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Stream/Area:
-
Subject:
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Paper ID:
IJSREM63999
Volume:
10 |
5 |
May 2026
Pages:
1-14
Publication Frequency:
Monthly (12 issues per year)
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.
Department of MBA
KG REddy college of Engineering & Technology
Department of MBA
KG REddy college of Engineering & Technology
Department of MBA
KG REddy college of Engineering & Technology
Department of MBA
KG REddy college of Engineering & Technology
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
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.
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Publication Date
5/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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