Behavioral Intention of Using Virtual Reality in Learning

800x600 Normal 0 false false false EN-ID X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-pagination:widow-orphan; font-size:10.0pt; font-family:Times New Roman,serif;} The purpose of this study is to identify Kolb’s learning style of seventh-grade students. Learning style is a combination of the way how the student absorbs then processes the information. Kolb's learning style, developed by David Kolb, combines students' learning tendency and produces four learning styles; they are diverger, assimilator, converger, and accommodator. Learning style is one of the factors can cause misconceptions. A misconception is a difference between a student's conceptions and the expert. Usually, the concepts are difficult to correct. This misconception will affect a student's achievement and disturb in understanding and developing a student's knowledge. The method applied in this study was a survey method with describtive qualitative technique. The data were collected using Kolb's Learning Style Inventory (KLSI) 3.1 version . The subjects were 78 students in three schools with high, medium, and low grades The results indicated that from 78 students, there were 40 divergers (51%), 12 assimilators (15%), ten convergers (13%), and 16 accommodators (21%). Each kind of learning style affects a student's misconception. This research benefits in identifying misconceptions probably faced by students in certain subjects of science learning.

This study explores the adoption of augmented reality (AR) in digital insurance services through the lens of the unified theory of acceptance and use of technology (UTAUT). As insurers increasingly turn to digital solutions to improve operational efficiency and customer engagement, AR emerges as a promising yet under-researched technology with applications in claims processing, risk communication, and policy visualization. By adapting core UTAUT constructs – performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), behavioral intention (BI), and actual use (AU) – the research employs a scenario-based quantitative design to assess user perceptions and BIs in a simulated AR insurance environment. Data were collected via an online survey targeting individuals with prior insurance experience and basic digital literacy. The structural model was tested using structural equation modeling (SEM). Results reveal that EE, SI, and FC have significant positive effects on BI, and that BI strongly predicts AU. However, PE does not significantly influence BI, suggesting that perceived ease of use and environmental support outweigh functional expectations in early-stage technology contexts like AR insurance. The findings provide both theoretical and practical contributions. Theoretically, the study extends UTAUT to a novel application domain, offering insights into user behavior around emerging InsurTech tools. Practically, it highlights the importance of intuitive design, social endorsement, and infrastructural readiness for fostering adoption. These insights can inform insurers aiming to integrate AR into their digital service strategies.

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R2 = 0.745), SI (β = 0.070, R2 = 0.745), and BI (β = 0.792, R2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R2 = 0.745) and FC (β = −0.004, R2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R2 = 0.317), SI (β = 0.462, R2 = 0.317), and EE (β = 0.139, R2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.

This review assesses the risk of a photic-induced seizure in a child during viewing of 3D (binocular 3 dimensional, stereoscopic) movies or games, either on standard video displays or when wearing a virtual reality (VR) headset. Studies published by pediatric epilepsy experts emphasize the low risk of 3D viewing even for children with known photosensitive epilepsy (PSE). The low incidence of PSE is noteworthy because the number of hours devoted to 2D or 3D screen viewing and/or VR headset use by children worldwide has increased markedly over the last decade. The medical literature does not support the notion that VR headset use poses a risk for PSE.

Understanding user intention to accept or reject a new technology is a key factor in implementation and success of new system technology in an organization (Gahtani & King, 1999). The purpose of this study was to explore surgeon’s acceptance of Virtual Reality Headset (VRH) as a surgical simulator. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied to explore the factors that affected surgeon’s acceptance. A structured questionnaire was conducted to capture information from international surgeons. The result explain 52% of variance associated with Behavioural Intention (R2 = 0.521), the three constructs of UTAUT model: Performance Expectancy (PE), Social Influence (SI) and Effort Expectancy (EE) have a significance and the biggest impact on surgeons behavioural intention to use the Virtual Reality Headset, therefore organizations must take into account the following: (1) emphasize the usefulness of the software (2) consider surgeon’s psychological aspects. (3) The system must be easy to use.

