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The disconnect between producers and consumers is a fundamental issue causing irregularities, inefficiencies and leakages in the agricultural sector, leading to detrimental impacts on all stakeholders, particularly farmers. Despite the potential benefits of Metaverse technology, including enhanced virtual representations of physical reality and more efficient and sustainable crop and livestock management, research on its impact in agriculture remains scarce. This study aims to address this gap by identifying the critical success factors (CSFs) for adopting Metaverse technology in agriculture, thereby paving the way for further exploration and implementation of innovative technologies in the agricultural sector.

The research employed integrated methodology to identify and prioritise critical success criteria for Metaverse adoption in the agricultural sector. By adopting a mixed-method technique, the study identified a total of 15 CSFs through a literature survey and expert consultation, focusing on agricultural and technological professionals and categorising them into three categories, namely “Technological”, “User Experience” and “Intrinsic” using Kappa statistics. Further, the study uses grey systems theory and the Ordinal Priority Approach to prioritise the CSFs based on their weights.

The study identifies 15 CSFs essential for adopting Metaverse technology in the agricultural sector. These factors are categorised into Technological, User Experience-related and Intrinsic. The findings reveal that the most important CSFs for Metaverse adoption include market accessibility, monetisation support and integration with existing systems and processes.

Identifying CSFs is essential for successful implementation as a business strategy, and it requires a collaborative effort from all stakeholders in the agriculture sector. The study identifies and prioritises CSFs for Metaverse adoption in the agricultural sector. Therefore, this study would be helpful to practitioners in Metaverse adoption decision-making through a prioritised list of CSFs in the agricultural sector.

The study contributes to the theory by integrating two established theories to identify critical factors for sustainable agriculture through Metaverse adoption. It enriches existing literature with empirical evidence specific to agriculture, particularly in emerging economies and reveals three key factor categories: technological, user experience-related and intrinsic. These categories provide a foundational lens for exploring the impact, relevance and integration of emerging technologies in the agricultural sector. The findings of this research can help policymakers, farmers and technology providers encourage adopting Metaverse technology in agriculture, ultimately contributing to the development of environment-friendly agriculture practices.

The study aims to explore the factors that motivate social media (SM) users to abstain from sharing pictures on SM platforms through the lens of user resistance theory (URT).

The study adopts a mixed-method approach and utilizes the URT as a lens to explore user attitudes towards SM engagement. Insights were derived from consultations with ten domain experts possessing diverse professional backgrounds. To prioritize the identified resistance factors, the Fuzzy-OPA multi-criteria decision-making (MCDM) technique was employed.

The study identifies 13 factors influencing users' choices to abstain from sharing images on SM platforms, categorized into 2 primary groups: personal and platform-related factors. Personal factors include privacy concerns, fear of negative judgment and anxiety over self-presentation. In contrast, platform factors include perceived risks of sharing images without consent, lack of control over the privacy settings and the lack of trust in SM platforms, contributing to users' reluctance to share pictures on SM.

The study utilized ten experts' opinions to classify and prioritize factors, but results may vary with more experts from diverse backgrounds. Additionally, resistance factors may differ across SM platforms like Instagram, Snapchat, Facebook, etc. The study contributes to theory by identifying and classifying personal and platform barriers to SM non-use, filling a gap in existing literature. It offers a framework for future research on technology adoption and non-use, emphasizing the role of privacy, self-presentation and identity factors in user decision-making. This classification aids in designing measurement tools for further research.

The study contributes to theory by identifying and classifying personal and platform barriers to SM non-use, filling a gap in existing literature. It offers a framework for future research on technology adoption and non-use, emphasizing the role of privacy, self-presentation and identity factors in user decision-making. This classification aids in designing measurement tools for further research.

While most of the research on SM platforms has examined the drivers behind their adoption, reasons for non-adoption, remain relatively underexplored. The study fills this gap by investigating why users limit sharing content on SM platforms.

Humanitarian supply chain (HSC) has been constantly challenged with multiple issues due to the complex dynamics of the disaster. These issues are inevitable and interrelated. The issues produce undesirable cascading effects that make performance measurement complicated. This paper aims to identify and model the critical barriers in the HSC.

The study identifies 17 barriers through the relevant literature and interviews with the stakeholders related to humanitarian organization (HO) in the developing economy. The barriers have been identified from strategic, technological, organizational, economic and operational point of view. Further, the barriers were ranked based on the degree of influence using the grey analytic network process (ANP) approach. The interrelationship among the influential barriers is established through modified total interpretive structural modelling (mTISM). To analyse and demonstrate the iterative consensus among the stakeholders, kappa statistics was adopted.

