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The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before.

Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which combines symmetric and asymmetric metrics.

The results show that the novelty mining system can detect novel and redundant blogs in the dataset of business blogs with a very high accuracy.

This paper shows that novelty mining techniques can be applied to business blogs to help organizations filter redundant information, and that the cosine and mixed metrics approaches produce better results.

Given that the recovery of the hospitality industry is hampered by worker shortages resulting from the loss of talents during the ongoing pandemic, the purpose of this study is to examine how professional identity affects hospitality employees’ psychological responses to the COVID-19 crisis and their intentions to leave the industry.

This study sample consisted of 1,188 US hospitality employees. The cross-sectional data were analyzed using partial least square structural equation modeling, analysis of variance and multigroup analysis.

A double-barreled effect of professional identity on career change intention was identified. Hospitality employees possessing a stronger professional identity were found to be more passionate and satisfied with their careers and less likely to switch to other industries. However, these individuals also feel more distressed by the pandemic crisis, which is associated with a heightened level of career change intentions.

The findings of this study confirm the importance of identity building as a means of sustaining the hospitality workforce. As nascent professionals possess a weaker identity and stronger intention to leave the industry, immediate attention should be paid to these individuals.

This study expands the knowledge surrounding the influences of hospitality professional identity as it exerts a double-barreled effect on career change intention. Further insights regarding how hospitality employees at various career stages respond differently to the COVID-19 crisis are uncovered by examining the moderating effects of industry experience.

The aim of this study was to integrate the hierarchical model of the perceived service quality (PSQ) theory with the bottom-up theory of satisfaction. It was hypothesised that satisfaction with sporting events would mediate the relationship between PSQ and life satisfaction. Study 1 was conducted to translate the Perceived Service Quality questionnaire (PSQQ) (Brady & Cronin, 2011) into Chinese and to validate it for sporting events. Study 2 was conducted to examine the main hypothesis. The results indicated that satisfaction-withevent partially mediated the relationship between PSQ and life satisfaction. The results are discussed in terms of both the examined theories.

This chapter provides a comprehensive review of research and developments relating to the use of Web 2.0 technologies in education. As opposed to early educational uses of the Internet involving publication of static information on web pages, Web 2.0 tools offer a host of opportunities for educators to provide more interactive, collaborative, and creative online learning experiences for students. The chapter starts by defining Web 2.0 tools in terms of their ability to facilitate online creation, editing, and sharing of web content. A typology of Web 2.0 technologies is presented to illustrate the wide variety of tools at teachers’ disposal. Educational uses of Web 2.0 technologies such as wikis, blogs, and microblogging are explored, in order to showcase the variety of designs that can be utilized. Based on a review of the research literature the educational benefits of using Web 2.0 technologies are outlined, including their ability to facilitate communication, collaborative knowledge building, student-centered activity, and vicarious learning. Similarly, issues surrounding the use of Web 2.0 tools are distilled from the literature and discussed, such as the possibility of technical problems, collaboration difficulties, and plagiarism. Two case studies involving the use Web 2.0 tools to support personalized learning and small group collaboration are detailed to exemplify design possibilities in greater detail. Finally, design recommendations for learning and teaching using Web 2.0 are presented, again based on findings from the research literature.

This study aims to examine the public’s acceptance of film-induced tourism and develops the relationship among placement marketing, involvement, place attachment and travel intention. The film Your Love Song shot in the Hualien and Taitung regions in Taiwan was selected as the case study.

An online sample survey was conducted using a structured questionnaire, and statistical tests and overall structural equation modeling analysis using the SPSS and AMOS statistical software packages, respectively, were performed.

This study results demonstrate that destination placement marketing has a significant positive effect on the level of destination involvement, place attachment and travel intention of viewers. Moreover, the level of involvement has some intermediary effect on the interrelationship between placement marketing and travel intention. Hence, this study suggests that relevant government agencies and tourism operators should promote local tourism through films and television shows and attract more tourists by retaining the original shooting scenes.

