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The purpose of this paper is to identify the challenges surrounding the implementation of digital technology (DT) agri-food supply chain (AFSC) and explore how these challenges relate to the various sustainability dimensions. Additionally, it aims to assess how these challenges are interconnected in relation to achieving sustainable development goals (SDGs).

The study employs a mixed-method approach utilizing the EFA-ISM-Fuzzy DEMATEL technique. To support and validate the findings, exploratory factor analysis (EFA) categorized 12 critical challenges in sustainable dimensions from 141 participants' responses. Furthermore, interpretive structural modeling (ISM) and decision-making trial and evaluation (DEMATEL) methods were used to obtain the interrelationship and hierarchical structure of the challenges.

The study identified 12 critical challenges while adopting DT in AFSC. These challenges were categorized into four sustainable dimensions: technological, economic, environmental and social. These challenges hinder the achievement of SDGs as well. Lack of regulatory and policy framework with security and privacy issues were the key challenges faced while adopting DT. These observations emphasize the necessity for government and policymakers to prioritize tackling the identified challenges to successfully endorse and execute DT initiatives in AFSC while also fulfilling the SDGs.

The implication underscores the need for collaboration among various stakeholders, such as governments, policymakers, businesses and researchers. By collectively addressing these challenges, DT can be leveraged optimally, fostering sustainable practices and making progress toward achieving the SDGs within the AFSC.

The study uses a combination technique of EFA and ISM-DEMATEL to identify the challenges faced in Indian AFSC while adopting DT and categorizes the interrelation between the challenges along with fulfilling the SDGs.

Supply chain disruptions can have severe negative consequences on companies. However, studies measuring the financial impacts of supply chain disruptions are largely confined to developed nations and large companies. Therefore, this study aims to analyze the impact of supply chain disruption on small companies in the context of an emerging nation. Further, an attempt has been made to classify supply chain disruptions and measure its impact by its type.

In this research, the event study on 335 supply chain disruption events for a 10 year period starting from 2009 to 2019 has been used.

The results state that the Indian small and medium companies lost −4.49% of shareholder wealth in disruption. The findings also indicate that the financial and environmental disruptions can have severe effect on shareholder wealth as compared to other category.

The study is confined to a developing country. Considering multiple countries can provide comparative results and therefore a global consensus could be achieved.

The outcomes of the results help managers to plan and prioritize supply chain disruptions, regulatory authorities can plug any possible insider trading practices for small companies in the event of supply chain disruptions. Investors can plan and take prudent investing decisions based on the nature of the disruptions.

To the best of the knowledge, this is the first study measuring the supply chain disruption effects on smaller companies in an emerging nation. The study is also novel in incorporating financial disruptions and measuring source wise impact on shareholder wealth.

This research identifies the supply chain performance indices and designs an evaluation framework to assess and compare the Indian petroleum supply chain performance. We presented a case study of three Indian petroleum companies. For this purpose, we identified fifteen performance criteria extracted from previous literature and expert inputs and classified them into four groups.

A fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) method is employed for evaluating the performance of the Indian petroleum supply chain.

The design and evaluation framework suggests that the top three performance measurement criteria, the purity of the products, compliance with environmental laws, and new technology adoption. The result findings also indicate that company C contributes to a maximum satisfaction level of 77%. Simultaneously, companies A and B hold satisfaction levels of 72% and 67%.

The managers should ensure that environmental standards, new technology adoption, and quality are significant concerns in the petroleum supply chain. The managers should follow national and international policies to preserve the environment and ensure safety in operational activities.

This paper makes two contributions in the domain of performance measurement of the petroleum supply chain. First, it identifies the prominent supply chain performance indices. Second, it proposes a model to assess and compare the performance of Indian petroleum companies.

Disruptive technologies can significantly contribute to the sustainability of operations in the petroleum supply chain. The present study aims to identify the prime sustainable dimensions and disruptive technologies implementation in the supply chain of the petroleum industry. The authors used content analysis in the literature and experts input to explore the sustainable dimensions and disruptive technologies in the supply chain.

This study used a hybrid method of hesitant fuzzy set and regret theory to identify the prominent sustainability dimensions and prominent disruptive technologies. This method emphasizes the decision-makers psychological characteristics under uncertain environments.

The result indicates that social responsibility, labor practices, safety and technical standards hold the most prominent sustainable dimensions in the petroleum supply chain. Further, the result also depicts that when consider an equal degree of regret and rejoice, artificial intelligence and big data could significantly enhance operations sustainability in the petroleum industry.

This study considers only 11 sustainable dimensions and 43 sustainable factors, whereas other dimensions and factors could also be considered in future research. The research uses hesitant fussy set and regret set theory to identify the prominent sustainable dimensions and disruptive technologies, whereas other multiple-criteria decision-making (MCDM) techniques can also be used.

To the best of the authors’ knowledge, this is the first paper to explore the sustainable dimensions (environmental, social and economic) and disruptive technologies in the supply chain of the petroleum industry. This research intended to guide the practitioners, policymakers and academicians to emphasize their effort toward sustainable operations supply chain management.

The purpose of this paper is to propose the microeconomics concept of elasticity to estimate the SERVQUAL gap elasticity to derive important insights for service providers to develop the right strategies to bridge the overall gap in service.

