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While spontaneous and voluntary knowledge contribution in online communities promotes value co-creation, dysfunctional knowledge behaviors hamper the effectiveness and development of such communities. The study conceptualizes physicians' proactive knowledge sharing and knowledge withholding behaviors in physician-driven online health communities (OHCs) and integrates the theories of role identity as well as communal and exchange relationships to understand the root causes and motivations behind these two types of knowledge behaviors.

The authors collected survey data from 166 users from one of the largest physician-driven OHCs in China and applied the covariance-based structural equation modeling approach to test the hypotheses.

The findings suggest that (1) physicians' professional role identity had a positive indirect effect on proactive knowledge sharing behaviors through communal motivation, and work pressure weakened this indirect effect; and (2) professional role identity had a negative indirect impact on knowledge withholding behaviors through exchange motivation.

This study extends proactive knowledge sharing and knowledge withholding behaviors from the organizational management domain to the online environment, exploring the underlying causes and motivations behind both behaviors in the unique context of physician-driven OHCs. The findings offer practical suggestions for the effective management of OHC platforms, as well as policy implications that respond to the workforce shortage of healthcare providers, a crisis that is unfolding globally.

The Internet of Things (IoT) offers substantial potential for improving efficiency and effectiveness in various applications, notably within the domain of smart construction. Despite its growing adoption within the Architecture, Engineering, and Construction (AEC) industry, its utilization remains limited. Despite efforts made by policymakers, the shift from traditional construction practices to smart construction poses significant challenges. Consequently, this study aims to explore, compare, and prioritize the determinants that impact the acceptance of the IoT among construction practitioners.

Based on the integrated model of Unified Theory of Acceptance and Use of Technology (UTAUT2), Task-Technology Fit (TTF), and perceived risk. A cross-sectional survey was administered to 309 construction practitioners in China, and the collected data were analyzed using structural equation modeling (SEM) to test the proposed hypotheses.

The findings indicate that TTF, performance expectancy, effort expectancy, hedonic motivation, facilitating conditions, and perceived risk exert significant influence on construction practitioners’ intention to adopt IoT. Conversely, social influence and habit exhibit no significant impact. Notably, the results unveil the moderating influence of gender on key relationships – specifically, performance expectancy, hedonic motivation, and habit – in relation to the behavioral intention to adopt IoT among construction practitioners. In general, the model explains 71% of the variance in the behavioral intention to adopt IoT, indicating that the independent constructs influenced 71% of practitioners’ intentions to use IoT.

These findings provide both theoretical support and empirical evidence, offering valuable insights for stakeholders aiming to gain a deeper understanding of the critical factors influencing practitioners’ intention to adopt IoT. This knowledge equips them to formulate programs and strategies for promoting effective IoT implementation within the AEC field.

This study contributes to the existing literature by affirming antecedents and uncovering moderators in IoT adoption. It enhances the existing theoretical frameworks by integrating UTAUT2, TTF, and perceived risk, thereby making a substantial contribution to the advancement of technology adoption research in the AEC sector.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

The purpose of this study is to prepare a new adsorbent activated carbon immobilized on polystyrene (ACPS) for uranium (VI) and thorium (IV) removal from an aqueous solution. Activated carbon (AC) was derived from biochar material by chemical activation to increase the active sites on its surface and enhance the adsorption capacity. Activated carbon (AC) was immobilized on polystyrene (PS) to improve the physical properties and facilitate separation from the working solution. A feasibility study for the adsorption of uranium (VI) and thorium (IV) on the new adsorbent (ACPS) has been achieved. Adsorption kinetics, isotherms, and thermodynamics models of the adsorption process were used to describe the reaction mechanism.

Activated carbon was synthesized from biochar charcoal by 2 M H₂SO₄. Activated carbon was immobilized on the pretreatment polystyrene by hydrothermal process forming new adsorbent (ACPS). Characterization studies were carried out by scanning electron microscope, energy-dispersive X-ray spectrometer, infrared spectroscopy and X-ray diffraction techniques. Different factors affect the adsorption process as pH, contact time, solid/liquid ratio, initial concentration and temperature. The adsorption mechanism was explained according to kinetic, isothermal and thermodynamic studies. Also, the regeneration of spent ACPS was studied.

The experimental results showed that pH and equilibrium time of the best adsorption were 6.0 and 60 min for U(VI), 4.0 and 90 min for Th(IV), (pH_PZC = 3.4). The experimental results fit well with pseudo-second order, Freundlich and Dubinin–Radushkevich models proving the chemisorption and heterogenous adsorption reaction. Adsorption thermodynamics demonstrated that the adsorption process is exothermic and has random nature of the solid/liquid interface. In addition, the regeneration of spent ACPS research showed that the adsorbent has good chemical stability. According to the comparative study, ACPS shows higher adsorption capacities of U(VI) and Th(IV) than other previous bio-adsorbents.

This study was conducted to improve the chemical and physical properties of bio-charcoal purchased from the local market to activated carbon by hydrothermal method. Activated carbon was immobilized on polystyrene forming new adsorbent ACPS for eliminating U(VI) and Th(IV) from aqueous solutions.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

Since there is lack of studies in determine factors that affecting enjoyment sentiment when using online learning system, this study aims to explore the antecedents of perceived online learning enjoyment by using extended technology acceptance model (TAM) and its effect on behavioral intentions (BIN) among higher education institutions students.

