Factors Driving Continuous Intention to Use Telemedicine in the Post-COVID-19 Era: An Integrated HBM and UTAUT Approach

A considerable amount of research using Unified Theory of Acceptance and Use of Technology (UTAUT) has been conducted worldwide to investigate the intention and actual usage of Learning Management Systems (LMS) by tertiary staff during COVID-19. However, there seems to be a lack of such research in developing countries like South Africa. Equally important is the examination of how UTAUT, in the context of developing countries, either supports or contradicts existing findings. Our motivation, therefore, was to determine whether the behavioural intention (BI) of tertiary staff within the context of a developing country aligns with or contradicts existing findings. Simultaneously, we aimed to explore the areas identified by UTAUT that should be addressed or considered based on these factors. We also incorporated additional context specific to developing countries. Guided by ten (10) hypotheses, we employed partial least squares structural equation modeling (PLS-SEM) to analyse the measurement and structural models using a survey of two hundred and sixty-four (264) respondents from one university in the Province of the Eastern Cape in South Africa. According to the UTAUT model, several factors such as performance expectancy (PE), effort expectancy (EE), attitude toward using technology (ATT), social influence (SF), self-efficacy (SE), anxiety (ANX), and facilitating conditions (FC) influence BI. However, the results obtained through path coefficient bootstrapping, using 10,000 subsamples, revealed that the intention to use LMS was explained by only three latent constructs: facilitating conditions (FC→ BI), performance expectancy (PE→ BI), and social-influence (SI→ BI). These three factors collectively accounted for approximately 44% of the variance (R-squared) in BI. We argue that instead of solely critiquing UTAUT, it is essential to consider its limitations and explore future research opportunities, particularly in connecting BI with actual usage. Overall, the theoretical implications of these results underscore the importance of acknowledging the influence of context and the limitations of existing theories in understanding technology acceptance in developing countries.

Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation. The results suggest the importance of the users' performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users' health motivation, thereby reducing users' effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users' habitual use behaviors.

As AI technologies with predictive capabilities increasingly spread, it has become necessary to leverage them in light of the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical Content Knowledge (TPACK) frameworks, especially in the English language. This study investigates the factors influencing English lecturers’ intentions to adopt artificial intelligence (AI) in teaching, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT) and Technological Pedagogical Content Knowledge (TPACK) frameworks. A quantitative research methodology was employed, collecting data from 174 English lecturers in Jordan through structured questionnaires. Structural Equation Modeling (SEM) was used to analyze the relationships between UTAUT constructs—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—and TPACK components, including Technological Knowledge (TK), Pedagogical Knowledge (PK), and Content Knowledge (CK). Reliability and validity measures confirmed the robustness of the instrument. The findings reveal that PE, EE, SI, and FC significantly predict lecturers’ Behavioral Intention (BI) to adopt AI tools. Furthermore, TPACK components, particularly Technological Pedagogical Knowledge (TPK) and Technological Content Knowledge (TCK), mediate the relationship between UTAUT factors and BI. Facilitating Conditions and Social Influence were found to have the strongest indirect impact through TPACK constructs. The model fit indices indicated a good fit, validating the proposed hypotheses. The study underscores the importance of professional development programs to enhance educators’ TPACK and emphasizes the need for institutional support to foster AI adoption. These findings contribute to the literature on technology adoption in education and provide actionable recommendations for integrating AI into English language teaching.

BackgroundEffective public health surveillance is essential for policymaking and resource allocation. The World Health Organization (WHO) supports the integration of mobile technologies to create mobile Oral (m-Oral) Health surveillance systems to enhance disease monitoring. The effectiveness and sustainability of electronic health information initiatives depend on users’ acceptance of new technologies. This research assessed dentists’ acceptance of electronic oral health surveillance systems (EOHSS) and related factors, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model.Materials and methodsA cross-sectional study included 1470 Egyptian dentists in an online survey from November 2023 to May 2024. The dentists were recruited from the five administrative regions in Egypt using convenience and snowball sampling. Participants responded to a questionnaire that was based on the UTAUT model. Structural equation model (SEM) was used for data analysis.Results83.4% of dentists intended to use EOHSS. Performance expectancy (PE) (ß = 0.240, 95% CI: 0.182, 0.295), training adequacy (TA) (ß = 0.232, 95% CI: 0.165, 0.291), and effort expectancy (EE) (ß = 0.231, 95% CI: 0.169, 0.289) had the greatest influence on behavioral intention (BI). In contrast, anxiety towards electronic systems (ANX) (ß = -0.140, 95% CI: -0.187, -0.095) had a significant negative effect on BI. Effort Expectancy (EE) had a significantly stronger positive impact on BI of females than males. Moreover, EE had a significantly stronger impact on BI of dentists older than 40 years old than those who were younger than 30 years old.ConclusionsEgyptian dentists’ intentions to use the EOHSS were influenced by PE, TA, and EE. However, anxiety related to technology may limit its adoption. EE had a greater positive impact on BI in females and in older dentists.

