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The purpose of this paper is to dig out enablers of sustainable industrial ecosystem to develop a framework.

To test the framework statistically, a structured questionnaire was designed. Measures for the questionnaire were adopted from an extensive literature review. Further, the questionnaire was pretested and further pilot study was conducted. Adding to this, the reliability and validity of the constructs was examined using confirmatory factor analysis followed by covariance-based structural equation modeling to test research hypotheses.

The statistical analyses suggest that the model exceeds the threshold limit for goodness of fit after undergoing through few iterations. Normative pressure has a low effect than rest of the factors.

The present study is a unique contribution in terms of its theoretical implications and practical use. Finally, research findings are concluded and further research directions is outlined.

As per the vision of promoting agricultural collectives, the government of India promoted the farmer producer organization (FPO). However, with the fast growth of FPOs, there is an issue with performance measurement. This study is aimed at the development of performance metrics for the FPOs.

In the first stage, we selected the measures from a secondary literature review and identified 11 parameters. Further, the Delphi round was conducted in the second stage with 26 experts working with FPOs and they were asked to rank these parameters. Based on the weightage of each parameter, the most important parameters were decided. The mean ranks and deviations of the performance parameters were analyzed. The hypothesis test and Kendall’s coefficient of concordance have been further used to validate the performance parameters. In the third stage, based on the inputs from the experts, a questionnaire was designed, and the data was collected from chief executive officers (CEOs) of the FPOs to identify the most important performance parameters.

The experts identified governance, financial support and professional management as important measures for FPOs. In the second round of the study, finance and governance were identified as the most important factors. It is important to note that finance and governance were the two most important factors in making an FPO successful. Finally, a 100-point metric was developed in seven major heads.

This study will be advantageous for all the stakeholders involved in the promotion of FPOs, including FPOs themselves, funding agencies providing funds to FPOs, skill-building organizations, etc.

This paper is one of its kind to develop a 100 points metrics for performance evaluation of FPOs.

The paper aims to evaluate how progressive stakeholders view the adoption of contemporary techniques such as virtual technology in driving sustainable quality in an emerging economy context.

The authors adopted a systematic literature review to develop the theoretical framework for virtual reality (VR) technology adoption in sustaining quality in agriculture production. The framework was refined after discussion with a panel of academic experts. The refined theoretical framework was further empirically validated using Partial Least Square Structure Equation Modelling.

The study focuses on the future perspective of the perception for progressive farming with the adoption of VR technology in an emerging economy. The data were collected from the stakeholders (farmers, collectives, cooperative, etc.), for their future perspectives for the adoption of VR technology and sustainable quality agriculture production. The study may help build up VR technology in emerging economies which may take years to be established.

The perception of the future perspective of VR technology study conducted has limitations. The findings are well established on technology adoption; however, the technology used will take many extra years to find its application in the agriculture sector. The study offers insightful theoretical, managerial and policy implications for sustainable quality in agriculture production through the adoption of virtual reality (VR) technology. The authors found very few works that focused on VR technology adoption.

The study discusses VR, which has an impact on sustaining the quality of agriculture production. The study has notable managerial and policy implications that suggest the future perspective for VR technology in agriculture production. The study is an unexplored area that needs research to capture future perspectives.

Agriculture value chains (AVCs) have experienced unprecedented disruption during the COVID-19 pandemic, with lockdowns and stringent social distancing restrictions making buying and selling behaviours complex and uncertain. This study aims provide a theoretical framework describing the stakeholder behaviours that arise in severely disrupted value chains, which give rise to inter-organisational initiatives that impact industry sustainability.

A mixed-methods approach is adopted, in which uncertainty theory and relational governance theory and structured interviews with 15 AVC stakeholders underpin the initial conceptual model. The framework is empirically validated via partial least squares structural equation modelling using data from an online survey of 185 AVC stakeholders based in India.

The findings reveal that buyer and supplier uncertainty created by the COVID-19 lockdowns gives rise to behaviours that encourage stakeholders to engage in relational governance initiatives. Progressive farmers and other AVC stakeholders welcome this improved information sharing, which encourages self-reliance that positively impacts agricultural productivity and sustainability.

The new framework offers farmers and other stakeholders in developing nations possibilities to sustain their AVCs even in dire circumstances. In India, this also requires an enabling ecosystem to enhance smallholders' marketing power and help them take advantage of recent agricultural reforms.

Research is scarce into the impact of buyer and seller behaviour during extreme supply chain disruptions. This study applies relational governance and uncertainty theories, leading to a proposed risk aversion theory.

The purpose of this paper is threefold: first, to investigate the role of big data and predictive analytics (BDPA) and social capital on the performance of humanitarian supply chains (HSCs); second, to explore the different performance measurement frameworks and develop a conceptual model for an HSC context that can be used by humanitarian organizations; and third, to provide insights for future research direction.

After a detailed review of relevant literature, grounded in resource-based view and social capital theory, the paper proposes a conceptual model that depicts the influence of BDPA and social capital on the performance of an HSC.

The study deliberates that BDPA as a capability improves the effectiveness of humanitarian missions to achieve its goals. It uncovers the fact that social capital binds people, organization or a country to form a network and has a critical role in the form of monetary or non-monetary support in disaster management. Further, it argues that social capital combined with BDPA capability can result in a better HSC performance.

The proposed model integrating BDPA and social capital for HSC performance is conceptual and it needs to be empirically validated.

Organizations and practitioners may use this framework by mobilizing social capital, BDPA to enhance their abilities to help victims of calamities.

Findings from study can help improve coordination among different stakeholders in HSC, effectiveness of humanitarian operations, which means lives saved and faster reconstruction process after disaster. Second, by implementing performance measurements framework recommended by study, donors and other stakeholders will get much desired transparency at each stage of HSCs.

The findings contribute to the missing link of social capital and BDPA to the existing performance of HSC literature, finally leading to a better HSC performance.

A major chunk of rural people live on agriculture and other allied activities viz animal husbandry, dairying and fisheries, etc. Rural development constitutes of lot of big data related to rural employment which has driven this study to address a research question that what is the application of big data in rural development with special reference to the world’s largest public works and wage employment generating poverty alleviation program – Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)? The concepts of MGNREGA are novel and innovative though the program continues to suffer from various rigidities depicted from the data. This drives us to the objectives of our research. The objective of the study is to explore literature and big data on rural development with special reference to MGNREGA, explore the upcoming challenges in rural employment with special reference to MGNREGA, identify gaps in existing literature and pave out future research direction. The present study paves various ways for future research directions for academicians, researchers and policy maker.

The major component of agriculture production includes the type of seed, soil, climatic conditions, irrigation pattern, fertilizer, weed control, and technology used. Soil is one of the prime elements in modern times for agriculture. Soil is also one of the primary and important factors for crop production. The available soil nutrient status and external applications of fertilizers decide the growth of crop productivity (Annoymous, 2017). The upcoming research question that needs to be addressed is What is the application of soil data on soil health management for sustaining agriculture? Driven by the need, the aim of the present study is (a) to explore the soil parameters of a district, (b) compare the values with the standards, and (c) pave a way for mapping the crops with suitability of soil health. This study will not only be beneficial for the district to take appropriate steps to improve the soil health but also would help in understanding the causal relationship among soil health parameters, cropping pattern, and crop productivity.