Tung-Lai Hu

Digital franchising is increasingly recognized as a technological advancement and a specialized subset of e-commerce, yet its unique entrepreneurial dynamics remain insufficiently explored in the existing literature. Previous studies have primarily focused on platform usability or general e-commerce adoption, often overlooking the motivational, contextual, and capability-based factors that influence individuals’ willingness to engage in digital franchising as either entrepreneurs or consumers. To address this research gap, the present study applies the Motivation-Opportunity-Ability (MOA) framework to examine how personal motivations (e.g., self-expression, financial rewards), perceived platform opportunities (e.g., support, attractiveness), and individual capabilities (e.g., digital literacy, self-efficacy) shape entrepreneurial intention and, in turn, influence consumption adoption intention in digital franchising environments. An online survey was conducted using a non-probability purposive sampling method. The final sample consisted of 491 respondents from Taiwan, all of whom were either entrepreneurs operating digital franchises in the fashion industry or consumers who had purchased fashion products through digital franchising platforms, thereby ensuring contextual relevance to the study’s focus. Data were analyzed using structural equation modeling (SEM). The results indicate that expected external rewards (β = 0.456, p < 0.001) and platform support (β = 0.315, p < 0.001) are the most influential factors in shaping entrepreneurial intention. Furthermore, entrepreneurial intention significantly mediates the relationship between MOA antecedents and consumption adoption intention (β = 0.176, p < 0.001), highlighting its role as a key behavioral mechanism. These findings extend the MOA framework to a new empirical setting and offer practical implications for platform developers, franchisors, and policymakers seeking to promote participation in digital franchising. Future research is encouraged to explore cross-industry comparisons, generational differences, and longitudinal approaches to further enrich the understanding of digital franchising adoption dynamics.

With the rise of digital marketing, videos have become pivotal in promoting agritourism by showcasing local features, attracting visitors, and building trust. This study examines the effects of video maturity and content empowerment on consumer behavior by focusing on dimensions such as clarity, coherence, emotional appeal, entertainment, information, and practicality. Survey data from 527 Taiwanese consumers, analyzed using structural equation modeling (SEM), reveal that video clarity (β = 0.105, p < 0.05) and coherence (β = 0.163, p < 0.01) significantly enhance trust, while flow experience exhibits an even stronger positive impact (β = 0.372, p < 0.001). Among content factors, knowledge has the most pronounced effect on flow experience (β = 0.440, p < 0.001), followed by emotionality, entertainment, and practicality. Moreover, both trust (β = 0.177, p < 0.001) and flow experience (β = 0.240, p < 0.001) positively influence purchase intentions, with the effect of flow experience being more dominant. These findings underscore the importance of high clarity, coherent narratives, and immersive emotional engagement in agritourism video marketing. The study makes significant theoretical contributions by refining the understanding of video-based consumer engagement, and it offers practical insights for enhancing agritourism promotional strategies.

Prognostic outcomes for patients with type 2 diabetes in the intensive care unit (ICU), including mortality and readmission rates, are critical for informed clinical decision-making. Although existing research has established a link between type 2 diabetes and adverse outcomes in the ICU, the potential of machine learning techniques for enhancing predictive accuracy has not been fully realized. This study seeks to develop and validate predictive models employing machine learning algorithms to forecast mortality and 30-day post-discharge readmission rates among ICU type 2 diabetes patients, thereby enhancing predictive accuracy and supporting clinical decision-making. Data were extracted and preprocessed from the MIMIC-III database, focusing on 14,222 patients with type 2 diabetes and their corresponding ICU admission records. Comprehensive information, including vital signs, laboratory results, and demographic characteristics, was utilized. Six machine learning algorithms—bagging, AdaBoost, GaussianNB, logistic regression, MLP, and SVC—were developed and evaluated using 10-fold cross-validation to predict mortality at 3 days, 30 days, and 365 days, as well as 30-day post-discharge readmission rates. The machine learning models demonstrated strong predictive performance for both mortality and readmission rates. Notably, the bagging and AdaBoost models showed superior performance in predicting mortality across various time intervals, achieving AUC values up to 0.8112 and an accuracy of 0.8832. In predicting 30-day readmission rates, the MLP and AdaBoost models yielded the highest performance, with AUC values reaching 0.8487 and accuracy rates of 0.9249. The integration of electronic health record data with advanced machine learning techniques significantly enhances the accuracy of mortality and readmission predictions in ICU type 2 diabetes patients. These models facilitate the identification of high-risk patients, enabling timely interventions, improving patient outcomes, and demonstrating the significant potential of machine learning in clinical prediction and decision support.

