In recent years, increased environmental awareness has led consumers to adopt more eco-friendly lifestyles, including choosing green products to reduce their impact on the environment. With a particular focus on the green consumption behavior with regard to battery electric vehicles (BEVs) from two leading brands, Tesla (automotive) and Gogoro (scooter), in Taiwan, this study investigated the relationships among green perceived quality (GPQ), green perceived value (GPV), green brand image (GBI), green trust (GTR), and green purchase intention (GPI). After collecting 203 environmentally conscious respondents who are familiar with Tesla and Gogoro BEVs, the PLS-SEM was used to verify the research framework. The findings demonstrate that GPQ significantly impacts GBI and GPV; GBI significantly impacts GPV and GTR; and GPV significantly impacts GTR. As the key determinants of purchasing BEVs, GPQ, GBI, and GTR have significant effects on GPI, whereas the effect of GPV on GPI is not significant. In addition, this study also explored the moderation effect on green purchase behavior, revealing that the brand-specific cognition of Tesla and Gogoro moderates the relationships between green attributes (such as GPQ, GBI, and GTR) and GPI. This study contributes a more comprehensive understanding of the psychological and behavioral drivers behind green consumption behavior within the context of BEVs. This study not only sheds light on consumer behavior within dual-market dynamics in Taiwan but also offers a framework for other markets with similar environmental and infrastructural challenges. These insights can assist BEV companies to develop their sustainable marketing strategies, emphasizing the importance of developing a strong green brand image and green trust to boost BEV sales from the perspective of green perceived quality.

Background: A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3283 abstracts). Methods: Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. Results: The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. Conclusions: It is suggested that all disciplines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

The COVID-19 pandemic has caused many medical issues. It has tested the impact of healthcare providers’ job demands, emotional exhaustion, and other pressures related to the impact on organizational leave intention. Accordingly, the purpose of this study was to verify the relationship between healthcare providers’ job demands, leisure involvement, emotional exhaustion, and leave intention under the COVID-19 pandemic. The questionnaire survey was used to address the issue of the present study. Convenience sampling was utilized to recruit 440 healthcare providers with a validity rate of 95%. Collected data were analyzed by structural equation modelling. Results indicated that healthcare providers’ job demands do not significantly influence leisure involvement. Job demands significantly influence emotional exhaustion. Job demands significantly influence leave intention. Emotional exhaustion significantly influences leave intention. Emotional exhaustion has a significant mediating effect between job demands and leave intention. Finally, relevant practical suggestions are provided based on the study results.

The random variable X is used to represent the normal process containing two important parameters—the process average and the process standard deviation. The variable is transformed using Y = (X − T)/d, where T is the target value and d is the tolerance. The average of Y is then called the accuracy index, and the standard deviation is called the precision index. If only the values of the accuracy index and the process precision index are well controlled, the process quality level as well as the process yield are ensured. Based on this concept, this paper constructed a control chart for the accuracy index and the precision index and derived the confidence intervals of the accuracy index and the precision index using in-control data, as the process was stable. This paper aims to control process quality via monitoring the accuracy and precision of the process. At the same time, fuzzy tests are developed for the indicators of process accuracy and precision to evaluate whether the process quality can reach the k-sigma quality level, as well as offer suggestions about directions of improvement when it fails to reach the k-sigma quality level. Obviously, the model in this paper cannot only evaluate whether the process meets the requirements of the quality level; it can also provide a decision regarding whether the process should be improved. It is very helpful for the enhancement of enterprises’ process capabilities.

Numerous advanced industrial countries emphasize green environmental protection alongside athletic healthcare. Many world-renowned sports brands are actively developing highly functional, environmentally friendly, and aesthetically pleasing products. For example, in the production of sports shoes, the eco-friendly yarn process is one of the important processes. This process involves multiple crucial larger-the-better quality characteristics closely tied to the functionality of sports shoes. Facing green environmental regulations and external competitors, it is evidently an imperative issue for enterprises to consider how to improve the quality of newly developed products, increase product value, and lower rates of both rework and scrap to accomplish the goals of saving energy and minimizing waste. Aiming to solve this problem, this study proposed a fuzzy evaluation model for products with multifunctional quality characteristics to assist the sporting goods manufacturing industry in evaluating whether all functional quality characteristics of its products meet the required quality level. This study first utilized the larger-the-better Six Sigma quality index concerning environmental protection for evaluation and then proposed product evaluation indicators for the eco-friendly yarn. Since the parameters of these indicators have not yet been determined, sample data need to be used for estimation. Enterprises require rapid response, so that the sample size is relatively small. Sampling error will increase the risk of misjudgment. Therefore, taking suggestions from previous studies, this study constructed the fuzzy evaluation model based on confidence intervals of quality indicators for the eco-friendly yarn. This method incorporated previous experience with data, thereby enhancing assessment accuracy.

The process capability index is a tool for quality measurement and analysis widely used in the industry. It is also a good tool for the sales department to communicate with customers. Although the value of the process capability index can be affected by the accuracy and precision of the process, the index itself cannot be differentiated. Therefore, the process incapability index is directly divided into two items, accuracy and precision, based on the expected value of the Taguchi process loss function. In fact, accuracy and precision are two important reference items for improving the manufacturing process. Thus, the process incapability index is good for evaluating process quality. The process incapability index contains two unknown parameters, so it needs to be estimated with sample data. Since point estimates are subject to misjudgment incurred by the inaccuracy of sampling, and since modern businesses are in the era of rapid response, the size of sampling usually tends to be small. A number of studies have suggested that a fuzzy testing method built on the confidence interval be adopted at this time because it integrates experts and the experience accumulated in the past. In addition to a decrease in the possibility of misjudgment resulting from sampling error, this method can improve the test accuracy. Therefore, based on the confidence interval of the process incapability index, we proposed the fuzzy testing method to assess whether the process capability can attain a necessary level of quality. If the quality level fails to meet the requirement, then an improvement must be made. If the quality level exceeds the requirement, then it is equivalent to excess quality, and a resource transfer must be considered to reduce costs.