thesis on technology acceptance

Technology acceptance model: a literature review from 1986 to 2013

• Published: 16 February 2014
• Volume 14 , pages 81–95, ( 2015 )

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• Nikola Marangunić 1 &
• Andrina Granić 1  

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With the ever-increasing development of technology and its integration into users’ private and professional life, a decision regarding its acceptance or rejection still remains an open question. A respectable amount of work dealing with the technology acceptance model (TAM), from its first appearance more than a quarter of a century ago, clearly indicates a popularity of the model in the field of technology acceptance. Originated in the psychological theory of reasoned action and theory of planned behavior, TAM has evolved to become a key model in understanding predictors of human behavior toward potential acceptance or rejection of the technology. The main aim of the paper is to provide an up-to-date, well-researched resource of past and current references to TAM-related literature and to identify possible directions for future TAM research. The paper presents a comprehensive concept-centric literature review of the TAM, from 1986 onwards. According to a designed methodology, 85 scientific publications have been selected and classified according to their aim and content into three categories such as (i) TAM literature reviews, (ii) development and extension of TAM, and (iii) modification and application of TAM. Despite a continuous progress in revealing new factors with significant influence on TAM’s core variables, there are still many unexplored areas of model potential application that could contribute to its predictive validity. Consequently, four possible future directions for TAM research based on the conducted literature review and analysis are identified and presented.

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This paper describes the results of research carried out within the project 177-0361994-1998 Usability and Adaptivity of Interfaces for Intelligent Authoring Shells funded by the Ministry of Science, Education and Sports of the Republic of Croatia.

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Marangunić, N., Granić, A. Technology acceptance model: a literature review from 1986 to 2013. Univ Access Inf Soc 14 , 81–95 (2015). https://doi.org/10.1007/s10209-014-0348-1

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Technology Acceptance Model: Which factors drive the acceptance of AI among employees?

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In recent years, artificial intelligence (AI) has rapidly moved from an ideal concept to a technology that can be deployed. Due to its capabilities of mimicking human intelligence, many firms in various industries have started integrating AI. However, AI cannot improve an organization’s performance if it is not used. Unfortunately, employees’ resistance to innovative technologies is a widespread problem. To minimize the adverse effects and costs of employees’ resistance, it is valuable to predict and better understand which factors drive the acceptance of AI among employees. This thesis addresses the ability to predict employees’ acceptance of AI. For this purpose, the traditional Technology Acceptance Model (TAM), which exists of behavioral intention to use (BI), perceived usefulness (PU), and perceived ease of use (PEOU), is extended with trust and social influence related factors such as compliance, image, technological trust, and behavioral uncertainty. In addition, the moderating effect of prior experience with AI was investigated. A survey was designed with 199 participants (N=199) to measure the magnitude and directionality of the effects of the driving factors of acceptance. Within the survey, participants were presented with a taxor audit-related case. The results demonstrate that the extended TAM model is a valid model to predict employees’ acceptance of AI. PU exhibited the strongest significant influence on BI. In contrast, no significant direct effect of PEOU on BI was found. The findings further demonstrated that acceptance behavior differs for experienced compared to inexperienced employees. Another significant result is that employees’ sentiment regarding their prior experience significantly affects the magnitude of the driving factors. These findings advance theory and contribute to future research focused on improving understanding of employees’ acceptance of AI.

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Technology acceptance and leadership 4.0: a quali-quantitative study.

1. Introduction

1.1. technology acceptance, 1.2. leadership 4.0, 1.3. study aims, 2. materials and methods, 2.1. procedure and participants, 2.2. measures for the quantitative study, 2.3. data analysis, 3.1. qualitative results, 3.1.1. industry 4.0: opportunities and risks.