Introduction. The coronavirus disease 2019 (COVID-19) pandemic prompted a shift from standard in-person consultation to non-patient contact methods such as telemedicine. To our knowledge, there was no published a priori evaluation of the telemedicine readiness and acceptance among the medical staff of the Philippine General Hospital (PGH) before implementing the institution’s telemedicine program. The lack of this vital pre-implementation step is understandable given the unprecedented crisis. However, if telemedicine programs will continue in the post-quarantine period, it is crucial to determine the facilitators and barriers to the use of telemedicine. Objective. This study determined the level of readiness and acceptance for telemedicine as an alternative method for patient consultation during the COVID-19 pandemic and post-enhanced community quarantine period among PGH medical staff (consultants, residents, fellows). Methods. The cross-sectional study was conducted from October 2020 to July 2021. Medical staff from the 16 clinical departments of the PGH were selected by systematic random sampling. Inclusion criteria included appointment as medical staff in PGH or University of the Philippines College of Medicine (UPCM), voluntary informed consent, internet access, and technical capacity to access e-mail and SurveyMonkey™. The online survey consisted of two questionnaires. It collected data on the demographic profile and outcomes of interest (e.g., telemedicine readiness and acceptance). Technological readiness was determined through the 16-item modified version of Technological Readiness Index (TRI) version 2.0, while telemedicine acceptance was determined through the modified version of the 19-item Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Descriptive and analytical statistics were performed at a 95% confidence interval. Results. The study had an 87% response rate with 205 respondents, 62% of whom were physicians in training (resident physicians and fellows). The respondents had a median age of 33 years and were mostly males. Only 19% had telemedicine experience before the pandemic. The majority (51%) learned telemedicine on their own. The most common devices used for telemedicine were mobile or smartphones (53%) and laptops (38%). The primary source of internet for telemedicine was mobile broadband (e.g., cellular data) (40%). The majority practiced telemedicine at their home or residence (51%), followed closely by the hospital or clinic (47%). The mean score of the respondents on TRI was 3.56 (very good technological readiness), and 4.00 (very good telemedicine acceptance) on UTAUT (behavioral intention to use the system). Performance expectancy (p = 0.02), effort expectancy (p = 0.03), and self-efficacy (p = 0.02) were significantly directly related to telemedicine adoption, while anxiety (p = 0.03) was significantly inversely related. Conclusion. The PGH medical staff were found to have very good telemedicine readiness and acceptance. This suggests a willingness to use telemedicine during the pandemic. Further studies on the organization and technical support system of the telemedicine program in the PGH are strongly recommended. The quality and efficiency of the program will strongly influence the continued use of telemedicine by the medical staff even after the pandemic.

This study developed and empirically tested a theoretical model to predict the factors affecting students’ acceptance and behavioral intentions toward using mobile learning (m-learning) in United Arab Emirates (UAE). The present study explored the behavioral intention to use m-learning from the angle of consumers by applying the extended Unified Theory of Acceptance and Use of Technology (UTAUT) model with the addition of service quality, perceived enjoyment and mobile self-efficacy and satisfaction. M-learning user data was derived from an online survey with 395 respondents and analyzed using structural equation modeling (SEM) and path analysis. The results revealed that (1) behavioral intention was significantly and positively influenced by satisfaction; (2) performance expectancy and effort expectancy were positively associated with satisfaction (3) perceived enjoyment, service quality and mobile self-efficacy had a significantly positive effect on performance expectancy and effort expectancy. SEM analysis showed that satisfaction was central to users’ behavioral intentions and mediated the effects of performance and effort expectancy on behavioral intentions. Our findings correspond with the UTAUT model. A number of implications for theory and practice are derived based on the findings.

