Technology Acceptance Model Constructs Research Articles

The technology acceptance model and adopter type analysis in the context of artificial intelligence

IntroductionArtificial Intelligence (AI) is a transformative technology impacting various sectors of society and the economy. Understanding the factors influencing AI adoption is critical for both research and practice. This study focuses on two key objectives: (1) validating an extended version of the Technology Acceptance Model (TAM) in the context of AI by integrating the Big Five personality traits and AI mindset, and (2) conducting an exploratory k-prototype analysis to classify AI adopters based on demographics, AI-related attitudes, and usage patterns.MethodsA sample of N = 1,007 individuals individuals (60% female; M = 30.92; SD = 8.63 years) was collected. Psychometric data were obtained using validated scales for TAM constructs, Big Five personality traits, and AI mindset. Regression analysis was used to validate TAM, and a k-prototype clustering algorithm was applied to classify participants into adopter categories.ResultsThe psychometric analysis confirmed the validity of the extended TAM. Perceived usefulness was the strongest predictor of attitudes towards AI usage (β = 0.34, p < 0.001), followed by AI mindset scale growth (β = 0.28, p < 0.001). Additionally, openness was positively associated with perceived ease of use (β = 0.15, p < 0.001). The k-prototype analysis revealed four distinct adopter clusters, consistent with the diffusion of innovations model: early adopters (n = 218), early majority (n = 331), late majority (n = 293), and laggards (n = 165).DiscussionThe findings highlight the importance of perceived usefulness and AI mindset in shaping attitudes toward AI adoption. The clustering results provide a nuanced understanding of AI adopter types, aligning with established innovation diffusion theories. Implications for AI deployment strategies, policy-making, and future research directions are discussed.

The effect of social media engagement on telemedicine adoption: an empirical study

Purpose This study aims to assess the collective impact of social media engagement and anxiety due to COVID-19 on telemedicine adoption intentions with other constructs of the Technology Acceptance Model (TAM) concerning anxiety linked to COVID-19 and the influence of privacy concerns on TAM constructs. These constructs encompass the perception of ease of use, perceived usefulness, one’s attitude toward telemedicine and the intention to utilize telemedicine. Design/methodology/approach A total of 178 comprehensive responses were gathered over a six-month period from residents in Mumbai, India, to examine the proposed model. The data was analyzed using software tools, including SPSS version 23 and IBM AMOS 21, to compute factor loadings, assess model fit, estimate path relationships and conduct hypothesis testing. Findings Privacy concerns with telemedicine usage had a significant negative impact on behavioral engagement (B = −0.20, SE = 0.08, p < 0.05) and positively impacted affective engagement (B = 0.25, SE = 0.06, p < 0.01). Similarly, anxiety due to COVID-19 had a negative impact on the perceived usefulness of telemedicine (B = −0.10, SE = 0.03, p < 0.05). Research limitations/implications This research addresses a void in the existing literature by merging the TAM and the Social Media engagement theory. This study reaffirms the impact of past and relevant experiences, privacy concerns and COVID-19-induced anxiety on various components of TAM, thus expanding and enriching the TAM model. Practical implications Healthcare administrators should implement strategies to alleviate privacy-related apprehensions associated with telemedicine platforms. Additionally, they should promote existing users to create and disseminate positive content to mitigate COVID-19-induced anxiety and foster meaningful engagement, thereby enhancing the willingness to adopt telemedicine. Social implications Providers and promoters of telemedicine platforms and services may lean toward employing digital marketing campaigns that rely on emotional persuasion, including tapping into the fear factor to boost subscription and service sales. Such practices raise ethical questions, underscoring the need for well-defined advertising standards to govern the marketing of these products. Originality/value This article is among the relatively rare studies that document the favorable influence of emotional engagement on the intention to utilize telemedicine, underscoring the significant role of emotions in shaping telemedicine adoption Intentions.

