Chinese Consumers' Attitudes toward and Intentions to Continue Using Skill-Sharing Service Platforms.

The sharing economy has rapidly transformed traditional consumption patterns worldwide. The emergence of skill-sharing services-which allow individuals to share their skills, abilities, and time through online platforms-has recently garnered attention. In China, the demand for skill-sharing services continues to grow, as these services effectively meet consumer needs. To understand this growing demand, this study aims to explore users' attitudes and intentions toward the use of skill-sharing service platforms in the Chinese market. A survey was conducted that incorporated 500 Chinese users who had used skill-sharing service platforms over the previous three months. A total of 409 datasets were analyzed, using structural equation modeling and multiple group analysis, in AMOS 24.0. The results showed that performance expectancy, effort expectancy, social influence, facilitating conditions, and self-efficacy positively influenced users' attitudes toward skill-sharing services, while privacy, functionality, and safety risks negatively affected these attitudes. Users' attitudes toward skill-sharing services significantly enhanced their intentions to continue using them, with the level of trust playing a crucial moderating role between attitude and the intention to continue using these services. These findings provide a significant theoretical and practical foundation for the further development of skill-sharing service platforms, the optimization of marketing strategies, and future research.

The acceptance of the potential use of social robots for children with autism spectrum disorder by Indonesian occupational therapists: a mixed methods study.

PURPOSE: Social robots have shown positive effects in treating children with autism spectrum disorder. The development of social robots in Indonesia has enabled their potential use in occupational therapy. This study aimed to investigate the factors influencing the acceptance of the potential use of social robots by Indonesian occupational therapists in clinical practice. METHODS: This study employed a mixed methods explanatory sequential design. An adapted unified theory of acceptance and use of technology model was utilised for the quantitative phase. The questionnaire explored the acceptance of social robots. The data were analysed using structural equation modelling. In the qualitative phase, semi-structured interviews with reflexive thematic analysis were conducted. The second phase aimed to explain the reasons behind the quantitative results and factors related to the acceptance of social robots in therapy. RESULTS: Occupational therapists showed high interest in using social robots in their sessions, as indicated by the significant positive relationship between performance expectancy and potential use. Three influential factors affecting acceptance emerged in the qualitative phase: occupational therapists' characteristics and competencies, social robots and occupational therapy interventions, and environmental influence. CONCLUSIONS: Indonesian occupational therapists have shown interest in using social robots. However, there are challenges regarding the practical application of social robots concerning individual differences in the factors that influence acceptance.

Social robots have been perceived as beneficial intervention tools for improving occupational therapists' performance with children with autism spectrum disorder.Environmental factors significantly influence the acceptance of social robots.The attitudes of occupational therapists influence their acceptance towards the potential use of social robots.

Understanding the Role of Technology Anxiety in the Adoption of Digital Health Technologies (DHTs) by Older Adults with Chronic Diseases in Shanghai: An Extension of the Unified Theory of Acceptance and Use of Technology (UTAUT) Model.

The unprecedented rapid growth of digital health has brought new opportunities to the health field. However, elderly patients with chronic diseases, as an important potential beneficiary group, are affected by the digital divide, leading to unsatisfactory usage of digital health technologies (DHTs). Our study focused on the factors influencing the adoption of DHTs among this vulnerable group. To extend the UTAUT theory, technology anxiety and several demographic predictors were included to address the age characteristics of the respondents. An on-site survey was conducted in general, district, and community hospitals in Shanghai (n = 309). Facilitating conditions negatively influenced technology anxiety. Technology anxiety hindered behavioural intention. Social influence had a significant but negative impact on behavioural intention. Education, whether older adults have had experience with DHTs and previous smartphone usage experiences were significantly associated with technology anxiety. The findings provide valuable information for multiple stakeholders, including family members of elderly users, product designers, and policymakers. Ameliorating facilitating conditions, improving devices' usage experience, encouraging attempts and focusing on groups with lower educational levels can help to reduce technology anxiety and promote DHT acceptance and use in older age groups.

Health professionals' acceptance of mobile-based clinical guideline application in a resource-limited setting: using a modified UTAUT model.

