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In contrast to other Chinese sports, which attract enormous commercial interests and relates to a kind of political kudos and ideological significance, Chinese baseball represents a singular case for the author to investigate the way in which China responds to sport globalisation due to its peripheral position in, and limited resource from, the domestic sport system and powerful stakeholders from the global society. The study examines the development of Chinese baseball and its interplay with the global sport giants and international events (i.e. the Olympic Games and the Major League Baseball [MLB]). Given the influence of sport globalisation, the author also identifies the consistent dominance of Chinese sport authorities and the spectrum of local stakeholders' reaction towards globalisation. The chapter ends with a discussion of the relationship between marginal position of Chinese baseball in the local sport system and the rationale of its response to sport globalisation.

Dysfunctional customer behavior (DCB) is costly and problematic for organizations. This research seeks to understand how DCB spreads and how businesses can effectively deal with it through employee intervention.

This research conducted a survey study and an experimental study to examine the proposed model.

Through two studies, we discovered that when an employee intervenes to stop DCB and is perceived as having high coping ability, observing customers learn from the employee’s action, resulting in reduced empathy toward the dysfunctional customer and diminished intentions to engage in DCB. Conversely, if they perceive the employee as having low coping ability, the intervention backfires, enhancing the observers’ empathy toward the dysfunctional customer and consequently leading them to engage in more DCB.

This research unveils an additional mechanism that explains the spread of DCB. It also contributes to the employee intervention literature by shedding light on when employee intervention can backfire. Further, our application of social learning theory along with the person-situation interaction literature offers a fresh perspective in explaining service exchanges.

Taiwan’s hotel industry was adversely impacted by the COVID-19 pandemic. This study aims to examine the effect of strategic choices by Taiwanese international tourist hotels before and during the pandemic environments.

A data envelopment analysis (DEA)-based Malmquist methodology is used in this study to provide a mechanism to assess Taiwanese hotel strategy performance. Changes in the productivity and performance of Taiwanese international tourist hotels were analyzed in the periods before and during the pandemic to uncover insights useful should a similar crisis occur in the future. Panel data were obtained from the annual report of international tourist hotels published by the Taiwan Tourism Bureau from 2017–2020. Two groups of hotels were analyzed in this study: city hotels and scenic hotels.

The findings of this study reveal that chain hotels tended to perform better than independent hotels in both city and scenic areas during the global pandemic. Specifically, the crisis caused a substantial decline in productivity and profitability for international tourist hotels in Taipei City during the COVID-19 period. Compared to city hotels, findings also indicate that most international tourist hotels in scenic areas were able to maintain better productivity, including larger-sized scenic hotels.

The DEA-based analysis provides unique and valuable insights for hotel firm leaders on how to better identify and make strategic choices when responding to future crises.

Assembly action recognition plays an important role in assembly process monitoring and human-robot collaborative assembly. Previous works overlook the interaction relationship between hands and operated objects and lack the modeling of subtle hand motions, which leads to a decline in accuracy for fine-grained action recognition. This paper aims to model the hand-object interactions and hand movements to realize high-accuracy assembly action recognition.

In this paper, a novel multi-stream hand-object interaction network (MHOINet) is proposed for assembly action recognition. To learn the hand-object interaction relationship in assembly sequence, an interaction modeling network (IMN) comprising both geometric and visual modeling is exploited in the interaction stream. The former captures the spatial location relation of hand and interacted parts/tools according to their detected bounding boxes, and the latter focuses on mining the visual context of hand and object at pixel level through a position attention model. To model the hand movements, a temporal enhancement module (TEM) with multiple convolution kernels is developed in the hand stream, which captures the temporal dependences of hand sequences in short and long ranges. Finally, assembly action prediction is accomplished by merging the outputs of different streams through a weighted score-level fusion. A robotic arm component assembly dataset is created to evaluate the effectiveness of the proposed method.

The method can achieve the recognition accuracy of 97.31% and 95.32% for coarse and fine assembly actions, which outperforms other comparative methods. Experiments on human-robot collaboration prove that our method can be applied to industrial production.