The Influence of TAM, Perceived Risk, and Trust on the Financial Performance of Multi-finance Companies Utilizing GPS Tracker Applications

This study investigates the influence of the Technology Acceptance Model (TAM), perceived risk, and trust on the financial performance of multi-finance companies utilizing GPS tracker applications. As technology adoption in the multi-finance sector grows, understanding these factors becomes crucial for optimizing financial outcomes. A quantitative research design involved a cross-sectional survey of multi-finance companies using GPS tracker applications. Data were collected from 150 managers and financial officers through structured questionnaires. Structural Equation Modelling (SEM) was used to analyze the relationships between TAM constructs (perceived ease of use and perceived usefulness), perceived risk, trust, and financial performance. The results indicate that perceived ease of use and usefulness significantly enhance trust in GPS tracker applications, positively impacting financial performance. Conversely, perceived risk negatively moderates the relationship between trust and financial performance.

Evaluating the Sabah Pay Mobile Application (e-Wallet) using the Technology Acceptance Model

The Sabah Pay e-wallet has been introduced as a digital payment solution in the state of Sabah, Malaysia, as part of a digital transformation initiative. However, there is a lack of comprehensive understanding regarding its user acceptance and the factors influencing it. Specifically, there is a need to investigate whether users perceive Sabah Pay as useful and easy to use. This research problem arises due to the importance of user acceptance in the success and sustainability of the e-wallet. Hence, we used the technology acceptance model (TAM) to analyse the perceived usefulness (PU) and customer satisfaction in using the Sabah Pay mobile application and the relationship between perceived ease of use (PEOU) and customer satisfaction using the Sabah Pay mobile application. The Cronbach's Alpha and composite reliability values are higher than 0.7, and the average variance extracted (AVE) is higher than 0.5, which supports the TAM constructs' convergence validity. The square root of AVE for each construct is higher than the corresponding correlations between constructs, which suggests the discriminant validity of the constructs. We used the Partial least squares structural equation modelling (PLS-SEM) to assess the data from the 173 respondents who reported using the Sabah Pay mobile application. We found that PU did not significantly affect customer satisfaction (CS), with a p-value of 0.207, although the path coefficient is 0.109, thus disproving the null hypothesis (H1). However, we supported the second hypothesis (H2) by observing that PEOU substantially impacted CS, with a p-value of 0.000 and a path coefficient of 0.614. The correlation indices demonstrated a satisfactory relationship between the data and the model. This research adds to the body of knowledge on mobile payment platforms by shedding light on perceived usefulness and ease of use factors that most affect users' happiness with Sabah Pay. The study suggests and identifies extensive research into potential customer satisfaction aspects like safety, trust, and value. This study's findings have real-world applications for practitioners and regulators looking to improve the mobile payment platform user experience and, by extension, boost customer happiness and adoption rates.

Advanced Predictive Models for the Startup Ecosystem Using Machine Learning Algorithms

The startup ecosystem, characterized by its dynamism, presents significant challenges in predicting its future trajectory. Traditional analytical methods often fall short in comprehensively addressing the myriad factors that shape this ecosystem. This research aims to enhance the predictability of trends within the startup landscape by integrating the Technology Acceptance Model (TAM) with the advanced Random Forest algorithm. While existing literature has extensively explored the challenges startups face and the nuances of stakeholder interactions, the integration of TAM's constructs with key empirical attributes, specifically Investment Dynamics, Startup Metrics, Stakeholder Interactions, Entrepreneurial Challenges, and Technological Infrastructure, is a pioneering approach. Drawing from a comprehensive dataset that spans a diverse array of startups, this study operationalizes TAM's constructs in conjunction with the specified attributes. The subsequent application of the Random Forest algorithm offers a novel predictive methodology. Initial results highlight the superior predictive capabilities of this integrated model compared to traditional approaches. The findings provide insights into the intricate relationship between technological perceptions, as framed by TAM, and the tangible realities of the startup domain. The fusion of TAM with state-of-the-art machine learning signifies a groundbreaking direction in startup ecosystem research. This innovative approach offers stakeholders an enhanced analytical tool, ensuring more informed decision-making and a deeper grasp of the multifaceted nature of startup ecosystems.

