Sandeep Jagtap

Digital transformation and sustainability are increasingly interconnected strategic priorities, requiring firms to address technological and environmental objectives simultaneously. However, resource orchestration research has largely examined digital and sustainability-oriented resources in separate streams, leaving limited understanding of how they can be jointly orchestrated. This study develops a Dual-Orchestration perspective to address this integration gap. A bibliometric analysis is conducted to identify the intellectual structure, major and emerging themes, and thematic evolution of resource orchestration research. The findings reveal growing connections among resource orchestration, digital transformation and technologies, sustainability and green development, dynamic capabilities, environmental uncertainty, resource allocation, stakeholder engagement, and strategic decision-making. Together, these patterns point to an emerging integration opportunity. Building on Resource Orchestration Theory and on the dynamic capabilities, environmental contingency, and managerial cognition perspectives, the study develops a conceptual framework in which digital resource orchestration and green resource orchestration converge through dual-orchestration integration. This integration facilitates the development and deployment of dynamic capabilities, which translate orchestrated resources into sustainable organizational outcomes. The environmental context shapes the relevance and configuration of orchestration, while managerial cognition and strategic decision-making shape orchestration and integration choices. Six conceptual propositions are derived from this mechanism, providing a theoretically grounded basis for future empirical investigation. The study contributes by integrating the fragmented digital and sustainability-oriented resource orchestration streams into a unified perspective that responds to the contemporary need for simultaneous digital and sustainable transformation.

Purpose: This research identifies key retailer-related issues causing inefficiencies in the Indian Dairy Supply Chain (DSC) and develops a hierarchical model aimed at guiding decision-makers towards a leaner and more productive DSC. The study addresses a significant gap in the lean DSC literature, focusing on the challenges retailers face in emerging markets such as India. Design/methodology/approach: The study employs a mixed-method approach, beginning with a comprehensive literature review and expert consultations to identify 15 critical retailer issues. A structured questionnaire was administered to industry professionals and academics, resulting in 290 valid responses. Exploratory Factor Analysis (EFA) was applied to uncover underlying constructs, followed by Total Interpretive Structural Modeling (TISM) to map relationships among these issues. The model was statistically validated using a one-sample t-test, and MICMAC analysis classified issues by their driving power and dependence. Findings: The study categorizes the identified retailer issues into four constructs: strategic, tactical, operational, and performance. Strategic issues (lack of top management commitment, insufficient financial support, and lack of assured long-term contracts) emerged as the most powerful drivers, underscoring their fundamental role in addressing other retailers' concerns. Tactical issues, including inadequate cold storage and ICT infrastructure, significantly influenced operational efficiency. Performance issues, such as retailer dissatisfaction, were the most dependent and were strongly influenced by strategic and tactical factors. Research limitations/implications: The study specifically focuses on the Indian DSC, limiting generalizability to other geographical or sectoral contexts. Additionally, the findings depend heavily on expert input, which may introduce subjectivity. Future research could validate these findings through broader empirical studies and comparative analysis in other regions or industries to enhance generalizability and robustness. Practical implications: The hierarchical model developed in this research provides clear prioritization guidelines for decision-makers and policymakers in the dairy industry. Strategic issues, notably management commitment and financial support, must be addressed first to resolve tactical and operational inefficiencies. Policymakers can use these insights to formulate targeted policies, invest strategically, and develop training and infrastructure programs that significantly enhance retailer performance and supply chain efficiency. Originality/value: This study contributes to the lean DSC literature by systematically identifying and modeling retailer-level barriers and by integrating EFA, TISM, and MICMAC into a data-driven hierarchical framework. The findings extend existing knowledge on supply chain performance management by demonstrating how strategic-level factors shape tactical and operational outcomes at the retail node of dairy supply chains. The developed model provides a structured decision-support mechanism to improve operational performance, reduce waste, and strengthen performance management practices within the DSC.

