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The purpose of this paper is to integrate job demands–resources theory with core self-evaluation theory to examine how and when sustainable project leadership stimulates sustainable project performance. Psychological empowerment is used as a mechanism between sustainable project leadership and sustainable project performance, whereas core self-evaluation acts as a moderator between sustainable project leadership and psychological empowerment.

This study adopts a time-lagged two-phase approach to collecting data from 257 software engineers working in medium-sized and large companies in the Polish financial sector.

This study reveals the direct and indirect (through psychological empowerment) effects of sustainable project leadership on sustainable project performance. It also confirms that the “sustainable project leadership–psychological empowerment” relationship strengthens with increasing level of project team members’ core self-evaluation.

This article explores a unique research framework. It significantly enriches the literature on sustainable project management, increasing the understanding of effective leadership practices that address the needs of software engineering teams to support sustainable project performance.

Managers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.

Using existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.

We identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.

Similar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.