Penerimaan Teknologi Artificial Intelligence (AI) di Proses Rekrutmen dan Seleksi Oleh Pelamar Kerja

Muhammad Putro; Joko Siswanto

doi:10.61221/jriem.v3i1.56

Authors

Muhammad Putro Institut Teknologi Bandung
Joko Siswanto Institut Teknologi Bandung

DOI:

https://doi.org/10.61221/jriem.v3i1.56

Keywords:

Recruitment and Selection, Artificial Intelligence, Job Applicant, Technology Acceptance

Abstract

The use of technology in the recruitment and selection process has evolved alongside organizational competition to identify, select, and retain top talent. Artificial Intelligence (AI) technology in recruitment and selection offers various advantages from an organizational perspective. However, job applicants' perceptions of AI technology have been rarely discussed in the literature, even though this issue significantly impacts their attitudes and work behaviors after the recruitment and selection process. This study focuses on job applicants' acceptance of AI technology using theoretical models of technology acceptance to understand the factors influencing their intention to use AI. The PLS-SEM statistical method was used for data processing with SmartPLS software. Based on data from 166 respondents in Jabodetabek and Bandung who had never participated in AI-based recruitment and selection, the study found that perceived usefulness, perceived ease of use, and trust positively influence the intention to use AI, with attitude acting as a mediator. Perceived usefulness and perceived ease of use also have a direct positive effect. Additionally, attitude and social influence positively affect the intention to use AI. However, perceived knowledge does not have a significant impact on job applicants' intention to use AI in recruitment and selection.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Brenner, F. S., Ortner, T. M., & Fay, D. (2016). Asynchronous video interviewing as a new technology in personnel selection: The applicant’s point of view. Frontiers in Psychology, 7(JUN). https://doi.org/10.3389/fpsyg.2016.00863

Buil, I., Catalán, S., & Martínez, E. (2020). Understanding applicants’ reactions to gamified recruitment. Journal of Business Research, 110(December 2019), 41–50. https://doi.org/10.1016/j.jbusres.2019.12.041

Choung, H., David, P., & Ross, A. (2022). Trust in AI and Its Role in the Acceptance of AI Technologies. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2022.2050543

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008

Gado, S., Kempen, R., Lingelbach, K., & Bipp, T. (2022). Artificial intelligence in psychology: How can we enable psychology students to accept and use artificial intelligence? Psychology Learning and Teaching, 21(1), 37–56. https://doi.org/10.1177/14757257211037149

Garcia, R. L. F., Huang, Y. K., & Kwok, L. (2023). Virtual interviews vs. LinkedIn profiles: Effects on human resource managers’ initial hiring decisions. Tourism Management, 94(September 2022), 104659. https://doi.org/10.1016/j.tourman.2022.104659

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). The Results of PLS-SEM Article information. European Business Review, 31(1), 2–24.

Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11(June), 100303. https://doi.org/10.1016/j.chbr.2023.100303

Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda. In Journal of Business Ethics (Vol. 178, Issue 4). Springer Netherlands. https://doi.org/10.1007/s10551-022-05049-6

Jia, Q., Guo, Y., Li, R., Li, Y., & Chen, Y. (2018). A conceptual artificial intelligence application framework in human resource management. Proceedings of the International Conference on Electronic Business (ICEB), 2018-Decem, 106–114.

Jones, J. W., Brasher, E. E., & Huff, J. W. (2002). Innovations in integrity-based personnel selection: Building a technology-friendly assessment. International Journal of Selection and Assessment, 10(1–2), 87–97. https://doi.org/10.1111/1468-2389.00195

Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77(March 2022), 101925. https://doi.org/10.1016/j.tele.2022.101925

Michaels, E., Axelrod, E. M. H. H. J. B., Handfield-Jones, H., Axelrod, B., Books24x7, I., & Press, H. B. (2001). The War for Talent. January 1998, 1–8. https://books.google.mv/books?id=simZCd_YUC4C

Ochmann, J., & Laumer, S. (2020). AI recruitment: Explaining job seekers’ acceptance of automation in human resource management. Proceedings of the 15th International Conference on Business Information Systems 2020 “Developments, Opportunities and Challenges of Digitization”, WIRTSCHAFTSINFORMATIK 2020. https://doi.org/10.30844/wi_2020_q1

Ore, O., & Sposato, M. (2022). Opportunities and risks of artificial intelligence in recruitment and selection. International Journal of Organizational Analysis, 30(6), 1771–1782. https://doi.org/10.1108/IJOA-07-2020-2291

Ployhart, R. E., Schmitt, N., & Tippins, N. T. (2017). Solving the Supreme Problem: 100 Years of selection and recruitment at the Journal of Applied Psychology. Journal of Applied Psychology, 102(3), 291–304. https://doi.org/10.1037/apl0000081

Ryan, A. M., & Ployhart, R. E. (2014). A Century of Selection. September 2013, 1–25. https://doi.org/10.1146/annurev-psych-010213-115134

van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with and complete digital, AI-enabled recruiting. Business Horizons, 62(6), 729–739. https://doi.org/10.1016/j.bushor.2019.07.004

van Esch, P., Black, J. S., & Arli, D. (2021). Job candidates’ reactions to AI-Enabled job application processes. AI and Ethics, 1(2), 119–130. https://doi.org/10.1007/s43681-020-00025-0

Venkatesh, V., & Davis, F. D. (2000). 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: Management Information Systems, 27(3), 425–478. https://doi.org/10.2307/30036540