Purpose This study investigates the factors shaping Chinese university students' behavioral intentions to adopt artificial intelligence (AI) for English as a Foreign Language (EFL) writing development. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and second language acquisition principles, it aims to understand how performance expectancy, effort expectancy, social influence, facilitating conditions, perceived learning resources, perceived enjoyment, and attitude influence students' willingness to use AI in their writing processes. Design/methodology/approach Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and second language acquisition principles, a cross-sectional survey was administered to 415 Chinese university students. Data were analysed using structural equation modeling (SEM) to examine the relationships among performance expectancy, effort expectancy, social influence, facilitating conditions, perceived learning resources, perceived enjoyment, attitude, and behavioral intention. Findings Path analysis revealed that performance expectancy, effort expectancy, facilitating conditions, and attitude significantly predict behavioral intention, with attitude being the strongest direct predictor. Surprisingly, social influence, perceived enjoyment, and perceived learning resources did not exert significant direct effects. Attitude fully mediated the effect of effort expectancy and partially mediated performance expectancy, while serving as an indirect-only mediator for social influence and perceived enjoyment. Originality/value This study challenges the universal applicability of Western technology acceptance models by highlighting the central role of attitude as a cultural-cognitive mediator in collectivist, exam-driven contexts. It proposes a cultural attunement model for AI adoption in EFL education and offers practical implications for pedagogy, policy, and culturally responsive AI tool design.

BackgroundArtificial intelligence (AI) shows great potential to improve clinical nursing practices. However, concerns and challenges related to its implementation have led to resistance among nurses, hindering the widespread use of AI tools in healthcare. This study aimed to apply the Unified Theory of Acceptance and Use of Technology (UTAUT) model to medical AI in Chinese nursing and to examine the relationships specified within the established theoretical framework.MethodsThis study employed a nationwide cross-sectional survey to assess Chinese nurses’ demographic characteristics, knowledge, and attitudes toward medical AI. Guided by the UTAUT framework, key constructs like performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioral intention were included, along with perceived risk and trust as additional variables. Data were collected from 8,514 valid questionnaires. Structural equation modeling (SEM) was employed for analysis. Confirmatory factor analysis (CFA) was conducted to construct validity of the extended UTAUT model, followed by evaluation of model fit and estimation of path coefficients. The Bootstrap method was utilized to examine the mediating effects.Results84.0% (7151/8514) of nurses had heard of medical AI, 79.5% (6771/8514) of nurses held optimistic views regarding its prospects, only 17.8% (1512/8514) of nurses had actually utilized it in clinical nursing work. The results of the SEM analysis revealed that performance expectancy (β = 0.074, P < 0.001), effort expectancy (β = 0.158, P < 0.001), social influence (β = 0.039, P < 0.001) and trust (β = 0.670, P < 0.001) had significant positive impacts on behavioral intention of nurses to adopt medical AI, while perceived risk had a significant negative impact (β=-0.013, P = 0.037). In contrast, facilitating conditions did not significantly affect behavioral intention (β=-0.010, P = 0.334). Additionally, the influence of performance expectancy (β = 0.148, P < 0.001), effort expectancy (β = 0.206, P < 0.001), social influence (β = 0.130, P < 0.001) and facilitating conditions (β = 0.074, P < 0.001) on behavioral intention was mediated by trust, highlighting the core role of trust in promoting intention to use AI. Notably, trust did not significantly mediate the relationship between perceived risk and behavioral intention (β=-0.007, P = 0.229).ConclusionsChinese nurses generally express high acceptance of medical AI, but its practical application rate remains low. To facilitate the widespread adoption of AI in nursing settings, future efforts should prioritize improving the accuracy, usability, and accessibility of medical AI. Moreover, initiatives should focus on creating a supportive environment, addressing perceived risks, and fostering greater trust in AI technologies.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12912-025-03748-9.

Despite social networks’ prevalence and unlimited benefits, their adoption rates are still unsatisfactory. This cross-country research aims to examine the impact of network externality (NE) and fear of missing out (FOMO) on the continuous use of social networks, which in turn, affects users’ self-esteem. To achieve this aim, a conceptual model is developed by extending the unified theory of acceptance and use of technology 2 (UTAUT2) with three new factors: NE, FOMO, and self-esteem. The model is tested using a quantitative research design based on data collected through online surveys from 841 social media users in Qatar and Jordan. The data were analyzed using partial least squares-structural equation modeling (PLS-SEM). The results indicated that the continuous use of social networks is positively affected by performance expectancy (PE), hedonic motivation (HM), and FOMO in both samples. The continuous use is also affected by effort expectancy (EE) in the Jordanian, but not the Qatari sample. In contrast, NE significantly affects the continuous use among Qatari respondents, while this relationship is not supported among their Jordanian counterparts. More interestingly, the continuous use of social networks positively impacts users’ self-esteem across the two samples. In summary, this research goes beyond what was examined in the UTAUT2 by investigating the consequences of continuous use on users’ self-esteem. The incorporated constructs extend the theoretical perspective of the UTAUT2 by integrating new determinants of the continuous use (i.e., FOMO and NE) and new outcomes of that use (i.e., self-esteem). The reflection of the impact of these factors in a cross-country comparison provides insights into the variation in using social networks between different countries.

