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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.

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 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.

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.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.

Construction waste reduction (CWR) plays a vital role in achieving sustainability in construction. A good CWR practice can result in optimizing material usage, conserving natural resources, limiting environmental pollution, protecting the environment and enhancing human health. In this regard, the purpose of the current study is to identify the most relevant organizational policies that aid in waste reduction and concurrently explores the congruent measures to be adopted during the construction process in the Indian high-rise building sector.

The research findings were obtained through a mixed- method approach. Content analysis was used to identify waste reduction measures (variables) targeting on the two domains of construction – “waste-efficient execution” and “waste – mitigating organizational policies.” Furthermore, the authors explored and documented the key measures from the identified waste reduction measures using the constraint value of the relative importance index. As the next step, the study listed the theoretical hypothesis based on expert interviews and tested the theory through confirmatory factor analysis.

The results revealed that “waste sensitive construction techniques and strategies” as the most significant category under the domain “Execution” with a path coefficient of 0.85. Concurrently, the study has also determined that “control procedures for budget, quality and resources” as the most effective organizational approach in reducing construction waste in the Indian building industry, with a path coefficient of 0.83.

The current research is context-sensitive to the Indian construction sector. It presents the stakeholder’s perspective on construction waste reduction and the relevant measures to be implemented to reduce construction waste in high-rise building projects in India. It can also act as a concordance for decision-makers to further focus on CWR management and aid in formulating policies suitable for the Indian context.

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.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

Cloud computing services are game-changing in empowering organizations to drive innovation and unlock new growth opportunities. Accordingly, this study aims to examine the determinants of cloud computing adoption in SMEs and assess their impact on firm financial performance, specifically focusing on the mediating role of organizational agility in driving cloud-enabled financial improvements.

Data were collected from 405 owners and managers of SMEs who used cloud computing. A “variance based-structural equation modelling” (PLS-SEM) was employed to test the hypothesis.

The PLS-SEM result shows that relative advantage, cost-effectiveness, compatibility and external environment support significantly influence cloud computing adoption in SMEs. However, complexity insignificantly impacts cloud computing adoption. The analysis also revealed that cloud computing adoption substantially influences organizational agility. Similarly, cloud computing adoption and organizational agility significantly influence firms’ financial performance.

The present research provides valuable suggestions to service providers, policymakers and managers on developing and expanding cloud computing in developed and developing nations. Additionally, cloud providers can recognize their role in creating a supportive and adaptable environment that ensures convenient access to cloud computing users.

This research is an initial attempt to blend the strength of diffusion of innovation (DOI) theory with two additional constructs (i.e. cost-effectiveness and external environment support) for making a comprehensive model of cloud computing adoption and its influence on firm financial performance. By doing this, the research adds to the empirical knowledge on cloud computing adoption and provides an institutional framework to interpret the impact of cloud-based information technology.

This study aims to optimize capital allocation across several stocks to maximize expected returns while minimizing risk. By enhancing the portfolio selection model with new constraints and a unique objective function, the research introduces improved strategies for achieving these financial objectives.

This research uses a Sharpe ratio index for portfolio comparison and introduces a self-adjusting algorithm based on a genetic algorithm, eliminating the need for manual parameter adjustments. The effectiveness of this methodology is assessed across various test cases, demonstrating its applicability and robustness in dynamic financial market conditions.

This study confirms that the proposed algorithm consistently outperforms traditional models, offering robustness across different market conditions. Results indicate significant risk management and return maximization, and improvements have been attributed to the innovative model enhancements and algorithmic adjustments.

Incorporating a new objective function prioritizing the price-to-earnings ratio and introducing technical analysis constraints significantly enhance portfolio profitability and risk management. Another key contribution is the self-adjusting algorithm streamlining parameter adjustments, fostering more dynamic and accurate responses to market changes. These contributions have not been observed in prior research, providing a novel approach to the portfolio selection problem.