Extending Technology Acceptance Model in AI-Enabled Recruitment: Perceived Ease of Use and Trust as Mediators of Applicant Attitudes
DOI:
https://doi.org/10.26905/jbm.v13i1.17193Keywords:
AI-Enabled Recruitment, Artificial Intelligence, Perceived Ease of Use, Perceived Trust, Technology Acceptance ModelAbstract
The rapid advancement of Artificial Intelligence (AI) has transformed contemporary recruitment practices through the automation of selection processes, including resume screening, recruitment chatbots, and AI-based interviews. Although AI is widely credited with improving recruitment efficiency, applicant acceptance remains a critical concern, primarily because of unresolved issues of trust, transparency, and system usability. Prior studies have largely examined these factors in isolation: some focus on Perceived Ease of Use (PEU) as a usability-driven determinant of technology adoption. While others emphasize Perceived Trust (PT) as dominant predictor of applicant attitudes toward AI-based selection. What remains absent, however, is an integrative model that simultaneously positions PEU and PT as parallel mediators linking applicant’s perception of AI use their attitudes toward it. This omission is non-trivial, because PEU and PT capture conceptually distinct mechanisms cognitive effort versus relational confidence that may operate jointly rather than independently in shaping acceptance. The present study directly addresses this gap by specifying and testing such an integrative mediation model. This study examines the effect of Perceived Use of AI in the Hiring Process (PUAHP) on Attitude Toward AI-Enabled Recruitment (ATT), both directly and through the mediating roles of Perceived Ease of Use (PEU) and Perceived Trust (PT). A quantitative explanatory study was conducted using a cross-sectional survey of 184 job applicants in Semarang City who had participated in AI-based recruitment processes within the preceding twelve months. The data were analysed using Structural Equation Modelling–Partial Least Squares (SEM-PLS). The findings shows that PUAHP has a positive and significant influence on ATT, PEU, and PT. Mediation analyses further demonstrate that PEU and PT significantly mediate the relationship between PUAHP and ATT, with PT emerging as the dominant mediator. These results indicate that applicants' trust in AI systems is the principal mechanism shaping favourable attitudes toward AI-enabled recruitment. Theoretically, this study extends the Technology Acceptance Model (TAM) by integrating trust into the analysis of AI-enabled selection. Practically, it suggests that organisations should strengthen the transparency, fairness, and usability of AI systems to strengthen applicant acceptance of AI-enabled recruitment technologies.
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Almeida, F., Carvalho, I., & Figueiredo, J. (2025). Perception of AI tools in the recruitment process: A study among HR professionals in Portugal. Journal of Human Resource Management, 28(1), 45–62. https://doi.org/10.3390/app15020746
Bazine, M. (2025). Perceived ease of use and technology adoption in digital hiring systems. International Journal of Information Management, 71, 102–115. https://doi.org/10.51244/IJRSI.2025.120600138
Bhatt, P., & Jhunjhunwala, S. (2025). Applicant perspectives on AI-based recruitment: The role of ease of use and fairness. Asian Journal of Management Research, 15(2), 112–128. https://doi.org/10.21863/ijbai/2025.13.2.005
Calluso, C., & Devetag, M. G. (2024). The impact of technology acceptance and personality traits on the willingness to use AI-assisted hiring practices. International Journal of Organizational Analysis. https://doi.org/10.1108/ijoa-06-2024-4562
Carolina, S. Y., Putri, R. L., & Djangkaru, R. R. (2025). Improving recruitment efficiency through artificial intelligence and automation: A systematic literature review. Jurnal Akuntansi, Manajemen dan Bisnis Digital, 4(1), 11–18.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4th ed.). Pearson Education.
