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The primary purpose of this study is to examine the factors leading to shopping cart abandonment and construct a model depicting interrelationship among them using interpretive structural modeling (ISM) and Matriced Impact Croises Multiplication Appliquee an un Classement (MICMAC).

Initially, 20 factors leading to shopping cart abandonment were extracted through a systematic literature review and expert opinions. Fifteen factors were finalized using the importance index and CIMTC method, for which consistency has been checked in SPSS software through a statistical reliability test. Finally, ISM and MICMAC approach is used to develop a model depicting the contextual relationship among finalized factors of shopping cart abandonment.

The ISM model depicts a technical glitch (SC8), cash on delivery not available (SC4), bad checkout interface (SC9), just browsing (SC11), and lack of physical examination (SC12) are drivers or independent factors. Additionally, four quadrants have been formulated in MICMAC analysis based on their dependency and driving power. This facilitates technical managers of e-commerce companies to focus more on factors leading to shopping cart abandonment according to their dependency and driving power.

Taking an expert’s opinion as a base may affect the results of the study due to biases based on subjectivity.

This study’s outcomes would accommodate practitioners, researchers, and multinational or national companies to indulge in e-commerce to anticipate factors restricting the general public from online shopping.

For the successful running of an e-commerce business and to retain the confidence of e-shoppers, every e-commerce company must make a strategy for controlling factors leading to shopping cart abandonment at the initial stage. So, this paper attempts to highlight the main factors leading to shopping cart abandonment and interrelate them using ISM and MICMAC approaches. It provides a clear path to technical heads, researchers, and consultants for handling these shopping cart abandonment factors.

The purpose of this study is to examine the effects of financial development on the economic growth of jurisdictions with systemically important Islamic finance.

The authors use several estimation methods. The primary analysis is based on the LSDVC method using a sample of 23 countries covering the period of 2000–2019.

The findings suggest that the financial sector may not be a significant factor in determining economic growth, or that it may decrease it depending on the proxy used. These results are in line with recent studies and robust across different estimation specifications and methods used.

Finance practitioners may reconsider the way they conduct their daily activities as their impact on economic growth is fading away. Similarly, policymakers should consider the role that financial development plays in economic growth alongside other factors that may influence its impact. It may be necessary to examine the moderating effects of institutional development on the relationship between finance and growth and consider the channels through which financial development can contribute to economic growth. Additionally, it would be useful to study the impact of Islamic finance on economic growth using different data sources.

Although the topic has been explored using different data sets and focusing on different samples, it has not been explored considering the impact of Islamic finance development on economic growth. Given the global appeal of the Islamic finance industry, it is worth investigating its significance for economic growth.

The study aims to estimate the short- and long-run effects of monetary policy on residential property prices in South Africa. Over the past decades, there has been a monetary policy shift, from targeting money supply and exchange rate to inflation. The shifts have affected residential property market dynamics.

The Johansen cointegration approach was used to estimate the effects of changes in monetary policy proxies on residential property prices using quarterly data from 1980 to 2022.

Mortgage finance and economic growth have a significant positive long-run effect on residential property prices. The consumer price index, the inflation targeting framework, interest rates and exchange rates have a significant negative long-run effect on residential property prices. The Granger causality test has depicted that exchange rate significantly influences residential property prices in the short run, and interest rates, inflation targeting framework, gross domestic product, money supply consumer price index and exchange rate can quickly return to equilibrium when they are in disequilibrium.

There are limited arguments whether the inflation targeting monetary policy framework in South Africa has prevented residential property market boom and bust scenarios. The study has found that the implementation of inflation targeting framework has successfully reduced booms in residential property prices in South Africa.

Accurately evaluating fluid flow behaviors and determining permeability for deforming porous media is time-consuming and remains challenging. This paper aims to propose a novel machine-learning method for the rapid estimation of permeability of porous media at different deformation stages constrained by hydro-mechanical coupling analysis.

A convolutional neural network (CNN) is proposed in this paper, which is guided by the results of finite element coupling analysis of equilibrium equation for mechanical deformation and Boltzmann equation for fluid dynamics during the hydro-mechanical coupling process [denoted as Finite element lattice Boltzmann model (FELBM) in this paper]. The FELBM ensures the Lattice Boltzmann analysis of coupled fluid flow with an unstructured mesh, which varies with the corresponding nodal displacement resulting from mechanical deformation. It provides reliable label data for permeability estimation at different stages using CNN.

The proposed CNN can rapidly and accurately estimate the permeability of deformable porous media, significantly reducing processing time. The application studies demonstrate high accuracy in predicting the permeability of deformable porous media for both the test and validation sets. The corresponding correlation coefficients (R²) is 0.93 for the validation set, and the R² for the test set A and test set B are 0.93 and 0.94, respectively.

