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The adoption of emerging information and communication technologies in construction project teams has engendered numerous virtual spaces, characterized by communication visibility and content persistence. As a result, the knowledge exchanged in these virtual spaces serves as a team’s digital resources. However, the extant literature mostly takes a process-based approach to examine the impact of knowledge sharing, thus failing to fully comprehend the process of converting digital resources into performance, resulting in a gap in the literature.

This study employs team resource-based theory to construct a theoretical model and develop hypotheses. Specifically, knowledge integration capability and team efficacy are hypothesized as two types of critical capabilities that mediate the links between knowledge sharing (quantity and quality) in virtual spaces and management performance. Data from 128 middle and senior construction project managers were collected to test the proposed theoretical model.

The results suggest that relationships between knowledge sharing (quantity and quality) and project management performance are both mediated by knowledge integration capability. Moreover, team efficacy could only partially translate knowledge sharing quantity into performance and couldn’t transform knowledge sharing quality into performance. Besides, knowledge integration is found to strengthen the link between knowledge sharing quantity and performance but weaken the relationship between knowledge sharing quality and performance.

This study explores how knowledge shared in virtual spaces could be leveraged for improving management performance in construction project teams. The findings in this study enhance the understanding of knowledge sharing in digital environments and afford important insights into transforming digital resources into performance within construction project teams.

In today’s turbulent and complex era, initiative behavior is becoming more drawn to construction projects but challenging to arouse as it is free of the established regulations in project practice. Given the prevalence of social media (SM) in modern workplaces, this study is thereby motivated to investigate whether and how SM use can act to drive initiative behavior of construction project members (PMs) in this context.

This study sharply examines two distinct types of SM use – work-related and social-related – to explore their roles in driving the initiative behavior of construction PMs. Additionally, self-determination theory is employed to explore their underlying translation mechanisms and associated boundary conditions. A survey dataset collected from 229 construction PMs is used to empirically test the proposed theoretical model.

Empirical results show that role-breadth self-efficacy, psychological safety and project identification, by satisfying basic psychological needs respectively, act as crucial bridging roles in translating SM use into initiative behavior of PMs. Such mediation effects are applied to both work-related and social-related SM use with varied mechanisms. Besides, prevention focus is found to be a contingent moderator on these relationships, with a strengthening role toward role-breadth self-efficacy and a weakening role toward project identification.

This study digs into the nuanced mechanisms of how SM use benefits construction projects, especially in terms of PMs’ initiative. The findings of this research afford new insights into effectively invigorating the initiative behavior of construction PMs under the current digital momentum.

High turnover of project managers is a common phenomenon in the construction industry, which has a negative impact on the productivity and performance of construction firms. The study investigates the mechanisms of person-environment fit on turnover intention of construction project managers and the mediating role of job embeddedness. The authors also tested the moderating role of perceived organizational support in the influence of job embeddedness on turnover intention.

The data were collected from managers of 62 construction and infrastructure projects in Wuhan. Based on person-environment fit theory, job embeddedness theory and social exchange theory (SET), the authors employ structural equation modeling (SEM) to examine the hypotheses.

Results show that if project managers are not well-fitted with the environment of organizations, it reduces their embeddedness in jobs, which in consequence makes them more inclined to leave. Job embeddedness mediates the relationship between person-environment fit and turnover intention. In addition, the authors validated the moderating effect of perceived organizational support, showing that the higher the employee's job embeddedness, the lower the employee's turnover intention.

Construction companies can retain project managers and stabilize management teams through effective management strategies, thus effectively reducing the separation costs of construction companies.

The objective of this study is to accurately predict the cost of green buildings to provide quantifiable criteria for investment decisions from investors.

This study proposes a hybrid prediction model ML-based for cost prediction of GBPs and obtains prediction parameters (PPs) associated with project characteristics through data mining (DM) techniques. The model integrates a principal component analysis (PCA) method to perform parameter dimensionality reduction (PDR) on a large number of raw variables to provide independent characteristic terms. Moreover, the support vector machine (SVM) algorithm is improved to optimize the prediction results and integrated with parameter dimensionality reduction and cost prediction.

The prediction results show that the mean absolute and relative errors of the hybrid prediction model proposed in this study are equal to 39.78 and 0.02, respectively, which are much lower than those of the traditional SVM model and MRA prediction model. Moreover, the hybrid prediction model with parameter dimensionality reduction also achieved better prediction accuracy (R² = 0.319) and superior prediction accuracy for different cost terms.

Theoretically, the hybrid prediction model developed in this study can reliably predict the cost while accurately capturing the characteristics of GBPs, which is a bold attempt at a comprehensive approach. Practically, this study provides developers with a new ML-based prediction model that is capable of capturing the costs of projects with ambiguous definitions and complex characteristics.

