The metaverse technology adoption process using UTAUT in digital business education

This study aims to analyze the factors influencing the behavior of using accounting applications among Micro, Small, and Medium Enterprises (MSMEs) in East Java using the Unified Theory of Acceptance and Use of Technology (UTAUT) model modified with the addition of a trust variable. Data were collected through a survey of 326 respondents using accounting applications and analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach. The results showed that performance expectancy, social influence, and trust significantly influenced behavioral intention, while effort expectancy did not. Facilitating conditions and behavioral intention significantly influenced use behavior. Moderation tests showed that only experience moderated the effect of social influence on behavioral intention, while gender, age, and voluntariness of use did not significantly influence.

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 study analyzes students' preferences regarding mobile banking usage in the Jabodetabek area, involving 250 respondents selected through purposive sampling. The research employs the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate key factors influencing mobile banking adoption. The Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique is used to analyze variables such as performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, and usage behavior. The findings show that effort expectancy, facilitating conditions, and behavioral intention have a significant impact on mobile banking adoption. These results suggest that perceptions of ease of use, good facilitating conditions, and a clear intention to use mobile banking are crucial in shaping user behavior. However, performance expectancy and social influence do not have a significant impact on the intention to use. These findings provide insights for banks to enhance service quality and meet students' expectations.

E-learning is emerging as an important strategy for providing broad and easy access to high quality education. In Indonesia, the concept of e-learning is usually applied in universities to support learning activities and student administration. This study aims to analyze the factors that affect the level of satisfaction by adopting the UTAUT (Unified Theory of Acceptance and Use of Technology) model and seeing the effect of user satisfaction. by intending to continue to use a new technology (e-learning). The UTAUT model was adopted in this study because it has a useful measurement tool for policy makers to assess the likelihood of the successful introduction of new technology. A quantitative research approach with a survey method is used to determine the factors that affect user satisfaction and the intention to use. The quantitative analysis of survey data was carried out using statistical techniques Partial Least Square Structural Equation Modeling (PLS-SEM) and utilizing SmartPLS 3.2.4 software. In this study, two factors were identified that affect user satisfaction, namely effort expectancy. Furthermore, user satisfaction affects intention to use.
 
 Keywords: e-learning, UTAUT, user satisfaction, intention to use, PLS-SEM

This study examines the factors influencing pre-service teachers’ behavioral intention to use AI-enabled educational tools during their practicum, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as the theoretical framework. The model includes the core UTAUT2 constructs such as performance expectancy, effort expectancy, hedonic motivation, social influence, facilitating conditions, price value, and habit. It also incorporates additional predictors including computer self-efficacy, computer anxiety, and computer playfulness. Data were collected from 563 pre-service teachers using a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that performance expectancy and hedonic motivation are the strongest predictors of behavioral intention. Computer self-efficacy, computer anxiety, and computer playfulness significantly influenced effort expectancy, although effort expectancy did not directly predict behavioral intention. Performance expectancy was significantly predicted by extrinsic motivation, job fit, relative advantage, and outcome expectations. Constructs such as social influence and facilitating conditions showed limited or inverse effects. These findings suggest that internal motivational, cognitive, and emotional factors are more influential than external or institutional factors in shaping the adoption of AI-enabled tools. The study highlights the importance of promoting personal relevance, confidence, and enjoyment in teacher preparation programs to encourage technology integration.

Background Artificial Intelligence (AI) is transforming service-oriented industries, yet its adoption in developing countries remains underexplored, particularly from the employee perspective. Employees’ perceptions, intentions, and organizational support are pivotal for effective AI integration in hospitality. Objective This study investigates the determinants of AI adoption among employees in Egypt’s hotel sector, drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT). It examines the effects of performance expectancy, effort expectancy, social influence, and facilitating conditions on AI usage, the mediating role of behavioral intention, and the moderating role of gender. Methods A quantitative design was applied using a structured online survey administered to 346 frontline employees in five-star hotels in Greater Cairo. Partial Least Squares Structural Equation Modeling (PLS-SEM) tested direct and indirect effects. Results Findings reveal that effort expectancy, social influence, and facilitating conditions directly and positively influence AI usage, while performance expectancy affects adoption indirectly via behavioral intention. BI mediates the link between UTAUT constructs and AI use, with gender moderating the intention–usage relationship: male employees exhibit stronger adoption tendencies. Conclusions The study underscores the role of user-friendly systems, inclusive training, and gender-sensitive strategies, extending UTAUT by establishing BI as a central mediator and revealing demographic contingencies.

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.

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. 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. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

This study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) as an analytical framework to systematically explore the multidimensional drivers of assistive devices (AD) usage behavior (UB) among people with visual impairments or blindness (PwVIB). The results indicate that four variables– Performance Expectation (PE), Effort Expectation (EE), Social Influence (SI), and Facilitating Conditions (FC) –significantly enhance Behavioral Intention (BI). Socioeconomic status exhibits significant moderating effects on UB, while BI plays a critical mediating role in the pathways through which PE, EE, SI, and FC influence UB. Based on these findings, the study proposes a dual-path strategy combining technological innovation and policy support to optimize AD and promote BI and UB. Specifically, increasing investments in technological R&D and improving policy frameworks, and establishing a systemic "economic support–targeted training–service extension" tripartite solution to comprehensively enhance AD, thereby integrating technological empowerment with social inclusive development.

