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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.

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.

Users contribute to online communities by posting and responding to discussion threads. Nonetheless, only a small fraction of threads gain popularity and shape community discourse. Prior studies have identified several factors driving thread popularity; however, despite their prevalence, the role of emotional expressions within discussion threads remains understudied. This study addresses this gap by investigating the impact of thread starters’ valence and embedded discrete emotions of anger, anxiety and sadness on thread popularity, drawing on the negativity bias and the emotion-as-social-information theories.

Using two samples from Reddit, this study employs negative binomial regression analysis to examine the hypothesized relationships.

The results demonstrate that negativity in thread starters significantly influences thread popularity; however, the expression of discrete emotions impacts popularity variously. In some contexts, such as COVID-19 vaccination subreddits, embedded anger in thread starters decreases thread popularity, whereas anxiety and sad expressions enhance it. In other contexts, such as professional discussions (e.g. r/Medicine subreddit), anger and anxiety expressions increase thread popularity, while sad expressions have no significant influence.

The study is limited by its focus on specific emotions and contexts. Future research could examine a broader range of emotions, post-content modalities and the impact of cultural and linguistic differences.

This study contributes to theory by offering a new definition of thread popularity and enhancing our understanding of the impact of emotions in online discussions. It also provides practical implications for online community members and moderators seeking to promote discussion posts that help achieve community goals.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

Considering the impact of significant economic and political events, this study investigates the return spillovers and connectedness among eight West African currencies from March 31, 2010, to March 28, 2024. It aims to enhance understanding of the interdependencies within the West African foreign exchange market, providing insights into the region’s risk management and diversification opportunities.

Using the time-varying parameter vector autoregression (TVP-VAR) method, this study analyzes daily exchange rate returns to capture the dynamic spillover effects and connectedness among the selected currencies. This approach identifies key transmitters and receivers of return shocks, reflecting the evolving interactions among the currencies over time.

The results show that the Sierra Leonean Leone, Cape Verdean Escudo, and West African CFA Franc are significant net transmitters of return shocks. At the same time, the Ghana Cedi, Nigerian Naira, Gambian Dalasi, Guinean Franc, and Liberian Dollar are net receivers, with the Gambian Dalasi being the most affected. These findings suggest relatively low regional spillover connectedness, offering favorable diversification opportunities.

This study provides a comprehensive analysis of the interconnectedness of West African currencies, contributing to the limited literature on this region. The findings have practical implications for investors and policymakers in managing foreign exchange risks and designing interventions to stabilize the market.

The purpose of this paper is to examine the underlying mechanisms influencing the social entrepreneurial intentions of individuals in China, adopting social capital theory, bottom-up and people-based approach. The interrelated effects of intellectual capital (social community trust, social capital bonding and social participation) on social innovation tendency were measured.

We recruited 502 Chinese individuals by utilising a reliable survey platform in China. This study used structural equation modelling as an analytical approach to examine the influence of social capital on social innovation and social entrepreneurship intention.

The findings showed that social innovation tendencies mediate the relationship between social community trust, social capital bonding and social participation and the social entrepreneurial intentions of individuals. The findings support the tested hypotheses that social innovation tendencies are the key mechanism to translate into stronger social entrepreneurial intentions. An iterative framework emphasising transparency and open collaboration among stakeholders, which are vital for fostering social entrepreneurial intentions, was proposed.

This is a novel empirical study to apply social capital theory to the field of social enterprises in the Chinese context using structural equation modelling, bottom-up, people-based approach and iterative model. The findings offer valuable practical insights for entrepreneurial and SME practices to foster social entrepreneurship through a people-based approach, emphasising the importance of fostering trust, strengthening social bonds, and encouraging active social participation.