A STIN Model Adoption for Chatbot in Higher Education Online Learning

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R2 = 0.745), SI (β = 0.070, R2 = 0.745), and BI (β = 0.792, R2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R2 = 0.745) and FC (β = −0.004, R2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R2 = 0.317), SI (β = 0.462, R2 = 0.317), and EE (β = 0.139, R2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.

This study examines a level and model for technology acceptability and use in online learning inside universities. The unified theory of UTAUT is used as an analysis tool. An associative quantitative method is used with a sample of 392 students. Data were collected by distributing questionnaires through a specially designed Google Form. The data obtained were then analyzed using variance-based SEM-PLS. The study findings show the adoption and utilization of technology in online education for university students are excellent. In addition, the structural analysis shows that all hypotheses developed in the model have a solid and significant direct and indirect correlation. Four predictors tested as a model, namely performance expectancy, effort expectancy, social influence, and facilitating conditions can predict behavioral intentions. Furthermore, behavioral intentions influence usage behavior positively and significantly. The conclusion of this study makes it clear that the UTAUT model can predict the acceptance and use of technology in online learning for university students. This study provides practical implications for university managers and policymakers to build students' trust in the technology offered by providing easy access and facilities according to their needs and expectations. Facilitating conditions including performance, adequate internet network access and compatible technology need to be considered by all parties so that the use of technology can be carried out smoothly.

Background and Aim: This research explores the key factors influencing nursing students' intention to use 3D body anatomy software at Liaoyang Vocational and Technical College, China. Based on these models, seven hypotheses and seven latent variables were proposed: Personal Innovation in Information Technology (PIIT), Performance Expectancy (PE), Effort Expectancy (EE), Subject Norms (SN), Attitude (ATT), Perceived Behavior Control (PBC), and Behavior Intention (BI). Materials and Methods: This study adopts a quantitative method, through an operationalized questionnaire, and conducts reliability and validity tests. The sampling method is purposive sampling and stratified sampling. Data analysis was performed through confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: The research findings indicate that all hypotheses are supported, with effort expectancy exerting the most significant influence on behavioral intention. The results show β =0.320, p < 0.001. This result indicates that EE has the greatest impact on BI. Next are Performance Expectancy (PE), Attitude (ATT), Subject Norms (SN), and Perceived Behavior Control (PBC). PIIT also shows a significant positive impact on PE and EE. Conclusion: This study indicates that in the comprehensive prediction of vocational nursing students' behavioral intention to use the 3Dbody anatomy software, performance expectancy (PE), effort expectancy (EE), subjective norm (SN), attitude (Att), and personal innovativeness (PIIT) all have a significant impact on behavioral intention (BI). According to the research results, software designers can simplify operations, while teachers can enhance students' confidence through guided learning. These improvements can enhance the user experience, thereby increasing students' adoption intention and usage retention.

This thesis evaluates the acceptance of the E-counseling system at the University of Jambi using the Unified Theory of Acceptance and Use of Technology 3 (UTAUT 3) model. This research is based on the problem of mental health levels in students supported by data from WHO and the Ministry of Health. The University of Jambi provides online counseling services to help students solve their problems, but from the pre-research it was found that the level of knowledge and use of E-counseling is still very low. This study aims to implement the UTAUT 3 model which is used to identify factors that influence the acceptance of E-counseling at the University of Jambi. This study took a sample of 100 respondents from all active undergraduate students at the University of Jambi. The analysis technique used in this study is PLS-SEM (Partial Last Structural Equation Modeling) which uses the SmartPLS 4 application in its data calculation. The results obtained from the data calculation show that there are 4 hypotheses that have a significant influence, namely Habit on Use Behavior, Personal Innovativeness on Behavioral Intention, Hedonic Motivation on Behavioral Intention, and Facilitating Conditions on Use Behavior and 8 other hypotheses that do not have a significant influence, namely Performance Expectancy on Behavioral Intention, Effort Expectancy on Behavioral Intention, Social Influence on Behavioral Intention, Facilitating Condition on Behavioral Intention, Price Value on Behavioral Intention, Habit on Behavioral Intention, Personal Innovativeness on Use Behavior and Behavioral Intention on Use Behavior.