INTRODUCTION: Clinical guidelines are crucial for assisting health professionals to make correct clinical decisions. However, manual clinical guidelines are not accessible, and this increases the workload. So, a mobile-based clinical guideline application is needed to provide real-time information access. Hence, this study aimed to assess health professionals' intention to accept mobile-based clinical guideline applications and verify the unified theory of acceptance and technology utilization model. METHODS: Institutional-based cross-sectional study design was used among 803 study participants. The sample size was determined based on structural equation model parameter estimation criteria with stratified random sampling. Amos version 23 software was used for analysis. Internal consistency of latent variable items, and convergent and divergent validity, were evaluated using composite reliability, AVE, and a cross-loading matrix. Model fitness of the data was assessed based on a set of criteria, and it was achieved. P-value < 0.05 was considered for assessing the formulated hypothesis. RESULTS: Effort expectancy and social influence had a significant effect on health professionals' attitudes, with path coefficients of (ß = 0.61, P-value < 0.01), and (ß = 0.510, P-value < 0.01) respectively. Performance expectancy, facilitating condition, and attitude had significant effects on health professionals' acceptance of mobile-based clinical guideline applications with path coefficients of (ß = 0.37, P-value < 0.001), (ß = 0.44, P-value < 0.001) and (ß = 0.57, P-value < 0.05) respectively. Effort expectancy and social influence were mediated by attitude and had a significant partial relationship with health professionals' acceptance of mobile-based clinical guideline application with standardized estimation coefficients of (ß = 0.22, P-value = 0.027), and (ß = 0.19, P-value = 0.031) respectively. All the latent variables accounted for 57% of health professionals' attitudes, and latent variables with attitudes accounted for 63% of individuals' acceptance of mobile-based clinical guideline applications. CONCLUSIONS: The unified theory of acceptance and use of the technology model was a good model for assessing individuals' acceptance of mobile-based clinical guidelines applications. So, enhancing health professionals' attitudes, and computer literacy through training are needed. Mobile application development based on user requirements is critical for technology adoption, and people's support is also important for health professionals to accept and use the application.

Pre-Service Teachers' GenAI Anxiety, Technology Self-Efficacy, and TPACK: Their Structural Relations with Behavioral Intention to Design GenAI-Assisted Teaching.

Generative artificial intelligence (GenAI) has taken educational settings by storm in the past year due to its transformative ability to impact school education. It is crucial to investigate pre-service teachers' viewpoints to effectively incorporate GenAI tools into their instructional practices. Data gathered from 606 pre-service teachers were analyzed to explore the predictors of behavioral intention to design Gen AI-assisted teaching. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this research integrates multiple variables such as Technological Pedagogical Content Knowledge (TPACK), GenAI anxiety, and technology self-efficacy. Our findings revealed that GenAI anxiety, social influence, and performance expectancy significantly predicted pre-service teachers' behavioral intention to design GenAI-assisted teaching. However, effort expectancy and facilitating conditions were not statistically associated with pre-service teachers' behavioral intentions. These findings offer significant insights into the intricate relationships between predictors that influence pre-service teachers' perspectives and intentions regarding GenAI technology.

An evaluation of factors influencing the adoption and usage of robotic surgery in lower limb arthroplasty amongst orthopaedic trainees: a clinical survey.

BACKGROUND: The rise of robotics in orthopaedic training, driven by the demand for better training outcomes and patient care, presents specific challenges for junior trainees due to its novelty and steep learning curve. This paper explores how orthopaedic trainees perceive and adopt robotic-assisted lower limb arthroplasty. METHODS: The study utilised the UTUAT model questionnaire as the primary data collection tool, employing targeted questions on a five-point Likert scale to efficiently gather responses from a large number of participants. Data analysis was conducted using partial least squares (PLS), a well-established method in previous technology acceptance research. RESULT: The findings indicate a favourable attitude amongst trainees towards adopting robotic technology in orthopaedic training. They acknowledge the potential advantages of improved surgical precision and patient outcomes through roboticassisted procedures. Social factors, including the views of peers and mentors, notably influence trainees' decision-making. However, the availability of resources and expert mentors did not appear to have a significant impact on trainees' intention to use robotic technology. CONCLUSION: The study contributes to the understanding of factors influencing trainees' interest in robotic surgery and emphasises the importance of creating a supportive environment for its adoption.

