ADOPTION OF ARTIFICIAL INTELLIGENCE TOOLS BY SELECTED STUDENTS OF THE CBAA AT LAGUNA UNIVERSITY: FACTORS INFLUENCING USAGE AND ACCEPTANCE

This study examines the relationship between technical readiness (TR) and the Unified Theory of Acceptance and Use of Technology (UTAUT) within the framework of Over-the-Top (OTT) media services, particularly Netflix. The study employs the UTAUT model and integrates the TR model's external factors of "optimism," "innovativeness," "inadaptability," and "insecurity" to examine their moderating influences on customer utilisation of Netflix. In 2024, 475 questionnaires were disseminated in this study, of which 453 were considered valid. The study revealed that customer trust in relationships significantly influences user involvement in 'Performance Expectancy' (PE), 'Effort Expectancy' (EE), 'Social Influence' (SI), and 'Facilitating Conditions' (FC). Furthermore, consumers' intention to interact with Netflix's 'Performance Expectancy' (PE), 'Effort Expectancy' (EE), and 'Social Influence' (SI) considerably and positively affects their behaviour. The research employed hierarchical regression analysis to assess the impact of customer willingness to "Intention to Use" on the UTAUT regarding Netflix consumption. The findings indicate that the Trustworthiness Rating of consumers substantially affects the UTAUT dimensions of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. Moreover, consumers' inclination to use Netflix is positively influenced by the constructs of Performance Expectancy, Effort Expectancy, and Social Influence. The results demonstrate that consumers' Trustworthiness Rating substantially influences the UTAUT aspects of Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. Moreover, the constructs of performance expectancy, effort expectancy, and social influence positively affect customers' inclination to utilise Netflix. The results of this study may inform strategic approaches to improve user engagement and satisfaction in the competitive OTT market.

Introduction: The rapid growth of financial technology has positioned cryptocurrencies as a promising alternative to traditional financial systems. In Malaysia, particularly in Selangor, however, there is very little adoption of cryptocurrency, as people are fearful of the regulatory concerns, security issues, and public knowledge gap. Given this, it is important to know what motivates the uptake of cryptocurrencies, in order for stakeholders who are looking to promote wider digital financial inclusion to strategize. Objectives: This study investigates the elements that lead to cryptocurrency adoption among Selangor citizens using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. It considers the performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, habit, perceived risks and cryptocurrency awareness on adoption intentions. In addition, it explores whether cryptocurrency awareness moderates the risk adoption association. Methods: Quantitative approach was used, where 244 respondents from Selangor were distributed the structured survey. Of these, 213 valid responses were filtered and analyzed using SPSS. Relating variables were assessed by use of statistical techniques such as reliability analysis, normality tests, correlation analysis, multiple regression and moderation analysis. Results: The results show that performance expectancy, effort expectancy, social influence and habit exert influence on cryptocurrency adoption, and other factors do not exert any influence. The strongest predictor was effort expectancy. Therefore, ease of use is indeed a critical role in the adoption decisions. Interestingly, cryptocurrency awareness was identified not to moderate the association between perceived risk and adoption, indicating that higher awareness does not mitigates the negative effect of perceived risk on adoption. Conclusions: To sum up, usability, social influence and prior experience drive cryptocurrency adoption in Selangor. This also calls for the need to improve awareness programs on risk concerns and enhancing public confidence. These findings are of great value to policymakers, financial institutions, and cryptocurrency technology developers in their efforts to encourage cryptocurrency adoption in Malaysia.

This work aims to study effective factors that could play an important role in the decision of Jordanian citizens to adopt e-government services. The study employed the UTAUT model with introducing new constructs, namely; 'website quality', 'trust of internet', 'trust of government', to study the adoption of e-government services in Jordan context. The data was collected using online survey from a total of 320 Jordanian citizen who are from both public and private sectors. The research model was evaluated using the structural equation modelling (SEM) technique. Based on the results, 'website quality', 'trust of internet', 'trust of government', 'performance expectancy', 'effort expectancy' and 'facilitating conditions' factors were shown to have a positive effect on behavioural intention to use e-government services. However, the influence of 'social influence' was found to be insignificant on the participants behavioural intention to use. Additionally, the 'website quality' factor was found to have a positive effect on the 'performance expectancy' of e-government services. The findings of this study will guide researchers, policy makers and professionals towards the Jordanian citizen' priorities in improving website design, website functions, website content quality, ease of use and security; thereby increasing the adoption of e-government services.