The barriers have been ranked to determine their extent of influence and modelled to reveal the interrelationship among them. The issues concerned with skills of personnel are the most influential barrier. Other three critical barriers identified are: chaotic operative environment, conflicting objectives of HO and lack of funding for information technology (IT) infrastructure. Appropriate funds need to be allocated towards IT infrastructure and personnel training.

Both quantitative and qualitative performance measurement frameworks had been proposed earlier for HSC. However, limited literature addresses the implementation issues with the available frameworks. This study advances the knowledge on performance measurement barriers of HSC and develops a functional description to identify the critical role of performance measurement in HOs. The study proposes a new version of the interpretive structure modelling, using mTISM technique, to determine the contextual interactions between various HSC performance measurement barriers.

The governments of different nations implemented various policy measures in response to the COVID-19 outbreak. These policy measures had a negative impact towards freight transportation. Further, the shock and ripple effect on the other supply chain complicates the recovery process of freight transportation. The transformation of freight transportation in the post-COVID-19 world was reported to be unsustainable. Thus, emerged the requirement to formulate the recovery measures in the context of freight transportation. This study aims to identify and model the recovery measures for sustainable freight transportation (SFT).

In this study, 13 critical recovery measures are established from the literature and finalized with the help of an expert panel. An integrated Grey Decision-Making trial and evaluation laboratory is used to prioritize and establish the cause-effect relationships among the identified critical recovery measures. Further, sensitivity analysis is performed to ensure the robustness of the proposed model.

The present study reflects that Efficient traffic management (M13), sustainability focused policies (M2), sensitization of stakeholders (M10), financial support (M9) and adoption of 4 R practices (M12) are the top five critical recovery measures for SFT. The results highlight that the transport sector needs to retain the learning from the COVID-19 period to operate under low workforce availability. Further, the emerging economies are suggested to promote local manufacturing to reduce the lead time and risk of unavailability. The study findings reflect that attaining sustainability without considering the social dimension of sustainability is impossible. Also, the results shed light on the controllable and uncontrollable recovery measures.

The findings from the study would assist policymakers and practitioners in re-formulating the recovery measures for freight transportation considering the aspect of sustainability.

The COVID-19 has affected the whole world and forced countries to impose lockdowns and restrict travel and transportation. This was followed by different countries formulating different policies, and when transportation resumed, there were some restrictions. Such strategies forced transporters to rethink mode choice decision making for freight transportation. The purpose of this study is to identify and rank the factors affecting freight transport mode choice decisions considering the spread of COVID-19 outbreak.

Initially, the factors affecting the mode choice decisions for freight transportation amid the COVID-19 outbreak have been extracted from a literature survey and group discussion with experts. Further, this paper employs the integration of grey-Decision making trial and evaluation laboratory (DEMATEL) with fuzzy Best Worst Method to analyze the identified factors and their sub-factors. The model's robustness and feasibility were then tested using sensitivity analysis.

The findings of the study showed that Disaster Characteristics and Operations Factors are the most and least influential factors, respectively. Panic, Travel restrictions, Border restrictions, Severity, and Mortality rate are the top five ranked sub-factors. Panic, Compliance to Social distancing, and Passenger and Freight integration are among the new sub-factors proposed. In the current circumstances, these sub-factors are crucial and relevant. In addition, various recommendations are offered to improve transportation services while guaranteeing safety, such as promoting passenger and freight integration, lowering the panic level, developing dynamic rules based on region characteristics, and so on. The study's findings will help practitioners and politicians reformulate the existing transportation infrastructure in the event of disease outbreaks.

The demographic context of experts, is one of the study's limitations. Only experts from the Indian subcontinent were considered in this study. In addition, future study work can be based on a comparison of the outcomes from various Multi-Criteria Decision Making techniques.

The present research work differentiates itself through the analysis of mode choice factors considering the ongoing pandemic across the globe. The results emanated from the study can guide the concerned stakeholders to make better decisions.

Block chain technology (BCT) has emerged as a promising solution for the co-ordination and aid mechanism issues in the context of humanitarian supply chain (HSC). However, implementation of BCT in HSC discerns several barriers. Therefore, the purpose of this study is to identify and model the block chain implementation barriers in the context of HSC.