“I imagine that behind the definition of Industry 4.0 lies much more and I am unaware of what this may be, to be completely honest”. [Plant Key Role]

“If in the past, only three people worked in the workplace and now there’ll only be two people with a collaborative robot, then it’s logical to think that this will have some kind of impact”. [Plant Key Role]

“It is something that needs to be accessible to everybody, because there is also a strong risk that people will fall behind, unable to keep up with the new technologies, and become more and more excluded”. [Plant Key Role]

3.1.2. How Will Workers React?

“In my experience, we’ve talked about it in training activities. People don’t know much. They are very curious but also skeptical, but they really don’t know much”. [Trainer]

“We often talked to workers about robotization and the introduction of automation in the industry. Of course, there’s always some sadness or regret in saying that people’s jobs could be taken away. However, everyone recognizes the fact that, if we look at it from a different perspective, robots could also take on tasks that are too stressful, cumbersome, or dangerous for us, and could work in dangerous environments. This is a unanimous thought”. [Trainer]

“There’s the fear of losing work or thinking that the machine could control them somehow. And this is something they would never want”. [Trainer]

3.1.3. Differences Linked to Personal Backgrounds

“We could say that the older generations are a little more skeptical. Young people tend to be more open; they experience it, almost as if it were an evolution in their day to day lives, but they also know very little about the changes”. [Trainer]

3.1.4. Acceptance or Refusal of the New Devices

“People need to develop complete trust towards these tools and create a relationship of trustworthiness with the robot. This is the big challenge, I think, in the use of collaborative robots of this kind”. [Plant Key Role]

“In terms of acceptance, I can’t envisage specific problems, even though the risk I foresee could be that the robot will be seen as man’s substitute. This is an issue”. [Plant Key Role]

“Many technicians have talked about these exoskeletons, and they say it’s like a bicycle with assisted pedaling. They’re not afraid, and I do not think they would react with fear”. [Trainer]

“Planning needs to be connected also to how much I can control this mechanism, or how much it controls me, the awareness and the certainty of being able to control it, to curb my fear, the fear that it might harm me and the fear that I may not be able to control it, or that he is controlling me”. [Trainer]

“With regard to the exoskeleton, the only thing that needs to be addressed is the physical management of the tool. The ability to use it and wear it. There could be people who feel uncomfortable having something on their body”. [Plant Key Role]

3.1.5. The Skills of the Future

“The 5.0 Industry may only need baboons. What need is there for humans? The more we move forward, the less is asked of people”. [Trainer]

“To avoid errors to ensure quality, we need someone who does not think. Someone who takes the piece—the only piece that is there—puts it in its position where the light is turned on”. [Trainer]

“I’m not all that convinced that people using these devices should not have more training than they have now. We’ve gone from people using a hammer, cutter or a screwdriver to people managing complex machinery. We need to change the professional profile”. [Trainer]

“Certainly, there will be more need for more technical vision of the role of the worker who in the future will have a technical/technological role”. [Plant Key Role]

3.1.6. Leadership 4.0

“Alongside the changes that the workers will have to make, it is also essential that the managers are aware of the types of technologies that are going to be used because the best way to manage people is to know what they have to do and in what conditions”. [Plant Key Role]

3.1.7. Interventions to Communicate the Change and Support Acceptance

“Awareness is an important key. I explain to you what this is, and you get to talk about it and give me feedback, tell me your opinion. So, any potential fears that you have will be addressed. At that point, there is no slamming on the brakes and there is no putting up a wall. On the contrary, there is active participation on behalf of the workers”. [Trainer]

3.1.8. Training

“We train the managers first so they know what is coming, and they can then be ready to address any question. We go from the manager and work down the ladder until reaching the newest employees”. [Plant Key Role]

3.2. Quantitative Results

4. discussion, limitations and directions for future research, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Molino, M.; Cortese, C.G.; Ghislieri, C. Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study. Int. J. Environ. Res. Public Health 2021 , 18 , 10845. https://doi.org/10.3390/ijerph182010845

Molino M, Cortese CG, Ghislieri C. Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study. International Journal of Environmental Research and Public Health . 2021; 18(20):10845. https://doi.org/10.3390/ijerph182010845

Molino, Monica, Claudio G. Cortese, and Chiara Ghislieri. 2021. "Technology Acceptance and Leadership 4.0: A Quali-Quantitative Study" International Journal of Environmental Research and Public Health 18, no. 20: 10845. https://doi.org/10.3390/ijerph182010845

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• v.9(3); 2018 Jul

A Systematic Review of the Technology Acceptance Model in Health Informatics

Bahlol rahimi.

