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The purpose of this paper is to identify the most critical antecedents of destination loyalty formation (DLF) and to develop a series of propositions for the relationships among the antecedents of loyalty formation and their direct and indirect impacts on loyalty formation.

This conceptual paper provides a comprehensive review of the previous studies that examined destination loyalty and posits a framework of tourist DLF titled Destination Loyalty Formation.

In the proposed conceptual model, the sequential relationships among the antecedents of tourist destination loyalty postulate that previous experiences are the most influential driver that could manipulate tourist destination loyalty. Place attachment and involvement constitute the second most influential factors of DLF. In addition to the above two variables, destination image is proposed to have direct and indirect effects on perception of service quality and satisfaction. Meanwhile, service quality and tourist satisfaction are proposed to have the largest magnitude of direct impacts on destination loyalty.

Previous studies examined most of the antecedents of destination loyalty separately. There is yet an effort to simultaneously consider antecedents of destination loyalty to examine how each antecedent relates to DLF. This conceptual paper attempts to address this issue by proposing a conceptual model that simultaneously considers antecedents of destination loyalty and examines how each antecedent relates to DLF.

This paper describes a computer supported ubiquitous learning environment for minority language learning. Hakka language involved various tones, art, craft, festivals, food, drink, medicine, religion and custom in different regions. The tones also vary across the dialects of Hakka. The majority of Hakka dialects have six tones in Taiwan. The context for Hakka language translation and use is extremely important. The purpose of this paper is to present the context-aware annotation service to help readers get the right information for Hakka language use and learning more easily.

This paper proposes the context-aware learning support system for Hakka culture-specific items or language, which is called Hakka culture-specific items annotation system (HACSIAS). HACSIAS is the context-aware annotation system, helping learners to add links of culture-specific items when they are reading the electronic journals, books, and web pages.

The HACSIAS provides learner appropriate information for Hakka culture-specific items deriving the learner’s situation and personal information. The context-aware computing will automatically provide translation service by searching a given culture-specific items database while these items or terminology appear in a document, either by displaying terms in the translation memory.

The context-aware annotation system for Hakka culture-specific language learning allows Hakka cultural resources to be smoothly integrated into learning materials.

The purpose of this paper is to explore the data connection, spatial distribution characteristics and trends in genealogical information. First, it implements a spatial-temporal visualization of the Hakka genealogical information system that makes these individual family pedigree charts appear as one seamless genealogy to family and researchers seeking connections and family history all over the world. Second, this study applies migration analysis by applying big data technologies to Hakka genealogies to investigate the migration patterns of the Hakka ethnic group in Taiwan between 1954 and 2014. This innovative library service enhances the Hakka genealogical migration analysis using big data.

The platform is designed for the exchange of genealogical data to be used in big data analysis. This study integrates big data and geographic information systems (GIS) to map the population distribution themes. The general procedure included collecting genealogical big data, geographic encoding, gathering the map information, GIS layer integration and migration map production.

The analytical results demonstrate that big data technology is highly appropriate for family migration history analysis, given the increasing volume, velocity and variety of genealogical data. The spatial-temporal visualization of the genealogical research platform can follow family history and migration paths, and dynamically generate roadmaps to simplify the cartographic steps.

Technology that combines big data and GIS is suitable for performing migration analysis based on genealogy. A web-based application for spatial-temporal genealogical information also demonstrates the contribution of innovative library services.

Big data play a dominant role in library services, and in turn, provide an active library service. These findings indicate that big data technology can provide a suitable tool for improving library services.

Online genealogy and family trees are linked with large-volume, growing data sets that are complex and have multiple, autonomous sources. The migration analysis using big data has the potential to help genealogy researchers to construct minority ethnic history.

For precisely presenting the swimming behavior of fish robots underwater and the practical implementation purpose, this paper aims to investigate a well-formulated fish robot model which integrates the nonlinear rigid body dynamics, kinematics and models of actuators.

This fish robot model is mainly built up by three basic parts: a balance mechanism, a four-links vibrator and a caudal fin. In the fish robot’s head, there is a balance mechanism used to control the rotations in pitch and roll directions of the fish robot by moving two movable masses. The four-links vibrator with three active joints actuated by DC motors is designed to vibrate the fish’s body. In the end of the fish robot body, a caudal fin which connects with the passive joint is developed to generate hydrodynamic thrust forces to propel the fish robot.

From the real stability tests and control verification, it is obvious that this proposed model can precisely present the swimming behavior of fish robots and possesses the potential to develop a fish-like robotic prototype.