In the present study, 14 potential barriers to BCT adoption in HSC have been identified through literature survey. The survey comprises white papers, pilot studies, conference proceedings and journal articles. Further, the identified barriers were finalised in consultation with a team of experts. The team comprised experienced stakeholders working in the humanitarian domain and BCT development. The barriers were categorised into four (technological, organisational, exogenous and economic) perspectives adopting the kappa statistics. Further, the barriers were prioritised using fuzzy best worst method (FBWM) approach. Later, sensitivity analysis was performed to check the robustness and viability of the model.

The findings from the study indicate that the barriers, such as “data privacy, ownership, and security issues” (B1), “funding issues and cost complexity” (B3) and “technological complexities” (B8), are relatively more influential. The HSC stakeholders and BCT developers are required to identify the safety mechanism against the misuse of victim’s data. The funding issues and technological complexities are interrelated and need synergetic cooperation between blockchain developers, donors, humanitarian organisations (HOs) and other HSC stakeholders. Further, “lack of awareness and understanding among stakeholders” (B6) and “interoperability, collaboration and cross-pollination among HOs” (B5) were identified as least influential barriers to BCT adoption in HSC.

In literature, limited study has been observed on determining barriers to BCT implementation. A more systematic method and statistical confirmation is necessary to establish further new confronting barriers. This study is limited to Indian context.

To the best of the authors’ knowledge, this study is first of its kind to use an FBWM approach for prioritising the barriers to BCT adoption in the context of HSC. The study provides potential barriers to BCT and categorises them into four different perspectives, along with their degree of influence.

Recent worldwide developments have altered how businesses operate. As a result, when making business decisions, the emphasis for many industries has shifted towards digital adoption to ensure sustainability, and the food supply chain is no exception. However, a substantial gap exists in assessing the barriers to a digitised food supply chain enabled by Industry 5.0 technologies. This study strives to bridge the gap by identifying and assessing the barriers to improved traceability.

For this study, a mixed method approach was used encompassing both qualitative and quantitative techniques, including an online survey, exploratory factor analysis (EFA), and the fuzzy evidential reasoning approach (FERA). The literature survey and expert opinion first yielded a list of 18 barriers, which were subsequently examined using EFA. As a result, four barriers were removed. The remaining 14 barriers were then assessed using FERA from the perspectives of the Technology, Organisation and Environment (TOE) framework. Further, a sensitivity analysis was performed to test the model’s reliability.

The present study resulted in the prioritisation of barriers from the TOE perspective. According to the findings, the top three barriers that impede the traceability of Industry 5.0-enabled digital food supply chains are Limited Digital and Physical Infrastructure, Inadequate Capital Investment, and the Intricate Supply Chain Framework.

The findings from this research will prove valuable for decision-makers, practitioners and policymakers in developing methods for improving traceability within the digital food supply chain. Concerned stakeholders may use the findings to identify and take immediate action for better decision-making.

This study’s originality lies in its position as one of the first to identify and examine the challenges to better traceability in an Industry 5.0-enabled digital food supply chain. It also adds value by broadening the TOE framework’s scope in the Industry 5.0-enabled digital food supply chain context.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

Pharmaceutical donations are a practical approach to increase medicine availability during disasters such as disease outbreaks. However, often donated pharmaceuticals are inappropriate and unsuitable. This convergence of inappropriate pharmaceuticals is a severe operational challenge and results in environmental hazards. This study explores the pharmaceutical supply chains (PSCs) during a disease outbreak to relieve the negative impact of the material convergence problem (MCP).

This study adopts a situation-actors-process learning-action-performance (SAP-LAP) linkage framework to understand the PSC dynamics. The problem-solving component of the SAP-LAP analysis provides the strategies catering to MCP. The findings from the SAP-LAP helped to develop the causal loop diagram (CLD). This study conducts several experiments on the proposed strategies by integrating CLD into a stock and flow diagram. Later, a disease outbreak case study accessed the pharmaceutical donations effect on PSC performance.

The study synthesises and evaluates propositions and strategies to incorporate circular economy (CE) principles in PSC. This study proposed two strategies; one to sort and supply and the other to sort, supply and resell. The reuse policy improves humanitarian organisations' finances in the simulation study. This study verified the operational improvement of PSC by reducing the transport and storage burden due to MCP.

This study comprehensively approaches the issue of drug donation and uniquely produced several propositions for incorporating a CE perspective in PSC. The study also proposed a unique simulation approach to model the donation arrivals in response to a disease outbreak using susceptible, exposed, infectious and recovered modelling.