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By investigating trust in the organization as a mediator and traditionality as a moderator, this study aims to examine the effect perceived organizational exploitation poses on frontline hotel employees’ service performance.

A three-wave survey that targets 219 supervisor–subordinate dyads from four Chinese hotels was conducted to test the hypotheses. The authors used SPSS 20.0 and AMOS 21.0 to analyze the data and verify the theoretical model.

This study found that perceived organizational exploitation exerts a destructive impact on frontline hotel employees’ service performance. Trust in the organization is a full mediator of the link connecting perceived organizational exploitation to service performance. Furthermore, traditionality weakens perceived organizational exploitation’s impact on trust in the organization and subsequent service performance.

The authors’ findings remind hotels to cease exploiting their employees to avoid compromising service performance. Hotels should also endeavor to instill trust among employees toward the hotel and allocate more attention to employees with lower levels of traditionality.

First, to the best of the authors’ knowledge, this study is among the first to explore the impact of perceived organizational exploitation on frontline hotel employees’ service performance. Second, this study reveals a novel mechanism underlying the connection between perceived organizational exploitation and service performance. Finally, this study identifies frontline hotel employees’ traditionality as a vital moderator that mitigates the negative relationships among perceived organizational exploitation, trust in the organization and service performance.

This study aims to investigate the mechanism of the misinformation spread based on the elaboration likelihood model and the effects of four factors – emotion, topic, authority and richness – on the depth, breadth and structural virality of misinformation spread.

The authors collected 2,514 misinformation microblogs and 142,006 reposts from Weibo, used deep learning methods to identify the emotions and topics of misinformation and extracted the structural characteristics of the spreading network using the network analysis method.

Results show that misinformation has a smaller spread size and breadth than true news but has a similar spread depth and structural virality. The differential influence of emotions on the structural characteristics of misinformation propagation was found: sadness can promote the breadth of misinformation spread, anger can promote depth and disgust can promote depth and structural virality. In addition, the international topic, the number of followers, images and videos can significantly and positively influence the misinformation's spread size, depth, breadth and structural virality.

The influencing factors of the structural characteristics of misinformation propagation are clarified, which is helpful for the detection and management of misinformation.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.

The integration of social media and e-commerce has resulted in a rising phenomenon among individual content providers (ICPs), who used to offer free content, to provide consumers with paid content, such as online courses, Q&As or consultations. Despite the prevalence of ICPs’ content monetization, empirical research has rarely studied its underlying mechanism. This paper examines how the characteristics of free content contributed by ICPs on social media platforms influence their paid content sales, focusing on the perspective of human brand.

The empirical setting is an online knowledge exchange platform, where users are allowed to provide free content (e.g. answers) on the social media platform and launch paid content (e.g. lectures) on the e-commerce platform. A machine learning technique is employed to construct measures for the characteristics of free content, and fixed-effects estimation is presented to confirm which factors have a significant influence on the sales of paid content.

The empirical results show that the quality, diversity and expertness of free content have a significant positive impact on the sales of the ICP-paid content, with the brand popularity of ICP playing a mediating role.

This study is the first attempt to demystify the relationship between content contribution and ICPs’ content monetization from the perspective of human brand. The findings validate the effectiveness of the “Selling by Contribution” strategy and provide valuable insights for ICPs and social media platforms.

Although idea implementation is a praised useful resource, the psychological and behavioral costs that employees may pay for idea implementation are rarely discussed. This study aims to examine the buffer effect of intrinsic interest on dark side of idea implementation.

Based on the conservation of resources theory, this study tested hypotheses with a multi-wave survey study of four information technology companies in China.

First, idea implementation increased emotional exhaustion. Second, emotional exhaustion mediated the relationship between employee idea implementation and negative workplace gossip about a leader. Third, intrinsic interest negatively moderated the relationship between idea implementation and emotional exhaustion. Fourth, idea implementation increased workplace negative gossip about a leader as a result of increased emotional exhaustion when intrinsic interest was low.

These findings are conducive to further understanding of the psychological mechanism and boundary condition of the negative impact of idea implementation. It provides practical guidance for buffering the dark side of idea implementation and effectively controlling the workplace negative gossip in the workplace.

This study aims to examine how virtual influencers (VIs) affect purchase intentions in tourism and hospitality e-commerce live streaming (THCLS) by focusing on the roles of VIs’ source credibility, trust in products, trust in VIs, emotional engagement, parasocial relationships and influencer–product congruence.