The emergence of digital economy has motivated PT Telkom Indonesia to launch SOBAT-UKM portal. It aims to support Small and Medium Enterprises (SMEs) in Indonesia. The portal has an e-consultation feature, which provides online business discussion between business experts and the SMEs. Despite its significant benefits, the utilization of e-consultation feature only reached 10.54% of the yearly target set by PT Telkom. To address this problem, a study was conducted to investigate factors affecting the low users’ acceptance. The Unified Theory of Acceptance and Use of Technology (UTAUT) was used to construct the research model and a questionnaire for data collection. The data were analyzed with Partial Least Square-Structural Equation Modeling (PLS-SEM). The results indicate that the influencing factors are Social Influence, Effort Expectancy, Facilitating Conditions, User Behavior, and Behavioral Intention.

Using the frameworks of the Theory of Use and Gratification (UGT) and the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study explores the factors that influence individual behavior and behavioral goals in the adoption of digital banking. Partial Least Square-Structural Equation Modeling (PLS-SEM) is used in the analysis of research data using the program SmartPLS 3.2.9 professional. There are 432 people in the research sample that filled out questionnaires. The results show that behavioral intentions are strongly influenced by the integration of UGT-UTAUT2 by 60.3%. Performance and effort expectations are influenced by cognitive needs, effort expectations are influenced by affective needs, and social influence is impacted by social needs. Behavioral intentions for the use of digital banking are shaped by a combination of factors such as price value, hedonic motivation, habits, facilitating conditions, and effort expectations. The relationship between behavioral intentions, affective and cognitive needs is mediated by effort expectations. In the context of using digital banking, habits and behavioral intentions are important factors that influence behavior; in contrast, cognitive needs, affective needs, performance expectations, and social influence have no direct effect on behavioral intentions.

Adopting digital finance solutions is crucial for enhancing efficiency and competitiveness within the financial services industry, particularly for Micro, Small, and Medium Enterprises (MSMEs). This study examines the factors influencing the use and acceptance of a sharing-based digital system enhanced with a Generative AI website (E-Mudharabah), employing the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). In this article, the Generative AI-enhanced profit-sharing digital systems called E-Mudharabah. It is a web-based management system facilitating capital management for financiers, consultants, and MSME actors. The research integrates key variables from both models, including Perceived Ease of Use, Perceived Usefulness, Performance Expectancy, Social Influence, Facilitating Conditions, Habit, and Technology Self-Efficacy, to assess their impact on Behavioral Intention and Actual Usage. The study utilizes a quantitative approach, gathering data through surveys and analyzing it using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. Results indicate significant positive effects of perceived usefulness, performance expectancy, and social influence on the behavioral intention to use E-Mudharabah. The findings underscore the role of user-friendly interfaces and societal acceptance in driving adoption. Perceived Usefulness was the most significant variable influencing Behavioral Intention and Actual Usage (p-value < 0.001). Additionally, Social Influence and Facilitating Conditions were shown to have substantial effects, highlighting the importance of user support and societal acceptance in technology adoption. The research also underscores the role of Technology Self-Efficacy in enhancing user confidence and engagement with the platform. These findings suggest that improving digital finance solutions' perceived benefits and ease of use while fostering a supportive environment can significantly boost their adoption rates.

Along with technological development, platforms that combine e-commerce and social media—known as social commerce—have emerged. In Indonesia, TikTok Shop has grown rapidly, as reflected by the increase in Gross Merchandise Value (GMV). TikTok Shop enables users to shop online while engaging with social media in a single application without switching platforms. This study aims to analyze the phenomenon of the growing use of TikTok Shop by examining the role of attributes from the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), such as performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, and gender, as well as additional variables including perceived price-saving orientation, risk, and trust. Data were collected through an online questionnaire involving 200 Generation Z respondents aged 17 to 29 years, residing in DKI Jakarta, who had made purchases through TikTok Shop within the past three months. Using a quantitative approach with the analysis of the Partial Least Squares Structural Equation Model (PLS-SEM), the results showed that habit, price-saving orientation, and trust exerted a positive and significant influence on the intention to use TikTok Shop. Conversely, performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and perceived risk did not demonstrate significant influence. Furthermore, gender did not moderate the relationships among these variables. These findings confirm that habit, cost-saving orientation, and trust play dominant roles in fostering the continuous intention of TikTok Shop use among Generation Z, offering valuable insights for service providers to develop more effective strategies.

The purpose of the study is to find out Information Technology challenges facing by practicing auditors using measurement variables used in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. This research study has adopted the sequential mixed method research in which the exploratory qualitative study is conducted with the qualified practicing auditors across Kerala to identify various Enterprise Resource Planning (ERP) software using in their audit firms. The results of this exploratory study are then empirically tested with the 321 respondents from qualified practicing-auditor accountants in Kerala using Structural Equation Modelling-Partial Least Squares (SEM-PLS). The findings reveal that there is a significant and positive relationship between variables used in the UTAUT model with practicing auditors. Thus, the results suggest that all the four constructs from UTAUT (performance expectancy, effort expectancy, social influence, and facilitating conditions) seem to be predominantly significant factors influencing practicing auditors.

This study evaluates the acceptance of an e-catalog application system for construction service procurement in government using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The use of e-catalogs in procurement offers substantial benefits, including increased transparency, efficiency, and accountability. This research aims to identify factors influencing user acceptance of e-catalogs through an explanatory quantitative approach. This research uses an explanatory quantitative method, A sample of 100 respondents was selected using accidental sampling, and data were gathered through questionnaires, analyzed via Partial Least Square (PLS) to evaluate relationships between variables. Findings reveal that Effort Expectancy, Facilitating Conditions, and Habit significantly impact Use Behavior, while Behavioral Intention is influenced by Facilitating Conditions and Effort Expectancy. Meanwhile, Performance Expectancy, Social Influence, Hedonic Motivation, and Price Value show no significant impact on Behavioral Intention or Use Behavior. The conclusions indicate that ease of use and environmental support are key factors in promoting e-catalog usage.