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AI-enabled human resource practices and organizational agility: The mediating role of employee voice and the moderating effect of AI-readiness culture in ASEAN countries

1Management Department, Universitas Bhayangkara Jakarta Raya, Indonesia, Indonesia

2Binus Business School, Bina Nusantara (BINUS) University, Jakarta, Indonesia, Indonesia

Open Access Copyright 2026 Diponegoro International Journal of Business under http://creativecommons.org/licenses/by-sa/4.0.

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Abstract

Our study examines how AI-HR management practices, including fair algorithms, analytics maturity, legal ethics and employee participation, can boost the retention rate of workforces and the agility of businesses in Southeast Asian countries. It further studies workforce retention as an intermediary mechanism and AI-motivated culture as an environmental moderator on this relationship.  Based on Resource-Based View, Socio-Technical Systems theory, and the Dynamic Capabilities approach, this study employs a cross-national quantitative research design. Data collected from organizations in Indonesia, Malaysia, Thailand and Singapore were analyzed using partial least squares structural equation modeling in SmartPLS software to test direct, mediating (indirect), and moderating effects.  Our research discovers that algorithmic fairness and digital ethics can directly and significantly promote employee voice; also importantly, an employee's voice contributes not marginally but greatly to workforce retention, and indirectly promotes organizational agility as well. HR analytics maturity indirectly reinforces corporate agility by supporting decision-making that is informed and staff engagement. Moreover, the relationship between employee voice and organizational agility is positively moderated by AI-ready culture--an earlier conclusion which confirms the effectiveness of these new HRM practices.  The paper progresses the AI-HRM literature by providing a unified model of several different countries which details that ethical, analytical and "distributed" AI practices serve to enhance the sustainability of workforce and enterprise-wide agility. This study provides new evidence to support theory on Southeast Asia, bringing valuable insights and guides to the establishment of sustainable, and future-oriented AI-HRM systems.

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AI-Enabled Human Resource Practices and Organizational Agility: The Mediating Role of Employee Voice and the Moderating Effect of AI-Readiness Culture in ASEAN Countries
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Keywords: artificial intelligence in HRM; algorithmic fairness; HR analytics maturity; digital ethics; employee voice; workforce retention; organizational agility; AI-readiness culture
Funding: NA

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  1. Agu, E., Efunniyi, C., Adeniran, I., Obiki-Osafiele, A., Abhulimen, A., & Osundare, O. (2024). Discussing ethical considerations and solutions for ensuring fairness in AI-driven financial services. International Journal of Frontline Research in Multidisciplinary Studies, 3(2), 001–009. https://doi.org/10.56355/ijfrms.2024.3.2.0024
  2. Alsaif, A., & Sabih Aksoy, M. (2023). AI-HRM: Artificial Intelligence in Human Resource Management: A Literature Review. Journal of Computing and Communication, 2(2), 1–7. https://doi.org/10.21608/jocc.2023.307053
  3. Arora, M., & Mittal, A. (2024). Employees’ change in perception when artificial intelligence integrates with human resource management: a mediating role of AI-tech trust. Benchmarking: An International Journal, 32(6). https://doi.org/10.1108/bij-11-2023-0795
  4. Bakhash, A., Fahmy, S. S., & Zran, J. (2025). Silencing the voices of discontent: How the new digital communication environment reinforces the spiral of silence in the Yemeni crisis. Journal of Arab & Muslim Media Research, 18(2), 239–257. https://doi.org/10.1386/jammr_00088_1
  5. Basnet, S. (2024). The Impact of AI-Driven Predictive Analytics on Employee Retention Strategies. International Journal of Research and Review, 11(9), 50–65. https://doi.org/10.52403/ijrr.20240906
