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.

Following the global financial crisis, the measurement of counterparty credit risk has become an essential part of the Basel III accord with credit value adjustment being one of the most prominent components of this concept. In this study, we extend the Merton structural credit risk model for counterparty credit risk calculation in the context of calculating the credit value adjustment mainly by estimating the probability of default. We improve the Merton model in a variance-convoluted-gamma environment to include default dependence between counterparties through a linear factor decomposition framework. This allows one to tackle dependence through a systematic common component. Our set-up allows for easier, faster and more accurate fitting for the credit spread. Results confirm that use of the variance-gamma-convolution clearly solves the vanishing credit spread problem for short time-to-maturity or low leverage cases compared to a Brownian motion environment and its modifications.

Little research has been done on female board representation in emerging market multinational enterprises (EMNEs). Our paper considers the role of female board representation and its impact on open innovation (OI) in the unique context of emerging markets. We draw on upper echelons and institutional theories to understand how female board representation and cross-country institutional contexts influence coupled OI. Combining a 10-year (2009-2019) dataset with a rich in-depth content analysis of 183 (EMNEs) engaged in OI, our results reveal a significant positive association between female board representation and a firm’s commitment to coupled OI initiatives. We also find that country-level institutional factors affect and positively moderate the relationship between female board representation and coupled OI. In emerging market environments where managerial perception and cultural beliefs sometimes hinder the promotion of females into top positions, our work has implications for EMNEs regarding how they harness diversity. We contribute to the OI literature by showing that female board representation enhances corporate OI investment within EMNEs.