Extended Unified Theory Of Acceptance And Use Of Technology Model Research Articles

Explaining primary school teachers’ intention to use digital learning platforms for students’ individualized practice: comparison of the standard UTAUT and an extended model

IntroductionThough technologies for individualization appear to benefit primary school students’ learning, studies suggest that their integration remains sparse. Technology acceptance research has largely focused on exploring teachers’ general acceptance of educational technologies, although factors might predict usage intentions differently depending on the specific usage purpose of an educational technology. Digital learning platforms for individualized practice are comparably challenging and complex to use and so far, predictors of primary school teachers’ intention to integrate such technologies into lessons are largely unknown. Meanwhile, research on teachers’ technology acceptance generally lacks comparability due to the absence of a shared theoretical model and usage purpose specification.MethodsIn a sample of 272 German primary school teachers, this study aims to identify predictors of teachers’ acceptance of digital learning platforms for students’ individualized practice in consideration of the unified theory of acceptance and use of technology (UTAUT). To ensure a shared understanding, teachers were provided with a video which specified the addressed usage purpose. Regarding teachers’ usage intention, the explanatory power of the standard UTAUT predictors was investigated and compared with an extended UTAUT model accounting for seven additional context-specific predictors.ResultsThe standard UTAUT significantly explained teachers’ usage intention, with performance expectancy, effort expectancy, and the availability of the necessary technical infrastructure showing significant associations with intention. However, neither a significant nor meaningful increase in explained variance was observed for the extended UTAUT model.DiscussionResults suggest that the standard UTAUT model is sufficient in explaining teachers’ usage intention and that its extension by context-specific predictors provides no added value. Acceptance facilitating interventions should therefore target performance and effort expectancy as well as the availability of technical infrastructure. Thus, underlining that successful implementation of complex educational technologies should consider both, individual and structural factors.

Examining the Reliability and Validity of Measuring Scales related to Informatization Instructional Leadership Using PLS-SEM Approach

During the COVID-19, university teachers’ informatization instructional leadership (TIIL) was adopted in many countries. This attracted widespread attention. This research derived six factors from the unified theory of acceptance and use of technology (UTAUT) including performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC) and behavioral intention (BI), added two new internal elements related to the individual teacher which are computer self-efficacy (CSE) and blended teaching competence (BTC). Before using the Partial Least Squares Structural Equation Modeling (PLS-SEM) to explore the contributing factors to TIIL by assessing interrelation between constructs within extended UTAUT model in this study. This pilot study aimed to examine the reliability and validity of modified scales incorporating Use Expectancy (UE) Scale including (PE Scale and EE Scale) used to measure use expectancy, SI Scale to measure social influence, FC Scale to measure facilitating conditions, CSE Scale to measure computer self-efficacy, BTC Scale to measure blended teaching competence, BI Scale to measure behavioral intention to adopt TIIL, and the TIIL Scale to measure teachers’ informatization instructional leadership. A total of 60 teachers from the large multi-disciplinary private undergraduate universities in Xi’an city of Shaanxi province in China participated in this research. The data was collected in November-December 2022 during the middle stages of COVID-19 pandemic. The PLS-SEM approach was used to evaluate the reliability and validity of the adapted scales. The internal consistency reliability was determined by composite reliability (CR) and Cronbach’s alpha. Convergent validity was assessed by outer loading and average variance extracted (AVE). Assessment of discriminant validity was measured by Fornell-Larcker criterion, Cross-loadings and Heterotrait-Monotrait Ratio (HTMT). Results showed after deleting nine items with lower than .40, Cronbach’s alpha values were all higher than .70. CR values were at a satisfactory level. All item values fulfilled the criteria of AVE, Fornell-Larcker criterion, cross-loadings, and HTMT. Research results revealed all adapted scales were valid and reliable to be used in future research. This study explored the influencing factors of TIIL in Chinese context, enriched the theory of TIIL, and provided practical support for the future development of TIIL.

