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Green entrepreneurship (GE) is a novel concept in business and enhances environmentally friendly production and operation activities for “sustainable development” (SD). The aim of this study is to determine the drivers that contribute to the growth and success of “micro, small, and medium enterprises” (MSMEs) in the manufacturing sector in India. The study also examines the mutual and cause-and-effect relationships among these identified drivers.

The study used integrated research methodology and identified nine key drivers of GE (GEDs) through extensive literature reviews, theoretical perspectives (i.e. “resource-based view” (RBV), “natural resource-based view” (NRBV) and “critical success factor theory” (CSFT)), and expert opinions. Further, “total interpretive structural modeling” (TISM) and “matrice d'impacts croisés multiplication appliquée á un classment” (MICMAC) analysis are used here to develop a hierarchical model and cluster the drivers, and fuzzy “decision-making trial and evaluation laboratory” (fuzzy-DEMATEL) is used to develop causal relationships among the drivers. Further, a sensitivity analysis is conducted to ensure the robustness of the results.

Results indicated that green manufacturing and operation capability development, green business process management and attitudes toward developing sustainable business models significantly impacted GE and SD. The findings of this study help managers, policymakers, and practitioners gain an in-depth understanding of the drivers of GE.

The study considers a limited number of drivers and is specific to Indian manufacturing MSMEs only. Further, a limited number of experts from different enterprises are considered for data analysis. This study is also based on interrelationships and their relative importance based on multicriteria decision-making techniques. This study aids government decision-making, policy formulation and strategic decision-making for manufacturing businesses in achieving SD goals. In addition, this research also encourages green entrepreneurs to start eco-driven companies and facilitate the use of environmentally friendly goods to offset environmental challenges and accomplish sustainable development goals.

This study proposes an integrated methodology that will benefit managers, practitioners and others in developing strategies and innovations to improve and develop green practices. This study further helps with responsive, sustainable business development in various manufacturing MSMEs.

This study aims to identify and prioritize the key practices and strategies for effective global sourcing and supply chain management (SCM).

The study uses a combination of Pareto analysis and multi-objective optimization based on ratio analysis research methodology to analyze and establish the relationships among the identified key practices and strategies. Pareto analysis enables organization to prioritize organizational efforts and resources by focusing on the most critical factors.

The study shows that the “eco-friendly sourcing strategy”, “lean manufacturing” and “tool cost analysis” are the top critical practices and strategy variables for global sourcing and SCM, whereas the “risk management”, “procurement strategy” and “leverage digital solutions” are the critical practices and strategy variables.

The findings of this research can also assist organizations in making informed decisions to optimize their global sourcing and supply chain operations.

By using these methods, this research paper gives valuable insights into the critical practices and strategies that can enhance efficiency, mitigate risks and drive success in global sourcing and SCM. The subjects and elements this study identified will serve as a framework and suggestions for further theoretical investigation and real-world implementations.

The study aims to add to the existing literature on food supply chains by specifically taking into the redesigning of the alignment of storage, packaging and distribution practices in the modern complex supply chain. The redesign of the food supply chain’s storage, distribution and packaging is a transformative endeavor ultimately aimed at enhancing efficiency, sustainability and reliability.

In order to identify, classify and prioritize the main challenges, this study conducted an extensive analysis of the literature and experts’ opinions in the areas of academia, information technology and the food supply chain (FSC) using combined compromise solution method (CoCoSo) and complex proportional assessment (COPRAS).

The top three classes of key indicators revealed in this study are dynamic route optimization and on-demand delivery pods (RD4), implementation of active packaging with nanotechnology (RP3) and collaborative last-mile (RD2). The findings reveal that dynamic route optimization and on-demand delivery pods (RD4) and collaborative last-mile (RD2) are maintaining a balance between collaborative delivery networks through route optimization which is a very discussable theme in recent literature.

The research provides fresh insights into how perishable food shelf life parameters and the use of distribution networks within the short supply chain can be taken into consideration when redesigning the storage, packaging and distribution system for food supply chains.

This study aims to investigate the relationship between patient satisfaction (PS) and the parameters in healthcare and supply chain management (HLSCM).

The structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) method have been employed to identify correlation and possible configuration of causal factors that influence PS, including lack of resilience (LS), lack of visibility (LV), cost management (CM) and integration and interoperability (II).

The results from SEM confirmed that PS is highly correlated with lack of visibility, CM and II as critical parameters. Moreover, fsQCA findings state that the configuration of high levels of both resilience and lack of visibility, as well as high levels of II, are crucial for PS.

The researchers also identified the configuration of factors that lead to low PS. The study’s results could assist healthcare providers in improving their supply chain operations, resulting in more effective and efficient healthcare service delivery and ultimately improving PS.

The fsQCA method used in the study provides a more nuanced understanding of the complex interplay between these factors. The inclusion of supply chain management characteristics as parameters in the evaluation of PS is a novel aspect of this research. Previous studies largely focused on more traditional factors such as physical care, waiting times and hospital amenities. By considering supply chain management factors, this study provides insights into an under-explored area of PS research, which has important implications for healthcare providers looking to improve their operations and PS.

Digital-only fashion represents an ideal fusion of sustainability and fashionability, garnering growing interest among fashion professionals. However, there is a noticeable gap in research focusing on digital-only fashion acceptance among consumers. Hence, this study aims to empirically examine consumers’ motivations, evaluations and acceptance of digital-only fashion based on the Functional Theory of Attitudes.

A US-based research agency was hired to collect data, resulting in 247 completed survey responses. Data analysis was conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach.