While previous studies have only analyzed two or three of the four concepts of film-induced tourism, placement marketing, travel intention, involvement and place attachment, this study completely integrates these four concepts and proves the correlation between them.

The purpose of this research is to survey whether consumer ethnocentrism and animosity will affect consumers' perceived betrayal and cause negative word-of-mouth (NWOM).

This study conducted a 2 (consumer ethnocentrism) × 3 (consumer animosity) between-subject experiment design to test the hypotheses. Comprised of 380 respondents, this study used ANOVA to examine the data.

The results showed that if a brand violates the perception of fairness, ethnocentrism and animosity will have a positive effect on perceived betrayal. In addition, low consumer animosity revealed a significant consumer ethnocentrism effect and low ethnocentrism revealed a significant animosity effect, while the relationship between perceived betrayal and word of mouth is negative.

The current research adds to the understanding about how the reaction to a domestic brand's marketing strategies that are viewed as unfair and hurt the domestic consumers' expectations.

The data sample used in this study is composed of 2,638 Chinese tourists who have travel experiences to the South Pacific region. This study examines the effects of memorable tourism experiences, destination cognitive and affective images, and satisfaction on revisit intention and their mechanisms from a cognitive–affective perspective. Results show that destination cognitive image, destination affective image, and satisfaction, respectively, play a mediating effect on the relationship between memorable tourism experiences and revisit intention. Memorable tourism experience is the most important predictor of revisit intention, and it mainly affects the cognitive image of a destination. In line with previous studies, this research has shown that memorable tourism experiences have significant impact on the destination image and tourists' revisit intention, which can provide significant implications for tourism practitioners and destination managers in the South Pacific islands.

To guarantee the asymptotic stability of discrete-time nonlinear systems, this paper aims to propose an evolved bat algorithm fuzzy neural network (NN) controller algorithm.

In evolved fuzzy NN modeling, the NN model and linear differential inclusion representation are established for the arbitrary nonlinear dynamics. The control problems of the Fisher equation and a temperature cooling fin for high-speed aerospace vehicles will be described and demonstrated. The signal auxiliary controlled system is represented for the nonlinear parabolic partial differential equation (PDE) systems and the criterion of stability is derived via the Lyapunov function in terms of linear matrix inequalities.

This representation is constructed by sector nonlinearity, which converts the nonlinear model to a multiple rule base for the linear model and a new sufficient condition to guarantee the asymptotic stability.

This study also injects high frequency as an auxiliary and the control performance to stabilize the nonlinear high-speed aerospace vehicle system.

For an enterprise, it is essential to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of the object of communication in order to employ an effective communication strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly in terms of time and labor, and it cannot acquire customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predictions for a large number of objects is an important issue. The paper aims to discuss these issues.

The diverse social media that have emerged in recent years represent a digital platform on which users can publicly deliver speeches and interact with others. Thus, social media may be able to serve the needs of automated personality predictions. Based on user data of Facebook, the main social media platform around the world, this research developed a method for predicting personality types based on interaction logs.

Experimental results show that the Naïve Bayes classification algorithm combined with a feature selection algorithm produces the best performance for predicting personality types, with 70-80 percent accuracy.

In this research, the dominance, inducement, submission, and compliance (DISC) theory was used to determine personality types. Some specific limitations were encountered. As Facebook was used as the main data source, it was necessary to obtain related data via Facebook’s API (FB API). However, the data types accessible via FB API are very limited.

This research serves to build a universal model for social media interaction, and can be used to propose an efficient method for designing interaction features.

This research has developed an approach for automatically predicting the personality types of network users based on their Facebook interactions.

Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly‐used hybrid models by classification+classification and clustering+classification hybrid approaches, respectively, in terms of customer value prediction.

To construct a hybrid model, multiple techniques are usually combined in a two‐stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre‐process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k‐means and self‐organizing maps for the clustering techniques to construct six different hybrid models.

The experimental results over a real case dataset show that the classification+classification hybrid approach performs the best. In particular, combining two‐stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/0.43 percent).

The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.