Journal of Human Resource Management Future Studies
Volume 1, Issue 1 (6-2026)
Journal of human resource management future studies 2026, 1(1): 65-87 |
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Tari G, Pabarjay Zanjani M, Hossein nejad Azhiri S. Perceived Procedural Justice in AI‑Based Recruitment: Testing the Technology Acceptance Model in the Iranian Context. Journal of human resource management future studies 2026; 1 (1) :65-87
URL: http://jhrmfs.khu.ac.ir/article-1-162-en.html
URL: http://jhrmfs.khu.ac.ir/article-1-162-en.html
Islamic Azad University, Marand Branch
Abstract: (220 Views)
experience.userimproveoBackground and Objectives: with the expansion of digital technologies and the use of artificial intelligence in human resource processes, especially recruitment, process efficiency has increased. However, the adoption of this technology by applicants depends on their perception of procedural justice. This study examines the effect of applicants' perception of AI use in the recruitment process on their percieved procedural justice, and the mediating roles of three Technilogy Acceptance Model constructs: percieved usefulness, percieved ease of use, and percieved trust.
Methods: This descriptive-correlational study with a quantitative approach used data collected from a questionaire adapted from Hossein et al. (2025) on a five-point Likert scale. The population consisted of 290 employees and branch manageres of an insurance company in Zanjan city. Cronbach's alpha=0.717, composite reliability=0.817, and AVE=0.518 were confirmed. Data analysis was conducted using Structural Equation Modeling in SmartPLS4.
Results: Findings showed that applicants' percieved use of AI in the recruitment process has a positive and significant relationship with percieved procedural justice. Percieved usefulness and percieved trust significantly mediate the relationship between AI use and procedural justice, while pecieved ease of use does not play this mediating role.
Conclusions: The results can provide a practical framework for designing intelligent recruitment systems that are fair, transparent, and trustworthy, and aligned with professional ethics and privacy to anhance acceptance and satisfaction among applicants. adding percieved trust to the Technology Acceptance Model and testing the mediating role of the three constucts addresses a research gap in emerging economies such as Iran and offers guideliness.
Methods: This descriptive-correlational study with a quantitative approach used data collected from a questionaire adapted from Hossein et al. (2025) on a five-point Likert scale. The population consisted of 290 employees and branch manageres of an insurance company in Zanjan city. Cronbach's alpha=0.717, composite reliability=0.817, and AVE=0.518 were confirmed. Data analysis was conducted using Structural Equation Modeling in SmartPLS4.
Results: Findings showed that applicants' percieved use of AI in the recruitment process has a positive and significant relationship with percieved procedural justice. Percieved usefulness and percieved trust significantly mediate the relationship between AI use and procedural justice, while pecieved ease of use does not play this mediating role.
Conclusions: The results can provide a practical framework for designing intelligent recruitment systems that are fair, transparent, and trustworthy, and aligned with professional ethics and privacy to anhance acceptance and satisfaction among applicants. adding percieved trust to the Technology Acceptance Model and testing the mediating role of the three constucts addresses a research gap in emerging economies such as Iran and offers guideliness.
Keywords: Digital Recruitment, Perceived Trust, Perceived Usefulness, Procedural Justice, Technology Acceptance Model (TAM)
Type of Study: Research |
Subject:
Special
Received: 2026/02/22 | Accepted: 2026/05/20 | Published: 2026/06/7
Received: 2026/02/22 | Accepted: 2026/05/20 | Published: 2026/06/7
References
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3. اخاتی، حسین. (1404). تأثیر هوش مصنوعی بر کیفیت تصمیمگیری در فرایندهای استخدام سازمانی. مدیریت، آموزش و توسعه در عصر دیجیتال، 2(2)، 1–13.
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9. Abdelhay, S., Korany, H., Marie, A., & Fareen, A. (2023). Artificial Intelligence Applications in the recruitment process opportunities and challenges. European Chemical Bulletin, 7, 1008–1019.
10. Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399–416.
11. Aghamohammadi, A., Jafari, K., & Parsa, A. (2024). A meta-analysis of artificial intelligence studies in organizations with an emphasis on challenges and opportunities. Human Resource Studies, 15(1), 119-142. DOI: [DOI:10.22034/JHRS.2025.485304.2317 [In Persian]]
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13. Akbari, A., & Tohmasabi, R. (2023). Identifying applications and requirements of artificial intelligence in the recruitment process. Organizational Culture Management, 21(1), 75–88. DOI: http://doi.org/10.22059/jomc.2021.320799.1008246 [In Persian]
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16. Da Motta Veiga, Figueroa-Armijos, M., & Clark, B. (2022). Artificial Intelligence in Hiring and Applicant Attraction: The Role of Ethical Perceptions. Academy of Management Proceedings, 2022(1), 10291. [DOI:10.5465/AMBPP.2022.10291abstract.]
