When Intention Is Not Enough: Post-Adoption Use Behavior of Islamic Mobile Banking within the UTAUT2 Framework

This study aims to investigate the technology usage behavior of tourists according to the modified acceptance and use of technology theory (UTAUT) model. Data were collected from a survey of 265 tourists using a random sampling technique. Partial least squares structural equation modeling (PLS-SEM) technique was used to analyze the data. The findings indicate that performance expectancy, hedonic motivation, and habit significantly influence tourists' behavioral intention to use information and communication technology, while effort expectancy, social influence, and facilitating conditions have no significant influence. However, actual usage behavior is highly dependent on tourists' behavioral intention and their habits, while facilitating conditions have no influence in this regard. The findings reveal the main factors influencing tourists' usage behavior that can help relevant stakeholders in designing tourism planning and marketing. The findings provide a strong contribution by extending the existing UTAUT-based literature by adding two new moderators in the relationship between behavioral intention and actual usage behavior.

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.

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.

This study examines the factors influencing Indonesian travellers’ behavioural intentions and use behaviours of Quick Response Code Indonesian Standard (QRIS), a popular QR-code mobile payment (M-Payment) system. Criterion-based purposive sampling was utilised to ensure data relevance. Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4 analysed data from 410 Indonesian travellers. Additionally, a multigroup analysis (MGA) examined differences between younger and older travellers. Results show that behavioural intention is strongly influenced by perceived enjoyment, promotional activities, and performance expectancy, which in turn predicts use behaviour. Although perceived trust does not moderate the intention–use behaviour link, perceived enjoyment mediates the relationship between performance expectancy, effort expectancy, and social influence on behavioural intention. MGA results reveal that promotional activities have a significantly stronger impact on younger travellers. Findings highlight the importance of optimising QRIS’s efficiency and user experience while leveraging promotional strategies to drive adoption. This is the first study to examine QRIS adoption among Indonesian travellers and provides new insights into M-Payment adoption in tourism by integrating promotional activities, perceived enjoyment, and perceived trust into the UTAUT model. Policymakers should also expand QRIS’s cross-border usability to facilitate international transactions for Indonesian travellers.

This study is important considering the rapid adoption of live streaming technology in the fashion industry, which has become a primary tool for boosting sales. Understanding the factors influencing fashion vendors' use of this technology is crucial, as it can help businesses and marketplace platforms design more effective and interactive marketing strategies. This study examines how behavioral intention affects the actual use of live streaming technology, while also considering the moderating role of gender in the relationships between these variables. The study adopts a quantitative approach, collecting data through a questionnaire survey. The population for this study consists of fashion vendors who actively use live streaming features on marketplaces such as Shopee, Tokopedia, and Lazada in the Tanah Abang area. Purposive sampling was employed as the sampling technique, as the study requires fashion vendors who meet specific criteria, namely, those who have used live streaming technology to sell their products on marketplaces. As a result, the sample size amounted to 211 participants. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the assistance of SmartPLS software to test the model’s validity and the relationships between variables. The findings show that trust significantly affects performance expectancy and effort expectancy. Both performance expectancy and effort expectancy significantly influence behavioral intention, and behavioral intention, in turn, positively affects the actual use of live streaming. This study offers new insights into how gender differences influence fashion vendors' perceptions and decisions when using live streaming technology as a marketing tool. Furthermore, it provides a deeper understanding of how trust in technology can strengthen performance and effort expectancies, ultimately enhancing behavioral intention and the actual use of live streaming technology.

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.

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.

