Factors Determining the Acceptance of E-Wallet among Gen Z from the Lens of the Extended Technology Acceptance Model

Recent rapid advances in artificial intelligence (AI) technologies have brought to the agenda how to encourage the use of these technologies in education. Teachers' acceptance of artificial intelligence technologies has an important place in this context. This study, based on the Technology Acceptance Model (TAM), investigates the factors affecting teachers' acceptance of artificial intelligence technologies. For this purpose, a five-structure structural model for AI technology was proposed by adding Self-Efficacy and Anxiety to TAM. Data were obtained from teachers working in Turkey. A trial form consisting of 21 items was prepared for EFA. 18 items were confirmed by CFA. Structural Equation Modeling (SEM) was used to analyze the data. In the proposed model, 7 hypotheses related to Self-Efficacy (SE), Artificial Intelligence Anxiety (AIA), Perceived Ease of Use (PEU), Perceived Utility (PU) and Behavioral Intention (BI) were tested. Six of the seven proposed hypotheses were confirmed while one was not. There was a significant negative effect of teachers' SE on their AIA, AIA on their PEU, and AIA on their BI (H1, H2, H7). In addition, a significant positive effect of teachers' PEU on their PU, SE on their BI and PU on their BI was found (H3, H4, H6). However, the hypothesis (H5) that teachers' PEU has a significant positive effect on their BI was not supported. In the study, it was found that teachers' acceptance of using AI technologies in teaching is predictable by teachers' self-efficacy towards AI, AI anxiety and perceived usefulness. The results of this study contributed to the extension of TAM. In addition, the results of the study can help future educational planning in the use of educational technologies.

Use of the Internet has increased remarkably in the past few decades and, therefore, has created a need to better understand the adoption of e-commerce across different cultures. Our study makes a significant contribution in different ways. First, an extended technology acceptance model (TAM) was developed and validated in an international setting, other than the U.S., in order to better understand the adoption of e-commerce across different cultures. This study extends McCoy et al. (2007) and Straub et al. (1997) work by validating TAM in the Pakistani culture. Contrary to our expectation, the predictive power of TAM seems robust and holds for both Pakistan and Canada. Second, the importance of perceived ease of use (PEOU) to intention to shop online was validated across the two cultures. The results of this study clarify an important issue in TAM studies, namely, when and why PEOU is important and influences intention to use a system (Gefen and Straub 2000; Keil et al. 1995). PEOU is more important than perceived usefulness (PU) in motivating users to accept a technology at the early adoption stage and its importance diminishes as users become familiar with the system. Practitioners, who might have confusions regarding the importance of PEOU due to previous TAM studies, should reconsider the extent to which PEOU affects online shopping at the early adoption stage. Similarly, in the case of Canadian customers, PU is the main factor that directly and indirectly affects intention to shop online. Furthermore, in developing programs to motivate customers to shop online, e-retailers must recognize the importance of trust on PEOU, PU, attitude and intention to shop as trust had the strongest effect on PEOU for the Pakistani sample and on PU for the Canadian sample.

Purpose: The factors influencing behavioural intention to use (BIU) were examined in this study. DigiLocker, a digital storage platform offered by the Indian government, among individuals in Mangaluru City. This study explored crucial constructs, including Perceived Usefulness (PU), Perceived Ease of Use (PEOU), self-efficacy (SE), and Social Influence (SI), to ascertain their effects on users' adoption intentions. Design/methodology/approach: A survey-based quantitative methodology encompassing 200 DigiLocker users in Mangaluru was employed. Structural Equation Modelling (SEM) was utilised to analyse the relationships among PU, PEOU, SE, SI, and BIU within the Technology Acceptance Model (TAM) framework. Findings: The results show that all four factors PU, PEOU, SE, and SI significantly influenced BIU. PU emerged as the most robust predictor, demonstrating that users valued the platform's perceived benefits. PEOU also significantly impacted BIU, albeit to a lesser extent than PU, suggesting that ease of use is an essential but secondary motivator. SE significantly contributes to BIU, indicating that users' confidence in their digital abilities fosters their adoption. While significant, SI exhibits a moderate influence on BIU, implying that social endorsements are less critical than individual perceptions of utility and usability. Practical implications: The findings indicate that DigiLocker developers and policymakers should prioritise enhancing the platform's practical benefits and user-friendliness to facilitate broader adoption. Furthermore, the provision of support resources could potentially augment user self-efficacy, aligning with the user requirements for competence and confidence in utilising digital platforms. Originality/value: This study contributes to digital adoption literature by examining DigiLocker acceptance in an urban Indian context, providing insights for policymakers regarding the significance of practical benefits over social factors in technology adoption. Type of the paper: Original Article