Purpose: This study used the UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) paradigm to investigate the factors influencing teachers' adoption of Digital Learning Resources (DLR). In addition to examining variations by gender, frequency of technology use, and academic field, this study assesses the influence of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), Price Value (PV), and Habit (HB) on behavioural intention (BI) and use behaviour (UB). Design/methodology/approach: Data were gathered from 502 teachers in nine taluks in Dakshina Kannada, India using a quantitative study approach and stratified random sampling. Google Forms and offline surveys were used to collect data, and structural equation modelling (SEM) was used for analysis. Group differences were also investigated using one-way analysis of variance (ANOVA) and independent sample t-tests. Findings: The findings showed that teachers’ BIs were highly influenced by PV, HM, SI, PE, and EE. Use behaviour is greatly influenced by behavioural aims and facilitating circumstances. FC did not affect BI, and HB had no discernible impact on BI or UB. Gender-based BI did not differ significantly according to the group analysis; however, the frequency of technology use and field of study did differ significantly. Practical implications: This study highlights the need for customised training, improved digital infrastructure, and targeted professional development to enhance the integration of digital tools in education. Originality/value: This study provides empirical insights into digital adoption patterns among teachers and offers valuable guidance to policymakers and teachers aiming to enhance technology-driven learning environments. Type of the paper: Original Article

PurposeThis study aims to investigate the factors that influence behavioral intention (BI) and usage of e-wallets by extending the unified theory of acceptance and use of technology (UTAUT) with constructs, namely, mobile self-efficacy, perceived enjoyment and satisfaction.Design/methodology/approachThis quantitative study used partial least squares structural equation modeling on a sample of 576 mobile e-wallet users surveyed online.FindingsThe key findings indicate that the model can explain 58.8% of the variance in behavioral intention and 53.8% in usage. Moreover, mobile self-efficacy has a significant influence on perceived enjoyment. Perceived enjoyment significantly affects satisfaction, effort expectancy and performance expectancy. Furthermore, effort expectancy significantly influences customer satisfaction in contrast to performance expectancy. In addition, although performance expectancy, social influence and satisfaction significantly impact consumers’ behavioral intention, effort expectancy and facilitating conditions condition have an insignificant influence on consumers’ behavioral intention. E-wallet stakeholders can use the findings of this study to make strategic decisions regarding the e-wallet ecosystem.Originality/valueAlthough previous studies have independently addressed the impact of mobile self-efficacy, perceived enjoyment and satisfaction on consumers’ behavioral intention and usage behavior, the expanded framework with the possible relationships proposed in this study has never been adequately studied in previous research in the context of e-wallets in developing countries based on an empirical analysis. This study represents one of the first attempts to improve the UTAUT by empirically analyzing these relationships.

Growing research has found that exposing patients to forest landscapes through technology improves their health. However, studies on the effects of nature therapy sessions on particularly vulnerable groups that require frequent regular treatment such as patients of chronic kidney disease (CKD) is lacking. This study aims to understand the effects of watching panoramic videos of forest walks through virtual reality (VR) headsets on patients. We also examine the effects of the frequency of virtual exposure to forests on the physiological and psychological parameters of patients undergoing dialysis. Twenty-four dialysis patients with a mean age of 65.11 year underwent a virtual nature intervention over the course of 3 weeks. The intervention consists of 3 numbers of 6-min 360-degree nature videos delivered using VR headsets. We measured heart rate variability (HRV) and heart rate (HR) using continuous electrocardiographic (ECG) monitoring and assessed their emotional states using a questionnaire survey and in-depth interviews. The results showed that the initial 6 min of watching of the panoramic videos through VR headsets resulted in significantly reduced HR and sympathetic nerve activity and increased positive emotional responses. However, repeated VR exposure did not further improve the physiological health of the respondents. Considering these results, the use of VR headsets to watch 6-min nature videos indoors can be used to enhance the positive psychological responses among dialysis and bed-ridden patients. As this study is preliminary, future random controlled trials are needed to compare and determine the best dose, content, and delivery methods of such an intervention.