The adoption of Islamic Mobile Banking (IMB) services remains a challenge in Pakistan, despite the increasing global growth of Islamic banking. Shariah compliance, security concerns, and trust issues significantly impact the acceptance of IMB services. This study integrates the Unified Theory of Acceptance and Use of Technology (UTAUT) with Islamic compliance to explore key factors influencing consumer adoption. Previous research suggests that regulatory confidence and performance expectancy play a pivotal role in technology acceptance (Venkatesh et al., 2012; Nasri, 2021; Hassan & Lewis, 2020). However, in the context of Islamic banking, additional variables such as financial security, awareness, and government regulations significantly shape user perceptions and adoption decisions (Mensah & Khan, 2024; Yusfiarto et al., 2024). A quantitative research methodology was employed, utilizing a structured survey to collect primary data from 400–500 respondents in Pakistan. The study applied Partial Least Squares Structural Equation Modeling (PLS-SEM) and SPSS for data analysis, ensuring validity and reliability (Hair et al., 2019). The results indicate that Government Regulation (GR) (β = 3.51, p < 0.01) and Performance Expectancy (PE) (β = 1.74, p < 0.05) are the strongest predictors of IMB adoption. Interestingly, Awareness (AWN) showed a negative impact (-3.20, p < 0.01), suggesting that consumer skepticism toward Shariah compliance and security may hinder adoption (Khan et al., 2023; Zouari & Abdelhedi, 2021). The findings highlight the critical role of regulatory transparency, perceived usefulness, and trust-building measures in driving IMB adoption. The study suggests that mere awareness campaigns are insufficient; rather, efforts should focus on strengthening Shariah governance, financial literacy, and cybersecurity measures to enhance user confidence (Ali & Hassan, 2022; Windasari et al., 2022). Future research should explore trust, risk perception, and ethical banking concerns as moderating factors influencing IMB adoption. Policymakers and financial institutions must adopt a holistic approach, ensuring that Islamic banking services align with consumer expectations while maintaining regulatory compliance. Expectancy (PE) (β = 1.74, p < 0.05) are the strongest predictors of IMB adoption. Interestingly, Awareness (AWN) showed a negative impact (-3.20, p < 0.01), suggesting that consumer skepticism toward Shariah compliance and security may hinder adoption (Khan et al., 2023; Zouari & Abdelhedi, 2021). The findings highlight the critical role of regulatory transparency, perceived usefulness, and trust-building measures in driving IMB adoption. The study suggests that mere awareness campaigns are insufficient; rather, efforts should focus on strengthening Shariah governance, financial literacy, and cybersecurity measures to enhance user confidence (Ali & Hassan, 2022; Windasari et al., 2022). Future research should explore trust, risk perception, and ethical banking concerns as moderating factors influencing IMB adoption. Policymakers and financial institutions must adopt a holistic approach, ensuring that Islamic banking services align with consumer expectations while maintaining regulatory compliance.

The integration of artificial intelligence (AI) applications, such as Google’s Little Language Labs and ChatGPT, is progressing rapidly, particularly in the field of language education. Pre-service English Language Teaching (ELT) students play a crucial role in determining how these technologies are used in instructional settings. This study investigates their acceptance of ChatGPT within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT2), a model that has rapidly gained substantial scholarly recognition since its introduction. Employing a cross-sectional survey design, data were collected in May 2025 from 456 pre-service ELT students enrolled at universities across Uzbekistan through an anonymous online questionnaire administered via Google Forms. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with a weighting path scheme in SmartPLS 4. The results indicate that habit, performance expectancy, effort expectancy, and personal innovativeness have significant positive effects on behavioral intention to use ChatGPT. By contrast, social influence, facilitating conditions, and hedonic motivation did not significantly predict behavioral intention. Furthermore, facilitating conditions, habit, and behavioral intention did not significantly influence actual use behavior. Gender and year of study were tested as moderating variables; however, neither demonstrated a significant moderating effect. These findings highlight the central importance of habit, performance expectancy, effort expectancy, and personal innovativeness in shaping pre-service ELT students’ intentions to adopt ChatGPT, while other UTAUT2 constructs appear less influential in this context. Notably, this is the first study to apply the UTAUT2 framework to examine ChatGPT acceptance among pre-service ELT students in Uzbekistan. Future research should extend this line of inquiry by including broader samples across universities and regions, thereby enabling more comprehensive insights into technology acceptance following cultural adaptation of the UTAUT2 framework in underexplored regions.

