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Although governments have used social media platforms to interact with the public in an attempt to minimize anxiety and provide a forum for public discussion during the pandemic, governments require sufficient crisis communication skills to engage citizens in taking appropriate action effectively. This study aims to examine how the National Health Commission of China (NHCC) has used TikTok, the leading short video–based platform, to facilitate public engagement during COVID-19.

Building upon dual process theories, this study integrates the activation of information exposure, prosocial interaction theory and social sharing of emotion theory to explore how public engagement is related to message sensation value (MSV), media character, content theme and emotional valence. A total of 354 TikTok videos posted by NHCC were collected during the pandemic to explore the determinants of public engagement in crises.

The findings demonstrate that MSV negatively predicts public engagement with government TikTok, but that instructional information increases engagement. The presence of celebrities and health-care professionals negatively affects public engagement with government TikTok accounts. In addition, emotional valence serves a moderating role between MSV, media characters and public engagement.

Government agencies must be fully aware of the different combinations of MSV and emotion use in the video title when releasing crisis-related videos. Government agencies can also leverage media characters – health professionals in particular – to enhance public engagement. Government agencies are encouraged to solicit public demand for the specific content of instructing information through data mining techniques.

The purpose of this paper is to explore the development of knowledge sharing from the perspectives of broaden-and-build theory and expectancy theory. Its research purpose is to understand how knowledge sharing is driven by such predictors as optimism, pessimism, and positive affect through their complex interactions with collectivism or power distance. In the proposed model of this study, knowledge sharing relates to optimism and pessimism via the partial mediation of positive affect. At the same time, the influence of optimism, pessimism, and positive affect on knowledge sharing are moderated by the national culture of collectivism and power distance, respectively.

This study’s hypotheses were empirically tested using data from high-tech firms across Taiwan and Malaysia. Of the 550 questionnaires provided to the research participants, 397 usable questionnaires were collected (total response rate of 72.18 percent), with 237 usable questionnaires from Taiwanese employees and 160 usable questionnaires from Malaysian employees. The data from Taiwan and Malaysia were pooled and analyzed using: confirmatory factor analysis for verifying data validity, independent sample t-tests for verifying the consistency with previous literature regarding cultural differences, and hierarchical regression analysis for testing relational and moderating effects.

This study demonstrates the integrated application of the broaden-and-build theory and expectancy theory for understanding optimism, pessimism, and positive affect in the development of knowledge sharing. The test results confirm that positive affect partially mediates the relationship between optimism and knowledge sharing and fully mediates the relationship between pessimism and knowledge sharing. Moreover, collectivism and power distance have significant moderating effects on most of the model paths between knowledge sharing and its predictors except for the relationship between pessimism and knowledge sharing.

This study extends the expectancy theory to justify how optimistic and pessimistic expectations are stable traits that dominate the way employees share their knowledge sharing. This study shows how collectivism and power distance of Hofstede’s cultural framework can be blended with the broaden-and-build theory and expectancy theory to jointly explain knowledge sharing. Besides, this study provides additional support to the adaptation theory of well-being that suggests psychosocial interventions, which manage to enhance well-being by leveraging positive affect, hold the promise of reducing stressful symptoms and boosting psychological resources among employees.

To deepen our understanding about the development of turnover intention, the purpose of this paper is to develop a conceptual model based on the stress theory to explain cross-country differences in the formation of turnover intention, complementing previous literature that mainly emphasizes the effect of monetary compensation on turnover intention without taking into account anxiety and pressure.

Empirical testing of this model by investigating personnel across Taiwan’s and Indonesia’s banks confirms the applicability of stress theory in cross-cultural business management. Of the 161 Chinese-language questionnaires distributed to the employees from the three large banks in Taiwan, 137 usable questionnaires were returned for a response rate of 85 percent. At the same time, of the 234 Indonesian-language questionnaires distributed to the employees from the two large banks in Indonesia, 219 usable questionnaires were returned for a response rate of 93.6 percent.

This research reveals that mental disengagement fully mediates the indirect relationship between performance-related anxiety and turnover intention, while positive reinterpretation fully mediates the indirect relationship between work pressure and turnover intention. Furthermore, the effects of performance-related anxiety and work pressure on turnover intention are moderated by cross-country differences.

First, the finding concerning the full mediating role of mental disengagement complements prior justifications of the conservation of resources theory. Second, the finding of this study regarding the full mediating role of positive reinterpretation complements the previous findings of Taylor’s (1983) theory of cognitive adaptation, which conceptualizes employees as active agents in restoring the psychological equilibrium in the aftermath of a competitive pressurized event.

To complement previous research on team performance, the purpose of this paper is to analyze the development of team performance and top management approval at the team level. In the proposed model, team performance and top management approval are influenced by the team leader’s charisma, teamwork exhaustion, and goal clarity via the full mediation of team planning. The effects of the leader’s charisma and goal clarity on team planning are moderated by teamwork exhaustion.

Empirical testing of this model based on hierarchical regression modeling, by investigating team personnel in high-tech firms, confirms the applicability of team planning among these firms’ work teams.

A team leader’s charisma and goal clarity positively relate to team planning, while teamwork exhaustion is not associated with team planning. Team planning further positively relates to team performance and top management approval, respectively. A team leader’s charisma negatively moderates the relationship between teamwork exhaustion and team planning, while goal clarity positively moderates the relationship between teamwork exhaustion and team planning.

While previous literature has focused in depth on team planning and its antecedents and outcomes, there still exists an important gap regarding potential moderation in the formation of team planning. This study provides some important findings that complement previous literature by examining three fresh exogenous determinants for explaining team planning, their interaction effects, and how they indirectly relate to team performance and top management approval via the full mediation of team planning.

