Ports are the main arteries of global trade, handling goods circulation and serving as hubs for information, capital, and technology. Integrating digital technology has become the key for green port development to achieve resource efficiency and ecological balance. The current literature overlooks how digital technology can facilitate greener port operations. This study integrates sustainable supply chain management and system dynamics theories based on an in-depth analysis of existing research results and expert interviews. The analysis focuses on three key dimensions: integrating digital technologies with infrastructure, optimizing digital management and operations, and improving environmental and safety management in a digitally driven setting. Using the fuzzy Decision Making Trial and Evaluation Laboratory (Fuzzy Dematel) methodology, we collaborated with domain experts in port logistics to identify and confirm 12 pivotal factors that support the green digital transformation of ports. The research shows that the most critical success factors for using digital technology to drive ports’ green transformation are green supply chain information platforms, intelligent vessel scheduling, traffic optimization, and digital carbon emission monitoring. This study significantly contributes to the literature on green port transformation, offering indispensable practical insights for port operators, government entities, and shipping firms in identifying and deploying these key success factors. The findings will help maritime supply chain stakeholders develop actionable digital strategies, improving port efficiency and ecological resilience.

Introduction: With the rapid expansion of digital gig platforms, counterproductive work behavior among gig workers has become increasingly prominent, adversely impacting the platform’s reputation, operational efficiency, and user experience. This study aims to explore how job demands and job resources influence counterproductive work behavior among gig workers.Methods: Grounded in the Job Demands-Resources model and Job Engagement Theory, this study develops a second-order chain mediation structural model to analyze the effects of job demands (Work pace/workload, physical demands, psychological demands, and customer-related social stressors) and job resources (Compensation, job security, learning opportunities, and opportunities for professional development) on counterproductive work behavior. Cognitive, emotional, and behavioral engagement are also examined as mediators.Results: Job demands positively influence counterproductive work behavior, while job resources have a negative impact. Cognitive, emotional, and behavioral engagement each negatively affect counterproductive work behavior. Additionally, platform formalization moderates the negative influence of gig workers’ engagement on counterproductive work behavior.Discussion: This research provides a comprehensive theoretical framework for digital gig platform managers to understand and predict gig workers’ counterproductive work behavior. It also offers practical implications for optimizing the work environment, enhancing job engagement, and mitigating counterproductive work behavior, thus fostering mutual development between gig workers and the platform.