Analysis of factors influencing electric vehicle adoption in Sub-Saharan Africa using a modified UTAUT framework by Qasim Ajao, Makhluk Hossain Prio, Lanre Sadeeq

“…The model incorporates facilitating conditions, trust, performance expectancy, social influence, network externalities, and effort expectancy as critical determinants. …”
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A Study of Determining Factors Influencing the Intention of Cryptocurrency Investors Using UTAUT 2 Approach by Ladia Puspanegara, Jono M. Munandar, Bagus Sartono

“…“The data were analyzed using two methods,“descriptive statistical analysis and SEM (Structural Equation Modeling).”The research findings indicate that several variables such as price value, social influence, hedonic motivation, performance expectancy, effort expectancy, and facilitating conditions have a positive and significant influence on BI (Behavioral Intention).”Additionally, the perceived risk variable has a negative and significant influence on behavioral intention. …”
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INFLUENCING FACTORS OF E-GOVERNMENT SERVICES ADOPTION IN BOSNIA AND HERZEGOVINA by Edin Osmanbegović, Zijad Lugavić

“…The survey results, conducted on a sample of 553 citizens’ of Bosnia and Herzegovina, found that determinants social influence, effort expectancy and performance expectancy are statistically most influential determinants of citizens’ adoption of e-government services. …”
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Motivated by Design: A Codesign Study to Promote Challenging Misinformation on Social Media by Selin Gurgun, Emily Arden-Close, Keith Phalp, Raian Ali

“…We applied the unified theory of acceptance and use of technology (UTAUT) as a theoretical framework and analysed our data based on the core constructs of this framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Our findings reveal four design considerations: creating secure and supportive environments, facilitating informed discussions through easy confrontation and access to reliable resources, leveraging recognition and social proof, and user support infrastructure. …”
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Design strategies for artificial intelligence based future learning centers in medical universities by Yang Xiaowen, Ding Jingjing, Wang Biao, Zhang Shenzhong, Wu Yana

“…Results Effort expectancy (EE), facilitating condition (FC), social influence (SI), and satisfaction (SA) significantly influence medical students’ continuance intention (CI) to use artificial intelligence tools. …”
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Web 2.0 Students Adoption Model for Learning in Universities: A Case of Muni University, Uganda by Phelix, Businge Mbabazi, Nicholas, Nkamwesiga, Ritah, Nafuna, Patricia, Kyomugisha

“…The model shows that students’ behavioral intention to use Web 2.0 depends on performance expectancy, effort expectancy, social influence and facilitating conditions. The study also showed that students use YouTube, Facebook and Google Apps but not LinkedIn, Social Bookmarking, Moodle, Zoom, Edx, MIT Courseware, and Dropbox among others.…”
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Private Parking Space Sharing Intention in China: An Empirical Study Based on the MIMIC Model by Ange Wang, Hongzhi Guan, Yan Han, Yangliu Cao

“…The results show that (1) the sharing behavioral intention (BI) is directly affected by perceived benefit (PB), perceived risk (PR), social influence (SI), and facilitating condition (FC) and indirectly affected by effort expectancy (EE), of which the total effect of PB is the largest; (2) exogenous variables have an indirect effect on BI through other psychological latent variables; among them, different sociodemographic and economic characteristics have a significant influence on different latent variables, while the built environment has no significant effect on latent variables. …”
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Social leadership and authority in the field of national and military security: problems of formation in the digital world by M. M. Kibakin

“…Based on the author’s sociological research, the paper analyses the self-assessment of young people of their leadership qualities, authority and social influence on other people in accord- ance with their status in social networks. …”
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The predictors of behavioral intention to use ChatGPT for academic purposes: evidence from higher education in Somalia by Ahmed-Nor Mohamed Abdi, Abukar Mukhtar Omar, Mohamed Hassan Ahmed, Abdullahi Abdulkadir Ahmed

“…The results uncovered that perceived usefulness, ease of use, social influence, hedonic motivation, and credibility have positively and significantly impacted students’ intention to use ChatGPT. …”
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Privacy and Security in Digital Health Contact-Tracing: A Narrative Review by Shehani Pigera, Paul van Schaik, Karen Renaud, Miglena Campbell, Petra Manley, Pierre Esser

