Chih-Ming Tsai

In this study, an innovative hierarchical porous oxide surface (HPOS) with an increased surface area was developed on biomedical 316L stainless steel (SS) implants to promote early bone and implant integration. The surface features and in vivo performance of the HPOS implant were examined using field-emission scanning electron microscopy, nanoindenter, roughness measurement instrument, contact angle goniometer, micro-computed tomography, and histological analysis. Results showed that the HPOS consisted of a hybrid micro-nano-pores structure and had a relatively low elastic modulus (150.6 ± 7.8 GPa, *p < 0.05), high roughness (8.6 ± 1.2 μm, **p < 0.01), and a low contact angle (15.6 ± 1.8°, ***p < 0.001). Additionally, rapid new bone formation was observed on the HPOS of the 316L SS implant modified at 5 V for 5 min, producing pore sizes from approximately 300 nm to 13.5 μm, with bone contact interfaces exceeding 64% at 12 weeks. The HPOS maintained mechanical interlocking ability at the microscale, which positively influenced osseointegration. Moreover, the difference in new bone formation thickness between the unmodified control group (0.45 ± 0.11 mm) and the modified 316L SS implant with HPOS (0.66 ± 0.13 mm) was statistically significant at 12 weeks post-implantation (*p < 0.05). These findings suggest that forming an innovative HPOS on a 316L SS implant could offer a potential solution to enhance early-stage osseointegration in clinical applications.

This study examines how government policy tools shape consumer adoption of battery electric vehicles (BEVs) in Taiwan. By extending the Technology Acceptance Model (TAM) focusing on three external government policy factors—legislative direction, monetary incentives, and usage-based benefits—this study uses two factors, including perceived usefulness (PU) and perceived ease of use (PEOU), to evaluate behavioral intention to use (BI), or purchase, BEVs. Utilizing PLS-SEM, survey data from 238 respondents were analyzed. The results suggest that legislative direction had no significant impact on PU or PEOU, while monetary incentives influenced only PEOU. In contrast, usage-based benefits strongly predicted both PU and PEOU. In addition, PU also partially mediates the relationship between PEOU and BI. These findings extend the TAM by situating public policy as a measurable driver of technology adoption, especially in the case of BEVs. For Taiwan, the results suggest that governmental policies focused on increased visibility and accessibility are more attractive than abstract regulatory frameworks in encouraging BEV adoption.

Purpose: This study addresses two key challenges in interval grey number prediction extensions: information loss in transformed sequences and sensitivity to weight selection. The research proposes a novel modeling approach for short-term time series forecasting, particularly in environments with limited data availability. Design/methodology/approach: The proposed approach integrates boxplots and Program Evaluation and Review Technique (PERT) with grey modeling through three main steps: data fuzzification using boxplots to infer upper and lower sequences, model construction employing various forecasting models and prediction aggregation using PERT concepts. Findings: The proposed procedure was validated using four datasets: two from previous studies, one from a simulation case and one from the UC Irvine Machine Learning Repository. The experimental results demonstrate that the proposed approach achieves superior prediction accuracy compared to Grey Model and its extensions. Practical implications: This approach is particularly valuable for industries with limited data availability. It provides a more reliable method for decision-making in environments where traditional data-driven approaches may be insufficient due to small sample sizes or fragmented datasets. Originality/value: The study introduces a novel combination of boxplots and PERT with grey modeling, offering an innovative solution to overcome the limitations of current interval grey number prediction methods. This approach provides a more robust framework for handling uncertain and non-uniform numerical distributions in short-term time series forecasting.

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.

This study examines the relationship between stylistic coach leadership style, team cohesion, and team performance for young student athletes who take part in a team sport. The stylistic coach leadership style is defined as a combination of both democratic and holistic coach leadership styles. In other words, the stylistic coach leadership style is when the coaches make the final decision based on what young student athletes think while ensuring that they are content and that the team is able to be successful overall. In order to determine the effect of stylistic coach leadership style on team cohesion and team performance, a study was conducted using an online questionnaire that was split into two parts. Young student athletes in grades 6 through 12 who were taking part in a team sport at the time were eligible to participate in this study. Demographic information was collected through the first part of the questionnaire while the second part measured each of the three constructs through questions that required the athletes to rank the statements based on the 5-point Likert scale. The results highlight that the stylistic coach leadership style directly affects both team cohesion and team performance, and team cohesion acts as a partial mediator in the relationship between stylistic coach leadership style and team performance. Finally, this study also provides some practical suggestions for coaches, with the aim of aiding them to be more efficient and effective in leading those young student athletes.

