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The disruptive pandemic has badly affected supply chain operations across the globe and implementing green supply chain strategies is challenging for manufacturing firms, especially in emerging countries. Therefore, this study aims to identify the significant challenges hindering the green supply chain as a pathway towards sustainability in the post-COVID-19 era.

Fuzzy Delphi Methodology (FDM), Interpretive structural modeling (ISM) and MICMAC were applied. FDM was applied to select the most relevant challenges and later ISM and Matrices d'Impacts cross-multiplication appliqúe a classmate MICMAC were used for modeling and classifying critical challenges.

Lack of trust between firms and supply chain partners, and difficulty in transforming positive environmental attitudes into action are the significant challenges to implementing green supply chain management. Lack of communication between government and Chinese firms is the least important factor which shows that the government is trying to support firms and reduce the negative effects after the drastic impacts of COVID-19. However, COVID-19 left a draconian effect on organization’s green supply chain and it’s not easy to overcome.

None of the previous studies applied mixed methodologies of FDM, ISM and MICMAC to evaluate Green supply chain as a pathway to sustainable operations in the post-COVID-19 era. Challenging factors of green supply chain operations in COVID-19 are different from earlier studies and contribute to the literature of emerging countries.

While the COVID-19 pandemic has given many firms the importance of business continuity, restaurants have yet to realize the essential enablers. In this regard, the study explored the potential enablers that can help firms attain business continuity in the post-COVID-19 era for casual dining restaurants.

This study used an extensive literature review and further supported 28 restaurant managers with an average experience of 8.5 years to identify the potential enablers for ensuring business continuity for casual dining restaurants. Subsequently, this study used the interpretive structural modelling coupled with Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis to understand the context-specific inter-relationships as a hierarchical structural model.

Findings suggest that financial capability forms the key driver for other enablers in the hierarchy of business continuity for casual dining restaurants. Furthermore, manpower (local and outstation) are the second-level drivers of restaurant process control, digital presence, online reviews and feedback management, scalability, food quality, food delivery management, employee safety and hygiene. The third-level factors further help restaurants to achieve the dynamic capability required to provide customer value and ensure business continuity.

The study is the foremost to explore a business continuity framework in the new normal for casual dining restaurants in the country. Furthermore, several contextual inter-relationships exhibiting the hierarchy are also established for the business continuity of restaurants in the new normal.

The research explores the shift to Quality 4.0, examining the move towards a data-focussed transformation within organizational frameworks. This transition is characterized by incorporating Industry 4.0 technological innovations into existing quality management frameworks, signifying a significant evolution in quality control systems. Despite the evident advantages, the practical deployment in the Indian manufacturing sector encounters various obstacles. This research is dedicated to a thorough examination of these impediments. It is structured around a set of pivotal research questions: First, it seeks to identify the key barriers that impede the adoption of Quality 4.0. Second, it aims to elucidate these barriers' interrelations and mutual dependencies. Thirdly, the research prioritizes these barriers in terms of their significance to the adoption process. Finally, it contemplates the ramifications of these priorities for the strategic advancement of manufacturing practices and the development of informed policies. By answering these questions, the research provides a detailed understanding of the challenges faced. It offers actionable insights for practitioners and policymakers implementing Quality 4.0 in the Indian manufacturing sector.

Employing Interpretive Structural Modelling and Matrix Impact of Cross Multiplication Applied to Classification, the authors probe the interdependencies amongst fourteen identified barriers inhibiting Quality 4.0 adoption. These barriers were categorized according to their driving power and dependence, providing a richer understanding of the dynamic obstacles within the Technology–Organization–Environment (TOE) framework.

The study results highlight the lack of Quality 4.0 standards and Big Data Analytics (BDA) tools as fundamental obstacles to integrating Quality 4.0 within the Indian manufacturing sector. Additionally, the study results contravene dominant academic narratives, suggesting that the cumulative impact of organizational barriers is marginal, contrary to theoretical postulations emphasizing their central significance in Quality 4.0 assimilation.

This research provides concrete strategies, such as developing a collaborative platform for sharing best practices in Quality 4.0 standards, which fosters a synergistic relationship between organizations and policymakers, for instance, by creating a joint task force, comprised of industry leaders and regulatory bodies, dedicated to formulating and disseminating comprehensive guidelines for Quality 4.0 adoption. This initiative could lead to establishing industry-wide standards, benefiting from the pooled expertise of diverse stakeholders. Additionally, the study underscores the necessity for robust, standardized Big Data Analytics tools specifically designed to meet the Quality 4.0 criteria, which can be developed through public-private partnerships. These tools would facilitate the seamless integration of Quality 4.0 processes, demonstrating a direct route for overcoming the barriers of inadequate standards.

This research delineates specific obstacles to Quality 4.0 adoption by applying the TOE framework, detailing how these barriers interact with and influence each other, particularly highlighting the previously overlooked environmental factors. The analysis reveals a critical interdependence between “lack of standards for Quality 4.0” and “lack of standardized BDA tools and solutions,” providing nuanced insights into their conjoined effect on stalling progress in this field. Moreover, the study contributes to the theoretical body of knowledge by mapping out these novel impediments, offering a more comprehensive understanding of the challenges faced in adopting Quality 4.0.

Farmers in India are hesitant to adopt organic farming (OF) despite high demand for organic products and favorable policy measures to encourage the practice. Therefore, this study aims to assess the OF adoption barriers faced by Indian farmers using a systematic method of multi-criteria decision making (MCDM).

