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Limited use of Internet of Things (IoT) technology on construction sites has restricted its value in the construction industry. To propel its widespread application, this paper explores the influencing factors and action paths of construction companies' IoT technology adoption behavior.

First, literature research, technology adoption theories, and semi-structured expert interviews were employed to build the adoption model. Second, a questionnaire survey was conducted among Chinese construction contractors to collect empirical data. Third, the structural equation model method and regression analysis were used to test the adoption model. Finally, the findings were further validated with interviews, case studies, and field observations.

External environmental pressure (EEP), perceived benefit (PB), top management support (TMS), company resource readiness (CRR), adoption intention (AI), and perceived compatibility (PCA) have a direct positive impact on adoption behavior (AB). In contrast, perceived cost (PC) and perceived complexity (PCL) exert a direct negative impact on AB. The EEP, PB, and PC are critical factors affecting AB, whereas AI is strongly affected by CRR and TMS. Besides, AI plays a part mediating role in the relationship between seven factors and AB. Company size and nature positively moderate AI's positive effect on AB.

This paper contributes to the knowledge of IoT technology adoption behavior in the construction sector by applying the technology adoption theories. Exploring the implementation barriers and drivers of IoT technology in construction sites from the perspective of organizational technology adoption behavior and introducing moderating variables to explain adoption behavior are innovations of this paper. The findings can help professionals better understand the IoT technology adoption barriers and enhance construction companies' adoption awareness, demand, and ability. This work also provides a reference for understanding the impact mechanism of the adoption behavior of other innovative technologies in construction.

Human–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.

Literature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.

The critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.

This paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

The purpose of this paper is to combine the Jiles-Atherton (J-A) hysteresis model with the field separation approach to realize the accurate simulation of dynamic magnetostrictive characteristics of silicon steel sheet.

First, the energy loss of silicon steel sheet is divided into hysteresis loss Why, classical eddy current loss Wed and anomalous loss Wan according to the statistical theory of losses. The Why is calculated by static J-A hysteresis model, Wed and Wan are calculated by the analytical formulae. Then, based on the field separation approach, the dynamic magnetic field is derived. Finally, a new dynamic magnetostrictive model is proposed by means of the quadratic domain rotation model.

Comparison of simulation and experimental results verifies that the proposed model has high accuracy and strong universality.

The proposed method improves the existing method’s problem of relying on too much experimental data, and the method ensures the calculation accuracy, parameter identification accuracy and engineering practicability. Consequently, the presented work greatly facilitates further explorations and studies on simulation of dynamic magnetostrictive characteristics of silicon steel sheet.

This study aims to identify which types of information quality influence purchase intentions the most in live streaming commerce and to examine the role of network size in this context.

We propose a model to investigate the correlation among the quality of different information in live streaming commerce, consumer trust, network size and purchase intention. An empirical analysis of 505 questionnaires was conducted by constructing a structural equation model.

The empirical findings indicate that information quality can directly enhance purchase intention and exert an indirect influence through the mediating factors of trust in products and streamers. Perceived network size positively moderates the relationship between information quality and trust in products. Of the five types of information, the quality of bullet-screen comments information is most important to consumers.

This study represents the first systematic analysis of how the quality of multiple types of information in live streaming commerce influences consumer trust and purchase intention, integrated within a unified framework. It uniquely introduces network size as a moderating variable, offering both theoretical insights and practical guidance for balancing information quality with network size in live streaming commerce environments.

In order to meet the different quality of service (QoS) requirements of vehicle-to-infrastructure (V2I) and multiple vehicle-to-vehicle (V2V) links in vehicle networks, an efficient V2V spectrum access mechanism is proposed in this paper.

A long-short-term-memory-based multi-agent hybrid proximal policy optimization (LSTM-H-PPO) algorithm is proposed, through which the distributed spectrum access and continuous power control of V2V link are realized.

Simulation results show that compared with the baseline algorithm, the proposed algorithm has significant advantages in terms of total system capacity, payload delivery success rate of V2V link and convergence speed.

The LSTM layer uses the time sequence information to estimate the accurate system state, which ensures the choice of V2V spectrum access based on local observation effective. The hybrid PPO framework shares training parameters among agents which speeds up the entire training process. The proposed algorithm adopts the mode of centralized training and distributed execution, so that the agent can achieve the optimal spectrum access based on local observation information with less signaling overhead.

In recent years, counterproductive knowledge behavior (CKB) and its types have received increasing interest in knowledge management as the degree of knowledge sharing and innovation in enterprises continues to increase. A rapidly growing number of studies have shed light on the important antecedents and consequences of employees’ CKB. However, the various labels, conceptualizations and operationalizations of CKB have fragmented this body of research. This study aims to systematically integrate the effects of the six types of organizational characteristics on CKB and further draws more general conclusions based on the results of previous studies.

Based on a survey of 103 effect values responsible for 52 CKB samples, the authors use the ABC theory to explore the effects of the six types of organizational characteristics on CKB. Moderator analysis were performed to resolve inconsistencies in empirical studies and understand the contexts under which CKB has the strongest or weakest effect.

The results showed that task interdependence and a positive organizational atmosphere, in general, negatively affect employees’ CKB in the moderation analysis. In contrast, workplace discomfort, negative organizational atmosphere, internal competition and time pressure positively and partly affect employees’ CKB. The direction and magnitude of these effects were affected by emotional factors, knowledge personnel types and sample sources. Discussing the theoretical, methodological and practical implications of these findings can offer a guiding framework for future research.