  6. Basnet, S. (2024). The Impact of AI-Driven Predictive Analytics on Employee Retention Strategies. International Journal of Research and Review, 11(9), 50–65. https://doi.org/10.52403/ijrr.20240906
  7. Böhmer, N., & Schinnenburg, H. (2023). Critical exploration of AI-driven HRM to build up organizational capabilities. Employee Relations: The International Journal, 45(5), 1057–1082. https://doi.org/10.1108/er-04-2022-0202
  8. Boddington, P. (2017) Towards a Code of Ethics for Artificial Intelligence Research. Ed. by Michael Wooldridge Barry O’Sullivan. Springer Publishing, Oxford, 1-5, 27-28
  9. Busuioc, M., Curtin, D., & Almada, M. (2022). Reclaiming transparency: contesting the logics of secrecy within the AI Act. European Law Open, 2(1), 79–105. https://doi.org/10.1017/elo.2022.47
  10. Buchan, J. (2010). Reviewing The Benefits of Health Workforce Stability. Human Resources for Health, 8(3). https://doi.org/10.1186/1478-4491-8-29
  11. Căvescu, A. M., & Popescu, N. (2025). Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath, 5(3), 99. https://doi.org/10.3390/appliedmath5030099
  12. Cath, C. (2018). Governing artificial intelligence: ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  13. Cheong, B. C. (2024). Transparency and accountability in AI systems: safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics, 6. https://doi.org/10.3389/fhumd.2024.1421273
  14. Chukwunweike, J., Lawal, O., Arogundade, J., & E, B. (2024). Navigating ethical challenges of explainable ai in autonomous systems. International Journal of Science and Research Archive, 13(1), 1807–1819. https://doi.org/10.30574/ijsra.2024.13.1.1872
  15. Căvescu, A. M., & Popescu, N. (2025). Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath, 5(3), 99. https://doi.org/10.3390/appliedmath5030099
  16. Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386
  17. Carmeli, A., Schaubroeck, J., & Tishler, A. (2011). How CEO empowering leadership shapes top management team processes: Implications for firm performance. The Leadership Quarterly, 22(2), 399–411. https://doi.org/10.1016/j.leaqua.2011.02.013
  18. Cowgill, B. (2019). Bias and Productivity in Humans and Machines. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3433737
  19. Căvescu, A. M., & Popescu, N. (2025). Predictive Analytics in Human Resources Management: Evaluating AIHR’s Role in Talent Retention. AppliedMath, 5(3), 99. https://doi.org/10.3390/appliedmath5030099
  20. Dastmalchian, A., Satish Kumar, M., Bacon, N., Steinke, C., Blyton, P., Tang, N., İmer, H. P., Auer-Rizzi, W., Jiang, Y., Kabasakal, H., Sugai, P., Habibi, M., Meo Colombo, C., Ertenu, B., Huang, H. J., Varnali, R., Cotton, R., Isa, C. R., Bayraktar, S.,Thang, T. T. N. (2020). High-performance work systems and organizational performance across societal cultures. Journal of International Business Studies, 51(3), 353–388. https://doi.org/10.1057/s41267-019-00295-9
  21. Davenport TH, Harris J, Shapiro J. Competing on talent analytics. Harv Bus Rev. 2010 Oct;88(10):52-8, 150. PMID: 20929194
  22. Detert, J. R., & Burris, E. R. (2007). Leadership Behavior and Employee Voice: Is the Door Really Open? Academy of Management Journal, 50(4), 869–884. https://doi.org/10.5465/amj.2007.26279183
  23. Doz, Y. L., & Kosonen, M. (2010). Embedding Strategic Agility. Long Range Planning, 43(2–3), 370–382. https://doi.org/10.1016/j.lrp.2009.07.006
  24. Dr Jolly Masih, D. A. J. (2023). Enhancing employee efficiency and performance in industry 5.0 organizations through artificial intelligence integration. European Economic Letters (EEL), 13(4), 300–315. https://doi.org/10.52783/eel.v13i4.589
  25. Du, J. (2024). Ethical and Legal Challenges of AI in Human Resource Management. Journal of Computing and Electronic Information Management, 13(2), 71–77. https://doi.org/10.54097/83j64ub9
  26. Du, J. (2024). Unlocking the Potential: Literature Review on the Evolving Role of AI in HRM. Frontiers in Management Science, 3(1), 28–33. https://doi.org/10.56397/fms.2024.02.05
