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In the context of exploring and implementing China's new urbanization, green eco-city have become a transformation model for urban development. Sharpening green buildings in the construction industry can significantly influence and determine China's economic growth trends, as well as the growth and overall development of its national economy. However, current green eco-city still lack appropriate standards and scientific theoretical basis to determine the target star program of green buildings. To fully implement the green building standards, establish and improve a sound technical standard system for the construction of green building demonstration areas, this study considers the spatial layout of green buildings as the core, adopts a plot potential evaluation method for evaluating a few green building plots, and utilizes four factors in verifying plots with great star potential. The study also establishes a system to calculate the star proportion of green buildings and applies the system in calculating the green building ratio of GM New District. Results indicate that the system can quantitatively analyze a plot potential, calculate the star proportion of green buildings scientifically and rationally, and provide some references for the construction of eco-city and the preparation of special planning for green buildings. The system construction is conducive to provide technical support for the construction of green eco-city. The improved system can be applied in the green building demonstration areas in China, and will be a reference model of constructing green building demonstration areas in the country.

This paper aims to make a comparative study on the latest version of green campus evaluation standard between China and America: Green Campus Evaluation Standard (GB/T51356-2019) and the sustainability tracking, assessment and rating system (STARS 2.2). The differences of evaluation methods and contents are analyzed and their respective characteristics and advantages are sorted out, so as to promote the development of sustainable campus evaluation standards.

The research mainly adopts the method of comparative study, which is carried out from three dimensions, namely, the related policies development of campus construction and world university sustainable rankings; the content of evaluation standards (including evaluation methods and evaluation categories and scores); the characteristics and current application of standards.

There are great differences between the evaluation standards of China and America in organization and participation mode, evaluation method and content. Public engagement, energy and campus engagement are the hot spots. Buildings, energy, food and dining and investment and finance will become the focus of sustainable campus in the future. Specific optimization strategies of key points, evaluation method and content and organization and participation mode of Chinese standard are put forward.

This paper clarifies the advantages and disadvantages of the current global sustainable campus, and provides the basis for the next stage of construction policy. At the same time, it is helpful for all countries, especially China, to formulate construction guidelines that not only meet their own actual needs but also conform to the trend of global sustainable campus development.

The connotation of sustainable campus is enriched, and the evaluation standards of sustainable campus are improved. The development of sustainable campus is promoted, so as to realize the sustainable development goals.

This research expands the scope of the study to the whole campus, rather than just one aspect of campus buildings. It compares the evaluation standard of green campus in China with STARS in the USA, and no longer compares leadership in energy and environmental design for schools. It discusses the campus building’s energy conservation while paying attention to the campus green consciousness, green management and green planning. Based on the relevant data currently used by STARS in the global evaluation, this paper analyzes the hot spots and shortcomings of the current global sustainable campus construction and puts forward some optimization suggestions for China’s green campus evaluation system.

In this chapter, the authors consider the progress and present state of star scholarship, while planting seeds for future inquiry where we believe fruitful opportunities await – both in furthering our understanding of stars and in more effectively situating this understanding in the talent management literature. Following a reflection on the multiple conceptualizations of stars that have been proposed and employed in star research in recent years, we suggest that the most useful conceptualization of stars is one that focuses simply on stars’ exceptional contributions to value creation – allowing the specific mechanisms of value creation to vary, as they do, across contexts. Next, the authors review recent progress in star scholarship – highlighting advances in scholarship on both the favorable and unfavorable influences of stars in organizations, as well as recent research shedding light on the professional experiences of stars as employees. The authors then turn their attention to future scholarship, specifically noting opportunities for research in two veins: how stars’ motivations, contributions, and experiences may evolve over the course of their careers and how stars and their broader work environments are best managed. Finally, we share thoughts on ways in which scholars can think about increasing the practical value of research on stars, primarily by integrating insights from research on stars with ideas rooted more squarely in the talent management literature which focuses on the deliberate identification, support, and management of individuals deemed to be equipped to create exceptional value in organizations.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

Using data from securities analysts, who are awarded status by the third-party organization Institutional Investor magazine, we examine the emergence of competition and articulate a model of competitive response among actors aware of the importance of status and some of the dimensions on which it may be gained. We predict analysts’ initiating or ceasing coverage of stocks in response to other analysts initiating coverage on stocks they cover. We find that competition can emerge because of status seeking rather than as a response to own capabilities or market needs, with compelling, and potentially negative, market implications for overt status seeking.

Retail investors use information provided by mutual fund rating agencies to make investment decisions. This paper aims to examine whether the ratings provide useful information to retail investors by analyzing the rating migration and closure risk of mutual funds that received Morningstar’s mutual fund ratings from 2005 to 2012.

The research design differentiates between buy-and-hold investment strategies and dynamic investment strategies. To assess the information content of mutual fund ratings for buy-and-hold investment strategies, the rating migration based on the first and the last mutual fund rating during two-, four-, six- and eight-year horizons is determined. With respect to dynamic investment strategies, the number of rating changes per fund on a monthly basis during these time horizons is calculated.

Mutual fund rating persistence is low or even inexistent, in particular, during longer time periods. Only for lower-rated funds, the rating appears to indicate higher risk of fund closure. In addition, mutual funds face a large number of up to 38 monthly rating changes in the eight-year window.

Mutual fund rating persistence has hardly been analyzed for funds offered to retail investors so far. This paper clearly points out that because of the extensive rating migration and the high number of monthly rating changes, retail investors barely benefit from using mutual fund ratings.