The study aims to better understand the impact of susceptibility to social influence (normative and informational) on perceived risk and the consequent impacts on attitudes towards counterfeiting and intention to purchase counterfeit brands.

A single cross-sectional descriptive research was employed, and questionnaires were used to collect data from 361 counterfeit buyers. Structural equation modelling (SEM) based on partial least squares (PLS-SEM) was applied to analyse data and test the research hypotheses.

Results showed that normative susceptibility to social influence significantly increased attitudes towards counterfeiting but not purchase intention; its impact on intention was mediated by perceived risk and attitudes. Although information susceptibility to social influence increased purchase intention, it had no significant impact on attitudes and perceived risk.

The current study empirically explores the relationship between susceptibility to social influence and perceived risk in the context of non-deceptive counterfeit consumption, by integrating the foundations of the theory of planned behaviour (TPB) and social cognitive theory (SCT).

This study aims to develop a low-cost additive-subtractive hybrid machine equipped with reverse scanning to fabricate high-precision dental surgical guides. The major focus of developing the hybrid additive manufacturing technology is to achieve clinical precision of dental tools at an affordable price.

The designed machine consists of a self-developed vat-photopolymerization-based 3D printer that can fabricate dental surgical guides. The 3D printer is integrated with a self-developed 3D scanner that will analyze the fabricated part and evaluate the dimensional discrepancies. Based on the data provided by the scanner, the integrated secondary milling process will successfully machine the part to meet the clinical precision and standard.

The efficacy of the newly developed hybrid machine is demonstrated with the fabrication of complex part, lower and upper dental surgical guides with the mean dimensional deviations of 198.1, 136.6 and 117.9 µm, respectively. The integration of the secondary scanning and machining system successfully enhanced the mean dimensional deviation of upper and lower guides by 11.88% and 28.75%, respectively. Furthermore, this study also benchmarked the dimensional accuracies achieved by this low-cost technology with the high-end commercial 3D printers. The overall cost of the machine is evaluated to be $2,399.

This paper proposes a novel hybrid additive manufacturing process with integrated reverse scanning and machining modules to fabricate high-precision dental guides. The developed machine is a low-cost alternative to the existing high-end commercial counterparts. The developed machine has the potential to make endodontic treatments more affordable.

The purpose of this research is to examine the influence of the Dark Triad personality traits (Machiavellianism, narcissism and psychopathy) on consumers’ purchase intentions for counterfeit luxury products via the mediating effect of moral disengagement, drawing on moral disengagement theory. Furthermore, descriptive norms are tested as a moderators of the mediated relationships between the Dark Triad personality traits and purchase intentions via moral disengagement based on trait activation theory.

To test the hypotheses, a survey design is employed to gather primary data from 205 consumers. The final data are analysed using partial least squares structural equation modelling.

This study finds that moral disengagement mediates the relationships between (1) Machiavellianism, (2) psychopathy and purchase intentions. Descriptive norms are found to moderate the indirect effect of moral disengagement between psychopathy and purchase intentions.

Dark Triad personality traits are linked to various unethical behaviours. However, no studies have explored how Dark Triad personality traits influence consumers’ purchase intentions for counterfeit luxury products. This study sheds light on how consumers with high Dark Triad personality traits are more likely to be morally disengaged, thereby leading to purchase intentions for counterfeit luxury products, drawing on moral disengagement theory. Furthermore, this study demonstrates descriptive norms as the boundary condition for the mediating relationship between Dark Triad personality traits and purchase intentions via moral disengagement, drawing on trait activation theory. The findings can be used to formulate better strategies to counteract the phenomenon of counterfeit luxury consumption.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

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.

This research focuses on developing a dual-nozzle slurry-based extrusion 3D printer capable of fabricating intricate zirconia structures. The designed 3D printer combines material extrusion and photopolymerization technologies to improve material diversity, precision and cost-effectiveness.

The 3D printer design incorporates ultraviolet curing to instantly cure extruded zirconia slurry thereby, eliminating the need for a step-wise curing procedure. Printing parameters were optimized to achieve high-quality prints, and supports made of polyethylene terephthalate glycol were used for intricate geometries. The printability and mechanical properties were evaluated for two different zirconia slurry compositions: 70 / 30 and 80 / 20 powder-to-resin weight percentages. The printed green body was subjected to a two-phase sintering process.

The 3D printer fabricated structures with features subtending angles greater than 50 degrees and a filling density above 80% without any supports. Shrinkage analysis showed the 80 / 20 composition resulted in higher density parts, with shrinkage ratios of 25.23%, 26.23% and 27.26% along the X, Y and Z axes, respectively. The sintered objects displayed hardness (1525 HV) and flexural strength (117 MPa), with minimal porosity.

This study demonstrates the development of a cost-effective dual-nozzle 3D printer that can effectively fabricate functional parts with complex material compositions and geometries that can cater to the futuristic requirements of high-end industries.