A qualitative study of factors influencing ePHR adoption by caregivers and care providers of Alzheimer's patients: An extension of the unified theory of acceptance and use of technology model.

Background and Aims: As the nowadays provision of many healthcare services relies on technology, a better understanding of the factors contributing to the acceptance and use of technology in health care is essential. For Alzheimer's patients, an electronic personal health record (ePHR) is one such technology. Stakeholders should understand the factors affecting the adoption of this technology for its smooth implementation, adoption, and sustainable use. So far, these factors have not fully been understood for Alzheimer's disease (AD)-specific ePHR. Therefore, the present study aimed to understand these factors in ePHR adoption based on the perceptions and views of care providers and caregivers involved in AD care. Methods: This qualitative study was conducted from February 2020 to August 2021 in Kerman, Iran. Seven neurologists and 13 caregivers involved in AD care were interviewed using semi-structured and in-depth interviews. All interviews were conducted through phone contacts amid Covid-19 imposed restrictions, recorded, and transcribed verbatim. The transcripts were coded using thematic analysis based on the unified theory of acceptance and use of technology (UTAUT) model. ATLAS.ti8 was used for data analysis. Results: The factors affecting ePHR adoption in our study comprised subthemes under the five main themes of performance expectancy, effort expectancy, social influence, facilitating conditions of the UTAUT model, and the participants' sociodemographic factors. From the 37 facilitating factors and 13 barriers identified for ePHR adoption, in general, the participants had positive attitudes toward the ease of use of this system. The stated obstacles were dependent on the participants' sociodemographic factors (such as age and level of education) and social influence (including concern about confidentiality and privacy). In general, the participants considered ePHRs efficient and useful in increasing neurologists' information about their patients and managing their symptoms in order to provide better and timely treatment. Conclusion: The present study gives a comprehensive insight into the acceptance of ePHR for AD in a developing setting. The results of this study can be utilized for similar healthcare settings with regard to technical, legal, or cultural characteristics. To develop a useful and user-friendly system, ePHR developers should involve users in the design process to take into account the functions and features that match their skills, requirements, and preferences.

Willingness of university students to continue using e-Learning platforms after compelled adoption of technology: Test of an extended UTAUT model.

The COVID-19 pandemic has prompted the adoption of an e-Learning pedagogy. This forced teachers and students to shift to online learning and thus was compelled to adopt online educational technology. Educational institutes have been facing challenges like insufficient infrastructure and a shortage of quality teachers. Online learning can help to address these challenges as online classes can accommodate more students. However, before implementing e-Learning technology management of institutes wants to be sure whether students will adopt new technology. Therefore, the purpose of this study was to unveil which factors are important to adopt new technology if implemented mandatorily. We tested the most popular technology acceptance model the UTAUT to understand students' intentions to continue using the e-Learning system in a mandatory environment. The study used a quantitative approach of research. The participants for this study were selected from a private university in India. The questionnaire for the study was adapted from previous studies. The survey was conducted by sharing an online link while students were attending classes online during the pandemic. Thus, the study utilized a convenience sampling technique. The data were analyzed using structural equation modelling. The findings revealed that the UTAUT model can partially explain the forceful adoption of technology. The study found 'Performance expectancy' and the 'availability of resources' as significant indicators of 'intention for continued usage'. This study recommends educational institutes should ensure students attain academic goals by using e-Learning platforms and ensuring the availability of essential resources to use the e-Learning technology.

A Correlational Study of Two U.S. State Extension Professionals' Behavioral Intentions to Improve Sustainable Food Chains through Precision Farming Practices.

