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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Analysis of Digital Marketing Adoption Behaviour in MSMEs in Bengkulu City using the UTAUT-3 Model by Nurul Renaningtias, Pradifta Irsyad Hidayat, Aan Erlansari, S.T.,M.Eng

“…The results of this study show that Performance Expectancy, Social Influence, Hedonic Motivation influence the behavioral intention of using digital marketing in MSME actors in Bengkulu City. …”
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Understanding the behavioral intentions of MaaS during public health events by Hang Luo, Hang Luo, Shoufeng Ma, Shoufeng Ma, Junfang Tian, Junfang Tian, Hongming Dong

“…Theoretical model, sourced from the Unified Theory of Acceptance and Use of Technology (UTAUT) model, is employed to elucidate the influence of six variables—performance expectancy, effort expectancy, social influence, ambiguity tolerance, perceived health threat, and policy cognition—on the behavioral intentions of MaaS.ResultsThe results show that three variables from the UTAUT positively impact behavioral intention and that there is a significant mediating effect of policy cognition on the relationship between these variables and the intention to use. …”
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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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Examining the effect of personality on user acceptance of conditionally automated vehicles by S. Nordhoff, E. Lehtonen

“…The results have shown positive effects of social influence, trust, and performance expectancy on the behavioral intention to use CondAVs. …”
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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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Factors influencing academic staff satisfaction and continuous usage of generative artificial intelligence (GenAI) in higher education by Maria Ijaz Baig, Elaheh Yadegaridehkordi

“…Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. …”
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Theoretical and methodological aspects of personal brand formation in the digital environment by G. V. Dovzhik, V. N. Dovzhik, S. A. Musatova

“…All of the above factors largely determine the nature and degree of social influence of influencers on the behavior of individuals in society.In addition, much attention is paid to the analysis of existing models of personal brand formation, which form the theoretical and methodological basis of a personal brand. …”
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Understanding pre-service teachers' intention to adopt and use artificial intelligence in Nigerian inclusive classrooms by Olufemi Timothy Adigun, Olufemi Timothy Adigun, Faisat Adeniran Tijani, Cynthy K. Haihambo, Simasiku Limbo Enock

“…Technological self-efficacy did, however, have a significant positive and indirect contribution to the effect of performance expectancy and social influence on the pre-service teachers' behavioral intention to adopt and use AI for inclusive education teaching, based on their technological self-efficacy.DiscussionThe implication of findings of this study points to the exigency of a need to strengthen institutional policies and teacher preparation curricula in a manner that would advance the infusion of the use of artificial intelligence for teaching of learners with special needs.…”
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What is the influence of psychosocial factors on artificial intelligence appropriation in college students? by Benicio Gonzalo Acosta-Enriquez, María de los Ángeles Guzmán Valle, Marco Arbulú Ballesteros, Julie Catherine Arbulú Castillo, Carmen Graciela Arbulu Perez Vargas, Isaac Saavedra Torres, Pedro Manuel Silva León, Karina Saavedra Tirado

“…Performance expectancy (β = 0.274), social influence (β = 0.355), and AI learning self-efficacy (β = 0.431) were found to have significant positive effects on AI adoption. …”
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Mondialisation et nouvelle mobilité des élites africaines by Michel Lesourd

“…Their financial capacities and their social influence allowed them to achieve this accumulation process. …”
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Cluster analysis of potential autonomous vehicle (AV) adopters in Indonesia’s new capital by Eko Agus Prasetio, Dita Novizayanti, Aghnia Nadhira Aliya Putri

“…Based on this clustering, we suggest staging strategies focusing on building trust and safety, enhancing positive perceptions through targeted marketing, leveraging social influence to broaden adoption, and sustaining enthusiasm with simplified approaches and incentives. …”
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