Despite the extensive academic interest in social media sentiment for financial fields, multimodal data in the stock market has been neglected. The purpose of this paper is to explore the influence of multimodal social media data on stock performance, and investigate the underlying mechanism of two forms of social media data, i.e. text and pictures.

This research employs panel vector autoregressive models to quantify the effect of the sentiment derived from two modalities in social media, i.e. text information and picture information. Through the models, the authors examine the short-term and long-term associations between social media sentiment and stock performance, measured by three metrics. Specifically, the authors design an enhanced sentiment analysis method, integrating random walk and word embeddings through Global Vectors for Word Representation (GloVe), to construct a domain-specific lexicon and apply it to textual sentiment analysis. Secondly, the authors exploit a deep learning framework based on convolutional neural networks to analyze the sentiment in picture data.

The empirical results derived from vector autoregressive models reveal that both measures of the sentiment extracted from textual information and pictorial information in social media are significant leading indicators of stock performance. Moreover, pictorial information and textual information have similar relationships with stock performance.

To the best of the authors’ knowledge, this is the first study that incorporates multimodal social media data for sentiment analysis, which is valuable in understanding pictures of social media data. The study offers significant implications for researchers and practitioners. This research informs researchers on the attention of multimodal social media data. The study’s findings provide some managerial recommendations, e.g. watching not only words but also pictures in social media.

Corporate social responsibility (CSR) is significant in the financial market. Despite plenty of existing research on CSR, few studies have quantified the fine-grained aspects of CSR and examined how diverse CSR aspects are associated with firms' trade credit. Based on the released CSR reports, this paper strives to measure the CSR fulfillment of firms and examine the relationships between CSR and trade credit in terms of textual features presented in these reports.

This research proposes a natural language processing-based framework to extract the overall readability and the sentiment of fine-grained aspects from CSR reports, which can signal the performance of firms' CSR in diverse aspects. Furthermore, this paper explores how the textual features are associated with trade credit through partial dependence plots (PDPs), and PDPs can generate both linear and nonlinear relationships.

The study’s results reveal that the overall readability of the reports is positively associated with trade credit, while the performance of the fine-grained CSR aspects mentioned in the CSR reports matters differently. The performance of the environment has a positive impact on trade credit; the performance of creditors, suppliers and information disclosure, shows a U-shaped influence on trade credit; while the performance of the government and customers is negatively associated with trade credit.

This study expands the scope of research on CSR and trade credit by investigating fine-grained aspects covered in CSR reports. It also offers some managerial implications in the allocation of CSR resources and the presentation of CSR reports.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.

The purpose of this paper is to draw on triadic reciprocal determinism and social exchange theory to examine how “induced-type” and “compulsory-type” union participation influence union commitment and job involvement, and how union participation in the west differs from that in China. It also examines whether the role of both organizational justice and employee participation climate (EPC) functions in the Chinese context.

Cross-sectional data are collected from 694 employees in 46 non-publicly owned enterprises, both Chinese and foreign, in the Pearl River Delta region of China. A multi-level moderated mediation test is used to examine the model of this research.

Union participation is positively related to organizational justice, union commitment and job involvement. In addition, organizational justice acts as the mediator among union participation, union commitment and job involvement. Specifically, the mediating role of organizational justice between union participation and union commitment, and between union participation and job involvement, is stronger in high-EPC contexts than low-EPC contexts.

Instead of examining the impacts of attitudes on union participation, as per most studies in the western context, this research examines the impacts of union participation in the Chinese context on attitudes, including union commitment and job involvement. It also reveals the role of both organizational justice and EPC in the process through which union participation influences union commitment and job involvement.

This paper is a study of the current trends and conditions of electronic resources for Chinese studies, based on a recent survey on the Internet of 29 Chinese libraries in North America and eight Chinese libraries in China, Taiwan and Hong Kong. The survey discussed current electronic resources for Chinese studies, with a union list of major Chinese language databases currently used in libraries in Asia and the US. Current views on the use and development of electronic resources for Chinese studies were summarised.

The purpose of this paper is to improve the existing differential evolution (DE) mutation operator so as to accelerate its convergence.

A new general donor form for mutation operation in DE is presented, which defines a donor as a convex combination of the triplet of individuals selected for a mutation. Three new donor schemes from that form are deduced.

The three donor schemes were empirically compared with the original DE version and three existing variants of DE by using a suite of nine well‐known test functions, and were also demonstrated by a practical application case – training a neural network to approximate aerodynamic data. The obtained numerical simulation results suggested that these modifications to the mutation operator could improve the DE's convergence performance in both the convergence rate and the convergence reliability.

Further research is still needed for adequately explaining why it was possible to simultaneously improve both the convergence rate and the convergence reliability of DE to that extent despite the well‐known “No Free Lunch” theorem. Also further research is considered necessary for outlining more distinctively the particular class of problems, where the current observations can be generalized.

More complicated engineering problems could be solved sub‐optimally, whereas their real optimal solution may never be reached subject to the current computer capability.

Though DE has demonstrated a considerably better convergence performance than the other evolutionary algorithms (EAs), its convergence rate is still far from what is hoped for by scientists. On the one hand, a higher convergence rate is always expected for any optimization method used in seeking the global optimum of a non‐linear objective function. On the other hand, since all EAs, including DE, work with a population of solutions rather than a single solution, many evaluations of candidate solutions are required in the optimization process. If evaluation of candidate solutions is too time‐consuming, the overall optimization cost may become too expensive. One often has to limit the algorithm to operate within an acceptable time, which maybe is not enough to find the global optimum (optima), but enough to obtain a sub‐optimal solution. Therefore, it is continuously necessary to investigate the new strategies to improve the current DE algorithm.