PurposeThe purpose of this study is to examine individuals' decisions to use health and fitness apps by applying the extended technology readiness and acceptance model (TRAM), which combines technology readiness (TR), the technology acceptance model (TAM) and perceived enjoyment (PEN). Moreover, this study explores the differences between users and non-users regarding their intentions to use health and fitness apps.Design/methodology/approachData collection (n = 206) was conducted using convenience sampling from four large universities in South Korea. The data were analysed by partial least squares structural equation modelling (PLS-SEM) using SmartPLS 3.0.FindingsThe results revealed that positive TR positively affects perceived ease of use (PEOU), perceived usefulness (PU) and PEN, while negative TR had a negative impact only on PEN. Furthermore, the significant relationships between PEOU, PU and PEN were identified. In addition, multigroup analyses indicated that the relationships between positive TR and PEN, between PEN and PEOU, between PEOU and PU, and between PU and behavioural intention were positively stronger for app users.Originality/valueThis study initially applied the TRAM to understand individuals' behavioural intentions to use health and fitness apps. Moreover, this study identified the distinct roles of positive and negative TR affecting individuals' cognition regarding using health and fitness apps. The differences in the psychological processes between app users and non-users offer insights and implications for practitioners.

Introduction:digital transformation in the Education 4.0 era has significantly reshaped higher education, including engineering education. In alignment with Sustainable Development Goal (SDG) 4: Quality Education, mobile-based gamification learning (MobGam) has emerged as an innovative approach to enhance learning quality. This study aims to analyze students’ behavioral intention (BI) to adopt MobGam by extending the Technology Acceptance Model (TAM) with two external constructs: Self-Efficacy (SE) and Perceived Enjoyment (PE).Method:a quantitative survey was conducted with 127 Industrial Electrical Engineering students in Indonesia. Data were collected using a five-point Likert-scale questionnaire and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) to evaluate both measurement and structural models.Results:the findings show that PE significantly enhances both Perceived Ease of Use (PEU) (β = 0.553, p = 0.001) and Perceived Usefulness (PU) (β = 0.301, p = 0.003). SE also demonstrates a strong positive effect on PEU (β = 0.424, p = 0.001) and PU (β = 0.296, p = 0.003). In the structural model, PEU significantly influences PU (β = 0.402, p = 0.001) and Attitude Toward Use (AT) (β = 0.270, p = 0.005), while PU strongly predicts AT (β = 0.526, p = 0.001) and Behavioral Intention (BI) (β = 0.392, p = 0.001). AT emerges as the most powerful determinant of BI (β = 0.591, p = 0.001). Overall, the model demonstrates high explanatory capacity, with BI predicted substantially (R² > 0.70), confirming that both cognitive (PEU, PU) and motivational (SE, PE) constructs jointly shape students’ acceptance of MobGam.Conclusions:this study extends the applicability of TAM in the context of MobGam by integrating SE and PE as key determinants of technology acceptance. Practically, the findings suggest that educators and instructional designers should emphasize PE, SE, PEU, PE, and AT when implementing MobGam systems to foster learning qulality and sustainable technology adoption in line with SDG 4.

Zahir Accounting Software use this caused a reaction on its self, in the form of acceptance or rejection. Due to the success of the application of information technology is subject to acceptance by the user as the user of technology. A technology acceptance model known as TAM (Technology Acceptance Model) can explain and predict the acceptance of the technology by the user. TAM models used to determine attitudes, intentions and behavior of users by using two main input variables of expediency and convenience. This study by spreading the questionnaire number 117 in the form of a closed question in the form of statements to know how to influence the perception of self-efficacy variable of Computer Self Efficacy (CSE), Perceived Ease of Use (PEoU), Perceived usefulness (PU), the Attitude Toward using (ATU), Behavioral Intention to Use (BITU) and Actual System Usage (ASU). Factors that affect the acceptance of the use of Software Zahir in Bogor BSI AMIK Computerized Accountancy Studies on research studies Zahir Accounting Software usage capabilities include themselves on the computer, perceived ease of use, perceived usefulness, the attitude to use, behavioral intention to use and actual use system. Keywords : Zahir Accounting, TAM, SEM, dan AMOS