An extended UTAUT model study on the adoption behavior of artificial intelligence technology in construction industry

BACKGROUND: In recent years, Despite the proven economic growth brought by AI technology globally, the adoption of AI in the construction industry faces obstacles. To better promote the adoption of AI technology in the construction domain, this study, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, delves into the key factors influencing the adoption of AI technology in the construction industry. By introducing personal-level influencing factors such as AI anxiety and personal innovativeness, the UTAUT model is extended to comprehensively understand users’ attitudes and adoption behaviors towards AI technology. METHODOLOGY: The research framework is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) with the added constructs of artificial intelligence anxiety and individual Innovativeness. These data were collected through a combination of online and offline surveys, with a total of 258 valid data collected and analyzed using structural equation modeling. RESULTS: The study found that Usage Behavior (UB) in adopting Artificial Intelligence (AI) is positively influenced by several factors. Specifically, Performance Expectancy (PE) (β= 0.266, 95%), Effort Expectancy (EE) (β= 0.262, 95%), and Social Influence (SI) (β= 0.131, 95%) were identified as significant predictors of UB. Additionally, Facilitating Conditions (FC) (β= 0.168, 95%) also positively influenced UB.Moreover, the study explored the moderating effects of Artificial Intelligence Anxiety and Individual Innovativeness on the relationships between Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) with the Usage Behavior of AI technology. PRACTICAL IMPLICATIONS: This study lie in informing industry stakeholders about the multifaceted dynamics influencing AI adoption. Armed with this knowledge, organizations can make informed decisions, implement effective interventions, and navigate the challenges associated with integrating AI technology into the construction sector.

Investigating the Adoption of Blockchain Technology in Agri-Food Supply Chains: Analysis of an Extended UTAUT Model

Against a backdrop of globalization, dynamic shifts in consumer demand, and climate change impact, the intricacies of agri-food supply chains have become increasingly convoluted, necessitating innovative measures to guarantee agri-food security and authenticity. Blockchain technology emerges as a promising solution, offering transparency, immutability, traceability, and efficiency in the overall supply chain. This study aims to investigate determinants impacting both the intention to use and the actual usage of blockchain-driven agri-food supply chain platforms. To achieve this, an expanded and adapted conceptual model rooted in the Unified Theory of Acceptance and Use of Technology (UTAUT) was formulated and empirically examined through Partial Least Squares Structural Equation Modeling using data from 175 respondents from agri-food companies across eight European countries. Agri-Food Supply Chain Partner Preparedness (FSCPP) emerged as the pivotal factor with the highest degree of influence on the intention to use blockchain-driven supply chain platforms. Additionally, the results from this study offer support for the significant influence of Performance Expectancy (PE), Effort Expectancy (EE), and Perceived Trust (PT) on usage intention, while also revealing the positive impact of Organizational Blockchain Readiness (OBR) on expected Usage Behavior (UB). This study provides significant insights into blockchain adoption within agri-food supply chains, contributing to the existing literature through an extended UTAUT framework.

The impact of credibility of streamers on the acceptance of live streaming commerce: An extended UTAUT model

This study aims to explore how innovation adoption, based on the Unified Theory of Acceptance and Use of Technology (UTAUT), influences online shopping through live streaming platforms. It focuses on understanding the factors that drive customer acceptance and usage behavior in the context of live streaming commerce. The research employed a quantitative methodology, utilizing a survey instrument to collect data. The participants comprised 237 online customers in Malaysia who were familiar with live streaming as a shopping medium. The SmartPLS software, which is a tool for structural equation modelling, was utilized for data analysis. The findings suggest that performance expectation, effort expectancy, and social influence, which are important components of the UTAUT theory, have a considerable impact on consumer acceptance and usage behaviour in live streaming commerce. Additionally, the credibility of streamers emerged as a critical factor influencing purchase intentions. These findings highlight the importance of streamer credibility in the live streaming shopping experience. This research offers valuable insights for online retailers and businesses utilizing live streaming platforms for commerce. Understanding the factors that encourage customer engagement and purchase can help in strategizing marketing and operational approaches. It also suggests that the streamers' credibility is a vital aspect of the shopping experience, pointing towards the need for selecting and training streamers effectively. Moreover, this study extends the applicability of the UTAUT theory to live streaming commerce, providing a framework for further research in this emerging field.