1 Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran

Hamed Nadri

2 Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran

Hadi Lotfnezhad Afshar

Toomas timpka.

3 Department of Computer and Information Sciences, Linköping University, Linköping, Sweden

4 Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

Background  One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM).

Objective  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.

Method  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.

Results  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.

Conclusion  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.

Background and Significance

New technologies are continuously being adopted in health services. 1 2 Modern information and communication technology (ICT) has been understood to improve service quality in the health service sector in general and in clinical medicine and at hospitals in particular, enhancing patient safety, staff efficiency and effectiveness, and reducing organizational expenses. 3 4 5 6 Meanwhile, progress in the life sciences has led to higher medical specialization and needs to exchange health information across institutional borders. 7 8 Despite these needs, health information systems development methods and research have focused on the technical aspects of the system design. 9 10 11 12 13 If the latter efforts are insufficient to meet the needs of progressive health service organizations and individual users, ICT investments will be spent ineffectively, and, potentially, patients put at risk. 14 Therefore, the impact on ICT adoption of different nontechnical and individual-level factors need to be established. 15 In this regard, it is positive that technology acceptance studies at the present are considered to stand as a mature field in information systems research. 16

During the past 30 years, several theoretical models have been proposed to assess and explain acceptance and behaviors in association with ICT introduction. Robust measures have been developed of how well a technology “fits” with user tasks and have validated these task–technology fit instruments. 17 The best known of these is the technology acceptance model (TAM), which was presented in 1989, 18 and has during this period been applied and empirically tested in a wide spectrum of ICT application areas. 19 20 Also, the TAM is one of the most popular research models to predict use, person's intention to perform a particular behavior, and acceptance of information systems and technology by individual users. 21 22 Originally, the TAM was derived from the social psychological theories of reasonable action (TRA) and planned behavior (TPB), 23 these three models focus on a person's intention to perform the behavior, 24 but the constructs of these three models are different and not exactly the same. The TAM has become the dominant model for investigating factors affecting users' acceptance of novel technical systems. 25 The basic model presumes a mediating role of perceived ease of use and usefulness in association between system characteristics (external variables) and system usage (as shown in Fig. 1 ). 26 Several reviews of TAM use encompassing the ICT field in total have been issued. Accounts of the first decade of TAM-related research and suggestions of future directions were offered in 2003 by Lee et al 27 and Legris et al. 25 The directions included a need for incorporating more variables related to human and social change processes and exploring boundary conditions. At that time, the original TAM had already been modified in the TAM2 version 28 by removal of the “Attitudes” concept and differentiating the “External variables” concept into social influence (subjective norm, voluntariness, and image), cognitive instrumental processes (job relevance, output quality, and result demonstrability), and experience. A few years later, Sharp continued to discuss the relative strengths of perceived usefulness (PU) and perceived ease and the role of attitudes in user acceptance, but also brought to the fore differences between volitional and mandatory use environments. 29 Venkatesh et al proposed a unified model—the unified theory of acceptance and use of technology (UTAUT)—based on studies of eight prominent models (in particular the TAM). The UTAUT is formulated with four core determinants of intentions and usage: performance expectancy, effort expectancy, social influence, and facilitating conditions, together with four moderators of key relationships: gender, age, experience, and voluntariness of use. 16 The same year, King and Jun conducted a statistical meta-analysis of TAM applications in various fields, reporting the TAM to be a valid and robust model that has been widely used. 30 In 2008, the TAM2 was extended with regard to determinants of perceived ease of use (PEOU) (TAM3). 31 The TAM3 is composed of four constructs: PEOU, PU, behavior intention, and use behavior.

