How HR Recruiters' Artificial Intelligence Awareness Effects Job Performance? Job Insecurity as Mediator

Abstract

The scope of the present research is to evaluate the level of Artificial Intelligence (AI) awareness among Human Resources (HR) Recruiters in the Middle East and its impact on their job performance and job insecurity, besides to explore whether job insecurity represents as a mediator in this relationship. The research used correlation, ANOVA and regression tests on data collected from 344 HR Recruiters to assess both direct and mediating relationships between the variables. SPSS 21 program was preferred in the analyses. The findings indicate that job performance had a negative correlation with AI awareness and a positive correlation with job insecurity. AI awareness was found to have a positive effect on job insecurity, and job insecurity was shown to partially mediate the relationship between AI awareness and job performance. This study provides valuable insights and contributes to the field of research on the effects of AI awareness on HR Recruiters’ job insecurity and job performance.

Keywords

• Artificial Intelligence (AI) awareness
• Job performance
• Job insecurity
• Human Resources (HR) & Recruitment

Ethical Statement

Bu çalışma için İstanbul Aydın Üniversitesinde ilgili birimden etik kurul onayı alınmıştır.

İK İşe Alma Uzmanlarının Yapay Zeka Farkındalığı İş Performansını Nasıl Etkiler? Aracı Olarak İş Güvencesizliği

Abstract

Bu araştırmanın amacı, Orta Doğu'daki İnsan Kaynakları (İK) İşe Alma Uzmanları arasında Yapay Zeka (YZ) farkındalık düzeyini ve bunun iş performansı ve iş güvencesizliği üzerindeki etkisini değerlendirmek ve ayrıca iş güvencesizliğinin bu ilişkide bir aracı görevi görüp görmediğini araştırmaktır. Araştırmada, değişkenler arasındaki hem doğrudan hem de aracılık eden ilişkileri değerlendirmek için 344 İK İşe Alma Uzmanından toplanan veriler üzerinde korelasyon, ANOVA ve regresyon testleri yapılmıştır. Analizlerde SPSS 21 programı tercih edilmiştir. Bulgular, iş performansının YZ farkındalığı ile negatif, iş güvencesizliği ile pozitif bir korelasyonu olduğunu göstermektedir. Araştırmada, YZ farkındalığının iş güvencesizliği üzerinde pozitif bir etkisi olduğu ve iş güvencesizliğinin YZ farkındalığı ile iş performansı arasındaki ilişkiye kısmen aracılık ettiği gösterilmiştir. Bu çalışma, YZ farkındalığının İK İşe Alma Uzmanlarının iş güvencesizliği ve iş performansı üzerindeki etkilerine ilişkin araştırma alanına değerli içgörüler sağlamakta ve katkıda bulunmaktadır.

Keywords

• Yapay Zeka (YZ) farkındalığı
• İş performansı
• İş güvencesizliği
• İnsan Kaynakları (İK) ve İşe alım

References

1. Adekiya, A. (2024). Perceived job insecurity and task performance: What aspect of performance is related to which facet of job insecurity? Current Psychology, 43, 1340–1358. https://doi.org/10.1007/s12144-023-04408-4
2. Alavi, M., & Leidner, D. E. (2021). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107–136. https://doi.org/10.2307/325096
3. Anand, A., Dalmasso, A., Vessal, S. R., Parameswar, N., Rajasekar, J., & Dhal, M. (2023). The effect of job security, insecurity, and burnout on employee organizational commitment. Journal of Business Research, 162, Article 113843. https://doi.org/10.1016/j.jbusres.2023.113843
4. Armstrong, M. (2014). Armstrong’s handbook of human resource management practice (13th ed.). Kogan Page.
5. Atrian, F., & Ghobbeh, M. (2023). Technostress and employee performance in AI-driven environments. Journal of Organizational Psychology, 45(2), 101–117. https://ideas.repec.org/p/arx/papers/2311.07072.html
6. Bai, S., Zhang, X., Yu, D., & Yao, J. (2024). Assist me or replace me? Uncovering the influence of AI awareness on employees’ counterproductive work behaviors. Frontiers in Public Health, 12, Article 1449561. https://doi.org/10.3389/fpubh.2024.1449561
7. Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
8. Baskaran, S., Lay, H., Ming, B., & Mahadi, N. (2020). Technology adoption and employee’s job performance: An empirical investigation. International Journal of Academic Research in Economics and Management Sciences, 9(1), 42–66. http://dx.doi.org/10.6007/IJAREMS/v9-i1/7443

