Expert-informed model maps CSR and HRM links in generative AI workplaces

An expert-informed structural modeling study of small and medium-sized enterprises identifies two foundational factors as independent drivers within a model of sustainability-oriented workforce management and employee well-being during generative artificial intelligence adoption. The analysis also identifies one dependent factor connected to assessing technology from a corporate social responsibility perspective, mapping perceived relationships among 16 human resource management and corporate social responsibility factors. The study, published as peer-reviewed research in Discover Sustainability , examines how human resource management and corporate social responsibility may interdepend when organizations implement generative AI.

It does not test an AI system, measure workers’ health directly or compare organizations before and after implementation. Instead, it uses total interpretive structural modeling and cross-impact matrix multiplication applied to the classification method, commonly called MICMAC, to develop a hierarchical relationship model based on expert input. The resulting framework contains 16 factors: nine attributed to human resource management and seven to corporate social responsibility.

Its central result is the classification of two foundational factors as independent drivers and one factor as dependent, with the latter related to the social assessment of technology from a CSR perspective. The model is designed to represent the perceived influence and dependency among factors rather than demonstrate that a particular policy causes measurable improvements in a workplace. For the SME context, the findings provide a structured way to examine the relative orientation of driving and dependency factors when managing a workforce alongside new technology.

The framework presents human resource management and corporate social responsibility as interconnected categories in the proposed model. In particular, the dependent factor tied to the social assessment of technology highlights how the study’s experts represented the relationship between technology evaluation and the CSR side of sustainable workforce management. Several limitations define the scope of the evidence.

The supplied findings describe an interpretive, expert-informed model rather than a longitudinal analysis, workplace trial or direct measurement of changes in employee health, productivity or corporate performance. They do not quantify the size of the relationships among the 16 factors or establish that particular management actions will improve outcomes.

Subject of Research: Expert-informed modeling of CSR, HRM, employee well-being and generative AI adoption in SMEs

Article Title: Extrapolating CSR–HRM interdependencies and employee well-being for sustainable workplaces in generative AI era

Article References: Chatterjee, R. S., Chaudhuri, R., Zvarikova, K., Vrontis, D., Gigauri, I., & Chatterjee, S. (2026). Extrapolating CSR–HRM interdependencies and employee well-being for sustainable workplaces in generative AI era. Discover Sustainability. https://doi.org/10.1007/s43621-026-04810-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04810-5

Keywords: generative AI, employee well-being, corporate social responsibility, human resource management, sustainable workplaces, SME management, structural modeling, MICMAC, workforce sustainability, Extrapolating, interdependencies, employee