User Acceptance of Virtual Technologies

The use of virtual teams in organizations has shifted upward exponentially since the onset of COVID-19, yet available research does not include findings based on workplace virtual team members, as opposed to student populations. Research is limited on what virtual workplace team members consider important in the performance coaching and personal development processes. Research on virtual team member perspectives is valuable for human resource professionals and organizational leaders and particularly helpful to organizations struggling to respond to the new work-at-home environment. The purpose of the mixed-methods study was to explore the perspectives of virtual team members about how performance coaching, mentoring, and training contribute to their personal development. Participants for the study were members of social network virtual team groups who have been members of virtual work teams. The sequential explanatory study data was from a sample of 149 virtual team members (12 for the pilot survey and 137 for the full study). The Kruskal–Wallis results led to the rejection of the null hypothesis at an overall significance level of 0.05, with significance levels of 0.014 for coaching and 0.008 for mentoring, indicating a perceived positive contribution from performance development efforts (coaching and mentoring) on the personal development of individual virtual team members. These results on the interpersonal aspects of virtual teams from team members and consideration of perspectives on their personal development provide organizations, leadership, and human resources professionals, valuable information to improve virtual teams and the team member experience in normal and unusual work situations.

Mobile commerce is defined as any direct or indirect transaction conducted and facilitated through a wireless telecommunication network such as payment, ticketing, auction and mobile banking. Despite the importance of mobile commerce, there have not been so many academic studies on the unique characteristics of mobile commerce environments. In this study, our research model is developed based on TAM(Technology Acceptance Model) to investigate the user's technology acceptance process in mobile commerce environment. The factors, such as ubiquity, compatibility, perceived usefulness, perceived ease of use, cost, security, attitude and behavior intention for mobile commerce were selected from pier study in information systems area. The data for empirical analysis of the research model is collected online-questionnaire of 167 mobile commerce users in South Korea. The hypotheses were analyzed using SPSS 15.0 and AMOS 7.0. The results of our study show that: (1) compatibility, perceived usefulness and security had influences on the attitude whereas perceived ease of use and cost did not have any significant impact on the attitude, (2) compatibility influenced perceived usefulness whereas perceived ease of use did not, and finally (3) ubiquity, perceived usefulness and attitude had considerable influences on intention for mobile commerce. Therefore, practitioners should focus on enhancing security, quality of contents and services as well as offering what mobile commerce users want. The results of this study may useful for academicians and practitioners alike.

With the growing use of technology in medical education, a framework is needed to evaluate learners' and educators' acceptance of these technologies. In this context, the Technology Acceptance Model (TAM) offers a valuable theoretical framework, providing insights into the determinants influencing users' acceptance and adoption of technology. This review aims to systematically synthesize the body of research in medical education that uses the TAM. An electronic literature search was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach in February 2024 on the Embase, MEDLINE, PsycINFO, PubMed, and Web of Science databases, yielding 680 articles. Upon elimination of duplicates and applying the exclusion criteria, a total of 39 articles were retained. To evaluate the quality of the study, the Medical Education Research Study Quality Instrument score was calculated for each analysis with a qualitative component. Studies using TAM in medical education began in 2010, with the model's application relatively rare up to 2016. Most of the studies were quantitative, operationalizing the TAM as a survey instrument, but it was also used as a research framework in qualitative data analysis. Structural equation modeling, descriptive analysis, and correlation analysis were the most common data analysis approaches in the studies. E-learning and mobile learning were the predominant learning interventions explored, but there were indications that novel learning technologies such as augmented reality, virtual reality, and 3D printing were being investigated. The study's findings reveal an expanding scholarly engagement with using TAM in medical education. Although the TAM has been mostly used as a survey instrument, it can also be adapted as a qualitative research framework to analyze data. This systematic review provides a foundation for future research to understand the factors influencing users' acceptance of technology, especially in medical education.

Successful implementation of Information Technology can be judged or predicted from the user acceptance. Technology acceptance model (TAM) is a model that is built to analyze and understand the factors that influence the acceptance of the use of technologies based on the users perspective. In other words, TAM offers a powerful explanation related to acceptance of the technology and its behavior. TAM model has been applied widely to evaluate various information systems (IS) or information technology (IT), but it is the lack of research related to the evaluation of the TAM model itself. This study aims to determine whether the model used TAM is still relevant today considering rapid development of information communication technology (ICT). In other words, this study would like to test whether the TAM measurement indicators are valid and can represent each dimension of the model. The method used is quantitative method with factor analysis approach. The results showed that all indicators valid and can represent each dimension of TAM, those are perceived usefulness, perceived ease of use and behavioral intention to use. Thus the TAM model is still relevant used to measure the user acceptance of technology.

