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This study aims to provide a systematic review and meta-analysis to explore the strength of the path relationship in the proposed model for online travel booking service adoption. It also explores the moderating effect of national or regional economic development levels and cultural differences.

A meta-analysis was conducted on the factors correlated with users' intention to adopt online travel booking services (OTBS) and the moderating effects of economic development levels and culture based on 42 empirical studies.

Perceived usefulness, perceived ease of use, attitude and perceived behavioral control have a significant positive impact on adoption intention. By contrast, subjective norms have a significantly negative impact on adoption intention. Furthermore, the economic development level of a country or region significantly moderates the relationships between perceived usefulness and perceived behavior control, attitude and perceived behavior control, and subjective norms and adoption intention. At the same time, national or regional cultural differences significantly moderate the relationships between attitudes and adoption intention, perceived usefulness and perceived behavior control, and subjective norms and adoption intention.

This study was conducted to obtain a unified conclusion regarding the research field of online travel booking service adoption. Its content was original. The conclusion provides theoretical references for follow-up research and the development of targeted marketing programs for online travel-booking service providers.

Internet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.

Method feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.

Based on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.

This paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

This study aims to elucidate the causal relationship between corporate greenwashing and celebrity leaders.

This study considers winning the National Model Worker Award as an external shock for producing celebrity leaders and conducts a difference-in-difference (DID) estimation with listed companies from 2009 to 2022 in the Chinese context.

The findings indicate an increase in greenwashing of companies with celebrity leaders in the post-award period. Stakeholder pressure can moderate the effect of celebrity leaders on corporate greenwashing.

This study enriches the research on the antecedents of greenwashing and the impacts of celebrity leaders. The findings advance the understanding of the real effect of celebrity leaders on environmental, social and governance (ESG) efforts and provide new insights into how celebrities respond to legitimacy pressures.

The anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort.

In addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks.

The results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver.

In future studies, the number of observations could be increased further.

The study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era.

The study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment.

In the digital era, creativity is pivotal in marketing, particularly in advertising, where mobile short-video advertising (MSA) has surged in popularity. However, the mechanism of how advertising creativity influences consumer decision-making remains understudied. We scrutinize how MSAs’ creativity influences consumers' purchase and sharing intentions.

This study uses 40 selected creative MSAs to collect a total of 666 valid questionnaires.

The results show that the exertion of creativity in MSAs positively impacts consumer intentions through perceived surprises and mental simulation, with the optimal stimulus level moderating these effects.

Our findings provide practical recommendations for brands and advertisers, mainly in terms of the impact of advertising creativity on advertising content strategy, helping them to create effective advertising to capture market and traffic by focusing on the content (relevance) and format (novelty) of advertising.

This study conducted in-depth research using the cognitive-affective-behavior (CAB) paradigm, integrated with mental simulation theory and the optimum stimulation level theory. Innovatively, we developed a model of the consumer decision-making process based on creativity, which enhances the research on the mechanisms underlying the consumer decision-making process.

The purpose of this paper is to study the tribological properties of Cu nanoparticles (NPs) as lubricant additives in three kinds of commercially available lubricants.

A four-ball machine is used to estimate the tribological properties of Cu NPs as lubricant additives in three kinds of commercially available lubricants. Three-dimensional optical profiler and electrical contact resistance are evaluated to investigate the morphology of the worn surfaces and the influence of Cu NPs on tribofilms.

Wear tests show that the addition of Cu NPs as lubricant additives could reduce wear and increase load-carrying capacity of commercially available lubricants remarkably, indicating that Cu NPs have a good compatibility with the existing lubricant additives in commercially available lubricants.

The tribological properties of Cu NPs as lubricant additives in three kinds of commercially available lubricants were investigated in this paper. These results are reliable and can be very helpful for application of Cu NPs as lubricant additives in industry.

This study examines whether common directors are guilty of contagion of corporate frauds from the customer side and, if so, how contagion occurs. Moreover, it explores a way to mitigate it, which is the increased digital orientation of firms.

Secondary data analysis is applied in this paper. We extract supply chain relations from the China Stock Market and Account Research (CSMAR) database as well as corporate fraud data from the same database and the official website of the China Securities Regulatory Commission (CSRC). Digital orientations are estimated through text analysis. Poisson regression is conducted to examine the moderating effect of common directors and the moderated moderating effect of the firms’ digital orientations.

By analysing the 2,096 downstream relations from 2000 to 2021 in China, the study reveals that corporate frauds are contagious through supply chains, while only customers’ misconduct can contagion to upstream firms. The presence of common directors strengthens such supply chain contagion. Additionally, the digital orientation can mitigate the positive moderating effect of common directors on supply chain contagion.

This study highlights the importance of understanding supply chain contagion through corporate fraud by (1) emphasising the existence of the contagion effects of corporate frauds; (2) understanding the potential channel in the process of contagion; (3) considering how digital orientation can mitigate this contagion and (4) recognising that the effect of contagion comes only from the downstream, not from the upstream.

The purpose of this study is to generate insight into the effects of entrepreneurship education in China by conducting a preliminary scoping study of the enterprising tendency of university students studying business.

This study used a self-administered questionnaire based on the General Measure of Enterprising Tendency v2 (GET2) test to measure the enterprising tendency of a group of Chinese university students. Decision trees, using the Chi-square automatic interaction detector (CHAID) approach, and multiple regression analyses were used to investigate the enterprising tendency of respondents.

The findings from this study indicate that the students have an overall medium level of enterprising tendency and strengths in some enterprising characteristics. The findings reveal that gender, family business, hometown and entrepreneurship education are significantly related to enterprising tendency but that age, household income, parents’ education and occupation are not.

Although the study is based on a relatively small sample taken from just one university in Beijing, the findings suggest that the enterprising tendency of students can be encouraged by entrepreneurship education. Combined with evidence that entrepreneurship education is at a relatively early stage of development in China, this finding suggests considerable scope to increase student’s enterprising tendency by extending, creating a more favourable environment for and improving the methods used to deliver entrepreneurship education. Enterprising tendency can be argued to naturally result in entrepreneurial intention; however, this extension is beyond the scope of this study, which is restricted to the analysis of enterprising tendency.

This study makes an original contribution to knowledge as it is one of the first studies to explore enterprising tendency among university students in China. It has value for government, policymakers and university program designers in that it provides direction for entrepreneurship education in China.

Smart community construction (SCC) and efficiency require resident participation. This paper aims to explore the determinants of residents’ participation intention (RPI) in the SCC.

Based on the theory of planned behavior (TPB), this study proposed an extended conceptual model to deeply analyze the RPI in the SCC. The relationship between all constructs was verified by processing and analyzing online survey data using confirmatory factor analysis (CFA), structural equation model (SEM), and bootstrapping method.

Participation attitude, perceived behavioral control, subjective norm, and perceived usefulness significantly and positively affected the RPI. Furthermore, intermediary effects in the extended conceptual model had been confirmed.

To fill the critical gap in the research on the determinants of the RPI in the SCC context, this study developed a novel conceptual model by extending the TPB to analyze the effects of self-driven and externally-driven factors on the RPI from the perspectives of residents’ psychology and external environment. The findings not only clarify the complex process of forming the RPI in the SCC but also provide a theoretical foundation for studying the RPI in similar community construction projects. Additionally, several strategies have been proposed to encourage residents’ participation in the SCC and promote the development of smart communities, such as clarifying residents’ participation obligations, improving the convenience services of smart communities, and diversifying residents’ participation approaches.