Is the Technology Acceptance Model just old wine in new wineskins? Exploring issues for further model development

For about three decades, the Technology Acceptance Model (TAM) has been a crucial source of information for understanding the factors and processes that influence the integration of technology. The TAM has been extensively studied in various educational settings, involving students and teachers across different academic levels and specific technologies. The diverse range of applications has generated a vast body of evidence affirming the model’s relevance in education. However, despite its success, the research on the TAM still has some unresolved issues and limitations. In this commentary, I address these existing concerns and describe further areas for improvement, including the model itself, the measurement of TAM constructs, and existing gaps and conflicting findings in the current body of evidence. By shedding light on these issues, I hope to guide future research endeavors that build upon and refine the TAM.

Integrating ECM, TAM, and SDT in Studying Accounting Lecturers' Continuance Intention for ODL: A Review

Lecturers are increasingly expected to use technology to prepare their students for 21st century skills (Ahmad et al., 2019). As a result, online learning will become a common trend in university education in the near future, particularly in accounting (Sarea et al., 2021), especially since the COVID-19 pandemic has brought enormous changes to the educational landscape. Thus, given that the ultimate success of Open and Distance Learning (ODL) hinges on the continuance of intention to use it, it is important to discuss this intention. This review paper aims to explore the factors that could influence accounting lecturers' continuance intention to use ODL by integrating three prominent theoretical models: the Expectation-Confirmation Model (ECM), the Technology Acceptance Model (TAM), and the Self-Determination Theory (SDT). This paper included 23 articles that covered studies from 2008 to 2024. The key ECM, TAM, and SDT constructs that have been used in the literature to predict continuance intention are identified in this paper. The findings suggest that perceived usefulness, perceived ease of use, satisfaction, and intrinsic motivation are potentially critical predictors. It is posited that the integration of the ECM, TAM, and SDT frameworks provide a comprehensive understanding of both extrinsic and intrinsic factors that drive the continued use of ODL among accounting lecturers, offering insights for designing effective online learning environments.

Use of Portable Air Cleaners in Washington State Schools: A Qualitative Analysis Based on the Technology Acceptance Model.

The US government allocated over $2.5 billion in "Elementary and Secondary School Emergency Relief (ESSER)" funds to Washington State for COVID-19 response and ventilation improvements. Despite available funding, gaps persist in supporting schools to successfully use portable air cleaners (PACs). We evaluated PAC needs within King County, Washington and characterized factors influencing schools' purchase and use of PACs. Public Health-Seattle & King County (PHSKC) assessed school's ventilation systems and IAQ improvements through a survey (N = 17). Separately, semi-structured interviews (N = 13) based on the technology acceptance model (TAM) were conducted with school personnel. A thematic analysis using inductive and deductive coding was conducted and logistic regression models assessed the predictive capability of the TAM. The PHSKC survey findings informed our recommendations. Positive attitudes, knowledge, and beliefs in ease of use and effectiveness of PACs were facilitators to PAC use. While barriers included a lack of training, education, and concerns about PAC maintenance and sustainability. TAM constructs of perceived usefulness (PU) and perceived ease of use (PEU) were predictive of having the intention to use PACs in schools. There is a critical need for solutions to circumvent challenges to implementing PACs in schools. This characterization provides insight for promoting PAC use in IAQ-impacted schools.

Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction

The aim of present study was to measure the relationship of UTAUT (Unified Theory of Acceptance and Use of Technology) and TAM (Technology Acceptance Model) variables regarding AI technology and AI-based applications acceptance in education sector. Research was carried out by using PRISMA (Preferred reporting items for systematic review and meta-analysis) guidelines. The relevant studies were searched from major databases that included a) Scopus, and b) Web of Science. Initial search retrieved 309 titles, and 30 relevant articles and conference papers were selected following the search process. Data was analysed using CMA (Comprehensive Meta-analysis) and Meta-Essential software. Findings exhibit that the relationship between UTAUT variables and BI to accept AI and AI-based applications in education was high (PE → BI), medium (EE → BI, SI → BI), and low (FC → BI). The magnitude of the relationship of TAM constructs remained high for all paths (PU → AT, PEOU → AT, PU → BI, and PEOU → BI). Theoretically, this meta-analysis provided a panoramic picture of two leading technology acceptance models regarding the acceptance/adoption of AI and AI-based technology in education sector. This meta-analysis provided a way forward for researchers to extend research on AI-based applications including ChatGPT, intelligent tutoring, AI-based robots, AI-based Chatbots, and AI-based voice assistants. Practically, findings are useful for IT companies, and decision makers of educational institutes in designing and implementing AI and AI-based applications.

Modelling the effects of emotional engagement and peer interaction on the continuous intention to use asynchronous e-learning.

Even though asynchronous e-learning has become popular among universities, few studies have examined how students intend to continue using it for their learning. This study proposed a theoretical model that aims to examine the effects of external factors-emotional engagement and peer interaction-and two constructs of technology acceptance model (TAM) on students' continuous intention to use asynchronous e-learning. A total of 259 students participated in a survey. The data were analysed using two steps in SEM AMOS. confirmatory factor analysis was conducted for assessing model measurement, and structural equation modelling was applied for assessing relationships among constructs and testing hypothesis. The results showed that emotional engagement had a significant effect on students' continuous intention to use asynchronous e-learning through the perceived ease of use of asynchronous e-learning and not through its perceived usefulness. Furthermore, peer interaction had a significant effect on students' continuous intention to use asynchronous e-learning. Moreover, the constructs of the technology acceptance model-perceived ease of use and perceived usefulness-had direct and significant effects on students' continuous intention to use asynchronous e-learning. Several implications and suggestions were discussed.

Comprehension, apprehension, and acceptance: Understanding the influence of literacy and anxiety on acceptance of artificial Intelligence

In this paper, we discuss how people's attitudes toward accepting Artificial Intelligence (AI) technologies are influenced by their self-reported literacy (i.e., their understanding and ability to use AI technology) and anxiety (i.e., their fear of the AI technology's impact on society). We conducted an anonymous survey, gathering 313 valid responses, and utilized Structural Equation Modelling to examine the relationships between these factors. Our model incorporates the constructs of Technology Acceptance Model (TAM) to provide a more comprehensive overview of AI acceptance. Our findings indicate that while literacy fosters a positive attitude towards acceptance, anxiety has a minimal, although significant, direct negative effect, but plays a significant role in mediating the influence of literacy. Specifically, learning and sociotechnical dimensions of AI anxiety serves as a complementary partial mediator between AI literacy and acceptance, meaning that a portion of the effect of literacy on acceptance is mediated through AI anxiety. In contrast, literacy still explains a portion of acceptance independent from anxiety. Our study also confirms that dimensions of technology acceptance, as suggested by TAM, significantly shape individuals' attitudes towards AI as for others digital technology. In this respect, AI literacy positively influences the perception of ease of use and usefulness, thereby contributing to the overall acceptance of AI-based technology. We also discuss the implications of these findings for the development of critical literacy in the context of AI-based technology.

Feasibility of NepaDengue mobile application for dengue prevention and control: user and stakeholder perspectives in Nepal

BackgroundDengue has emerged as a significant public health issue in Nepal since 2006, necessitating innovative approaches to disseminating timely information and promoting preventive behaviour. Our study focuses on developing and...

Adoptability of digital payments for community health workers in peri-urban Uganda: A case study of Wakiso district.