  27. Faheem, M. (2024). Ethical AI: Addressing bias, fairness, and accountability in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 23(2), 1703–1711. https://doi.org/10.30574/wjarr.2024.23.2.2510
  28. Fenwick, A., Frangos, P., & Molnar, G. (2024). The critical role of HRM in AI-driven digital transformation: a paradigm shift to enable firms to move from AI implementation to human-centric adoption. Discover Artificial Intelligence, 4(1). https://doi.org/10.1007/s44163-024-00125-4
  29. Greenberg, J. (1987). A Taxonomy of Organizational Justice Theories. The Academy of Management Review, 12(1), 9. https://doi.org/10.2307/257990
  30. Grimmelikhuijsen, S. (2022). Explaining Why the Computer Says No: Algorithmic Transparency Affects the Perceived Trustworthiness of Automated Decision‐Making. Public Administration Review, 83(2), 241–262. https://doi.org/10.1111/puar.13483
  31. Harrington, J. R., & Lee, J. H. (2014). What Drives Perceived Fairness of Performance Appraisal? Exploring the Effects of Psychological Contract Fulfillment on Employees’ Perceived Fairness of Performance Appraisal in U.S. Federal Agencies. Public Personnel Management, 44(2), 214–238. https://doi.org/10.1177/0091026014564071
  32. Huriye, A. Z. (2023). The Ethics of Artificial Intelligence: Examining the Ethical Considerations Surrounding the Development and Use of AI. American Journal of Technology, 2(1), 37–45. https://doi.org/10.58425/ajt.v2i1.142
  33. Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue. Human Resource Management, 57(3), 679–684. https://doi.org/10.1002/hrm.21916
  34. Hmoud, B. I., & Várallyai, L. (2020). Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants. International Journal of Engineering and Management Sciences, 5(1), 749–765. https://doi.org/10.21791/ijems.2020.1.65
  35. Ismail, O., & Ahmad, N. (2025). Ethical and Governance Frameworks for Artificial Intelligence: A Systematic Literature Review. International Journal of Interactive Mobile Technologies (iJIM), 19(14), 121–136. https://doi.org/10.3991/ijim.v19i14.56981
  36. Jia, X., & Hou, Y. (2024). Architecting the future: exploring the synergy of AI-driven sustainable HRM, conscientiousness, and employee engagement. Discover Sustainability, 5(1). https://doi.org/10.1007/s43621-024-00214-5
  37. Jöhnk, J., Wyrtki, K., & Weißert, M. (2020). Ready or Not, AI Comesu2014 An Interview Study of Organizational AI Readiness Factors. Business & Information Systems Engineering, 63(1), 5–20. https://doi.org/10.1007/s12599-020-00676-7
  38. Khan, M. I., Hussain, S., & Parahyanti, E. (2024). The Role Generative AI in Human Resource Management: Enhancing Operational Efficiency, Decision-Making, and Addressing Ethical Challenges. Asian Journal of Logistics Management, 3(2), 104–125. https://doi.org/10.14710/ajlm.2024.24671
  39. Khan MA (2025), "Investigating the intersection of organizational behavior, supply chain practices, economic outcomes, financial excellence and CSR for corporate identity improvement". Measuring Business Excellence, 29(3), 551–572, doi: https://doi.org/10.1108/MBE-06-2024-0083
  40. Kayusi, F., Mishra, R., Keari Omwenga, M., Gonzalez Vallejo, R., Chavula, P., Juma, L., & Agura Kayus, B. (2025). AI-Driven HR Analytics: Transforming Talent Management and Employee Engagement. Revista Multidisciplinaria Voces de América y El Caribe, 2(1), 558–582. https://doi.org/10.69821/remuvac.v2i1.214
  41. Kim, S. Y., & Fernandez, S. (2017). Employee empowerment and turnover intention in the U.S. federal bureaucracy. The American Review of Public Administration, 47(1), 22–44
  42. Kirkman, B. L., & Shapiro, D. L. (2001). The impact of cultural values on job satisfaction and organizational commitment in self-managing work teams: the mediating role of employee resistance. Academy of Management Journal, 44(3), 557–569. https://doi.org/10.2307/3069370
  43. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
  44. Knoll, M., Schyns, B., Meyer, B., & Neves, P. (2020). A Multi‐Level Approach to Direct and Indirect Relationships between Organizational Voice Climate, Team Manager Openness, Implicit Voice Theories, and Silence. Applied Psychology, 70(2), 606–642. https://doi.org/10.1111/apps.12242
