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Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

This study aims to elucidate the dynamics of social media discourse during global health events, specifically investigating how users across different platforms perceive, react to and engage with information concerning such crises.

A mixed-method approach was employed, combining both quantitative and qualitative data collection. Initially, thematic analysis was applied to a data set of social media posts across four major platforms over a 12-month period. This was followed by sentiment analysis to discern the predominant emotions embedded within these communications. Statistical tools were used to validate findings, ensuring robustness in the results.

The results showcased discernible thematic and emotional disparities across platforms. While some platforms leaned toward factual information dissemination, others were rife with user sentiments, anecdotes and personal experiences. Overall, a global sense of concern was evident, but the ways in which this concern manifested varied significantly between platforms.

The primary limitation is the potential non-representativeness of the sample, as only four major social media platforms were considered. Future studies might expand the scope to include emerging platforms or non-English language platforms. Additionally, the rapidly evolving nature of social media discourse implies that findings might be time-bound, necessitating periodic follow-up studies.

Understanding the nature of discourse on various platforms can guide health organizations, policymakers and communicators in tailoring their messages. Recognizing where factual information is required, versus where sentiment and personal stories resonate, can enhance the efficacy of public health communication strategies.

The study underscores the societal reliance on social media for information during crises. Recognizing the different ways in which communities engage with, and are influenced by, platform-specific discourse can help in fostering a more informed and empathetic society, better equipped to handle global challenges.

This research is among the first to offer a comprehensive, cross-platform analysis of social media discourse during a global health event. By comparing user engagement across platforms, it provides unique insights into the multifaceted nature of public sentiment and information dissemination during crises.

This study aimed to develop a moderated mediation model to explain the relationship between artificial intelligence (AI) awareness and counterproductive work behavior, turnover intention. In this model, the authors assumed that interpersonal conflict mediates and that perceived organizational support and competitive psychological climate moderates the relationship between AI awareness and counterproductive work behavior, turnover intention.

An empirical study based on a sample of 1,129 Vietnamese employees at some enterprises of 6 fields with the highest level of AI application. Structural equation modelling analysis was used for hypothesis testing.

Analysis of the data demonstrates that AI awareness has a relationship with counterproductive behavior, interpersonal conflict and turnover intention. At the same time, the research results also confirm that interpersonal conflict affects counterproductive behavior and turnover intention. Moreover, interpersonal conflict mediates the effect of AI awareness on counterproductive behavior and turnover intention, and the moderating roles of perceived organizational support and competitive psychological climate has been confirmed.

Sample data was only collected at a few Vietnamese enterprises in 6 fields with the highest level of application which are e-commerce, transportation and logistics, education, real estate, finance and agriculture, which may be limiting generalizability of research results. Future studies could include data from enterprises in different sectors or focus on a specific sector.

The authors offer several significant implications to reduce counterproductive work behavior and turnover intention in enterprises, such as by paying attention that the penetration and spread of AI or other smart technologies is inevitable in the future, ensuring make sure support from organization is available for the employees and creating a working environment of integrity and honesty in all situations based on trust, respect and fairness.

The study developed and verified a moderated mediated model on the relationship between AI awareness and counterproductive work behavior, turnover intention. The authors confirmed the mediating role of interpersonal conflict and the moderating role of perceived organizational support and competitive psychological climate in the relationship among AI awareness and counterproductive work behavior, turnover intention.

The negative effects of job stress and burnout on construction professionals (CPs) at the construction site have been widely concern in the construction industry. The purpose of this study is committed to explore the impact of job stress on CPs on the construction site, especially in the context of the widespread use of social media to express their emotions.

This study developed a job-related stress-burnout-health conditions-turnover intention (S-B-HT) framework to explore the direct and lagged effects of job stress, we also examined the moderating effects of online emotions, operationalized in terms of emotional intensity and expression pattern, on the relationship between job stress with job burnout under two evolution paths (i.e. health conditions or turnover intention). This study collected 271 samples through a survey questionnaire for empirical testing, and introduced structural equation models to validate the proposed conceptual model.

The results show that job stress has a significant positive effect on job burnout, and job burnout maintains a positive relationship with health conditions (or turnover intention) under the interference mechanism. Simultaneously, the online emotions expressed in social media have a positive moderating effect in two stages of the evolution path.

The findings of this study remind the project manager need to timely find and solve the job burnout characteristics of CPs due to excessive job stress, especially to prevent the accidental consequences caused by job burnout.

On this basis, this study provides an important value of using social media to express emotions for the project team to alleviate the adverse of professionals under job stress.

During the COVID-19 pandemic, an infodemic erupted on social media, leading to a surge in negative disclosure behaviors such as expressing dissatisfaction and releasing negative emotions. By extending the elaboration likelihood model and the Big Five personality theory to the domain of online self-disclosure, we aimed to identify the factors that influence negative disclosure behavior.

We investigated how the features of negative information content, information sources and recipients’ social perceptions influence how social media users disclose negative information. We also examined the moderating roles of personality traits in this process. To validate the model and test our hypotheses, we collected cross-sectional data from 456 social media users.

