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The literature that is presently available on sustainable supply chain management (SSCM) combining optimization and industry 4.0 techniques falls short in its depictions of the recent developments, budding pertinent areas and the importance of SSCM in the growth of industrial economies around the world. This article's main objective is to analyze current trends, highlight the latest initiatives and perform a meta-analysis of the literature that is currently accessible in the SSCM area with a special focus on optimization and industry 4.0 techniques. The paper also proposes a conceptual framework that will assist in illuminating how the ideas of optimization and industry 4.0 may contribute to realizing sustainability in supply chains.

The proposed study systematically reviews 85 research publications published between 2010 and 2022 in referenced peer-reviewed journals in diverse fields, including engineering, business and management, services and healthcare. Numerous categories are considered throughout the examination of the literature, including year-wise publications, prominent journals, type of research design, concerned industry and research technique used.

The study demonstrates a deeper comprehension of the literature in the field and its evolution throughout numerous industry sectors, which is helpful for both practitioners and academics. The results from the content analysis highlight various future research opportunities in the domain.

This is one of the first research articles that have attempted to establish, analyze and highlight the current trends and initiatives in the SSCM domain from an optimization and industry 4.0 techniques viewpoint. The cluster-based future research propositions also enhance the novelty of the study.

Although earlier research suggests a positive relationship exists between engaging in entrepreneurial marketing activities and firm performance, there may be contingent issues that impact the association. This investigation unpacks the relationship between entrepreneurial marketing behaviour and firm performance under the moderating role of coopetition, in an immediate post-COVID-19 period.

A resource-based theoretical lens, alongside an outside-in perspective, underpins this study. Following 20 field interviews, survey responses via an online survey were obtained from 306 small, passive exporting wine producers with a domestic market focus in the United States. The data passed all major robustness checks.

The statistical findings indicated that entrepreneurial marketing activities positively and significantly influenced firm performance, while coopetition provided a non-significant moderation effect. Field interviews suggested that entrepreneurs’ attemps to scale up from passive to more active export activities in an immediate post-pandemic period helped explain the findings. Owner-managers rejoined trustworthy and complementary pre-pandemic coopetition partners in the immediate aftermath of coronavirus disease 2019 (COVID-19) for domestic market activities. In contrast, they had to minimise risks from dark-side/opportunistic behaviour when joining coopetition networks with partners while attempting to scale up export market activities.

Unique insights emerge to unpack the entrepreneurial marketing–performance relationship via the moderation effect of coopetition, namely, with the temporal setting of an immediate post-COVID-19 period. Firstly, new support arises regarding the likely performance-enhancing impact of owner-managers’ engagement in entrepreneurial marketing practices. Secondly, novel findings emerge in respect of the contrasting role of coopetition in both domestic and export market activities. Thirdly, new evidence arises in relation to a resource-based theoretical lens alongside an outside-in perspective, whereby, strategic flexibility in pivoting facets of a firm’s business model needs effective management following a crisis.

This paper aims to analyze the heterogeneous effect of prudential regulation on the stability of banks in the West African Economic and Monetary Union (WAEMU).

The author uses in this study individual bank data from balance sheets, income statements of banks in the WAEMU space and annual reports of the banking commission formed into a three-year panel from the period 2017 to 2019. First, this study uses hierarchical clustering based on specific banking characteristics to determine whether the WAEMU region’s banking markets are heterogeneous or not. Second, this study uses quantile regression approach with fixed effects to explore how that prudential regulation affects the conditional distribution of WAEMU bank stability.

The analysis reveals heterogeneity resulting in two distinct groups. Using the quantile regression approach, this study demonstrates that prudential regulation has a significantly more substantial and positive effect on the upper quantiles than on the lower quantiles of the conditional distribution of WAEMU bank stability. Furthermore, the effect of banking regulation also varies among pan-African cross-border banks, national banks and foreign banks. Among these types of banks, pan-African cross-border banks remain the most stable by adopting prudential regulation. The results remain robust and vary across different WAEMU countries.