Teaching and learning processes can be enhanced through the adoption of Virtual Reality (VR) Technology in the classroom. However, educators in Malaysia have been slow to adopt VR approaches to classroom content delivery. Little is known about the specific factor that impact to the intention to employ VR in educational environment. The purpose of this present research paper is to investigate and provide a preliminary analysis of a framework that predicts level of technology acceptance in a post secondary institution in Perak, Malaysia. A questionnaire based on factor identified in the Unified Theory of Acceptance and the Use Of Technology (UTAUT) was administered to 41tutoring level educators to measure the effect of performance expectancy (PE), Effort Expectancy (EE), and Social Influence (SI) on Behavioral Intention (BI) towards VR. Regression analysis identified that EE and SI significantly influenced BI. The need for future study of technology acceptance in education was discussed.

Dalam era perkembangan teknologi informasi, metode baru dalam pembelajaran telah muncul, memberikan perspektif berbeda terhadap konsep pendidikan. Universitas Brawijaya merespon dengan menerapkan LMS BRONE, platform pembelajaran daring berbasis Moodle. Namun, pengguna masih mengeluhkan beberapa masalah seperti harus login berkali-kali, pengingat tenggat waktu tugas yang tidak konsisten, dan tampilan yang tidak efisien. Populasi ditetapkan sebagai mahasiswa Universitas Brawijaya S1 dan Diploma yang pernah menggunakan LMS BRONE dalam proses pembelajaran. Metode penelitian yang diaplikasikan yaitu kuantitatif dengan pengumpulan data melalui penyebaran kuesioner dengan teknik purposive sampling kepada mahasiswa Universitas Brawijaya dan berhasil mendapatkan 194 responden. Penelitian ini menggunakan model Unified Theory of Acceptance and Use of Technology (UTAUT) untuk mengeksplorasi faktor-faktor yang mempengaruhi penerimaan pengguna terhadap LMS BRONE di Universitas Brawijaya. Hasil analisis data menggunakan PLS-SEM dengan alat bantu SmartPLS menunjukkan bahwa lima hipotesis diterima. Dari analisis jalur, dapat disimpulkan bahwa Performance Expectancy, Effort Expectancy, dan Social Influence memiliki pengaruh signifikan terhadap Behavioral Intention, sementara Facilitating Conditions tidak berpengaruh signifikan terhadap Behavioral Intention, tetapi berpengaruh signifikan terhadap Use Behavior. Behavioral Intention juga berpengaruh signifikan terhadap Use Behavior. Hasil penelitian ini mendukung teori UTAUT dan memberikan wawasan penting tentang penerimaan pengguna LMS BRONE di Universitas Brawijaya. Secara keseluruhan, penerimaan pengguna terhadap LMS BRONE di Universitas Brawijaya dapat dikategorikan positif. LMS BRONE memudahkan aksesibilitas materi, pengumpulan tugas, dan membantu dalam manajemen waktu. Namun, diperlukan perbaikan dalam konsistensi penggunaan oleh dosen dan optimalisasi fitur untuk perangkat mobile. Upaya sosialisasi dan pelatihan penggunaan LMS BRONE juga diperlukan agar adopsi sistem ini lebih optimal di kalangan pengguna. Abstract In the era of information technology development, new learning methods have emerged, offering different perspectives on educational concepts. Universitas Brawijaya has responded by implementing BRONE LMS, an online learning platform based on Moodle. However, users have reported several issues such as the need to log in repeatedly, inconsistent task deadline reminders, and an inefficient interface. The population was defined as undergraduate and diploma students at Universitas Brawijaya who had used the BRONE LMS in the learning process. The research applied a quantitative method by collecting data through a questionnaire distributed via purposive sampling to Universitas Brawijaya students, resulting in 194 respondents. This study utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to explore the factors influencing user acceptance of the BRONE LMS at Universitas Brawijaya. The data analysis, conducted using PLS-SEM with SmartPLS software, showed that five hypotheses were supported. From the path analysis, it was concluded that Performance Expectancy, Effort Expectancy, and Social Influence had a significant impact on Behavioral Intention, while Facilitating Conditions did not significantly affect Behavioral Intention but did significantly influence Use Behavior. Behavioral Intention also had a significant impact on Use Behavior. The results of this study support the UTAUT theory and provide valuable insights into user acceptance of the BRONE LMS at Universitas Brawijaya. Overall, user acceptance of the BRONE LMS at Universitas Brawijaya can be categorized as positive. The BRONE LMS facilitates access to materials, assignment submissions, and helps with time management. However, improvements are needed in the consistency of use by lecturers and optimization of features for mobile devices. Efforts in socialization and training for the use of the BRONE LMS are also necessary to enhance system adoption among users.