Purpose: The purpose of this paper is to assess the impact of dimensions of technology readiness (TR) on accounting office staffʼs intention to use artificial intelligence (AI), using the mediating factors of the technologyʼs perceived usefulness (PU) and perceived ease of use (PEOU). Methodology/research approach: The study was based on a purposive sample of 200 staff members of accounting offices in Poland. Structural equation modelling (SEM) was used to test the relationships between the TR personality dimensions and the cognitive dimensions (PU and PEOU) of the technology acceptance model (TAM). Findings: The findings indicate that respondentsʼ optimism affected the perceived usefulness of technology, while innovativeness affected the perceived ease of use of AI technologies. In addition, the cognitive dimensions of the TAM were confirmed to in-fluence respondents' intention to use AI tools; thus, PU and PEOU mediate the relationship between TR dimensions and the intention to use technology. Research limitations/implications: The study focuses on a limited number of accounting practitioners. Moreover, the basis for the analysis was only two TR modera-tors: PU and PEOU. Originality/value: The articleʼs integration of knowledge from psychology, manage-ment and technology fosters a holistic approach to the issue of adopting technological innovations such as AI. The adopted research perspective enriches the theoretical contribution to the literature by identifying factors in AI technology adoption among Polish accountants. It also facilitates the application of the research results in practice as it takes into account the organisational and individual context of this process. Furthermore, the content of the article and the analysis contained therein can provide valuable support to organisations planning to use AI technologies, allowing them to improve their implementation strategies by considering both technical and human aspects.

Introduction: The use of shared automated vehicles (SAVs) should lead to several societal and individual benefits, including reduced greenhouse gas emissions, reduced traffic, and improved mobility for persons who cannot safely drive themselves. We define SAVs as on-demand, fully automated vehicles in which passengers are paired with other riders traveling along a similar route. Previous research has shown that younger adults are more likely to report using conventional ridesharing services and are more accepting of new technologies including automated vehicles (AVs). However, older adults, particularly those who may be close to retiring from driving, stand to greatly benefit from SAV services. In order for SAVs to deliver on their aforementioned benefits, they must be viewed favorably and utilized. We sought to investigate how short educational and/or experiential videos might impact younger, middle-aged, and older adult respondents’ anticipated acceptance and attitudes toward SAVs. Knowing what types of introductory experiences improve different age groups’ perceptions of SAVs will be beneficial for tailoring campaigns aiming to promote SAV usage. Methods: We deployed an online survey using the platform Prolific for middle-aged and older respondents, and our departmental participant pool for younger adults, collecting 585 total responses that resulted in 448 valid responses. Respondents answered questions regarding their demographic attributes, their ridesharing history, preconceptions of technology, as well as their anticipated acceptance attitudes towards SAVs as measured by the dimensions of the Automated Vehicle User Perception Survey (AVUPS). After this, respondents were randomly assigned to an intervention condition where they either watched 1) an educational video about how SAVs work and their potential benefits, 2) an experiential video showing a AV navigating traffic, 3) both the experiential and educational videos, or 4) a control video explaining how ridesharing works. Anticipated acceptance attitudes towards SAVs were measured again after this intervention and difference scores calculated to investigate the effect of the intervention conditions. Prolific respondents were paid at a rate of $9.50/hour and younger adults received course credit. Results: Controlling for preconceptions of technology and ridesharing experience, a MANOVA was run on the difference scores of the dimensions of the AVUPS (intention to use, trust/reliability, perceived usefulness (PU), perceived ease of use (PEOU), safety, control/driving-efficacy, cost, authority, media, and social influence). Both older and middle-aged adults expressed significantly greater increases in PEOU and PU of SAVs than younger adults. We also observed an interaction between age and condition for both PU and PEOU. For PU, older adults’ difference scores were found to be significantly greater than younger adults’ for the control video condition. With PEOU, older adults’ difference scores were significantly greater than both younger adults’ for the control video condition, and middle-aged adults had greater difference scores for the educational-only video condition than younger or older adults. Discussion: The increases in PU observed for older adults in the control condition suggests that educating them on how to use currently available ridesharing services might transfer to and/or highlight the benefits that automated ridesharing might provide. The PEOU interactions also suggest that middle-aged adults might respond more positively than younger or older adults to an educational introduction to SAVs. Conclusion: The positive findings pertaining to PU and PEOU show that exposure to information related to SAVs has a positive impact on these attitudes. PU’s and PEOU’s positive relationship to behavioral intentions (BI) in the Technology Acceptance Model, coupled with the findings from this study, bode well for higher fidelity interventions seeking to inform and/or give individuals experience with SAVs. Providing information on how currently available ridesharing services work helped our older adult respondents recognize the potential usefulness of SAVs. Knowing that different age groups may respond better to educational versus experiential interventions, for example middle-aged adults in this study responding more positively to the educational video condition than younger or older adults, may be useful for targeted promotional campaigns.