Does a gamified website matter? An extended UTAUT model with regulatory fit, user experience, engagement, and attitudes

Using the unified theory of acceptance and use of technology (UTAUT) model with user experience, engagement, and attitude toward a website, this study aims to draw on recent theoretical insights for the impact of regulatory fit derived for prevention-focused (promotion-focused) users faced with a more utilitarian (hedonic) website on their intentions to use the website and recycle resources. This study employed survey data from 472 Taiwanese with internet experience. Structural equation modeling was applied to analyze the developed model and hypotheses. This study shows that intention to use the website has a significantly positive effect on the practice of recycling resources. Further, social influence, facilitating conditions, and user attitude toward the website are key determinants of intention to use the website and recycle resources. This analysis also identifies the mediating effects of user experience, performance expectation, effort expectation, and engagement on the association of regulatory fit with intention to use the website and recycle resources. This study extends and validates the role played by promotion- and prevention-focused users encountering a hedonic versus utilitarian website (i.e., generation of regulatory fit) in enhancing user experience, engagement, performance expectation, and effort expectation, and in turn, intensifying behavioral intentions. In addition, it confirms the mediating effects of such experience, engagement, and expectation in an online gamification context.

Enhancing Business Efficiency through Low-Code/No-Code Technology Adoption: Insights from an Extended UTAUT Model

The growing need for new applications and software has driven developers to seek quick development options. As a result, the low-code/no-code technology platform emerges as a potential option, leading to the adoption of low-code and no-code technologies in enterprises becoming a focus of inquiry. This study aims to examine users' behavioral intentions toward the adoption of the low-code/no-code technology platform, considering the increasing need for new applications. The Extended Unified Theory of Acceptance and Use of Technology (UTAUT) model serves as the theoretical framework for understanding the factors influencing individuals’ intentions to adopt low-code or no-code technologies. The study focused on the five key components of the model: Performance Expectation (PE), Effort Expectation (EE), Social Influence (SI), Perceived Risk (PR), and Perceived Cost (PC). Based on the surveys and data analysis techniques, the findings show relationship between these five categories with an individual's Behavioral Intention (BI) to adopt low-code/no-code technologies. Furthermore, the analysis identifies the most significant BI construct. These findings are beneficial to businesses seeking to enhance efficiency and expedite application development processes in response to increasing digital demands. In general, this study contributes to the topic of technology adoption and improves our understanding of the practicality of the Extended UTAUT model. Doi: 10.28991/HEF-2024-05-01-07 Full Text: PDF

Mobile learning using extended UTAUT model during COVID-19: evidence from developed countries

This research evaluates the extended unified theory of acceptance and use of technology (UTAUT) model from the context of mobile-based learning using smartphones amid COVID-19. It specifically examines the impact of exogenous variables such as social isolation besides standard dimensions and mediating variables such as perceived compatibility, perceived anxiety, and perceived value on mobile learning technology. The research also explores the impact of service quality and technological innovation as moderating variables on the modified and extended UTAUT model. The data for this research was gathered from 898 students in technologically advanced countries, for instance, Canada, the United Kingdom, Spain, France, the United States, Australia, and Germany. The outcomes of this research show that the exogenous dimensions of the UTAUT model, such as social isolation, have an affirmative and significant association with the behavioral intent to adopt mobile-based learning in an online education environment. The study’s findings further exhibited that the mediating dimensions, such as perceived anxiety, perceived compatibility, and perceived value, have a robust and affirmative association between exogenous and endogenous factors. Moreover, the results demonstrated a strong influence of technological innovation and service quality on the association between independent and dependent factors. Overall, the research findings have significant implications for both industry and academia regarding management and theory.