User acceptance is a precondition for successful implementation of hospital information systems (HISs). Increasing investment in information technology by healthcare organisations internationally has made user acceptance an important issue in technology implementation and management. Despite the increased focus on hospital information systems, there continues to be user resistance. The present study aimed to investigate the factors affecting hospital information systems nurse-user acceptance of HISs, based on the Unified Theory of Acceptance and Use of Technology (UTAUT), in the Shiraz University of Medical Sciences teaching hospitals. A descriptive-analytical research design was employed to study nurses' adoption and use of HISs. Data collection was undertaken using a cross-sectional survey of nurses (n=303). The research model was examined using the LISREL path confirmatory modeling. The results demonstrated that the nurses' behavioural intention (BI) to use hospital information systems was predicted by Performance Expectancy (PE) (β= 2.34, p<0.01), Effort Expectancy (EE) (β= 2.21, p<0.01), Social Influence (SI) (β= 2.63, p<0.01) and Facilitating Conditions (FC) (β= 2.84, p<0.01). The effects of these antecedents of BI explained 72.8% of the variance in nurses' intention to use hospital information systems (R2 = 0.728). Application of the research model suggested that nurses' acceptance of HISs was influenced by performance expectancy, effort expectancy, social influence and facilitating conditions, with performance expectancy having the strongest effect on user intention.

Objective: Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this study aims to examine patients’ behavioral intention (BI) toward accepting electroacupuncture analgesia and identify key determinants that influence its adoption. Methods: We conducted a multicenter cross-sectional study with questionnaire-based data collection and analyzed associations using structural equation modeling (SEM). Reporting followed the STROBE guidelines. Results: A total of 325 participants were included in the analysis. Performance expectancy (PE) ( β = 0.272, p &lt; 0.001), effort expectancy (EE) ( β = 0.610, p &lt; 0.001), and facilitating conditions (FC) ( β = 0.586, p &lt; 0.001) exhibited significant positive effects on BI. Social influence did not exert a significant effect ( β = 0.217, p = 0.090). FC had a significant negative effect on use behavior (UB) ( β = −0.617, p &lt; 0.001), forming a competitive mediation model with its positive indirect effect via BI. Significant differences in BI were observed across participants of different age groups, social backgrounds, educational levels, occupations, and income levels ( p &lt; 0.05). The model exhibited a good fit, supporting the applicability of the theoretical framework. Conclusion: EE, PE, and FC are key factors influencing patients’ acceptance of electroacupuncture analgesia. FC exhibited a competitive mediation effect on UB, highlighting the necessity of optimizing convenience conditions to meet the higher expectations of experienced users. Future initiatives should focus on developing tailored strategies for diverse demographic groups to enhance the clinical adoption and therapeutic effectiveness of electroacupuncture technology.

Development technology in the field health own positive impact. On the field Health services are available developed mobile applications Forgive Health services to public by online. The Mobile JKN Application has objective For give service health to community and make it easier power health For give service optimally. On research This analyze about reception user The Mobile JKN application uses the Unified Theory of Acceptance and Use of Technology (UTAUT). Respondent data obtained during spread questionnaire that is as many as 110 respondents. Respondent data Then analyzed using Structural Equation Model (SEM) research results This show that Performance Expectancy (PE) and Social Influence (SI) have an influence significant to Behavioral Intention (BI). However, for Effort Expectancy (EE) it is not influential significant to Behavioral Intention (BI) with t-test values are 2.224, 2.224, and 1.198 respectively. Third variable the can explain influence to Behavioral Intention (BI) was 34.3%. Whereas for Facilitating Conditions (FC) and Behavioral Intention (BI) influence significant towards Use Behavior (UB) with t-test values are 4.013 and 5.636 respectively. As well as second variable the can explain influence on Use Behavior (UB) of 59.3%. So on research explain reception user to use Mobile JKN application with using UTAUT.