The basic technology acceptance model. 18

Turning the attention from theory building to use environments, Turner et al concluded that care should be taken when using a particular version of the TAM outside the context in which the version originally was validated. 32 Proceeding with the analyses of model validity across use environments, Hsiao and Yang used cocitation analyses to identify three main application contexts for TAM use: (1) task-related systems, (2) e-commerce systems, and (3) “experiential” (or “hedonic”) systems. 33

Task-related systems are designed to improve task performance and efficiency. These systems can be categorized as automation software, office systems, software development, and communication systems such as electronic health record (EHR). Clinical practice guidelines, linked educational content, and patient handouts can be part of the EHR. This may permit finding the answer to a medical question while the patient is still in the examination room. 34 e-Commerce is the activity of buying or selling of products on online services or over the Internet. 35 The “hedonic” information systems are usually connected to home and leisure activities, focusing on the fun or novel aspect of information systems includes online gaming, online surfing, online shopping, and even online learning while perusing enjoyment at the same time. 33

In 2010, Gagnon et al conducted a systematic review to investigate factors influencing the adoption of ICT by health care professionals. In this review, including all ICT acceptance models in health services, it was concluded that PU of system and PEOU were the two most influential factors. 36 These two factors are the main components of the original TAM. 22 Regarding applications in specific health services areas, Strudwick concluded from a review of TAM applications among nursing practitioners that a modified TAM with variables detailing the health service context and user groups added could provide a better explanation of nurses' acceptance of health care technology. 37 Further, Ahlan and Isma'eel reported from an overview of patient acceptance of ICT that the TAM is one of the most useful models for studying patients' perceptions and behaviors. 38 Also, Garavand et al concluded from their general review of the most widely used acceptance models in health services that the TAM is the most important model used to identify the factors influencing the adoption of information technologies in the health system. 39

The objective of this systematic review was to compile published research on TAM use in health information systems development and implementation with regard to application areas and model modifications after its initial introduction, and also to gain understanding of the existing research and debates relevant to a particular topic or area of study. In the present setting, the development of health services requires parallel adjustments of ICT support, and accordingly, of TAMs.

We used systematic search processes to identify all published original articles related to TAM applications in health services from 1989, the year when the TAM was introduced, to February 2017. The PubMed, Scopus, and Web of Science databases were searched and English-only publications selected. The broad keywords used for the initial search are displayed in Table 1 . The authors, title, journal, year of publication, and abstract for each article were collected in an Excel spreadsheet. First, the publication's titles, and abstracts, were assessed together by two of the four authors, after reviewing all abstracts and eliminating those categorized with exclusion criteria or lacking inclusion criteria; the full texts of the relevant articles were then reviewed by three authors together. The full texts of the remaining articles were read for eligibility, and the qualified publications were retained in a list. A search of the recent reviews and hand-searching references from articles were made to get related articles. The TAM has been used in many technological and geographical contexts. Several major technologies like mobile and telemedicine have variety of applications. 40 41 In a separate phase, the technologies and applications as a subset of major technological contexts and characteristics of each tested model for user groups were identified by three authors together. Finally, the publications in the list were classified into three categories according to their aim and content:

• Original TAM: Applications of the original TAM. In this category, the relationship between the main constructs of the original TAM is examined. These relationships include the relationship between PU and perceived ease to use with intention to use and also the relationship between perceived ease to use and PU.
• Development and Extension of TAM: Reports of new insights related to the core elements of TAM and/or development of new TAM versions by integrating new factors and other acceptance theory variables with the original TAM. These factors incorporate into the constructs of the original TAM as predictive and moderating variables.
• Comparisons of the TAM with other technology acceptance models: The TAM and other theoretical models are compared by examining factors associated with the adoption of a particular technology.

A total of 492 document references were retrieved from the database searches. After removal of 44 duplicates, 448 publications were entered into the selection process. Results of the screening process in the analysis are noted in the flow diagram in Fig. 2 . First, 448 publications' titles and abstracts were assessed together by two of the four authors. At this stage, 120 articles unrelated to the topic were excluded from the review. The full texts of the relevant articles were then reviewed by three authors together. The titles and abstracts of the relevant articles were then reviewed by three authors. When the title or abstract was deemed significant for inclusion in the review, the full text was scanned to ensure that the content was relevant. At this stage, 209 articles that were unrelated to acceptance of technology in health care, TAM constructs, or only addressed separate components of the TAM and other acceptance models were excluded. When there was disagreement, the authors evaluated their assessment until consensus was reached. A search of the recent reviews and hand-searching references from articles yielded an additional 15 papers. The systematic search of the literature identified 134 articles that reported original empirical research on the use of the TAM within health services.

