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This study aims to explore economic, social, psychological and political empowerment and dis-empowerment of women caused by microfiance interventions. Women tend to face the brunt of societal discrimination created by economic, social, psychological and political disempowerment. This led to the emergence of the microfinance model for the rural poor and specifically focused on women as an agency for social change.

This study is based on a systemic literature review to examine microfinance-led women empowerment to reduce the ambiguity in theoretical and empirical underpinning.

The study’s findings suggest that even though microfinance as a developmental model is not a runaway success, it did make some positive impact on the status of women.

This study shows that the microfinance program empowers women and reduces societal inequalities to some extent, but literature also suggests that microfinance as a model has failed to make the requisite socio-economic change, and in some cases, there is adverse impact.

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 purpose of this paper is to propose a novel integrated approach using analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) methods for evaluation and prioritization of appropriate manufacturing flexibility type required in the face of multiple environmental uncertainties.

Using a case study of an Indian fashion apparel firm, the study demonstrates the application of the proposed integrated framework for evaluation and prioritization of manufacturing flexibility. The study uses AHP method to determine importance weight of environmental uncertainty criteria and subcriteria and then employs TOPSIS method to determine the final ranking of manufacturing flexibility types required to cope up with these uncertainties.

The findings of the case suggest that the proposed integrated approach is feasible and practically implementable for manufacturing flexibility assessment.

AHP has been extensively studied and used, but the major limitation of this proposed approach is the involvement of large number of pairwise comparisons leading to difficulty in maintaining consistency in pairwise comparisons.

The proposed approach can work as a benchmarking tool to practitioners in evaluating and prioritizing manufacturing flexibility alternatives and to suggest strategic allocation of resource by prioritizing different manufacturing flexibilities types.

Unlike conventional approaches, the study provides meaningful knowledge to decision makers by demonstrating a simple, flexible, and efficient method to evaluate and rank the appropriate manufacturing flexibility types.

Usage of updated technology is continuously empowering customer relationship management (CRM) to be convenient and user friendly, where customers are kept engaged with knowledge and information. This enables them on decision-making and managing their portfolio, especially in mutual fund investments. To improve toward a positive decision, certain quality related variables needed to be considered. Thus, this study aims to estimate the mediation effect of relationship quality and outcome (RQO) between CRM and investment decision-making in mutual funds (MFD).

The descriptive study adopted the constructs from existing empirical literatures to conceptualize the model with three higher order constructs with 12 dimensions. Survey method is used, and with a structured questionnaire, a total of 323 mutual fund investors were approached using nonprobability criterion sampling technique, of which 262 relevant responses were considered for estimating the structural model. Smart PLS was used to establish the relationship of the constructs.

The result emphasizes a significant direct and indirect relationship indicating that investors are more inclined to MFD through technology-enabled CRM and RQO plays a vital role in explaining the direct relationship between CRM and MFD. The results of the study are in-line with the existing literature.

The study highlights that financial institutions must focus not only on technological diffusion but also needs to ensure quality service by providing knowledge and information during every access of transactions by customers, making them independent and confident during investments.

This study indicates how capacity efficiency, which is a part of service productivity, can be managed without affecting the outcome efficiency by incorporating technology in the place of human interaction during relationship acquiring and retaining process.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

The purpose of this article is to investigate and analyze the interactions between economic and sustainable development elements in the context of remanufacturing in India.

To comprehend the hierarchical and contextual link among factors impacting remanufacturing in India, the study used interpretive structural modeling (ISM) and decision-making trial and evaluation laboratory (DEMATEL) methodologies.

The integrated ISM-DEMATEL approach identifies optimal utilization of the resources as the most crucial factor influencing remanufacturing in India, followed by reducing landfills, conserving energy and low cost. The study also reveals that optimal utilization of resources, reduction of landfills, conservation of energy and incorporated advanced technology impacts most of the factors but get affected by a few factors.

Industry practitioners and policymakers should consider the remanufacturing process to achieve sustainable and economic development. The government and other stakeholders can use the ISM framework and cause-and-effect diagram to classify the impact factors and their impact on the Indian economy and environment.

This study supports the process to save the landfills and curbing pollution, conserve energy and optimize utilization of the resources, generate employment and supporting the development of the economy. Remanufacturing will undoubtedly contribute to the development of an environment and economy in India that benefits both.

ISM and DEMATELs strategy offers a tiered model and a cause-and-effect relationship between the variables affecting remanufacturing in India.

This paper aims to assess the factors favoring the adoption of the challenges faced and support mechanism, which will lead to the proliferation of glass fiber-reinforced gypsum (GFRG) technology in India.

Semi-structured interviews with 35 experts, including construction developers, architects, contractors, government officials and design consultants, were conducted. This qualitative data was analyzed using thematic analysis and matrix analysis.