The marketplace application is one of the most widely used applications today to support the fulfilment of needs. To provide a channel for their customers, PT.ABC has developed a marketplace application and have been put in playstore since 2017. Through this application, customer can make various transactions including purchasing and selling goods to others. The company have been facing some serious problems regarding the use of this application. This triggers the study. Based on our preliminary studies regarding the information technology evaluation, we decide to adopt the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The UTAUT model has four main variables namely Facilitating Condition, Effort Expectancy, Performance Expectancy, and Social Influence, and four moderating variables, namely: Gender, Age, Experience, Voluntariness of Use. Using the model, this study aims to learn the contribution of those factors to customers’ intention to use behaviour. To test the model, we developed a questionnaire and collect the data from 375 users as samples. The samples were selected using the snowball sampling technique. We model the problem as SEM (Structural Equation Modelling) and use SmartPLS to analyze the data. The result shows that performance expectancy, facilitating conditions and social influence contribute significantly to customers’ use behavior which in turn significantly influences the behavior intention to use and there is a positive influence between behavior intention and use behavior in the use of mobile applications. Meanwhile, gender only moderates the relation between effort expectancy, social influence and performance expectancy to customers’ behavior intention.

<sec> <title>BACKGROUND</title> Intelligent technologies are transforming healthcare delivery, necessitating that nursing curricula prepare students for digitally enhanced practice environments. However, empirical evidence examining nursing students' readiness for technology adoption, particularly through established theoretical frameworks, remains limited in Middle Eastern educational contexts. </sec> <sec> <title>OBJECTIVE</title> The objectives of this study were to (1) assess the level of awareness among nursing students regarding the use of artificial intelligence (AI) applications in nursing education; (2) examine nursing students' perceptions of the potential benefits and challenges associated with AI adoption, guided by the core constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT); and (3) evaluate nursing students' readiness and preparedness for artificial intelligence adoption by analysing the relationships between UTAUT-based theoretical constructs and behavioural intention to use AI technologies in educational and clinical training contexts. </sec> <sec> <title>METHODS</title> A cross-sectional survey was conducted among 314 undergraduate nursing students at a university in the United Arab Emirates. Data were collected using a validated questionnaire (Cronbach α=.89; content validity index=0.92) measuring UTAUT constructs including performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention. Analysis utilized IBM SPSS Statistics version 28.0, including descriptive statistics, Pearson correlation, and multiple regression </sec> <sec> <title>RESULTS</title> Students demonstrated high awareness (298/314, 94.9%) and training interest (261/314, 83.1%), with favourable perceptions of artificial intelligence's educational benefits. However, practical confidence remained lower (186/314, 59.2%), and three-quarters indicated needing substantial support. Performance expectancy (mean 3.96, SD 0.72) and facilitating conditions significantly predicted behavioural intention. The regression model explained 58% of variance in behavioural intention (R²=0.58; F6,307=71.24; P&lt;.001). Performance expectancy emerged as the strongest predictor (β=.38; P&lt;.001), followed by social influence (β=.20; P&lt;.001) and effort expectancy (β=.19; P=.001). A pronounced gap emerged between theoretical readiness (mean 3.78-4.09) and actual preparedness (mean 2.00-2.56), with insufficient training (mean 2.03, SD 0.76) and limited practical experience (mean 2.13, SD 1.34) as primary deficits. </sec> <sec> <title>CONCLUSIONS</title> The Extended UTAUT framework effectively explained nursing students' artificial intelligence adoption intentions. While students demonstrate positive attitudes, the readiness-preparedness gap highlights urgent needs for structured competency development, faculty training, and phased implementation strategies in nursing education. </sec>