The authors explored eighteen barriers to OF adoption by conducting a literature survey and discussion with experts on OF. Then the authors used a combined method of Grey Decision Making Trial and Evaluation Laboratory (DEMATEL) and Interpretive Structural Modeling (ISM) methodology to rank the barriers and analyze their interactions.

The analysis reveals that “Lack of knowledge and information,” “lack of financial capacity of farmers’ and “lack of institutional support” are the cause (independent) barriers that significantly impact other barriers. The top three effect (dependent) barriers are “lack of availability of organic inputs,” “personal characteristics such as age, attitudes and beliefs” and “lack of premium pricing,” which are affected by the other barriers.

This research work will help the decision makers understand the barriers to OF adoption in India and their interrelationships. The proposed framework enables them to focus on the high-priority independent barriers, which will subsequently impact the other dependent barriers.

Previous research on OF adoption barriers lacked a multifaceted scientific approach, which is necessary because OF is a complex system and needs a thorough investigation to assess the interaction between the barriers. The research attempts to fill this gap and addresses the complex nature of adoption barriers.

Behavioral characteristics and the existence of personal knowledge and skills that are essential in forming a capable manager with specialized insight, is one of the most important evaluation processes in appointing a manager. Wisdom as a trait of a set of personal characteristics and cognitive knowledge of a capable manager will gain competitive advantages for the company and external stakeholders. The purpose of this study is to present the framework of managerial financial wisdom through qualitative and quantitative research methodologies.

The study uses grounded theory methodology to identify factors for managerial financial wisdom and uses a MICMAC process to evaluate the power of driving and dependence among factors. MaxQDA software was used for data coding and analysis. The MICMAC process by determining the location of each of the sub-themes in the four-dimensional matrix, seeks to determine the most effective criterion in the context of the study. The method of data collection is mixed and in terms of philosophical nature, this study is inductive/comparative.

The results of the research in the qualitative part indicate the presentation of a framework based on two categories, four components, and thirty themes. The results of the quantitative part of the study also showed that the most important factor of managerial financial wisdom is market-oriented values. Finally, it was found that the social trust caused by managerial financial wisdom is considered a factor in gaining more shares in the capital market due to the positive stimulus in this dimension.

The study is relevant for both practitioners and academia and has significant implications. For practitioners, the structural linkages identified will help enhance managerial wisdom effectiveness and drive the critical metrics for financial decisions and shareholders' rights protection. The study may help financial market practitioners better understand future policies. Academia and researchers can take reference from methodologies used in this study for exploring factors of interest and developing matrix linkages among them.

Nowadays, the tourism industry is considered the largest and most diverse industry in the world, which can play a significant role in the economic growth and development of a country as the main source of income and employment. In this regard, the purpose of this study is to focus on evaluating the competitiveness indicators of Shiraz city tourism destination based on a combined model.

For this purpose, a questionnaire with 78 questions was used to evaluate the indicators, in which a total of 1,432 tourists participated, including 927 domestic tourists and 505 international tourists. The collected data were analyzed to determine the hierarchical relationship between the indicators using equation structural modeling.

The research findings indicate that the most effective and influential indicators in the field of tourism competitiveness are natural and handmade resources, and the most influential factors are demand conditions and cultural and natural resources. In this hierarchy, each factor affects its previous level and influences the next level.

This study offers significant potential for uncovering credible and robust approaches to further investigate the contextualization of tourism competitiveness on both national and international scales, thereby generating valuable new insights. By conceptualizing the diverse dimensions of tourism competitiveness and delving into the variations in its impacts across multiple levels, this research not only challenges existing notions but also aids destinations in maintaining and enhancing their market position and share over time. This study offers valuable insights and practical implications for both researchers and practitioners in the field of urban tourism. It enhances the understanding of destination competitiveness, informs policy decisions, facilitates benchmarking and best practices, guides strategic decision-making and promotes sustainable tourism development.

Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.

This study investigates the integration of Industry 4.0 (I4.0) technologies with condition-based maintenance (CBM) in upstream oil and gas (O&G) operations, focussing on developing countries like Nigeria. The research identifies barriers to this integration and suggests solutions, intending to provide practical insights for improving operational efficiency in the O&G sector.

The study commenced with an exhaustive review of extant literature to identify existing barriers to I4.0 implementation and contextualise the study. Subsequent to this foundational step, primary data are gathered through the administration of carefully constructed questionnaires targeted at professionals specialised in maintenance within the upstream O&G sector. A semi-structured interview was also conducted to elicit more nuanced, contextual insights from these professionals. Analytically, the collected data were subjected to descriptive statistical methods for summarisation and interpretation with a measurement model to define the relationships between observed variables and latent construct. Moreover, the Relative Importance Index was utilised to systematically prioritise and rank the key barriers to I4.0 integration to CBM within the upstream O&G upstream sector.

The most ranked obstacles in integrating I4.0 technologies to the CBM strategy in the O&G industry are lack of budget and finance, limited engineering and technological resources, lack of support from executives and leaders of the organisations and lack of competence. Even though the journey of digitalisation has commenced in the O&G industry, there are limited studies in this area.

The study serves as both an academic cornerstone and a practical guide for the operational integration of I4.0 technologies within Nigeria's O&G upstream sector. Specifically, it provides an exhaustive analysis of the obstacles impeding effective incorporation into CBM practices. Additionally, the study contributes actionable insights for industry stakeholders to enhance overall performance and achieve key performance indices (KPIs).

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.