“…The data were analysed using thematic analysis. (3) Results: Eight main themes were derived: privacy, data protection and control, trust, technical issues, perceived benefit, knowledge and awareness, social influence, and psychological factors. (4) Conclusions: Improving privacy standards and the awareness of the digital contact-tracing process will encourage the acceptance of contact-tracing apps.…”
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Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients by Guanqing Chen, A. James O’Malley

“…Abstract When an hypothesized peer effect (also termed social influence or contagion) is believed to act between units (e.g., hospitals) above the level at which data is observed (e.g., patients), a network autocorrelation model may be embedded within a hierarchical data structure thereby formulating the peer effect as a dependency between latent variables. …”
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The unified theory of acceptance and use of DingTalk for educational purposes in China: an extended structural equation model by Yukun Hou, Zhonggen Yu

“…The findings indicated that (1) effort expectancy (EE), performance expectancy (PE), facilitating conditions (FC), self-efficacy (SE), and received feedback (RF) could significantly impact users’ attitudes toward behavior (ATB); (2) social influence (SI), FC, RF, and ATB could be significant predictors of user behavioral intention (BI); (3) FC, RF, and BI were found to have a significant effect on use behavior (UB); (4) the extended UTAUT model could explain 60.9% of the variance of users’ behavioral intention of DingTalk in China; (5) the study identified ATB and BI as joint mediators between certain variables in the model. …”
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Assessment of physical education teachers’ use of distance teaching behavior under the influence of the COVID-19 pandemic by Hsiu-Chin Huang, Ya-Tzu Kung, Ruey-Rong Huang, Wui-Chiu Mui, Yu-Chien Su

“…The model contains four independent variables: performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC), two dependent variables: behavioral intention (BI) and use behavior (UB) and three moderating variables: gender, age, and experience. …”
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Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study by Latifah H Alenazi

“…The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. …”
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Public Acceptance of Driverless Buses in China: An Empirical Analysis Based on an Extended UTAUT Model by Jian Chen, Rui Li, Mi Gan, Zhiyan Fu, Fatao Yuan

“…The results showed that PI and PR are the most critical factors that affect the public’s acceptance intention; effort expectancy (EE), performance expectancy (PE), social influence (SI), and facilitating condition (FC) can also determine the acceptance intention to a certain extent; gender, age, and education level have exhibited significantly different moderating effects on the influencing factors. …”
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Improved population coverage of the human papillomavirus vaccine after implementation of a school-based vaccination programme: the Singapore experience by Karuppiah Vijayalakshmi, Anne Hui Yi Goei

“…Timely assessment of knowledge lapses and targeted intervention, strong partnerships with stakeholders, constant on-site adaptation and positive social influence contributed to its success. This model can be applied to future school health programmes.…”
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Why do people use a mobile wallet? The case of fintech companies in Jordan by Ohoud Khasawneh, Rana AlBahsh

“…Meanwhile, perceived value, security, privacy, and social influence had a moderate effect. The attractiveness of alternatives and attitudes towards m-wallet usage showed lesser impact, with R-square values at 26.7% and 22.8%, respectively, illustrating varied influences on adoption rates in determining consumer adoption of m-wallet services in Jordan.This paper enhances research on mobile commerce in developing economies, focusing on Jordan. …”
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Young Muslim generations and sadaqah through digital platforms: Do sadaqah literacy and religiosity matter? by Ahmad Santoso, Heri Sudarsono, Weni Hawariyuni

“…However, business expectations, social influence, and facilitating conditions have no effect on this intention. …”
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Hybrid learning in post-pandemic higher education systems: an analysis using SEM and DNN by Alvin Muhammad ‘Ainul Yaqin, Ahmad Kamil Muqoffi, Sigit Rahmat Rizalmi, Faishal Arham Pratikno, Remba Yanuar Efranto

“…Data were collected through questionnaires evaluating key factors, including social influence, perceived interactivity, perceived usefulness, ease of use, facility conditions, attitude, satisfaction, and user intention. …”
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Determinants of BSI mobile banking adoption intentions: DeLone & McLean and UTAUT Model integration with religiosity by Erlinda Sholihah, Ika Septiana Windi Antari, Rizqi Fajar Rochimawati, Ulwiyyah

“…Findings − The findings show that from the factors identified, Service Quality, Information Quality, Performance Expectancy, Effort Expectancy, Social Influence, and Religiosity are critical variables in BSI mobile banking adoption intention. …”
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