  45. Le-Nguyen, H.-T. (2024). Ethical Dilemmas of AI Perspectives Towards Common Digital Art and Digital Crafting (pp. 226–257). Igi Global. https://doi.org/10.4018/979-8-3693-1950-5.ch013
  46. Lee, M. K. (2018). Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management. Big Data Society, 5(1). https://doi.org/10.1177/2053951718756684
  47. Leventhal, G. S. (1980). What Should Be Done with Equity Theory? In Social Exchange (pp. 27–55). Springer US. https://doi.org/10.1007/978-1-4613-3087-5_2
  48. Levenson, A. (2017). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685–700. https://doi.org/10.1002/hrm.21850
  49. Ludviga, I., & Kalvina, A. (2023). Organizational agility during crisis: do employees’ perceptions of public sector organizations’ strategic agility foster employees’ work engagement and well-being? Employee Responsibilities and Rights Journal, 36(2), 209–229. https://doi.org/10.1007/s10672-023-09442-9
  50. Mdhlalose, D. (2024). An examination of employee rewards and work environment on employee creativity and innovation. SEISENSE Journal of Management, 7(1), 21–34. https://doi.org/10.33215/rewfe541
  51. Madanchian, M., & Taherdoost, H. (2025). Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors. Information, 16(1), 51. https://doi.org/10.3390/info16010051
  52. Martin, K. D., & Murphy, P. E. (2016). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45(2), 135–155. https://doi.org/10.1007/s11747-016-0495-4
  53. Marler, J. H., & Boudreau, J. W. (2016). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3–26. https://doi.org/10.1080/09585192.2016.1244699
  54. Milliken, F. J., Morrison, E. W., & Hewlin, P. F. (2003). An exploratory study of employee silence: Issues that employees don't communicate upward and why. Journal of Management Studies, 40(6), 1453–1476. https://doi.org/10.1111/1467-6486.00387
  55. Morrison, E. W. (2014). Employee Voice and Silence. Annual Review of Organizational Psychology and Organizational Behavior, 1(1), 173–197. https://doi.org/10.1146/annurev-orgpsych-031413-091328
  56. Muduli, A. (2015). High performance work system, HRD climate and organisational performance: an empirical study. European Journal of Training and Development, 39(3), 239–257. https://doi.org/10.1108/ejtd-02-2014-0022
  57. Newell, S., & Marabelli, M. (2015). Strategic Opportunities (and Challenges) of Algorithmic Decision-Making: A Call for Action on the Long-Term Societal Effects of “Datification.” SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2644093
  58. Nguyen, A., & Mateescu, A. (2019). Explainer: Algorithmic management in the workplace. In Proceedings of the Data & Society Research Institute. https://datasociety.net/library/explainer-algorithmic-management-in-the-workplace/
  59. Owolabi, O. S., Uche, P. C., Islam, R. B., Ihejirika, C., Adeniken, N. T., & Chhetri, B. J. T. (2024). Ethical Implication of Artificial Intelligence (AI) Adoption in Financial Decision Making. Computer and Information Science, 17(1), 49. https://doi.org/10.5539/cis.v17n1p49
  60. Park, H., Ahn, D., Lee, J., & Hosanagar, K. (2021). Human-AI Interaction in Human Resource Management: Understanding Why Employees Resist Algorithmic Evaluation at Workplaces and How to Mitigate Burdens. 1–15. https://doi.org/10.1145/3411764.3445304
  61. Polisetty, A., Chakraborty, D., G, S., Kar, A. K., & Pahari, S. (2023). What Determines AI Adoption in Companies? Mixed-Method Evidence. Journal of Computer Information Systems, ahead-of-print(ahead-of-print), 370–387. https://doi.org/10.1080/08874417.2023.2219668
  62. Pickering, B. (2021). Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies. Future Internet, 13(5), 132. https://doi.org/10.3390/fi13050132
  63. Prikshat, V., Islam, M., Patel, P., Malik, A., Budhwar, P., & Gupta, S. (2023). AI-Augmented HRM: Literature review and a proposed multilevel framework for future research. Technological Forecasting and Social Change, 193, 122645. https://doi.org/10.1016/j.techfore.2023.122645
  64. 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