The purpose of this paper is to report on research findings from a teaching and learning intervention that explored whether undergraduate university students can be taught to articulate their employability skills effectively to prospective employers and to retain this ability post-course.

The study included 3,400 students in 44 courses at a large Canadian university. Stage 1 involved a course-level teaching and learning intervention with the experimental student group, which received employability skills articulation instruction. Stage 2 involved an online survey administered six months post-course to the experimental group and the control group. Both groups responded to two randomly generated questions using the Situation/Task, Actions, Result (STAR) format, a format that employers commonly rely on to assess job candidates’ employability skills. The researchers compared the survey responses from the experimental and control groups.

Survey results demonstrate that previous exposure to the STAR format was the only significant factor affecting students’ skills articulation ability. Year of study and program (co-operative or non-co-operative) did not influence articulation.

The findings suggest that universities should integrate institution-wide, course-level employability skills articulation assignments for students in all years of study and programs (co-op and non-co-op).

This research is novel because its study design combines practical, instructional design with empirical research of significant scope (institution-wide) and participant size (3,400 students), contributing quantitative evidence to the employability skills articulation discussion. By surveying students six months post-course, the study captures whether articulation instruction can be recalled, an ability of particular relevance for career preparedness.

A growing body of research from various domains has investigated Airbnb, a two-sided market platform for peer-based accommodation sharing. The authors suggest that it is due time to take a step back and assess the current state of affairs. This paper aims to conflate and synthesize research on Airbnb.

To facilitate research on Airbnb and its underlying principles in electronic commerce, the authors present a structured literature review on Airbnb.

The findings are based on 118 articles from the fields of tourism, information and management, law and economics between 2013 and 2018. Based on this broad basis, the authors find that: research on Airbnb is highly diverse in terms of domains, methods and scope; motives for using Airbnb are manifold (e.g. financial, social and environmental); trust and reputation are considered crucial by almost all scholars; the platform’s variety is reflected in prices; and the majority of work is based on surveys and empirical data while experiments are scarce.

Based on the present assessment of studied topics, domains, methods and combinations thereof, the authors suggest that research should move toward building atop of a common ground of data structures and vocabulary, and that attention should focus on the identified gaps and hitherto scarcely used combinations. The set of under-represented areas includes cross-cultural investigations, field experiments and audit studies, the consideration of dynamic processes (e.g. based on panel data), Airbnb’s “experiences” and automated pricing algorithms and the rating distribution’s skewness.

This study provides a comprehensive overview of work on the accommodation sharing platform Airbnb, to the best of the auhtors’ knowledge, representing the first systematic literature review. The authors hope that researchers and practitioners alike will find this review useful as a reference for future research on Airbnb and as a guide for the development of innovative applications based on the platform’s peculiarities and paradigms in electronic commerce practice. From a practical perspective, the general tenor suggests that hotel and tourism operators may benefit from: focusing on their core advantages over Airbnb and differentiating features and aligning their marketing communication with their users’ aspirations.

The purpose of this study is to examine investors’ intention and behavior concerning ex ante information acquirement and ex post claims from the micro-level perspective with the deepening of the initial public offering (IPO) reform of China.

The authors made surveys and collected 932 valid questionnaires from investors in China. The authors also conducted interviews with sophisticated investors, investment bankers and government regulators to obtain first-hand information. Based on the survey results, the authors make the empirical analysis.

Investors’ attention to the first-hand information of the IPO prospectuses is inadequate. Individuals rely more on second-hand information, while institutions conduct more surveys. The higher the institutional practitioners’ degree of education, the more surveys they make. Only 1/3 investors intend to seek judicial remedy when getting fraud information due to high litigation costs and proof collecting difficulties. The investors who read more about prospectuses in advance are more likely to seek judicial protection afterwards. Compared with investors who know less about government administrative protection measures, those who know more have a low probability to choose “not to seek judicial protection.”

The authors enrich the research studies of IPO information acquisition and investor protection by conducting surveys to get first-hand data. Previous literature mostly makes empirical tests by using proxy variables.

The purpose of this paper is to analyze the occurrence of terms to identify the relevant topics and then to investigate the area (based on topics) of hospitality services that is highly associated with relationship quality. This research represents an opportunity to fill the gap in the current literature, and clarify the understanding of guests’ affective states by evaluating all aspects of their relationship with a hotel.

This research focuses on natural opinions upon which machine-learning algorithms can be executed: text summarization, sentiment analysis and latent Dirichlet allocation (LDA). Our data set contains 47,172 reviews of 33 hotels located in Las Vegas, and registered with Yelp. A component-based structural equation modeling (partial least squares (PLS)) is applied, with a dual – exploratory and predictive – purpose.

To maintain a truly loyal relationship and to achieve competitive success, hospitality managers must take into account both tangible and intangible features when allocating their marketing efforts to satisfaction-, trust- and commitment-based cues. On the other hand, the application of the PLS predict algorithm demonstrates the predictive performance (out-of-sample prediction) of our model that supports its ability to predict new and accurate values for individual cases when further samples are added.

LDA and PLS produce relevant informative summaries of corpora, and confirm and address more specifically the results of the previous literature concerning relationship quality. Our results are more reliable and accurate (providing insights not indicated in guests’ ratings into how hotels can improve their services) than prior statistical results based on limited sample data and on numerical satisfaction ratings alone.