Click to Ride: Understanding E-Ticketing Adoption in Emerging Economies Using an Extended UTAUT2 Framework

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.

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.

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.

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.

Purpose: This study explores online hotel booking intentions among Bangladeshi tourists. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the most widely used and cited IT adoption model, was employed in this investigation as the theoretical framework for analyzing customers' online hotel booking intentions. In the UTAUT2 paradigm, this work introduces three new concepts called service affordability, online reviews, and website trust. Methods: The study targeted individuals in Bangladesh with prior experience in online hotel booking. Data were gathered from 400 participants via a self-administered online questionnaire in Google Forms, using convenience sampling, and responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results: According to the findings, performance expectancy, social influence, habit, price value, service affordability, and website trust are the most critical determinants of behavioral intention to book hotels online. In contrast, online reviews, effort expectancy, facilitating conditions, and hedonic motivation do not affect customers' intention to book hotels online. Implications: This study provides practical insights for legislators, marketers, and hospitality managers to improve users' trust, perceived affordability, and overall acceptance of online hotel booking platforms. The findings facilitate the formulation of more user-centric digital strategies in the hospitality sector by identifying key variables related to booking intention and usage. Originality: This study represents a pioneering effort in Bangladesh by extending the UTAUT2 model to examine online hotel booking behavior. It introduces additional constructs, such as service affordability, online reviews, and website trust, thereby offering a more comprehensive framework for understanding technology adoption in emerging digital tourism markets.

The increasing prevalence of online scams poses significant threats to financial security, trust in digital systems, and national economic stability. To address this growing concern, Malaysia established the National Scam Response Centre (NSRC) as a centralized platform for scam reporting and intervention. Despite the NSRC’s potential, limited empirical research has examined factors influencing its utilization. This study investigates the impact of citizens’ readiness factors including awareness, perceived risk, performance expectancy, effort expectancy, and social influence on NSRC utilization, while also examining the moderating role of education. Drawing upon Protection Motivation Theory (PMT), Perceived Risk Theory (PRT), and the Unified Theory of Acceptance and Use of Technology (UTAUT), a survey was administered to 416 respondents across Malaysia. Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed for data analysis. The results reveal that all five readiness factors significantly influence NSRC utilization, with awareness being the strongest predictor. Education was found to significantly moderate the relationship between awareness and utilization but did not moderate other predictors. The findings underscore the importance of enhancing public awareness and digital literacy to strengthen citizen engagement with NSRC services.

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.

IntroductionThis study investigates the determinants of undergraduate learners’ interest in AI-supported mathematics education. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory, a structural model was proposed and empirically tested, incorporating mathematics anxiety, self-efficacy, performance expectancy, and effort expectancy.MethodsData were collected from 247 Chinese undergraduates and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsThe findings indicate that performance expectancy, effort expectancy, and self-efficacy are significant predictors of learning interest, with effort expectancy exerting the strongest influence. Self-efficacy also indirectly affects learning interest through its influence on perceptions of the system’s usefulness and ease of use. Contrary to prior research, mathematics anxiety did not significantly predict either self-efficacy or learning interest, suggesting that AI-facilitated environments may buffer negative emotional effects. Academic major moderated the relationship between mathematics anxiety and learning interest, reflecting disciplinary differences in motivational dynamics.DiscussionThis research contributes to theory by integrating motivational constructs into technology acceptance models and extending AI applications to cognitively demanding domains. Practical implications include prioritizing user-centered design and targeted self-efficacy interventions to enhance learner engagement.

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.

The unified theory of acceptance and use of technology 2 (UTAUT2) is applied and extended to explore the adoption and use of mobile shopping apps in India. This study adds impulsiveness construct to the original model. The data gathered from 328 respondents were evaluated using the partial least square structural equation modelling (PLS-SEM) method. All construct (i.e., performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, habit and impulsiveness), except for social influence and price value were found to significantly relate to behavioural intention to use mobile shopping applications. Behavioural intention, habit and impulsiveness were found to significantly influence use behaviour of mobile shopping apps, except for facilitating conditions. Moreover, significant mediating influences were reported in the study. The findings of the study provide valuable insights and suggestions for mobile marketers and developers of shopping apps to enable their increased adoption and use. Further, the mediating roles of performance expectancy, effort expectancy and habit have been explored in the model.

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 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.

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

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.