Examining the Sustainable Integration of Artificial Intelligence in Human Resource Digitalization in the Context of Industry 4.0

Subin Thomas; Dhanya S. Nair; Jeena Joseph; Sijimon G. Srampical; Regina Sibi Cleetus

doi:10.31637/epsir-2026-2044

Autores/as

Subin Thomas Girideepam Business School
Dhanya S. Nair Girideepam Business School
Jeena Joseph Marian College Kuttikkanam Autonomous
Sijimon G. Srampical St. Berchmans College
Regina Sibi Cleetus Mar Ivanios College (Autonomous)

DOI:

https://doi.org/10.31637/epsir-2026-2044

Palabras clave:

HR Agility, Human Resource Functions, Industry 4.0, Structural Equation Modeling

Resumen

Introduction: Artificial Intelligence (AI) is reshaping workplace innovation through technologies such as robotics, augmented reality, IoT, and the metaverse. Within the Industry 4.0 paradigm—centered on flexibility, resilience, precision, and productivity—Human Resources (HR) must adapt to align human potential with technological advancements. Methodology: This study explores AI integration in HR functions, focusing on HR automation, organizational network analysis, and structural design, in the context of technological convergence and agile practices. Results: The analysis reveals that all five AI dimensions—employee performance, occupational safety, salary administration, employee morale, and real-time feedback—significantly affect organizational network analysis. However, only employee performance, salary administration, and morale impact HR digitalization. In terms of organizational design, morale, safety, and real-time feedback are key influencing factors. The conceptual model shows that 75% of the variation in organizational network analysis is explained by the five AI dimensions. Discussion: These results underscore the critical role of AI in supporting agile HR practices that enhance talent retention, performance alignment, and workforce optimization. Conclusions: Strategically integrating AI into HR functions is essential for creating synergy between human capital and technological infrastructure, fostering innovation, operational efficiency, and sustainable growth within Industry 4.0 environments.

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Biografía del autor/a

Subin Thomas, Girideepam Business School

Is an Associate Professor and Dean at Girideepam Business School, Kottayam. With extensive experience in academia and leadership, he contributes significantly to the fields of business education and management studies. His research interests span strategic management, innovation, and organizational development.

Dhanya S. Nair, Girideepam Business School

Is an Associate Professor at Girideepam Business School, Kottayam. Her academic and research contributions lie in the areas of marketing, consumer behavior, and business analytics. She is actively involved in developing industry-relevant pedagogy and student engagement practices.

Jeena Joseph, Marian College Kuttikkanam Autonomous

Is a faculty member in the Department of Computer Applications at Marian College Kuttikkanam (Autonomous). Her research interests include artificial intelligence, human-computer interaction, and educational technology. She brings a cross-disciplinary approach to her teaching and research.

Sijimon G. Srampical, St. Berchmans College

Is associated with St. Berchmans College, Changanachery. He is involved in educational, pastoral, and academic roles, with an interest in values-based education and social development.

Regina Sibi Cleetus, Mar Ivanios College (Autonomous)

Is a faculty member in the Post Graduate and Research Department of Commerce at Mar Ivanios College (Autonomous). Her research spans accounting, sustainability, and higher education policy. She is a published scholar contributing to both academic and professional forums.

Citas

Arias, E. (2021). Chatbots: The future of HR and employee benefits communication. Benefits Quarterly, 37(1), 7-12.

Sivathanu, B., & Pillai, R. (2018). Smart HR 4.0–how industry 4.0 is disrupting HR. Human Resource Management International Digest, 26(4), 7-11. DOI: https://doi.org/10.1108/HRMID-04-2018-0059

Bäck, A., Hajikhani, A., Jäger, A., Schubert, T., & Suominen, A. (2022). Return of the Solow-Paradox in AI?: AI-Adoption and Firm Productivity. Centre for Innovation Research (CIRCLE), Lund University.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74-94. DOI: https://doi.org/10.1177/009207038801600107

Barman, A., & Das, K. (2018). Internet of Things (IoT) as the Future Smart Solution to HRM-How would wearable IoT bring organizational efficiency. In International Conference Dec.