A well-formulated model with dynamics of actuators is integrated for presenting the swimming behavior of carangiform locomotion type fish robots in this investigation. From the simulation results and the practical test of a real fish robot, the feasibility of this proposed model for building up real fish robots can be proven, and this proposed model is accurate enough to effectively present the swimming behavior of fish robots.

To support the standardized evaluation of bicyclist automatic emergency braking (AEB) systems, test scenarios, test procedures and test system hardware and software tools have been investigated and developed by the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis. This paper aims to focus on the development of test scenarios and bicyclist surrogate for evaluating vehicle–bicyclist AEB systems.

The harmonized general estimates system (GES)/FARS 2010-2011 crash data and TASI 110-car naturalistic driving data (NDD) are used to determine the crash geometries and environmental factors of crash scenarios including lighting conditions, vehicle speeds, bicyclist speeds, etc. A surrogate bicyclist including a bicycle rider and a bicycle surrogate is designed to match the visual and radar characteristics of bicyclists in the USA. A bicycle target is designed with both leg pedaling and wheel rotation to produce proper micro-Doppler features and generate realistic motion for camera-based AEB systems.

Based on the analysis of the harmonized GES/FARS crash data, five crash scenarios are recommended for performance testing of bicyclist AEB systems. Combined with TASI 110-car naturalistic driving data, the crash environmental factors including lighting conditions, obscuring objects, vehicle speed and bicyclist speed are determined. The surrogate bicyclist was designed to represent the visual and radar characteristics of the real bicyclists in the USA. The height of the bicycle rider mannequin is 173 cm, representing the weighted height of 50th percentile US male and female adults. The size and shape of the surrogate bicycle were determined as 26-inch wheel and mountain/road bicycle frame, respectively. Both leg pedaling motion and wheel rotation are suggested to produce proper micro-Doppler features and support the camera-based AEB systems.

The results have demonstrated that the developed scenarios, test procedures and bicyclist surrogate will provide effective objective methods and necessary hardware and software tools for the evaluation and validation of bicyclist AEB systems. This is crucial for the development of advanced driver assistance systems.

Urban MICE competitiveness research consists of two clusters, one that is public-statistics-based and another that is questionnaire-based. Supply-side research on urban MICE competitiveness is rare. Based on the findings of Chen (2014) and other scholars, the purpose of this paper is to design counterpart statistical indicators to empirically analyze CMCA member cities.

After calculating the standardized Z value of the original statistical data for 17 CMCA member cities, the authors conducted confirmatory factor analysis for the first-level principal components, based on which hierarchical clustering was performed; then, regression analysis was conducted with the MICE profit factor as the dependent variable and the cost factor, tight support factor and facilitating factor as the independent variables to support publishing articles.

The confirmatory factor analysis showed that the urban MICE competitiveness indicators from the supply-side perspective include the profit factor, cost factor, tight support factor and facilitating factor.

On the basis of research findings from the demand perspective and the literature review, the authors constructed an urban MICE competitiveness indicator system from the perspective of the supply side and conducted principal component analysis. However, because of the inaccessibility of panel data, the current data were only sufficient to conduct the research. If panel data could be acquired, further research could be conducted to perfect the current indicator system for urban MICE competitiveness.

The findings suggest that tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The tight support factor exerts a significant positive influence on urban MICE competitiveness from the supply-side perspective, while the cost factor exerts a significant negative influence. The findings suggest that the tourism total income, tourism foreign exchange income, inbound tourist number, number of exhibitions, exhibition area, number of UFI member cities and number of ICCA member cities were the main reason for the gap between different cities’ competitiveness and the reform focus for improving urban MICE competitiveness. The cost factor had a significantly negative influence on urban MICE competitiveness, implying that the higher the average hotel room price and revenue per available room, the less competitive the MICE host city is.

The research bridge the empirical statistics and the questionnaire-based perception study on urban MICE tourism image, and advance to construct an empirical statistics based indicator system for urban MICE tourism image.

The crowdfunding market has experienced rapid growth in recent years. However, not all projects are successfully financed because of information asymmetries between the founder and the providers of external finance. This shortfall in funding has made factors that lead to successful fundraising, a great interest to researchers. This study draws on the social capital theory, human capital theory and level of processing (LOP) theory to predict the success of crowdfunding projects.