Purpose: This study aims to collect empirical data on the impact of three critical variables in blockchain adoption – performance expectancy, effort expectancy, and social influence – on the Intention to use blockchain in private higher education institutions in Indonesia. Using the UTAUT theoretical framework, this study aims to understand how these three factors influence the decision to adopt blockchain technology in accounting information systems. Methodology/approach: This study investigates how private universities in Indonesia perceive the possibility of implementing blockchain technology in the accounting system. By applying the UTAUT model, the study examined three aspects that may influence the willingness to adopt blockchain technology: performance expectancy, effort expectancy, and social influence. Data was collected through distributing questionnaires to 136 leaders of finance departments in various private universities. Findings: The results show that performance and efficacy expectancy significantly influence the Intention to use blockchain technology in private universities. In contrast, social influence does not show a significant influence. Practical and Theoretical contribution/Originality: This research makes a practical contribution by highlighting the importance of perceived usefulness and ease of use in driving blockchain adoption in the higher education sector. From a theoretical perspective, this study extends the applicability of the UTAUT model in the context of private universities in Indonesia. Research Limitation: The Object of this research is only private higher education institutions in Indonesia.

This study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain why citizens frequently abandon digital public services despite substantial government investment in e‑government platforms. It focuses on Riau Province, Indonesia, and positions User Satisfaction as a central mediator linking four UTAUT antecedents—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—to actual usage behavior. Adopting a deductive quantitative design, the research uses a stratified random survey of 240 e‑government users and analyzes the data with PLS‑SEM, supported by rigorous tests of reliability, validity, common‑method bias, and predictive relevance. The model explains 80.1% of the variance in User Satisfaction and 80.2% in User Behavior, indicating strong explanatory and predictive power. Results show that Performance Expectancy, Social Influence, and Facilitating Conditions significantly increase satisfaction, while Performance Expectancy, Facilitating Conditions, and User Satisfaction itself are key direct predictors of continued use. User Satisfaction also mediates the effects of performance expectancy, social influence, and facilitating conditions on behavior. Although Effort Expectancy is not statistically significant at the 5% level, it exhibits the largest effect size on satisfaction, underscoring the structural importance of ease of use. Theoretically, the study validates an under‑explored affective pathway in mandatory settings; practically, it offers a roadmap for shifting from technology‑centric to citizen‑centric digital governance.

This study delves into the implications of incorporating AI tools, specifically ChatGPT, in higher education contexts. With a primary focus on understanding the acceptance and utilization of ChatGPT among university students, the research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the guiding framework. The investigation probes into four crucial constructs of UTAUT—performance expectancy, effort expectancy, social influence and facilitating conditions—to understand their impact on the intent and actual use behaviour of students. The study relies on data collected from six universities in two countries and assessed through descriptive statistics and structural equation modelling techniques, and also takes into account participants' gender and study level. The key findings show that performance expectancy, effort expectancy, and social influence significantly influence behavioural intention. Furthermore, behavioural intention, when considered alongside facilitating conditions, influences actual use behaviour. This research also explores the moderating impact of gender and study level on the relationships among these variables. The results not only augment our comprehension of technology acceptance in the context of AI tools but also provide valuable input for formulating strategies that promote effective incorporation of ChatGPT in higher education. The study underscores the need for effective awareness initiatives, bespoke training programmes, and intuitive tool designs to bolster students' perceptions and foster the wider adoption of AI tools in education. Practitioner notes What is already known about this topic ChatGPT is a tool that is quickly gaining worldwide recognition. ChatGPT helps with writing essays and solving assignments. ChatGPT raises ethical concerns about authorship, plagiarism and ethics. What this paper adds This study explores students' acceptance of ChatGPT as an aid in their education, which has not been studied previously. We used the extended Unified Technology Acceptance and Use of Technology theory to test what factors mostly influence the use of ChatGPT by students. We conducted a multiple study in Poland and Egypt based on sampling strategy from six universities. Implications for practice and/or policy ChatGPT is a global game changer and should be incorporated into study programmes. The limitations of ChatGPT should be well explained and known since it is prone to making mistakes. Higher education teachers should be aware of ChatGPT's capabilities.