  65. Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 469–481). FAT* ’20: Conference on Fairness, Accountability, and Transparency. ACM. https://doi.org/10.1145/3351095.3372828
  66. Rodgers, W., Murray, J. M., Stefanidis, A., Degbey, W. Y., & Tarba, S. Y. (2022). An artificial intelligence algorithmic approach to ethical decision-making in human resource management processes. Human Resource Management Review, 33(1), 100925. https://doi.org/10.1016/j.hrmr.2022.100925
  67. Rafi, N., Shafique, I., Kalyar, M. N., & Ahmed, A. (2021). Knowledge management capabilities and organizational agility as liaisons of business performance. South Asian Journal of Business Studies, 11(4), 397–417. https://doi.org/10.1108/sajbs-05-2020-0145
  68. Siradhana, N. K., & Arora, R. G. (2024). Examining the Influence of Artificial Intelligence Implementation in HRM Practices Using T-O-E Model. Vision: The Journal of Business Perspective. https://doi.org/10.1177/09722629241231458
  69. Stahl, B. C., Timmermans, J., & Mittelstadt, B. D. (2016). The Ethics of Computing. ACM Computing Surveys, 48(4), 1–38. https://doi.org/10.1145/2871196
  70. Sulistiawan, J., Moslehpour, M., Diana, F., & Lin, P.-K. (2022). Why and When Do Employees Hide Their Knowledge? Behavioral Sciences, 12(2), 56. https://doi.org/10.3390/bs12020056
  71. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(sici)1097-0266(199708)18:7<509::aid-smj882>3.0.co;2-z
  72. Teece, D., Peteraf, M. A., & Leih, S. (2016). Dynamic Capabilities and Organizational Agility: Risk, Uncertainty and Entrepreneurial Management in the Innovation Economy. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2771245
  73. T, T., E, U. M., Bakkiyaraj, M., Muthuvel, S., Johari, L., & Maharudrappa, M. (2024). AI-Powered HR Technology Implementation for Business Growth in Industrial 5.0 (pp. 171–200). Igi Global. https://doi.org/10.4018/979-8-3693-2432-5.ch009
  74. Tripathi, A. (2024). Organizational Learning Culture and Firm Performance: The Mediating Role of Learning Agility. Vikalpa: The Journal for Decision Makers, 49(2), 129–142. https://doi.org/10.1177/02560909241254996
  75. van den Broek, Elmira; Sergeeva, Anastasia; and Huysman, Marleen, (2019) "Hiring Algorithms: An Ethnography of Fairness in Practice. ICIS 2019 Proceedings. 6. https://aisel.aisnet.org/icis2019/future_of_work/future_work/6
  76. van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics. Journal of Organizational Effectiveness: People and Performance, 4(2), 157–178. https://doi.org/10.1108/joepp-03-2017-0022
  77. Van Dyne, L., Ang, S., & Botero, I. C. (2003). Conceptualizing employee silence and employee voice as multidimensional constructs. Journal of Management Studies, 40(6), 1359–1392. https://doi.org/10.1111/1467-6486.00384
  78. Wang, W., & Siau, K. (2019). Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity. Journal of Database Management, 30(1), 61–79. https://doi.org/10.4018/jdm.2019010104
  79. Warner, K. S. R., & Wäger, M. (2019). Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal. Long Range Planning, 52(3), 326–349. https://doi.org/10.1016/j.lrp.2018.12.001
  80. Watts, S., & Munir, T. (2025). Bridging the gap: exploring innovation enablers, challenges and AI adoption for enhanced workforce productivity. International Journal of Productivity and Performance Management, 1–17. https://doi.org/10.1108/ijppm-01-2025-0001
  81. Weiner, E. B., Dankwa-Mullan, I., Nelson, W. A., & Hassanpour, S. (2025). Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice. PLOS Digital Health, 4(4), e0000810. https://doi.org/10.1371/journal.pdig.0000810
  82. Yu, L., & Li, Y. (2022). Artificial Intelligence Decision-Making Transparency and Employees’ Trust: The Parallel Multiple Mediating Effect of Effectiveness and Discomfort. Behavioral Sciences, 12(5), 127. https://doi.org/10.3390/bs12050127
  83. Zerilli, J., Knott, A., Gavaghan, C., & Maclaurin, J. (2018). Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard? Philosophy & Technology, 32(4), 661–683. https://doi.org/10.1007/s13347-018-0330-6
  84. Zuboff, Shoshana (2019) The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. New York: PublicAffairs

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