The contribution of this study to the literature is multifaceted. First, this study uses individual bank-level constituted in panel data from the WAEMU region to assess the effect of prudential regulation on the stability of the WAEMU’s banking sector. This approach allows for a more granular analysis as this study considers individual regional banks’ specific characteristics and behaviors. Second, this study considers the heterogeneous effect of regulation on the stability of banks within the WAEMU space. This means that this study acknowledges that not all banks are affected similarly by prudential regulations, and this research aims to identify and quantify these differences.

This study aims to conduct a bibliometric analysis on “intertemporal choice” behavior of individuals from journals in the Scopus database between 1957 and 2023. The research covered the data on the said topic since it first originated in the Scopus database and carried out performance analysis and content analysis of papers in the business management and finance disciplines.

Bibliometric analysis, including science mapping and performance analysis, followed by content analysis of the papers of identified clusters, was conducted. Three clusters based on cocitation analysis and six themes (three major and three minor) were identified using the bibliometrix package in R studio. The content analysis of the papers in these clusters and themes have been discussed in this study, along with the thematic evolution of intertemporal choice research over the period of time, paving a way for future research studies.

The review unpacks publication and citation trends of intertemporal choice behavior, the most significant authors, journals and papers along with the major clusters and themes of research based on cocitation and degree of centrality and relevance, respectively, i.e. discounting experiments and intertemporal choice, impulsivity, risk preference, time-inconsistent preference, etc.

Over the past years, the research on “intertemporal choice” has flourished because of the increasing interest of researchers and scholars from different fields and the dynamic and pervasive nature of this topic. The well-developed and scattered body of knowledge on intertemporal choice has led to the need of applying a bibliometric analysis in the intertemporal choice literature.

Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This study aimed to identify subgroups of esports players based on their gaming behavior patterns across game genres and compare self-efficacy, social efficacy, loneliness and three dimensions of quality of life between these subgroups.

324 participants were recruited from prolific academic to complete an online survey. We employed latent profile analysis (LPA) to identify subgroups of esports players based on their behavioral patterns across genres. Additionally, a one-way multivariate analysis of covariance (MANCOVA) was conducted to test the association between cluster memberships and development and well-being outcomes, controlling for age and gender as covariates.

LPA analysis identified five clusters (two single-genre gamer groups, two multigenre gamer groups and one all-genre gamer group). Univariate analyses indicated the significant effect of the clusters on social efficacy, psychological health and social health. Pairwise comparisons highlighted the salience of the physical enactment-plus-sport simulation genre group in these outcomes.

This study contributes to the understanding of the development and well-being benefits experienced by various esports consumers, as well as the role of specific gameplay in facilitating targeted outcomes among these consumer groups.

The aim of this study is to offer evidence-based knowledge of the most popular research topics in studies on spiritual leadership (SL) and the research approaches and theories in use. Another aim is to create a comprehensive research framework covering the antecedents and outcomes of SL, as well as the underlying mechanisms and conditional factors. This study also synthesizes future research avenues presented in the literature.

This study used a systematic literature review method. The presented analysis covered both bibliometric studies and in-depth manual content analysis. In total, 274 articles indexed in the Scopus database were analyzed, with a particular focus on 126 empirical papers.

This study shows that most of the research took place in developing countries and focused on the links between SL and workplace spirituality, employee well-being and engagement. It provides a complex research framework which orders previous variables according to their levels. Future research is required that would use a multilevel research approach and determine the impact of SL on society and the leaders themselves, as well as determining the reverse impact of organizational performance on the development of SL.

This study takes advantages of both bibliometric and in-depth content analysis to expand the understanding of the state of the art in SL research. It demonstrates how different factors contribute to SL and how they subsequently influence outcomes. It also offers numerous future research directions which go beyond those identified so far in the literature to further develop the theory of SL.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.