This study explores the impact of social media marketing activities and ESG green brand involvement on the repurchase intention of green products among members of Taiwan’s green product social media groups. Using the Theory of Reasoned Action, the study employs a quantitative approach, collecting data through an online survey from 438 valid responses. Structural equation modeling (SEM) was applied to analyze the relationships between social media marketing activities (independent variable), green values, environmental concerns, brand image, and brand involvement (mediating variables), and repurchase intention (dependent variable). The findings reveal that social media marketing activities significantly enhance green values, environmental concerns, and brand image, which positively influence brand involvement. Brand involvement significantly mediates the relationship between green values, environmental concerns, brand image, and repurchase intention. This confirms that effective social media marketing can strengthen consumer loyalty and repurchase intentions for green products. The study suggests that companies should integrate green values, environmental concerns, and brand image into their social media marketing strategies to enhance consumer engagement and repurchase intentions. Leveraging social media influencers and interactive content can effectively promote green products and foster a culture of sustainability, creating a win–win situation for companies, consumers, and the environment. This research contributes to the literature by highlighting the mediating roles of green values, environmental concerns, and brand involvement in social media marketing for green products. It provides practical insights for companies aiming to enhance green product repurchase intentions through targeted social media strategies and ESG initiatives, illustrating the strategic use of social media marketing to boost consumer loyalty and sustainable consumption.

Global warming, exacerbated by greenhouse gas emissions from non-green product manufacturing, necessitates a shift towards green production to safeguard ecosystems and finite resources. The United Nations Environment Programme emphasizes the importance of environmental considerations in corporate ESG criteria. Despite the higher costs of designing and manufacturing green products, which deter some companies and lead to greenwashing, efforts persist to promote sustainable goods. This study explores enhancing consumer repurchase intentions for green products through social media marketing, emphasizing green values, environmental concerns, brand image, and involvement. Findings reveal social media's role in boosting these factors, thus encouraging green repurchases. Insights are valuable for green businesses, social media groups, environmental agencies, and future research.

With the popularization and maturity of Internet technology, it has become an inevitable trend of agricultural development to transform the way of agricultural development and connect the Internet with agricultural products. The development of “Internet + agriculture” is rearranging the entire agricultural product industry chain and it also affects the direction of agricultural development. In this article, we make full use of Internet technology, aiming at the development status of the agricultural product industry chain, and design a layer-by-layer inversion model based on partial differential equations to construct an “Internet +” agricultural product industry chain optimization system. This research strives to achieve the coupling of hardware infrastructure and software service systems. A system platform consisting of one platform, two modules, five services, and twelve functional submodules is designed, and a layer-by-layer inversion method of partial differential equations is introduced. The optimization of the agricultural product industry chain from the control of the production side and the sales side is realized. On this basis, further analysis of the accuracy of partial differential equations, system response time, greenhouse climate change control, agricultural product production capacity, and brand value are further analyzed. The following five conclusions are drawn: first, the response time of each interface is 1 to 2 seconds and in the interval of 500 milliseconds, the intensive range is the densest, and the system performance is further improved. Second, for the three spatial fractional partial differential equations, the equations used in this system basically achieved the best accuracy results. Third, at 13: 00, the maximum temperature difference between indoor and outdoor is 6.3°C, which is the moment when the outdoor solar radiation is strongest. The average temperature in the greenhouse is higher than that outdoors, and the trend of the two changes is the same. Fourth, after the optimization of the industrial chain, the highest utilization rate of agricultural product production capacity is 97% and the net profit can reach 150%. Fifth, the brand value of agricultural products has increased from 560 million Yuan in 2010 to 3.153 billion Yuan in 2020, and the quality value has achieved rapid growth. The results show that the “Internet +” agricultural product industry chain optimization system in this article has a reasonable structure, stable operation, easy expansion, and strong applicability and further accelerates the process of modern agriculture.