Empirical results reveal that (1) information overload, topic relevance, attractiveness of information sources, peer approval of negative disclosure and social influence on negative information strengthen the intention to disclose negative information. (2) The perception of social risk weakens the intention to disclose negative information. (3) Openness to experience, extraversion and neuroticism strengthen the relationship between the intention to disclose negative information and actual disclosure behavior.

Our results not only provide new perspectives on the decision-making mechanisms behind negative disclosure behavior but also extend personality research within the context of the dissemination of negative information. Furthermore, it offers insights into negative information dissemination on social media platforms, with significant implications for various stakeholders.

In the era of social media, containing and managing online rumors poses a significant challenge. Therefore, a thorough investigation into the factors influencing rumor dissemination becomes increasingly crucial. Key rumor spreaders wield considerable influence over audience behavior and public opinion dissemination, making them pivotal in curbing the spread of rumors. This study aims to categorize these key spreaders and delve into the distinct characteristics of each category.

This paper introduces a method for identifying key rumor spreaders through social network analysis. By utilizing text mining and sentiment analysis on data from typical rumor events on Weibo, we extract 14 characteristics of key spreaders. Subsequently, we develop a method for classifying the roles of these key spreaders and conduct empirical research to validate our findings.

Our study reveals significant variations in the characteristics of key spreaders across different roles. These insights enable managers to implement differentiated management strategies for key spreaders based on their respective roles.

This research sheds light on the diverse characteristics exhibited by key rumor spreaders on social media, providing a novel reference point for enhancing the effectiveness of rumor intervention and control strategies.

As an important engine of economic growth, the digital economy is bringing new opportunities for the promotion of entrepreneurship. However, key questions regarding the extent of the effect of the digital economy on entrepreneurship remain unanswered. This study examines how the digital economy influences entrepreneurship in China using provincial data from 2011–2020, applying convergence tests and spatial econometric models.

Based on theoretical analysis and using macro provincial data covering the period of 2011–2020, we adopt a diversified empirical analytical method and apply a combination of the convergence trend test, spatial auto correlation test, and spatial Durbin model to test the research hypotheses.

First, there is spatial correlation between the digital economy and entrepreneurship. Second, the overall trend of China’s digital economy shows s convergence, with the whole country and the eastern region showing absolute β convergence and the whole country as well as the central and western regions showing β conditional convergence. Third, the digital economy can significantly promote entrepreneurship and has spatial spillover effects. Moreover, higher education has a negative moderating effect on the process of digital economy empowering entrepreneurship.

Studying the spatially correlated impacts of the digital economy on entrepreneurship enhances our understanding of its contribution to economic growth. Policy-makers can use these findings to develop targeted digital infrastructure investments in lagging provinces, guide entrepreneurs to better grasp the opportunities of the digital economy, and provide support for innovation and entrepreneurship. The findings also could offer Chinese experience that can be used to guide developing countries in utilizing the digital economy to enable entrepreneurship.

This paper expands and enriches the analytical focus on digital economy-empowered entrepreneurship and complements the current theoretical research on the moderating effect of the digital economy in empowering entrepreneurship.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This study aims to review and synthesize the burgeoning field of auditing research utilizing Chinese data. Over the past decades, there has been a remarkable rise in such research, driven by China’s abundant audit data, distinctive institutional features and enduring cultural influences. The purpose is to comprehensively review auditing studies featured in top-tier accounting journals, shedding light on the unique contributions made possible by Chinese data. By identifying key themes across domains, this paper aims to underscore the cultural and contextual disparities between China and Western countries, predominantly the USA, within the area of auditing.

This study presents a systematic review of China-themed auditing research, primarily published in seven leading global accounting journals. The researchers conducted a comprehensive search of the websites of these journals, identifying relevant articles using search terms such as “China auditing,” “Chinese Stock Market and Accounting Research (CSMAR),” “institutional environment,” and “internal control.” After the initial search, 54 relevant articles were selected and reviewed. The study covers all China-specific auditing research, categorizing key themes into six areas to explore how scholars use Chinese data to address important auditing questions.

The findings reveal a significant increase in auditing research utilizing Chinese data, prominently featured in top-tier academic journals. This study categorizes six central themes, highlighting the broad range of topics explored using Chinese audit data. More importantly, the research identifies substantial cultural and contextual differences between China and Western nations, particularly the USA, that influence the auditing profession and markets. Exploring these themes underscores the invaluable insights derived from Chinese data, shedding light on areas not previously addressed by studies relying solely on Western datasets.

The value of this study lies in its comprehensive examination of seminal auditing studies using Chinese data, making a distinctive contribution to the auditing literature. This paper highlights the inadequacies of Western datasets in addressing certain auditing questions and emphasizes the unique advantages offered by China’s extensive public audit data, institutional characteristics and cultural determinants. The identified gap in the literature underscores the unexplored opportunities for further research in the Chinese auditing context. This study, therefore, provides a roadmap for future scholars, encouraging the exploration of new avenues and fostering a deeper understanding of the cultural nuances influencing auditing practices in China.