Flow diagram of the study.

Publications dealing with the original TAM had peaked ( n  = 3, 2.2% of all articles) in 2013 and 2015, publications on development and extension of TAM peaked ( n  = 16, 11.9%) in 2013, while publications reporting comparisons of TAMs had peaked ( n  = 2, 1.5%) in years 2010 and 2013 ( Fig. 3 ). A general increase in reports of TAM use suggests a persisting interest in understanding technology acceptance in health services. Also, there was a noteworthy leap in reports of TAM extensions in 2012 ( Fig. 3 ), which implies a recent highlighting of the influence from external factors on technology acceptance. The 134 articles reporting on TAM use had been published in 72 scientific journals, and originated from 30 countries; 29 (21.6%) studies from the United States, 28 (20.9%) from Taiwan, 14 (10.4%) from Spain, while the remaining articles originated from countries in Europe, Asia, and Africa. The journals with the highest numbers of articles were International Journal of Medical Informatics with 11 studies (8.14%), Telemedicine and e-Health with 10 studies (7.4%), and BMC Medical Informatics and Decision Making, with 8 studies (5.9%).

Frequency of articles reporting technology acceptance model use according to the three study categories displayed by year.

The first study of a TAM use in health services was reported in 1999, 42 analyzing physicians' intentions associated with the adoption of the telemedicine technology in a Hong Kong hospital setting. The ICT application area in which the TAM was first more frequently applied was EHR for which a peak in publications was observed in 2009. Publications reporting the TAM applications in telemedicine reached its peak in 2014, while the use of the TAM for analyses of mobile applications did peak in 2015. The first integration of several acceptance models with the TAM in health services was reported from Finland for examining acceptance of mobile systems among physicians. 43 In this study, the TAM was combined with the UTAUT and Personal Innovativeness in the Domain of Information Technology (PIIT) models.

Three main technological contexts were identified for applications of the TAM ( Table 2 ): (1) Telemedicine with 25 studies (18.6%), (2) EHR with 21 studies (15.7%), and (3) mobile applications with 15 studies (11.2%). Researchers in different countries have focused on different specific technologies: researchers in Taiwan on telemedicine (8 articles), mobile applications ( n  = 5), and hospital information systems (HIS) ( n  = 4); in the United States on EHRs ( n  = 8), computers, handheld (personal digital assistants [PDAs]) ( n  = 4), telemedicine, and personal health records ( n  = 2); and in Spain on telemedicine ( n  = 6), while researchers from Iran have focused on EHR ( n  = 3) technology ( Fig. 4 ).

Technological contexts in using the technology acceptance model between geographical contexts. The parenthesized value is number of studies.

Abbreviations: CPOE, computerized physician order entry; HIT, health information technology; ICT, information and communication technology; MeSH, Medical Subject Headings; PACS, picture archiving and communication system; PDA, personal digital assistant; TAM, technology acceptance model.

Note: The parenthesized value is number of studies.

Telemedicine, the area where the TAM has been most widely applied, is also the first technology that was studied using the TAM ( Fig. 5 ). TAM application on mobile technologies was initiated in 2006 43 and these studies peaked in 2015. As shown in Table 3 , most studies have emphasized the acceptance of physicians ( n  = 43, 32%) and nurses ( n  = 34, 25.3%). Other users of technology acceptance include patients and clients of health services, pharmacists, and other medical professionals.

Distribution of three main technological contexts in using the technology acceptance model by year.

Applications of the Original TAM

As shown in Table 4 , 23 (17.1%) of the identified articles reported application of the original TAM. In most studies using the original TAM to assess technology acceptance, the main constructs (i.e., PU and perceived ease to use) of TAM were supported. The most frequent ICT application areas were telemedicine, n  = 6 (26%) and PDA, n  = 2 (8.6%). The study participants ranged from 10 to 1,942, with an average of 184. The user category involved in the most studies was nurses ( n  = 4, 17%) followed by physicians and patients (both n  = 3, 13%).