9. Basnet, S. (2024). Artificial intelligence and machine learning in human resource management: Prospect and future trends. International Journal of Research Publication and Reviews, 5, 281–287. https://doi.org/10.55248/gengpi.5.0124.0107
10. Basu, S., Majumdar, B., Mukherjee, K., Munjal, S., & Palaksha, C. (2023). Artificial intelligence–HRM interactions and outcomes: A systematic review and causal configurational explanation. Human Resource Management Review, 33(1), Article 100893. https://doi.org/10.1016/j.hrmr.2022.100893
11. Berente, N., Gu, B., Recker, J, & Santhanam, R. (2021). Managing artificial intelligence. MIS Quarterly, 45(3), 1433–1450. https://doi.org/10.25300/MISQ/2021/16274
12. Bessen, J. E. (2019). AI and jobs: The role of demand (NBER Working Paper No. 24235). National Bureau of Economic Research. https://www.nber.org/papers/w24235
13. Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency (pp. 149–159). https://doi.org/10.48550/arXiv.1712.03586
14. Breaugh, J. A., & Starke, M. (2000). Research on employee recruitment: So many studies, so many remaining questions. Journal of Management, 26(3), 405–434. https://doi.org/10.1177/014920630002600303
15. Brougham, D., & Haar, J. (2018). Smart technology, artificial intelligence, robotics, and algorithms (STARA): Employees’ perceptions of our future workplace. Journal of Management & Organization, 24(2), 239–257. https://doi.org/10.1017/jmo.2016.55
16. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.
17. Campbell, J. P. (1990). Modeling the performance prediction problem in industrial and organizational psychology. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organizational psychology (Vol. 1, pp. 687–732). Consulting Psychologists Press.
18. Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10, Article 20. https://doi.org/10.1057/s41599-023-02079-x
19. Cheng, X. (2020). Obtain employee turnover rate and optimal reduction strategy based on neural network and reinforcement learning. https://doi.org/10.48550/arXiv.2012.00583
20. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
21. Cowgill, B., Dell’Acqua, F., Deng, S., Simcoe, T., & Zbikowski, M. (2020). Biased programmers? Or biased data? A field experiment in operationalizing AI ethics. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 128–137). https://doi.org/10.48550/arXiv.2012.02394
22. Çalışkan, A., & Köroğlu, Ö. (2022). Job performance, task performance, contextual performance: Development and validation of a new scale. Uluslararası İktisadi ve İdari Bilimler Dergisi, 8(2), 180–201. https://doi.org/10.29131/uiibd.1201880
23. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
24. De Angelis, M., Mazzetti, G., & Guglielmi, D. (2021). Job insecurity and job performance: A serial mediated relationship and the buffering effect of organizational justice. Frontiers in Psychology, 12, 694057. https://doi.org/10.3389/fpsyg.2021.694057
25. De Witte, H. (2005). Job insecurity: Review of the international literature on definitions, prevalence, antecedents, and consequences. SA Journal of Industrial Psychology, 31(4), 1–6. https://doi.org/10.4102/sajip.v31i4.200
26. Devi, B. R., & Banu, P. V. (2014). Introduction to recruitment. SSRG International Journal of Economics and Management Studies, 1(2), 5–8. https://doi.org/10.14445/23939125/IJEMS-V1I2P102
27. Field, A. (2013). Discovering statistics using IBM SPSS Statistics (4th ed.). SAGE Publications.
28. Filippi, E., Banno, M., & Trento, S. (2023). Automation technologies and their impact on employment: A review, synthesis and future research agenda. Technological Forecasting and Social Change, 191, 122448. https://doi.org/10.1016/j.techfore.2023.122448