This study aims to identify if and how Industry 5.0 technologies support achieving Sustainable Development Goal (SDG)-2 in relation to agri-food supply chains. This research employs an agri-food supply chain-oriented approach to backcasting the SDGs in which Industry 5.0 is considered to comprise discontinuous innovations. The focus is on analysing six dominant consumer trends expected to continue into 2030. Agri-food supply chains need to advance beyond linear distribution within information silos to digital transformation (Industry 5.0) to be able to achieve SDG-2 by 2030. Using agri-food supply chain strategies to offset world hunger by the backcasting method is methodologically lmited by the quality and diversity of data available. The theoretical scope of the study is limited to six dominant trends in consumer demand that are expected to continue into 2030. The theoretical contribution explains the strategic process by which agri-food supply chains can implement Industry 5.0 technologies towards the achievement of the SDGs using the knowledge-based theory of the firm extended to supply chains. This research contributes to advancing progress on SDG-2 and related SDGs. Business and other stakeholders will better understand the process of implementing Industry 5.0 technologies to reflect the ambitions of SDG-2 related to consumer trends.

Purpose: This study aims to examine the factors influencing metaverse adoption in education, addressing a context that remains underexplored and contested. It aims to identify and structure the key determinants shaping stakeholder adoption decisions while highlighting the metaverse’s implications for knowledge management through immersive and experiential learning. Design/methodology/approach: The study adopts a multimethod design combining the task-technology fit model and the extended resource-based view. Expert insights were analysed using fuzzy ordinal priority approach (F-OPA) to prioritise factors, modified Total Interpretive Structural Modeling (m-TISM) to examine interrelationships and develop a hierarchical framework and MICMAC analysis to classify factors by driving power and dependence value. Findings: Fourteen factors influencing metaverse adoption were identified and grouped into four categories: infrastructure, pedagogy, accessibility and socioeconomic conditions. The findings show that adoption depends on both enabling capabilities and contextual constraints. The metaverse can enhance knowledge creation, sharing and application through immersive learning, but adoption is limited by infrastructure readiness, equitable access and socioeconomic disparities. Originality/value: This study offers novel empirical evidence on metaverse adoption in education by integrating task-technology fit with the extended resource-based view and applying a combined F-OPA, m-TISM and MICMAC approach to generate prioritised and interpretive insights.

Food industry plays major role in public health, economic and environmental sustainability. Integration of machine learning (ML) is increasingly reshaping elements like safety protocols, quality management and supply chain optimization, though outcomes vary substantially by context and scale. This paper comprehensively synthesizes the ML applications by employing a systematic literature review (SLR) and a bibliometric analysis of 234 peer-reviewed articles published since 2014 and identifies prominent ML techniques like neural networks, support vector machines, and ensemble techniques. Various applications of these methods are food processing, fraud detection, personalized nutrition, and predictive maintenance. Collaborative pattern along with possible research directions are predicted using keywords co-occurrence and co-authorship network. Explainable AI (XAI), federated learning (FL) along with ethical rules are essential to ensure implementation and navigate AI driven revolution in food sector. Deep learning models have achieved very high classification accuracy in controlled food-quality trials, while early ML-driven dynamic-pricing pilots in SME grocery settings have reported substantial reductions in food waste and modest revenue gains; however, these results are context-specific and remain unconfirmed across broader, resource-constrained SME contexts.

The vehicle routing problem is one of the most often studied optimization problems. In this study, an improved artificial bee colony (ABC) algorithm is proposed which is structured specifically to address the capacitated vehicle routing problem (CVRP), a significant challenge in combinatorial optimization. The proposed algorithm integrates several novel features to improve its performance on CVRP instances. These include a unique initialization strategy that spreads non-repeated customers during initialization, an innovative dual group strategy in the employed bee phase, and an improved scout bee phase with perturbation techniques. Additionally, the proposed algorithm effectively handles infeasible solutions using a novel penalty function formula. The Chebyshev Minkowski’s distance is utilized for route evaluation, enhancing spatial relationship representation over traditional Euclidean distances. Moreover, the classical CVRP model is extended by introducing new variables and constraints to capture changing demand at each customer location within a route. The algorithm’s efficiency was extensively evaluated using benchmark data sets comprising 73 instances from data sets A, B, and P sourced from the VRP instances library site. This rigorous testing enables comprehensive assessments and meaningful comparisons with other algorithms. Overall, the proposed ABC algorithm offers a promising solution for addressing CVRP challenges, providing advancements in solution quality, robustness, and adaptability. Finally, the optimal results are compared in terms of their statistical significance using Friedman and Wilcoxon rank tests with three well-known optimizer algorithms in the literature.

