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Social commerce (s-commerce) has gained widespread popularity as a social platform where customers engage in resource-sharing activities such as information exchange, advice-seeking and expressing their opinions on mutual interests. However, existing studies have not fully comprehended the drivers of electronic customer-to-customer interaction (eCCI) and how such behavior contributes to the customer “stick” on s-commerce sites. This study develops the Motivation–Opportunity–Ability (MOA) theory and investigates the impact of MOA factors on eCCI, which in turn affects customer stickiness.

A survey was used to acquire data from 455 valid respondents, and the research employed a combination of fuzzy-set qualitative comparative analysis (fsQCA) and structural equation modeling.

The results revealed associations between perceived self-efficacy, intrinsic motivation, tie strength with other customers, eCCI and customer stickiness.

Considering the limited availability of complete eCCI frameworks in existing scholarly works, the authors present valuable perspectives on the role of consumer characteristics as both antecedents and consequences of eCCI. Additionally, this study proposes a research agenda for the field of eCCI on s-commerce sites.

This study aims to identify an antecedent that hinders knowledge sharing, namely, the perceived climate of Cha-xu. Based on the social exchange perspective, the authors propose a theoretical model that links the perceived climate of Cha-xu to employee knowledge sharing. This model focuses on the mediating role of two types of trust (vertical and horizontal trust) and the moderating role of task interdependence in influencing the mediation.

Using a sample of 509 Chinese employees, this study carried out a survey on an online platform. This study developed a structural equation model and tested the moderated mediation hypothesis by using Mplus 8.0.

The results showed that two types of trust act as mediators in the relationship between the perceived climate of Cha-xu and knowledge-sharing processes. The mediating effect of horizontal trust is stronger. Most significantly, findings show that this mediated relationship is contingent on the level of task interdependence.

This paper provides evidence for distinguishing vertical trust and horizontal trust in the field of knowledge management. From a managerial perspective, this study identifies traditional cultural factors for hindering knowledge-sharing processes within Chinese organizations.

In this paper, the authors take the central environmental protection inspection (CEPI) as an exogenous shock to study the reaction of the stock market in China. Using the event study method, the authors check how the first round of the first batch of CEPI supervision affects the cumulative abnormal return (CAR) of the listed firms on the Shenzhen or Shanghai stock exchange. This paper aims to discuss the aforementioned objective.

In this paper, the authors take the first round of the first batch of CEPI supervision as a clean exogenous shock to study its effects on the capital market. The authors collect daily trading data from the China stock market and accounting research (CSMAR) database, with the sample containing 1,950 Chinese firms listed on either the Shenzhen or Shanghai stock exchanges. And detailed information on CEPI supervision is obtained from the official website of the Ministry of Ecology and Environment of the People's Republic of China. The event study method is adopted to analyze the reaction of the stock market under CEPI supervision. Specifically, the authors constructed the cumulative abnormal return of each firm around the event day of CEPI. To capture the deterrent effects of CEPI supervision, the authors examine the situation of polluting and non-polluting firms in the supervised provinces, adjacent provinces and provinces that are not supervised or close to the supervised provinces, respectively.

This paper throws light on the following: (1) the polluting firms in the supervised provinces were negatively impacted by CEPI within 20 trading days of the event day, and its effects spread to the polluting firms in the neighboring provinces; (2) CEPI had a favorable impact on the non-polluting businesses in the provinces that are neither supervised nor close to the supervised provinces. The authors contend that it is because the investment is being forced out of the polluting sector and into the non-polluting sector, which is more pronounced in the provinces not directly or indirectly targeted by CEPI; (3) by comparison, the “looking back monitoring of the first round” has had no discernible detrimental impact on the firms' CAR, indicating an important role of psychology anticipation of investors in the stock market performance; (4) although not physically located in the supervised provinces, the downstream enterprises of the polluting firms suffer significantly from CEPI shock; (5) the effectiveness of CEPI supervision in the supervised provinces depends on the level of local environmental regulation and the ownership structure of the company. Private firms in the provinces with stronger environmental regulations suffer more from the CEPI shock; (6) the multivariate analysis shows that while enterprises with high ROE and financial leverage may be at risk of CAR loss, older, larger firms are less likely to experience CEPI shock; (7) the study of persistent effect reveals that the strike of CEPI supervision can last for at least 10 months after the event day and deterrent effect can be spread within the whole polluting industry.