Using the frameworks of the Theory of Use and Gratification (UGT) and the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study explores the factors that influence individual behavior and behavioral goals in the adoption of digital banking. Partial Least Square-Structural Equation Modeling (PLS-SEM) is used in the analysis of research data using the program SmartPLS 3.2.9 professional. There are 432 people in the research sample that filled out questionnaires. The results show that behavioral intentions are strongly influenced by the integration of UGT-UTAUT2 by 60.3%. Performance and effort expectations are influenced by cognitive needs, effort expectations are influenced by affective needs, and social influence is impacted by social needs. Behavioral intentions for the use of digital banking are shaped by a combination of factors such as price value, hedonic motivation, habits, facilitating conditions, and effort expectations. The relationship between behavioral intentions, affective and cognitive needs is mediated by effort expectations. In the context of using digital banking, habits and behavioral intentions are important factors that influence behavior; in contrast, cognitive needs, affective needs, performance expectations, and social influence have no direct effect on behavioral intentions.

PurposeThis study aims to investigate the factors that influence behavioral intention (BI) and usage of e-wallets by extending the unified theory of acceptance and use of technology (UTAUT) with constructs, namely, mobile self-efficacy, perceived enjoyment and satisfaction.Design/methodology/approachThis quantitative study used partial least squares structural equation modeling on a sample of 576 mobile e-wallet users surveyed online.FindingsThe key findings indicate that the model can explain 58.8% of the variance in behavioral intention and 53.8% in usage. Moreover, mobile self-efficacy has a significant influence on perceived enjoyment. Perceived enjoyment significantly affects satisfaction, effort expectancy and performance expectancy. Furthermore, effort expectancy significantly influences customer satisfaction in contrast to performance expectancy. In addition, although performance expectancy, social influence and satisfaction significantly impact consumers’ behavioral intention, effort expectancy and facilitating conditions condition have an insignificant influence on consumers’ behavioral intention. E-wallet stakeholders can use the findings of this study to make strategic decisions regarding the e-wallet ecosystem.Originality/valueAlthough previous studies have independently addressed the impact of mobile self-efficacy, perceived enjoyment and satisfaction on consumers’ behavioral intention and usage behavior, the expanded framework with the possible relationships proposed in this study has never been adequately studied in previous research in the context of e-wallets in developing countries based on an empirical analysis. This study represents one of the first attempts to improve the UTAUT by empirically analyzing these relationships.

Pharmaceutical care can be improved via mobile health (mHealth) applications (apps); however, the engagement of these apps depends on healthcare providers' acceptance. Thus, identifying barriers and facilitators to utilizing mHealth apps is needed to develop and facilitate their use. The objective of this research was to investigate the various elements that influence the acceptability and utilization of mHealth apps in the delivery of pharmaceutical care services, as perceived by pharmacists working in the government health sector of Kuwait. A cross-sectional survey was conducted, and the results were used in the mobile health technology acceptance model (m-TAM) to measure the behavioral intentions of pharmacists with regard to the acceptance and use of mHealth apps. Multiple elements influence the behavioral intention of pharmacists to use mHealth apps, including Compatibility (CO), Performance Expectancy (PE), Personal Innovativeness (PI) and Effort Expectancy (EE) were statistically significant predictors. The mediating role of PE was found to be statistically significant in the link between CO and BI and in the association between EE and BI. The substantial mediation effects of Effort Expectancy (EE) and Performance Expectancy (PE) were seen in the three associations between job-related mental demands (MA) and burnout (BI), professional identity (PI) and burnout (BI), and perceived social support at work (PSA) and Behavioural Intention (BI). The results further demonstrate that EE substantially mediates the connections between MA and PE, MSE and PE, PI and PE, and PSA and PE. These results suggest that the acceptance of mHealth apps by pharmacists in Kuwait is multifaceted and requires evaluating a chain of effects. As a result, developing apps that provide pharmaceutical care requires consideration of each one of these factors. Results can direct policymakers and stakeholders in their efforts to implement mHealth in Kuwait.

Purpose – The purpose of this paper is to explore the factors that influence users’ behavioral intention (BI) to adopt mobile social networking sites (mSNS) in facilitating formal/informal learning. Specifically, the study also investigates the association of mobility, reachability and convenience with performance expectancy (PE) and effort expectancy (EE). Design/methodology/approach – Partial least squares structural equation modeling (PLS-SEM) approach was applied to test on 266 valid responses. Findings – The findings indicated that learning compatibility (LC), PE, EE and copyright clearance (CC) has a significant effect on BI. The results also revealed that EE is influenced by mobility, reachability and convenience. PE however was found to be influenced by convenience. Practical implications – The results of this study provides valuable insights and references for practitioners and mobile network providers in developing mSNS in facilitating learning. Originality/value – While mSNS have the potential to become a new research area with numerous benefits for the learning community, there is little research on the adoption factors on mSNS in facilitating learning. This study therefore attempts to close the research gap by contributing to the mobile literatures.

