Modern Studies in Management and Organization

Modern Studies in Management and Organization

Structural-Interpretive Modeling of Smart Green HRM's Drivers and Outcomes: A Pathway to Sustainable Innovation Adoption and Competitive Resilience

Document Type : Original Article | English

Author
Mohammad Ghasemi
Associate Professor, Department of Management, University of Sistan and Baluchestan, Zahedan, Iran.
10.22034/jmsmo.2026.601401.1057
Abstract
Purpose: This study identifies and hierarchically models the drivers and consequences of Smart Green Human Resource Management (Smart Green HRM) in promoting sustainable organizational innovation adoption, addressing the research gap regarding structural relationships among AI-enabled HR practices, dynamic capabilities, and innovation outcomes.
Methodology: Using a mixed-methods approach, Fuzzy Delphi and Interpretive Structural Modeling (ISM) with MICMAC analysis were applied. A panel of 15 experts from Sistan and Baluchestan Province (Iran) was selected through purposive snowball sampling. Variables were screened via Fuzzy Delphi (threshold: 0.70), and 12 final variables were structured hierarchically through ISM level partitioning.
Findings: The ISM model revealed five hierarchical levels. Institutional Pressures (C1), Top Management Support (C2), and AI Infrastructure (C3) emerged as foundational drivers (Level V). Smart Green HR practices (C4–C7) occupied Level IV, while dynamic capabilities (C8–C10) were positioned at Level III. Sustainable Innovation Adoption (C11) and Competitive Advantage (C12) constituted top-level outcomes (Levels I–II). MICMAC analysis confirmed C1, C2, and C3 as strategic pillars with highest driving power.
Conclusion: This study extends Dynamic Capabilities Theory by introducing "Algorithmic Synchrony," demonstrating that Seizing and Reconfiguration co-evolve simultaneously in AI-augmented environments. The hierarchical model provides practitioners with a roadmap for prioritizing infrastructure and leadership investments. The framework guides organizations navigating the Twin Transition, particularly in developing regions.
Keywords
Smart Green HRM
Sustainable Innovation
Interpretive Structural Modeling
Dynamic Capabilities
Twin Transition
Subjects
Cao, C., Chen, Z., & Liu, Y. (2023). Linking digital transformation with green innovation: The mediating role of dynamic capabilities and green human resource management. Journal of Environmental Management, 345, Article 118720. https://doi.org/10.1016/j.jenvman.2023.118720
DiMaggio, P. J., & Powell, W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147–160. https://doi.org/10.2307/2095101
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albort-Morant, G., Al-Busaidi, A. S., Bown, R. B., Choi, J., Cremer, D. D., Dawes, S., Diwanji, V., Dhar, R., ... Wright, R. (2023). "So what if the chatbot wrote it?" Multidisciplinary perspectives on Opportunities, Challenges and Implications of Generative AI for research, practice and policy. International Journal of Information Management, 71, Article 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI in business and management: Opportunities, challenges, and research agenda. Electronic Markets, 34(1), Article 8. https://doi.org/10.1007/s12525-024-00696-8
George, G., Merrill, R. K., & Schillebeeckx, S. J. D. (2023). Digital sustainability and the Twin Transition: Organizational responses to environmental challenges. Academy of Management Journal, 66(5), 1321–1348. https://doi.org/10.5465/amj.2021.1526
Jorzik, P., Siegel, R., & Spraul, K. (2026). Navigating the Twin Transition in HR: A conceptual framework for Smart Green Human Resource Management. Human Resource Management Review, 36(1), Article 101045. https://doi.org/10.1016/j.hrmr.2025.101045
Kannukollu, H., Sharma, M., & Kumar, A. (2024). Interpretive structural modeling and MICMAC analysis in operations and supply chain management: A methodological review and best practices. International Journal of Production Research, 62(8), 2840–2865. https://doi.org/10.1080/00207543.2023.2245210
Li, X., Zhang, W., & Wang, L. (2026). Enterprise AI readiness and sustainable performance: The mediating mechanism of green process innovation and algorithmic HRM. Technological Forecasting and Social Change, 200, Article 123150. https://doi.org/10.1016/j.techfore.2025.123150
Mota, R., Santos, B., & Ferreira, F. (2026). Smart Green HRM practices and employee eco-innovative behavior: A fuzzy-Delphi and survey study on the Twin Transition. Journal of Cleaner Production, 434, Article 139980. https://doi.org/10.1016/j.jclepro.2025.139980
Ren, S., Tang, G., & Jackson, S. E. (2023). Green human resource management research: A review and classification framework. Human Resource Management Review, 33(1), Article 100920. https://doi.org/10.1016/j.hrmr.2022.100920
Singh, S. K., Del Giudice, M., Chierici, R., & Graziano, D. (2024). Green innovation and environmental performance: The role of green transformational leadership and green HRM. Technological Forecasting and Social Change, 198, Article 123010. https://doi.org/10.1016/j.techfore.2023.123010
Sushil. (2018). How to check the correctness of total interpretative structural models? Annals of Operations Research, 270(1), 473–487. https://doi.org/10.1007/s10479-016-2382-3
Teece, D. J. (2010). Business models, business strategy and innovation. Long Range Planning, 43(2–3), 172–194. https://doi.org/10.1016/j.lrp.2009.07.003
Teece, D. J. (2020). Hand in glove: Open innovation and the dynamic capabilities framework. Strategic Management Review, 1(2), 233–253. https://doi.org/10.1561/111.00000010
Teng, X., Wu, Z., & Yang, F. (2025). Digital-green synergistic development: How smart HR technologies drive sustainable product and process innovation. IEEE Transactions on Engineering Management, 72, Article 103850. https://doi.org/10.1109/TEM.2024.3389012
Warfield, J. N. (1974). Developing interconnected matrices in structural modeling. IEEE Transactions on Systems, Man, and Cybernetics, SMC-4(1), 87–91. https://doi.org/10.1109/TSMC.1974.5408524
Modern Studies in Management and Organization
Volume 2, Issue 4 - Serial Number 8
This issue is designated as Winter 2026 and was published with a delay in September 2026.
Winter 2026
Pages 51-70

  • Receive Date 22 January 2026
  • Revise Date 08 March 2026
  • Accept Date 17 March 2026
  • Publish Date 07 September 2026