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Given the widespread adoption of empowerment in the workplace, increasing research has investigated the influences of empowering leadership. However, previous research has found confounding effects of it. This study aims to examine how and when empowering leadership exhibits “double-edged sword” effects on followers’ work outcomes.

The authors used a three-wave survey with a final sample of 215 full-time employees to test the research model.

The results indicate that followers’ role-breadth self-efficacy (RBSE) interacted with empowering leadership to predict their hindrance-related stress, subsequently influencing their turnover intention. Specifically, empowering leadership is found to elicit hindrance-related stress among followers with low RBSE. Furthermore, empowering leadership indirectly affects turnover intention by eliciting hindrance-related stress only among followers with low RBSE.

This study broadens the exploration of the “dark side” of empowering leadership, offering a more nuanced explanation of how it can lead to both beneficial and detrimental outcomes. It refines the understanding of empowering leadership’s effectiveness by highlighting the role of followers’ RBSE rather than focusing solely on the degree of empowerment. In addition, by contributing to the stress theory, this research demonstrates how individual differences influence followers’ cognitive appraisal of stress, shaping distinct stress experiences and driving the adoption of varying work-related coping strategies.

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.

Linear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.

Determining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.

Arithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.

This study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.

The study aims to explicate how Metaverse boosts learners’ cognition, decision confidence and active participation in Metaverse-based learning (MBL).

A survey is designed with 523 respondents. Structural equation modeling (SEM) is conducted using online data to verify a research model.

Results demonstrate that Metaverse-related characteristics, namely interactivity, corporeity, persistence, immersion and personalized experience, aid in strengthening learners’ cognitive processing and decision confidence, whilst escapism does not influence decision confidence in MBL. Furthermore, user-related dimensions, including personal innovativeness and perceived trendiness, are the underlying motivations for decision confidence. Additionally, cognitive processing is positively associated with decision confidence, which considerably fosters learners’ active participation in MBL.

Limited studies have been conducted to illuminate a mechanism of cognitive processing, decision confidence and active participation among learners toward MBL in light of the Stimulus-Organism-Response (S-O-R) paradigm. Therefore, a substantial amount of knowledge is supplemented to enlighten whether learners in a developing country may generate their engagement with MBL.

This study aims to explore the relationship between artificial intelligence (AI) awareness and physical and psychological withdrawal behaviors of enterprises employees through the mediating roles of job security and emotional exhaustion as well as the moderating role of emotional intelligence.

Data were collected through a self-administered questionnaire from six fields with the highest level of AI application with a sample of 1,129 Vietnamese enterprises employees. Data were analyzed using SmartPLS, a bootstrapping technique was used to analyze the data. The mediating effect of job security and emotional exhaustion and the moderating effect of emotional intelligence were performed.

The research showed that the proposed moderated mediation model was accepted because the relationships between the constructs were statistically significant. The results of the data analysis supported a positive relationship between AI awareness and physical and psychological withdrawal behaviors, as well as a mediating effect of job security and emotional exhaustion. The findings also confirmed that there is a moderating effect of emotional intelligence between AI awareness and job security, emotional exhaustion, physical and psychological withdrawal behaviors.

Sample data was only collected at a few Vietnamese enterprises in six fields with the highest level of AI application which are e-commerce, transportation and logistics, education, real estate, finance and agriculture, which may be limiting generalizability of research results.

This study offers several practical and useful management implications, such as anticipating negative attitudes, feelings and behaviors of employees to prepare a response plan; conducting interviews, investigate employees’ AI awareness and do their best to minimize its negative effects on employees’ psychological states and behaviors; and paying attention to recruiting and selecting employees with good emotional intelligence.

This study contributes to the growing literature on AI by elucidating the mediating roles of job insecurity and emotional exhaustion in the relationship between AI awareness and physical and psychological withdrawal behavior. This study also makes a significant step forward in examining the moderating mechanisms of emotional intelligence in attenuating the effects of AI awareness on job insecurity, emotional exhaustion, physical and psychological withdrawal behavior.