The conventional method of education has shrunken adequate information access and acquisition capability. However, this as further widened the educational knowledge gap. This research study examined the prospect of adopting e-learning in the Nigerian educational system. The Unified Theory of acceptance and use of technology (UTAUT)modelwas utilized in other to properly investigate the adoption of e-learning for an improved educational system in Nigeria. Adescriptive survey design was employed, and a quantitative research method was used for data gathering and analysis. A total of 574 responses was obtainedfrom the research study respondents. The study analysis result showed that the Average variance extracted (AVE) for actual use, behavioural intention, experience, effort expectancy, facilitating condition, performance expectancy and social influence was o.738,0.790,0.670,0.804,0.749,0.861,0.514 respectively, and the discriminant value for actual use, behavioural intention, experience, effort expectancy, facilitating condition, performance expectancy and social influence were 0.859,0.889,0.897,0.819,0.865,0.928 and 0.717 respectively. This analysis result suggests that the research model convergent and discriminate validity were acceptable. Furthermore, approximately 59.7% of the variance of behavioural intention (BI) to adopt eLearning was illustrated by the PE (Performance Expectancy), EE (Effort Expectancy), and SI (Social Influence); Where R2 = 0.597. Furthermore, about 77.5% of the variance of actual adoption (AC) of eLearning was explained by behavioural intention (BI) to adopt eLearning Where R2 = 0.775. The result suggests that Performance expectancy (PE), Effort Expectancy (EE) and Social Influence (SI) have a positive effect of the behavioural intention to adopt e-Learning and the behavioral intention would lead to the actual adoption of eLearning. Additionally, Facilitating condition (FC) and Experience (E) have a positive effect on the actual adoption of e-Learning. The result of the research study suggests that the adoption of e-Learningin Nigeria educational system is influenced by the ease of use, performance gain, public sway, adequate support, and proficiency.

Purpose In response to the rising cost of living, European travellers are increasingly turning to online discount platforms (ODPs) to find affordable travel deals. Despite their widespread use, limited research has investigated the psychological mechanisms underlying consumer acceptance of such platforms. This study aims to address this gap by extending the Unified Theory of Acceptance and Use of Technology by incorporating perceived value and perceived risk to better understand the factors that influence the intention to purchase leisure travel through discount websites. Design/methodology/approach A quantitative research approach was used. Data were collected via an online survey of 425 European users of discount travel websites. Partial Least Squares Structural Equation Modelling was used to examine the relationships between performance expectancy, effort expectancy, social influence, facilitating conditions, perceived value, perceived risk and behavioural intentions. Findings Perceived value was found to be the strongest predictor of purchase intention towards ODPs, followed by performance expectancy and social influence. Perceived risk had a significant negative effect. Effort expectancy and facilitating conditions were revealed to be not significant. The model explained 55% of the variance in behavioural intentions. Research limitations/implications This study extends Unified Theory of Acceptance and Use of Technology-based literature by incorporating perceived value and risk to deepen understanding of consumer intentions in leisure travel purchases via ODPs, highlighting perceived value as the strongest predictor and emphasising the utility of second-order constructs for modelling multidimensional consumer behaviour. This study relied on self-reported data, which may introduce common method bias. In addition, generalisability is limited by the use of a European non-probability sample. Practical implications The findings of this study provide strategic guidance for tourism marketers by underscoring the primacy of perceived value and the need to emphasise emotional appeal, trust and social recognition over functionality, as digitally savvy consumers no longer prioritise ease of use or support in mature markets. Originality/value By integrating perceived value and perceived risk into the UTUAT framework, this study showed that in digitally mature markets, usability and infrastructure are baseline expectations rather than drivers of intention. Instead, perceived value (emotional, functional and social) emerges as the strongest predictor, calling for adoption models that account for digital maturity. These insights advance theory on hedonic and utilitarian drivers of consumer decision-making in price-sensitive leisure travel and offer practical guidance for platform operators and tourism providers in post-pandemic, inflationary context.

