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The present study aims to investigate the factors of loan access that affect entrepreneurial self-efficacy (ESE) and operating efficiency of Indian Micro, Small and Medium Enterprises (MSMEs). Furthermore, the study intended to investigate the influence of ESE on the operating efficiency of Indian MSMEs and its mediating role.

In this study, exploratory research design is used. The study heavily relies on the primary data which has been collected by using the survey research method from a cross-section of 617 women-owned MSMEs, located in urban, rural, suburban and exurban areas of Haryana, Uttarakhand, Himachal Pradesh and NCR-Delhi. The partial least square structural equation modeling method version 3.3.3 has been used to evaluate.

In terms of the selected factors affecting access to finance, it has been established that the Loan Formalities, Banking Process, Loan Process, Staff Responsiveness and Incentive Scheme have a positive and significant influence in enhancing accessibility to finance and improving the self-efficacy and operating performance of firms. The findings also show that ESE mediates the relationship between various factors of loan access and the operating efficiency of MSMEs.

The study’s findings show that entrepreneurial capacity is significantly and favorably impacted by attitudes toward entrepreneurship, ESE, perceived access to findings and business operations. It has also been demonstrated that entrepreneurial intentions are strongly and favorably influenced by entrepreneurial ability to access commercial bank financing for small businesses and the impact of the same on the women-owned MSMEs in India. It also revealed unfavorable loan terms, limited collateral, fear of repaying of loan and intricate loan application were among the many reasons for loan denial.

The study offers a comprehensive approach that simultaneously considers financial accessibility and ESE. This all-encompassing method offers a thorough grasp of the variables affecting MSMEs' operational efficiency (OE). In contrast to earlier research that might have concentrated only on direct relationships, this study explores the mediating mechanisms involved. This study examines how ESE modulates the influence of financing availability on OE, providing a comprehensive understanding of the underlying mechanisms. By taking into account particular MSME sector characteristics like size, industry or regional variations, the study may provide a unique contextual lens. Understanding how these contextual factors interact with entrepreneurial attributes and access to finance adds depth to the analysis.

Good management strives to align and corporate processes for more attention being paid to supply chain management. Firms realize that greater co-operation and improved coordination can help to manage the entire supply chain more efficiently. The imperfect quality item is one of the most important issues that affect the expected profit of green supply chain. The imprecise cost with screening process of poor quality items posed in supply chain is the subject of this study.

The present study explores production model for imperfect items having uncertain cost parameters with three-layer supply chain encompassing supplier, manufacturer and retailer. The model is considering the impact of business tactics such as order size, production rate, production cost and appropriate times in various sectors on collaborative marketing systems. Due to imprecise cost parameters, the pentagonal fuzzy numbers are set to fuzzify the total cost and defuzzifition by using graded mean integration.

This study offers an explicit condition in uncertain environment to manage the imperfect quality item to increase the potential profit of the supply chain. The influence of changes in parameter values on the optimal inventory policy under fuzziness is provided managerial insights.

This model makes a significant contribution to fuzzy inference. The results of the study provide a trading strategy for the industry to avoid losses. The prescribed study can be suitable for the industries like sculpture, jewelry, pottery, etc.

Central banks globally are actively investigating the issuance of central bank digital currencies, a digital form of fiat money. In this light, this study aims to explore and empirically validate the factors that enable or inhibit user behavioral intentions to adopt the digital rupee in India.

The study employed dual-factor theory (DFT) to capture the users’ perceptions of both enablers and inhibitors of the digital rupee. The authors gathered survey data from 351 individuals in India through online questionnaires. The authors used partial least squares structural equation modeling and multigroup analysis (MGA) to evaluate the proposed conceptual model.

The findings reveal that enablers such as perceived government support, trialability and similarity positively influence users’ attitudes toward the digital rupee. In contrast, inhibitors such as usage, value and risk barriers increase users’ resistance. Attitude has a significant positive impact on the intention to use the digital rupee, while resistance significantly reduces the intention to adopt it. MGA results highlight the importance of gender and income status in understanding intention to use the digital rupee.

By applying DFT, the study identifies a set of enablers and inhibitors that influence the behavioral intention to use the digital rupee in India. It provides actionable insights for governments and central bankers to devise effective policies, design considerations and targeted interventions, ensuring a sustainable environment for the successful implementation of the digital rupee.

The sporting body in Indian sports studies has been studied in the context of changing power relations in society, and this essay continues in a similar vein. The significance of sport in India evolved from being used as a means of control by the coloniser to becoming a point of resistance and then a tool for nation-building for the colonised peoples. Post-liberalisation wrought significant changes to the social and cultural landscape and brought with it a singular focus upon the individual, as the idea of the nation receded to the background and the link between sport and self-making became prominent. In the context of the changing cultures of sport and the body from colonial to post-liberalisation India and the growing prominence of sport in popular culture, this chapter turns a critical eye towards the representation of sport in three Nike advertisements between 2007 and 2016 with the understanding that these representations are shaped by the workings of power and ideology in society and therefore provide a window to access the evolution of sports discourse over time. Through this examination, it explores the complex dynamic between the liberation of the postcolonial sporting body from discursive shackles and its evolution – and possible entrapment – into becoming a placard of the neoliberal vision and what this means in terms of the decoloniality discourse.

The demand for green personal care products (GPPs) has been growing globally due to increasing health-care concerns. However, the purchase rate of these products among consumers remains low. This study aims to identify and model the key barriers to the purchase of GPPs.