Telemedicine has become increasingly important in healthcare, especially with the COVID-19 pandemic. Despite, Younger generations are more fluent in using technology, previous study shows that older generations (Gen-X) are more satisfied in using telemedicine compared to younger generations. This study aims to identify the factors influencing user satisfaction and behavioral intention toward Alodokter's telemedicine service application usage among Millennials and Gen-Z in Indonesia. A quantitative cross-sectional study was conducted using a purposive sampling technique. A total of 160 Millenials and Gen-Z respondents who had used the Alodokter telehealth application within the past year were chosen in this study. The data were collected by means of an online questionnaire that was distributed through widely used social media platforms. The questionnaire consisted of 30 questions that assessed variables, such as performance expectancy, effort expectancy, social influence, price value, customer satisfaction, and behavioral intention. Data were analyzed using Partial Least Square–Structural Equation Modeling (PLS–SEM) with SmartPLS software version 3.2.9. The findings reveal that customer satisfaction positively affects behavioral intention. Performance expectancy, effort expectancy, price value, and social influence positively impact customer satisfaction. Price value was shown to have the most positive influence on behavioral intention. This study provides insights into the factors influencing user satisfaction and behavioral intention toward telemedicine service application usage among younger generations in Indonesia. The results can be used to improve telemedicine services and enhance the experience of users, particularly Millennials and Gen-Z.

BackgroundThe application of virtual reality (VR) in gastroscopic operation teaching can be safe and effective, but the advantages can be realized only when students accept and use it. This study aims to identify the factors influencing Chinese clinical medical postgraduates on their intention to use the 3D gastroscopic model constructed based on VR technology using Unified Theory of Acceptance and Use of Technology (UTAUT) model. Students’ demographic factors are also taken into consideration.MethodsAll methods were carried out in accordance with relevant guidelines. Data were collected from clinical medical postgraduates students in China using stratified sampling. A total of 292 questionnaires including valid responses were used in this study. Data were processed using Amos 24.0 and SPSS 26.0 software and the statistical analysis technique was based on structural equation modeling (SEM).ResultsThe results showed that different from the mediator of home location and year of clinical learning, mediator of gender, university kind and graduate degree did not affect the behavioral intention. In addition, performance expectancy, facilitating condition, and social influence directly and indirectly have effect on behavioral intention. Also, the significance between social influence and performance expectancy, social influence and effort expectancy were verified.ConclusionsThis study manifested that the proposed framework based on the UTAUT had explanatory power to identify the factors influencing the students’ behavioral intention to use the 3D gastroscopic model constructed based on VR technology. Whereas, an important variable of effort expectancy in the frame of the SEM were not certified, thereby indicating that particular attention should be paid to this variable by universities and teachers before applying 3D gastroscopic model constructed based on VR technology in teaching. Added preparatory work is required such as explaining the basic knowledge of the operating steps of VR model and make students adequately understand its accessibility, which can probably improve the intentions of them to use it. The positive effects of social influence on performance expectancy and effort expectancy we proposed was also verified in this study, which provided a direction for future research.

Purpose: This study used the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) paradigm to investigate the factors influencing teachers' adoption of Digital Learning Resources (DLR). In addition to examining variations by gender, frequency of technology use, and academic field, this study assesses the influence of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HB) on behavioural intention (BI) and use behaviour (UB). Design/methodology/approach: Data were gathered from 502 teachers in nine taluks in Dakshina Kannada, India using a quantitative study approach and stratified random sampling. Google Forms and offline surveys were used to collect data, and structural equation modelling (SEM) was used for analysis. Group differences were also investigated using one-way analysis of variance (ANOVA) and independent sample t-tests. Findings: The findings showed that teachers’ BIs were highly influenced by PV, HM, SI, PE, and EE. Use behaviour is greatly influenced by behavioural aims and facilitating circumstances. FC did not affect BI, and HB had no discernible impact on BI or UB. Gender-based BI did not differ significantly according to the group analysis; however, the frequency of technology use and field of study did differ significantly. Practical implications: This study highlights the need for customised training, improved digital infrastructure, and targeted professional development to enhance the integration of digital tools in education. Originality/value: This study provides empirical insights into digital adoption patterns among teachers and offers valuable guidance to policymakers and teachers aiming to enhance technology-driven learning environments. Type of the paper: Original Article

The study aimed to assess the effect of eight factors that shape employees’ behavioural intentions regarding the use of AI systems in their professional environments: performance and effort expectancy, social influence, facilitating conditions, relative advantage, compatibility, complexity and trialability. These factors were used as predictors of behavioural intention and use behaviour. Data for PLS-SEM analysis were collected from tourism employees using an online survey. Findings revealed a positive and substantial correlation between factors like performance expectancy, effort expectancy and social influence on the one hand and behavioural intentions on the other. In addition, facilitating conditions, compatibility, complexity and trialability were found to be positively and significantly correlated with behavioural intentions, which were also correlated with employees’ use of AI. The study contributes to a more nuanced understanding of the human dimension in the implementation of AI, offering insights for organisations seeking to navigate the complex behavioural landscape of technological change.

