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The customers are demanding the products which are not only healthy but also clean and environment friendly i.e. call for sustainable consumption products. Therefore, this study aims to identify the important drivers of organic food purchase intention.

A cross-sectional research design involving the collection of primary data from 234 respondents was adopted in this study. Responses were gathered from the consumers of organic food representative of the Indian population. Structural equation modelling was applied to analyze data and validate the research model.

The findings of the study would help practitioners understand the factors leading to the purchase intention of organic food products in a growing consumer market. This knowledge would help them devise marketing and communication strategies to increase the consumption of organic food products.

The present study advances existing literature on organic food consumption by extending the theory of planned behaviour with factors, namely, environmental concern, convenience and trust, and establishing their role in developing the purchase intention for organic food products.

Los consumidores demandan productos no sólo saludables, sino también limpios y respetuosos con el medio ambiente, es decir, productos de consumo sostenible. Por lo tanto, este estudio pretende identificar los principales factores que influyen en la intención de compra de alimentos ecológicos.

En este estudio se adoptó un diseño de investigación transversal que incluía la recogida de datos primarios de 234 encuestados. Las respuestas procedían de consumidores de alimentos ecológicos representativos de la población india. Se aplicó un modelo de ecuaciones estructurales para analizar los datos y validar el modelo de investigación.

Las conclusiones del estudio ayudarán a los profesionales a comprender los factores que conducen a la intención de compra de productos alimentarios ecológicos en un mercado de consumidores en crecimiento. Este conocimiento les ayudaría a diseñar estrategias de marketing y comunicación para aumentar el consumo de alimentos ecológicos.

El presente estudio avanza la literatura existente sobre el consumo de alimentos orgánicos mediante la ampliación de la TPB con factores, a saber, la preocupación por el medio ambiente, la conveniencia y la confianza, y el establecimiento de su papel en el desarrollo de la intención de compra de productos alimenticios orgánicos.

顾客要求的产品不仅是健康的, 而且是清洁和环保的, 即呼吁可持续消费产品。因此, 本研究旨在确定有机食品购买意向的重要驱动因素。

本研究采用横断面研究设计, 从234名受访者中收集原始数据。受访者的回答来自于代表印度人口的有机食品消费者。采用结构方程模型来分析数据并验证研究模型。

本研究的结果将有助于从业者了解在不断增长的消费市场中导致有机食品购买意向的因素。这些知识将帮助他们制定营销和沟通策略, 以增加有机食品的消费。

本研究通过扩展TPB的因素, 即环境关注、便利性和信任, 并确定它们在发展有机食品购买意向中的作用, 从而推进了现有的关于有机食品消费的文献。

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.

The operational activities of manufacturing organizations are continuously degrading the environment. Circular economy adoption can help industries optimize their resources along with minimal waste generation. The purpose of this study is to identify and analyze the barriers that hinder the adoption of circular economy (CE) in the manufacturing sector.

The barriers are extracted from a critical review of the literature and listed into three categories as social, economic and environmental. The barriers in each category are then analyzed using the fuzzy-based analytic hierarchy process method. The approach is capable to consider the fuzziness in the preference of barriers and determine their priority.

The pairwise comparison and weight of all the main and sub-criteria are computed, which helps in deciding the ranking of barriers. The results show that social criteria are having the highest importance followed by economic and environmental criteria. Among all the sub-criteria, low demand and acceptance of remanufactured products is at the highest level followed by lack of government support and legislation barrier.

The outcome of this study would be helpful for the decision makers and business managers in the manufacturing sector to focus on the barriers in each category and accordingly formulate strategies for CE adoption.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

This study proposes strategies for vaccine center allocation for coronavirus disease (COVID) vaccine by determining the number of vaccination stations required for the vaccination drive, location of vaccination station, assignment of demand group to vaccination station, allocation of the scarce medical professional teams to station and number of optimal days a vaccination station to be functional in a week.

The authors propose a mixed-integer nonlinear programming model. However, to handle nonlinearity, the authors devise a heuristic and then propose a two-stage mixed-integer linear programming (MILP) formulation to optimize the allocation of vaccination centers or stations to demand groups in the first stage and the allocation of vaccination centers to cold storage links in the second stage. The first stage optimizes the cost and average distance traveled by people to reach the vaccination center, whereas the second stage optimizes the vaccine’s holding and storage and transportation cost by efficiently allocating cold storage links to the centers.

The model is studied for the real-world case of Chandigarh, India. The results obtained validate that the proposed approach can immensely help government agencies and policymaking body for a successful vaccination drive. The model tries to find a tradeoff between loss due to underutilized medical teams and the distance traveled by a demand group to get the vaccination.

To the best of our knowledge, there are hardly any studies on a vaccination program at such a scale due to sudden outbreaks such as Covid-19.

There are several methods developed in the recent past to predict the spread of COVID-19 in different countries. However, due to changing scenarios in terms of interaction among people, none could predict the case close to the actual figures. An attempt to simulate people's interaction due to economic reopening concerning the confirmed cases at various places as per changing situation has been made. The scenario development method's base lies in the hypothesis that if there were no inter-state transportation during India's lockdown after May 24th, the number of infection cases would have started lowering down in a normalized progression.

This study has developed three scenarios from the worst to the business-as-usual to the best in order to project the COVID-19 infections in India concerning infections observed from January 30th till May 24th, 2020, since the domestic flights became operational from May 25th, 2020, in India.

