This study examines how organizations in Cambodia intend to adopt generative artificial intelligence (AI) and how these intentions are associated with changes in work processes and employee arrangements. Although generative AI is attracting strong managerial interest, most empirical evidence comes from developed economies. This has resulted in a limited understanding of how adoption unfolds in resource-constrained settings where digital readiness, managerial support, and strategic evaluation may not carry equal weight.
This study uses a quantitative, cross-sectional survey of 347 respondents from Cambodian organizations engaged in AI-related initiatives. The proposed relationships were assessed using partial least squares structural equation modeling (PLS-SEM), drawing on organizational change theory and socio-technical systems theory to link organizational antecedents to adoption intention and reported organizational change outcomes.
Technological readiness, managerial support, and perceived strategic value were positively associated with the intention to adopt generative AI. Technological readiness and managerial support showed stronger relationships, whereas perceived strategic value had a smaller, though still significant, effect. Adoption intention was positively associated with changes in work processes and employee arrangements. The model explained these two downstream outcomes modestly, suggesting that additional organizational and employee-level factors are also likely shape how change unfolds in practice.
This study contributes context-specific evidence from Cambodia, a setting that remains underrepresented in the generative AI literature. Rather than proposing a new theory of AI adoption, it shows how established organizational change and socio-technical perspectives explain the intention to adopt generative AI in an emerging economy. The findings suggest that, in this context, adoption intention depends more immediately on readiness and internal managerial support than on a fully developed strategic evaluation. This extends the current debate by showing that generative AI adoption in emerging economies may be organizationally consequential, yet uneven, exploratory, and shaped as much by practical capacity constraints as by strategic ambition.