GFRG-based buildings produce much less carbon footprints as compared to traditional ones and can be safely recommended as a promising, environmentally sensitive technology of the future. The major drivers in its adoption are its efficient construction capability, energy and soil conservation and significant waste reduction. Some of the challenges in implementation are long planning time, lack of skilled labor, lack of awareness about green building technologies and myopic perception of high cost incurred in green building adoption in people’s minds.

This study establishes that the construction industry has the potential to contribute toward creating a sustainable and green planet. It does so by evaluating and then positively positioning GFRG as an environmentally friendly building system.

The harmful effects of continuous environmental manipulation by humans leading to its degradation is a critical discussion agenda for most nations of the world. The issue has been taken up seriously by developing countries, and now, developing countries are also becoming sensitised to it. Several policies toward the attainment of this goal have been formulated and are being implemented by government and private bodies. Although some authors have studied the issues and challenges related to the adoption of green buildings, their attempts mostly focused on developed countries. Moreover, research that investigated the evaluation of the GFRG building system as a successful green technology of the future is inadequate.

This paper aims to present a systematic literature review and bibliometric analysis of research papers published on chat generative pre-trained transformer (ChatGPT), an OpenAI-developed large-scale generative language model. The study’s objective is to provide a comprehensive assessment of the present status of research on ChatGPT and identify current trends and themes in the literature.

A total of 328 research article data was extracted from Scopus for bibliometric analysis, to investigate publishing trends, productive countries and keyword analysis around the topic and 34 relevant research publications were selected for an in-depth systematic literature review.

The findings indicate that ChatGPT research is still in its early stages, with the current emphasis on applications such as natural language processing and understanding, dialogue systems, speech processing and recognition, learning systems, chatbots and response generation. The USA is at the forefront of publishing on this topic and new keywords, e.g. “patient care”, “medical”, “higher education” and so on are emerging themes around the topic.

These findings underscore the importance of ongoing research and development to address these limitations and ensure that ChatGPT is used responsibly and ethically. While systematic review research on ChatGPT heralds exciting opportunities, it also demands a careful understanding of its nuances to harness its potential effectively.

Overall, this study provides a valuable resource for researchers and practitioners interested in ChatGPT at this early stage and helps to identify the grey areas around this topic.

The purpose of this study is to present a systematic review of all the enablers of manufacturing flexibility and to provide a path for future research in the area of manufacturing flexibility.

Research papers were collected from electronic databases to search academic journals dealing with manufacturing flexibility, environmental uncertainty, business strategy, organizational attributes, technology, innovation and product types. The study consists of a systematic review of 101 research papers and assessment of these papers in terms of their role in enabling flexibility.

It is seen from the literature review that most of the research in the field of flexibility was done in developed countries, and further empirical research with respect to manufacturing flexibility and firm performance is required to be done in developing countries. It is also seen that financial performance has often been used in many of the studies as a proxy to measure firm performance and one can explore alternative metrics for firm performance. The review reveals that an integrated framework to assess manufacturing flexibility and firm performance is largely missing in literature. This review highlights some research gaps for future research in the area of manufacturing flexibility.

The literature review was done using search terms restricted to manufacturing flexibility, environmental uncertainty, business strategy, organizational attributes, technology, innovation and product types. There may be various unexploited areas for future research in terms of indirect influence of several different variables. The proposed framework is only conceptual in nature and thus requires rigorous empirical testing to develop a comprehensive list of enablers and their respective criteria and attributes.

The study synthesizes existing literature for assessing manufacturing flexibility in an organization and addresses gaps in the research area of manufacturing flexibility. It provides a comprehensive review of all relevant studies from 1992 to January 2013 for the use of both academicians and practitioners.

The purpose of this paper is to explore different drivers and volume flexibility strategies employed in manufacturing firms. It also examines contingent factors, which determine volume flexibility requirement in manufacturing firms.

Using a multi-case study approach, the study examines and analyses drivers, strategies and contingent factors that affect volume flexibility in four Indian manufacturing plants belonging to automobiles, auto ancillary, fashion apparel and electrical industry.

The empirical analysis suggests various drivers of volume flexibility and different strategies employed by firms to enhance flexibility. The study also illustrates various contingent factors that determine the need of volume flexibility in firms.

The sample of the study is majorly confined to the northern region of India. Methodologically, the analysis is solely based on the qualitative data.

The study suggests practitioners to consider a range of contingent factors, while evaluating the need of volume flexibility in manufacturing plants. Rather than relying on a single strategy, a mix of strategies should be used to develop volume flexibility in firms.

The identification of a range of strategies employed by volume flexible firms as well as contingent factors that need to be evaluated before employing volume flexibility are the major contributions of this study.