The research framework was empirically evaluated using a cross-sectional research design and the data was collected from 715 undergraduate students from public higher education institutions in Malaysia using an online survey method. A structural equation modeling using partial least square method was used to examine the hypothesized model.

The results of partial least squares structural equation modeling indicated that the main predictive variables of TAM along with the extended variables were significantly influence the perceived online learning enjoyment. Meanwhile, the analysis also identified that perceived online learning enjoyment can significantly generate positive BIN for using online learning platforms as well as it also plays as a significant mediator role.

This study has significant implications for higher education institutions that wish to develop online learning environment for their students by providing answers to higher education institutions on how to successfully use the learning management system to assist students' learning performance from the aspect of online learning enjoyment sentiment.

This study is remarkable because it is the first attempt to explore the effect of these five predictors on students' learning enjoyment toward online learning platforms and subsequently on BIN to use this learning platforms, especially in the context of Malaysian higher education system. It is also unique in the way to extend the use of TAM predictive variables with others variables to produce more informative results about the study. Hence, this study also has a new contribution in the literature in the domain of digital learning.

Mobile messaging groups (MMGs) have been widely adopted in construction practice, yet, little is known about how to foster knowledge sharing (KS) in MMGs, characterized by communication visibility. This study is thus motivated to investigate mechanisms for KS in this context.

The paper employs knowledge governance theory to construct a theoretical model and develop hypotheses. Specifically, psychological safety is identified as a mediator between knowledge governance mechanism (KGM) and KS, and promotion regulatory focus is identified as a moderator between KGM and psychological safety. Data from 208 Chinese construction project team members are collected to test the proposed theoretical model.

The results suggest that both formal and informal KGM positively affect psychological safety, which in turn improves KS (quantity and quality). Moreover, the mediating role of psychological safety is confirmed, and the moderating role of promotion regulatory focus is validated.

This study explores how to foster KS in MMGs, which are pervasive in today’s digital age. The findings in this study enhance the understanding of KS in digital environments and afford important insights into knowledge management within construction project teams.

Chatbots have been explored as a novel approach to enhancing consumer engagement by delivering more enjoyable, personalized services. This research aims to investigate the mechanism through which anthropomorphic elements of chatbots influence consumers' intentions to use the technology.

This research introduces five key concepts framed through the “computers-are-social-actors” (CASA) paradigm: form realism (FR), behavioral realism (BR), cognitive trust (CT), entertainment (EM) and chatbot usage intention (CUI). An online questionnaire garnered 280 responses from China and 207 responses from Indonesia. Data collection employed a combination of purposive and snowball sampling techniques. This research utilized structural equation modeling through the analysis of moment structures (AMOS) 27 software to test the hypotheses.

(1) FR positively predicts CT and EM, (2) FR negatively predicts CUI, (3) BR positively predicts CT and EM, (4) BR positively predicts CUI and (5) Both CT and EM mediate the relationship between FR and CUI, as well as between BR and CUI.

This research enriches the current literature on interactive marketing by exploring how the anthropomorphic features of chatbots enhance consumers' intentions to use such technology. It pioneers the exploration of CT and EM as mediating factors in the relationship between chatbot anthropomorphism and consumer behavioral intention. Moreover, this research makes a methodological contribution by developing and validating new measurement scales for measuring chatbot anthropomorphic elements.

This article aims to clarify the impact of stock market liberalization on corporate green technology innovation, analyze its mechanism from the perspectives of financing constraints and environmental management level and explore heterogeneity.

Using the panel data of Chinese enterprises from 2010 to 2020, this article adopts the multi-point difference-in-difference (DID) method to test the impact of stock market liberalization on enterprise green technology innovation and its conduction pathway.

The outcomes demonstrate that stock market liberalization contributes to the furthering of green technology innovation. The heterogeneity test reveals that this promotion is more pronounced for private companies, small-scale companies and companies with high information transparency. The mediating effect test shows that stock market liberalization boosts green technology innovation by alleviating corporate financing constraints and improving corporate environmental management.

This article elucidates the impact path of stock market liberalization on corporate green innovation based on alleviating corporate financing constraints and improving corporate environmental management levels. From the perspective of corporate green technology innovation, this article provides evidence from emerging market countries for the economic effects of capital market opening, which helps to further improve the level of green innovation.

The recent livestreaming commerce has magnified the role of influencer marketing, where the influencers are partnering with brands for product promotion. This study examines the impact of influencer attributes, interaction strategies and parasocial relationships on impulsive buying in livestreaming commerce.

A survey with 368 livestreaming commerce users was analyzed using the symmetric-thinking approach – partial least squares structural equation modeling (PLS-SEM) and asymmetric thinking approach – fuzzy set qualitative comparative analysis (fsQCA).

The results of PLS-SEM indicate that influencer trustworthiness, influencer interactivity and self-disclosure determine parasocial relationships, which in turn influence impulsive buying. The fsQCA finding returned three configurations with various combinations of the causal conditions (i.e. influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty) explaining the formation of impulsive buying.

These findings provide unique linear and nonlinear insights to explain the combinatory effects of influencer attributes, interaction strategies, parasocial relationships, perceived fit uncertainty and perceived quality uncertainty on impulsive buying in livestreaming commerce.