The author proposes a novel framework for assembly action recognition, which simultaneously leverages the features of hands, objects and hand-object interactions. The TEM enhances the representation of dynamics of hands and facilitates the recognition of assembly actions with various time spans. The IMN learns the semantic information from hand-object interactions, which is significant for distinguishing fine assembly actions.

Breast cancer has become the largest cancer in the world today. Health problems for women with breast cancer need to be addressed urgently. This study aims to select the best method for preparing temperature-sensitive sports underwear, and to verify the feasibility of using K-type thermocouple threads in underwear fabrics.

In the experiments, two samples were designed for temperature-sensitive performance tests and the effects produced by different outer layer structures were investigated. In the second step, K-type thermocouple wires were integrated into sports underwear. The comfort and feasibility of the temperature-sensitive underwear were investigated.

It was finally verified to obtain the best comfort and temperature-sensing performance of K-type thermocouple filaments integrated into sports underwear with plain stitching.

The underwear has a certain prospect for the application of smart apparel based on breast cancer health monitoring, which is of some significance for monitoring smart apparel.

Sustainability is a major global concern, and research has suggested a bidirectional relationship between participatory sport events and the natural environment. Against this background, we examined the influence of runners’ environmental consciousness on their perceptions of the quality of green initiatives and their supportive intention at a running event.

We collected questionnaire responses from 496 runners at an event held in Taiwan, and we used partial least squares structural equation modeling for our measurement and structural models.

Our findings revealed that environmental consciousness had a positive relationship with green perceived quality, and that green perceived quality, in turn, positively affected supportive intention. Green perceived quality also mediated the relationship between environmental consciousness and supportive intention, and running frequency moderated the relationship between environmental consciousness and supportive intention.

Stakeholders should promote the environmental consciousness of event participants and implement sustainable initiatives to enhance participants’ supportive intention towards participatory sport events.

This study contributes to the literature by examining the role of environmental consciousness, green perceived quality and supportive intention in the context of a running event. The findings highlight the importance of environmental sustainability in participatory sport events and provide valuable insights for event organizers and stakeholders in designing and implementing sustainable initiatives.

The term sustainable tourism has been in existence for many years, yet the concept is still lacking universal agreement in terms of definition, a situation reflected in the difficulties encountered in producing, particularly implementing and related policies. The broad range of issues involved with sustainable development makes integrated policy and governance difficult even in the more specific fields such as sustainable tourism. This chapter begins by reviewing the basic elements of sustainable tourism, its links with the parent subject of sustainable development, and the issues involved in applying the ‘triple bottom line’ concept to tourism. It then examines the relationship between policy formulation and application in the context of governance, and the links between governance and power at different levels. While one of the achievements of sustainable tourism has been its widespread acceptance and official adoption of the concept in many countries, regions and communities, this acceptance has not been translated very often into effective action, owing partly to the lack of integration between stakeholders and partly to the multiple viewpoints making up the tourism landscape which rarely achieve unanimity of policy issues. The chapter includes a discussion of case studies at different scales to illustrate the positive and negative results of the implementation of such policies and concludes with a general summation.

The announcement of the SDGs in 2015 marked a dramatic shift in global development, culminating in an age of international cooperation where the international community bands together to tackle the world's most pressing challenges. This chapter discusses the peculiar connection between Islamic financing and the SDGs and focuses on the possible harmony between these two sectors. It emphasizes the need for significant financial resources to advance sustainable development. Researchers argue it is crucial to bring digital technologies together with Islamic finance to reach sustainable economic growth through innovations like artificial intelligence (AI), blockchain, and fintech. In addition, the chapter explains core principles of Islamic finance that conform to Shari'ah, which will eradicate poverty and promote conscientious consumption. Islamic finance opens the opportunity for financially and socially excluded groups, poverty reduction, and environmental sustainability through programs like renewable energy projects. However, SDG integration with Islamic finance still poses some challenges, namely, Shari'ah scholars' understanding of the SDGs, regulatory and operational difficulties, the necessity for innovation, and measuring nonfinancial benefits. Through case studies, the authors provide pragmatic insights into successful integration processes as well as practical lessons for the concerned parties. The chapter ends by making policy recommendations grounded on active support by governments, compulsory educational initiatives, new interesting Islamic finance products, and shared efforts (or undertakings) among various stakeholders. In short, this chapter positions Islamic finance as a potent catalyst for SDGs, furnishing a sustainable economic and social development framework while acknowledging and addressing challenges on the integration journey.