Students' Behavioural Intention towards Adoption of Online Education: A Study of the Extended UTAUT Model

Online education (o-education) is gaining significant attention from education providers, students, and policy makers. This research explored students’ behaviours and intentions towards online education. The present research utilised the Unified Theory of Acceptance and Use of Technology (UTAUT) as a model to investigate these intentions and behaviours. The study incorporated UTAUT predictors along with three additional independent variables. This study adopted a cross-sectional design and used a closed-end questionnaire to collect data from 403 respondents. The data were analysed with SPSS and AMOS by utilising structural equation modeling (SEM) techniques. The results of the SEM analysis indicate that perceived value (PV) and convenience (Cnv) have positive influences on behavioural intention (BI), while perceived risk has a negative impact on behavioural intention (BI). Additionally, facilitating conditions (FC) and behavioural intention (BI) are found to positively influence use behavior (UB). The research findings suggest that demographics, including age, education and gender, influence behavioural intention (BI). The findings of the study have theoretical and practical implications, which are elaborated in the last section of this article.

Gravitating towards Fintech: A study on Undergraduates using extended UTAUT model

One of the most pressing issues affecting people worldwide is the accessibility of financial services in the wake of the COVID-19 outbreak. While peoples' adoption of technological advancements has spiraled over time, the acceptance of Fintech services edged up for instilling resilience during the pandemic. Hence, the study aims at exhuming the Fintech adoption for sustainable financial inclusion among young undergraduate users through the Unified Theory of Acceptance and Use of Technology (UTAUT) model. To test the model, the researchers obtained data from 375 undergrads. The study adopted the Structural Equation Modelling (SEM) approach in AMOS software to examine the determinants that affect the users to embrace Fintech services. The findings revealed that performance expectancy, effort expectancy, and facilitating conditions significantly influence the students' intention to use Fintech, whereas, the users’ facilitating condition and behavioral purposes positively influence them to use the financial technology. Interestingly, the social influence and personal innovativeness did not affect their intention to accept and adopt Fintech. Therefore, the study results will help explore the expectations, preferences, and actual level of use of Fintech among undergraduates and contribute to the escalation of Fintech use, leading to sustainable financial inclusion.

Adoption intention of mobile QR code payment system among marginalized street vendors: an empirical investigation from an emerging economy

Purpose Recently, the Government of India has emphasized digital financial inclusion for promoting cashless transactions with a vision to transform India from a traditional cash-based economy into a cashless economy. Technology-driven payment apps are facilitated greater access to cashless financial services and improve the speed, efficiency, accuracy and effectiveness of financial transactions. This study aims to explore the determinants of quick response (QR) code mobile payment (m-payment) adoption intention among marginalized street vendors in India. Design/methodology/approach The proposed research model was tested using 320 responses from QR code m-payment users. An interview schedule was performed using the structured questionnaire from marginalized street vendors by adopting a purposive sampling technique. The proposed research framework of this study developed on the Unified Theory of Acceptance and Use of Technology (UTAUT). In addition to the existing variables proposed in the UTAUT model, three more variables have been added, namely, digital financial literacy (DFL), personal innovativeness (PI) and perceived trust (PT). Besides, the study used confirmatory factor analysis and structural equation modeling techniques to analyze the data. Findings This study confirms that factors such as performance expectancy, effort expectancy, facilitating conditions, PT and customers’ DFL are significant determinants of street vendors’ intention to use QR code m-payment services. However, social influence and PI have shown an insignificant relationship with adopting a QR code m-payment system. Research limitations/implications The results provide insights for policymakers and service providers. Specifically, government and bankers design promotional campaigns emphasizing the ease of use, perceived benefits, security and faster business transactions to accept and use the QR code m-payment system to encourage prospective users to achieve a cashless economy. Originality/value Many prior studies have widely concentrated on m-payment adoption intention in India. However, only a few studies have attempted to examine the factors influencing the adoption of QR code m-payment services among merchants from emerging economies. There is a dearth of studies on QR code adoption from an unorganized sector perspective, specifically marginalized street vendors. Therefore, this study explicitly examines the extent to which the determinants of adoption intention toward QR code-based m-payment services among marginalized street vendors within the framework of the extended UTAUT model by incorporating DFL, PI and PT. The findings of this study contribute, theoretically and practically, to the existing literature.