This study aims to investigate the willingness of clinical nurse educator to adopt virtual reality technology, while also examining the underlying mechanisms that influence this willingness through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). A convenience sampling method was employed to select 225 clinical nurse educator, all of whom possess a professional qualification certificate as nurse practitioners, from a tertiary hospital in Wuhan City, Hubei Province. The study utilized an adapted UTAUT model theory-based design to develop several questionnaires: the performance expectancy questionnaire (11 items), the effort expectancy questionnaire (4 items), the social influence questionnaire (6 items), the facilitating conditions questionnaire (7 items), and the behavioral intention questionnaire (4 items). These instruments were designed to assess the clinical nurse educators' willingness to adopt VR technology. Furthermore, a regression model was established to analyze the factors influencing this willingness, utilizing SPSS 26.0 for statistical analysis and validating the model through path analysis with AMOS 24.0, where a p-value of less than 0.05 was considered statistically significant. The questionnaire demonstrated strong reliability and validity, yielding a total of 222 valid samples, comprising 209 females (94.14%) and 13 males (5.86%). Among the clinical nurse educators, 163 (73.42%) reported a willingness to use virtual reality technology, with scores of 4 or higher. Pearson correlation analysis revealed positive correlations between performance expectancy, effort expectancy, social influence, and facilitating conditions with behavioral intention (p < 0.05). Furthermore, regression analysis indicated that performance expectancy, effort expectancy, social influence, and facilitating conditions had a positive impact on behavioral intention (p < 0.05). The path model exhibited a good fit, and the results were consistent with the regression analysis, showing that the effects of performance expectancy, effort expectancy, and social influence on the behavioral intention to use virtual reality technology were 0.231, 0.150, 0.236, and 0.247, respectively. Clinical nurse educators exhibit a robust willingness to engage with VR technology. Moreover, improving factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions can substantially enhance their readiness to adopt this technology.

As primary end-users, nurses' intention or motivation to use the hospital electronic medical records can influence its adoption and effective usage. This study explores nurses' intention to use the Next Generation Electronic Medical Record, a novel electronic medical record implemented in Singapore, as compared to first-generation systems. Cross-sectional surveys were conducted at 2 time points in a tertiary hospital where nurses were surveyed using the Unified Theory of Acceptance and Use of Technology to capture their intention to use the first-generation and novel systems. Welch t test was used to compare means between time points. Multivariable regression analysis was used to determine the predictors of nurses' intention to use the novel system. At each time point, 1152 and 723 nurses responded, where most were registered nurses aged 31 to 40 years old. Positive predictors include performance expectancy, effort expectancy, and social influence, while negative predictors include technological savviness and nursing designation. Effort expectancy, the perceived ease of use, was the strongest predictor. This study found that nurses' intentions remained positive across time points. Key predictors like effort expectancy could be targeted to promote effective use of the novel electronic medical record. Suggested strategies include continuous, robust training and involving end-users in the design and implementation process.

The purpose of this study was to establish factors hindering teachers’ acceptance of electronic performance system used as an appraisal tool. The study was guided by the following specific research objectives: To establish effects of performance expectancy, effort expectancy, social influence and facilitating condition on teachers’ acceptance of electronic performance system. The study adopted a survey research design subjecting public secondary school teachers in Bomet Central sub-county. Krejcie and Morgan's table and a formula of sample size determination was used to select 196 respondents for the study. Purposive sampling technique was used to target principal, stratified sampling technique with be used to sample from each strata; Languages, Sciences, Humanities, and Creative Arts. Simple random sampling technique was used to sample respondents from each strata. The quantitative data collected was coded and analyzed using the Pearson’s (r) Correlation, regression analysis (R) and ANOVA, SPSS, tables and graphs. Regressing analysis revealed R squared of 0.702, implying that the variables used in this study; resource-based conditions, performance expectancy, effort expectancy and social influence jointly explained 70.2 percent of the variation in the adoption of electronic performance by public secondary school teachers in Bomet Central Sub-County. The study concludes that the selected factors adopted by this study which included resource-based conditions, performance expectancy, effort expectancy and social influence have positive and significant effect on the adoption and implementation of electronic performance by public secondary school teachers in Bomet Central Sub-county. The study thus recommends that the managements of public secondary schools in Bomet central sub county and the country at large should strive to embrace resource-based conditions, performance expectancy, effort expectancy and social influence, since they have been found to have positive effect on the adoption of electronic performance by public secondary school teachers in Bomet Central Sub-county. Keywords: Selected Factors, Performance expectancy, Effort expectancy, Social influence, Resource-based conditions, Adoption of Electronic performance system.

