Modeling organizational performance through AI driven green organizational citizenship behavior using PLS SEM and ANN framework in the Indonesian chemical industry

This research examines how green organizational citizenship behavior (GOCB), the integration of artificial intelligence (AI), employee engagement, and organizational performance are interrelated in the chemical industry. Specifically, the study was conducted in Banten Province, Indonesia in 2024, involving 245 respondents from the top management of 50 leading companies in the basic chemical sector. This research addresses a gap in prior studies, which largely examined GOCB and AI separately and relied on linear models, lacking an integrated perspective and the ability to capture nonlinear interactions. To fill this gap, a dual analytical approach was employed, using PLS-SEM to assess causal relationships and mediation effects, and ANN to model nonlinear patterns and enhance predictive accuracy. Utilizing a quantitative research method, data were gathered through questionnaires answered by the targeted managerial staff. A dual-stage analytical approach was employed, combining PLS-SEM to examine the causal relationships among constructs, and ANN to capture nonlinear patterns and enhance predictive accuracy. The results indicated that both GOCB and AI integration have a significant and positive effect on employee engagement and the overall organizational performance. Additionally, employee engagement was found to play an important mediating role with a positive impact. The novelty of this study lies in the simultaneous integration of GOCB, AI, and engagement into a holistic model applied specifically in the basic chemical industry of Indonesia, using a dual-stage PLS-SEM + ANN approach to capture both linear and nonlinear effects. The findings highlight the importance for the basic chemical industry to prioritize GOCB and AI integration as a strategy to boost employee involvement and enhance organizational outcomes. Support from company leadership, government, and employees is also essential to facilitate these initiatives. The study’s limitations include issues related to the generalizability of its findings, indicating a need for future studies with a wider focus and more detailed approaches to gain a deeper insight into AI adaptability in the chemical industry.