Understanding consumer adoption of mobile payment in Pakistan

Purpose This study aims to explore the consumers’ choices of mobile payments (m-payments) using a comprehensive unified model. The financial technology for digital m-payment has been increasingly introduced in the market, yet their acceptance has remained low. Design/methodology/approach This study uses the unified theory of acceptance and use of technology (UTAUT) with additional constructs of social influence, trust, anxiety, personal innovativeness and grievance redressal (GR). Structural equation modeling is used to evaluate the predictive model of attitudes toward m-payment. Individuals’ responses to questions regarding their attitude and intention to accept m-payment were gathered and examined through the lens of extended UTAUT model. Findings While the model supports TAM classical role, empirical examination of the model revealed that users’ attitudes and intentions are influenced by trust, personal innovativeness and social influence. Moreover, intention to use and GR are significant positive predictors of m-payment usage behavior. Originality/value M-payment provides customers with new digital payment platforms while providing businesses and marketing agents with more alternatives for online marketing. However, there is not much reported about m-payment adoption in Pakistan. This research introduces and evaluates new constructs that were not included in the original model. In Pakistan, to the best of the authors’ knowledge, this is a first of its kind of research which is purely based on the customers’ perspective of m-payment adoption.

Determining the Acceptance of Digital Cardiac Rehabilitation and Its Influencing Factors among Patients Affected by Cardiac Diseases.

Cardiac diseases are a major global health issue with an increasing prevalence of affected people. Rehabilitation following cardiac events is underutilized, despite its proven effectiveness. Digital interventions might present a useful addition to traditional cardiac rehabilitation. This study aims to assess the acceptance of mobile health (mHealth) cardiac rehabilitation and to investigate the underlying factors of acceptance in patients with ischemic heart disease and congestive heart failure. A cross-sectional study was conducted from November 2021 to September 2022 with N = 290 patients. Sociodemographic, medical, and eHealth-related data were assessed. The Unified Theory of Acceptance and Use of Technology (UTAUT) was applied. Group differences in acceptance were examined and a multiple hierarchical regression analysis was conducted. The overall acceptance of mHealth cardiac rehabilitation was high (M = 4.05, SD = 0.93). Individuals with mental illness reported significantly higher acceptance (t(288) = 3.15, padj = 0.007, d = 0.43). Depressive symptoms (β = 0.34, p < 0.001); digital confidence (β = 0.19, p = 0.003); and the UTAUT predictors of performance expectancy (β = 0.34, p < 0.001), effort expectancy (β = 0.34, p < 0.001), and social influence (β = 0.26, p < 0.001) significantly predicted acceptance. The extended UTAUT model explained 69.5% of the variance in acceptance. As acceptance is associated with the actual use of mHealth, the high level of acceptance found in this study is a promising basis for the future implementation of innovative mHealth offers in cardiac rehabilitation.

Influence of achievement goal on technology use among undergraduates in Malaysia

ABSTRACT Previous studies had identified the potential link between achievement goal and students’ technology use. However, the literature on this topic is extremely scarce. The purpose of this study was to investigate the antecedents to technology use among undergraduates via an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with achievement goal as an external variable. Data were collected from 599 undergraduates using a 38-item self-report questionnaire and analyzed using structural equation modeling. Results of this study showed that the extended UTAUT model had a good fit to the data of this study, with 35% of the variance in behavioral intention and 32% of the variance in use behavior explained by their antecedents. This study found that performance expectancy, effort expectancy, and achievement goal had significant influences on undergraduates’ technology behavioral intention to use technology, while social influence and facilitating conditions from the original UTAUT model did not. It was also found that achievement goal and behavioral intention were significant antecedents that influenced undergraduates’ technology use behavior. The findings of this study contribute to both theory, practice, and policy that enhance our understanding of students’ learning and technology use.