This study examined the influence of the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs—Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions—on consumers’ behavioral intention to purchase seafood products through digital platforms in Surigao del Sur, Philippines. Employing a descriptive-correlational quantitative design, data were collected from 100 respondents selected through stratified random sampling. A validated questionnaire measured perceptions of each UTAUT construct and behavioral intention using a five-point Likert scale. Descriptive statistics, Pearson correlation, and multiple linear regression were utilized for data analysis. Results revealed that all UTAUT constructs were positively perceived by respondents, indicating favorable attitudes toward digital seafood marketing. Correlation analysis showed strong interrelationships among constructs, with Performance Expectancy exhibiting the highest and most significant association with Behavioral Intention (r = 0.7169, p &lt; 0.01). Regression analysis further identified Performance Expectancy (β = 0.4013, p = 0.002) and Facilitating Conditions (β = 0.4559, p = 0.003) as significant predictors of Behavioral Intention, whereas Effort Expectancy and Social Influence showed moderate yet non-significant effects. These findings suggest that consumers’ online seafood purchasing intentions are primarily driven by perceived usefulness and enabling infrastructure rather than ease of use or social persuasion. The study affirms the applicability of the UTAUT model in the digital seafood market context and underscores the importance of improving technological support, platform performance, and consumer trust to enhance adoption in emerging rural economies.

&lt;span&gt;The use of learning management systems (LMS), such as Moodle, for personalized mathematics learning (PML) is successful; nonetheless, its performance depends on several factors. This study investigates the factors influencing the utilization of LMS for PML at the Maldives National University (MNU). A correlational study was conducted involving 120 randomly selected pre-university students using an online questionnaire to measure performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and student commitment (SC) toward LMS usage behavior (UB). Data analysis with IBM statistical package for the social sciences (SPSS) statistics version 25.0 revealed significant positive relationships between LMS UB and PE (r=0.624**), EE (r=0.644**), FC (r=0.533**), and SC (r=0.696**), with SC being the strongest predictor. SI showed a weaker positive relationship (r=0.204). The study also discovered a multiple correlation (R) value of 0.807 and an analysis of variance (ANOVA) (F (5, 114)=42.497, p=0.000). The study’s findings underscore the significance of these factors in promoting LMS adoption and effective use concluding that focusing on these key predictors can enhance PML and improve student engagement and performance.&lt;/span&gt;

The purpose of this study is to examine the structural relationship among self-efficacy, social influence, effort expectancy, performance expectancy, and behavioral intention of mobile learning, which is based on the extended technology acceptance model. We performed a study to determine the impacts that social influence, performance expectancy, and effort expectancy have on behavioral intention of mobile learning through self-efficacy. Appropriate measures were developed and tested on 226 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The path relationship of the research model was analyzed by structural equation modeling (SEM) using AMOS 18.0. The results revealed that firstly, self-efficacy has positive effects on performance expectancy, social influence, and effort expectancy. Second, social influence has positive effects on performance expectancy, behavioral intention, and effort expectancy. Third, effort expectancy has positive effects on performance expectancy and behavioral intention. Fourth, performance expectancy has a positive effect on behavioral intention. Managers of mobile learning should focus on self-efficacy to enhance behavioral intention.

This study investigated factors affecting use of computer statistical applications among undergraduate students of Economics in Ambrose Alli University, Ekpoma. The study was concerned with determining the relationship between predictor variables (performance expectancy, effort expectancy, social influence and facilitating conditions) and use of statistical applications. 400 students were drawn by stratified random sampling technique from the population of 808 regular students in the Department of Economics. Instrument used for the collection of data was a survey questionnaire adapted and modified from the work of Abdulwahab &amp; Dahalin (2010). Linear regression technique was used to establish the relationship between the dependent and independent variables at 0.05 level of significance. Findings showed that performance and effort expectancy have no significant relationship with students’ statistical application usage (p&gt;0.05) while social influence and facilitating conditions are significantly related with students’ use of the applications (p&lt;0.05). R-square (R2) was 0.76 depicting that 76.0% change in students’ use of computer statistical applications was determined by the predictors (performance expectancy, effort expectancy, social influence and facilitating conditions). It was recommended for the faculty to create a social forum where students can meet to share knowledge on data analysis and computer statistical application usage.Keywords: Statistical Applications, Performance expectancy, Effort expectancy, Social influence, facilitating conditions