From Intention to Reflection: Understanding Self-Directed Learning in the Use of Generative AI in Vietnam

This study examines the determinants of corporate tax compliance in Thailand, partic-ularly in the context of increasing digitalization. While prior studies have separately addressed technology acceptance and behavioral intention, few have integrated these domains to explain voluntary tax compliance. This research employs a hybrid model that combines the Technology Acceptance Model (TAM), the Theory of Planned Be-havior (TPB), and taxpayer responsibility theory. Using data from 500 corporate ac-countants, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM), Necessary Condition Analysis (NCA), and Importance–Performance Map Analysis (IPMA) to analyze the data. The results demonstrate that perceived ease of use, subjec-tive norms, and perceived behavioral control significantly influence tax intention, which in turn affects compliance behavior. Furthermore, attitudes toward technology and perceived usefulness indirectly support compliance, while taxpayer responsibility ex-hibits a strong direct effect. These findings contribute to the theoretical understanding of compliance behavior and offer practical insights for enhancing technology-enabled tax systems in emerging markets. This study examines the determinants of corporate tax compliance in Thailand, partic-ularly in the context of increasing digitalization. While prior studies have separately addressed technology acceptance and behavioral intention, few have integrated these domains to explain voluntary tax compliance. This research employs a hybrid model that combines the Technology Acceptance Model (TAM), the Theory of Planned Be-havior (TPB), and taxpayer responsibility theory. Using data from 500 corporate ac-countants, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM), Necessary Condition Analysis (NCA), and Importance–Performance Map Analysis (IPMA) to analyze the data. The results demonstrate that perceived ease of use, subjec-tive norms, and perceived behavioral control significantly influence tax intention, which in turn affects compliance behavior. Furthermore, attitudes toward technology and perceived usefulness indirectly support compliance, while taxpayer responsibility ex-hibits a strong direct effect. These findings contribute to the theoretical understanding of compliance behavior and offer practical insights for enhancing technology-enabled tax systems in emerging markets.

Background The use of generative artificial intelligence (GenAI) in informal digital learning of English (IDLE) foregrounds the need to understand the conditions under which learners adopt and continue using these tools. Objectives This study integrated GenAI literacy into Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to explain behavioral intention and actual use of GenAI for IDLE. It further examined both net effects and configurational pathways for high GenAI usage. Methods We recruited 475 Chinese university students with prior IDLE experience. We used partial least squares structural equation modelling (PLS‐SEM) to test the extended UTAUT2 model and applied fuzzy‐set qualitative comparative analysis (fsQCA) to identify configurations. Results and Conclusions Effort expectancy, social influence, and habit significantly predicted behavioral intention, whereas performance expectancy, price value, hedonic motivation, and facilitating conditions were not significant. Habit, facilitating conditions, and behavioral intention predicted actual usage of GenAI for IDLE. GenAI literacy also showed direct positive effects on behavioral intention and actual usage. It negatively moderated the relationship between social influence and behavioral intention, but strengthened the effects of habit and behavioral intention on actual usage. Incorporating GenAI literacy improved the explanatory capacity of the UTAUT2 model in GenAI‐IDLE contexts. fsQCA analysis indicated that high levels of GenAI use can emerge from four configurations. Across them, GenAI literacy, hedonic motivation, and habit appeared as core contributors. These findings provide directions for future research and educational design to support effective informal language learning with GenAI.

This study seeks to establish the influence of the Big Five personality traits, which include Openness, Neuroticism, Conscientiousness, Agreeableness, and Extraversion, on growers’ willingness to embrace crop insurance schemes. Furthermore, it explores the role of Attitude, Subjective Norms, and Perceived Behavioral Control, as proposed in the Theory of Planned Behavior (TPB), on this relationship. Using a structured questionnaire, data were collected from 412 growers of arecanut and pepper. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via Smart-PLS 3.3. The analysis revealed that Perceived Behavioral Control (β = 0.462**), Subjective Norms (β = 0.260**), and Attitude (β = 0.115**) positively influenced growers’ behavioral intentions. Interestingly, the Big Five personality traits themselves did not have a direct effect on these intentions. Further mediation analysis demonstrated that Attitude and Subjective Norms fully mediated the effects of Extraversion (α = 0.026**, β = 0.069), Neuroticism (α = 0.019**, β = –0.016), and Openness (α = 0.024**, β = 0.069) on Behavioral Intention. However, these variables did not mediate the relationship between Agreeableness (α = 0.011, β = 0.058), Conscientiousness (α = –0.017, β = –0.080), and Behavioral Intention. Additionally, perceived behavioral control mediated the link between personality traits and intention, though this was not the case for Conscientiousness. This study contributes to the application of the TPB by incorporating the Big Five personality traits and exploring their interaction with the TPB dimensions.