Factors determining behavioural intention of nursing students to use mobile learning: an application and extension of the UTAUT model

Mobile learning (m-learning) promotes not only the quality of learning process but also learning performance. Therefore, this study proposed a theoretical framework model to predict the factors affecting behavioural intentions of nursing students to use m-learning. We evaluated the unified theory of acceptance and use of technology (UTAUT) model and extended it to include attitude, mobile self-efficacy, and motivation, with mobile literacy as the moderating factor. For this purpose, the structural equation model was used to analyse data from an online survey of 792 nursing students from six universities in Taiwan. The results confirm that: 1) attitude was an important determinant of the behavioural intention of nursing students to use m-learning; 2) amongst the four major constructs of UTAUT, performance expectancy had the most significant impact on behavioural intention; 3) M-SE and motivation were important external factors of PE and effort expectancy; 4) mobile literacy had a significant moderating effect on the relationship among PE, facilitating conditions, and behavioural intention. The results of this study provide support for an extended UTAUT model and significant practical recommendations for researchers, nursing students, and coherent education units, in order to improve the feasibility of m-learning.

Determinants of the Behavioural Intention of Open Distance Learning Students to Use Digital Tools and Resources for Learning in Nigeria

This study investigated determinants of the behavioural intention of Open Distance Learning (ODL) students to use digital tools and resources for learning in Nigeria. The study utilized an online questionnaire to gather data from 522 ODL students from an ODL institution in Nigeria. The Unified Theory of Acceptance and Use of Technology (UTAUT) was the main framework used for the analysis in which attitude was included as an additional variable to seek out a much better model capable of improving the understanding of ODL students’ behavioural intention to use digital tools and resources for learning. Thus, an extended UTAUT model was developed and tested in this study. The model consisted of six constructs: performance expectancy, effort expectancy, social influence, facilitating conditions, attitude and behavioural intention. A Partial Least Square-Structural Equation Modelling (PLS-SEM) was used for data analysis. Results revealed that the proposed model successfully explained critical factors that determine ODL students’ behavioural intention to use digital tools and resources for learning. The study suggested that attitude, performance expectancy and facilitating conditions are the major determinants of behavioural intention to use digital tools and resources but attitude is the most prominent factor. Thus, implications and suggestions for further studies were highlighted.

Understanding Users' Continuance Usage Behavior Towards Digital Health Information System Driven by the Digital Revolution Under COVID-19 Context: An Extended UTAUT Model.

BackgroundImproving the health status of users through the use of digital health information systems has drawn the attention of practitioners and academics under the tide of the digital revolution, specifically in the time of global pandemic of COVID-19, and as a result, online medical consultation has developed rapidly.PurposeEmpirical studies, however, are lacking in terms of gaining insight into use digital health information system driven by the digital revolution under COVID-19 and identifying the factors for retaining users and encouraging their continuing use. To solve this problem, this study seeks to explore the factors that influence users’ intention to use digital health information system.MethodsThis study extended the Unified Theory of Acceptance and Use of Technology (UTAUT) model by introducing components of perceived risks into the model. Structural equation modeling was adopted to evaluate the research model based on an empirical survey of 241 users in China.ResultsAs indicated by the results, users’ continuance usage behavior to digital health information system is shaped by intention to use and facilitating conditions, with effort expectancy, social influence, perceived ease of use and perceived enjoyment exerting indirect positive effects on continuance usage behavior via intention to use. In contrast, perceived risk and perceived cost have indirect negative impact on continuance usage behavior.ConclusionThe findings of this study can not only help the practitioners better understand the users’ continuance usage behavior towards digital health information system driven by digital revolution in the time of COVID-19 pandemic and further tap into the potential market but also make up the short of traditional technology acceptance model explanatory by the extended UTAUT model.