MSMEs in Indonesia are expected to be able to face competition in the era of industrial revolution 4.0. However, there are many problems and obstacles in competitiveness, especially facing global competition, including access to capital, access to information and technology, access to organization and management, and access to business networks and partnerships. Besides, it is often difficult for them to get additional capital through banks or other lenders to increase their business scale. Moreover, a lack of financial and digital literacy causes the low validity of MSMEs' data to lenders. The adoption of blockchain technology is one of the considerations to minimize these MSMEs problems. Meanwhile, this technology is still relatively new to be applied to MSMEs but positively impacts the future. This study aims to measure and analyze MSMEs' readiness in using blockchain technology on a business scale with the TRAM model. This model integrates the Technology Readiness Index (TRI) and Technology Acceptance Model (TAM) models. This study aims to test several variables, including TRI, perceive ease of use, perceive ease of usefulness, attitude toward, and intense use of blockchain technology. Data processing uses the partial least square path modelling (PLS-PM) method. The results showed that TRI was significant on perceived ease of usefulness and perceived ease of usefulness. Then, perceive ease of use is significant towards perceive ease of usefulness and intention to use. Besides, perceive ease of usefulness is significant for attitude. The attitude toward variable is significant for the intention to use in the acceptance of blockchain technology.

PurposeThe study aims to validate a mobile learning readiness scale through the technology readiness and acceptance model (TRAM), thereby assessing students' readiness to adopt m-learning in teaching and learning, including its acceptance.Design/methodology/approachA structured questionnaire was administered to open and distance learning (ODL) students in Odisha, India, to assess their readiness and acceptance of m-learning. 665 valid responses were collected, and collected data was analysed using statistical packages for social sciences (SPSS) and SmartPLS.FindingsThe findings of the study reveal that optimism contributes positively to perceived ease of use (PEOU) and perceived usefulness (PU) of m-learning (β = 7.921, p < 0.001; β = 2.123, p < 0.05), whereas innovativeness positively contributes to PEOU of m-learning (β = 2.227, p < 0.05), but not PU of m-learning. ODL student's optimism improves his/her PEOU and PU of m-learning, but innovativeness improves only his/her PEOU. Further, the impact of innovativeness is higher than that of optimism in the TRAM and innovativeness is the strong predictor to adopt m-learning. It also shows that the PU of m-learning positively influences behavioural intention to use m-learning (β = 4.757, p < 0.001). Integrating technology readiness (TR) with technology acceptance model (TAM) to predict students' acceptance of m-learning is very useful.Practical implicationsThe paper will help decision-makers to adopt and use m-learning in higher educational institutions.Originality/valueThis paper is the first to explore the readiness and acceptance of m-learning in higher education in India.

This study aims, first, to propose a model and to test an acceptance model to identify the students' intention to use e-learning system and to identify the use of e-learning used as a supplementary tool in a conventional learning context. A TAM model proposed includes System Functionality (SF), System Interactivity (SI), Usability (U), Self-Efficacy (SE), Internet and Computer Experience (ICE), Socio-Environment Factor (SEF), Perceived Ease of Use (PEU), Perceived Usefulness (PU), Use of Supplementary Learning (USL), Use for Distance Learning (UDL) as the factors influencing the use of the system. The Technology Acceptance Model (TAM) is developed based on hypotheses and related factors. The result showed TAM can be used to examine the acceptance of blended learning. In this study Perceived of Usefulness (PU) is the main factor of the construct followed by Perceived Ease of Use (PEU) and Use of Supplementary Learning (USL) to explain the causal relation in TAM model.