A feature set is extracted and correlations between project success and features are utilized to order the features. The artificial neural network (ANN) is popularly applied to analyze the dependencies of the input variables to improve the accuracy of prediction. However, the problem of overfitting may exist in such neural networks. This study proposes a neural network method based on ensemble machine learning and dropout methods to generate several neural networks for preventing the problem of overfitting. Four machine learning techniques are applied and compared for prediction performance.

This study shows that the success of crowdfunding projects can be predicted by measuring and analyzing big data of social media activity, human capital of funders and online project presentation. The ensemble neural network method achieves highest accuracy. The investments rose from early projects and another platform by the funder serve as credible indicators for later investors.

The managerial implication of this study is that the project founders and investors can apply the proposed model to predict the success of crowdfunding projects. This study also identifies the most influential features that affect fundraising outcomes. The project funders can use these features to increase the successful opportunities of crowdfunding project.

This study contributes to apply a new machine learning modeling method to extract features from activity data of crowdfunding platforms and predict crowdfunding project success. In addition, it contributes to the research on the deployment of social capital, human capital and online presentation strategies in a crowdfunding context as well as offers practical implications for project funders and investors.

Recent years have witnessed the development of a variety of rating systems but the authors have little knowledge about their impact on users’ perceptions of information quality, cognitive decision effort, and enjoyment. The purpose of this paper is to understand the potential cognitive fit underlining the relationship between rating systems types (i.e. five-star, binary-visual, and binary-textual) and tasks (i.e. purchase-decision and browsing tasks) in the context of shopping websites.

A total of 191 subjects were obtained. This study conducted an experiment with a 2×3 between-subject factorial design. The first dimension is a task that has two conditions (purchasing vs browsing). The second dimension is a type of rating system that has three different types (binary-textual, binary-visual, and five-star).

The results show that the cognitive fit may occur when individuals use a five-star rating system to help them make a purchasing decision and when they use a binary-visual rating system while browsing. This fit might increase perceived information quality while decrease cognitive decision efforts, and in turn raise intention to adopt the systems. Moreover, five-star rating systems can make users feel more fun and enjoyment than binary-textual and visual rating systems, regardless of task type.

This study focuses on three main rating systems that are popular on shopping websites. Nevertheless, few other rating systems exist in the market such as unitary or ten-star rating systems. Further studies can consider other types of rating systems and address, in addition to representation, the issue of information granularity (i.e. unitary, binary, five-star, and ten-star rating systems).

The results of this study could provide design principles for web designers in determining which rating systems best match the websites they are developing. If the websites or specific webpages are more utilitarian oriented (hedonic oriented), five-star rating systems (binary-visual rating systems) are more appropriate.

This study contributes to the rating system literature by examining the cognitive fit underlining the relationship between rating systems types and tasks. Importantly, this study extends cognitive fit theory by considering affective responses, that is, perceived enjoyment and intention to continue to use.

Given the unique cultural-political context of China, this paper aims to investigate two research questions: What has been the development trajectory of policy-making on consumer privacy protection in China, and what factors have shaped its development over the years?

This paper adopts a historical approach and examines the development of Chinese consumer privacy policy during four periods: 1980s, 1990s, 2000s and 2010-present.

Chinese policy-making on consumer privacy protection has made steady advancement in the past few decades due to factors such as technological development, elite advocacy and emulation of other markets; however, the effects of these factors are conditioned by local forces.

To date, most studies of consumer privacy issues have focused on Western countries, especially the European Union and the USA. A better understanding of how consumer privacy policy has developed in China provides important lessons on the promotion of consumer privacy protection in other developing countries.

Online dating services have been growing rapidly in recent years. However, adopting these services may involve high risk and trust issues among potential users toward both online dating services and the daters they introduce to users. The purpose of this paper is to investigate how perceived benefits vs risks, and trust vs distrust affect user adoption vs non-adoption intentions toward using this rather controversial information and communications technology in the context of online dating.

Structural equation modeling was used to evaluate the research model using data from a survey of 451 single individuals.

The results indicated that perceived benefits play more essential roles in adoption, while perceived risks affect non-adoption more. Individuals' trust in online dating service predicts a major portion of the variation in user benefit perceptions, while distrust in online dating service and in daters that users might select significantly influence perceived risks. Moreover, benefit and risk perceptions can mediate the impacts of trust and distrust on both adoption and non-adoption decisions.

This study extends theories of decision-making in the use of controversial information technologies such as in the case of online dating. It investigates the coexistence of various trust and distrust beliefs as well as benefit and risk perceptions, and their different impacts on adoption and non-adoption in online dating services.