17. Danks, D., & London, A. J. (2017). Algorithmic bias in autonomous systems. 26th International Joint Conference on Artificial Intelligence, 4691–4697. Pittsburgh, USA, August 2017.
18. Dattner, B., Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The legal and ethical implications of using AI in hiring. Harvard Business Review, 25, 1490–1523.
19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
20. Davis, F. D., Bagozzi, R. P., & Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
21. Dehghan Manshadi, F., Tabavar, A., & Ghasem, M. (2025). Strategic analysis of artificial intelligence–based human resource management development. Entrepreneurship Development, 18(2), 108–148. [In Persian] [DOI:10.22059/jed.2025.392646.654508]
22. Dehghan Monshadi, F., Tabaavar, A., & Qasem, M. (2024). Strategic analysis of human resource management development based on artificial intelligence. Entrepreneurship Development, 18(2), 108-148. DOI: [DOI:10.22059/jed.2025.392646.654508 [In Persian]]
23. Ekhati, H. (2025). The impact of artificial intelligence on decision-making quality in organizational recruitment processes. Management, Education, and Development in the Digital Age, 2(2), 1-13. [In Persian]
24. Etemadi, M., Chitsaz, A., Koushki, S., & Jafari, S. M. A. (2024). Artificial intelligence versus human-led approaches in human resource recruitment assessment: A meta-synthesis of advantages and disadvantages. Sustainable Human Resource Management Quarterly, 6(11), 191-214. [In Persian]
25. Fathiazar, M., & Bagzadeh, Sh. (2025). Application of artificial intelligence in recruitment and performance evaluation processes. In Proceedings of the Ninth International Conference on Management and Industry. [In Persian]
26. Figueroa-Armijos, M., Clark, B. B., & da Motta Veiga, S. P. (2023). Ethical perceptions of AI in hiring and organizational trust: The role of performance expectancy and social influence. Journal of Business Ethics, 186(1), 179–197.
27. Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
28. Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37, 735–757.
29. Frail, J., & László, V. (2021). A literature review: Artificial intelligence impact on the recruitment process. International Journal of Engineering and Management Sciences, 6(1), 108–119.
30. Gilliland, S. W. (1993). The perceived fairness of selection systems: An organizational justice perspective. Academy of Management Review, 18(4), 694–734.
31. Hausknecht, J. P., Day, D. V., & Thomas, S. C. (2004). Applicant reactions to selection procedures: An updated model and meta analysis. Personnel Psychology, 57(3), 639–683.
32. Hiemstra, A. M. F., Oostrom, J. K., Derous, E., Serlie, A. W., & Born, M. P. (2019). Applicant perceptions of initial job candidate screening with asynchronous job interviews. Journal of Personnel Psychology, 18(3), 138–147.
33. Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11(4), Article 100303.
34. Hosain, M. S., & Mustafi, M. A. A. (2023). Social networking information and job applicants’ background check: Mediating and moderating effects of employers’ behavioral intention and legal consideration. Middle East Journal of Management, 10(5), 467–498.
35. Hosain, M. S., Liu, P., & Mustafi, M. A. A. (2021). Social networking information and pre-employment background check: Mediating effects of perceived benefit and organizational branding. International Journal of Manpower, 42(7), 1279–1303.
36. Huang, T. L., & Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness. Electronic Commerce Research, 15(2), 269–295, 2015.
37. Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection:, 178(4), 977–1007. A review and research agenda. Journal of Business Ethics
38. Ingold, P. V., & Langer, M. (2021). Resume= resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes. Computers in Human Behavior, 114, Article 106573.
39. Jaser, Z., Petrakaki, D., Starr, R. R., & Oyarbide-Magaña, E. (2022). Where automated job interviews fall short. Harvard Business Publishing Education. Web article https://hbr.org/2022/01/where-automated-job-interviews-fall-short. (Accessed 20 September 2023).
40. Lee, C., & Cha, K. (2023). FAT-CAT—explainability and augmentation for an AI system: A case study on AI recruitment-system adoption. International Journal of Human-Computer Studies, 171(1), Article 102976.
41. Upadhyay, K. A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258.
42. 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.
43. 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, 215–222.