Abbreviations: BCMA, bar code medication administration; BI, business intelligence; EHR, electronic health record; IT, information technology; PHN, public health nurse; TAM, technology acceptance model.

Development and Extension

Of all studies, 102 (76.1%) studies reported development or extension of the TAM. In these studies, different factors and theories were incorporated to the original TAM ( Table 5 ). The factors investigated in the most commonly used technological contexts such as health information technology systems in general, telemedicine, EHR, mobile apps, HIS, E-prescription, PDAs, and personal health record are briefly provided. According to the results in various technological contexts, it is possible to draw basic factors that incorporate with the original TAM for each technological context. The most common factors added to the TAM in almost all technological contexts were, in order of importance and frequency of repetition, compatibility, subjective norm, self-efficacy, experience, training, anxiety, habit, and facilitators. These factors can be a basic model for most technological contexts with the incorporation of the original TAM and separate variables regarding a context.

Abbreviations: DOI, diffusion of innovation; HIV, human immunodeficiency virus; ICT, information and communication technology; ICU, intensive care unit; IS, information system; IT, information technology; NHS, National Health Service; USB, Universal Serial Bus; UTAUT, unified theory of acceptance and use of technology.

Adding separate variables to develop contextualized TAM versions allows optimizing specific dimensions of the TAM in particular settings and thereby improving predictions in these contexts. A full summary of the additions to the original TAM displayed by technology application area in health services, theories integrated, and new factors and variables inserted is shown in Table 6 . The most commonly integrated theories were classic acceptance models such as UTAUT, TRA, Diffusion of Innovation theory, and the TPB. In addition to the theories, the conditions and technologies forming the particular context in specific settings have been used to add further concepts and variables, i.e., some factors were not derived from any technology acceptance theory and were instead specific to a certain technology (such as technology features, environmental conditions, user types, etc.). Among the 102 articles, only two studies were conducted on the TAM3.

Abbreviations: DOI, diffusion of innovation; HIT, health information technology; ICT, information and communication technology; IDT, innovation diffusion theory; IS, information system; PDA, personal digital assistant; TAM, technology acceptance model; TPB, theory of planned behavior; TRA, theories of reasonable action; TTF, task-technology fit; UTAUT, unified theory of acceptance and use of technology.

Comparison of Other Technology Acceptance Models with TAM

Nine (6.7%) studies compared TAM with other TAMs. The most common ICT application area for these comparisons was mobile technology, n  = 3 (33.3%). Typically, Hsiao and Tang 44 used different variables to investigate the introduction of mobile technologies from the perspective of the elderly people in Taiwan. Their results supported the validity of the TAM variables, and also the inclusion of novel factors such as perceived ubiquity, personal health knowledge, and perceived need for health care. Day et al 45 conducted a study to evaluate hospice providers' attitudes and perceptions regarding videophone technology in settings where the technology was introduced but underutilized. Findings indicate that the TAM provides a good framework for an understanding of telehealth underutilization.

In two studies on telemedicine acceptance among physicians in China and the United States, respectively, the TAM and the TPB model were compared. Interestingly, the findings from China suggested that the TAM was more valid than the TPB, while the TPB was more valid than the TAM in the United States. 46 47 Another study comparing the TAM and the UTAUT among physicians concluded that the usage intentions were strongly associated with the performance expectancy on attitude and attitude concepts. 48 Manimaran and Lakshmi 49 formulated an integrated TAM for Health Management Information System and concluded that health workers' innovativeness and voluntariness had a direct and positive influence on these intentions. Similarly, Smith and Motley 50 found that e-prescribing acceptance was predicted by the technological sophistication, operational factors, and maturity factors constructs, i.e., ease-of-use variables derived from the TAM. Liang et al 51 examined whether TAM can be applied to explain physician acceptance of computerized physician order entry (CPOE), and found that data analysis provided support for all relationships predicted by TAM but failed to support the relationship between ease of use and attitude. A follow-up analysis showed that this relationship is moderated by CPOE experience (more details of the nine studies are shown in Table 7 ).

