Journal of Business Logistics最新文献

Supply chains are clearly instrumental to firm-level environmental performance. Yet in research examining these effects, distinctions between arms-length relationships (largely transactional buyers and suppliers) and other influencers, such as strategic research partners (i.e., entities who jointly maintain legal commitments to shared knowledge and resources, with common service or product development interests) are often ambiguous. In our work, we aim to investigate this distinction. Combining arguments that reflect institutional theory, stakeholder theory, and expectancy disconfirmation theory, we anticipate positive associations between the environmental performance of strategic partners and the future performance of related focal firms. We posit these associations to be more easily observable than those between a firm and its arms-length relations. We further suggest that, due to the level of integration and codependency with strategic research partners, losses in environmental performance (backsliding) will be associated with dampened links between strategic partner performance and subsequent firm performance. By weaving together evidence from thousands of firm-year observations, merging representative fields from FactSet, CSRHub, and Compustat sources, we find support for these associations. Implications for future research and practice are discussed.

This study investigates the role of artificial intelligence (AI) in enhancing sustainability and efficiency within the fragmented supply chain of the tea industry. Small-scale tea gardens often face logistical inefficiencies, inconsistent quality control, and economic constraints, limiting their competitiveness. This research bridges the gap in literature by proposing an AI-enabled collaborative supply chain model tailored for small-scale tea gardens. Using a mixed-method research design incorporating extensive field studies and structural equation modeling (SEM), this study validates the model's effectiveness. Findings indicate that AI can significantly improve coordination, predictive analytics, and automation in supply chain processes, enhancing operational efficiency, profitability, and sustainability. Additionally, AI-driven collaboration fosters more transparent and data-driven decision-making among supply chain partners, reducing dependency on intermediaries. This study contributes to the theory of collaborative advantage by demonstrating AI's role in fostering cooperative synergies in agricultural supply chains. The proposed AI-enabled framework offers a scalable model for broader application in agribusiness, presenting significant policy and managerial implications.

Airports drive economic growth, enable global connectivity, and support millions of passengers and cargo every year. However, rapid increases in air traffic pose escalating operational and environmental challenges. To tackle these pressures, the aviation sector is increasingly turning to digitalization and emerging technologies to improve operational and environmental performance. This study provides robust empirical evidence on the effectiveness of one such technology: airport collaborative decision making (A-CDM), a blockchain-based application designed to improve coordination among airport stakeholders. By analyzing data from all European airports that implemented A-CDM between 2010 and 2023, we demonstrate its significant impact on both operational and environmental performance. Improvements in operational performance foster greater resilience, enabling airports to better recover from disruptions such as traffic congestion, adverse weather conditions, and unexpected events. Meanwhile, the enhancements in environmental performance contribute to sustainability by reducing carbon emissions, energy consumption, and overall environmental impact. This study provides valuable, evidence-based insights for policymakers and airport managers, highlighting how investments in digitalization can simultaneously improve both resilience and sustainability in the aviation sector.

Despite its substantial potential to enhance supply chain resilience (SCRes), artificial intelligence (AI) remains underexplored as an autonomous entity within supply chains (SCs), particularly in terms of its social capabilities and interactions within sociotechnical systems. Current literature has yet to address how AI can transform SC collaboration and network stability from a forward-looking perspective, creating a critical research gap. This study aims to address this gap by examining the social abilities introduced by AI and their implications for SCRes. The exploratory research design of this study is grounded in the actor-network theory and the Gioia method and includes 23 semi-structured interviews with experts. The findings reveal that AI actively influences decision-making processes, power dynamics, and trust among SC actors. By interacting with both human and non-human actors, AI emerges as a critical tool for redefining collaboration and network stability. While AI-driven SCs can enhance organizations' efficiency and adaptability, they also introduce challenges, including ethical considerations, responsibility allocation, and unintended consequences of algorithmic decision-making. This study contributes to the discourse on the embeddedness of AI in SCs by offering theoretical insights into its sociotechnical integration and managerial implications for its governance. Understanding AI as an active tool is crucial for designing resilient and future-proof SCs. The findings emphasize the need for organizations to develop strategies that balance the benefits and risks of AI, ensuring its responsible and effective deployment across SC networks.

In an era where financial resilience and ethical integrity are pivotal to supply chain success, this study explores how firms can harness digitalization capability to enhance supply chain finance (SCF) performance, with a particular emphasis on its role in promoting supply chain transparency (SCT) and mitigating partner firms' unethical behavior. Specifically, we develop a theoretical model based on the organizational information processing theory and social exchange theory and test it by employing partial least squares structural equation modeling analysis on data from 172 Chinese manufacturing firms. The results reveal that firms' digitalization capability advantage (DCA) is an effective means to achieve the fit between information processing needs and capabilities in improving SCT and reducing the unethical behavior of partner firms. This fit is further strengthened when firms and their partners exhibit a higher level of cultural congruence. Additionally, the study demonstrates that improving SCT and mitigating unethical behavior are crucial mechanisms that facilitate reciprocal resource interactions, fostering high-quality social relationships between focal firms and their partners, and ultimately contributing to enhanced SCF performance. Overall, this study provides a clear and theoretically grounded explanation of how DCA can be utilized to enhance supply chain operations and SCF performance.