to predict interactions. 1–18.
Bao, Y., & Du, Z. (2023). Face Recognition Technology Risks and Regulatory Issues. 652–656. https://doi.org/10.2991/978-94-6463-040-4_99
Beltr, G., Sousa, S., & Lamas, D. (2023). Trust in Facial Recognition Systems : A Perspective from the Users. 379–388.
Boadu, R. O., Adu, V. W., Boadu, K. A. O., Ibrahim, B., Akey, P., Mensah, A. A., Adzakpah, G., & Mensah, N. K. (2025). Examine frameworks policies and strategies for effective information governance in healthcare organizations. Plos One, 20(7 July), 1–22. https://doi.org/10.1371/journal.pone.0327496
Choong, Y. O., Ng, L. P., & Lau, T. C. (2025). Beyond fairness: exploring organizational citizenship behavior through the lens of self-efficacy and trust in principals. Humanities and Social Sciences Communications, 2025, 1–14. https://doi.org/10.1057/s41599-025-04611-7
Damanik, F. S., Widayanti, A. W., Wiedyaningsih, C., Pharmacy, F., Muslim, U., Umn, N., & Washliyah, A. (2025). Exploring Technology Acceptance Models : A Systematic Review of Health Application Adoption in Asian Countries. 15(2), 110–120. https://doi.org/10.22146/jmpf.96755
Deng, K., Zhang, C., Song, M., & Hu, X. (2026). Exploring Key Factors Influencing Generation Z Users ’ Continuous Use Intention on Human-AI Collaboration in Secondhand Fashion E-Commerce Platforms. 1–36.
Dixit, R. S., Choudhary, S. L., & Govil, N. (2025). Analyzing the impact of artificial intelligence on the online purchase decision ‑ making process through the lens of the UTAUT 2 model. Discover Computing. https://doi.org/10.1007/s10791-025-09575-5
Gamage, A. N. K. K. (2025). A comparative analysis of qualitative and mixed methods research : Strengths , limitations , and practical applications. 25(03), 2040–2046.
Gillioz, C., Nicolet-dit-félix, M., Delplanque, S., Mortillaro, M., Sander, D., & Fiori, M. (2025). How do emotionally intelligent individuals react to other people ’ s emotions ? A study on emotional and facial reactions.
Habibu, T., Luhanga, E. T., & Sam, A. E. (2025). Assessment of How Users Perceive the Usage of Biometric Technology Applications. 1–11.
Hair, J. F. (2019). When to use and how to report the results of PLS-SEM. 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hausinger, T., Probst, B., Hawelka, S., & Pletzer, B. (2025). Own-gender bias in facial feature recognition yields sex differences in holistic face processing. Biology of Sex Differences, 16(1), 1–15. https://doi.org/10.1186/s13293-025-00695-7
Jangid, M., Gupta, S., & Sharma, S. (2025). Zero trust biometric attendance : A secure face recognition framework. 15(May), 2437–2449.
Jiang, T., Sun, Z., Fu, S., & Lv, Y. (2024). Human-AI interaction research agenda : A user-centered perspective. Data and Information Management, 8(4), 100078. https://doi.org/10.1016/j.dim.2024.100078
Kandasamy, G., Almanasef, M., Almeleebia, T., Orayj, K., Shorog, E., Alshahrani, A. M., Khaled, A., Prabahar, K., Alqifari, S. F., Mani, V., Alsuhim, G. Y. A., Alahmari, W. M. D., Ahmed, R., & Paulsamy, P. (2025). Prevalence of anxiety and depression among university students in Southern Saudi Arabia based on a cross sectional survey. Scientific Reports, 15(1), 1–9. https://doi.org/10.1038/s41598-025-00695-y
Kelkay, J. M., Wubante, S. M., Anteneh, D. S., & Takilo, M. K. (2025). Intention to use eLearning ‑ based continuing professional development and its predictors among healthcare professionals in Amhara region referral hospitals , Ethiopia , 2023 : using modified UTAUT ‑ 2 model. BMC Health Services Research. https://doi.org/10.1186/s12913-025-12317-4
Kenesei, Z., Kökény, L., Ásványi, K., & Jászberényi, M. (2025). The central role of trust and perceived risk in the acceptance of autonomous vehicles in an integrated UTAUT model. European Transport Research Review, 17(1). https://doi.org/10.1186/s12544-024-00681-x
Kiran, K. P. (2025). Assessing Unified Payments Interface (UPI) adoption and usage through the interplay of UTAUT factors.
Lee, P., Li, J., Rafiee, Y., Jones, B. C., & Shiramizu, V. K. M. (2025). Further evidence that averageness and femininity , rather than symmetry and masculinity , predict facial attractiveness judgments. 1–9.
Li, C., & Li, H. (2023). Telematics and Informatics Disentangling facial recognition payment service usage behavior : A trust perspective. Telematics and Informatics, 77(August 2022), 101939. https://doi.org/10.1016/j.tele.2023.101939
Liu, M., Wang, C., & Hu, J. (2025). Understanding older adults’ adoption of facial recognition payment: An integrated model of TAM and UXT. Plos One, 20(7 July), 1–29. https://doi.org/10.1371/journal.pone.0325291
Lyu, T., Guo, Y., & Chen, H. (2025). Understanding people ’ s intention to use facial recognition services : the roles of network externality and privacy cynicism. 20. https://doi.org/10.1108/ITP-10-2021-0817
Mansur, D. M., Liestyowati, L., & Maryadi, A. F. (2022). Understanding The Behavior and Perceptions of Millennial Generation as Guidance in Developing Service Products and Marketing Strategies: Evidence from West Java, Indonesia. Jurnal Manajemen Bisnis.