Abbreviation: TAM, technology acceptance model.

The review showed that the TAM initially was applied to task-related ICT systems such as EHRs. These were often connected to educational processes leading to that system's impacts on learning and competence were natural critical influences on use intentions. Since the purpose of task-related systems is to enhance the users' task performance and improve efficiency, educational concepts can be expected to continue to play a dominant role within TAM in this domain. In other words, for the task-related systems such as EHRs, PU and self-efficacy related to learning can be expected to have stronger effects on usage than PEOU, 33 i.e., clinical users are likely to accept a new technology mainly if they recognize that it can help them to improve their work performance and build efficacy. 52 In addition to PU and self-efficacy, system quality, information quality, physicians' autonomy, security and privacy concerns, and cultural and organizational characteristics were found to be important for adoption of task-related technologies, such as EHRs and HISs.

The second aggregation of TAM research was focused on communication systems and telemedicine. The rapid development of worldwide Internet infrastructures has facilitated development of systems in this domain. Telemedicine applications have in particular allowed to introduce new organizational structures in health services 40 and consequently led to an interest in the use of the TAM to facilitate the organizational adaptation. Health care policy makers are still debating why institutionalizing telemedicine applications on a large scale has been so difficult, 53 and why health care professionals are often averse or indifferent to telemedicine applications. 40 54 We believe that user rejection is one of the important factors in institutionalizing various types of telemedicine applications. Therefore, it is important to examine the effective factors in accepting telemedicine applications by health care professionals. Consequently, when using the TAM on this category of systems, the validity of analyses with regard to the organizational fit of the novel ICT application is central. 55 56 Other factors commonly associated with technology adoption in this context include subjective norm, security and confidentiality, facilitators, accessibility, and self-efficacy.

Finally, the most recent trend in TAM use—on mobile technologies—is characterized by involving also patients as users. In this setting, the notion of “hedonic” system aspects, denoting factors associated with pleasure or happiness is of importance. 57 Different from the task-related systems, the concept of hedonic systems focuses on the enjoyable aspect of ICT use and consequently requires other types of factors and variables for analyses of use intentions. Intrinsic motivational factors such as usability and perceived liveliness are in this setting as influential as the PU. The progress from EHRs to mobile technologies in ICT applications has required also the TAM to be dynamically adapted. Based on this, progress of technology introduction in health services cannot be seen to decrease, and a need to modify the TAM to keep up with the new application areas can be also foreseen in the future. Common factors for hedonic such as mobile apps include usability, user satisfaction, reliability, privacy, compatibility, innovativeness, subjective norm, self-efficacy, technical support and training, anxiety, and communication. Also, a theory that integrates with the original TAM to examine the hedonic systems is the self-determination theory (SDT). SDT is a theory of motivation that is concerned with supporting our natural or intrinsic tendencies to behave in effective and healthy ways. 58

In the extensions of the TAM observed in the review, a wide range of technological context factors and circumstances were introduced. Examples of such factors include physicians' autonomy, doctor–patient relationship, project team competency, clinical safely, job fit, and optimism, as well as patient user group, 59 voluntariness of the ICT use, and whether the ICT systems were prototypes, trial systems, to-be-implemented systems, or implemented systems. Other revisions had more to do with explicitly stating contextual circumstances, rather than extensions per se. For instance, over the life course of an ICT application, the relationships in the TAM may change, e.g., usability may initially be critical but less important later on. Two methods to add novel concepts and variables to the TAM were highlighted in this review. The first, theory-based additions can be expected to allow comparisons between ICT application areas and harmonization between ICT applications and different organizational processes.

However, it has been suggested that a main reason for inconsistent predictive performance of the TAM in health services is the poor match between construct operationalization and the context in which the construct is measured, 29 The second method to expand the TAM is to add contextualized TAM concepts that increase predictive power. One method to derive such contextualized concepts is belief elicitation 60 which was also the process used to fit general behavioral theory to the ICT context when developing the TAM. 20 However, this step-wise method is less suitable for comparisons between application areas and analyses of the organizational fit of new ICT applications from a general health service perspective. The results of this review suggest that consensus is needed upon how the TAM extension processes should be designed for uses in health services.