Purpose This research uses a mixed-methods approach to identify predictors of Generative artificial intelligence (Gen-AI) adoption and usage among academics and educational researchers. It examines drivers and barriers to adoption and usage based on the diffusion of innovation theory (DIT) and the theory of planned behaviour (TPB). Design/methodology/approach A qualitative investigation was carried out by conducting interviews of academic researchers who used the Gen-AI tools such as ChatGPT. Based on the DIT, TPB and the qualitative analysis results, an integrated model was proposed and tested using survey data collected from academic researchers and analysed using partial least squares-structural equation modelling (PLS-SEM). Findings The study demonstrated that relative advantages and observability influence attitude and subjective norms, and these in turn impact behavioural intentions. Researchers' perception of relative advantage and their intentions to use Gen-AI tools were found to lead to positive usage behaviours. However, technical limitations and ethical concerns acted as key moderators between attitude and intention and subjective norms and intention, respectively. Mediation effects were also observed. Research limitations/implications This study utilised TPB and DIT as its base models, and future research could incorporate additional constructs from other technology adoption theories. The study concentrated on researchers who had utilised Gen-AI tools and subsequently reported significant factors affecting its adoption and usage. Future studies should also consider the perspective of non-users of Gen-AI tools. Further, geographical focus was on researchers in India, future research should broaden the geographical scope. Practical implications The academic community must unite to develop ethical guidelines for using Gen-AI tools and plagiarism concerns in research. The focus should be on emphasising the importance of ethical usage of Gen-AI tools. This study highlights the need for establishing standards, tools and comprehensive guidelines for researchers to use such tools transparently within an ethical framework. Originality/value The study results can greatly enhance understanding of Gen-AI tools usage among researchers, particularly in light of concerns about its impact on research integrity and potential negative consequences for researchers.

Generative artificial intelligence (GenAI) is rapidly transforming higher education, yet empirical evidence remains limited on the factors associated with its acceptance and usage among medical students, especially in non-Western, high-stakes educational contexts such as China. A clear and contextualized understanding of these mechanism is essential to effectively integrate GenAI into medical curricula and prepare future healthcare professionals for AI-augmented clinical practice. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study systematically investigated the relationships between core UTAUT constructs, and Chinese medical students' behavioral intention (BI) and actual usage (AU) of GenAI, testing direct, mediating, and exploratory moderated pathways. A cross-sectional online survey was administered to students at a public medical university in China from October 2024 to January 2025, yielding 1781 valid responses. Validated scales were used to measure core UTAUT constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), BI, and AU. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships. The model demonstrated strong explanatory power, accounting for 67.6% of the variance in BI and 66.3% in AU. PE (β = 0.377, p < 0.001), FCs (β = 0.333, p < 0.001) and SI (β = 0.212, p < 0.001) were positively associated with BI. EE showed no significant direct association with BI (β = 0.038, p = 0.209) but had a weak yet significant direct association with AU (β = 0.057, p = 0.045). BI served as a significant mediator in the relationships between PE, SI, FCs, and AU (all p < 0.001) but failed to mediate the association between EE and AU (p = 0.219). Age was the only significant moderator for the path from EE to BI (β = 0.071, p = 0.043) and the path from BI to AU (β = 0.024, p = 0.022); gender, major, and academic level showed no moderating effects. This study empirically validates and extends the UTAUT framework within Chinese medical education. Key findings underscore the important roles of PE, FCs and SI, reveal the context-dependent role of EE, and identify the moderating effect of age. Strategic interventions including demonstrating GenAI's tangible utility, improving technical infrastructure, leveraging peer / faculty advocacy, and tailing strategies to age-related differences are recommended. These insights provide evidence-based guidance for educators, policymakers, and AI developers to support responsible integration of GenAI into medical education, ultimately preparing future healthcare professionals for an AI-driven healthcare ecosystem.