Precision farming provides one of the most important solutions for managing agricultural production to advance global food security. Extending professionals' competencies to promote precision farming practices can increase the adoption rate, ultimately impacting food security. Many studies have addressed barriers to the adoption of precision farming technologies from the farmers' perspective. However, few are available data on the perspectives of extension professionals. Agricultural extension professionals play an important role in innovative agricultural technology adoption. Thus, this study applied four constructs from the unified theory of acceptance and use of technology (UTAUT) model to investigate behavioral intentions to promote precision farming among extension professionals from two extension systems. In total, 102 (N = 102) agricultural extension professionals were surveyed. The results indicated that performance expectancy and social influence were individually significant predictors of extension professional behavioral intentions to promote precision farming technologies. There were no significant differences between the professionals of two extension systems. Gender, age, and years of service did not affect extension professionals' intention to promote precision agriculture technologies. The data suggested the need for training programs to develop advanced competencies to promote agricultural innovation. This study contributes to the future professional development programs for extension professionals on communicating innovations to address food security and sustainability issues.

Factors affecting Malaysian ESL teachers' behavioral intentions for technology use in the post-COVID-19 era.

This study aimed to investigate English as a Second Language (ESL) teachers' technology acceptance levels and to identify the factors affecting their behavioral intentions (BI) with respect to technology use in the post-COVID-19 era. A cross-sectional survey of 361 Malaysian ESL teachers was conducted. Participants were recruited via convenience sampling, and they answered an online survey questionnaire that was designed with reference to past studies. The collected data were analyzed via descriptive statistics, Pearson's correlation, and multiple regression analyses. The findings revealed that Malaysian ESL teachers generally had a high level of technology acceptance in the post-COVID-19 era. Their BIs had a significant relationship with three factors: performance expectancy (PE), effort expectancy (EE), and social influence (SI), of which EE was identified as the most significant factor influencing their BI with respect to technology use in the post-COVID-19 era. Conversely, the presence of facilitating conditions did not have a substantial connection with ESL teachers' behavioral intentions for technology use after the pandemic, despite the fact that there was weak positive relationship with each other. This study provides insights for the field of educational psychology by identifying the current trends in ESL teachers' behavioral intentions in adopting technology in the post-COVID-19-era ESL classrooms. The findings of this study may also support investigations into technology acceptance in ESL teaching, illustrating a growing need to provide adequate educational and technological tools, resources, and facilities to facilitate the delivery of lessons by ESL teachers. Future studies should conduct longitudinal research and investigate more variables from different technology acceptance models.

What Determines the Acceptance and Use of eHealth by Older Adults in Poland?

An aging population is considered a major challenge for governments and healthcare planners. eHealth is perceived as a tool with the potential to ensure efficient healthcare. Moreover, eHealth services may help older adults to maintain longer life in good health. However, there are still several challenges to the large-scale implementation of these solutions among older adults. Therefore, the aim of this study was to explore determinants of the acceptance and use of eHealth by older adults in Poland. Data was collected by the questionnaire, and the UTAUT model was employed. This research covered older adults aged 60 to 69. The analysis of the results using nested regression analysis showed that performance expectancy has a strong significance on the older adults' acceptance and use of eHealth, followed by effort expectancy and social influence. In contrast, facilitating conditions do not have a significant influence on the acceptance and use of eHealth. These findings may also be beneficial for the government to provide relative policies to support the development and usage of eHealth services as well as for the healthcare devices industry to design more older adult-oriented products.

Social Acceptance of Mobile Health among Young Adults in Japan: An Extension of the UTAUT Model.

The unprecedented development of information and communication technologies has opened up immense possibilities in the field of health care. Mobile health (mHealth) is gaining increasing attention as an important technology for solving health-related problems. Although a high rate of smartphone usage among young people in Japan has been identified, smartphone usage for health management is not high. As Japanese youth are important potential users of mHealth, it is necessary to explore theories that influence the behavioral intention of Japanese youth to adopt mHealth. This study conducted a questionnaire survey in a Japanese university and collected 233 valuable responses. This study was adapted and extended from the unified theory of acceptance and use of technology (UTAUT) model to measure eight constructs: health consciousness, social influence, facilitation conditions, perceived risk, trust, performance expectancy, effort expectancy, and behavioral intention. Structural equation modeling was used for hypothesis testing. We found that trust, performance expectancy, and effort expectancy directly influenced the behavioral intention to use mHealth. Health consciousness and social influence indirectly influence behavioral intention through trust and performance expectancy. Facilitation conditions indirectly influenced behavioral intention through effort expectancy. This study makes a vital theoretical contribution to policymakers and product developers for the further diffusion of mHealth among young people in Japan.

Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network algorithm.

With the advancement of artificial intelligence, robotics education has been a significant way to enhance students' digital competency. In turn, the willingness of teachers to embrace robotics education is related to the effectiveness of robotics education implementation and the sustainability of robotics education. Two hundred and sixty-nine teachers who participated in the "virtual human education in primary and secondary schools in Guangdong and Henan" and the questionnaire were used as the subjects of study. UTAUT model and its corresponding scale were modified by deep learning algorithms to investigate and analyze teachers' acceptance of robotics education in four dimensions: performance expectations, effort expectations, community influence and enabling conditions. Findings show that 53.68% of the teachers were progressively exposed to robotics education in the last three years, which is related to the context of the rise of robotics education in schooling in recent years, where contributing conditions have a direct and significant impact on teachers' acceptance of robotics education. The correlation coefficients between teacher performance expectations, effort expectations, community influence, and enabling conditions and acceptance were 0.290 (p = 0.000<0.001), -0.144 (p = 0.048<0.05), 0.396 (p = 0.000<0.001), and 0.422 (p = 0.000<0.001) respectively, indicating that these four core dimensions both had a significant effect on acceptance. Optimization comparison results of deep learning models show that mDAE and AmDAE provide a substantial reduction in training time compared to existing noise-reducing autoencoder models. It is shown that time-complexity of the deep neural network algorithm is positively related to the number of layers of the model.

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

Background: Improving 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. Purpose: Empirical 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. Methods: This 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. Results: As 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. Conclusion: The 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.

Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology.

BACKGROUND: Marked advances in artificial intelligence (AI)-based technologies throughout industries, including healthcare, necessitate a broader understanding their use. Particularly, intent to use AI-based healthcare technologies and its predictors among nursing students, who are prospective healthcare professionals, is required to promote the utilization of AI. OBJECTIVE: This study conducted a path analysis to predict nursing students' intent to use AI-based healthcare technologies based on the unified theory of acceptance and use of technology. DESIGN: A cross-sectional survey was performed. PARTICIPANTS: The participants were 210 nursing students from two nursing schools in Korea. METHODS: This study established hypothetical paths for the influence of performance expectancy, effort expectancy, social influence, facilitating conditions, self-efficacy, and anxiety on intent to use AI-based technologies. Mediation of positive and negative attitudes and facilitating conditions' direct effects on intent to use were examined. RESULTS: Positive attitude toward AI (ß = 0.485, p = .009) and facilitating conditions (ß = 0.117, p = .045) predicted intent to use, whereas the path from negative attitude to intent to use was not significant. Performance expectancy, self-efficacy, and effort expectancy predicted positive attitude. Performance expectancy and self-efficacy had a negative effect on the path to negative attitude, whereas anxiety had a positive effect. Facilitating conditions did not significantly predict positive or negative attitude and only directly predicted intent to use. Social influence did not have a significant effect on intent to use. CONCLUSIONS: Intervention programs and other measures should be developed to provide education and information to boost performance expectancy, effort expectancy, facilitating conditions, and self-efficacy regarding the use of AI to lower anxiety and foster positive attitude toward AI-based health technologies.

Adoption of mobile health services using the unified theory of acceptance and use of technology model: Self-efficacy and privacy concerns.

Mobile health (mHealth) services have been widely used in medical services and health management through mobile devices and multiple channels, such as smartphones, wearable equipment, healthcare applications (Apps), and medical platforms. However, the number of the users who are currently receiving the mHealth services is small. In China, more than 70% of internet users have never used mHealth services. Such imbalanced situation could be attributed to users' traditional concept of medical treatment, psychological factors (such as low self-efficacy) and privacy concerns. The purpose of this study is to explore the direct and indirect effects of mHealth users' self-efficacy and privacy concerns on their intention to adopt mHealth services, providing guidelines for mHealth service providers to enhance users' intention of adoption. A questionnaire was designed by the research team and 386 valid responses were collected from domestic participants in China. Based on the unified theory of acceptance and use of technology (UTAUT) model, a research model integrated self-efficacy and privacy concerns was constructed to investigate their effects on users' intention to adopt mobile mHealth services. The results show that self-efficacy could facilitate users' intention to adopt mHealth services, and had a significantly positive effect on perceived ubiquity, effort expectancy, performance expectancy and subjective norm. This study verifies the direct and indirect effects of self-efficacy and privacy concerns on users' intention to adopt mHealth services, providing a different perspective for studying mHealth adoption behavior. The findings could provide guidelines for mHealth service providers to improve their service quality and enhance users' intention of adoption.