29. Golgeci, I., Ritala, P., Arslan, A., McKenna, B., & Ali, I. (2025). Confronting and alleviating AI resistance in the workplace: An integrative review and a process framework. Human Resource Management Review, 35, Article 101075. https://doi.org/10.1016/j.hrmr.2024.101075
30. Greenhalgh, L., & Rosenblatt, Z. (1984). Job insecurity: Toward conceptual clarity. Academy of Management Review, 9(3), 438–448. https://doi.org/10.2307/258284
31. Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. The Guilford Press.
32. He, G., Zheng, X., Li, W., Tan, L., Chen, S., & He, Y. (2023). The mixed blessing of leaders’ artificial intelligence (AI)-oriented change behavior: Implications for employee job performance and unethical behavior. Applied Research in Quality of Life, 19(2), 469–497. https://doi.org/10.1007/s11482-023-10250-4
33. Hellgren, J., & Sverke, M. (2003). Does job insecurity lead to impaired well-being or vice versa? Estimation of cross-lagged effects using latent variable modelling. Journal of Organizational Behavior, 24(2), 215–236. https://doi.org/10.1002/job.184
34. Hermina, U., & Yosepha, S. (2019). The model of employee performance. International Review of Management and Marketing, 9, 69–73. https://doi.org/10.32479/irmm.8025
35. Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
36. Janssen, O., & Van Yperen, N. W. (2004). Employees’ goal orientations, the quality of leader–member exchange, and the outcomes of job performance and job satisfaction. Academy of Management Journal, 47(3), 368–384. https://doi.org/10.2307/20159587
37. Jovanovic, T. (2025, March). Unlocking the future of talent acquisition: Integrating AI and innovative tools for enhanced efficiency. In International Conference on 44th Organizational Science Development: Human Being, Artificial Intelligence and Organization (pp. 359–369). https://doi.org/10.18690/um.fov.2.2025.29
38. Joshi, A., Singh, R., & Rani, S. (2024). Strategic adoption of artificial intelligence for human resource management practices transforming healthcare sector. The International Journal of Education Management and Sociology, 3(3), 151–163. https://doi.org/10.58818/ijems.v3i3.133
39. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
40. Kaplan, R. S., & Norton, D. P. (1996). The balanced scorecard: Translating strategy into action. Harvard Business Press.
41. Kassa, B. Y., & Worku, E. K. (2025). The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity, 11(1), Article 100474. https://doi.org/10.1016/j.joitmc.2025.100474
42. Kim, B.-J., & Kim, M.-J. (2024). How artificial intelligence-induced job insecurity shapes knowledge dynamics: The mitigating role of artificial intelligence self-efficacy. Journal of Innovation & Knowledge, 9(4), Article 100590. https://doi.org/10.1016/j.jik.2024.100590
43. Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd ed.). Guilford Press.
44. Kong, H., Yuan, Y., Baruch, Y., Bu, N., Jiang, X., & Wang, K. (2021). Influences of artificial intelligence (AI) awareness on career competency and job burnout. International Journal of Contemporary Hospitality Management, 33(2), 717–734. https://doi.org/10.1108/IJCHM-07-2020-0789
45. Kumar, S., Verma, A. K., & Mirza, A. (2024). Digital revolution, artificial intelligence, and ethical challenges. In Digital transformation, artificial intelligence and society (Chap. 11). Springer.
46. Lane, M., & Saint-Martin, A. (2021). The impact of artificial intelligence on the labour market: What do we know so far? OECD Social, Employment and Migration Working Papers, 256, 1–61. https://doi.org/10.1787/7c895724-en
47. Liang, X., Guo, G, Shu, L., Gong, Q., & Luo, P. (2022). Investigating the double-edged sword effect of AI awareness on employee’s service innovative behavior. Tourism Management, 92, 104564. https://doi.org/10.1016/j.tourman.2022.104564
48. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717. https://doi.org/10.1037/0003-066X.57.9.705
49. Makarius, E. E., Mukherjee, D., Fox, J. R., & Burton, J. P. (2020). Rising with the machines: A sociotechnical framework for bringing artificial intelligence into the organization. Journal of Business Research, 120, 262–273. https://doi.org/10.1016/j.jbusres.2020.07.045
50. Meshram, R. (2023). The role of artificial intelligence (AI) in recruitment and selection of employees in the organisation. Russian Law Journal, 11(9S), 322–333. https://doi.org/10.52783/rlj.v11i9s.1624
51. Motowidlo, S. J. (2003). Job performance. In W. C. Borman, D. R. Ilgen, & R. J. Klimoski (Eds.), Handbook of psychology: Industrial and organizational psychology (Vol. 12, pp. 39–53). Wiley.
52. Murugesan, U., Subramanian, P., Srivastava, S., & Dwivedi, A. (2023). A study of artificial intelligence impacts on human resource digitalization in Industry 4.0. Decision Analytics Journal, 7, Article 100249. https://doi.org/10.1016/j.dajour.2023.100249
53. Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
54. Niesen, W., Van Hootegem, A., Handaja, Y., Battistelli, A., & De Witte, H. (2018). Quantitative and qualitative job insecurity and idea generation: The mediating role of psychological contract breach. Scandinavian Journal of Work and Organizational Psychology, 3(1), Article 3. https://doi.org/10.16993/sjwop.36
55. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.
56. Organisation for Economic Co-operation and Development. (2023). The impact of AI on the workplace: Evidence from OECD case studies of AI implementation. https://doi.org/10.1787/2247ce58-en
57. Parasa, S. K. (2024). Impact of AI in recruitment and talent acquisition. Human Resource and Leadership Journal, 9(3), 78–83. https://www.carijournals.org/journals/index.php/HRLJ/article/view/2117/2509
58. Peterson, R. A. (1994). A meta-analysis of Cronbach’s coefficient alpha. Journal of Consumer Research, 21, 381–391. https://doi.org/10.1086/209405
59. Piccoli, B., Callea, A., Urbini, F., Chirumbolo, A., & Ingusci, E. (2021). Job insecurity and job performance: A serial mediated relationship. Frontiers in Psychology, 12, 694057. https://doi.org/10.3389/fpsyg.2021.694057
60. Presbitero, A., & Teng-Calleja, M. (2023). Job attitudes and career behaviors relating to employees’ perceived incorporation of artificial intelligence in the workplace: A career self-management perspective. Personnel Review, 52(4), 1169–1187. https://doi.org/10.1108/PR-02-2021-0103
61. Rafferty, A. E., & Griffin, M. A. (2006). Refining individualized consideration: Distinguishing developmental leadership and supportive leadership. Journal of Occupational and Organizational Psychology, 79(1), 37–61. https://doi.org/10.1348/096317905X3673
62. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 469–481). https://doi.org/10.1145/3351095.3372828
63. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
64. Rathore, S. P. S. (2023). The impact of AI on recruitment and selection processes: Analyzing the role of AI in automating and enhancing recruitment and selection procedures. International Journal for Global Academic & Scientific Research, 2(2), 51–63. https://doi.org/10.55938/ijgasr.v2i2.50
65. Setiawan, I., & Purba, H. H. (2020). A systematic literature review of key performance indicators (KPIs) implementation. Journal of Industrial Engineering and Management, 1(3), 200–208. https://doi.org/10.7777/jiemar.v1i3.79