Whereas digital payments have been identified as a solution to health payment challenges, evidence on their adoptability among Community Health Workers (CHWs) is limited. Understanding their adoptability is crucial for sustainability. This study assessed the adoptability of digital payments for CHWs in Wakiso district, Uganda. A convergent parallel mixed-methods study was conducted between November and December 2022, in Wakiso district, Uganda. We surveyed a random sample of 150 CHWs using a structured questionnaire and conducted key informant interviews among three purposively selected Digital payment coordinators. The study utilized the Technology Acceptance Model (TAM) framework to assess the adoptability of digital payments among CHWs. Factor analysis was performed to extract composite variables from the original constituting variables. Using the median, the outcome was converted to a binary variable and logistic regression was conducted to assess the association between the TAM constructs and adoptability of digital payments by CHWs. Quantitative data was analyzed using STATA 14, while qualitative data was transcribed verbatim and analyzed using ATLAS.ti 22. Nearly all participants (98.0%; n = 49) had previously received payments through mobile money, a digital payment method. (52%; n = 78) of CHWs said they intend to use digital payment modalities. Perceived risk of digital payments was associated with 83% lower odds of adoptability of digital payment modalities (OR = 0.17;95%CI:0.052, 0.54), while perceived trust had nearly three times higher odds of adoptability of digital payment modalities (OR = 2.82;95%CI:1.41, 5.67). Qualitative interviews showed that most CHWs reported positive experiences with digital health payments, including effectiveness and completeness of payments except for delays associated with mobile money payments across payment providers. Mobile money was reported to be easy to use, in addition to fostering financial responsibility compared to cash. CHWs in Wakiso district intend to use digital payment modalities, particularly mobile money/e-cash. Perceived risk of the payment method and trust are key determinants of adoptability. Synergized efforts by both payment providers to manage payment delays and mitigate risks associated with digital payments could attenuate perceived risk and build trust in digital payment modalities.

The influence of affinity on the continuance intention of local government information systems using the technology acceptance model

Affinity is precisely used for user acceptance of the local government information system (SIPD), which is a mandatory system. Affinity is capable of influencing users’ perceptions of interest and common interests, thereby encouraging users to continue using the SIPD system. This research aims to empirically test perceived affinity towards the continuance intention (CI) of using the SIPD by using technology acceptance model (TAM) constructs as mediating factors. A total of 100 respondents, who are active users of the SIPD in 24 districts/cities, participated in this study. Data collection was conducted using an online questionnaire. Data processing was performed using structural equation model (SEM) in the Smart PLS. The research results show that affinity significantly influences the CI of using the SIPD system, both directly and through the mediation of TAM constructs, namely perceived usefulness and perceived ease of use. Thus, this research provides an important contribution by demonstrating that affinity plays a relevant role and is suitable for implementation in mandatory systems like SIPD. By understanding user affinity, authorities can better comprehend users’ perceptions and attitudes, which in turn can help enhance the acceptance and sustainability of SIPD system usage in the South Sulawesi region.

Factors affecting the use of climate information services for agriculture: Evidence from Iran

The use of Climate Information Services (CIS) is considered the most important solution for the long-term adaptation of the agricultural sector in dealing with the challenges caused by climate change. While there are examples of successful CIS programs in the agricultural sector of developed countries, there are barriers to successfully using CIS programs for farmers in developing countries. In this regard, this research was carried out with two general objectives: (i) identifying the factors affecting the use of CIS by farmers, and (ii) providing practical policies for applying this information in the agricultural sector of Iran. A comprehensive Technology Acceptance Model (TAM) theory was used as the theoretical framework for this research, and self-efficacy (SE), social norm (SN), and perceived trust (PT) were added as variables. This research was conducted using structural equation modeling (SEM), and a designed questionnaire was used as the data-gathering instrument. The statistical population of this research includes all farmers of Dezful city in Khuzestan province (southwest of Iran). The findings of the research showed that the initial TAM explains 0.537 % of the variance of farmers' behavioral intention in using CIS. The three primary TAM constructs included Attitude, Perceived Usefulness (PU), and Perceived Ease of Use (PEOU), all of which had positive effects on farmers' willingness. Most importantly, by including SE, SN, and PT variables, the developed TAM can increase the model's ability to predict farmers' intentions by 13.5 %.

