We simulated the manufacturing production line in a micro, small, and medium enterprise (MSME) to assess the efficiency of a helmet product organization, using ARENA simulation modelling software version 15.10.00000. The process and standard time for each process in the production line were estimated from data provided by the enterprise’s management and direct observation. The enterprise line was engaged in six different processes to manufacture a singular product type. ARENA was used to analyse data. The simulation results showed an increase in workers’ utilization and reduced production duration for restructuring worker allocations, while maintaining a constant throughput rate.

Mental health issues have seen a significant rise globally, with Malaysia exhibiting the highest percentage of mental disorders in Southeast Asia. Particularly in low-income areas, employees believe that skilled, capable, and committed leaders are their most important organizational resource for enhancing their psychological health. Therefore, this study aims to examine the effects of engaging leadership, psychological capital, and psychological well-being among low-income workers in Malaysia. A total of 229 sets of data were collected from the bottom 40% of households in Malaysia using judgmental sampling. The hypotheses were analyzed using Partial Least Squares-Structural Equation Modelling (PLS-SEM). The findings demonstrated that engaging leadership positively affects psychological capital, and psychological capital positively affects psychological well-being. Further, the role of psychological capital as a mediator in the relationship between engaging leadership and psychological well-being was confirmed. This study recommends that various stakeholders, organizations, and employees foster employee mental health by considering and leveraging the positive impacts associated with engaging leadership. The findings contribute to a better understanding of how to engage leadership in promoting psychological well-being, particularly among low-income workers in the Malaysian context.

This study adopts a computational data-driven framework integrating Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (MGA) to examine the heterogeneous effects of destination competitiveness attributes on tourists’ revisit intentions in rural China. Using 608 valid samples from three representative destinations—Lugu Lake, Hemu Village, and Hongcun—the model incorporates destination type as a moderating factor to quantitatively identify contextual heterogeneity. Results show that community support is the strongest predictor of revisit intention, while infrastructure has no overall effect yet displays opposite impacts across destinations—negative at Lugu Lake but positive at Hongcun. These findings highlight the contextual sensitivity of competitiveness factors and demonstrate the value of computational modeling and data analytics in enhancing contingency-based tourism research. The study contributes to tourism management and computational social science by providing a reproducible, data-intensive approach for analyzing behavioral mechanisms and informing differentiated strategies for sustainable rural destination development.