The primary threats to the validity of this review are concerned with the search strategy employed. First, it may be possible that we have not identified all relevant publications. The completeness of the search is dependent upon the search criteria used and the scope of the search, and is also influenced by the limitations of the search engines used. Publication bias is possibly a further threat to validity, in that we were primarily searching for literature available in the major computing digital libraries. It is possible that, as a result, we included more studies reporting positive results of the TAM as those publications reporting negative results are less likely to be published. Since we have been unable to undertake a formal meta-analysis, we are equally unable to undertake a funnel analysis—using a series of events that lead toward a defined goal—to investigate the possible extent of publication bias. Finally, it must be remembered that the TAM does not measure the benefit of ICT use, 57 implying that measures of technology acceptance and use intentions should not be mistaken for measures of technology value. Separate studies using measures of effectiveness or productivity are needed to assess the organizational value of the new technology.

The review was limited to those articles describing only the TAM and its application in health care service. By restricting our review to a narrow segment of this literature, we may have inadvertently eliminated meaningful details from other acceptance models and factors in health technologies acceptance. Also, there are books and book chapters that deal with the TAM in health care. These types of publications are not included in our review, but may contain information relevant to this review. Finally, our review includes only articles in English language and languages other than English might have information about the TAM in health care.

The result showed that telemedicine applications peaked between 1999 and 2017 and is the ICT application area most frequently studied using the TAM, implying that acceptance of telemedicine applications during this period was a major challenge when exploiting ICT to develop health service organizations. 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. Finally, it is suggested that the common investigated factors in the previous studies ( Table 6 ), for each technological contexts and user groups, should tested empirically in real settings. If these factors confirmed, it is recommended that they will be applied as a basic model for each technological contexts and user groups.

Clinical Relevance statement

This systematic review showed that between 1999 and 2016, telemedicine applications were the ICT application area most frequently studied using the TAM, implying that acceptance of the telemedicine technology during this period was a major challenge for health service organizations. The construct validity of the model is showcased by its broad applicability to various technologies in health care. With the increasing number of technologies in the health care environment, the use of technology acceptance models is needed to guide implementation processes across health service contexts and user groups. This review has indicated continuous progress in revealing new aspects critical for ICT implementation having significant influence on health service processes and outcomes.

Multiple Choice Questions

• (1) Hospital information system (HIS), (2) mobile applications, and (3) electronic health record (EHR).
• (1) Telemedicine, (2) hospital information system (HIS), and (3) computers, handheld (PDAs).
• (1) Telemedicine, (2) electronic health record (EHR), and (3) mobile applications.
• (1) Electronic health record (EHR), (2) e-prescription systems, and (3) hospital information system (HIS).

Correct Answer: The correct answer is option c. The study identified three main technological contexts for using TAM in health care: (1) Telemedicine, (2) electronic health records (EHR), and (3) mobile applications. The geographical contexts of using TAM between different countries: Taiwan (telemedicine and mobile applications), U.S. and Iran (EHR), and Spain (telemedicine).

• Subjective norm, self-efficacy, compatibility, experience, training, anxiety, habit, and facilitators.
• Job relevance, age, communication, image, information quality, and uncertainty avoidance.
• Power distance, time orientation, project team competency, acceptability, and organizational characteristics.
• Training, management support, user interface, autonomy, cluster ownership, personal innovativeness, and loyalty.

Correct Answer: The correct answer is option a. The most common factors added to the original TAM in almost all technological contexts were, in order of importance and frequency of repetition, compatibility, subjective norm, self-efficacy, experience, training, anxiety, habit, and facilitators.

Acknowledgments

This article was developed as a part of the research study code: 1395–01–52–2759 and by the supports of Urmia University of Medical Sciences. Also, we are very thankful to the editorial board of Applied Clinical Informatics journal for their valuable and constructive comments that made us very encouraged to reread and integrate all the comments.

Funding Statement

Funding None.

Conflict of Interest None.

Protection of Human and Animal Subjects

Not applicable.