Better control of employees’ CKB is not achieved by adjusting organizational characteristics alone but by combining personal characteristics and mood changes with it to balance organizational characteristics and CKB. Furthermore, the large-sample joint study integrated the conceptual definition of CKB. The multivariate data study provided more reliable conclusions and a solid theoretical foundation for CKB research areas.

This paper aims to explore the underlying mechanisms and boundary conditions through which equipment manufacturing enterprises can capture market value from digital transformation, with a specific focus on the roles of knowledge search and knowledge recombination.

This study uses a double fixed-effects model to test the hypotheses, using a unique data set of “firm-year” observations from 739 publicly listed equipment manufacturing companies in China, spanning the period from 2018 to 2022.

Digital transformation drives market value creation in equipment manufacturing enterprises through both breakthrough knowledge recombination (BKR) and progressive knowledge recombination (PKR). In addition, the analysis of marginal conditions reveals that diversified knowledge search serves as a substitute for digital transformation in promoting BKR, while also positively moderating the relationship between digital transformation and PKR.

Grounded in the knowledge-based view theoretical framework, this study introduces the novel concepts of BKR and PKR and systematically examines how digital transformation impacts market value in equipment manufacturing enterprises.

This study aims to investigate the effects of different dimensions of social capital on individual knowledge transfer to nurture the organization’s intellectual capital, as well as the interactions among these dimensions and explore the potential moderators.

This study conducted a meta-analysis with 108 independent empirical studies to examine the different dimensions of social capital–knowledge transfer relationships and the effects of moderators and used meta-analytic structural equation modeling (MASEM) to test the internal relationships among social capital dimensions.

The results show that structural, relational and cognitive social capitals are all positively related to knowledge transfer. In addition, different dimensions of social capital act as complements to one another. Further examinations reveal that the level of economic development has no significant moderating effect on the relationship between social capital and knowledge transfer. Then, the cultural context and profit climate characteristics moderate the relationship between social capital and knowledge transfer.

Leveraging the trilogy of signaling, learning and spillover effects, this meta-analytic study quantitatively integrates the relationships between different dimensions of social capital and knowledge transfer. It reconciles the present disparate findings, demonstrates the validity of different dimensional social capital interactions and obtains highly generalized conclusions. This study also introduces a dichotomy, saturation versus reinforcement, to explain the mixed results, which enriches social capital theory.

The purpose of this study is to prepare trivalent chromium conversion (TCC) film on the Zn-Ni electrodeposited film on the surface of 2024 aluminum alloy and to ensure that the TCC film has good corrosion resistance and electrical conductivity.

The morphology of the TCC film was studied by scanning electron microscopy, and the elemental composition of the TCC film was characterized by X-ray energy dispersive spectroscopy. The TCC film was tested and the roughness was analyzed by 3D morphology (white light interference). The electrochemical behavior and corrosion resistance of TCC films were studied by the Tafel polarization curve and electrochemical impedance spectroscopy, and the conductivity was tested.

The TCC films were uniformly black and bright in appearance and were mainly compounds of Zn, Ni and Cr with O. The electrochemical impedance of the TCC film is larger than that of the Zn-Ni film, the corrosion current (I_corr) is smaller than that of the Zn-Ni film and the corrosion potential (E_corr) is larger than that of the Zn-Ni film, which has excellent corrosion resistance. TCCs were performed on the appropriate size of the shell sample, and the resistance of the shells was 1.5 mVDC, which meets the total resistance requirements of the test standard for composite connector accessories.

In this study, TCC film was successfully prepared on the Zn-Ni coating on the surface of 2024 aluminum alloy. The TCC film has good corrosion resistance and electrical conductivity.

This study aims to provide a new perspective and path to reduce the unsafe behavior of new generation of construction workers (NGCWs) in China. The purpose of this study is to explore the influencing mechanism of work-family balance on the unsafe behavior of NGCWs and test the mediating effect of job satisfaction and the moderating effect of group safety climate.

A theoretical model on the influencing mechanism of work-family balance on unsafe behavior of NGCWs was constructed through theoretical analysis. Research data were collected from 502 NGCWs via a questionnaire survey, and research hypotheses were testified with regression analysis.

The results show that work-family balance not only directly reduces NGCWs’ unsafe behavior but also indirectly reduces it through job satisfaction, which plays a partial mediating role. In addition to positively moderating the relationship between work-family balance and NGCWs’ unsafe behavior, group safety climate can also moderate the relationship between work-family balance and job satisfaction in a positive way.

This study provides practical implications for construction companies to reduce the unsafe behaviors of NGCWs from the perspective of work-family balance. Specifically, construction companies should adopt more flexible work rules, such as flexible organization and rotation systems, to increase their work autonomy. Meanwhile, construction companies need to improve the work environment and basic conditions for NGCWs, establish a reasonable salary system and provide attractive promotion opportunities to increase their job satisfaction. In addition, construction companies should provide active safety lectures and training, and supervisors should improve safety communication and interaction levels. Co-workers should remind workers about their safety attitudes and behaviors promptly. A good group safety climate will be created through the efforts of construction companies, supervisors and co-workers.

This study clarifies the influencing mechanism of work-family balance on the NGCWs’ unsafe behavior and further tests the partial mediating role of job satisfaction and the positively moderating effect of group safety climate on the influence relationship of work-family balance on job satisfaction and NGCWs’ unsafe behavior, which defines the boundary conditions of the relationship between work-family balance and NGCWs’ unsafe behavior, and promotes the effective integration of social exchange theory and theoretical system of influencing mechanism of construction workers’ unsafe behavior.