Goyal, C., & Patwardhan, M. (2021). Strengthening work engagement through high performance human resource practices. International Journal of Productivity and Performance Management, 70(8), 2052-2069. DOI: https://doi.org/10.1108/IJPPM-03-2020-0098

Seal, C. (2019). The Agile HR Function: Redesigning HR as A Strategic Business Partner. Kogan Page Publishers.

Cayrat, C., & Boxall, P. (2023). The roles of the HR function: A systematic review of tensions, continuity and change. Human Resource Management Review, 33(4), 100984. DOI: https://doi.org/10.1016/j.hrmr.2023.100984

Chowdhury, S., Budhwar, P., Dey, P. K., Joel-Edgar, S., & Abadie, A. (2022). AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework. Journal of Business Research, 144, 31-49. DOI: https://doi.org/10.1016/j.jbusres.2022.01.069

Czarnitzki, D., Fernández, G. P., & Rammer, C. (2022). Artificial intelligence and firm-level productivity. Discussion Paper, (22-005), ZEW-Centre for European Economic Research. DOI: https://doi.org/10.2139/ssrn.4049824

Dolan, E. G., Schuler, R. S., & Jackson, S. E. (2022). Artificial intelligence and human resource management: Advancing theory and research. Journal of Management, 48(1), 59-85.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. DOI: https://doi.org/10.1177/002224378101800104

Hundleby, J. D. (1967). Reviews: Nunnally, Jum. Psychometric Theory, 640, 431-433. DOI: https://doi.org/10.2307/1161962

Jerman, A. J., Pejić Bach, M., & Aleksić, A. (2020). Transformation towards smart factory system: Examining new job profiles and competencies. Systems Research and Behavioral Science, 37(2), 388-402. DOI: https://doi.org/10.1002/sres.2657

Kim, S., Su, Z. X., & Wright, P. M. (2018). The “HR–line-connecting HRM system” and its effects on employee turnover. Journal of Human Resource Management, 57(5), 1219-1231. DOI: https://doi.org/10.1002/hrm.21905

Leaders, B., Policymakers, P., Index, C. P., Indexes, P. P., Occupation, W. D., Demographics, E., et al. (2022). Growth trends for selected occupations considered at risk from automation. Growth.

Li, Y., Liu, Q., Cheng, S., Wang, J., & Li, H. (2023). Real-time performance tracking and improvement for employee engagement using artificial intelligence. Journal of Business Research, 149, 675-684.

Maganti, S. (2023). Transformative impact: Exploring the integration of artificial intelligence in human resource management. South India Journal of Social Sciences, 21(1), 172.

Mohamed, S. A., Mahmoud, M. A., Mahdi, M. N., & Mostafa, S. A. (2022). Improving efficiency and effectiveness of robotic process automation in human resource management. Sustainability, 14(7), 3920. DOI: https://doi.org/10.3390/su14073920

Mohanty, S., & Mishra, P. C. (2020). Framework for understanding Internet of Things in human resource management. Rev. ESPACIOS, 41(12).

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, 100249. DOI: https://doi.org/10.1016/j.dajour.2023.100249

Ngai, E. W. T., Chan, T. K. H., & Moon, K. K. L. (2020). Artificial intelligence applications in healthcare: A thematic analysis. Journal of Health Management, 22(2), 220-234.