This study examines sustainable practices in the child immunisation vaccine supply chain (VSC) in Uttar Pradesh, India, to support the UN’s Sustainable Development Goals (SDGs). This analysis examines the impact of social, economic, and environmental sustainability practices on the VSC’s efficacy and efficiency, as well as their contribution to achieving SDG goals, including improved healthcare access and enhanced community resilience. This study employs Structural Equation Modelling (SEM) to analyse the relationships between sustainability practices and vaccine supply chain performance. Data were collected through a structured survey from different stakeholders, including healthcare workers, supply chain managers, and policymakers involved in vaccine distribution, storage, and delivery. A total of 180 valid responses were analysed to assess the direct and indirect effects of sustainability practices on the VSC. The findings highlight the interrelated nature of sustainability factors across the VSC. Social sustainability emerged as the most influential component (path coefficient: 0.868), emphasising the importance of fair labour practices, community well-being, and ethical leadership. Economic sustainability had a moderate-to-high impact (0.666), highlighting the importance of profit optimisation and cost reduction. Meanwhile, environmental sustainability had a smaller effect (0.542), indicating that, while important, it must be supported by social and economic policies for maximum benefit. The study emphasises the value of external collaboration and integrated sustainable practices in building the VSC. The research provides evidence for developing targeted interventions and policies that improve vaccine supply chain sustainability, supporting broader healthcare goals and sustainable development. Unlike previous research, which has mostly focused on logistical issues in vaccine delivery, this study incorporates sustainability factors into the examination of vaccine supply chains. This study provides policymakers, healthcare professionals, and stakeholders with valuable guidance for developing sustainable immunisation strategies in Uttar Pradesh and other similar regions worldwide, offering a comprehensive framework that connects sustainability practices to improved healthcare outcomes.

The convergence of Information Technology (IT) and Operational Technology (OT) in Industry 4.0 has intensified the need for timely, trustworthy, and explainable cyber threat intelligence (CTI) for Industrial Control Systems (ICS). However, existing AI-enabled and Large Language Model (LLM)-based CTI solutions are predominantly designed for conventional IT environments and do not adequately address the safety, latency, governance, and operational constraints of industrial settings. This paper presents an AI-assisted CTI framework tailored to ICS and Industry 4.0 environments, integrating multi-source data ingestion, a Retrieval-Augmented Generation (RAG) knowledge store, a modular chain-of-agents architecture, and an explicit human-in-the-loop verification gate. Following a Design Science Research approach, the framework was evaluated through expert assessment involving twelve cybersecurity practitioners with experience in industrial and Security Operations Centre (SOC) environments and complemented by a proof-of-concept artefact instantiation based on the APT41 DUST campaign. The prototype integrated five heterogeneous CTI evidence sources and executed the automated analytical workflow in approximately 25 s (25.29 s) while illustrating evidence-grounded retrieval, specialized agent orchestration, and human-supervised intelligence generation. Practitioner feedback indicated that AI-assisted contextual intelligence and agent-based reasoning were perceived as valuable, while successful adoption depends primarily on governance, explainability, trust, and alignment with existing operational workflows rather than algorithmic sophistication alone. The study contributes a design-science artefact that combines retrieval-augmented intelligence, modular AI agents, and human oversight, providing practical design guidance for trustworthy AI-assisted CTI deployment in safety-critical Industry 4.0 environments.