Social media has become an important part of most people’s lives since the pandemic and lockdowns. With little to do during lockdowns, some people created and managed social media accounts and became influencers. Following this trend, companies, governments, and individuals related to the leisure and hospitality industry turned to social media and influencer marketing to promote tourism in specific destinations in hopes of spurring an economic recovering. Since the pandemic, consumers have turned to social media to gather information and ideas for travel plans. To better understand the connection between what consumers read online and their desire to travel, this study aims to analyse the impacts of influencers, including their content and the fitness of destination, on consumers, who have their own perceived value and desire for the destination. A total of 332 Taiwanese respondents aged 18 to 55 were sampled to conduct a data analysis using the structural equation model (SEM). The results showed that content has a positive impact on the fitness of destination and the desire to travel, with no significant impact on perceived value, and the fitness of destination has a positive impact on perceived value and the desire to travel; in contrast, perceived value has a negative impact on the desire to travel. The mediation effect of influencer fitness of destination was also investigated through the bootstrap method. Fitness of destination acts as a full mediator between content and perceived value. Finally, the findings and managerial implications are further discussed to examine how the desire to travel among consumers can be affected by influencer marketing in the post-pandemic era. Practical suggestions for applying influencer marketing strategies based on the results are also proposed to help the tourism industry boost tourism revenue and achieve sustainable development goals after the pandemic.

Nowadays, the dissemination of news information has become more rapid, liberal, and open to the public. People can find what they want to know more and more easily from a variety of sources, including traditional news outlets and new social media platforms. However, at a time when our lives are glutted with all kinds of news, we cannot help but doubt the veracity and legitimacy of these news sources; meanwhile, we also need to guard against the possible impact of various forms of fake news. To combat the spread of misinformation, more and more researchers have turned to natural language processing (NLP) approaches for effective fake news detection. However, in the face of increasingly serious fake news events, existing detection methods still need to be continuously improved. This study proposes a modified proof-of-concept model named NER-SA, which integrates natural language processing (NLP) and named entity recognition (NER) to conduct the in-domain and cross-domain analysis of fake news detection with the existing three datasets simultaneously. The named entities associated with any particular news event exist in a finite and available evidence pool. Therefore, entities must be mentioned and recognized in this entity bank in any authentic news articles. A piece of fake news inevitably includes only some entitlements in the entity bank. The false information is deliberately fabricated with fictitious, imaginary, and even unreasonable sentences and content. As a result, there must be differences in statements, writing logic, and style between legitimate news and fake news, meaning that it is possible to successfully detect fake news. We developed a mathematical model and used the simulated annealing algorithm to find the optimal legitimate area. Comparing the detection performance of the NER-SA model with current state-of-the-art models proposed in other studies, we found that the NER-SA model indeed has superior performance in detecting fake news. For in-domain analysis, the accuracy increased by an average of 8.94% on the LIAR dataset and 19.36% on the fake or real news dataset, while the F1-score increased by an average of 24.04% on the LIAR dataset and 19.36% on the fake or real news dataset. In cross-domain analysis, the accuracy and F1-score for the NER-SA model increased by an average of 28.51% and 24.54%, respectively, across six domains in the FakeNews AMT dataset. The findings and implications of this study are further discussed with regard to their significance for improving accuracy, understanding context, and addressing adversarial attacks. The development of stylometric detection based on NLP approaches using NER techniques can improve the effectiveness and applicability of fake news detection.

The over-the-top services, especially Netflix, are currently gaining popularity around the world. The purpose of this study is to investigate the intentions of people in Taiwan using Netflix. The target respondents were people age ranged from 16 to 25, the young generation also would soon become main consumer in market since they obtain entertainment online more often compared to the older generation. Among 340 questionnaire responds gathered, 278 were used since part of the responds were not from the target population or were invalid. The Technology Acceptance Model (TAM) is applied in the study to respondents’ intentions using Netflix, while confirmatory factor analysis and structural equation model (SEM) are adopted for the purpose to clarify the interactions among the following four main factors that each are shown as a result creates positive impact on one another: perceived ease of use, perceived usefulness, perceived value, intention to use. The results of the study could be implicated on Netflix's marketing strategy, and the model may also apply to broader streaming services in organizing customer's preferences.

The cosmetic industry is currently thriving as people gain satisfaction in purchasing beauty-enhancing products. The purpose of this study is to investigate the impact of eWOM and perceived value on young customers’ purchase intention for open-shelf cosmetics products shopping at physical drugstores. The target respondents ranged from 15 to 24 years old, as the young generation contributes to a large portion of the cosmetic market. 243 valid questionnaires were received from people who have purchased cosmetic products once in six months in Taiwan. The confirmatory factor analysis and structural equation model were adopted to analyze the relationship between three constructs: eWOM, perceived value, and purchase intention. The results suggest that perceived value impacts purchase intention positively and directly, while eWOM impacts purchase intention indirectly. This result regarding the consumer behavior of young customers could be utilized in the cosmetic industry's marketing strategy to enhance the sales profit and product competitiveness.