Based on the observed cases till May 24th, the rise of cases is projected further in a random progression and superimposed to the normal progression. The results obtained in the three scenarios present that worst case needs complete lockdown, business-as-usual case needs regulatory lockdown and best case assures complete lockdown release by the second week of September 2020. This study suggests the preparedness and mitigation strategy for a threefold lockdown management scheme in all-inclusive.

The work has been done on a hypothesis which is solely original.

The purpose of this paper is to investigate long-run commonality in liquidity using multiple proxies computed from limited order book data of NIFTY50 stocks. The findings indicate the existence of systematic liquidity or commonality on NIFTY50 market and comprising industries.

The sample comprises all intraday transactions corresponding to NIFTY 50 stocks for April 2015. The study runs firm by firm time series regressions to test the concept of long-run commonality, while controlling other effects.

Strong evidence is found in support of long-run commonality across three liquidity measures. On the basis of significance (10%) of long-run commonality beta (β_LR), the strength of long-run commonality is found to be highest in natural resources and infrastructure sector. Portfolios having greater exposure to these sectors will face diversification risk to a great extent.

Knowledge of long-run commonality helps portfolio managers in formulating diversification strategies and reshuffling the portfolio over the period. Commonality risk being non-diversifiable is a policy concern for regulators and central bankers. Its empirical evidence will assist in managing exchange organization and thus preventing market crashes because of sudden liquidity evaporation.

Although there are recent studies documenting commonality in short run, little empirical work has been done on commonality in the long run and in emerging markets such as India. This research contributes to the literature by testing concept of commonality in long-run on NIFTY50 stocks using detailed transaction data from National Stock Exchange.

Journal of Enterprise Information Management (JEIM) is a leading journal that publishes studies on applied information management relevant to industry personals, academicians and researchers. This study uses bibliometric tools to present a retrospective analysis of the journal's outcomes.

The authors applied bibliometric tools for analysing the impact, topic coverage, renowned authors with affiliation, citation, methodology and analysis of the JEIM corpus. Additionally, they used bibliographic coupling to develop a graphical visualisation and analyse the journal's thematic evolution.

With 16 yearly articles, JEIM contributed 656 research articles on various themes. The major themes that have come to define the JEIM over this time include information and systems, supply chain management, manufacturing resource planning, communication technologies and small- to medium-sized enterprises. Empirical methodology, quantitative techniques with descriptive analysis and regression methods are the most preferred. The article's primary research purpose shows the majority of theory-verifying articles. Co-authorship analysis reveals that the single-author trend is decreasing and the journal now has articles with international collaborations.

This study is the retrospective analysis of the JEIM, which is useful for aspiring contributors and the journal's editors.

The manufacturing industry is one of the most disrupted systems as a result of the global spread of the Covid-19 pandemic. Manufacturing firms are looking for strategies and policies to deal with the situation while also meeting customer demands. This study aims to discuss and analyze the barriers that have impacted manufacturing systems during this period.

The barriers and performance measures were extracted from the extant literature and further discussed with academic and industry experts. Based on the response of experts, a list of ten barriers and five performance measures were selected for further analysis. The interpretive ranking process (IRP) is applied to analyze the inter-relationship among the barriers with respect to performance variables. The cross-interaction matrices and the dominance profile are created to prioritize the barriers. Based on dominance value, an IRP-based manufacturing barrier evaluation model is developed for validation.

The impact of the pandemic on the manufacturing industry is analyzed through the list of barriers and a structured ranking model is proposed. The research findings of the study indicate that “Financial constraints” is the most influential barrier to manufacturing due to the outbreak of Covid-19, followed by “Government imposed restrictions” and “Setbacks in logistics services.”

The ranking of barriers and developed interpretive ranking process model would be helpful for practitioners and policymakers to formulate strategies for manufacturing organizations to deal with the pandemic situation. The finding can be beneficial as it promotes similar studies in other sectors.

This study contributes to the manufacturing sector by developing a contextual relationship among the set of identified barriers against various performance measures. As per the author's knowledge, this is the first study that provides a relationship and ranking of manufacturing barriers due to the outbreak of Covid-19.

This study aims to conduct an extensive bibliometric analysis of research across COVID-19 and human resource management (HRM). It captures an exhaustive conceptual understanding of theoretical foundations, research trends, developments and research directions in the HRM domain.

A set of 505 HRM and COVID-19-specific articles collected from the Scopus database were systematically analyzed using a two-tier method. In the first tier of analysis, the evolution and current state of research are identified using citation analysis. In the second tier, network analysis and content analysis of research clusters and thematic mapping are done to identify the prominent research themes and research gaps and suggest future research directions.

The study highlights the emergence of six research clusters: SHRM and competitive advantage, employer branding and employee engagement, crisis management and resilience, challenges, career shock and job demand resources and burnout. The thematic mapping categorizes the themes into four categories: motor, basic, emerging or declining, and niche research themes published on COVID-19 and HRM. To understand the socio-cultural dynamics and cross-cultural issues during human resource management, the findings emphasized the need for the increased contribution of researchers and practitioners, especially from the developing and emerging nation’s context. Increased co-authorship among influential authors and institutions will also help formulate strategies and policies to effectively deal with similar pandemics.

Unlike the previous literature review, the present findings provide meaningful insights for formulating people management techniques, policies, and practices in response to COVID-19 or similar pandemics.