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Due to increasing uncertainty in the global business scenario, research on supply chain resilience is gaining significance. The outbreak of the COVID-19 pandemic has accelerated and magnified the issues already pertaining in the supply chain thereby increasing the vulnerabilities in the network. This study attempts to build the concept of pseudo-resilience in supplier selection and evaluation for supply chain sustainability.

A combination of multi-criteria decision-making methods AHP and R is adopted, and an integrated method called Combined AHP–R method is used to identify and include the property of pseudo-resilience into supplier selection processes.

The authors identified various factors contributing to pseudo-resilience considering supplier selection process and found the most important attribute. Using the combined AHP–R method, the suppliers were evaluated, considering the attributes contributing to the pseudo-resilience of supply chains and best supplier was selected.

To the best of our knowledge, this is the first study addressing a supplier selection problem for sustainable supply chains, considering pseudo-resilience. Also, this is the first study to apply the AHP–R method for supplier selection in the resilience or sustainability context.

The author identifies the traits of consumer resilience in emerging markets, classifies these major traits into five categories and analyses the influence relationships among them with distinctive focus on the psychological and personal resilience aspects.

The influence relations among the traits of consumer resilience from an expert perspective were identified with typical focus on electronic supply chains, and later the same was analysed through an intelligent influence modelling method, the grey causal modelling (GCM).

The major traits were analysed using the GCM, where the cause–consequence relations were observed for various objectives and the situational effects are noted. By constructing a magnitude plot and further a causal magnitude table, the important influence traits of consumer resilience for the considered case were observed and the same were auxiliary validated using an interpretive structural modelling (ISM) based approach.

As perceived from the results, it is evident that social support and recommendations from customers emerge as the principal influence traits of consumer resilience from an expert perspective, considering the case. The study can be further extended empirically to validate the findings.

Altogether, the author can recommend for practitioners that the influence of family, society, friends, peers as well as ratings from the customers can determine the level of consumer resilience. Hence, practitioners of customer relationship management can focus on improving the product and brand awareness among customers, so that more customers may recommend for typical products.

Consumer resilience depend on several factors, where the author has identified 25 major traits of the same and classified them into five major categories, including individual psychological factors, individual attitudes, individual socio demographic factors, micro environmental factors and macro environmental factors and the influence relations among them were studied from an expert perspective.

Over the years, the fruit and vegetable supply chain has encountered several challenges. From the harvesting stage until it reaches the consumer, a significant portion of fruits and vegetables gets wasted in the supply chain. As a result, the present study attempts to identify and analyze the growth barriers in the fresh produce supply chain (FPSC) in the Indian context.

An integrated grey theory and DEMATEL based approach is used to analyze growth barriers in the FPSC. The growth barriers were analyzed and sorted based on their influence and importance relations.

The results emphasize that the most critical growth barriers in the FPSC that should be addressed to ensure food waste reduction are as follows: Lack of cold chain facilities (B2), lack of transportation or logistic facilities (B1), lack of collaboration and information sharing between supply chain partners (B3), lack of proper quality and safety protocols (B15), a lack of processing and packaging facilities (B14), and poor productivity and efficiency (B13). Results are also verified by conducting a sensitivity analysis.

The results are highly useful for policymakers to exploit growth barriers within the FPSC that require more attention. The obtained results show that the managers and policymakers need to utilize more funds to develop the cold chain facilities and logistics facilities to develop the FPSC. By improving the cold chain facilities, it is possible to improve the quality of food, make the food safe for human consumption, reduce waste, and increase the efficiency and productivity of the supply chain. Also, this study may encourage policymakers and industrial managers to adopt the most influential SCM practices for food waste reduction.

Many researchers have attempted to analyze the causes of food waste and growth barriers in the FPSC using various decision-making methods. Still, no attempts are made to explore the causal relations among various growth barriers in FPSC through the integrated Grey-DEMATEL technique. Also, we devise policy implications in the light of the new farm bills or the Indian agricultural acts of 2020. Lack of cold chain facilities (B2) was found to be the critical driving barrier in the FPSC, as it influences multiple barriers. Also, there is a dire need for cold chain facilities and transportation systems to enhance productivity and efficiency.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is also studied considering select firms in the Indian context.

The author has proposed an advanced grey prediction model, the first-entry grey prediction model (FGM (1, 1)) for forecasting the sustainability governance performances of firms. The proposed model is tested using the periodic data of sustainability governance performances of 10 Indian firms.

The author observes that the majority of firms (6 out of 10) show dipping performances for sustainability governance for the future predicted period. This throws insights into the direction of improving good governance practices for Indian firms.

The idea and motivation for sustainability-focussed governance need a bi-directional focus from the side of managers that act as the agents and from the side of shareholders that act as the principals, as seen from an agency theory perspective for sustainability governance.

Sustainability governance culture can be inculcated to a firm at the strategic level by having a bi-directional focus from managers and shareholders, so as to enhance the social and environmental sustainability performances.

The governance performance evaluations for firms particularly in developing countries were not dated back more than a decade or two. Hence, the author implements a prediction model that can be best suited, when there are small periodic data sets available for prediction.

This study aims to examine empirically the nature of supply chain disruptions caused by the COVID-19 pandemic, particularly on the Indian automobile sector.