Priyanka, R., Ravindran, K., Sankaranarayanan, B., & Ali, S. M. (2023). A fuzzy DEMATEL decision modeling framework for identifying key human resources challenges in start-up companies: Implications for sustainable development. Decis. Anal. J. 6, 100192. DOI: https://doi.org/10.1016/j.dajour.2023.100192

Randhawa, M. (2019). What does agile mean to HR? Retrieved from https://www.myhrfuture.com/blog/2019/10/30/what-does-agile-mean-to-hr

Rydén, P., & El Sawy, O. (2022). Real-time management: When AI goes fast and flow. In Platforms and Artificial Intelligence: The Next Generation of Competences (pp. 225–243). DOI: https://doi.org/10.1007/978-3-030-90192-9_11

Sarkar, S., Pramanik, A., Maiti, J., & Reniers, G. (2021). COVID-19 outbreak: A data-driven optimization model for allocation of patients. Comput. Ind. Eng, 161, 107675. DOI: https://doi.org/10.1016/j.cie.2021.107675

Urba, S., Chervona, O., Panchenko, V., Artemenko, L., & Guk, O. (2022). Features of the application of digital technologies for human resources management of an engineering enterprise. Ingénierie des Systèmes d’Information, 27(2). DOI: https://doi.org/10.18280/isi.270204

Chakraborty, S. C., Bhatt, V., & Chakravorty, T. (2019). Impact of IoT adoption on agility and flexibility of healthcare organization. Int. J. Innov. Technol. Explor. Eng., 8(11), 2673-2681. DOI: https://doi.org/10.35940/ijitee.K2119.0981119

Sharma, A., Tyagi, R., Verma, A., & Paul, A. (2022). Review on digitalisation and artificial intelligence in human resource function of energy sector. Water Energy Int, 65(2), 38-46.

Strohmeier, S. (2020). Smart HRM–a delphi study on the application and consequences of the Internet of Things in Human Resource Management. International Journal of Human Resource Management, 31(18), 2289-2318. DOI: https://doi.org/10.1080/09585192.2018.1443963

Subramaniam, A., Smith-Jackson, T. L., & Heidel, R. E. (2021). Artificial intelligence in workplace ergonomics: A review of current trends and future research directions. Journal of Occupational Health Psychology, 26(2), 135-146.

Tabiu, A., Pangil, F., & Othman, S. Z. (2016). Examining the link between HRM practices and Employees’ performance in Nigerian public sector. Management Science Letters, 6(2016), 395-408. DOI: https://doi.org/10.5267/j.msl.2016.4.006

Ugwu, C. C., & Abdelrahman, M. (2020). Stress detection in the workplace using artificial intelligence and Internet of Things technologies. Journal of Ambient Intelligence and Humanized Computing, 11(1), 89-98.

Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A., & Trichina, E. (2022). Artificial intelligence, robotics, advanced technologies and human resource management: A systematic review. International Journal of Human Resource Management, 33(6), 1237-1266. DOI: https://doi.org/10.1080/09585192.2020.1871398

Tarken, W. (2019). How to measure your agile HR operating performance? Retrieved from https://acortar.link/jA74iK

Wang, L., Li, Y., Du, J., & Huang, X. (2020). An Artificial Intelligence-enabled health and safety management system for industry 4.0. Safety Science, 124, 104618.

Qamar, Y., Agrawal, R. K., Samad, T. A., & Jabbour, C. J. C. (2021). When technology meets people: the interplay of artificial intelligence and human resource management. J. Enterprise Inform. Manag., 34(5), 1339-1370. DOI: https://doi.org/10.1108/JEIM-11-2020-0436

Yu, X., & Lee, J. Y. (2020). An intelligent chair system for personalized sitting comfort management. Sensors, 20(16), 4478.

Zadorozhnyi, Z. M., Muravskyi, V., Muravskyi, V., & Pochynok, N. (2022). Transformation of accounting methods with the use of robotic equipment with Artificial Intelligence. In 2022 12th International Conference on Advanced Computer Information Technologies, ACIT (pp. 285-289). IEEE. DOI: https://doi.org/10.1109/ACIT54803.2022.9912753

Zhang, Q., Zhou, B., He, Z., Xu, Y., & Liu, S. (2021). Intelligent workplace comfort management based on Internet of Things and Artificial Intelligence. IEEE Access, 9, 143659-143666.