Advancement in technologies, such as smartphones and social networking sites (SNSs), are transforming traditional school-based communication in education. School-based SNSs are a web-based system that enables administrators and teachers to (1) create or join a semi-public online school community within a bounded system, (2) construct a virtual classroom with individual student profiles, or avatars, (3) invite parents and guardians to create a profile and link with their child's profile, (4) and communicate with students, parents, and guardians about students' school experiences using the classroom management and communication platform. ClassDojo, a school-based SNS, has over three million teachers and 35 million students using the platform (Williamson, 2017a). Teachers create and manage the virtual community; therefore, it is crucial to understand teachers' end-user attitudes towards adopting school-based SNSs. An extension of the Technology Acceptance Model (TAM) examined K-8 teachers' end-user attitudes to integrate school-based SNSs in United States' primary and middle schools. The TAM's foundation, extensions, and correlation to teachers' attitudes towards technology presented as an ideal model to ground the study. Thus, using theoretical and empirical studies related to teachers' adoption of technology and SNSs, this research study extended TAM using the following factors: (1) perceived usefulness (PU), (2) perceived ease of use (PEOU), (3) security awareness (SA), (4) subjective norm (SN), (5) attitude toward using SNSs (ATT), and (6) intention to use SNSs (ITU). TAM research traditionally relies on obtaining self-reported data from participants through survey. This survey-research collected data from 264 kindergarten to eighth-grade teachers throughout the United States. The survey data was used to analyze descriptive statistics between TAM variables, as well as perform path analyses on the relationships between the TAM variables. In this study, the TAM was extended to include subjective norm (SN) and security awareness (SA). In summary, a majority of K-8 teachers had a generally favorable attitude about ClassDojo's: (1) perceived usefulness, (2) perceived ease of use, (3) security awareness, (4) subjective norm, (5) attitude towards use, and (6) intention to use. Path analysis with latent factors utilized multiple regressions to assess the direct and indirect influences of variables within a model (Hatcher, 2013). The extended TAM model was reliable and illustrated that seven out of the eight path analyses were statistically significant. Teachers' attitudes towards ClassDojo use had the most statistically significant influence on teachers' intentions to use ClassDojo. Similar to findings from traditional TAM studies, perceived usefulness had the largest statistically significant influence on teachers' attitudes toward ClassDojo use. A thematic analysis of teachers' comments about ClassDojo provided support for the extended TAM path analysis. In conclusion, this study synthesized other TAM variables to establish, the Teacher Technology Acceptance Model of Social Networking Sites (T-TAMS), to identify and explore factors that positively influenced K-8 teachers' end-user attitudes towards school-based SNSs use. Lastly, limitations and future research were presented. This study advanced research on teachers' TAM of SNSs, teachers' end-user attitudes toward ClassDojo, and school-based communication. Thus, these findings can be used to boost ClassDojo's adoption rates among K-8 schools in the United States.

Based on the Technology Acceptance Model (TAM) and supplemented by social cognitive theory and gender schema theory, this study investigated the interactive mechanism of TAM by incorporating self-efficacy and gender as two factors in students’ usage of sports bracelets. Data were collected from 682 Chinese college freshmen. Results indicated that perceived usefulness (PU), perceived ease of use (PEU) and attitude towards technology (ATT) significantly influenced students’ intention to use sports bracelets. Self-efficacy (SE) not only was positively associated with PU and PEU, but, more importantly, self-efficacy moderated the relationship between PEU and behavioural intention (BI), and also between ATT and BI marginally. The effects of SE towards BI were found to be more apparent for low PEU and ATT students. In the case of sports bracelets, gender was found to moderate the relationships between PU and BI, and BI increased with PU for males but remained unchanged for females. Implications were discussed.

Extending the technology acceptance model and empirically testing the conceptualised consumer goods acceptance model

The purpose of this research is to analyze the user’s acceptance of the Accrual-Based Financial SIMDA by using Technology Acceptance Model 3 adapted approach. The research has 7 variables, namely: Result demonstrability (RES), Computer Playfulness (CPLAY), Perceived Enjoyment (ENJ) , Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention (BI), and System Usage (USE). This research is a quantitative research held at the Local Government of Brebes. The method of gathering data is survey by distributing questionnaires to the respondents. The research uses proportionate stratified random sampling. A total of 108 questionnaires were distributed in this study, 106 questionnaires could be collected. The result shows that the System Usage (USE) is affected by three variables, namely: Behavioral Intention (BI), Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Behavioral Intention (BI) is affected by Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Perceived Usefulness (PU) is affected by Result Demonstrability (RES) and Perceived Ease of Use (PEOU). Meanwhile, Perceived Ease of Use (PEOU) is affected by the Computer Playfulness (CPLAY) and Perceived Enjoyment (ENJ). This research expected to provide a solution to the problems related to user acceptance of the Accrual-Based Financial SIMDA and to increase user acceptance.