The authors evaluate the stock market performance of individual company and its quantitative relationship to certain variables related to company’s supply chain.

The authors analysed the company’s operations considering several ratios like asset intensity, company size, labour intensity and inventory to revenue.

The results of analysis can help the companies to understand how disruptions in the supply chain can affect the company’s operations and how it is perceived by the investors in the stock market.

Also, investors are benefitted, as they can understand how different companies with different operational characteristics react to global disruptions in supply chains, which in turn would help them to find better investment opportunities.

Although there is some literature available on the qualitative as well as quantitative analysis, the authors go further to analyse the impact of supply chain disruption on the stocks of the automobile sector.

Sustainability has been receiving increasing attention in recent times, due to increasing pressures from consumers and stakeholders. Based on few selected indicators, we suggest a method for calculating and forecasting the degree of sustainability supply chain considering the case of the IKEA Group.

In order to predict the sustainability of IKEA’s supply chain, utilizing IWAY fulfillment scores, this research uses the concept and theory of grey prediction models and moving probability-based Markov models.

According to the findings of prediction, we observe that the level of supply chain sustainability is declining for the case in the forecast year 2022. The results are perceived as per the outcomes of the first-order, one-variable-based grey prediction model (GM (1, 1) model) and the grey moving probability state Markov model-based error correction.

Operationalizing sustainability, we consider the contribution a company’s supply chain toward the advancement of human rights, ethical labor practices, environmental improvement and anti-corruption principles into the account of supply-chain sustainability.

In order to understand the future trends in the supply chain sustainability performances of the firms and make corrective actions, managers may take a note on the results of prediction and they can subsequently work on the policy implications.

We build an advanced prediction model for forecasting the level of sustainability performances for a case firm using the indicator of human rights, ethical labor practices, environmental improvement and anti-corruption principles.

The author explores the challenges to implementation of Industry 5.0 in the manufacturing sector, considering the developing economy context and studying the causal relationships among factors using an advanced causal modelling technique, the Grey Influence Analysis (GINA). The challenges were further classified based on importance and their influencing power.

The author uses the novel causal modelling technique of GINA to study and understand the influence relations among the challenges to implementation of Industry 5.0.

Based on the results from the expert response-based study in the Indian manufacturing industry, it is seen that the Regulatory challenges (RGC) appear to be the most important challenge that needs to be tackled first, while implementing Industry 5.0. Integration with existing systems and Ethical challenges (ETC) emerge as the second and third most important in the category of challenges, as per the results from the GINA analysis.

The RGC and the ETC need to be addressed prior to implementation and it is necessary to check whether the technologies comply with regulations and whether it creates serious job displacements. While implementation, the challenges with integration to existing systems can be appropriately tackled.

As a practical implication of the study, the author suggests that a proactive and reactive approach can be adopted to implement the Industry 5.0 concepts to reality. The RGC can be viewed for the adoption of technology, integration challenges can be viewed by understanding the existing systems, and ETC can be addressed by understanding the workforce in combination with technologies.

The shift in focus on sustainability and resilience has transformed the conventional industries to think beyond efficiency and productivity, toward being more responsible to society. The study analyzes the challenges to implementation of Industry 5.0 and the causal relations among them considering an expert response-based study.

This study aims to examine the factors that influence the adoption of social media to meet the service needs of persons with disabilities.

This study outlines the social media adoption model in disability services by using theories of persons with disabilities’ resistance, extreme-ableist expressions and exploit-ecological theories. A questionnaire survey is conducted among citizens who participate in disability-related activities.

The adoption of social media is influenced by disability norms, administrative and platform support. Emerging trends, such as disability politics and inclusion, are inconsequential.

Results implicate that frequent sharing of disability rights perspectives and awareness initiatives can enhance social media platforms for disability services.

In regional disability services, the scope of social media is hampered by a lack of reporting capabilities and a paucity of digital content sensitive to disability.

The disability interest group reported less specialised services enabled by social media from developing nations. The current study addresses this research gap.

The utilization of the new track construction (NTC) machine in the dedicated freight corridor (DFC) project in India proved to be counterproductive due to various uncertain and dynamic factors. This study aims to develop the model and simulate the productivity of the NTC machine considering system dynamic factors encountered during the construction phase of the project.

The study employs a fuzzy system dynamics (FSD) model to analyse the productivity of the NTC machine. Initially, 21 variables related to the construction productivity of equipment-intensive activities are identified using exploratory factor analysis (EFA). A stock-and-flow diagram is then developed to examine the interdependencies among these variables. The FSD model integrates hard relationships via mathematical formulas and soft relationships using fuzzy measures and integrals. The model’s structure and behaviour are validated through dimension consistency, structure verification and pattern verification tests.

The results indicate that key system variables, such as site restrictions, weather and site conditions, unscheduled breaks and functional range, significantly influence the productivity of the NTC machine. The study also indicates the importance of human expertise, operator’s experience and crew coordination skill as influencing system variables in equipment productivity.

This study provides insights in managing complex and dynamic factors affecting the productivity of the NTC machine used large-scale rail construction projects. This study offers a novel application of FSD to address productivity challenges of this special equipment in the new rail track construction projects.