In this paper, the authors only concentrate on the market reaction within 20 trading days after the event day. An analysis of long-term effects should be valuable to get a deeper knowledge of the capital market reaction to the CEPI policy. In addition, the paper only focuses on the first round of the first batch of CEPI. Since CEPI has been built as a constant regulation of local environmental performance, further study may need to track both the reaction of listed firms and investment behavior in the capital market.

Policy implications of the paper are as follows: First, for the policymakers, it is important to construct a constant environmental regulation system instead of a campaign movement. Second, for investors, as environmental issues are receiving increasing attention from both the government and the public, investment decisions should take into account firms' environmental performance, which can help reduce the risk from environmental regulations. Third, the firms in the polluting industry should take more action to reduce pollutant releases and adopt green technology, which is essential for sustainable development under environmental protection.

This paper contributes to the existing literature in the following aspects. First, the authors provide new evidence on the effects of environmental regulations as a shock to the stock market, which has been wildly concentrated in the literature about environmental policies evaluation and capital market reaction. Second, the authors supplement the literature on green finance and sustainability transformation, which has got increasing attention in recent years. Theoretically, by guiding investment and affecting the stock market performance, environmental regulations are considered to be an efficient way to stimulate polluting firms to transform into green development. The results of the paper support this intuition by showing that the CAR of the non-polluting firms in non-supervised provinces in fact benefit from the CEPI supervision.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.

This paper aims to address three research questions (1) Who is the main stakeholder that shapes Airbnb experience, (2) Does Airbnb offers an authentic travel experience? and (3) What should be the future research trends in Airbnb?

This paper uses the systematic literature review (SLR) with a well-defined protocol, research strategy and methods to answer the research questions.

The review revealed that while Airbnb plays a significant role as the platform provider, the stakeholders influencing the experiences are multifaceted. Hosts, guests, local communities and even regulatory bodies all contribute to shaping the overall Airbnb Experience ecosystem. Hosts, in particular, have a crucial role in curating and delivering unique experiences, which significantly impacts the quality and authenticity of the offerings. On the question of whether Airbnb offers an authentic travel experience, the review uncovered mixed findings. For examples, some studies emphasized the potential for Airbnb to provide authentic and local experiences, allowing travelers to engage with the community and cultural aspects of a destination. However, other studies raised concerns about the commodification and standardization of experiences, leading to a potential loss of authenticity.

This paper is different from previous SLR where previous research systematically reviewed; motivations to use and choose Airbnb, institutionalization of Airbnb, stakeholders of Airbnb. This paper addresses authentic experience as a factor that influences activity participation.

This study aims to explore the effects of cat-assisted therapy (Ca-AT) on a patient in their homes, specifically investigating the effects on patient’s memory, behavioral pathology and ability to perform activities of daily living, independently.

A case study design was used in patient’s homes using three measuring scales, namely, Mini-Mental State Examination (MMSE), Barthel index (BI) and Behavioral Pathology in Alzheimer’s Disease (AD) Rating Scale.

The MMSE and BI mean scores were increased, whereas the Behavioral Pathology mean score was decreased. Patient negative behaviors were improved specifically, aggressiveness, anxieties, phobias, and caregiver burden was decreased.

Patients with AD could significantly benefit from Ca-AT in their own homes, and it could decrease caregiving burden.

Ca-AT is a newly developed type of animal-assisted therapy that uses cats to treat patients, especially elderly people with AD, in their homes.

Given the increasing importance of engineering insurance, it is still unclear which specific factors can enhance the role of engineering insurance as a risk transfer tool. This study aims to propose a hybrid approach to identify and analyze the key determinants influencing the consumption of engineering insurance in mainland China.

The empirical analysis utilizes provincial data from mainland China from 2008 to 2019. The research framework is a novel amalgamation of the generalized method of moments (GMM) model, the quantile regression (QR) technique and the random forest (RF) algorithm. This innovative hybrid approach provides a comprehensive exploration of the driving factors while also allowing for an examination across different quantiles of insurance consumption.

The study identifies several driving factors that significantly impact engineering insurance consumption. Income, financial development, inflation, price, risk aversion, market structure and the social security system have a positive and significant influence on engineering insurance consumption. However, urbanization exhibits a negative and significant effect on the consumption of engineering insurance. QR techniques reveal variations in the effects of these driving factors across different levels of engineering insurance consumption.

This study extends the research on insurance consumption to the domain of the engineering business, making theoretical and practical contributions. The findings enrich the knowledge of insurance consumption by identifying the driving factors specific to engineering insurance for the first time. The research framework provides a novel and useful tool for examining the determinants of insurance consumption. Furthermore, the study offers insights into the engineering insurance market and its implications for policymakers and market participants.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.