The coronavirus has created significant disruptions and exposed supply chain (SC) vulnerabilities. This crisis started a discussion about SC sustainability and performance. Therefore, the implementation of advanced technologies and supply chain collaboration could mitigate this disruption with the help of government support and policies. Considering this situation, this paper examines how COVID-19 influences advanced technologies (Artificial Intelligence, the Internet of Things, Blockchain, Digital twins, and Big Data Analytics) and supply chain collaboration (SCC) with a moderating role of government support and policies (GSP) in Pakistan. The study encompasses a comprehensive assessment carried out via structural equation modeling and data collected from Pakistani companies engaged in SCM or those operating within the SC divisions of manufacturing enterprises. According to the empirical findings, it is evident that COVID-19 outbreaks have a significant influence on SSCP; However, they do not show a similar impact on advanced technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration, the influence of COVID-19 on SSCP was effectively mediated through advance technologies (AI, IoT, Blockchain, DT, and BDA) and supply chain collaboration. This research contributes to the existing literature on SSCP by emphasizing the importance of the resource-based view, dynamic capability view, and institutional theories. SC and logistics managers can apply the theoretical framework proposed in this study to mitigate the impact of the COVID-19 epidemic or disruptions in logistics and SC operations, thereby improving profitability in the context of an epidemic.

Purpose: In the context of the environmental degradation challenge in manufacturing firms, greening the supply chain (SC) is the most widely endorsed method to mitigate the adverse repercussions of climate change. Based on organizational learning and resource dependence theories, the aim of this research is to know how green supply chain external integration (GSCEI) and green supply chain internal integration (GSCII) influence ambidextrous green innovation (AGI). It also examines the mediating roles of green absorptive capacity (GAC) and green knowledge integration capability (GKIC), as well as the moderating role of green technology dynamism (GTD).Design/methodology/approach: To assess the hypothesized model, data were obtained with 386 questionnaires from managers employed in manufacturing firms in Pakistan applying a cross-sectional approach. A partial least square structural equation modeling technique was implemented to evaluate the data.Findings: The results revealed that GSCEI and GSCII substantially impact AGI. Moreover, GAC and GKIC serve as mediators between GSCEI and AGI. GAC and GKIC also intervene in the relationship between GSCII and AGI. GTD was significant as a moderator for the correlation between GSCEI and AGI. However, it does not moderate the relationship between GSCII and AGI.Practical implications: This research offers significant comprehension and an innovative approach for manufacturing organizations to curb environmental corrosion by stimulating AGI through green SC integration. It suggests to practitioners that integrating internal knowledge with external partners expands communication and collaboration to ensure that resources connected with environmental preservation flow smoothly.Originality/value: This research is a valuable addition to the field, as it explores for the first time the missing link among the studied constructs. It opened the black box of how knowledge-related capabilities facilitate knowledge resources to elicit AGI, an area that has not yet been explored.

Purpose: The emergence of the COVID-19 epidemic has considerably increased the intricacy of information, exacerbating the difficulties firms encounter in efficiently processing and understanding accurate data and knowledge. Consequently, the COVID-19 epidemic has profoundly exacerbated production ambiguity for firms, thereby disrupting their regular business operations and supply chain activities. Digital technologies (DTs) are essential tools for firms to process and interpret information and knowledge, thereby improving their resilience against supply chain interruptions.Design/methodology/approach: This research investigates the effect of digital technologies on firm resilience throughout COVID-19, utilizing PLS-SEM and artificial neural networks (ANN) derived from a comprehensive survey of Pakistani manufacturing firms.Findings: Our research assesses the mediating role of supply chain integration, memory, and absorptive capacity, as well as the moderating influence of information complexity. The outcomes demonstrate that supply chain integration (SCI), memory (SCM), and absorptive capacity (SCAC) mediate digital technologies’ influence on firm resilience. Moreover, in situations where information is highly complex, DTs have a greater effect on a firm’s resilience.Originality/value: The results enhance our comprehension and awareness of the resilience-related effects of DTs and offer significant management insights for strengthening firm resilience in the setting of the COVID-19 pandemic.

