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.

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.

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.

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.

Process Capability Indices (PCIs) are devices widely used in the industry to evaluate process quality. The commonly used process capability indices all contain accuracy indices and precision indices. As the accuracy index is closer to zero, the process accuracy is higher. The precision index mainly represents the extent of process variation. As the value is smaller, the process variation is smaller, that is, the precision is higher. In fact, process capability indices are the functions of accuracy indices and precision indices. Obviously, as long as accuracy indices and precision indices are controlled, the process capability indices can be controlled as well. Therefore, this study first derived accuracy and precision control charts to observe not only process accuracy but also process precision. Then, this study adopted in-control data to acquire a 100 (1 − $\alpha$)% confidence region of an accuracy index and a precision index, with which statistical tests were performed. Subsequently, according to the definition of the six sigma quality level, both indices were examined. Furthermore, based on the testing results, suggestions for process improvement were proposed, including correcting the direction of process deviation and deciding whether to reduce process variation. Finally, this study demonstrated the applicability of the proposed model using an empirical example.

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.