Dadaboyev, S. M. U., Abdullayeva, J., Abbosova, N., Suleymenova, A., & Mamadjanova, K. (2025). Role of artificial intelligence in employee recruitment: Systematic review and future research directions. Discover Global Society, 3(1). https://doi.org/10.1007/s44282-025-00246-w
Daly, S. J., Wiewiora, A., & Hearn, G. (2025). Shifting attitudes and trust in AI: Influences on organizational AI adoption. Technological Forecasting and Social Change, 215, 124108. https://doi.org/10.1016/j.techfore.2025.124108
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Journal of Risk and Uncertainty, 18(3), 321–325. https://doi.org/10.1023/a:1011156710779
Deepa, R., Sekar, S., Malik, A., Kumar, J., & Attri, R. (2024). Managerial capabilities required for adopting AI in HRM: A systematic literature review and bibliometric analysis. Technological Forecasting and Social Change, 203, 123381. https://doi.org/10.1016/j.techfore.2024.123381
Dhingra, M., & Mudgal, R. K. (2019). Applications of perceived usefulness and perceived ease of use: A review. https://doi.org/10.1109/SMART46866.2019.9117404
Du, J. (2024). Do humans trust AI in HRM? Why do? Why not? Insights from a decade of research. Journal of Research in Social Science and Humanities, 3(7), 20–48. https://doi.org/10.56397/jrssh.2024.07.04
Fatein, B. (2025). Adoption of AI in talent acquisition: A quantitative study on HRM practitioners using the Technology Acceptance Model (TAM). المجلة العلمية للدراسات والبحوث المالية والتجارية. https://doi.org/10.21608/cfdj.2025.352695.2145
Geddam, S. M., Nethravathi, N., & Hussian, A. A. (2024). Understanding AI adoption: The mediating role of attitude in user acceptance. Journal of Informatics Education and Research, 4(2). https://doi.org/10.52783/jier.v4i2.975
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hanson, W. E., Creswell, J. W., Clark, V. L. P., Petska, K. S., & Creswell, J. D. (2005). Mixed methods research designs in counseling psychology. Journal of Counseling Psychology, 52(2), 224–235. https://doi.org/10.1037/0022-0167.52.2.224
Horodyski, P. (2023). Applicants' perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11(11). https://doi.org/10.1016/j.chbr.2023.100303
Hosain, M. S., Amin, M. B., Debnath, G. C., & Rahaman, M. A. (2025). The use of artificial intelligence (AI) in the hiring process: Job applicants' perceptions of procedural justice. Computers in Human Behavior Reports, 19, Article 100713. https://doi.org/10.1016/j.chbr.2025.100713
Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection: A review and research agenda. Journal of Business Ethics, 178(4), 977–1007. https://doi.org/10.1007/s10551-022-05049-6
Makhdom, H., & Emam, N. (2025). Mediation and moderation in AI recruitment: The roles of trust and challenges in shaping perceived benefits and future use. Journal of Statistics Applications & Probability, 14(4), 601–612. https://doi.org/10.18576/jsap/140408
Mansori, S. F. A. (2025). Exploring the future of technology acceptance models in the age of artificial intelligence. International Science and Technology Journal. https://doi.org/10.62341/sfam1509
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080335
Moravec, J., Hoffman, B., & Kline, T. (2024). AI-enhanced decision-making in talent acquisition: Predictive accuracy and consistency. Personnel Psychology, 77(1), 31–57. https://doi.org/10.1111/peps.12540
Or, C. (2024). Watch that attitude! Examining the role of attitude in the Technology Acceptance Model through meta-analytic structural equation modelling. International Journal of Technology in Education and Science. https://doi.org/10.46328/ijtes.575
Palvia, P. (2009). The role of trust in e-commerce relational exchange: A unified model. Information & Management, 46(4), 213–220. https://doi.org/10.1016/j.im.2009.02.003
Qureshi, O. A., & Saoud, L. S. (2025). From perception to action: Empowering AI-human-centric project management through the mediating role of consumer trust. Proceedings of the 13th IPMA Research Conference. https://doi.org/10.56889/yfrk1589
Radner, R., & Rothschild, M. (1975). On the allocation of effort. Journal of Economic Theory, 10(3), 358–376. https://doi.org/10.1016/0022-0531(75)90006-x
Rukadikar, A., & Khandelwal, K. (2025). Exploring perceptions of Indian graduates on the effectiveness of artificial intelligence in higher education campus hiring. Discover Artificial Intelligence, 5(1). https://doi.org/10.1007/s44163-025-00526-z
Sewang, A., Rizal, M., & Arifin, Z. (2025). Pemanfaatan kecerdasan buatan dalam rekrutmen: Efisiensi waktu dan biaya organisasi. Jurnal Administrasi Bisnis, 14(1), 22–38.
Shao, C., Nah, S., Makady, H., & McNealy, J. (2024). Understanding user attitudes towards AI-enabled technologies: An integrated model of self-efficacy, TAM, and AI ethics. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2331858
Skarzyńska, E., & Beyer, K. (2025). The impact of trust on adopting artificial intelligence technology: The perspective of small and medium-sized enterprises (SMEs). In Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities (Vol. 1). https://doi.org/10.18276/978-83-8419-028-9-16
Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258. https://doi.org/10.1108/shr-07-2018-0051
van Esch, P., Black, J. S., & Ferolie, J. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90(1), 215–222. https://doi.org/10.1016/j.chb.2018.09.009
van Esch, P., Black, J. S., & Moro, M. (2020). Job candidates' reactions to AI-enabled job application processes. AI and Ethics. https://doi.org/10.1007/s43681-020-00025-0
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
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. https://doi.org/10.2307/30036540
Verheijen, F., Lub, X., & Zhang, R. (2025). "Dear applicant, the AI will see you now!" Job applicants' reactions to AI-enhanced selection. In 38th Bled eConference: Empowering Transformation: Shaping Digital Futures for All: Conference Proceedings. https://doi.org/10.18690/um.fov.4.2025.13
Xiong, Y., & Kim, J. K. (2025). Who wants to be hired by AI? How message frames and AI transparency impact individuals' attitudes and behaviors toward companies using AI in hiring. Computers in Human Behavior: Artificial Humans, 3, 100120. https://doi.org/10.1016/j.chbah.2025.100120
Yadav, I., & Satsangi, P. (2025). The future of recruitment: Evaluating AI-based screening tools. Shodhai: Journal of Artificial Intelligence, 2(2). https://doi.org/10.29121/shodhai.v2.i2.2025.52
Zhang, L., & Yencha, C. (2022). Examining perceptions towards hiring algorithms. Technology in Society, 68, 101848. https://doi.org/10.1016/j.techsoc.2021.101848
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