The linear economic business model was deemed unsustainable, necessitating the emergence of the circular economy (CE) business model. Due to resource scarcity, increasing population, and high food waste levels, the food sector has been facing significant sustainability challenges. Small and medium-sized enterprises (SMEs), particularly those in the food sector, are making efforts to become more sustainable and to adopt new business models such as the CE, but adoption rates remain low. Industry 4.0 and its associated technological applications have the potential to enable CE implementation and boost business competitiveness. In the context of emerging economies facing significant resource scarcity constraints and limited technology availability, CE principles need to be adapted. CE could create a new job economy in emerging economies, bringing scale and a competitive advantage. This study explores the enablers of and barriers to Industry 4.0 adoption for CE implementation in fruit and vegetable SMEs in India from a resource-based perspective. The purpose is to develop an evidence-based framework to help inform theory and practice about CE implementation by SMEs in emerging economies. Fifteen semi-structured interviews were conducted with experts in food SMEs. The interview transcripts were first subjected to thematic analysis. The analysis was then complemented with sentiment and emotion analyses. Subsequently, hierarchical cluster analysis, k-means analysis, and linear projection analysis were performed. Among others, the findings suggest that Industry 4.0 plays a key role in implementing CE in SMEs in emerging economies such as India. However, there are specific enablers and barriers that need to be considered by SMEs to develop the resources and capabilities needed for CE competitive advantage.

Increasing reliance on digital technologies has led to a significant shift in how businesses operate, with many now relying heavily on digital platforms for effective planning, communication, sales, marketing, supply chain, and logistics management. In this context, knowledge sharing platforms enable academic–industry collaboration in which exchange of ideas, opinions, experience, and expertise brings collective intelligence in cooperative learning ecosystem thereby expediting decision making. However, establishing long-term commitment among the partners, allocation of time and resources for sharing tacit knowledge, collaboration among partners with different strategic priorities, and real-time knowledge sharing capabilities are essential for effective and rapid learning in knowledge sharing platforms. The present article will examine these benefits and challenges in knowledge sharing and its impact on supplier selection platforms in Asian automakers. The findings of this article will be helpful for researchers and practitioners intending to explore the role of cooperation in knowledge sharing and digital transformation amid competitive environment prevalent in the automotive industry. The potential supplier database is first examined for qualifying the capability requirements put forth in this article and further prioritized using a multicriteria decision-making technique and analytic hierarchy process. The article results reveal that the manufacturer has highly prioritized firms’ financial transparency for supplier evaluation followed by the suppliers’ cost control, quality control, and manufacturing capabilities. The article has significant theoretical and practical implications for developing robust supplier evaluation criteria for automobile industry and a digital ecosystem for original equipment manufacturers in making supplier related decisions.

The literature on the imperativeness of government support for firm survival since the onset of COVID-19 is vast, but scholars have scarcely considered the impact of such assistance on managers' time, nor the extent to which support measures induce resilience and export activity. Accordingly, this study assesses the impact of government support on (1) bureaucracy and (2) resilience using data from 535 Moroccan SMEs. It further evaluates the influence of resilience on direct versus indirect exports, and espouses the institutional voids, resource-based and strategy-creation view to explain the associations through a contingency lens. The results demonstrate that (1) government support increases bureaucracy which, (2) surprisingly triggers and enhances resilience. Furthermore, (3) resilience has a positive impact on direct exports but (4) adversely affects indirect exports. Theoretically, the findings acquiesce extant calls for measurement specificity in export performance. Practically, stakeholders' attention is drawn to the value of managers' time well spent.

The competitive landscape of multiple e-commerce platforms and the vast amount of product reviews associated with these platforms have supported both consumers’ online shopping decision making and also served as a reference for product attribute performance improvement. This paper proposes a sentiment-driven fuzzy cloud multi-criteria model for online product ranking and performance to provide purchase recommendations. In this novel model, Bi-directional Long Short-Term Memory Network-Conditional Random Fields (BiLSTM-CRF), sentiment analysis, and K-means clustering are first integrated to mine product attributes and compute sentiment values based on reviews from various platforms. Next, considering the confidence of the sentiment value, the cloud model is combined with q-rung orthopair fuzzy sets to define the new concept of the q-rung orthopair fuzzy cloud (q-ROFC) and the interaction operational laws between q-ROFCs are given. The sentiment values of each product attribute from different platforms are cross-combined and transformed into a type of q- ROFC, while multiple interactive information matrices are established. To investigate the correlation among homogeneous attributes, the q-ROFC interaction weighted partitioned Maclaurin Symmetric mean operator is proposed. Finally, we provide real-world examples of online mobile phone ranking and attribute performance evaluation. The results show that our proposed method offers significant advantages in dealing with customer purchase decisions for online products and problems with performance direction identification. Managerial implications are discussed.