BackgroundMobile health (mHealth) as an innovative form of information and communications technology can efficiently deliver high-quality health care by enhancing communication and health management, reducing costs, and increasing access to health services. An individual’s internal health locus of control (HLOC) is found to be associated with the behavioral intent to adopt mHealth. However, little is known about the underlying mechanism of this association.ObjectiveThe primary objective of this study was to test the mediation influence of the Unified Theory of Acceptance and Use of Technology (UTAUT) on the relationship between internal HLOC and the behavioral intention to use mHealth.MethodsA total of 374 responses were collected from Malaysian adult users of mHealth, using convenience and snowball sampling methods. Partial least squares structural equation modeling was used to analyze the data. Data were collected for variables, including demographics, internal HLOC, and modified UTAUT constructs (ie, performance expectancy, effort expectancy, and social influence).ResultsThe results showed that there was no direct relationship between internal HLOC and the behavioral intention to use mHealth (β=−0.039, P=.32). The indirect relationship between internal HLOC and the intent to adopt mHealth was supported, indicating that the UTAUT constructs performance expectancy (β=0.104, P<.001), effort expectancy (β=0.056, P=.02), and social influence (β=0.057, P=.002) mediated this relationship. The results showed full mediation, with total variance explained at 47.2%.ConclusionsThis study developed an integrative model, where a health-related disposition (internal HLOC), mHealth-related beliefs (performance expectancy and effort expectancy), and normative pressure (social influence) were combined to explain the underlying mechanism of the behavioral intent to adopt mHealth. The results showed that the intention to adopt mHealth is mediated by the influence of UTAUT factors, while HLOC has no direct effect on adoption intention. The findings provide insights into augmenting mHealth adoption among the public by enhancing the perceived benefits of mHealth, helping design more effective and user-friendly mHealth tools, and capitalizing on social normative influence to adopt mHealth. This study utilized the constructs of the UTAUT model to determine the intention to use mHealth. Future research should focus on other health- and technology-related theories to ascertain other possible factors influencing the behavioral intent of mHealth adoption.

The aim of this study was to empirically examine the extended unified theory of acceptance and use of technology 2 (UTAUT2) model by adding “personalization” as one of the antecedents, as well as a moderator to determine the key factors for the continuous use intention of mobile news applications (apps). For this study, an online and manual sample survey of 309 respondents, who had used the news app earlier, was collected and analyzed, using quantitative methods such as explanatory and confirmatory factor analysis, structural equation modeling, and Hayes process for finding moderating effects among variables. The findings of the direct effect demonstrated that performance expectancy (PE) has the most influential effect on continuous use intention, followed by habit (HT), hedonic motivation (HM), and facilitating conditions (FC). Furthermore, the outcome of tests for the moderating effect of personalization between UTAUT2 constructs and continuous use intention (CUI) showed that personalization has a significant moderating effect on performance expectancy and habit. Therefore, this research establishes the key role of PE, HT, HM, and FC as main factors that trigger the users’ continuous use intention of news apps and provides an integrated framework to assess the moderating effect of personalization on technology acceptance. The findings of the research expand the existing literature on news applications and provide foundation for future research studies in the area of mobile news apps.

PurposeThis study aims to explore the factors affecting investment behaviour in cryptocurrencies among Jordanian investors. Specifically, it aims to assess how various motivational and behavioural drivers impact the intention to use cryptocurrencies, grounded in the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework. The choice of Jordan as the research context is particularly relevant due to the lack of adequate regulations on cryptocurrency investment.Design/methodology/approachThis study uses a quantitative research approach, using an online survey as the primary method for data collection. The final data set consists of 285 responses collected through a self-administered questionnaire to cryptocurrency users in Jordan. Next, structural equation modelling (SEM) was used to test the developed theoretical framework based on the UTAUT2 model.FindingsThe findings reveal that performance expectancy, trust, hedonic motivation and price value significantly enhance the intention to invest in cryptocurrencies, with performance expectancy acting as a mediator. Effort expectancy is not directly related to behavioural intention; however, it positively impacts performance expectancy, validating the mediation hypothesis. Trust affects both the intention to use and the performance expectancy, reinforcing its role as a mediator in cryptocurrency adoption. Hedonic motivation and price value also positively affect the intention to use cryptocurrency. In contrast, social influence and facilitating conditions do not significantly impact behavioural intention, suggesting that cryptocurrency adoption decisions are less influenced by external opinions or the availability of necessary conditions. The findings also show that the demographic profiles of the cryptocurrency users were young, educated males, which suggests a demographic skew in cryptocurrency usage in Jordan.Originality/valueThis study innovatively adapts the UTAUT2 model, focusing on the mediating role of performance expectancy between effort expectancy, trust, and behavioural intention. This study pioneers by examining the mediation effect of performance expectancy, showing how users' ease in using cryptocurrencies positively affects their belief in positive outcomes, subsequently influencing their behavioural intention to use cryptocurrencies. Moreover, this study sheds light on the factors driving cryptocurrency adoption in developing countries like Jordan. It also underscores the demographic trends in cryptocurrency use and proposes targeted recommendations for policymakers and cryptocurrency platforms to foster more inclusive and informed investment environments.