User acceptance of information technology has been a significant area of research for more than two decades in the field of information technology. This study assessed the acceptance of information technology in the context of Health Information Management (HIM) by utilizing Technology Acceptance Model (TAM) which was modified and applied to assess user acceptance of health information technology as well as viability of TAM as a research construct in the context of HIM. This was a descriptive- analytical study in which a sample of 187 personnel from a population of 363 personnel, working in medical records departments of hospitals affiliated to Tehran University of Medical Sciences, was selected. Users' perception of applying information technology was studied by a researcher-developed questionnaire. Collected data were analyzed by SPSS software (version16) using descriptive statistics and regression analysis. The results suggest that TAM is a useful construct to assess user acceptance of information technology in the context of HIM. The findings also evidenced the perceived ease of use (PEOU) and perceived usefulness (PE) were positively associated with favorable users' attitudes towards HIM. PU was relatively more associated (r= 0.22, p = 0.05) than PEOU (r = 0.014, p = 0.05) with favorable user attitudes towards HIM. Users' perception of usefulness and ease of use are important determinants providing the incentive for users to accept information technologies when the application of a successful HIM system is attempted. The findings of the present study suggest that user acceptance is a key element and should subsequently be the major concern of health organizations and health policy makers.

Purpose This study aims to investigate the intention to use blockchain technology (BT) in the context of supply chain (SC) operations through an integrated technology adoption framework using two well-known models, the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM). Moreover, the study looked at the direct effect of TAM and UTAUT elements on attitude toward BT, as well as the role of attitude toward BT as a mediator between TAM and UTAUT elements and intention to use BT. Design/methodology/approach The study used a quantitative research method, and a structured questionnaire was used to gather primary data. The final sample, drawn using a convenience sampling that consisted of 273 managers from the Jordanian manufacturing sector. Structural equation modeling statistical method was conducted using the Smart PLS program to test hypotheses in the proposed study framework. Findings The study has provided intriguing results. It found that two UTAUT elements, namely performance expectancy and social influence and one TAM element, namely perceived usefulness, have a significant impact on the attitude toward BT. Besides that, the study found that attitude toward BT significantly mediated the relationship between UTAUT-TAM elements and intention to use BT. The findings revealed that three elements namely performance expectancy, social influence and perceived usefulness have statistical significance on intention to use BT through the mediation of attitude. Finally, there is a direct significant positive relationship between the attitude toward BT and intention to use it. Research limitations/implications The study helps decision-makers, South Carolina practitioners and academics recognize the fundamental factors that increase manufacturing firms’ intentions to use blockchain in their SCs. This gives decision-makers a better understanding of why users accept or reject BT, as well as how to improve user acceptability through technological design. Future studies should seek for a bigger sample size and use random sampling techniques. Furthermore, the study should be replicated in other industries or developing countries to validate the findings. Originality/value There is a scarcity of studies identifying the factors that increase blockchain adoption intention in SCM and developing countries. This study differs in that it examines BT intention to use in the context of SC using an integrated technology adoption framework that uses two well-known models, UTAUT and TAM, whereas other studies typically use only one model/theory. Moreover, given the importance of attitude in behavior, this study also investigated the effect of TAM-UTAUT elements on attitude toward BT, as well as the role of attitude toward BT as a mediator between TAM and UTAUT elements and intention to use BT.

PurposeThe purpose of this paper is to propose and verify that the technology acceptance model (TAM) can be employed to explain and predict the acceptance of mobile learning (M‐learning); an activity in which users access learning material with their mobile devices. The study identifies two factors that account for individual differences, i.e. perceived enjoyment (PE) and perceived mobility value (PMV), to enhance the explanatory power of the model.Design/methodology/approachAn online survey was conducted to collect data. A total of 313 undergraduate and graduate students in two Taiwan universities answered the questionnaire. Most of the constructs in the model were measured using existing scales, while some measurement items were created specifically for this research. Structural equation modeling was employed to examine the fit of the data with the model by using the LISREL software.FindingsThe results of the data analysis shows that the data fit the extended TAM model well. Consumers hold positive attitudes for M‐learning, viewing M‐learning as an efficient tool. Specifically, the results show that individual differences have a great impact on user acceptance and that the perceived enjoyment and perceived mobility can predict user intentions of using M‐learning.Originality/valueThere is scant research available in the literature on user acceptance of M‐learning from a customer's perspective. The present research shows that TAM can predict user acceptance of this new technology. Perceived enjoyment and perceived mobility value are antecedents of user acceptance. The model enhances our understanding of consumer motivation of using M‐learning. This understanding can aid our efforts when promoting M‐learning.

