Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers’ decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers’ technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis approach to account for the complexity of customers’ decision-making processes in adopting (...) new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude and behavioral intention found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology. (shrink)

Interactive Whiteboard has recently been used to replace the TWB, with many of its features being observed to help teachers in educational activities. This is based on effectively and efficiently increasing the teacher-student interaction. Therefore, this study aims to analyze the determinants of Behavioral Intention and the use of interactive whiteboards by K-12 teachers, in remote and rural Chinese areas. The Modified-Unified Theory of Acceptance and Use of Technology 2 model was used in this analysis, as a learning medium to (...) deliver the subject matter to students. The sample and population were also the teachers in the Guangxi Zhuang Autonomous Region, China, where 171 voluntary respondents participated in this study. Furthermore, the obtained data were processed using a Structural Equation Model approach, through the Smart-PLS software. The results showed that Habit and Hedonic Motivation had a significant influence on the Behavioral Intention of teachers, toward the utilization of IWB in remote and rural areas. Besides this, Facilitating Conditions and BI also had a significant positive effect on Usage Behavior. Based on these results, important information was provided to school principals, local governments, and teachers for education quality improvement, regarding the patterns of increasing IWB utilization in remote and rural areas. (shrink)

The purpose of this study was to develop and empirically test a model for predicting the key factors affecting nurses’ behavioral intention to use mobile learning. We explored behavioral intention from users’ perspectives by applying an extended unified theory of acceptance and use of technology model with the addition of information quality, system quality, technostress, and satisfaction. We conducted a survey of the district and regional hospitals in central Taiwan. Data were derived from 434 respondents. Structural equation modeling was applied (...) to analyze the causal effects of 15 hypothesized predictive factors. We determined that satisfaction, social influence, performance expectancy, facilitating conditions, and effort expectancy positively impacted nurses’ behavioral intention to use m-learning. In addition, technostress was a negative antecedent of effort expectancy. Information quality and system quality had significantly positive effects on satisfaction, performance expectancy, and effort expectancy. This study provides hospital managers with a reference when assessing future developments and informs approaches to promote m-learning. (shrink)

Mobile payment has emerged as a popular payment method in many countries. While much research has focused on the antecedents of mobile payment adoption, limited research has investigated the consequences of mobile payment usage relating to how it would influence consumer behaviors. Here, we propose that mobile payment not just reduces the “pain of paying,” a traditional view explaining why cashless payment stimulates spending, but it also evokes the “pleasure of paying,” raising from the enhanced processing fluency in completing transactions. (...) We tested this new conceptualization of “pleasure of paying” using EEG, complementing other behavioral measures. In two studies, we found that mobile payment effectively enhanced purchase likelihood and such an enhancement is generalizable to both hedonic and utilitarian products. By employing EEG measures, we provided the first neural evidence of “pleasure of paying” in addition to the signal of “pain of paying.” Critically, we demonstrated that the “pleasure of paying” is a distinctive psychological mechanism that is induced by mobile payment usage and that the “pleasure of paying” joins the “pain of paying” to mediate the increased purchase intention. We discuss the contributions and implications of these results to the ongoing evolution of cashless payment societies. (shrink)

Artificial intelligence technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology and the theory of perceived risk, this study identified six factors that influence students' (...) willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy, performance expectancy, and social influence were all positively related to college students' WTA AI-assisted learning environments. Psychological risk significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments. (shrink)

Against the backdrop of frequent food safety problems, the importance of establishing food traceability systems has become increasingly important and urgent to address the contradiction between consumer information on safe food choices and the proliferation of problematic foods. The purpose of this study is to empirically study the influencing factors of Chinese consumers on the food traceability system in the food safety field. In this study, multiple models—push factor, pull factor, mooring factor, and switching intention—were integrated into the push-pulling-mooring theory (...) to form a conceptual PPM comprehensive model framework to study the switching intentions of two-dimensional code traceability technology for dairy products of Chinese consumers. By collecting the questionnaire survey, 305 valid questionnaires were collected from the consumers of middle- and high-end dairy products in China, and the influencing factors of thrust, pull, and mooring force were identified. The results showed that 10 of the 11 hypotheses were positive, but the impact of perceived risk on user satisfaction was negative. The important value of this study is to conduct a comprehensive empirical analysis of the key factors influencing consumer choice of traceable safe food through an integrated multi-model framework to help identify ways to establish and improve consumer willingness to use QR code traceable system products, to increase consumer confidence in the use of traceable and safe food choices. (shrink)