Mansur, D. M., Sule, E. T., Kartini, D., Oesman, Y. M., Putra, A. H. P. K., & Chamidah, N. (2019). Moderating of the role of technology theory to the existence of consumer behavior on e-commerce. Journal of Distribution Science, 17(7), 15–25. https://doi.org/10.15722/jds.17.07.201907.15
Matheis, T. K., Lehnhart, R., Carolina, A., Costa, C., & Tontini, J. (2026). Analysis of the determinants of behavioral intention to use digital financial channels : user perspectives.
Moradi, H. (2025). Integrating AI in higher education: factors influencing ChatGPT acceptance among Chinese university EFL students. International Journal of Educational Technology in Higher Education, 22(1). https://doi.org/10.1186/s41239-025-00530-4
Mutanda, B., & Tapera, J. (2025). Leveraging Digital Technologies to Enhance Business Resilience in Risk Management. 249–272. https://doi.org/10.4018/979-8-3693-5912-9.ch010
Of, P., Contribution, T. H. E., Collaborative, O. F., To, E., Sustainable, T. H. E., & Goalsa, D. (2025). Percepción de la Contribución de la Economía Colaborativa a los Objetivos de Desarrollo Sostenible. 11(1), 1–14.
Paliwal, G., Kumar, A., Prasad, K., Deepshika, S., Vijay, B., & Shrimal, M. (2025). Transformative impact of explainable artificial intelligence : bridging complexity and trust. Discover Artificial Intelligence. https://doi.org/10.1007/s44163-025-00281-1
Prabhavathi, B., Tanuja, V., & Madhu, V. (2021). Attendance Management System Using Face Recognition Method Attendance Management System Using Face Recognition Method. https://doi.org/10.1088/1742-6596/2089/1/012078
Prayogi, A., Navea, R. F., Dian, R., Akbar, M., Pane, S., Siregar, R. M., Sugianto, A., Fachri, H., & Simbolon, S. (2024). Direct implementation of AI-Based Facial Recognition for students. Journal of Information System and Technology Research, 3(3), 107–116.
Qiu, J., Zhou, C., Rui, Y., & Feng, F. (2025). Advances in Numerical Computation and Mathematical Modeling for Geotechnical Engineering. 15–18.
Quan, Z., Yang, X., Pan, W., Wang, R., Cui, D., & Xu, Q. (2025). Unconscious processing of facial race and emotional information. BMC Psychology, 13(1). https://doi.org/10.1186/s40359-025-02906-7
Setiya Putra, Y. W. (2023). Implementasi Model TAM pada Sistem Informasi Presensi Online Menggunakan Face Recognition dan GPS. Journal of Applied Computer Science and Technology, 4(2), 147–154. https://doi.org/10.52158/jacost.v4i2.577
Shiau, W. L., Liu, C., Cheng, X., & Yu, W. P. (2024). Employees’ Behavioral Intention to Adopt Facial Recognition Payment to Service Customers: From SQB and Value-Based Adoption Perspectives. Journal of Organizational and End User Computing, 36(1), 1–32. https://doi.org/10.4018/JOEUC.359894
Susanty, A., Puspitasari, N. B., Siahaan, G. S., & Setiawan, S. (2025). Factors influencing the intention of textile and garment SMEs to adopt digital technologies and its impact on performance. 1–25.
Syahputra, R. R., & Daryanto, E. (2025). Implementation of Facial Recognition Identification System in Indonesia ’ s Visa Services to Strengthen the Selective Policy of the Directorate General of Immigration in Supporting National Security. 4(5), 1–10.
Varma, P., Shen, L., Postnova, S., King, K., Howard, M. E., Aidman, E., Rajaratnam, S. W. M., & Sletten, T. L. (2025). Understanding sleep health challenges of defence shift workers to design a digital , sleep and circadian management tool. Scientific Report, 1–13.
Yu, T., Teoh, A. P., Liao, J., & Wang, C. (2025). Factors influencing behavioral intention to use facial recognition payment in the restaurant industry : a comparative analysis of China and Malaysia. Journal of Travel & Tourism Marketing, 42(2), 222–247. https://doi.org/10.1080/10548408.2025.2455421
Yu, T., & Wang, C. (2024). Acceptance of or resistance to facial recognition payment : A systematic review. February, 1–19. https://doi.org/10.1002/cb.2385
Yu, T., Wang, C., Bian, Q., & Teoh, A. P. (2024). Acceptance of or resistance to facial recognition payment: A systematic review. Journal of Consumer Behaviour, August. https://doi.org/10.1002/cb.2385
Zakaria, Z. (2025). Face Recognition Technology : Benefits , Applications , and Challenges. 13(03), 2096–2102. https://doi.org/10.18535/ijsrm/v13i03.ec05
Zhang, Y. (2024). International Journal of Information Impact of perceived privacy and security in the TAM model : The perceived trust as the mediated factors. International Journal of Information Management Data Insights, 4(2), 100270. https://doi.org/10.1016/j.jjimei.2024.100270
Zl, P. G. İ., & Kalari, İ. L. İ. K. P. O. L. İ. T. İ. (2025). USER DATA AND DIGITAL PRIVACY : PRIVACY POLICIES OF SOCIAL MEDIA PLATFORMS. 15(1), 225–239. https://doi.org/10.7456/tojdac
Степанов, А. Г. (2023). Разработка метода сопоставления трехмерных изображений лица с данными компьютерной томографии Developing methods of matching 3D facial images with computed tomography data Prosthodontics. 150–157. https://doi.org/10.37988/1811-153X
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Jurnal Manajemen Bisnis
This work is licensed under a Creative Commons Attribution 4.0 International License.