<p>Mobile information systems (IS) such as field force automation and mobile office applications are rapidly being adopted by a large number of organizations. Despite its popularity and widespread adoption, the body of knowledge regarding user acceptance of mobile information systems in the workplace still is largely anecdotal. The purpose of this study was to develop and rigorously test a model of the factors that influence user acceptance of mobile information systems in the workplace. A thorough review of relevant literature in electronic business, mobile business, user acceptance of technology and user acceptance of mobile technology provided the basis for the development of the conceptual model that guided this research. The model hypothesized that temporal, spatial and structural characteristics of the portfolio of tasks performed by users of mobile information systems in the workplace (namely, temporal requirements of job, spatial dispersion of job, spatial dependence of job, job structuredness and job interdependence) would influence their perceived individual need for mobile information systems (PINMIS). It also suggested that the perceived individual need for mobile IS would influence the performance expectancy as well as intention to use mobile IS. In addition, the model posed that system portability would influence effort expectancy and intention to use mobile IS. In order to develop a research instrument, construct domains were specified and an initial set of items was generated. This was followed by an extensive purification process which consisted of card sorting and expert review rounds, survey pre-tests as well as a pilot study with 234 respondents from a large telecommunications company in New Zealand. The results obtained in this stage helped to refine the measurements and provided the foundations for the main study. The main study was based on a survey with 309 respondents from a wide range of organizations in New Zealand. Using Partial-Least-Squares (PLS) the data collected in the main study was used to test the model. The model was successfully validated and statistically significant evidence was provided that temporal requirements of job, spatial dispersion of job, spatial freedom of job and job interdependence positively influenced PINMIS. On the other hand, job structuredness did not significantly influence PINMIS. It was also found that PINMIS significantly influences performance expectancy and that system portability has a positive effect over effort expectancy as well as intentions to use mobile IS.</p>

The technology acceptance model (TAM) is a popular measure of user adoption and acceptance of technology. The pharmaceutical marketing industry has largely incorporated technology-based applications to enhance operational efficiency, effectiveness, and client engagement in the past decade. No study has explored user acceptance by pharmaceutical executives in the context of technology's impact on performance. The study aims to explore the relationship between perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BIU) in the context of the perceived impact of technology on performance (TechIMP). Hypotheses were tested using factor-based structural equation modeling. A random sample of 282 marketing executives was drawn from pharmaceutical companies in Nigeria using an online questionnaire. The developed model provided acceptable measures of fit and validity. Significant positive relationships exist between PEOU, PU, and BIU, explaining 58% of the variance in TechIMP. PEOU had a stronger impact on BIU compared to PU. BIU was a significant link between PEOU and PU to TechIMP. Multigroup analysis showed key differences between male and female executives. The study adds to the existing literature by extending TAM to include TechIMP. Managers should enhance positive user perception and acceptance by engaging in simulated training before introducing new technology and ensuring flexibility of technology use.