The dimensions of SERVQUAL adopted from Parasuraman et al. (1988) and Kumar et al. (2009) are first verified for their unidimensionality using structural equation modeling and reliability in the context of United Arab Emirates banking industry. Furthermore, the technique of dominance analysis is used to derive the relative importance of dimensions for different groups of banks. Finally, the stepwise log-linear regression models are used to estimate the gap elasticity to measure the responsiveness of the overall SERVQUAL gap to a change in customers’ perception on different dimension.

The results reveal that the dimension which is prioritized as the most important dimension need not to be the one to be targeted under the resource constraint to react faster to the changes of customers’ banking behavior.

This is probably the first attempt to examine the service quality through gap elasticity. This method is especially useful when the traditional approach to measure relative importance of critical factors fails to clearly discriminate between two or more dimensions, which, in turn, may lead to failure in decision making to choose the right strategies to bridge the overall gap in the service.

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.

In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.

In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.

In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.

Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.

Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.

Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

The purpose of this paper is to design and analyze a robust numerical method for a coupled system of singularly perturbed parabolic delay partial differential equations (PDEs).

Some a priori bounds on the regular and layer parts of the solution and their derivatives are derived. Based on these a priori bounds, appropriate layer adapted meshes of Shishkin and generalized Shishkin types are defined in the spatial direction. After that, the problem is discretized using an implicit Euler scheme on a uniform mesh in the time direction and the central difference scheme on layer adapted meshes of Shishkin and generalized Shishkin types in the spatial direction.

The method is proved to be robust convergent of almost second-order in space and first-order in time. Numerical results are presented to support the theoretical error bounds.

A coupled system of singularly perturbed parabolic delay PDEs is considered and some a priori bounds are derived. A numerical method is developed for the problem, where appropriate layer adapted Shishkin and generalized Shishkin meshes are considered. Error analysis of the method is given for both Shishkin and generalized Shishkin meshes.

The current study aims to investigate the determinants of nonperforming loans (NPLs) in the GCC economies during the period spanning 2000 to 2018. It also examines whether the worldwide financial crisis of 2007–2008, which brought the issue of non–performing loans to the greater attention of academics and policymakers, had a substantial impact on NPLs in this region.

The sample consists of 53 conventional banks from GCC countries, and the basic data for the study is obtained from various sources such as Bankscope, IMF World Economic Outlook, World Bank and Chicago Board of Options Exchange Market Volatility Index. The estimations were done by dynamic panel data regression modeling using system generalized methods of moments.

The findings reveal that both, the non-oil real GDP growth rate and inflation have favorable effects on NPLs. On the other hand, domestic credit to the private sector and the volatility index have an adverse effect on NPLs. Furthermore, the period-wise analysis shows that the relevance and significance of the determinants of NPLs vary between the precrisis and postcrisis periods. It is also reflected through the intercept dummy, which is found to be significant, indicating that the financial crisis, as a global economic factor, had a significant impact on NPLs. A number of robustness tests are applied, which indicate that the results are mostly robust and consistent in terms of the significance of the explanatory variables and the direction of their relationship with the dependent variable.

Policymakers and bank authorities must strive to maintain a healthy economy and implement macroprudential policies to improve the financial stability of banks and reduce credit risk.

To the best of the authors’ knowledge, this is likely the first study that empirically investigates the influence of the financial crisis on NPLs in the context of GCC economies. In addition, the research spans 19 years to produce more conclusive results.

This paper aims to report the results of an experiment undertaken to investigate the effect of country of origin (COO) in shielding the company’s image and retaining the trust and supportive behavioural intentions when it faces a crisis and the interactive effect of COO and the company’s pre-crisis reputation in shielding the company’s image.

A quasi-experimental study was undertaken to test the proposed hypotheses. Specifically, a two (pre-crisis reputation: low versus high) × two (country of origin: Indian versus Non-Indian) between-subjects factorial experimental design is configured and operationalized.

The results demonstrate that COO of a company fails to protect trust and supportive behaviour on its own, but, in the presence of a high pre-crisis reputation, it shields trust in the company more effectively. However, the interaction of COO and reputation does not induce supportive behaviour for the company during a crisis.

The findings of this research may help organizations to enhance trust/supportive behaviour toward their brand/company using attributes such as COO and pre-crisis reputation of the company.

This paper aims to suggest a parameter independent and simple speed estimator for primary field-oriented control of a promising electro-mechanical energy conversion device in the form of brushless doubly-fed reluctance machine (BDFRM) drive.

The speed estimation algorithm, in this context, is formulated using a modified secondary winding active power (mPs)-based model reference adaptive system (MRAS). The performance of the proposed estimator is verified through computer aided MATLAB simulation study, compared with conventional active power-based MRAS and further supported with experimental validation using a 1.6 kW BDFRM prototype run by a dSPACE-1103 controller.

The formulation of mPs-MRAS is insensitive to any machine parameters and does not involve any integration/differentiation terms. Thus, any deviation therein does not hinder the performance of the mPs-MRAS-based speed estimator. The proposed speed estimator shows stable behavior for variable speed-constant load torque operation in all the four quadrants.

The formulation of mPs-MRAS is insensitive to any machine parameter and does not involve any integration/differentiation terms.