66. Singer, V., & Gupta, V. (2025, January 17). Jobs and the future of work, AI and beyond: How every career can navigate the new tech landscape. World Economic Forum. https://www.weforum.org/stories/2025/01/ai-and-beyond-how-every-career-can-navigate-the-new-tech-landscape/
67. Staufenbiel, T., & König, C. J. (2010). A model for the effects of job insecurity on performance, turnover intention, and absenteeism. Journal of Occupational and Organizational Psychology, 83(1), 101–117. https://doi.org/10.1348/096317908X401912
68. Stiglbauer, B., Selenko, E., Batinic, B., & Jodlbauer, S. (2012). On the link between job insecurity and turnover intentions: Moderated mediation by work involvement and well-being. Journal of Occupational Health Psychology, 17(3), 354–364. https://doi.org/10.1037/a0028565
69. Sverke, M., Hellgren, J., & Näswall, K. (2002). No security: A meta-analysis and review of job insecurity and its consequences. Journal of Occupational Health Psychology, 7(3), 242–264. https://doi.org/10.1037/1076-8998.7.3.242
70. Şentürk, F. K., & Ertem, M. (2020). The mediating role of organizational commitment in the effect of commitment to change perception on job satisfaction. In 28th National Management and Organization Congress (pp. 64–79).
71. Tabachnick, B. G., & Fidell, L. S. (2019). Using multivariate statistics (7th ed.). Pearson.
72. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48, 1273–1296. https://doi.org/10.1007/s11165-016-9602-2
73. Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
74. Tarafdar, M., Cooper, C. L., & Stich, J.-F. (2019). The technostress trifecta—Techno eustress, distress, and strain. Journal of Business Research, 103, 69–82. https://doi.org/10.1111/isj.12169
75. Tusquellas, N., Santiago, R., & Palau, R. (2025). Professional development analytics: A smart model for Industry 5.0. Applied Sciences, 15(4), Article 2057. https://doi.org/10.3390/app15042057
76. Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for human resource management. Strategic HR Review, 17(5), 237–244. https://doi.org/10.1108/SHR-07-2018-0051
77. Van Hootegem, A., De Witte, H., De Cuyper, N., & Vander Elst, T. (2018). Job insecurity and the willingness to undertake training: The moderating role of perceived employability. Journal of Career Development, 46(4), 395–409. https://doi.org/10.1177/0894845318763893
78. Vander Elst, T., De Witte, H., & De Cuyper, N. (2014). The job insecurity scale: A psychometric evaluation across five European countries. European Journal of Work and Organizational Psychology, 23(3), 364–380. https://doi.org/10.1080/1359432X.2012.745989
79. Wang, H., Lu, C., & Siu, O. L. (2015). Job insecurity and job performance: The moderating role of organizational justice and the mediating role of work engagement. Journal of Applied Psychology, 100(4), 1249–1258. https://doi.org/10.1037/a0038330
80. Xie, L., Huang, J., Jin, Z., Fu, D., Lu, L., & Chen, W. (2024). Enhancement or inhibition: The double-edged sword effect of AI awareness on employees’ work engagement. https://doi.org/10.2139/ssrn.5042724
81. Yan, B., & Teng, Y. (2025). The double-edged sword effect of artificial intelligence awareness on organisational citizenship behaviour: A study based on knowledge workers. Behaviour & Information Technology, 1–17. https://doi.org/10.1080/0144929X.2025.2462267
82. Yuan, Q., Kong, J., Liu, C., & Jiang, Y. (2025). Understanding the effects of specific techno-stressors on strain and job performance: A meta-analysis of the empirical evidence. Information Technology & People, 38(2), 787–826. https://doi.org/10.1108/ITP-08-2022-0639
83. Zhao, H, Ye, L., Guo, M., & Deng, Y. (2025). Reflection or dependence: How AI awareness affects employees’ in-role and extra-role performance? Behavioral Sciences, 15(2), Article 128. https://doi.org/10.3390/bs15020128