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This study investigates whether logistics and supply chain resilience strategies (SCREST) can help mitigate the negative impacts of disruptions on firm performance and logistics and supply chain (SC) activities of companies, using the COVID-19 pandemic as a case study.

The authors collected primary data on the implementation of different types of SCRESTs and measured the impact of COVID-19 in terms of firm performance and logistics and SC metrics through a survey of Japanese manufacturing companies in four sectors. The authors used these data to illustrate whether the companies benefitted from SCRESTs in mitigating the negative impacts of COVID-19. A questionnaire comprising structured and open-ended questions was sent to 8,000 companies all over Japan that met the selection criteria, using a combination of mail and web-based media. The respondents were logistics and SC professionals. A combination of qualitative and quantitative analysis was performed for data analysis and interpretation.

Research conducted within the case of the Japanese context revealed that findings varied depending on the methodology applied. The use of a direct analysis approach and qualitative analysis suggested that the implementation of SCRESTs is beneficial in addressing the negative impacts of COVID-19 on firm performance and logistics and SC activities, whereas the application of indirect analysis approach yielded mixed results. The analysis also indicated a shift in the preferred SCRESTs during COVID-19.

To the best of the authors’ knowledge, this is the first study to examine the benefits of implementing SCRESTs using primary data from the manufacturing sector of Japan. Furthermore, empirical research on this topic is generally lacking.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

Digital healthcare once again emerges due to pandemic (Covid-19). Digital healthcare can be minimising the issue of accessibility, availability, accuracy and affordability of healthcare service during a pandemic. Digital healthcare playsa significant role to provide healthcare equity during the pandemic. This article presents the current trends and scenario of digital healthcare with a focus on health equity. The main objective of this chapter is to review the four aces of health equity in the digital healthcare literature. The scope and challenges faced by the policymakers to implementation of digital healthcare to improve health equity. This chapter considers the hybrid literature review based on the bibliometric and the systematic literature based on the various theme, sub-theme, concept and context-related health equity through digital healthcare. This study provides the previous and current research trends and preposition for the future researcher, healthcare professional, policymakers and digital healthcare innovators to invent the tool which leads the health equity through the digital healthcare in the healthcare.

This study aims to examine current research on the relationship between Operational Excellence and Healthcare 4.0 (H4.0) for healthcare organizations.

The authors have performed a systematic literature review of 102 documents published between 2011 and 2022 from the Scopus database to identify the research trends on Operational Excellence and H4.0. Through a descriptive bibliometric analysis, this study has highlighted the year-wise trend in publication, top authors, prominent sources of publications, the country-wise spread of research activities and subject area analysis. Furthermore, through content analysis, this study has identified four clusters and proposed directions for future research of each identified cluster.

Results reflect overall growth in this area, with a few parts of the world being underrepresented in research related to Operational Excellence and H4.0. The content analysis focused on describing challenges pertaining to healthcare industries and the role of Operational Excellence tools and H4.0 technologies in dealing with various healthcare delivery aspects. The authors concluded their analysis by proposing a theoretical framework and providing theoretical and managerial implications of the study.

To the best of the authors’ knowledge, the paper is one of the first to analyze the existing literature on the healthcare sector at the interface of Operational Excellence and H4.0 technologies. The conceptual framework and cluster-wise future research prepositions are some of the unique offerings of the study.

The purpose of this paper is to examine whether board characteristics moderate the relationship between capital adequacy regulation and bank risk-taking of universal banks in Sub-Saharan Africa (SSA).

The paper uses 700 bank-year observations of universal banks in SSA between 2009 and 2019. The paper further uses the two-step generalized method of moments as the baseline estimator.

The paper finds that capital adequacy regulation is positively related to overall bank and liquidity risks. Nonetheless, capital adequacy regulation increases credit risk in the sampled banks. The paper further reports that board characteristics individually and significantly moderate the relationship between capital adequacy regulation and risk-taking.

The findings have implications for regulators of universal banks that board characteristics matter for capital adequacy regulation to impact risk-taking behavior.

The paper extends the existing literature on the effect of board characteristics on the capital adequacy regulations and risk-taking behavior nexus of universal banks.

In this study, STEM resources reviewed in Goodreads were investigated to determine their authorship, linguistic characteristics and impact. The analysis reveals gender disparity favoring titles with male authors.

This paper applies theoretical concepts of knowledge commons to understand how individuals leverage the affordances of the Goodreads platform to share their perceptions of STEM-related books.

The analysis reveals gender disparity favoring titles with male authors. Female-authored STEM publications represent popular science nonfiction and juvenile genres. Analysis of the scholarly impact of the reviewed titles revealed that Google Scholar provides broader and more diverse coverage than Web of Science. Linguistic analysis of the reviews revealed the relatively low aesthetic disposition of reviewers with an emphasis on embodied experiences that emerged from the reading.

This study contributes to the understanding of the impact of popular STEM resources as well as the influence of the language of user-generated reviews on production, consumption and discoverability of STEM titles.

The study responds to the common concerns of authors, reviewers, and editors on writing and publishing high-quality literature review (LR) studies. First, we evolved the background and decision elements on the five parameters of a quality LR paper: Planning, Operationalizing, Writing, Embedding, and Reflecting (POWER), from the editorials and guiding literature. Statistical procedure and refinement of 256 responses from writers, reviewers, and editors revealed 37 decision elements. Finally, a multicriteria-decision-making approach was applied to the detailed responses from the lead editors of ABDC, Scopus, ABS, and WoS journals, and 31 decision elements were found strong enough to represent these five parameters on the quality of LR studies. All five parameters are found important to be considered. However, a high priority is suggested for embedding (the results coming out of the review) and operationalizing (the process of conducting the review), whereas reflection, writing, and planning of LR papers still remain important. The purpose of the POWER framework is to overcome the challenges and decision dilemmas faced by writers and decision-makers. The POWER framework acts as a guiding tool to conduct LR studies in general and business management scholars in specific ways. In addition, this study provides a checklist (Table 6) and template (Appendix A1) of a quality LR study to its stakeholders.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.