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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.

This paper considers the role of gender in urban governance, taking Mumbai (formerly Bombay) in India as case study. Findings are presented from a research programme involving both female and male politicians and administrators in order to explore the ways in which women and men undertake the duties of public office. In attempting to take account of the historical traditions of Indian local governance, in the current post‐colonial context, characterized not simply by difference, division and hierarchy but also by tolerance, diversity, locality and collective values, it is argued: first that women are held in high regard, seen as competent, fair and flexible in their dealings with others; and second that women’s increasing presence may be bringing enhanced levels of respectability to local governance.

The global outbreak of COVID-19 and its subsequent varied impacts on different economic activities necessitate to examine its disruptions and impacts on livestock sector in India due to its recently surging potential as an unrivaled alternative to boost farmer’s income.

The studies for review were identified through search in different databases using relevant keywords. Only full text papers written in English language were reviewed. The review was organized and streamlined using Covidence software.

Analysis of the literature reveals adverse effects of COVID-19 on functioning of input and output stages of livestock supply chains. This has resulted in upstream and downstream economic losses that affect livelihoods of the producers.

Scale of unprecedented crisis due to COVID-19 pandemic requires creative policy decisions to make livestock production systems robust, resilient and sustainable. Organized production systems are required to integrate with livestock-tech startups to modernize their supply chains, whereas local supply chains are required to reorient with government’s intervention in terms of developing on-farm production and postproduction processing facilities.

Although there exist some evidence on COVID-19-related impacts on livestock sector of India, but an integrated review of evidence on COVID-19 related disruptions at all the stages (from input supply to marketing) of livestock supply chains was missing.

Since the start of the COVID-19 pandemic, one dark social-media phenomenon in particular has experienced a significant rise: consumer-to-consumer (C2C) conflicts, i.e. consumers who verbally attack each other in response to COVID-19 service failures. The aim of this paper is to uncover the sources of such conflicts and to gain an insight into the corresponding conflict moderation strategies that international brands adopt.

The methodology consists of non-participatory netnographic observations of 13 national, international, and global online brand communities (OBCs) on Facebook. The authors use purposeful sampling to collect relevant data on conflict sources and brand moderation strategies during COVID-19 service failures and a hybrid approach to thematic analysis to derive distinct themes from these data.

The paper identifies five C2C conflict sources: brand attack, brand dissatisfaction, brand skepticism, brand contention and brand defense; these are then classified as having either an individualistic (self-oriented) or collectivistic (other-oriented) orientation. The authors also uncover several moderation strategies: non-engaging, automated, bolstering, asserting (direct, indirect) and informing (factual, empathetic, apologetic), which are broadly categorized into two levels based on their passive vs active approach and authoritative vs cooperative orientation. The paper further highlights that brands adapt their moderation strategies to specific sources of C2C conflicts, thereby producing a range of OBC outcomes.

The study's empirically informed framework comprising sources of undesirable conflicts and brand moderation strategies offers a practical tool that can aid marketing managers in nurturing civil C2C engagement and interactive behaviors in their OBCs. By adopting our framework, brand and marketing practitioners can tailor their communication strategies toward different sources of C2C conflict and minimize their adverse consequences, thus, fostering an overall constructive OBC engagement.

The authors offer a novel framework to international marketing research, consisting of C2C conflict sources and corresponding moderation strategies that take place in response to service failures during the COVID-19 pandemic. These insights, in turn, inform international marketers about new ways of transforming the dark side of OBCs into a source of competitive advantage based on real-world brand practice.

Diabetes is one of the life-threatening chronic diseases, which is already affecting 422m people globally based on (World Health Organization) WHO report as at 2018. This costs individuals, government and groups a whole lot; right from its diagnosis stage to the treatment stage. The reason for this cost, among others, is that it is a long-term treatment disease. This disease is likely to continue to affect more people because of its long asymptotic phase, which makes its early detection not feasible.

In this study, the authors have presented machine learning models with feature selection, which can detect diabetes disease at its early stage. Also, the models presented are not costly and available to everyone, including those in the remote areas.

The study result shows that feature selection helps in getting better model, as it prevents overfitting and removes redundant data. Hence, the study result when compared with previous research shows the better result has been achieved, after it was evaluated based on metrics such as F-measure, Precision-Recall curve and Receiver Operating Characteristic Area Under Curve. This discovery has the potential to impact on clinical practice, when health workers aim at diagnosing diabetes disease at its early stage.

This study has not been published anywhere else.