44. Zhang, P. (2024). Application of Artificial Intelligence (AI) in recruiting and selection: The case of company A and company B. Journal of Business and Management Studies, 6(3), 224–235.
45. آقامحمدی، علی، جعفری، کاظم و پارسا، امیر. (1403). فراتحلیل مطالعات هوش مصنوعی در سازمانها با تأکید بر چالشها و فرصتها. مطالعات منابع انسانی، 15(1)، 119–142.
46. عباسی، رضا و اسماعیلی، محمد. (1403). هوش مصنوعی و فرایندهای دیجیتال منابع انسانی: کاربردها و چالشها. مطالعات منابع انسانی، 14(1)، 116–140.
47. اخاتی، حسین. (1404). تأثیر هوش مصنوعی بر کیفیت تصمیمگیری در فرایندهای استخدام سازمانی. مدیریت، آموزش و توسعه در عصر دیجیتال، 2(2)، 1–13.
48. اکبری، امیر و طهماسبی، رضا. (1402). شناسایی کاربردها و الزامات هوش مصنوعی در فرایند جذب و استخدام. مدیریت فرهنگ سازمانی، 21(1)، 75–88.
49. اعتمادی، مریم، چیتساز، الهام، کوشکی، سارا و جعفری، سید محمدعلی. (1403). هوش مصنوعی در برابر رویکردهای انسانی در ارزیابی جذب منابع انسانی: فراترکیب مزایا و معایب. فصلنامه مدیریت منابع انسانی پایدار، 6(11)، 191–214.
50. دهقان منشادی، فاطمه، طبّاور، الهام و قاسم، محمد. (1404). تحلیل راهبردی توسعه مدیریت منابع انسانی مبتنی بر هوش مصنوعی. توسعه کارآفرینی، 18(2)، 108–148.
51. فتحیآذر، مهدی و باقیزاده، شهرزاد. (1404). کاربرد هوش مصنوعی در فرایندهای جذب و ارزیابی عملکرد. در مجموعه مقالات نهمین کنفرانس بینالمللی مدیریت و صنعت.
52. Abbasi, R., & Esmaeili, M. (2024). Artificial intelligence and digital human resource processes: Applications and challenges. Human Resource Studies, 14(1), 116–140. [In Persian] [DOI:10.22034/JHRS.2024.195965]
53. Abdelhay, S., Korany, H., Marie, A., & Fareen, A. (2023). Artificial Intelligence Applications in the recruitment process opportunities and challenges. European Chemical Bulletin, 7, 1008–1019.
54. Acikgoz, Y., Davison, K. H., Compagnone, M., & Laske, M. (2020). Justice perceptions of artificial intelligence in selection. International Journal of Selection and Assessment, 28(4), 399–416.
55. Aghamohammadi, A., Jafari, K., & Parsa, A. (2024). A meta-analysis of artificial intelligence studies in organizations with an emphasis on challenges and opportunities. Human Resource Studies, 15(1), 119-142. DOI: [DOI:10.22034/JHRS.2025.485304.2317 [In Persian]]
56. Akati, H. (2025). The impact of artificial intelligence on decision-making quality in organizational recruitment processes. Journal of Management, Education, and Development in the Digital Era, 2(2), 1–13. [In Persian]
57. Akbari, A., & Tohmasabi, R. (2023). Identifying applications and requirements of artificial intelligence in the recruitment process. Organizational Culture Management, 21(1), 75–88. DOI: http://doi.org/10.22059/jomc.2021.320799.1008246 [In Persian]
58. Chen, Z. (2023). Collaboration among recruiters and artificial intelligence: Removing human prejudices in employment. Cognition, Technology & Work, 25(1), 135–149.
59. Colquitt, J. A., Conlon, D. E., Wesson, M. J., Porter, C. O. L. H., & Ng, K. Y. (2001). Justice at the millennium: A meta-analytic review of 25 years of organizational justice research. Journal of Applied Psychology, 86(3), 425–445.
60. Da Motta Veiga, Figueroa-Armijos, M., & Clark, B. (2022). Artificial Intelligence in Hiring and Applicant Attraction: The Role of Ethical Perceptions. Academy of Management Proceedings, 2022(1), 10291. [DOI:10.5465/AMBPP.2022.10291abstract.]
61. Danks, D., & London, A. J. (2017). Algorithmic bias in autonomous systems. 26th International Joint Conference on Artificial Intelligence, 4691–4697. Pittsburgh, USA, August 2017.
62. Dattner, B., Chamorro-Premuzic, T., Buchband, R., & Schettler, L. (2019). The legal and ethical implications of using AI in hiring. Harvard Business Review, 25, 1490–1523.
63. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
64. Davis, F. D., Bagozzi, R. P., & Warshaw, P.R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
65. Dehghan Manshadi, F., Tabavar, A., & Ghasem, M. (2025). Strategic analysis of artificial intelligence–based human resource management development. Entrepreneurship Development, 18(2), 108–148. [In Persian] [DOI:10.22059/jed.2025.392646.654508]
66. Dehghan Monshadi, F., Tabaavar, A., & Qasem, M. (2024). Strategic analysis of human resource management development based on artificial intelligence. Entrepreneurship Development, 18(2), 108-148. DOI: [DOI:10.22059/jed.2025.392646.654508 [In Persian]]
67. Ekhati, H. (2025). The impact of artificial intelligence on decision-making quality in organizational recruitment processes. Management, Education, and Development in the Digital Age, 2(2), 1-13. [In Persian]
68. Etemadi, M., Chitsaz, A., Koushki, S., & Jafari, S. M. A. (2024). Artificial intelligence versus human-led approaches in human resource recruitment assessment: A meta-synthesis of advantages and disadvantages. Sustainable Human Resource Management Quarterly, 6(11), 191-214. [In Persian]
69. Fathiazar, M., & Bagzadeh, Sh. (2025). Application of artificial intelligence in recruitment and performance evaluation processes. In Proceedings of the Ninth International Conference on Management and Industry. [In Persian]
70. Figueroa-Armijos, M., Clark, B. B., & da Motta Veiga, S. P. (2023). Ethical perceptions of AI in hiring and organizational trust: The role of performance expectancy and social influence. Journal of Business Ethics, 186(1), 179–197.
71. Fishbein, M., & Ajzen, I. (1975). Beliefs, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
72. Folger, N., Brosi, P., Stumpf-Wollersheim, J., & Welpe, I. M. (2022). Applicant reactions to digital selection methods: A signaling perspective on innovativeness and procedural justice. Journal of Business and Psychology, 37, 735–757.
73. Frail, J., & László, V. (2021). A literature review: Artificial intelligence impact on the recruitment process. International Journal of Engineering and Management Sciences, 6(1), 108–119.
74. Gilliland, S. W. (1993). The perceived fairness of selection systems: An organizational justice perspective. Academy of Management Review, 18(4), 694–734.
75. Hausknecht, J. P., Day, D. V., & Thomas, S. C. (2004). Applicant reactions to selection procedures: An updated model and meta analysis. Personnel Psychology, 57(3), 639–683.
76. Hiemstra, A. M. F., Oostrom, J. K., Derous, E., Serlie, A. W., & Born, M. P. (2019). Applicant perceptions of initial job candidate screening with asynchronous job interviews. Journal of Personnel Psychology, 18(3), 138–147.
77. Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11(4), Article 100303.
78. Hosain, M. S., & Mustafi, M. A. A. (2023). Social networking information and job applicants’ background check: Mediating and moderating effects of employers’ behavioral intention and legal consideration. Middle East Journal of Management, 10(5), 467–498.
79. Hosain, M. S., Liu, P., & Mustafi, M. A. A. (2021). Social networking information and pre-employment background check: Mediating effects of perceived benefit and organizational branding. International Journal of Manpower, 42(7), 1279–1303.
80. Huang, T. L., & Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness. Electronic Commerce Research, 15(2), 269–295, 2015.
81. Hunkenschroer, A. L., & Luetge, C. (2022). Ethics of AI-enabled recruiting and selection:, 178(4), 977–1007. A review and research agenda. Journal of Business Ethics
82. Ingold, P. V., & Langer, M. (2021). Resume= resume? The effects of blockchain, social media, and classical resumes on resume fraud and applicant reactions to resumes. Computers in Human Behavior, 114, Article 106573.
83. Jaser, Z., Petrakaki, D., Starr, R. R., & Oyarbide-Magaña, E. (2022). Where automated job interviews fall short. Harvard Business Publishing Education. Web article https://hbr.org/2022/01/where-automated-job-interviews-fall-short. (Accessed 20 September 2023).
84. Lee, C., & Cha, K. (2023). FAT-CAT—explainability and augmentation for an AI system: A case study on AI recruitment-system adoption. International Journal of Human-Computer Studies, 171(1), Article 102976.
85. Upadhyay, K. A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258.
86. 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.
87. 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, 215–222.
88. Zhang, P. (2024). Application of Artificial Intelligence (AI) in recruiting and selection: The case of company A and company B. Journal of Business and Management Studies, 6(3), 224–235.
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