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The purpose of this study is to campare the corrosion behavior of Az91 films and bulk sample, in the objective to provide reference for the corrosion resistance improvement of Mg alloys.

AZ91 films with various thickness values are produced by magnetron sputtering technique, and the corrosion behavior was characterized by immersion tests and electrochemical measurements.

The AZ91 films exhibited a preferred orientation with basal planes parallel to the surface and increased densification with the increase of thickness, and a superior corrosion resistance for the AZ91 films compared with the bulk sample.

The preferred (0002) basal planes in AZ91 films benefited the corrosion resistance and the nanoscale AZ91 films facilitated the development of a dense passivation film. Consequently, AZ91 film exhibited a superior corrosion resistance.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

This study mainly focused on the long-term effect of different risk exposure levels and prior anti-epidemic experience of healthcare workers in mitigating COVID-19 on their work stress in the post-COVID era.

The study sample included 359 physicians, 619 nurses, 229 technicians and 212 administrators, for a total of 1,419 healthcare workers working in the Lanzhou area during the investigation. Data were analyzed by multivariate regression models.

Our findings indicated that the interaction between pandemic effect mitigation experience and high-risk exposure significantly affected healthcare workers in the post-COVID era by increasing their work stress (p < 0.001) and reducing their rest time (p < 0.001). Healthcare workers may have experienced worse outcomes in the long term if they had higher levels of risk exposure and more experience in fighting epidemics. Furthermore, poor mental health (p < 0.001) and prior experience with SARS (p < 0.001) further amplified these adverse effects. However, surprisingly, we did not observe any effect of prior anti-epidemic experience or high-risk exposure on the mental health of healthcare workers in the post-COVID era (p > 0.1).

The adverse impact of COVID-19 may have left long-lasting effects on Health professionals (HPs), particularly those with high Risk exposure (RE) and more mitigation experience. Poor Mental health (MH) and previous experience in mitigating previous similar outbreaks (such as SARS) are risk factors that should be considered. Support programs must be designed and promoted to help HPs respond and improve their performance.

Our study presents compelling evidence that the COVID-19 pandemic will have long-term detrimental effects on the work stress of healthcare workers.

Workforce agility (WFA) is an emergent research topic in volatile times. However, there is a lack of research in understanding the leadership dimension that triggers such an attribute in organizations. Our study aims to understand the impact of workforce agility on empowering leadership behavior and employee performance dimensions (task performance, contextual performance and counterproductive work behavior).

We collected data from 236 employees using reliable, validated scales and conducted various statistical analyses.

Our results demonstrated that WFA (1) partially mediated the relationship between empowering leadership and contextual performance (CP), (2) has not mediated the relationship between empowering leadership and counterproductive behavior (CWB) and (3) mediated the relationship between empowering leadership and task performance (TP).

Our research has practical implications for management practitioners. It suggests hiring and developing an agile workforce through appropriate training and development programs can significantly impact organizational performance. Furthermore, it provides insights into building leadership capabilities that sustain workforce agility practices, empowering leaders to make informed decisions.

Our research fills a significant gap in the existing literature by exploring the effects of WFA on leadership and performance. This novel approach provides a fresh perspective on the dynamics of organizational behavior, making it a valuable addition to the field.

This study aims to develop a novel thermal modeling strategy to simulate electron beam powder bed fusion at part scale with machine-varying process parameters strategy. Single-bead and part-scale experiments and modeling were studied. Scanning strategies were described by the process controlling functions that enabled modeling.

The finite element analysis thermal model was used along with the powder bed fusion with electron beam experiments. The proposed strategy involves dividing a part into smaller sections and creating meso-scale models for each subsection. These meso-scale models take into consideration the variable process parameters, including power and velocity of the moving heat source, during part building. Subsequently, these models are integrated to perform partscale simulations, enabling more realistic predictions of thermal accumulation and resulting distortions. The model was built and validated with single-bead experiments and bulky parts with different features.

Single-bead experiments demonstrated an average error rate of 6%–24% for melt pool dimension prediction using the proposed meso-scale models with different scanning control functions. Part-scale simulations for three different geometries (cantilever beams with supports, bulk artifact and topology-optimized transfer arm) showed good agreement between modeled temperature changes and experimental deformation values.

This study presents a novel approach for electron beam powder bed fusion modeling that leverages meso-scale models to capture the influence of variable process parameters on part quality. This strategy offers improved accuracy for predicting part geometry and identifying potential defects, leading to a more efficient additive manufacturing process.

Financial institutions actively seek to leverage the capabilities of artificial intelligence (AI) across diverse operations in the field. Especially, the adoption of AI advisors has a significant impact on trading and investing in the stock market. The purpose of this paper is to test whether AI advisors are less preferred compared to human advisors for investing and whether this algorithm aversion diminishes for trading.