district employees in Nagan Raya Regency using the Unified Theory of Acceptance and Use of Technology (UTAUT). The sample of this study was 354 sub-district employees who filled out the research questionnaire. Data were analyzed using the Ordinary Least Square (OLS) method to test the effect of independent variables on the dependent variable. The results showed that performance expectancy, effort expectancy, social influence, and facilitating conditions had a significant effect on behavioral intention to use e-SKP. Performance expectancy was the strongest predictor, followed by social influence, facilitating conditions, and effort expectancy. In addition, behavioral intention had a significant effect on the use behavior of e-SKP. The findings of this study confirm the Unified Theory of Acceptance and Use of Technology (UTAUT) model in the context of implementing e-SKP on sub-district employees in Nagan Raya Regency. The implication is that Performance expectancy Effort expectancy moderated by gender, Social influence moderated by gender, age, experience and voluntariness of use affect behavioral intention. Furthermore, facilitating conditions moderated by age and experience and behavioral intention influence use behavior.Kata kunci: UTAUT, e-SKP, performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, use behavior.

This research examines factors influencing the acceptance of internet banking (IB) in Lebanon. It extends the unified theory of acceptance and use of technology (UTAUT) model by including trust as a proxy and then investigates the moderating effect of a set of socio-demographic variables (gender, age, experience) in shaping consumer perceptions towards using IB. A cross-sectional survey was used to collect data from 408 IB consumers in Lebanon. Structural equation modelling was employed as the main method of analysis. The results show that behavioral intention (BI) was significantly influenced by performance expectancy (PE), social influence (SI), trust (TRU) and effort expectancy (EE) in their order of influencing power. Moreover, facilitating conditions (FC) and BI significantly influenced use behavior (UB). Gender moderated the relationship between PE_BI and SI_BI, age moderated the relationship between PE_BI, EE_BI, and FC_UB, and experience moderated the relationship between EE_BI and SI_BI. The theoretical and practical implications are discussed at the end of the article.

The COVID-19 pandemic has brought outward unparalleled difficulties in the field of education, emphasizing the need for unique solutions. This study aimed to evaluate the determinants that affect the effective implementation of the automated student academic advisory system at the University of Nizwa of the Sultanate of Oman, with a particular emphasis on its influence on students' emotional intelligence during the pandemic The research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate how factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions influence students' behavioral intentions. In addition, the study examined the behavioral intention effects on the use of the automated system, emotional intelligence effects and the experience of COVID-19 specifically influences the actual use of the system. The study employed a case study methodology in combination with a quantitative survey method to gather data from 272 students and advisors at the University of Nizwa. The collected data was analyzed using SmartPLS, a technique known as structural equation modeling. The research provides useful information on the adoption of technology in an educational environment and its impact on student well-being and emotional intelligence. The study found that only performance expectancy and facilitating condition factors had a substantial impact on behavioral intention, whereas effort expectancy and social influence did not. Behavioral intention showed a positive correlation with actual usage, but performance expectancy, effort expectancy, and social influence did not directly influence the actual utilization. Moreover, behavioral intention served as a mediator for the indirect impact of performance expectancy and facilitating conditions on actual usage. However, it did not mediate the indirect impact of social influence and effort expectancy on actual utilization. Furthermore, the impact of COVID-19 and emotional intelligence did not influence the relationship between facilitating conditions and actual use. These findings offer valuable information into understanding educational technology, particularly in situations of global crises, and offer practical recommendations for educators, legislators, and academic institutions seeking to enhance student participation, support, and general well-being.&#x0D;