Survey data from 416 active viewers of VIs in THCLS were analysed using partial least squares structural equation modelling.

This study highlights the importance of the VIs’ source credibility, which positively affects trust in the product, trust in VIs and emotional engagement. However, source credibility does not have a positive impact on parasocial relationships. Trust in products positively influences trust in VIs. Emotional engagement and trust in VIs significantly influence parasocial relationships, which, positively affects purchase intentions. Influencer–product congruence strengthens the link between parasocial relationships and purchase intentions but does not moderate the relationship between trust in VIs and purchase intentions. No significant gender differences were observed, although minor discrepancies were noted in the effect of trust in products on trust in VIs. The importance–performance map analysis revealed that parasocial relationships are the most important factor influencing purchase intentions, while influencer–product congruence has the highest performance, trust in products is the least important and VIs’ source credibility has the lowest performance.

This study provides actionable insights for marketers leveraging VIs in the THCLS sector, emphasizing strategies to enhance VI credibility, foster parasocial relationships, ensure influencer–product congruence and adopt gender-neutral marketing approaches to effectively influence purchase intentions.

This study offers theoretical and practical insights into the role of VIs in THCLS, illuminating their impact on consumer behaviour and purchase intentions.

This study aims to investigate the rising trend of virtual influencers – digitally created characters with human-like attributes. It aims to evaluate and compare their effectiveness with human influencers in terms of brand attitudes and purchase intentions. It uncovers the mechanisms underlying the differences in effectiveness observed between virtual influencers and their human counterparts.

The research uses a 2 (influencer type: human vs virtual) × 3 (product type: functional vs symbolic vs experiential) between-subjects design. Through a pilot study (n = 334) and a main study (n = 352), the research examines the interactive effects of influencer and product type on brand attitude and purchase intentions. Hypotheses were developed and tested using moderating mediation models centered on authenticity and product–endorser fit.

Virtual influencers are perceived as less authentic than human influencers across all product types, negatively influencing brand attitude and purchase intention. However, the extent to which influencer type affects brand attitudes and purchase intentions, mediated by product–endorser fit, varies based on the product type.

This research emphasizes two key mechanisms – authenticity and product–endorser fit – influencing the effectiveness of virtual influencers. It suggests that aligning virtual influencers with suitable product types can offset their perceived authenticity deficit, significantly affecting their endorsement effectiveness.

This paper aims to investigate why followers have low perceptions of leader openness and thus feel reluctant to communicate novel ideas by examining leader–follower relationship conflict (i.e. interpersonal incompatibility) and a follower’s power distance orientation (i.e. an acceptance of uneven power distribution in organizations) as antecedents.

The research administrators conducted a three-wave work behavior survey in Study 1, a laboratory experiment in Study 2, and an online experiment in Study 3.

The results demonstrated that leader–follower relationship conflict reduced followers’ perceptions of leader openness. However, the negative impact of relationship conflict became non-significant when followers have high power distance orientations (i.e. an acceptance of uneven power distribution in organizations). The findings also showed an indirect interaction effect of leader–follower relationship conflict and followers’ power distance orientation on the followers’ communication of novel ideas through the followers’ perceptions of leader openness.

The research suggests that followers with higher power distance orientations are more likely to communicate novel ideas consistently because their relationship conflicts with their leaders do not negatively influence their perceptions of leader openness. Although researchers traditionally view cultures with a high level of power distance value as an obstacle to employee creativity, the present study reveals the benefits of an individual-level power distance orientation.

By introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.

This paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.

(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.

The originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

The study seeks to shed light on the estimates of the carbon shadow price in the literature relying on frontier techniques. The shadow price of undesirable outputs, such as greenhouse gas emissions, assists policymakers in determining the most cost-effective methods for reducing emissions.

The study relies on the PSALSAR and PRISMA approaches for a systematic literature review. The Web of Science and Scopus databases were used for the references.

Both parametric and nonparametric methods have been employed in the literature to estimate the shadow prices of undesirable outputs. Also, results were discussed according to the methodological and application aspects, and broad conclusions on obtained results were provided, bridging climate change mitigation policies and the shadow price of undesirable outputs.

The present study applies an integrated method, PSALSAR, to conduct a systematic review of 53 studies published between 2014 and 2023 in which efficiency models were applied to estimate the shadow price of undesirable outputs, especially CO₂. After presenting the most applicable parametric and nonparametric estimation models, a systematic summary of included articles was provided, highlighting the key features of publications.