Influences of Online Practice Community on Instructors’ Blended Teaching Behaviors: Analysis Based on an Extended UTAUT Model

During the implementation of China’s Vocational Skill Level Certificate, the Vocational Skill Curriculum Development Organization built an online teaching community of practice (COP) by inviting instructors from several schools to participate. Through this community, instructors constructed a blended teaching system together through multiparty collaboration modes like cooperation, communication, sharing, and mutual assistance. As demonstrated by the research team, when teachers participate in a community of practice, the curriculum development model implemented within that community can strengthen the blended teaching intentions and ability of instructors, thus exerting positive effects on their blended teaching behaviors. Based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT), a theoretical model concerning the influencing factors of online teaching communities of practice on the blended teaching behaviors of instructors was constructed. Through a questionnaire survey and field interviews of 204 instructors, data were analyzed by SPSS and AMOS software and an empirical test of the constructed conceptual model was carried out. The research results demonstrate that performance expectancy influences the blended teaching intentions of instructors only slightly, while effort expectancy and facilitating conditions have significantly positive influences on the blended teaching intentions of instructors. Interactions between the instructors themselves and interactions between instructors and the organization in the online community of practice have significantly positive influences on the blended teaching ability of instructors. The blended teaching intentions and blended teaching ability of instructors both have significantly positive influences on their blended teaching behaviors. In this research conclusion, various methods for online COPs to improve the blended teaching behaviors of instructors were summarized.

Factors Affecting Learners’ Adoption of an Educational Metaverse Platform: An Empirical Study Based on an Extended UTAUT Model

This study examined the factors affecting learners’ adoption of an educational metaverse platform using an extended UTAUT (unified theory of acceptance and use of technology) model and incorporating perceived risk. Data were derived from a survey of 495 respondents from China and analyzed using structural equation modeling. The results revealed that (i) performance expectancy, effort expectancy, social influence, and facilitating conditions had significantly positive effects on learners’ satisfaction with the Eduverse; (ii) learners’ satisfaction had a positive effect on their continued usage intention; (iii) learners’ intention to use the Eduverse was reduced after they perceived risks. Our study provided empirical evidence of the validity of the UTAUT model in explaining learners’ adoption of the Eduverse. Our findings have significant practical implications for enterprises, educational institutions, and governments.

Determining the Influencing Factors on Acceptance of eHealth Pain Management Interventions Among Patients With Chronic Pain Using the Unified Theory of Acceptance and Use of Technology: Cross-sectional Study

Chronic pain is a complex disease with high prevalence rates, and many individuals who are affected do not receive adequate treatment. As a complement to conventional therapies, eHealth interventions could provide many benefits to a multimodal treatment approach for patients with chronic pain, whereby future use is associated with the acceptance of these interventions. This study aims to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the influencing factors on acceptance. A further objective of the study is to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and compare it with its extended version in terms of explained variance of acceptance. We performed a cross-sectional web-based study. In total, 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited through flyers, posters, and web-based inquiries between December 2020 and July 2021. In addition to sociodemographic and medical data, the assessment included validated psychometric instruments and an extended version of the well-established UTAUT model. For statistical analyses, group comparisons and multiple hierarchical regression analyses were performed. The acceptance of eHealth pain management interventions among patients with chronic pain was overall moderate to high (mean 3.67, SD 0.89). There was significant difference in acceptance among age groups (W=9674.0; r=0.156; P=.04). Effort expectancy (β=.37; P<.001), performance expectancy (β=.33; P<.001), and social influence (β=.34; P<.001) proved to be the most important predictors of acceptance. The extended UTAUT (including the original UTAUT factors as well as sociodemographic, medical, and eHealth-related factors) model explained 66.4% of the variance in acceptance, thus supporting the viability of the model. Compared with the original UTAUT model (performance expectancy, effort expectancy, and social influence), the extended model explained significantly more variance (F25,278=1.74; P=.02). Given the association between acceptance and future use, the knowledge of the influencing factors on acceptance should be used in the development and promotion of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. In total, 8 predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as the factors that influence acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.