PurposeThis study relied on the Theory of Planned Behavior (TPB) to assess factors that affected event fans’ decisions regarding their intention to attend events by using social network websites. The purpose of this study is to examine the impact of event fans’ attitudes, subjective norms and perceived behavioral control on their intentions to go to events based on social networking sites (SNSs) marketing. In addition, the researchers examined the impact of perceived enjoyment on event fans’ attitudes towards events pages on SNS.Design/methodology/approachThis study used a quantitative research method and used an online survey distributed on Qualtrics and based on the TPB. Populations in the study were followers of events pages on Facebook, Twitter and Instagram. The sample was convenience.FindingsBy using the partial least square-structural equation modeling (PLS-SEM), the study found that all the research hypothesis were supported except (H2). While event fans’ attitudes had not a statistically significant impact on their behavioral intentions towards using social media to go to events (H2), perceived enjoyment had a statistically significant impact on event fans’ attitudes towards events pages on SNS (H1). According to the research findings, event fans were influenced by their subjective norms (H3) and perceived behavioral control (H4). These factors significantly influenced event fans’ behavioral intention, which led to their actual behavior (H5).Practical implicationsThis study provided evidence supporting that subjective norms and perceived behavioral control were effective in forming intention towards events page, which in turn affected actual behavior, while perceived enjoyment was effective in forming events fan attitudes towards events’ social media pages. This may indicate a need for positive images of the events depicted through social media. In addition, when using social media as a marketing medium for events, event marketers and organizers should understand how other important people’s opinions and perceptions affect the intention and behavior formation. This implies the need to stress the social acceptance of the events, and use family ties, family and other social-units elements of events.Social implicationsThis study provided statistical evidence supporting the applicability of the TPB within the context of event marketing and using social media. This implies a better understanding of the rational decision-making process, along with the social factors affecting the process of forming behavioral intentions and intentions. Furthermore, perceived enjoyment was incorporated within the model. Perceived enjoyment was effective in forming positive attitudes towards events’ social media pages. This highlights the need to provide information and contents in an enjoyable and user-friendly way.Originality/valueThe value of this study is derived from its aim to highlight the importance of social media as an effective marketing tool for events. Moreover, this study sought to contribute to the literature on social media by exploring how social media affected event attendees’ behavior and attitudes and by gauging the impact of social media on the event industry.

Introduction: Graduate students face a high risk of adopting a sedentary lifestyle due to academic demands and stress. Although physical activity is crucial for health, there remains a gap in understanding the psychosocial factors that influence this behavior in specific populations. This study aimed to test an expanded Theory of Planned Behavior (TPB) model by incorporating Anticipated Affect and Habit to analyze the determinants of intention and physical activity behavior among postgraduate students in Yogyakarta. Methods: Using a quantitative approach, 249 postgraduate students (Master’s and Doctoral) were recruited via voluntary sampling. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The research instrument measured core TPB constructs (Attitude, Subjective Norms, Perceived Behavioral Control – PBC), additional constructs (Habit, Anticipated Affect), Intention, and actual physical activity behavior (T_MET) using the International Physical Activity Questionnaire Short Form (IPAQ-SF). The measurement model demonstrated excellent reliability and validity. The structural model showed a high predictive power for Intention, explaining 75.1% of its variance (R² = 0.751). Anticipated Affect (β = 0.353), Habit (β = 0.221), and PBC (β = 0.205) were the strongest predictors of Intention. In contrast, the model demonstrated a very weak association with actual physical activity behavior (T_MET), with R² = 0.002. Results: All hypothetical pathways to Behavior (from Intention, Habit, and PBC) were found to be statistically insignificant. These findings suggest a weak intention–behavior correspondence among graduate students. Although emotional factors and habitual tendencies strongly shape intention, they are insufficient to translate into actual physical activity. Conclusion: Contextual limitations, including unmeasured environmental factors, may contribute to this limited translation from intention to behavior. Health interventions on campuses may benefit from integrating volitional strategies (such as implementation intentions) and creating a supportive environment to help students bridge the intention–behavior gap.

ObjectiveThis study investigates the factors influencing physicians’ acceptance and adoption of artificial intelligence (AI) technologies in clinical practice, integrating the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM), while also examining the mediating role of trust.MethodsA structured survey was conducted among 414 physicians assessing their perceptions of AI technologies using constructs from TPB, TAM, and trust-related factors. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for data analysis.ResultsFindings confirm that TPB and TAM effectively explain physicians’ AI acceptance, with TPB exhibiting a stronger predictive power compared to TAM. Trust emerged as a critical determinant in AI adoption, fully mediating the relationship between perceived behavioral control (p < 0.001), subjective norms (p < 0.05), perceived usefulness (p < 0.001), ease of use (p < 0.001), and behavioral intention. Notably, perceived ease of use (p < 0.001) had the strongest direct impact on trust, while perceived usefulness (p < 0.001) significantly influenced behavioral intention. Attitude toward AI showed a significant effect (p < 0.01). Subjective norms and perceived behavioral control had weaker direct influences (p < 0.05 and p = 0.07, respectively).ConclusionTrust plays a pivotal role in AI adoption, shaping physicians’ acceptance beyond traditional TPB and TAM factors. Healthcare administrators, policymakers, and technology developers should focus on enhancing trust by improving AI transparency, interpretability, and user-friendly design.