The present study examines the impact of sustainable transitions on the energy and environmental efficiency (EEE) of nations across the developed and developing world. It studies the temporal shift in the share of renewable sources in energy generation. It also analyses the shift in the efficiency frontier of nations using data envelopment analysis (DEA). Further, it studies the macro-level drivers of EEE in the countries.

As the first step, we benchmark the EEE of the developed and developing nations using DEA. Subsequently, we look at the influence of institutional quality, human capital, R&D and knowledge systems on EEE, to develop a comprehensive understanding of the macro-level drivers of EEE.

Our analyses reveal that a country’s institutional quality, human capital and R&D are critical determinants of EEE. The results show that while human capital has a significant positive impact on EEE, R&D expenditure alone has no substantial impact. The findings also suggest that knowledge diffusion disperses best practices across nations and bridges EEE gaps.

Attempts to promote sustainable energy transitions and improve EEE have met with varying levels of success. The results of this study will provide a useful guideline for the governments to achieve the goal of EEE through sustainable energy transitions (SET).

Unlike previous studies, we adopt a multi-factor EEE assessment. We also examine additional influences like the human capital of a nation and its knowledge management system to develop a comprehensive understanding of the macro-level drivers of EEE.

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.

This study aimed to analyze the impact of enterprise digital transformation on bank loan contracting from 2007 to 2021.

This study used an empirical approach to examine the impact of digital transformation on bank loan interest rates and the possible mechanisms. The digital transformation data of firms were obtained by Python, and the bank loan contracting information of Chinese listed firms was hand-collected from the notes of the annual report.

The results show that digital transformation can significantly reduce the bank loan interest rate and show heterogeneity in the nature of property rights, industry type and firm size. The above results remain significant after conducting a series of robustness tests. Channel tests suggest that digital transformation can promote total factor productivity, improve firms’ information environment and reduce the risk of financial distress, thus helping them reduce their loan interest rate. In addition, banks’ digital transformation can also affect the link between enterprise digital transformation and bank loan interest rates.

First, this paper deeply investigates the relationship between enterprise digital transformation and bank loan contracting, and the mechanisms behind it which expands the research on economic consequences of digitalization. Developing digitalization society has been a top priority in China as it is an urgent task to survive in the competitive global economic environment as a developing country. Second, in developed countries, the evidence relating to bank loan contracting is plentiful. However, the Chinese studies are still very limited as it has no database like Dealscan in US.

This paper aims to investigate the relationship linking hard technology innovation with the high-quality development (HDP) of SRDI firms. SRDI firms are typically classified as medium-sized to moderately scaled businesses renowned for their specialized, refinement, differentiation and innovation (SRDI), with a focus on providing exceptional products or services to gain a competitive advantage in specific market segments. These firms are dedicated to expanding market share and enhancing innovation capacities both locally and globally. The research also aims to scrutinize the contextual effects of digital transformation within this framework.

Hard technology innovation consists of three essential components: innovative characteristics, newly developed technology-based intellectual property rights and the volume of R&D initiatives. The evaluation of HDP was performed utilizing the entropy method, with a specific emphasis on assessing value creation and value management capabilities. Subsequently, this study explores the impact of technological innovation on the HDP of firms using a dual-dimension fixed effects model.

Every aspect of hard technology innovation is essential for promoting the HDP of businesses. The digital transformation of businesses exerts a heterogeneous moderating influence in this process. This is evident in the constructive impact on the connection between innovation attributes and the volume of fruitful R&D initiatives, as well as the HDP of firms. Conversely, the moderating effect is deemed insignificant in the association between new technology-based intellectual property and HDP.

This research delves deeper into the underlying mechanisms that underlie the promotion of HDP through hard technology innovation, thereby expanding the scope of our exploration on the HDP of SRDI firms. It establishes a theoretical framework and practical directives for achieving enhanced development quality amidst the evolving landscape of digital transformation within firms.

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.