This study explores the adoption of augmented reality (AR) in digital insurance services through the lens of the unified theory of acceptance and use of technology (UTAUT). As insurers increasingly turn to digital solutions to improve operational efficiency and customer engagement, AR emerges as a promising yet under-researched technology with applications in claims processing, risk communication, and policy visualization. By adapting core UTAUT constructs – performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), behavioral intention (BI), and actual use (AU) – the research employs a scenario-based quantitative design to assess user perceptions and BIs in a simulated AR insurance environment. Data were collected via an online survey targeting individuals with prior insurance experience and basic digital literacy. The structural model was tested using structural equation modeling (SEM). Results reveal that EE, SI, and FC have significant positive effects on BI, and that BI strongly predicts AU. However, PE does not significantly influence BI, suggesting that perceived ease of use and environmental support outweigh functional expectations in early-stage technology contexts like AR insurance. The findings provide both theoretical and practical contributions. Theoretically, the study extends UTAUT to a novel application domain, offering insights into user behavior around emerging InsurTech tools. Practically, it highlights the importance of intuitive design, social endorsement, and infrastructural readiness for fostering adoption. These insights can inform insurers aiming to integrate AR into their digital service strategies.

Objective: Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aims to examine patients’ behavioral intention (BI) toward accepting electroacupuncture analgesia and identify key determinants that influence its adoption. Methods: We conducted a multicenter cross-sectional study with questionnaire-based data collection and analyzed associations using structural equation modeling (SEM). Reporting followed the STROBE guidelines. Results: A total of 325 participants were included in the analysis. Performance expectancy (PE) ( β = 0.272, p < 0.001), effort expectancy (EE) ( β = 0.610, p < 0.001), and facilitating conditions (FC) ( β = 0.586, p < 0.001) exhibited significant positive effects on BI. Social influence did not exert a significant effect ( β = 0.217, p = 0.090). FC had a significant negative effect on use behavior (UB) ( β = −0.617, p < 0.001), forming a competitive mediation model with its positive indirect effect via BI. Significant differences in BI were observed across participants of different age groups, social backgrounds, educational levels, occupations, and income levels ( p < 0.05). The model exhibited a good fit, supporting the applicability of the theoretical framework. Conclusion: EE, PE, and FC are key factors influencing patients’ acceptance of electroacupuncture analgesia. FC exhibited a competitive mediation effect on UB, highlighting the necessity of optimizing convenience conditions to meet the higher expectations of experienced users. Future initiatives should focus on developing tailored strategies for diverse demographic groups to enhance the clinical adoption and therapeutic effectiveness of electroacupuncture technology.

This study is important considering the rapid adoption of live streaming technology in the fashion industry, which has become a primary tool for boosting sales. Understanding the factors influencing fashion vendors' use of this technology is crucial, as it can help businesses and marketplace platforms design more effective and interactive marketing strategies. This study examines how behavioral intention affects the actual use of live streaming technology, while also considering the moderating role of gender in the relationships between these variables. The study adopts a quantitative approach, collecting data through a questionnaire survey. The population for this study consists of fashion vendors who actively use live streaming features on marketplaces such as Shopee, Tokopedia, and Lazada in the Tanah Abang area. Purposive sampling was employed as the sampling technique, as the study requires fashion vendors who meet specific criteria, namely, those who have used live streaming technology to sell their products on marketplaces. As a result, the sample size amounted to 211 participants. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS software to test the model’s validity and the relationships between variables. The findings show that trust significantly affects performance expectancy and effort expectancy. Both performance expectancy and effort expectancy significantly influence behavioral intention, and behavioral intention, in turn, positively affects the actual use of live streaming. This study offers new insights into how gender differences influence fashion vendors' perceptions and decisions when using live streaming technology as a marketing tool. Furthermore, it provides a deeper understanding of how trust in technology can strengthen performance and effort expectancies, ultimately enhancing behavioral intention and the actual use of live streaming technology.