This study examines the factors influencing seniors’ adoption of virtual reality (VR) cognitive training games by integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM). Beyond the traditional constructs of these models, the research incorporates perceived enjoyment (PE), personal innovativeness (PI), and computer anxiety (CAX) to address seniors’ cognitive health needs and explore VR-based non-pharmacological interventions. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study analyzed direct and indirect effects on adoption within a questionnaire-based framework. The research was conducted at community care centers in Taoyuan City, Taiwan, with 150 seniors aged 65–80. Participants engaged in a 20-min session with Nintendo Switch VR games and subsequently completed a questionnaire assessing their experiences. Results revealed that perceived ease of use (PEOU), PE, and subjective norms (SN) significantly shaped attitudes toward VR adoption, which strongly influenced behavioral intention (BI). BI emerged as a robust predictor of actual usage behavior (AUB), while CAX negatively impacted BI, highlighting the need to address emotional barriers. This study underscores the importance of designing intuitive, engaging, and accessible VR cognitive training games tailored for seniors. It also highlights the pivotal role of family and caregivers in fostering adoption, alongside the necessity of providing social and emotional support. These findings contribute to the theoretical understanding of VR-based games adoption in older adults and offer practical guidance for developers and healthcare providers aiming to create effective interventions for cognitive health.

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.

Purpose – The purpose of this paper is to explore the factors that influence users’ behavioral intention (BI) to adopt mobile social networking sites (mSNS) in facilitating formal/informal learning. Specifically, the study also investigates the association of mobility, reachability and convenience with performance expectancy (PE) and effort expectancy (EE). Design/methodology/approach – Partial least squares structural equation modeling (PLS-SEM) approach was applied to test on 266 valid responses. Findings – The findings indicated that learning compatibility (LC), PE, EE and copyright clearance (CC) has a significant effect on BI. The results also revealed that EE is influenced by mobility, reachability and convenience. PE however was found to be influenced by convenience. Practical implications – The results of this study provides valuable insights and references for practitioners and mobile network providers in developing mSNS in facilitating learning. Originality/value – While mSNS have the potential to become a new research area with numerous benefits for the learning community, there is little research on the adoption factors on mSNS in facilitating learning. This study therefore attempts to close the research gap by contributing to the mobile literatures.

The fast pace of generative artificial intelligence (AI) has already revolutionized several creative fields, especially pop music production, by introducing fresh composition, production, and sound design tools. This research explores the determinants of generative AI adoption within pop music production based on Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine its influence on creativity, production practices, and industry sentiment. The investigation hypothesizes that generative AI improves creative production by changing music creation procedures and influencing listener commitment. The investigation examines several important factors, such as user engagement (UE), artist autonomy (AA), collaborative perception (CP), emotional effect (EI), perceived quality of AI-generated music (PQ), creativity enhancement (CE), and music production efficiency (MPE). A total of 100 professional composers and 200 aspiring artists along with 500 music listeners formed the sample that completed surveys and collaborated with AI in music creation activities. SmartPLS 3.2.9 performed statistical testing using path analysis techniques, which examined direct and indirect variable relationships through structural equation modeling procedures. The results indicate that CE (β = 0.298, p < 0.01) and UE (β = 0.248, p < 0.05) have a significant impact on the adoption of AI tools in music production, while PQ plays a substantial role in determining both behavioral intention (BI) and actual usage behavior (UB). The findings propose that while AI tools are widely included by fresher artists for their creative latent, concerns regarding authorship rights, originality, and the role of human participation in music-making remain. This exploration contributes to a deeper empathy of how AI is reshaping pop music creation and offers valuable insights into its broader implications for the music industry.

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.