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Paper ID:IJSREM63999

Emerging HR with Technology: Transforming Human Resource Management in the Digital Age

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63999

Volume:

10 |

5 |

May 2026

Pages:

1-14

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63999

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

KY

Karunakar Y

Department of MBA

KG REddy college of Engineering & Technology

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PB

Pranitha B

Department of MBA

KG REddy college of Engineering & Technology

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NB

Nimmagadda Babu

Department of MBA

KG REddy college of Engineering & Technology

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Mentors

CC

Chandana Chilaka

Department of MBA

KG REddy college of Engineering & Technology

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Abstract

The convergence of digital technologies and Human Resource Management (HRM) has inaugurated a new strategic epoch in which data-driven decision-making, automation, and intelligent systems collectively reshape the entire employee lifecycle. Grounded in an integrated theoretical framework synthesising the Technology Acceptance Model (TAM; Davis, 1989), the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003), the Resource-Based View (RBV; Barney, 1991), and the e-HRM typology (Strohmeier, 2007), this study investigates how Artificial Intelligence (AI), Machine Learning (ML), Robotic Process Automation (RPA), cloud-based Human Capital Management (HCM) systems, predictive people analytics, and immersive technologies (VR/AR) collectively transform talent acquisition, learning and development, performance management, employee engagement, and workforce planning. Drawing on a sequential mixed-methods design — a structured survey of 310 HR practitioners across five Indian industry sectors and ten semi-structured CHRO interviews — this research constructs and empirically tests a comprehensive framework linking technology adoption with human capital outcomes and organisational performance. Confirmatory findings indicate that AI-enabled hiring reduces mean time-to-hire by 38.1% (t(308) = 11.42, p < 0.001; β = −0.419); predictive attrition models achieve 74% early-detection accuracy; and advanced digital learning ecosystems accelerate skill acquisition velocity by 46.3%. Critically, a moderation analysis (Hayes PROCESS macro; H5: interaction term β = −0.263, p < 0.01) reveals that perceived algorithmic unfairness substantially erodes employee trust, effectively neutralising engagement gains from AI adoption. Ethical challenges — including algorithmic bias, employee privacy under India’s Digital Personal Data Protection Act (DPDPA) 2023, digital equity, and change-management resistance — are analysed as primary determinants of sustainable HRTech value, not secondary concerns.

KEYWORDS

HR Technology Artificial Intelligence People Analytics Digital HRM Automation Talent Management Employee Experience Future of Work HRTech India

Cite This Article

Karunakar Y, Pranitha B, Nimmagadda Babu, Chandana Chilaka, "Emerging HR with Technology: Transforming Human Resource Management in the Digital Age," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-14, May 2026. https://doi.org/10.55041/IJSREM63999

Full Article/Conclusion

This study examined how emerging technologies are disrupting HRM across the talent lifecycle and produced empirical evidence of their effects on critical HR and organisational outcomes. The findings converge on a cautiously optimistic but rigorously qualified assessment. AI-enabled recruitment tools, people analytics platforms, digital learning ecosystems, and employee experience technologies each demonstrate statistically significant positive associations with efficiency, retention, engagement, and skill outcomes. The cumulative effects — a 38.1% reduction in time-to-hire, a 7.3 percentage point improvement in retention, a 46.3% acceleration of skill-development velocity — represent tangible value that justifies significant investment. Concurrently, the study establishes that algorithmic fairness, employee privacy, digital equity, and change-management readiness are not peripheral implementation concerns but primary determinants of whether HRTech investments yield sustainable human capital value or generate backlash, erode trust, and fracture organisational cohesion. The moderation finding — that perceived algorithmic unfairness substantially erodes employee trust and nullifies engagement gains for approximately two-fifths of the sample — constitutes perhaps the study’s most policy-actionable contribution: HRTech ROI is fundamentally contingent on perceived procedural justice.

References

Ajunwa, I. (2021). The quantified worker: Law and technology in the modern workplace. Cambridge University Press. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108 Bersin, J. (2022). The definitive guide to people analytics. Bersin & Associates LLC. Bondarouk, T., & Brewster, C. (2016). Conceptualising the future of HRM and technology research. International Journal of Human Resource Management, 27(21), 2652–2671. https://doi.org/10.1080/09585192.2016.1232296 Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15. Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE Publications. Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight Datta, A., Tschantz, M. C., & Datta, A. (2015). Automated experiments on ad privacy settings: A tale of opacity, choice, and discrimination. Proceedings on Privacy Enhancing Technologies, 2015(1), 92–112. 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 Deloitte (2022). Global robotic process automation survey. Deloitte Insights.

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Publication Date

5/27/2026

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