Household pharmaceutical waste disposal in Malaysia is inadequate, largely due to limited awareness and a lack of safe disposal facilities. Community pharmacies are not legally required to collect unused or expired medications and face various challenges in offering disposal services. Therefore, it is essential to investigate the factors influencing community pharmacists' intention to provide safe medication disposal. This study aims to identify the attitude, subjective norm and perceived behavioural control affecting Malaysian community pharmacists' intention to provide medication take-back service and to identify the predictors of this intention. A cross-sectional survey was conducted with a stratified random sample of 424 community pharmacists in the Klang Valley. The Theory of Planned Behaviour (TPB) served as the theoretical framework, with constructs measured using a 4-point Likert scale. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse how TPB constructs and external factors influence pharmacists' intention. Of the 424 surveys, 310 responses were received (response rate: 73.1%). The PLS-SEM model explained 66% of the variance in pharmacists' intention (R2=0.662, adjusted R2=0.656). While attitude was not a significant predictor, subjective norm e.g. patient demand and external factors e.g. business competitiveness were significant positive predictors. Conversely, perceived behavioural control factors, including workload, costs, space and authority negatively impacted intention. This study identifies key constructs influencing community pharmacists' intention to offer medication take-back service. A multifaceted approach involving clear regulatory frameworks, government support and pharmaceutical industry participation, and public education is necessary to enhance this intention.

is study investigates the behavioural intentions of Small Enterprises (SEs) to participate in public procurement, using the Theory of Planned Behavior (TPB) as the guiding framework. While TPB traditionally posits that attitude, subjective norms and perceived behavioural control (PBC) directly predict behavioural intention, this study extends the model by including perceived benefits as an additional determinant and testing the moderating role of attitude. A cross-sectional survey design was employed, collecting data from 189 SEs owners and managers across Dar es Salaam and Dodoma. Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was used to deduce the findings. Results showed that perceived benefits and PBC significantly predicted SEs’ intention to engage in procurement, while subjective norms and attitude exhibited weak direct relationships. A moderation analysis of these features showed that attitude, whereas moderating on the subjective norms side, also had the effect of negatively moderating the relationship between PBC and intention (hypothetical overconfidence where both variables are high). The effect of attitude on perceived benefits was insignificant, meaning that these practical evaluations drive intention independently. The study adds to TPB by integrating the perspective of a developing economy and demonstrates that SEs’ procurement participation was determined mainly by pragmatic assessments rather than attitudinal and normative aspects. The findings have practical implications in terms of developing capacities, financial access, transparent procurement processes, as well as promoting the availability of a supportive network to increase SEs involvement.

The rapid advancement of generative AI tools, such as ChatGPT, has sparked widespread debate over their impact on academic integrity and educational practices. As these tools become increasingly accessible to students, understanding the factors that influence their adoption in academic settings is essential. The current study explores the application of generative artificial intelligence (AI) tools by college students, such as ChatGPT and many others, for completing homework assignments. Drawing on the Task-Technology Fit (TTF) framework and the concept of moral obligation, this research aims to investigate the factors influencing students' behavioral intentions to use generative AI in academic contexts. Data were collected through an online survey of 136 Taiwanese college students. The results indicate that perceived technology characteristics and self-efficacy significantly enhance task-technology fit, positively affecting behavioral intention. Conversely, moral obligation shaped by perceived teacher attitudes negatively influences students' intention to use AI tools for coursework. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses and explains a substantial proportion of the variance in behavioral intention. These findings provide theoretical insights into how technological and ethical considerations jointly influence AI adoption in education. The study also offers practical suggestions for educators and institutions aiming to guide the responsible use of generative AI in learning environments. This study contributes a novel framework for understanding responsible AI use in higher education.

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.