This book provides an interdisciplinary critical perspective regarding risk, uncertainty, and governance challenges of cryptocurrencies. It considers the perspectives of several disciplines including accounting, cybersecurity, cyberlaw, economics, ethics, finance, financial regulation, shariah (Islamic) law and technology. Distinguished from other books on similar topic, our in-depth analysis and critical discourse on cryptocurrency risk categories are supplemented by research evidence gathered from surveys and interviews with stakeholders. The inclusion of an Islamic insight matters given mixed views at present regarding the permissibility of cryptocurrencies albeit some countries have imposed somewhat restricted function of cryptocurrencies for non-religious reasons. It is envisaged that this book will help enlighten stakeholders on this aspect of uncertainty and inspire fit and proper governance strategies for the public interest.

This article highlights the generative properties of context for consumer experiences of racism and discrimination. Drawing from conceptualizations of context in social anthropology and human geography, it develops a framework to systematically catalogue intersections of various micro- and macro-social contexts that configure within and across marketplace geographies and inform racism and discrimination. The framework is applied to an integrative review of studies on marketplace racism and discrimination. The review illuminates that: 1) application of intersectional perspectives varies significantly across cultural difference dimensions; 2) knowledge is clustered within specific micro-social context expressions of cultural difference dimensions; 3) studies intersecting micro- and macro-social expressions commonly reveal underexplored discrimination instances; and 4) knowledge on macro-social contextual forces significantly lacks non-western perspectives. Drawing on the review findings, a list of areas of advancement for future scholarship is presented, along with recommendations for marketing practitioners acting towards identifying, understanding, and counteracting racism and discrimination.

We assess, for the first time in the literature, the impact of green credit on bank efficiency. We find that green credit has a negative impact on bank efficiency. However, the effect is heterogeneous among different types of banks. While small and low capitalized banks are more affected, the impact is lower in banks with higher levels of risk. On the other hand, we find that highly capitalized banks can offset the negative effects of green credit, while large banks and those highly involved in green credit, benefit from this activity.

The current study contributes to the literature in efficiency analysis in two ways: 1) we build on the existing studies in Dynamic Network Data Envelopment Analysis (DNDEA) by proposing a sequential structure incorporating dual-role characteristics of the production factors; 2) we initiate the efforts to complement the proposal of our innovative sequential DNDEA through a behavioural-causal analysis. The proposal of this statistical analysis is very important considering it does not only validate the proposal of the efficiency analysis but also our practice can be generalized to the future studies dealing with designing innovative production process. Finally, we apply these two different analyses to the banking industry. Using a sample of 43 Chinese commercial banks including five different ownership types (state-owned, joint-stock, city, rural, and foreign banks) between 2010 and 2018, we find that the inefficiency level is around 0.14, although slight volatility has been observed. We find that the highest efficiency is dominated by state-owned banks, and although foreign banks are less efficient than joint-stock banks, they are more efficient than city banks. Finally, we find that rural banks have the highest inefficiency.

This paper presents an assessment of the Chinese healthcare system in 31 provinces for a 10-year period in light of relevant physical and human resource variables. First, a novel TEA-IS (Trigonometric Envelopment Analysis for Ideal Solutions) model is developed to assess healthcare efficiency at the province level. Machine learning methods are also employed to predict high-low performance and the synergistic Chinese healthcare province in terms of contextual variables. The results indicate that synergy has played a pivotal role in the Chinese healthcare systems, not only by triggering higher performance levels due to the progressive adoption of best practices over the course of time, but also by being closely related to different socioeconomic and demographic variables, such as the illiteracy rate. It is possible to claim that healthcare performance has remained stable in China over the past two decades, performance and synergy at the province level are still heterogeneous.