Testing results highlight that consumer acceptance of digital-only fashion is directly influenced by both overall attitude and self-expressive attitude. Self-expression is particularly pivotal in digital-only fashion acceptance. Adorning avatars and dressing realistic on-screen bodies are distinct yet complementary aspects of using digital-only fashion. Consumers with positive environmental beliefs about digital-only fashion are concerned about how well digital-only fashion items allow them to express such beliefs.

This study innovatively applied the functional theory of attitudes to the emerging domain of digital-only fashion and identified consumers’ four functional attitudes toward digital-only fashion, along with the underlying motivations served by each functional attitude. Furthermore, this study provides valuable practical insights across the digital-only fashion value chain.

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.

The growing pressure on businesses to balance environmental sustainability with profit maximisation has led to the development of green entrepreneurial orientation (GEO), which proactively integrates green practices into core business operations. Grounded in the ecological modernisation theory, GEO acts as a green management practice which helps in achieving competitiveness through environmental innovation. However, despite the growing emphasis on GEO, there remains a gap in understanding how specific drivers influence the organisational structures and processes that lead to GEO. Therefore, the study addresses this gap by analysing the key drivers of GEO using an integrated approach.

In this study, total interpretive structural modelling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) were used to identify and interpret the interrelationship between key drivers of GEO. Here, TISM technique aided in constructing a contextual relationship-based structural model of drivers, whereas MICMAC assisted in categorising the drivers based on their driving and dependence power. A case evaluation was also carried out in the Indian textile industry to validate the TISM model.

The result indicates that institutional pressure, managerial environment concern, organisational resilience and big data analytical capabilities are the most influential drivers of GEO at organisational level, and other drivers act as secondary and linked variables in this process. The MICMAC analysis further supports the results of TISM. In addition, the overall TISM model is validated in the Indian textile sector.

The study findings will assist researchers and policymakers in adopting a systematic approach to prioritise GEO in pollution intensive industries. Moreover, it will help managers in leveraging GEO to achieve strategic advantages amid environmental challenges.

This study is amongst the first to employ an integrated qualitative approach to analyse drivers of GEO.

In the present era, artificial intelligence (AI) is transforming and redefining the lifestyles of society through its applications, such as chatbots. Chatbot has shown tremendous growth and has been used in almost every field. The purpose of this study is to identify and prioritize the factors that influence millennial’s technology acceptance of chatbots.

For the present research, data were collected from 432 respondents (millennials) from Punjab. A fuzzy analytical hierarchy process was used to prioritize the factors influencing millennials’ technology acceptance of chatbots. The key factors considered for the study were information, entertainment, media appeal, social presence and perceived privacy risk

The findings of the study revealed media appeal as the top-ranked prioritized factor influencing millennial technology acceptance of chatbots. In contrast, perceived privacy risk appeared as the least important factor. Ranking of the global weights reveals that I3 and I2 are the two most important sub-criteria.

Data were gathered from the millennial population of Punjab, and only a few factors that influence the technology acceptance of chatbots were considered for analysis which has been considered as a limitation of this study.

The findings of this study will provide valuable insights about consumer behaviour to the business firm, and it will help them to make competitive strategies accordingly.

Existing literature has investigated the factors influencing millennials’ technology acceptance of chatbots. At the same time, this study has used the multi-criteria decision-making technique to deliver valuable insights for marketers, practitioners and academicians about the drivers of millennials’ technology acceptance regarding chatbots which will add value to the prevailing knowledge base.

Limited studies in the mobile payment segment have attempted at understanding the factors that resist customers from using financial apps or mobile payment services (MPSs). This study aims at identifying the barriers from online customer reviews and examine how these barriers affect customers’ negative emotions (anger, fear, sadness), customer ratings and recommendation intentions.

This study, divided into three phases, has adopted a text-mining based mixed-method approach on 14,043 reviews present in Google PlayStore or App Store pages about financial apps used in India.

Phase 1 identified barriers like, “bad user experience”, “UPI failure”, “trust issues”, “transaction delays” from the reviews. Phase 2 found that “bad user experience” and “UPI failure” trigger both “anger” and “sadness”. “Transaction delays” and “money lost in transaction” stimulate “fear”. From the IRT stance, in Phase 3 this study has found that barriers like, “transaction error”, “UPI failure” (usage), “bad user experience” (image) and “trust issues” (tradition) have a significant negative impact on both customer ratings and recommendation intention.

The current study contributes to the existing literature on MPSs by identifying barriers from user generated content. Additionally, this study has also examined the impact of the barriers on customers’ negative emotions and recommendation intention.

Concerns over environmental degradation, alongside the global legalization of hemp cultivation, have prompted applications of hemp-derived textile products. However, research examining consumers’ acceptance of such products is lacking. It is critical to understand how young consumers adopt these products as they prioritize sustainability and lead the future purchasing power. Thus, this study aims to investigate young consumers’ perceptions, attitudes, and purchase intentions regarding hemp fiber textiles and clothing based on the Theory of Consumption Value and the Spiral of Silence Theory.

A US-based research company was hired to recruit survey participants, yielding 314 valid data. Data analysis, including Exploratory Factor Analysis and Partial Least Squares Structural Equation Modeling, was conducted using the R programming language and RStudio software.

Purchase intentions of hemp-based clothing and textiles among young consumers are influenced by attitude, perceived social value and future trend perception. Perceived social value also impacts attitude and future trend perception. High environmental involvement correlates with positive attitudes. Consumers’ subjective knowledge of hemp and objective knowledge regarding hemp fiber impact their perceptions of future trends in such products.

This research represents a seminal consumer study focusing on the resurgence of industrial hemp fiber textiles and clothing. This study empirically validates the significant role of social factors in young consumer acceptance of such products at both personal and societal levels. This study expands current knowledge and provides actionable insights for practitioners.