Data-Driven Inclusive HR and Leadership Strategies for Enhancing Innovation and Employee Engagement
In the digital era, inclusive Human Resource (HR) practices and leadership commitment are increasingly recognized as strategic levers for driving innovation and employee engagement. This study presents a data-driven investigation into how inclusive HR strategies such as diversity training, inclusive recruitment, mentorship programs, and employee resource groups interact with leadership support to influence organizational outcomes. Utilizing a mixed-methods approach, the research analyzes quantitative data from 200 respondents across multiple industries using structural equation modeling (SEM) with SmartPLS, alongside qualitative insights from 15 semi-structured interviews. The analysis reveals that leadership commitment to diversity strongly predicts both employee engagement (r = 0.80) and innovation (r = 0.75). Inclusive HR initiatives also show significant positive correlations with retention, productivity, and collaborative performance. Thematic analysis of interview data reinforces these findings, highlighting the role of digital HR systems and inclusive culture in shaping innovative work environments. This research contributes a validated predictive model that can inform the development of HR analytics platforms and AI-enabled decision support systems, offering actionable insights for engineering managers and HR technologists. The results emphasize that data-driven inclusive HR practices, when aligned with strategic leadership, not only advance equity but also deliver measurable performance advantages in dynamic organizational contexts.
Authors
- Ninda Lutfiani (ORCID: https://orcid.org/0000-0001-7019-0020)
- Umi Rusilowati (ORCID: https://orcid.org/0000-0003-2420-0843)
- Muhammad Farhan Kamil
- Arul Immanuel
- Rifqa Nabila Muti (ORCID: https://orcid.org/0009-0008-2980-3823)
- Fidyanti Mudrika
Institutions
- Durban University of Technology (ZA)
- Universitas Muhammadiyah Tangerang (ID)
- Universitas Pamulang (ID)
Publication Details
- Published
- 2025-08-15
- DOI
- https://doi.org/10.1109/iccit65724.2025.11167031
- Primary Topic
- AI and HR Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00