Exploring Accounting Application Adoption among East Java MSMEs through UTAUT Model

The metaverse, a digital space where users interact through virtual and augmented reality, is set to transform business education in higher learning. This study explores the adoption of metaverse technologies in universities, utilizing the Unified Theory of Acceptance and Use of Technology (UTAUT). The UTAUT model, which includes performance expectancy, effort expectancy, social influence, and facilitating conditions, is used to examine the factors influencing adoption, with moderating variables such as Gender, Age, Experience, and Voluntariness of Use. The research targets undergraduates from the Faculties of Economics at State University of Jakarta and State University of Makassar. Data were gathered through surveys and analyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM) with Variance Based (VB) SEM. The results indicate that performance expectancy and social influence significantly impact students' behavioral intention and usage behavior, particularly among different genders. Facilitating conditions also critically affect technology adoption. The study underscores the metaverse's potential to enrich educational experiences by offering immersive learning environments. However, the success of these technologies hinges on students' readiness and willingness to engage. The findings suggest that younger students, especially those influenced by social factors, are more likely to adopt metaverse technologies, guiding institutions in enhancing digital learning tools.

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 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.

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.

SMA Negeri 1 Kota Jambi has been implementing the INLISLite library information system for seven years. However, there has not been a comprehensive evaluation of the level of success of this system. This study aims to measure the level of user acceptance of the INLISLite library information system and the factors that influence it, using the UTAUT (Unified Theory of Acceptance and Use of Technology) model. The variables used in the UTAUT model include performance expectancy, effort expectancy, social influence, and facilitating conditions. This study also considers the moderating effect of gender and age. Data were collected through questionnaires distributed to 285 respondents, consisting of teachers, students, and staff of SMA Negeri 1 Kota Jambi. Data analysis was carried out using SEM (Structural Equation Modeling) techniques through SmartPLS 4.0 software. The results showed that effort expectancy and facilitating conditions significantly influence the acceptance of INLISLite users. However, performance expectancy and social influence do not have a significant effect. Gender and age do not moderate the relationship between variables in the UTAUT model

As the Malaysian public sector undergoes digital transformation, its electronic records management faces challenges, including security issues, maintaining record integrity and record authenticity, audit trails, and trust in existing systems. Blockchain technology has the potential to solve these challenges through features such as distributed records, transparency, restricted immutability, and cryptographic security. However, the adoption of this technology in the Malaysian public sector is still underexplored. The main objective of the study is to identify factors that influence the adoption of blockchain technology in Electronic Records Management in the Malaysian public sector and to develop an adoption model. A quantitative method was used to collect data from 253 public-sector officials directly involved in electronic records management. The conceptual framework was developed by integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Data analysis was conducted using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach to test the relationship between variables and confirm the study hypotheses. Results show that users' behavioral intentions have a significant effect on actual usage of blockchain technology (H10 is accepted), with an R² of 0.633, indicating that the independent construct explains 63.3% of the variance in behavioral intentions. Individual, organizational, and environmental factors, including performance expectations, effort expectations, social influences, and facilitating conditions, have a significant effect on users’ behavioral intentions, emphasizing that technology adoption requires a holistic approach. The study also finds that behavioral intentions act as a critical mediator before actual usage of the technology can occur.

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.

Purpose This study used extended Unified Theory of Acceptance and Use of Technology (UTAUT) model to examine the effect of personal expectancy (PE), efforts expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV) and habit (H) on behavioural intention (BI) to use Chat Generative Pre-Trained Transformer (ChatGPT) in higher education for an Indian context. The study also examined moderating effects of students’ self-innovativeness (SIN) and integrity (INT) on the relationship between BI and use behaviour (UB) for ChatGPT. Design/methodology/approach A sample of 311 students has been selected from Northern states of India by applying stratified proportionate random sampling method and four disciplines – engineering, business administration, science and fine arts were used as different strata in the sample selection process. Partial least squares structural equation modelling (PLS-SEM) approach has been used in data analysis to assess proposed theoretical model. Findings The study found PE, FC, PV and H significantly account for students’ BI and actual use of ChatGPT, whereas EE, SI and HM showed a negligible impact on BI. The BI was also found non-significant in predicting the usage behaviour for ChatGPT. The moderation effects of SIN and INT of students were also found non-significant on the relationship between BI and UB. Research limitations/implications A limited sample size of study and its focus on Northern states of India constrain the generalizability of findings to the other parts of world. Originality/value The study helps to understand the nuances associated with advanced artificial intelligence (AI)-driven tool such as ChatGPT in higher education for the application of best-practices. Therefore, this study aims to bridge this gap by examining determinants of ChatGPT usage with extended version of UTAUT model in Indian context by using additional constructs such as SIN and INT.

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.

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.

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.

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.

Background: Teledentistry facilitates treatment planning, patient monitoring and education in dentistry by enhancing the access to and outcomes in oral health. This study aimed to assess the undergraduate students' use and acceptance of teledentistry services using a tool based on the UTAUT model. Methods: The implementation of teledentistry into the curriculum of 241 undergraduate students receiving training for teleconsultation was evaluated. The students' behavioral intention to adopt teledentistry was measured with questions based on the Unified Theory of Acceptance and Use of Technology (UTAUT) constructs, such as performance expectancy, effort expectancy, social influence facilitating conditions, and trust. Results: A total of 105 students completed the UTAUT model questionnaire. The valid hypotheses considered the following constructs: performance expectancy (H1), social influence (H3) and trust (H5) showed positive impacts, whereas effort expectancy (H2) had a non-significant negative influence and facilitating conditions (H4) had no significance. Therefore, performance expectancy, social influence and trust had an impact on the students' behavioral intention to use teledentistry. Discussion: The constructs performance expectancy, social influence and trust impacted the behavioral intention of undergraduate students in using teledentistry, meaning that these factors must be considered during the implementation of this telehealth service.

Despite the increasing use of generative artificial intelligence (GenAI) tools among students, limited research has examined the underlying mechanisms guiding their adoption in English as a Foreign Language (EFL) education. Drawing on the unified theory of acceptance and use of technology 2 (UTAUT2) and self-determination theory (SDT), this study examines the factors shaping Indonesian EFL students’ intention and actual use of GenAI for English learning. Using a cross-sectional survey design, data were collected using an online questionnaire from 462 EFL students at three private universities in Indonesia and analyzed with the partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4. The results indicated that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, habit, autonomy, and relatedness significantly impacted EFL students’ intention to adopt GenAI for English learning. While competence did not significantly influence behavioral intention, both the SDT components (autonomy, competence, and relatedness) and behavioral intention were predictors of actual usage. The integrated framework explained a high proportion of variance (R² = 83.1% for behavioral intention; R² = 77.1% for actual use). These insights advance understanding of the technological and motivational determinants of GenAI adoption in EFL education and offer evidence-based direction for designing effective, equitable, and ethically responsible AI-supported English learning practices.

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.