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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.

In recent years, the development of metaheuristic algorithms for solving optimization problems within a reasonable timeframe has garnered significant attention from the global scientific community. In this work, a new metaheuristic algorithm inspired by the inflection mechanism of the avian influenza virus H5N1 in poultry and humans, taking into account its mutation mechanism, called H5N1.

This algorithm aims to explore optimal solutions for optimization problems by simulating the adaptive behavior and evolutionary process of the H5N1 virus, thereby enhancing the algorithm’s performance for all types of optimization problems. Additionally, a balanced stochastic probability mechanism derived from the infection probability is presented. Using this mechanism, the H5N1 algorithm can change its phrase, including exploitation and exploration phases. Two versions of H5N1, SH5N1 and MH5N1, are presented to solve single-objective optimization problems (SOPs) and multi-objective optimization problems (MOPs).

The performance of the algorithm is evaluated using a set of benchmark functions, including seven unimodal, six multimodal, ten fixed-dimension multimodal to solve SOPs, ZDT functions and CEC2009 has been used to demonstrate its superiority over other recent algorithms. Finally, six optimization engineering problems have been tested. The results obtained indicate that the proposed algorithm outperformed ten algorithms in SOPs and seven algorithms in MOPs.

The experimental findings demonstrate the outstanding convergence of the H5N1 algorithm and its ability to generate solutions of superior quality.

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.

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.

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.

This study aims to gain a better understanding of artificial serendipity – pre-planned surprises intentionally crafted through deliberate designs – in online marketplaces. By exploring the key features of artificial serendipity, this study investigates whether serendipity can be intentionally designed, particularly with the use of artificial intelligence (AI). The findings from this research broaden the scope of serendipity studies, making them more relevant and applicable in the context of the AI era.

A narrative study was conducted, gathering insights from 32 Chinese online consumers through diaries and interviews. The data were analysed in close collaboration with participants, ensuring an authentic reflection of their perceptions regarding the features of artificial serendipity in online marketplaces.

Findings reveal that artificial serendipity, particularly when designed by AI, is still regarded by online consumers as genuine serendipity. It provides a sense of real surprise and encourages deeper reflection on personal knowledge, affording the two central qualities of genuine serendipity: unexpectedness and valuableness. However, since artificial serendipity is pre-planned through intentional design, consumers cannot have entire control over it. Therefore, compared to natural serendipity – fortune surprises arising from accidental correspondence between individuals and contexts – artificial serendipity tends to be more surprising yet less valuable.

For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity.

Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This study stands out as one of the few to provide a nuanced understanding of artificial serendipity, offering valuable insights for both research and practice. For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity. Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This work intends to evaluate experimentally the ratcheting behaviour of AM MS300. Furthermore, cyclic plasticity modelling (modified Abdel-Karim and Ohno model) is examined as a means of predicting ratcheting.

Uniaxial stress-controlled cyclic loading histories were utilised to evaluate ratcheting for Maraging Steel 300 (MS300) fabricated via laser powder bed fusion (LPBF) additive manufacturing (AM). Heat-treated and as-built AM and conventionally manufactured (CM) MS300 coupons were tested at room temperature, under constant and incrementally variable stress amplitude and mean stress. Two sets of AM test coupons were used, printed at horizontal and vertical built orientation. The AM material ratcheting was predicted via constitutive modelling and numerical simulation. The Abdel-Karim and Ohno cyclic plasticity model was modified by introducing a memory surface, to improve ratcheting prediction.

The hysteresis stress–strain response and low cycle fatigue (LCF) life were obtained from the different loading histories. Both the AM and CM MS300 exhibited an accumulation of axial strain (ratcheting) for all tests, attributed to the application of non-zero mean stress. The AM MS300 has demonstrated a higher ratcheting accumulation rate than the CM material. The achieved agreement between the numerical results of the new model and the experimental data offers an indication on the suitability and the robustness of this model.

The ratcheting behaviour of the AM MS300 material has been characterised for the first time in the published literature, for a variety of loading histories selected. A modified Abdel-Karim and Ohno plasticity model has been developed to account for the ratcheting performance of this material.

This study aims to explore the factors influencing the evolution of emerging technology innovation network (ETIN) in combination with the key attributes and life cycle of emerging technologies, particularly the impact of multiple knowledge attributes and technology life cycle on the ETIN evolution.

This study collects 5G patent data and their citation information from the Derwent Innovations Index to construct a 5G technology innovation network (5GIN) as a sample network and conducts an empirical analysis of the 5GIN using the temporal exponential random graph model (TERGM).

The results indicate that during the 5GIN evolution, the network scale continues to expand and exhibits increasingly significant core-periphery structure, scale-free characteristic, small-world characteristic and community structure. Furthermore, the findings suggest that the multiple knowledge attributes based on the key attributes of emerging technologies, including knowledge novelty, coherence, growth and impact, have a significant positive influence on the ETIN evolution. Meanwhile, the temporal evolution of ETIN is also found to be correlated with the life cycle of emerging technologies.

This study extends the exploration of emerging technology research from a complex network perspective, providing a more realistic explanatory framework for the factors influencing ETIN evolution. It further highlights the important role that multiple knowledge attributes and the technology life cycle play within this framework.

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 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.