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The purpose of this is to classify the social and economic factors which impact the involvement of women in self-help groups (SHGs) for their economic as well as social empowerment.

The research has been conducted in Nainital district of Uttarakhand (India) in 2018. Primary data have been gathered from women respondent only on factors relating to the ownership of asset, housing characteristics and other demographic details. Both SHG and non-SHG women members have been chosen as key informants during the survey. Multi-stage purposive and stratified random sampling has been used for the selection of respondents and SHGs. The logit regression model has been formulated to describe the causes that influence the participation of women in SHGs. Also, an empowerment index has been constructed to measure the effect of SHGs on women empowerment.

The results show that factors including age, education, family type and distance from the market have a significant impact on the participation of women in SHGs. Also, there is a significant difference in both these values which suggests that the value of the empowerment index gets significantly increased after joining the SHGs.

Analytically derived factors have been used to develop an empowerment index. Hence, the present research is valuable for marketing practitioners, entrepreneurs and professionals from the development sector who intend to work amongst SHGs, primarily with women. The paper is valuable for academic researchers in this area so that the limited body of knowledge, on the empowerment index, could be developed.

The present research is unique because the authors did not find work, especially in the context of rural India, in the said area. Factors impacting the participation of women in SHGs along with the impact of participation on empowerment have been explored using the logit regression model, leading to the development of an empowerment index.

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。

Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.

The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.

Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.

This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.

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.