Search results

In today’s digital era, talent recruitment can help address the growing shortage of skilled labor in the construction industry and promote sustainable growth. While existing research has explored the association between talent acquisition and local labor productivity or economic progress, the impact on construction growth deserves further study. This study aims to (1) explore the influence of talent recruitment on the growth of the construction industry and (2) analyze whether different regional characteristics shape the differential impact of talent acquisition on construction growth.

This research employs a quantitative approach, focusing on 35 major cities in China. A panel data regression model is utilized to analyze annual data from 2013 to 2018, considering variables like the construction talent recruitment index, value added in construction, gross regional product per capita and others. The study also examines regional heterogeneity and conducts robustness tests to validate the findings.

The results reveal a positive and significant correlation between talent recruitment and construction industry growth. This correlation is more pronounced in economically advanced and infrastructure-rich regions. The study also finds that factors like capital investment, educational attainment and housing prices significantly contribute to industry growth. Talent recruitment not only transforms local labor market dynamics but also drives demand for construction services, promoting industry growth through economies of scale.

This research constructs a new measurement for talent recruitment and provides new insights into the pivotal role of talent recruitment in the sustainable growth of the construction industry. It underscores the need for construction firms to tailor talent acquisition policies to their specific circumstances and regional developmental conditions. The findings offer practical guidance for driving regional growth within the sector, emphasizing the importance of talent recruitment as a key yet previously underappreciated factor in industry development.

This paper aims to objectively present the research dynamics of China in the field of information behavior and its development trends. Firstly, it incorporates China’s research in the field of information behavior into the global research network of information behavior, analyzing the changes in the status of Chinese scholars and their research institutions in the global research network from 1991 to 2022, the trends in publication volume and the cooperation relationships with other countries. Then, it conducts a detailed analysis of China’s research categories, groups, theoretical models and hot topics in different information contexts in the past five years (2018–2022).

The study retrieved research literature related to information behavior in China from 1991 to 2022 in the Web of Science database. It then utilized a national/institutional cooperation network map to analyze the changes in the status of Chinese scholars/institutions in the global research network during this period, publication volume trends and cooperation relationships with other countries. Furthermore, it employed keyword co-occurrence network maps to analyze the key categories, groups, theories and models of China’s research in different information contexts in the past five years. Based on this, it used keyword clustering network maps to analyze the hot topics of China’s research in different information contexts in the past five years.

(1) China’s research in the field of information behavior started relatively late, but the volume of publications has grown rapidly since 2004, currently ranking second globally in cumulative publication quantity. However, the influence of the literature published by China is limited, and there is a lack of research institutions with global influence. (2) In the last five years, China has conducted extensive research in various information contexts. Among these, most research was conducted in work contexts, followed by healthcare contexts, especially studies related to epidemics. (3) Current research on information behavior in China is characterized by expanded and refined research groups, diversified research categories, continuous expansion and enrichment of research contexts, increased interdisciplinary nature of research and continuous innovation in research methods and theoretical models.

This study, utilizing a scientific knowledge map, elucidates China’s position in global information behavior research, with a specific emphasis on analyzing China’s research hot topics and trends in this field over the past five years. It aims to provide valuable resources for scholars interested in understanding the status of information behavior research in China and to offer some guidance for scholars currently or intending to engage in information behavior research.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

In the dynamic landscape of 2023, global challenges are amplified by escalating conflicts and the COVID-19 aftermath. Developing nations like Vietnam face a critical juncture, requiring diversified economies for enhanced livelihoods and poverty reduction. However, this growth necessitates increased energy consumption, potentially escalating carbon emissions. Green innovation (GI) emerges as a beacon of hope, offering products and services designed for a minimal carbon footprint. Beyond socio-economic advancement, GI aligns with sustainable development goals. This study aims to  examine the influence of knowledge management (KM) and digitalization (DG) on GI, particularly in sustainable competitive advantage.

Using structural equation modeling and drawing upon a survey administered to 301 Vietnamese enterprises.

The findings illuminate diverse underpinnings between green product and process innovation, unravel the intricate relationship between KM, DG and GI, and provide crucial insights for firms seeking sustainable competitive edges.

This multidimensional approach significantly enriches the understanding of these pivotal elements in contemporary business landscapes.

This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.

This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.

Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.

Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.

This study aims to build on social comparison theory to develop a theoretical model of leader–member exchange (LMX) relationship to workplace ostracism through perceived organizational status by coworkers and envy. This study further proposes that warmth and competence may potentially moderate these two indirect effects.

This study tested the hypotheses in a battery manufacturing company located in South China by a survey of 216 employees organized in 55 work teams, using different sources. Additionally, the authors conduct two online vignette experiments to test this study’s mediation, proving the causality.