Exploring technology acceptance model with system characteristics to investigate sustainable building information modeling adoption in the architecture, engineering, and construction industry: The case of the Philippines

The widespread adoption of building information modeling (BIM) within the architectural, engineering, and construction (AEC) industry has changed productivity and project outcomes, highlighting the need to explore its acceptance, especially in developing economies. This study extended the technology acceptance model (TAM) to asses determinants shaping the adoption of BIM in the Philippines. The proposed hypothetical model used system quality, user interface, and personal innovativeness as the antecedent factors of TAM. Using partial least squares-structural equation modeling on a sample of 527 respondents, the measurement model fit indices showed acceptable validity and reliability. The findings of the path analysis theoretically supported the suggested model, with ten out of fourteen hypothesized relationships statistically supported. With nearly half of the respondents aged between 18 and 25, our study offers empirical evidence indicating that a significant portion of current AEC professionals, primarily digital natives, holds potential for BIM adoption within their respective industries. The significance of the system interactivity, user interface design, and personal innovativeness to the TAM constructs indicate that AEC companies may invest in BIM software, especially since these antecedent constructs influence the behavioral intention to use BIM. Practical insights into the design of strategic BIM implementation are outlined.

Perception of Generative Artificial Intelligence in Higher Education Research

Integrating Generative Artificial Intelligence (AI) into higher education research holds great promise, but understanding researchers' perceptions and addressing associated challenges is crucial for success. This study investigates how researchers perceive the potential benefits of Generative AI, its ease of use, and the challenges affecting its adoption in higher education research. It aims to uncover diverse perspectives and insights for effective integration. Using a qualitative case study approach, data was collected from 33 researchers in social sciences at one institution through online surveys and interviews. Thematic analysis, guided by the Technology Acceptance Model (TAM), identified recurring themes. The findings reveal significant engagement with AI adoption, with most participants integrating Generative AI into their research. However, readiness to adopt varied, reflecting different levels of enthusiasm and preparedness. Participants' perceptions ranged from recognizing Generative AI's convenience to expressing concerns about potential negative impacts such as becoming overly reliant, aligning with TAM constructs. Additionally, participants stressed the importance of responsible and ethical Generative AI usage through training and awareness initiatives. These findings lay the foundation for strategies promoting Generative AI adoption and responsible integration in higher education research. Implications for institutions aiming to integrate Generative AI effectively emphasize the need to understand researchers' perspectives and address their concerns.

Involving Patients and Clinicians in the Design of Wireframes for Cancer Medicines Electronic Patient Reported Outcome Measures in Clinical Care: Mixed Methods Study.

Cancer treatment is a key component of health care systems, and the increasing number of cancer medicines is expanding the treatment landscape. However, evidence of the impact on patients has been focused more on chemotherapy toxicity and symptom control and less on the effect of cancer medicines more broadly on patients' lives. Evolving electronic patient-reported outcome measures (ePROMs) presents the opportunity to secure early engagement of patients and clinicians in shaping the collection of quality-of-life metrics and presenting these data to better support the patient-clinician decision-making process. The aim of this study was to obtain initial feedback from patients and clinicians on the wireframes of a digital solution (patient app and clinician dashboard) for the collection and use of cancer medicines ePROMs. We adopted a 2-stage, mixed methods approach. Stage 1 (March to June 2019) consisted of interviews and focus groups with cancer clinicians and patients with cancer to explore the face validity of the wireframes, informed by the technology acceptance model constructs (perceived ease of use, perceived usefulness, and behavioral intention to use). In stage 2 (October 2019 to February 2020), the revised wireframes were assessed through web-based, adapted technology acceptance model questionnaires. Qualitative data (stage 1) underwent a framework analysis, and descriptive statistics were performed on quantitative data (stage 2). Clinicians and patients with cancer were recruited from NHS Greater Glasgow & Clyde, the largest health board in Scotland. A total of 14 clinicians and 19 patients participated in a combination of stage 1 interviews and focus groups. Clinicians and patients indicated that the wireframes of a patient app and clinician dashboard for the collection of cancer medicines ePROMs would be easy to use and could focus discussions, and they would be receptive to using such tools in the future. In stage 1, clinicians raised the potential impact on workload, and both groups identified the need for adequate IT skills to use each technology. Changes to the wireframes were made, and in stage 2, clinicians (n=8) and patients (n=16) indicated it was "quite likely" that the technologies would be easy to use and they would be "quite likely" to use them in the future. Notably, clinicians indicated that they would use the dashboard to enable treatment decisions "with around half" of their patients. This study emphasizes the importance of consulting both patients and clinicians in the design of digital solutions. The wireframes were perceived positively by patients and clinicians who were willing to use such technologies if available in the future as part of routine care. However, challenges were raised, and some differences were identified between participant groups, which warrant further research.