The increasing presence of Artificial Intelligence (AI) and Generative AI (GenAI) tools in education highlights the need for teachers to develop specific digital competencies for effective integration into curriculum planning. This study aimed to design and psychometrically validate a diagnostic instrument, developed by the authors, grounded in an extended version of the Technology Acceptance Model (TAM), in order to assess in-service teachers’ behavioural intentions and its relationship with teachers’ pedagogical digital competencies for the integration of GenAI tools in curriculum design. A sample of 434 in-service teachers from the Dominican Republic participated in the study. The model included factors such as Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, Self-Efficacy, Attitude of Use, and Behavioural Intention, analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). Results confirmed all hypothesized relationships, with Behavioural Intention emerging as the main predictor of digital competence for using GenAI tools in curriculum planning. The model demonstrated strong reliability, convergent and discriminant validity, and good explanatory power. The findings emphasize the importance of teachers’ motivation, self-efficacy, and perceived benefits in adopting AI technologies. Educational policies and teacher training programs should place particular emphasis on enhancing these dimensions to foster effective pedagogical use of GenAI tools in teaching practice.

The extent to which entrepreneurship education and exposure to role models influence the antecedents of entrepreneurial intention, entrepreneurial intention, and subsequent entrepreneurial behaviour has yielded mixed results in prior research. Furthermore, limited attention has been given to the role of risk-taking propensity in shaping attitude towards behaviour and perceived behavioural control within the Theory of Planned Behaviour (TPB) framework. To address these gaps, this study investigates the influence of entrepreneurship education and role models on the antecedents of entrepreneurial intention, entrepreneurial intention, and entrepreneurial behaviour, drawing on the TPB. In addition, the study examines the effect of risk-taking propensity on both attitude towards behaviour and perceived behavioural control, the relationships between the TPB antecedents and entrepreneurial intention, as well as the direct effects of perceived behavioural control and entrepreneurial intention on entrepreneurial behaviour. Data were collected from 496 final-year diploma students enrolled at a University of Technology and a TVET College in Gauteng, South Africa, using a structured, self-administered online questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data and test the hypothesised relationships. The findings revealed that entrepreneurship education significantly influences all the antecedents of entrepreneurial intention but does not have a direct influence on entrepreneurial intention or behaviour. Role models had a significant positive effect on perceived behavioural control, subjective norms, and entrepreneurial behaviour, but no effect on attitude towards behaviour or entrepreneurial intention. Risk-taking propensity had a positive effect on both attitude towards behaviour and perceived behavioural control. Furthermore, attitude towards behaviour and perceived behavioural control significantly predicted entrepreneurial intention, while subjective norms did not. Both entrepreneurial intention and perceived behavioural control exerted a significant direct effect on entrepreneurial behaviour. This study highlights the critical role of entrepreneurship education, exposure to entrepreneurial role models, and risk-taking propensity as drivers of entrepreneurial intention and behaviour.

The present investigation explored behaviours towards Information and Communication Technology (ICT) among 239 primary teachers (59.4% male, 40.6% female) in Dammam, Saudi Arabia. A convenience sampling technique was used to choose the sample. Based on the Decomposed Theory of Planned Behaviour (DTPB) model, the study addressed a research gap investigating technology adoption in a centralised education system. The analysis employed Partial Least Squares Structural Equation Modelling (PLS-SEM) and the mediating effects of attitudes on the path between perceived ease of use towards ICT (β = 0.131, p = 0.003) and perceived usefulness of ICT (β = 0.386, p &lt; 0.001) and behavioural intention towards ICT was statistically significant. Subjective norms mediated peer (β = 0.128, p &lt; 0.001) and superior (β = 0.070, p = 0.001) influences on behavioural intention; however, student influence was not significant, contrary to a similar Western investigation. The results indicate that behavioural intention significantly predicts actual use (β = 0.305, p &lt; 0.001). Contrary to DTPB theory, the results indicate that perceived behavioural control did not mediate any relationships. The model explained 26.1% of the variance in ICT use (R² = 0.261), lower than that identified in a related Western investigation. A notable distinction with differences identified in explanatory power (R²) and predictive relevance (Q²) indicated some theoretical constraints. The findings highlighted the compatibility assumptions associated with technology adoption in a centralised education system. The recommendations highlighted the importance of modifying teachers' attitudes and developing peer networks, rather than providing resources, to increase technology use within the Saudi educational system. Keywords: ICT usage, attitudinal beliefs, subjective norms, perceived behavioural control, the DTPB model, Saudi primary education.