The authors found that high-level LMX leads to both envy and perceived organizational status by coworkers, which results in a mixed blessing on workplace ostracism toward the employee with high-level LMX. The focal employee’s warmth and competence moderate these indirect relationships.

The authors use LMX to explore antecedents of workplace ostracism and explain how and when these focal employees suffer workplace ostracism from their coworkers. The authors extend the research on LMX by examining the interpersonal risk of being a focal employee. The authors discover two critical boundary conditions – warmth and competence.

This study suggests that it is important to balance the level of the differential LMX; appropriately endorsing other members is a good way to avoid eliciting envy and opposition. Meanwhile, person-oriented citizenship behaviors such as demonstrations of concern or help may shortly build up an employee’s warm impression on their coworkers.

By discovering the bright and dark sides of LMX, this paper has the potential to advance theories on LMX and workplace ostracism. Therefore, the authors believe the current research will have an important impact on relevant research in the future.

The purpose of this study is to examine the impact of cross-ownership on corporate digital innovation and their specific mechanisms. Cross-ownership, who hold equity in two or more companies simultaneously, have two different types of governance effects in the capital market: governance synergistic effects and competitive collusion effects.

This paper uses a panel model, selecting A-share company data from 2011 to 2021 in China. In total, 23,853 valid data were obtained, which came from the CSMAR database and Wind database. For some missing data, they were manually supplemented by consulting the company's annual report and Sina Finance. Data processing was conducted using EXCEL and Stata16.0 software.

The results show that cross-ownership promote corporate digital innovation by leveraging governance synergies. Further grouping tests show that the synergistic effects of cross-ownership are significant in non-state-owned, high-tech, weakly competitive and higher analyst attention enterprises. Mechanism testing shows that cross-ownership can empower corporate digital innovation in three ways: reducing information asymmetry, alleviating financing constraints and improving corporate governance.

The conclusion of this paper provides new empirical evidence for a comprehensive understanding of the role of cross-ownership in corporate development, enriches the economic consequences research of chain institutional investors in China and broadens the research perspective of corporate digital innovation. It also provides important references for the digital transformation of enterprises and the healthy development of the capital market.

This study aims to explore the mechanism of boundary-spanning search on firm’s innovation performance under environmental dynamics from the perspective of strategic knowledge integration.

A survey was conducted among Chinese firm managers and R&D personnel, resulting in the collection of 315 valid samples. Hierarchical regression analysis was mainly adopted to demonstrate the hypothesized relationships, while the Sobel test and bootstrap method were used to further validate the mediating effects.

The results demonstrate that boundary-spanning search in different dimensions is a critical factor in the improvement of firm innovation performance (FIP). Two types of strategic knowledge integration are the main factors causing FIP and mediate the influence of boundary-spanning search on FIP. Furthermore, environmental dynamics moderate the relationship among boundary-spanning search, strategic knowledge integration and FIP.

Managers need to strengthen the boundary-spanning search for market and technical knowledge, which will promote firm innovative performance. Managers also need to implement strategic knowledge integration, which specifically includes using planned strategic knowledge integration to compensate for knowledge deficiencies, thereby achieving predetermined objectives; and using emergent strategic knowledge integration to update their understanding of internal and external environments, and to reset strategic objectives. In dynamic environments, managers should emphasize strategic knowledge management activities more.

From a strategic management perspective, this study categorizes strategic knowledge integration into planned and emergent forms. By applying the logic of knowledge acquisition, integration and creation, it explores how boundary-spanning search affects FIP through strategic knowledge integration as the intermediary and the boundary conditions of environmental dynamics. This not only provides a deeper understanding of the nature and effects of boundary-spanning research but also enhances the theory of strategic knowledge management.

Based on the perspectives of social capital, image motivation and motivation affordances, this paper explores the direct and moderation effects of different kinds of motivations (i.e. relationship-based motivation, community-based motivation and individual-based motivation) on users' continuous knowledge contributions in social question and answer (Q&A) communities.

The authors collect the panel data of 10,193 users from a popular social Q&A community in China. Then, a negative binomial regression model is adopted to analyze the collected data.

The paper demonstrates that social learning, peer recognition and knowledge seeking positively affect users' continuous contribution behaviors. However, the results also show that social exposure has the opposite effect. In addition, self-presentation is found to moderate the influence of social factors on users' continuous use behaviors, while the moderation effect of motivation affordances has no significance.

First, this study develops a comprehensive motivation framework that helps gain deeper insights into the underlying mechanism of knowledge contribution in social Q&A communities. Second, this study conducts panel data analysis to capture the impacts of motivations over time, rather than intentions at a fixed time point. Third, the findings can help operators of social Q&A communities to optimize community norms and incentive mechanisms.