A respectable amount of work dealing with Technology Acceptance Model (TAM) clearly indicates a popularity of TAM in the field of technology acceptance in general. Nevertheless, there is still a gap in existing knowledge regarding representative academic literature that underlie research on TAM in educational context. The main objective of this systematic literature review is to provide an overview of the current state of research efforts on TAM application in the field of learning and teaching for a variety of learning domains, learning technologies and types of users. Through systematic search by the use of EBSCO Discovery Service, the review has identified 71 relevant studies ranged between 2003 and 2018. The main findings indicate that TAM and its many different versions represent a credible model for facilitating assessment of diverse learning technologies. TAM's core variables, perceived ease of use and perceived usefulness, have been proven to be antecedent factors affecting acceptance of learning with technology. The paper identifies some gaps in current work and suggests areas for further investigation. The results of this systematic review provide a better understanding of TAM acceptance studies in educational context and create a firm foundation for advancing knowledge in the field. Practitioner Notes What is already known about this topic Technology acceptance research in teaching and learning context has become an attractive trend. A number of reviews and meta‐analysis focused on specific topics related to technology acceptance in education have been conducted. The Technology Acceptance Model (TAM) is the key model in understanding predictors of human behaviour towards potential acceptance or rejection of the technology. What this paper adds The state of current research on Technology Acceptance Model application in educational context lacks comprehensive reviews addressing variety of learning domains, learning technologies and types of users. The paper presents systematic review of relevant academic literature on Technology Acceptance Model (TAM) in the field of learning and teaching. The paper provides empirical evidence on the predictive validity of the models based on TAM presented in selected literature. The findings revealed that TAM, along with its many different versions called TAM++, is a leading scientific paradigm and credible model for facilitating assessment of diverse technological deployments in educational context. TAM's core variables, perceived ease of use and perceived usefulness, have been proven to be antecedent factors that have affected acceptance of learning with technology. Implications for practice and/or policy The systematic review adds to the body of knowledge and creates a firm foundation for advancing knowledge in the field. By following the most common research objectives and/or by filling current gaps in applied research methods, chosen sample groups and types of result analysis, an own study could be conducted. Future research may well focus on identifying additional external factors that could further explain acceptance and usage of various learning technologies.

Often joint human–automation performance depends on the factors influencing the operator’s tendency to rely on and comply with automation. Although cognitive engineering (CE) researchers have studied automation acceptance as related to task–technology compatibility and human–technology coagency, information system (IS) researchers have evaluated user acceptance of technology, using the Technology Acceptance Model (TAM). The parallels between the two views suggest that the user acceptance perspective from the IS community can complement the human–automation interaction perspective from the CE community. TAM defines constructs that govern acceptance and provides a framework for evaluating a broad range of factors influencing technology acceptance and reliance. TAM is extensively used by IS researchers in various applications and it can be applied to assess the effect of trust and other factors on automation acceptance. Likewise, extensions to the TAM framework use the constructs of task–technology compatibility and past experience to extend its description of the role of human–automation interaction in automation adoption. We propose the Automation Acceptance Model (AAM) to draw upon the IS and CE perspectives and take into account the dynamic and multi-level nature of automation use, highlighting the influence of use on attitudes that complements the more common view that attitudes influence use.

One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM). This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction. An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM. The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered. Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.

This systematic review examines how traditional Technology Acceptance Models (TAMs) can be adapted to better understand user acceptance of AI technologies. The study analyzed 80 peer-reviewed articles published between 2010 and 2025, identified through a comprehensive search of EBSCOhost, Embase, Inspec, Scopus, and Web of Science databases using keywords related to AI acceptance factors and technology acceptance models. Our analysis employed descriptive statistics, thematic analysis, and narrative synthesis to identify key factors influencing AI acceptance, including perceived usefulness, perceived ease of use, trust, transparency, explainability, and ethical considerations. We found that trust in AI systems' reliability, fairness, and privacy protection plays a crucial role in user acceptance, with low trust leading to resistance. Transparency and explainability of AI decision-making processes were identified as critical for building user trust. The review also highlights limitations of current research, including inconsistent definitions of AI technologies, inadequate exploration of cultural and contextual differences in AI acceptance, and the predominance of cross-sectional research designs. By addressing these areas, future research can provide a more comprehensive theoretical foundation for understanding user acceptance of AI technologies, guiding the development and ethical application of AI systems. Keywords: Artificial Intelligence (AI), Trust and Explainability in AI, Technology Acceptance Models (TAM).