For this purpose, the study used innovation resistance theory (IRT) as a framework to identify key barriers to the purchasing of GPPs. The barriers were identified through a systematic literature review and validated by industry and academia experts. Furthermore, using interpretive structural modelling and Matrice d’Impacts Croisés Multiplication Appliquée a un Classement, the study identifies the interrelationships among the barriers and categorizes them based on their driving and dependence power.

The findings reveal that limited availability, improper labelling standard and certification, poor performance of products and lack of government regulations are key barriers to the purchase of GPPs.

The study contributes to the existing literature on green purchase behaviour. Furthermore, it informs marketing strategies to overcome the identified barriers and increase the purchase of GPPs.

This study is the foremost empirical study that identifies and analyses the industry specific barriers to GPPs based on experts’ input and under the purview of IRT.

This study aims to address the critical issue of machine breakdowns in industrial settings, which jeopardize operation economy, worker safety, productivity and environmental compliance. It explores the efficacy of a predictive maintenance program in mitigating these risks by proactively identifying and minimizing failures, thereby optimizing maintenance activities for higher efficiency.

The article implements Logical Analysis of Data (LAD) as a predictive maintenance approach on an industrial machine maintenance dataset. The aim is to (1) detect failure presence and (2) determine specific failure modes. Data resampling is applied to address asymmetrical class distribution.

LAD demonstrates its interpretability by extracting patterns facilitating the failure diagnosis. Results indicate that, in the first case study, LAD exhibits a high recall value for failure records within a balanced dataset. In the second case study involving smaller-scale datasets, enhancement across all evaluation metrics is observed when data is balanced and remains robust in the presence of imbalance, albeit with nuanced differences in between.

This research highlights the importance of transparency in predictive maintenance programs. The research shows the effectiveness of LAD in detecting failures and identifying specific failure modes from diagnostic sensor data. This maintenance strategy exhibits its distinction by offering explainable failure patterns for maintenance teams. The patterns facilitate the failure cause-effect analysis and serve as the core for failure prediction. Hence, this program has the potential to enhance machine reliability, availability and maintainability in industrial environments.

Conscious consumers have been influenced to either cut back on their fashion consumption or switch to ethical clothing (EC) as a result of the detrimental social effects of fast fashion that have been seen over the past 10 years. It also reflects how the ethical belief of the young generation influences them to be conscious of the ill effects of their fashion choices or behave like an ignorant irresponsible buyer. This study aims to examine this issue in detail to find out the prevalence and impact of such beliefs on consumption choices over a period of time.

This study uses the cross-sectional data of 525 respondents from India to explore and unearth the EC phenomenon in emerging markets. It follows a two-step approach consisting of confirmatory factor analysis and structural equation modeling to examine the proposed hypotheses using AMOS 22 software.

It was found that consumers in developing economies are concerned about the ethical standards followed by the fashion industry (FI), which is reflected in the form of inhuman working conditions for FI workers.

This study emphasizes understanding attitude, subjective norms, behavioral control and EC related to ethical buying behavior and their interaction mechanisms that transform it into the actual buying intention of EC.

It was an eye-opener that collective societal culture and standards do not influence ethical purchase decisions but it is rather the individual’s own ethical rules which is a result of established core family values that significantly shape fashion consumption. This study advances existing literature by empirically verifying the relationship between consumer attitude, consumers’ subjective norms, perceived behavioral control, environmental concern with ethical buying behavior and ethical purchase intention. It could provide insightful information and support academic research as well as real-world marketing and environmental initiatives.

Even though the pandemic years resulted in a significant increase in massive open online courses (MOOCs), there are still countries where penetration is low. The rejection of MOOCs can inhibit individual and societal advancements. The purpose of this study is to explore what is behind the resistance to MOOCs in these regions. Using the theoretical framework of innovation resistance theory, it defines the factors that inhibit the adoption of MOOCs.

The research is based on two studies. In the first study, in-depth interviews were used to explore factors that may cause barriers to adoption. Following the results of the first phase, a survey was conducted to investigate resistance to MOOCs, including both users and nonusers of such platforms.

Structural equation modeling revealed the presence of functional and psychological barriers, with the most significant being usage and value-related barriers. The lack of information and the need for interaction were identified as the main factors contributing to these barriers.

The results help increase the acceptance and effective integration of MOOCs into different educational environments, especially in countries with high resistance.

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

This paper aims to explore the key drivers of a resilient supply chain for the Indian pharmaceutical industry for enhanced and sustainable export pursuits in a disruptive business time.

This study literarily identifies the key drivers of supply chain resilience in the Indian pharmaceutical industry and methodologically appraises, analyzes and assesses them to derive a structural model that catalytically acts as facilitators and drivers for enhanced export performance. By reviewing relevant literature, it identifies research and development (R&D), procurement and sourcing, manufacturing, distribution and reverse logistics factors. It collects Likert scale feedback from stakeholders and probes interrelationships among them in achieving the enhanced export performance of India’s pharmaceutical sector.

The results lucidly identify the key enablers affecting the export performance of India’s pharmaceutical sector. These primarily include developing excellence in manufacturing, reverse logistics and distribution. Furthermore, sourcing and R&D are the antecedents of manufacturing expertise, catalytically contributing to improved export outcomes. Based on the derived structural model, the interrelationships are evaluated to explore causal links in achieving supply chain resilience and simultaneously elucidating the managerial and socio-economic interventions.

Narrating the results, this paper scholarly identifies the key drivers that need to be factored in for enhanced export outcomes of India’s pharmaceutical sector. The paper explains how to achieve supply chain resilience for export performance in India’s pharmaceutical sector.