This study proposes an innovative approach with the CNN to rapidly estimate permeability in porous media under dynamic deformations, guided by FELBM coupling analysis. The fast and accurate performance of CNN underscores its promising potential for future applications.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

The principal research objective entails examining the nexus between green supplier selection and green performance while scrutinizing the moderating role of governance mechanisms, specifically process control and outcome control, in shaping this association.

To assess our hypotheses, this study obtained data from Chinese manufacturing sectors and utilized regression analysis on a dataset consisting of 295 samples.

This study enriches the sustainable supply chain management literature by emphasizing the influence of green supplier selection on a firm’s green performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This study enriches the sustainable supply chain management literature by emphasizing the influence of supplier selection on a firm’s environmental performance and the moderating effects of outcome and process control, offering practical insights for industry professionals.

This paper aims to identify and empirically analyze useful and applicable metrics for measuring and managing the sustainability performance of small and medium-sized enterprises (SMEs).

To achieve the objective of the paper, potential metrics were adopted from previous research related to industrial sustainability and an empirical analysis was carried to assess the applicability of the metrics by collecting empirical data from Italian footwear SMEs using a structured questionnaire. The SMEs were selected using a convenience sampling method.

The results of the within-case analysis and the cross-case analysis indicate that the majority of the metrics were found to be useful and applicable to each of the SMEs and across the SMEs, respectively. These metrics emphasized measuring industrial sustainability performance related to financial benefits, costs and market competitiveness for the economic sustainability dimension; resources for the environmental sustainability dimension; and customers, employees and the community for the social sustainability dimension.

Apart from the within-case analysis and cross-case analysis, it was not possible to conduct statistical analysis since a small number of SMEs were accessible to collect empirical data.

The findings of the paper have considerable academic, managerial and policy implications and will provide a theoretical basis for future research on measuring and managing industrial sustainability performance. By providing a set of empirically supported metrics based on the triple bottom line approach (i.e. economic, environmental and social metrics), this paper contributes to the existing knowledge in the field of industrial sustainability performance measurement.

The purpose of this paper is to identify the differences between Hungarian family-owned businesses (FOBs) and non-family-owned businesses (NFOBs) concerning the elements of SME competitiveness and financial performance.

The research covers the Hungarian data set of the Global Competitiveness Project (GCP, www.sme-gcp.org) of 738 (data collection between 2018 and 2020) non-listed SMEs, of which 328 were FOBs. The study uses the comprehensive, multidimensional competitiveness measurement of the GCP built on the resource-based view (RBV) and the configuration theory. Financial performance was captured with two composite indicators: short-term and long-term financial performance (LTFP). The comparative analysis between FOBs and NFOBs was conducted using binary logistic regression.

The results show that FOBs are more prone to focusing on local niche markets with higher longevity and LTFP than NFOBs. However, FOBs have lower innovation intensity and less organised administrative procedures. The most contradicting finding is that the FOBs’ higher LTFP is accompanied by significantly lower competitiveness than in the case of NFOBs.

This study goes beyond other GCP studies by including composite financial performance measures among the variables examined. The combination of performance-causing (resources and capabilities) and performance-representing (financial performance) variables provides a better understanding of the non-listed SMEs in terms of family ownership. The results help academia to enrich the RBV-competitiveness, the non-listed SME management and finance literature, and policymakers to design business development and support schemes. They also show future entrepreneurs the impact of family ownership on entrepreneurial success.

Digital transformation misalignment refers to disparities in digital transformation levels between suppliers and buyers across the production and operation process. It has negatively affected supply chain stability. However, the existing research concerning the economic consequences has not been adequately addressed. Therefore, this paper aims to investigate whether such digital transformation misalignment increases supplier financial risk and to identify the factors influencing this relationship.

This paper examines binary combinations of suppliers and buyers listed on China’s A-share market between 2011 and 2021. This group constitutes a sample to empirically test the influence of digital transformation misalignment on the supplier’s financial risk, as well as the moderating effect of the geographical and organizational distances.

The paper’s findings demonstrate that digital transformation misalignment has indeed a significant increase in the supplier’s financial risk. Moreover, the impact is more intense when the geographical or organizational distance between the supplier and the buyer is relatively large.

The existing literature rarely explores the potential risks arising from digital transformation misalignment between supply chain partners. Therefore, this paper fills a notable gap as it is the first to study the impact of digital transformation misalignment on the supplier’s financial risk and the specific applied mechanisms. The contribution significantly improves the field of corporate digital transformation, particularly, within the context of supply chain management.

In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.