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Despite various governmental efforts, female entrepreneurship in the UAE is still not a popular option among women. The purpose of this study is to identify the enablers to female entrepreneurship and to establish relationships among them.

This research uses a two-phased approach. In the first-phase, an empirical study on female entrepreneurs was conducted to find out the most important enablers from among a set of variables identified through a comprehensive literature review. In the second-phase, using interpretive structural modeling, a hierarchy-based model is developed among the most important enablers. Further, these enablers are also classified depending on their driving power and dependence.

Survey results indicate that female entrepreneurs consider enablers as the driving force in creating an entrepreneurial culture. Further, the hierarchy-based model developed in this research helps to identify variables that are of strategic importance and require utmost attention.

The framework presented in this study can be used effectively by the policymakers to develop suitable strategies for improving entrepreneurial behavior among women in the United Arab Emirates.

The novelty of this approach is the integration of questionnaire and interpretive structural modeling methodology and classifying enablers in four clusters. The research provides useful insights about the perception of female entrepreneurs about the entrepreneurial culture in the United Arab Emirates, and a relationship model that may serve as a decision tool for improving female entrepreneurship.

The key objectives of this study were to investigate the interactions among the lean, green management practices and organizational sustainable performance measures and explore the possibility of simultaneous implementation of these concepts for improving the organizational sustainable performance.

Using the interpretive structural modeling (ISM) technique, the interactions among the lean, green practices and organizational sustainable performance measures were established. A focus group which consisted of purposively selected 15 experts was utilized in the primary data collection.

In Sri Lankan context, water and material consumption reduction, energy efficiency, water pollution and greenhouse gas reduction were identified as the dominant green practices, while pull production, lot size reduction, continuous improvement, preventive maintenance, employee involvement and cycle time reduction were the dominant lean practices. Inventory level, profitability, quality, cost, employee satisfaction, customer satisfaction, lead time, resources consumption (material, water, energy) and waste generation were determined as the dominant sustainable performance measures. The resulting ISM-based structural model which consisted of eight levels concluded that firstly lean practices influence the green practices and afterward green practices affect the sustainable performance measures.

The aim of this study was to develop a hypothetical structural model to explain the interactions among the lean, green management practices and organizational sustainable performance measures. But this hypothetical model was not empirically tested in the current study. So further study is required to empirically test the proposed model.

Currently organizations who practice for sustainable performance engages, lean and green practices separately without understanding on which practices are stared when and how. So, through the findings of this study, organization who desire to improve the sustainable performance are recommended to begin the journey with lean practices and subsequently move in to green and handle both lean and green initiatives through one functional unit.

The existing literature does not possess a model for explaining the lean–green synergy and organizational sustainable performance and this study successfully fills this gap. Also the study proposes for the practitioners, when and how the lean and green practices should be initiated and implemented for rising the sustainable performance of an organization.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

In the scope of the immense growth of corporate frauds and scandals, reporting unethical practices could be considered as an important mechanism to control them and ultimately improve organizational quality. To this end, this study proposes the conceptual framework comprising the enablers impacting employees' tendencies and behaviors to reporting misdemeanor in the workplace.

Systematic review of literature has been carried out. To understand the complexities among various enablers and to analyze their driving power and contingencies, a modified total interpretive structural modeling (TISM) approach has been adopted.

The findings indicate that enablers such as moral identity (MI) and job satisfaction (JS) having higher driving power (come at the bottom of the hierarchy) are relatively more important. Furthermore, perceived personal cost (PC), moral courage (MC), self-efficacy (SE) and anger have high dependent power of factors. Finally, the paper provides two paths that can lead to whistleblower's ethical decision.

A conceptual framework delivered in this paper requires to test against the field data. However, the conceptual understanding of driving enablers paves the way to top management in recruiting and hiring people in the workplace.

This study represents the first attempt to apply TISM for whistleblowing phenomenon. It provides a comprehensive conceptual framework in order to address the relative importance of various individual enablers in developing reporting tendencies against misdemeanors.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

This research aims to design a comprehensive resilience model in the banking industry for identifying the dimensions and components that can enhance organizational resilience in the industry, which can contribute to the existing literate as a promising comprehensive model.

After reviewing the literature and studying the models of organizational resilience, semistructured interviews were conducted with managers and prominent experts in the banking industry. To analyze the interviews, the thematic analysis technique was used with three coding stages. After designing the research model in two main dimensions of micro and macro management in the banking industry, the relation between the main components and subcomponents was identified by using Interpretive Structural Modeling (ISM) and DEMATEL techniques.

The study findings indicating that proper observation and predicting the bank's problems and making suitable connections with the government are two major indicators of the resilience of the banking network, which can realize through influencing the components of risk management, financial resource management and system corruption. The results of this research can lead to the expansion of theoretical foundations of the past research and the concept of organizational resilience in the field of financial services and especially the banking industry.

This paper provides the components with a more significant impact, which bank managers should consider the relationship among them to enhance organizational resilience for more effectiveness of their decisions.

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.

Tourism is one of the upcoming service industry in India with high potentials for future growth, particularly in rural areas. Many potential barriers are affecting the growth of tourism in rural India. Therefore, it is essential to explore and prioritize the barriers to tourism growth in rural India.

Qualitative and quantitative responses from “16” experts related to tourism and hospitality management from central India are collected for this study. An integrated Multi-Criteria Decision Making (MCDM) based framework is adopted to identify and relate significant barriers to tourism growth in India.

The result of the study identified many significant barriers and their importance to tourism growth in rural India.

The findings of this study add to the knowledge base of tourism research in line with the previous literature. This study offers an in-depth understanding of barriers focusing on rural tourism growth and devising both the plan of action and the suggestive measures in dealing with rural tourism.

The study provides a robust framework by integrating Interpretive Structural Modelling(ISM) and Decision Making Trial and Evaluation Laboratory (DEMATEL) to explore and prioritizing the critical barriers to rural tourism growth in India. The results of this study can help the decision-maker to fundamentally improve the economy of India through the growth of rural tourism.

This study aims to develop a hierarchical model that uncovers the relationships between challenges confronting Africa's organ transplant supply chain systems.

Eleven challenges (variables) were identified after a comprehensive review of the existing literature. The contextual interactions among these variables were analysed from the perspectives of health-care stakeholders in two sub-Saharan Africa (SSA) countries (Nigeria and Uganda), using Delphi-interpretive structural modelling-cross-impact matrix multiplication applied to classification (MICMAC) techniques.

The findings reveal that weak regulatory frameworks, insufficient information systems and a lack of necessary skills make it challenging for critical actors to perform the tasks effectively. The interaction effects of these challenges weaken organ supply chains and make it less efficient, giving rise to negative externalities such as black markets for donated organs and organ tourism/trafficking.

This paper establishes a solid foundation for a critical topic that could significantly impact human health and life once the government or non-profit ecosystem matures. The MICMAC analysis in this paper provides a methodological approach for future studies wishing to further develop the organ supply chain structural models.

The study provides valuable insights for experts and policymakers on where to prioritise efforts in designing interventions to strengthen organ transplantation supply chains in developing countries.

This study is one of the first to empirically examine the challenges of organ transplant supply chains from an SSA perspective, including theoretically grounded explanations from data collected in two developing countries.