The four hypotheses regarding the direct and indirect relationships between variables are tested in five experiments that collect data from Prolific.

The results of the five experiments reveal that, for investing, consumers are less likely to use AI advisors in comparison to human advisors. However, this reluctance to AI advisors decreases for trading. The author identifies the perceived importance of careful decision-making for investing and trading as the psychological mechanism. Specifically, the greater emphasis on careful decision-making in investing, as compared to trading, leads to consumers’ tendency to avoid AI advisors.

This research is the first to investigate whether algorithm aversion varies based on whether one’s approach to the stock market is investing or trading. Furthermore, it contributes to the literature on carefulness by exploring the interaction between a stock market approach and the lay belief that algorithms lack the capability to deliberate carefully.

This study aims to analyze the convergence of environmental degradation clubs in the Association of Southeast Asian Nations (ASEAN). In addition, this study also analyzes the influence of renewable energy and foreign direct investment (FDI) on each club as an intervention to change the convergence pattern in each club.

This study analyzes the club convergence of environmental degradation in an effort to find out the distribution of environmental degradation reduction policies. This study uses club convergence with the Phillips and Sul (PS) convergence methodology because it considers multiple steady-states and is robust. This study uses annual panel data from 1998 to 2020 and ASEAN country units with ecological footprints as proxies for environmental degradation. After obtaining the club results, the analysis continued by analyzing the impact of renewable energy and FDI on each club using panel data regression and the Stochastic Impacts by Regression on Population, Affluence and Technology model specification.

Based on club convergence, ASEAN countries can be grouped into three clubs with two divergent countries. Club 1 has an increasing pattern of environmental degradation, while Club 2 and Club 3 show no increase. Club 1 can primarily apply renewable energy to reduce environmental degradation, while Club 2 requires more FDI. The authors expect policymakers to take into account the clubs established to formulate collaborative policies among countries. The result that FDI reduces environmental degradation in this study is in line with the pollution halo hypothesis. This study also found that population has a significant effect on environmental degradation, so policies to regulate population need to be considered. On the other hand, increasing income has no effect on reducing environmental degradation. Therefore, the use of renewable energy and FDI toward green investment is expected to intensify within ASEAN countries to reduce environmental degradation.

This research is by far the first to apply PS Club convergence to environmental degradation in ASEAN. In addition, this study is also the first to analyze the influence of renewable energy and FDI on each club formed, considering the need for renewable energy use that has not been maximized in ASEAN.

In today’s rapidly evolving business landscape, innovation is the cornerstone for every organization. Knowledge management (KM) is crucial for developing sustainable competitive advantage by fostering innovation. This study aims to identify the key drivers of KM in the context of digital transformation through qualitative research.

This study employs a qualitative approach based on in-depth interviews with senior KM officers, including chief knowledge officers and directors who spearhead KM in their respective organizations. This research identifies four key dimensions, shedding new light on the drivers of KM in the context of digital transformation.

This study’s findings reveal that the integration of important drivers from the lens of social-technical system (STS) theory is categorized into the four dimensions of KM, namely, motivation, technology, people interaction and organizational drivers. These factors jointly impact and design the effectiveness of KM in the digital age.

This study makes a unique contribution to the field of digital transformation. It presents a conceptual framework from the lens of the STS theory that encompasses four critical dimensions of KM: motivation, technology, people interaction and organizational dimensions, each with sub-codes. This framework can be utilized by practitioners and scholars alike.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Based on the stressor-detachment theory, this study aims to investigate the effect of daily customer mistreatment on proactive service performance and ego depletion, mediated by psychological detachment inhibition during the evening. Additionally, this study endeavors to investigate the dual moderating role of prosocial motivation.

A time-lagged, diary daily survey involving 74 participants over 8 consecutive workdays was conducted to test the hypotheses.

The findings indicate that the psychological detachment inhibition during the evening of Day t mediates the impact of Day t’s customer mistreatment on Day t + 1’s proactive service performance and ego depletion. Furthermore, although prosocial motivation was found to intensify the impact of customer mistreatment on psychological detachment inhibition, it alleviated the negative association between psychological detachment inhibition and proactive service performance.

When employees experience customer mistreatment, hospitality managers should not only provide emotional reassurance and resolve any related issues promptly but also encourage employees to engage in activities that distract them and help them to relax and recharge, especially for those who exhibit high prosocial motivation. Moreover, hiring employees with high prosocial motivation is recommended for hospitality organizations to enable them to maintain high service performance.

This study focuses on psychological detachment inhibition during the evening linking within-person design and daily spill-over impact, enriching the mechanisms through which the repercussions of daily customer mistreatment extend beyond the immediate workday and affect individuals’ outcomes. This study also expands upon the existing literature by clarifying the dual aspects – both detrimental and beneficial – of prosocial motivation.