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The model of pay-for-knowledge incentivizes individuals with financial rewards for sharing their expertise, facilitating a transactional exchange between knowledge providers (sellers) and seekers (buyers). While this model is effective in promoting paid contributions, its influence on free knowledge exchanges remains ambiguous, creating uncertainty about its overall impact on platform knowledge ecosystems. This study aims to explore the mechanim of how knowledge payment influences free knowledge contribution. Based on relational signaling theory, this study posits that a buyer’s payment for knowledge acts as a positive relational signal in the buyer–seller relationship and examines how the signaling effect varies across different social contexts through attribution theory.

This paper empirically tests the hypotheses by analyzing a data set comprising 630 instances from 359 unique knowledge sellers on Zhihu, a prominent knowledge-sharing platform in China. This paper use zero-inflated negative binomial models to conduct this analysis.

The findings reveal that when buyers pay for knowledge, this action positively influences sellers to contribute knowledge for free. However, the strength of this influence is moderated by the platform’s social functions: appreciation feedback tends to weaken this effect, while social network ties enhance it.

Prior research has predominantly focused on the financial incentives of pay-for-knowledge and its spillover effects on unpaid users’ activities. This study shifts the focus to the social dimensions of pay-for-knowledge, arguing that buyer-initiated knowledge payments signal buyers’ commitment to foster reciprocal relationships with sellers. It expands the literature on the relationship between knowledge payment and contribution, moving beyond financial incentives to include social factors, thus enriching our understanding of the interplay between paid and free knowledge activities. Additionally, the empirical evidence supports the efficacy of pay-for-knowledge in promoting both free and paid contributions within knowledge-sharing platforms.

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.

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.

Assessing performance art has shifted toward an audience-centric approach, with various factors impacting audiences’ holistic experiences in a theater. Existing theater marketing research has predominantly focused on servicescapes, leaving a research gap regarding audiences’ spatial experience within theater buildings.

To address this gap, this case study collected crowdsourced data from mainstream social media platforms, including rating scores, textual reviews and reviewer-uploaded photos. The aim was to explore to what extent the theater architecture design impacted audiences’ theatrical experience. The old and new facilities of the case study theater were compared, and a series of quantitative and qualitative techniques were applied for data analysis, including statistics, content analysis, sentiment analysis and thematic analysis.

The study identified five major themes in social media review: the show, architectural design and attributes, staff and service, neighborhood and amenities, and financial consideration. Comments about theater architecture constituted a substantial portion of reviews, with seating comfort, intimacy and stage visibility being frequently discussed features. Seven subthemes related to architectural design emerged as key contributors to audiences’ spatial experiences, including aesthetics, design and spatial configuration; the bar and lobby; the new facility; seats; sets and stage; acoustics; and intimacy and atmosphere.

The study places the individual experiences of audience members as central and uses an inductive approach to analyze their self-generated data. The research results offer valuable insights into theater design and confirm the belief that architectural design has a significant impact on the overall theatrical experience of audiences.

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).

The purpose of this study is to investigate the impact of brand hate, personality traits (jealousy and sadism) and corporate social irresponsibility (CSI) on schadenfreude and the effects of brand hate, CSI and schadenfreude on purchase intention for counterfeit luxury brands (CLBs).

The statistical population of this study is consumers of luxury and CLB. Simple random sampling method was used, and data from 200 questionnaires were analyzed through structural equation modeling and SPSS 20 and AMOS 20 software.

The results indicated that brand hate, personality traits (jealousy and sadism) and CSI have a direct positive effect on schadenfreude. In addition, brand hate, CSI and schadenfreude have a direct positive effect on purchase intention for CLB.

TO the best of the authors’ knowledge, this research is among the first to evaluate the impact of brand hate, personality traits and CSI on schadenfreude and purchase intention.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

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.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.