Introduction:The objective of this study was to understand the factors that affect the adoption and acceptance of technology, in particular to analyse the underlying mechanisms by which age affects acceptance of mobile IT in a healthcare context.Understanding how and why people accept or reject technologies has been the subject of a large body of information systems research including the Technology Acceptance Model [2] [3].The Unified Theory of Acceptance and Use of Technology model 2 [4] combines the dispersed literature into a unified model and looks at technology acceptance from a consumer use context thus making it of particular use for the public health environment.The authors of the model describe the moderating effect of age on each of the seven constructs [4], and show empirically that age does have an impact using longitudinal studies; however the rationale behind the underlying mechanisms of this is scant.Age is often an important consideration, especially with technologies introduced in healthcare.Individuals show declines in psychomotor skills [5], processing capacity, and increased distraction by side issues when using technologies with increased age; however, they can continue to use technology to a high level [6].Methods: A review of the current literature on mobile technology was conducted, in addition to qualitative primary data collection.Focus groups were conducted for three different age groups, 15-30, 31-50, and 51+ in order to explore views on acceptance of mobile technology in healthcare and the factors that affect the adoption of technology.Thematic analysis was performed to analyse focus group responses.A mobile web application for recording Patient Reported Outcome Measures was used as an example for focus group participants to interact with.Patient reported outcome measures are validated questionnaires, completed by patients to measure their perceptions of their own functional status and wellbeing.They are currently collected in a paper format, but an application has been created to improve response rates.We used our finding to make recommendations to the current Patient Reported Outcome Measures application to improve user acceptance.Results and Discussion: Our results from the literature review and primary data collection extend the Unified Theory of Acceptance and Use of Technology 2 model from a healthcare perspective.Our findings confirm that age does moderate the seven constructs of user acceptance but shows that it does so via the 17 mechanisms that we indentified.These 17 mechanisms are outcome expectancy, convenience expectancy, time expectancy, base ease of use, ease of learning, impact of disability, practitioner influence, support, security and confidentiality, prompts, usage enjoyment, novelty enjoyment, hardware costs, software costs, technology preference, location preference and reminders.The model highlights the factors that need to be considered when developing mobile IT in healthcare.We used our finding to make recommendations on how to improve uptake of the current Patient Reported Outcome Measures App with respect to age with a view on improving response rates.

Aim/Purpose: This study aims to examine the ten factors from the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and Unified Theory of Acceptance and Use of Technology (UTAUT) theories in order to analyze behavioral intentions to use the Indonesian online capital market investment platforms and the effect of behavioral intentions on actual usage. Background: The potential growth of capital market investors in Indonesia is large, and the low use of the Internet for investment purposes makes it necessary for stakeholders to understand the factors that affect people’s intentions to invest, especially through online platforms. Several previous studies have explained the intention to use online investment platforms using the TAM and TPB theories. This study tries to combine TAM, TPB, and UTAUT theories in analyzing behavioral intentions to use an online capital market investment platform in Indonesia. Methodology: The research approach employed is a mixed method, particularly explanatory research, which employs quantitative methods first, followed by qualitative methods. Data were collected by conducting interviews and sending online surveys. This study was successful in collecting information on the users of online capital market investment platforms in Indonesia from 1074 respondents, which was then processed and analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with the IBM AMOS 26.0 application. Contribution: This study complements earlier theories like TAM, TPB, and UTAUT by looking at the intention to use online capital market investment platforms from technological, human, and environmental viewpoints. This study looks at the intention to use the online capital market investing platform as a whole rather than separately depending on investment instruments. This study also assists practitioners including regulators, the government, developers, and investors by offering knowledge of the phenomena and factors that can increase the capital market’s investment intention in Indonesia. Findings: Attitudes, perceived ease of use, perceived behavioral control, subjective norm, and national pride were found to be significant predictors of the intention to use online investment platforms in Indonesia, whereas perceived usefulness, perceived risk, perceived trust, perceived privacy, and price value were not. Recommendations for Practitioners: All practitioners must be able to take steps and strategies that focus on factors that have a significant impact on increasing usage intentions. The government can enact legislation that emphasizes the simplicity and convenience of investment, as well as launch campaigns that encourage people to participate in economic recovery by investing in the capital market. Meanwhile, the developers are concentrating on facilitating the flow of investment transactions through the platform, increasing education and awareness of the benefits of investing in the capital market, and providing content that raises awareness that investing in the capital market can help to restore the national economy. Recommendation for Researchers: Further research is intended to include other variables such as perceived benefits and perceived security, as well as other frameworks such as TRA, to better explain individuals’ behavioral intentions to use online capital investment platforms. Impact on Society: This study can help all stakeholders understand what factors can increase Indonesians’ interest in investing in the capital market, particularly through online investment platforms. This understanding is expected to increase the number of capital market participants and, as a result, have an impact on economic recovery following the COVID-19 pandemic. Future Research: Future research is expected to investigate additional factors that can influence individuals’ behavioral intention to use an online capital market investment platform, such as perceived benefits and perceived security, as well as the addition of control variables such as age, gender, education, and income. International research across nations is also required to build a larger sample size in order to examine the behavior of investors in developing and developed countries and acquire a more thorough understanding of the online capital market investment platform.