Purpose: Environmental sustainability emerged as a crucial focus for manufacturing firms, serving as a pivotal strategy for preserving ecological balance and overall business performance improvement across environmental, financial and social aspects. Considering that supply chain (SC) management is a central and strategic function for manufacturing firms, companies are now embracing green supply chain management practices (GSCMP) and green supply chain integration (GSCI) to limit resource wastage and pollution levels, depletion of ecological resources, and emissions. Therefore, this study delves deep into the influence of GSCMP and GSCI on sustainable performance (SP) and the mediating impacts of green innovation (GI) and green absorptive capacity (GAC), alongside the moderating effect of big data analytics capability (BDAC). Design/methodology/approach: Empirical data were sourced from the manufacturing firm’s managers working in Pakistan and analyzed through partial least square structural equation modelling (PLS-SEM). Findings: Results indicated significant insights, demonstrating a positive effect of GSCMP and GSCI on GI and GAC, both of which ultimately result in improved SP. Thus, GI and GAC act as significant mediators and BDAC as a moderator among GSCMP, GSCI and SP. Practical implications: The outcomes put forward that by adopting a synergistic approach to greening the SC and implementing green strategies, firms can reduce carbon emissions, enhance performance and work towards a greener future with carbon neutrality. Originality/value: This research considerably adds to the field’s current knowledge by stressing the need for a holistic approach to greening the entire SC by integrating internal (GSCMP, GI, GAC) and external (GSCI) efforts strategically to minimize the contribution of manufacturing SCs to environmental demolition.

Purpose: Due to extensive industrial activities, the manufacturing sector is deteriorating the environment through resource depletion and rising pollution levels which led to a significant transition toward green supply chain practices (GSCP). Therefore, internal and external GSCP and green training (GT) gaining momentous attention. This study aims to explore the interconnections among the internal and external GSCP, GT, green innovation (GI), pro-environmental behavior (PEB), competitive advantage (CA), green knowledge sharing (GKS), green self-efficacy (GSE), environmental and financial performance (EP) and (FP). Design/methodology/approach: To check the hypothesized model, researchers used cross-sectional data based on survey questionnaires which were gathered from Pakistani manufacturing firms. The theoretical framework was validated through the utilization of partial least square structural equation modeling. Findings: The findings suggest that internal and external GSCP and GT are correlated with GI, PEB, CA, EP and FP. Additionally, this study discovers that PEB and GI act as intermediaries among internal and external GSCP, GT and CA. GKS positively moderates the connection among internal and external GSCP, and GT, GI, and PEB. Similarly, GSE also serves as a moderator among between PEB and GI. Research limitations/implications: This study is a significant contribution to the literature by studying potential mediators and moderators that improve the association among outlined constructs. Moreover, findings suggest that firms should adopt an integrated and holistic green approach to combat environmental deterioration, maintain environmental integrity and attain sustainable development. Originality/value: There is a scarcity of studies concerning the holistic framework of interrelated constructs studied in this research and it is the pioneer research to offer insights with an innovative model and empirical evidence.

Purpose: This paper presents a comprehensive overview of existing academic research on the application of Generative AI (Gen-AI) and ChatGPT within agricultural supply chains, highlighting current trends, leading publishers, thematic areas and proposing a future research agenda. Design/methodology/approach: A bibliometric analysis was conducted using VOSviewer software on a sample of 79 articles retrieved from the Web of Science (WoS) and Scopus databases to identify emerging research topics. Findings: The number of publications in this area has increased, and since 2023, academic interest in the application of Gen-AI and ChatGPT to agricultural supply chains has shown a consistent upward trend. Key areas of focus include enhancing prediction accuracy, coordination, automation, employee performance, optimizing supply chain management and improving overall business outcomes. However, concerns also exist, like hallucination, data privacy, security risks, lack of accuracy, copyright issues and ethical implications. Consequently, the risk of ungoverned Gen-AI outputs without human oversight potentially disrupting operations underscores the need for robust governance frameworks to ensure its reliable integration in agricultural contexts. Practical implications: Gen-AI and ChatGPT provide several advantages for agricultural supply chains. They enhance forecasting capabilities and coordination among partners, automate stakeholder interactions to increase efficiency, reduce manual tasks to improve employee performance and support data-driven decision-making through advanced analytics, ultimately strengthening supply chain management. Originality/value: This study offers a structured and in-depth examination of the Gen-AI and ChatGPT phenomenon in agricultural supply chains. Moreover, it contributes to the literature by identifying existing research gaps and suggesting potential directions for future investigation.