Technology acceptance is a measure of that technology’s usefulness. Oil palm is one of the biggest contributors to Indonesia’s revenues, thus fueling its economy. Using remote sensing would allow a plantation to monitor and forecast its production and the amount of fertilizer used. This review aims to provide a policy recommendation in the form of a strategy to improve the added value of Indonesia’s oil palm and support the government in increasing oil palm production. This recommendation needs to be formulated by determining the users’ acceptance of remote sensing technology (state-owned plantations, private plantation companies and smallholder plantations).

This review’s methodology used sentiment analysis through text mining (bag of words model). The study’s primary data were from focus group discussions (FGDs), questionnaires, observations on participants, audio-visual documentation and focused discussions based on group category. The results of interviews and FGDs were transcribed into text and analyzed to 1) find words that can represent the content of the document; 2) classify and determine the frequency (word cloud); and finally 3) analyze the sentiment.

The result showed that private plantation companies and state-owned plantations had extremely high positive sentiments toward using remote sensing in their oil palm plantations, whereas smallholders had a 60% resistance. However, there is still a possibility for this technology’s adoption by smallholders, provided it is free and easily applied.

Basically, technology is applied to make work easier. However, not everyone is tech-savvy, especially the older generations. One dimension of technology acceptance is user/customer retention. New technology would not be immediately accepted, but there would be user perceptions about its uses and ease. At first, people might be reluctant to accept a new technology due to the perception that it is useless and difficult. Technology acceptance is the gauge of how useful technology is in making work easier compared to conventional ways.

Therefore, technology acceptance needs to be improved among smallholders by intensively socializing the policies, and through dissemination and dedication by academics and the government.

The social implications of using technology are reducing the workforce, but the company will be more profitable and efficient.

Remote sensing is one of the topics that people have not taken up in a large way, especially sentiment analysis. Acceptance of technology that utilizes remote sensing for plantations is very useful and efficient. In the end, company profits can be allocated more toward empowering the community and the environment.

The purpose of this paper is to provide adaptive access to learning resources in the digital library.

A novel method using ontology-based multi-attribute collaborative filtering is proposed. Digital libraries are those which are fully automated and all resources are in digital form and access to the information available is provided to a remote user as well as a conventional user electronically. To satisfy users' information needs, a humongous amount of newly created information is published electronically in digital libraries. While search applications are improving, it is still difficult for the majority of users to find relevant information. For better service, the framework should also be able to adapt queries to search domains and target learners.

This paper improves the accuracy and efficiency of predicting and recommending personalized learning resources in digital libraries. To facilitate a personalized digital learning environment, the authors propose a novel method using ontology-supported collaborative filtering (CF) recommendation system. The objective is to provide adaptive access to learning resources in the digital library. The proposed model is based on user-based CF which suggests learning resources for students based on their course registration, preferences for topics and digital libraries. Using ontological framework knowledge for semantic similarity and considering multiple attributes apart from learners' preferences for the learning resources improve the accuracy of the proposed model.

The results of this work majorly rely on the developed ontology. More experiments are to be conducted with other domain ontologies.

The proposed approach is integrated into Nucleus, a Learning Management System (https://nucleus.amcspsgtech.in). The results are of interest to learners, academicians, researchers and developers of digital libraries. This work also provides insights into the ontology for e-learning to improve personalized learning environments.

This paper computes learner similarity and learning resources similarity based on ontological knowledge, feedback and ratings on the learning resources. The predictions for the target learner are calculated and top N learning resources are generated by the recommendation engine using CF.