Understanding the factors of mobile payment continuance intention: empirical test in Saudi Arabia

Purpose: The objective of this study is to explore the determinants of individuals' intentions to continue utilize mobile payment services by examining integrated model of the constructs of the Technology Acceptance Model (TAM) (perceived ease of use and perceived usefulness) and the Theory of Planned Behavior (TPB) (attitude, subjective norm, and perceived behavioral control). Furthermore, the study investigates the relationship between user satisfaction and their intention to persist in using mobile payment. Methodology: In this quantitative investigation, a sample of 474 mobile e-wallet users was surveyed online. Partial least squares structural equation modeling was employed for data analysis. Findings: The study reveals that the constructs from the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and user satisfaction collectively influence users' intentions to use e-wallet services. Notably, the model demonstrates its ability to elucidate 59.1% of the variance in behavioral intention to continue utilizing e-wallets. Additionally, both user satisfaction and perceived behavioral control significantly impact the decision to persist in using e-wallets. The insights gleaned from this study can be leveraged by e-wallet stakeholders to inform strategic decisions within the e-wallet ecosystem. Practical implications: The inclusion of TAM and TPB constructs enhances our understanding of e-wallet service utilization. These findings offer valuable insights for service providers, enabling them to tailor their marketing strategies more effectively, thereby ensuring customer satisfaction and continued e-wallet usage. Originality: This study introduces a comprehensive and integrative approach to elucidate the factors influencing the sustained use of e-wallet services. By amalgamating two foundational adoptions above theories, it extends our understanding of mobile payment adoption dynamics.

What factors influence the intention to use electric motorcycles in motorcycle-dominated countries? An empirical study in Vietnam

Transitioning to electric motorcycles (EMs) can reduce dependence on carbon-based fuels, mitigate air pollution, and lower greenhouse gas emissions in countries where motorcycles dominate the transportation landscape. However, the factors influencing fleet electrification in these countries remain largely unknown. Given that this challenge pertains to the prior adoption of new technology, technology acceptance theories can provide valuable insights into the adoption of EMs. Additionally, misinformation about EM risks and self-assessment of knowledge could interact as determinants of adoption. This study integrates the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and perceived risk to elucidate the impact of psychosocial factors on consumers' intentions to use EMs. Furthermore, it examines the direct and moderating effect of subjective knowledge about EM technology on the formation of this behavioral intention. Structural Equation Modeling (SEM) analysis was applied to investigate data collected from 762 respondents through personal interviews using a questionnaire. The results confirm that all original TAM and TPB constructs significantly influence behavioral intention, aligning with theoretical expectations. As hypothesized, perceived risk was found to exert a negative influence on the intention to adopt EMs. Additionally, subjective knowledge was observed to moderate the relationship between perceived usefulness and subjective norms concerning the intention to use EMs. These findings offer a roadmap for developing strategies aimed at encouraging adoption intentions toward EMs, particularly in countries like Vietnam.