Digital interventions for antimicrobial prescribing and monitoring: a qualitative meta-synthesis of factors influencing user acceptance.

OBJECTIVE: To understand and synthesize factors influencing user acceptance of digital interventions used for antimicrobial prescribing and monitoring in hospitals. MATERIALS AND METHODS: A meta-synthesis was conducted to identify qualitative studies that explored user acceptance of digital interventions for antimicrobial prescribing and/or monitoring in hospitals. Databases were searched and qualitative data were extracted and systematically classified using the unified theory of acceptance and use of technology (UTAUT) model. RESULTS: Fifteen qualitative studies met the inclusion criteria. Eleven papers used interviews and four used focus groups. Most digital interventions evaluated in studies were decision support for prescribing (n = 13). Majority of perceptions were classified in the UTAUT performance expectancy domain in perceived usefulness and relative advantage constructs. Key facilitators in this domain included systems being trusted and credible sources of information, improving performance of tasks and increasing efficiency. Reported barriers were that interventions were not considered useful for all settings or patient conditions. Facilitating conditions was the second largest domain, which highlights the importance of users having infrastructure to support system use. Digital interventions were viewed positively if they were compatible with values, needs, and experiences of users. CONCLUSIONS: User perceptions that drive users to accept and utilize digital interventions for antimicrobial prescribing and monitoring were predominantly related to performance expectations and facilitating conditions. To ensure digital interventions for antimicrobial prescribing are accepted and used, we recommend organizations ensure systems are evaluated and benefits are conveyed to users, that utility meets expectations, and that appropriate infrastructure is in place to support use.

Dataset of international students' acceptance of online distance learning during COVID-19 pandemic: A preliminary investigation.

The dataset describes factors affecting international students' acceptance of Online Distance Learning (ODL) mode while pursuing oversea education during COVID-19 pandemic. The recruited respondents comprised of international students who were pursuing undergraduate degree programmes in the institutions of higher learning (IHLs) in Malaysia. Respondents were invited to participate in an online survey via Google Forms. A purposive sampling technique was adopted in this research whereby a total of 207 valid questionnaires were obtained and used for data analysis. Data outputs such as respondents' profile, Partial Least Squares Structural Equation Modelling, and importance-performance matrix analysis were presented. The data can be used as a reference source to identify areas of improvement by educators, academic management, and policy makers of IHLs.

Psychotherapists' acceptance of telepsychotherapy during the COVID-19 pandemic: A machine learning approach.

OBJECTIVE: This study aimed to develop predictive models of three aspects of psychotherapists' acceptance of telepsychotherapy (TPT) during the COVID-19 pandemic, attitudes towards TPT technology, concerns about using TPT technology and intention to use TPT technology in the future. METHOD: Therapists (n = 795) responded to a survey about their TPT experiences during the pandemic, including quality of the therapeutic relationship, professional self-doubt, vicarious trauma and TPT acceptance. Regression decision tree machine learning analyses were used to build prediction models for each of three aspects of TPT acceptance in a training subset of the data and subsequently tested in the remaining subset of the total sample. RESULTS: Attitudes towards TPT were most positive for therapists who reported a neutral or strong online working alliance with their patients, especially if they experienced little professional self-doubt and were younger than 40 years old. Therapists who were most concerned about TPT were those who reported higher levels of professional self-doubt, particularly if they also reported vicarious trauma experiences. Therapists who reported low working alliance with their patients were least likely to use TPT in the future. Performance metrics for the decision trees indicated that these three models held up well in an out-of-sample dataset. CONCLUSIONS: Therapists' professional self-doubt and the quality of their working alliance with their online patients appear to be the most pertinent factors associated with therapists' acceptance of TPT technology during COVID-19 and should be addressed in future training and research.