The Evolution of the Technology Acceptance Model (TAM): From Early Foundations to the AI Era

Abstract

The Technology Acceptance Model (TAM) is one of the most widely used theoretical models for explaining the acceptance and use of information technology. Developed by Fred D. Davis and published in 1989, this model identifies perceived usefulness and perceived ease of use as the two primary constructs influencing technology acceptance. As technology has evolved and user needs have become more diverse, TAM has undergone several extensions, from TAM2 and TAM3 to its integration with the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2. The advancement of digital technologies, mobile applications, internet-based services, Internet of Things (IoT), and artificial intelligence (Artificial Intelligence/AI) has further expanded the use of TAM in various studies. This article aims to examine the evolution of TAM from its original model to research developments in 2026 through a literature review approach. The discussion shows that TAM remains relevant due to its simple structure, ease of implementation, and adaptability to various technological characteristics. However, the acceptance of modern technology can no longer be explained solely by usefulness and ease of use. Factors such as trust, risk, social influence, privacy, transparency, and ethics are increasingly important, especially in the implementation of AI. Therefore, while TAM remains a vital foundation for technology acceptance research, its application must consider evolving social, organizational, and technological conditions.

Keywords: Technology Acceptance Model, TAM2, TAM3, UTAUT, technology acceptance, artificial intelligence.

Abstract

The Technology Acceptance Model (TAM) is one of the most widely used theoretical models for explaining the acceptance and use of information technology. Developed by Fred D. Davis and published in 1989, the model identifies perceived usefulness and perceived ease of use as two fundamental constructs influencing technology acceptance. As technological developments and user needs have become increasingly diverse, TAM has undergone several extensions, including TAM2 and TAM3, while also contributing to broader technology acceptance frameworks such as the Unified Theory of Acceptance and Use of Technology (UTAUT) and UTAUT2. The advancement of digital technologies, mobile applications, internet-based services, the Internet of Things (IoT), and artificial intelligence (AI) has further expanded the application of TAM across various research contexts. This article aims to examine the evolution of TAM from its original formulation to research developments in 2026 through a narrative literature review. The discussion indicates that TAM remains relevant due to its simplicity, adaptability, and applicability across different technologies. However, the acceptance of contemporary technologies cannot be adequately explained solely through perceived usefulness and perceived ease of use. Factors such as trust, perceived risk, social influence, privacy, transparency, and ethical considerations have become increasingly important, particularly in AI adoption. Therefore, TAM continues to provide a valuable theoretical foundation for technology acceptance research, although its application requires consideration of evolving technological characteristics and social and organizational contexts.

Keywords: Technology Acceptance Model, TAM2, TAM3, UTAUT, technology acceptance, artificial intelligence.

A. Introduction

The development of information technology has brought major changes to human life, spanning education, economics, business, government, and daily activities. Various digital systems have been developed to improve work effectiveness and efficiency. However, the success of technology implementation is determined not only by the sophistication of the system but also by the user's willingness to accept and utilize it.

A technology may possess various advanced features, yet it may not be used optimally if users find it difficult, do not understand its benefits, or lack trust in the system. Therefore, user acceptance has become a critical aspect of information systems research.

One of the models widely used to explain this phenomenon is the Technology Acceptance Model (TAM). This model was developed by Fred D. Davis and published widely in 1989. TAM aims to explain the factors influencing an individual's acceptance of a technology, primarily through perceived usefulness and perceived ease of use (Davis, 1989).

According to Ilmi et al. (2020), TAM has been used in various information systems studies in Indonesia. The model is considered quite flexible as it can be applied across various sectors, including government, education, and business.

Conceptually, TAM explains that individuals tend to accept technology if they perceive it as beneficial and easy to use. Perceived usefulness refers to the belief that using the technology will improve one's performance. Meanwhile, perceived ease of use relates to the belief that the technology can be used without requiring excessive effort.

Nevertheless, technological advancements have presented new challenges in understanding user behavior. Social factors, experience, habits, organizational support, security, and trust have become increasingly important in determining technology acceptance.

These developments led to the emergence of TAM2 in 2000 and TAM3 in 2008. On the other hand, models integrating various technology acceptance theories have also emerged, namely UTAUT in 2003 and its extension, UTAUT2, in 2012.

Entering the era of artificial intelligence, technology acceptance has become increasingly complex. AI technology does not merely assist users in performing tasks but is also capable of automatically generating information, recommendations, predictions, and content.

This condition raises new questions regarding trust in AI, data security, system transparency, and accountability for the decisions generated by the technology.

Based on these developments, this article aims to explain the evolution of TAM from its initial model to 2026. The discussion is conducted through a literature review of various works concerning TRA, TPB, TAM, TAM2, TAM3, UTAUT, UTAUT2, and AI acceptance research.

B. Discussion

1. Theory of Reasoned Action (TRA) as an Initial Foundation

Theory of Reasoned Action (TRA) is one of the behavioral theories that served as the basis for the development of TAM. This theory was introduced by Martin Fishbein and Icek Ajzen through their work in 1975.

TRA explains that an individual's behavior is primarily influenced by the intention to perform an action. This intention is formed through two main factors: attitude toward behavior (attitude toward behavior) and subjective norm (subjective norm).

Attitude toward behavior describes an individual's positive or negative assessment of a specific action. Meanwhile, subjective norm relates to perceptions regarding social pressure or expectations from people considered important.

For example, someone might have the desire to use a digital payment application because they feel the technology is practical and beneficial. However, the decision to use it may also be influenced by family, friends, or the work environment.

According to Ilmi et al. (2020), TRA provides a basis for understanding the relationship between beliefs, attitudes, intentions, and individual behavior. This concept was later used in the development of technology acceptance models.

Davis adapted this behavioral approach to better suit information systems usage. The difference is that TAM places special attention on user perceptions of technological characteristics, particularly their usefulness and ease of use.

2. Theory of Planned Behavior (TPB)

Theory of Planned Behavior (TPB) is an extension of TRA that was explained extensively by Ajzen in 1991.

The main difference between TPB and TRA lies in the addition of the variable perceived behavioral control or perceived behavioral control.

This variable explains the extent to which an individual feels they have the ability, resources, and opportunity to perform an action.

In TRA, intention is the primary determinant of behavior. However, in reality, an individual cannot always perform the desired action due to certain limitations.

For example, a student might have the desire to use a digital learning application. However, that desire may not be realized if the student does not have adequate devices or faces limited internet access.

TPB considers these conditions through three main components: attitude toward behavior, subjective norm, and perceived behavioral control.

Ajzen (1991) explained that these three components can help predict an individual's intention to perform an action, while intention and perceived behavioral control play a role in explaining actual behavior.

Although TPB is not a direct extension of TAM, both models are linked within the tradition of behavioral and technology acceptance research. Both are also used as part of the conceptual foundation in the development of broader technology acceptance models.

3. Technology Acceptance Model (TAM) in 1989

The TAM developed by Davis became one of the most important models in information systems research because it offers a relatively simple approach to explaining technology acceptance.

The two main constructs of TAM are perceived usefulness (PU) and perceived ease of use (PEOU).

Perceived Usefulness (PU)

Perceived usefulness describes the extent to which an individual believes that using the technology will improve their performance.

In a work context, technology is considered useful if it can help users complete tasks faster, increase productivity, or produce better work.

For example, an employee is likely to accept the use of accounting software if the system can speed up transaction recording and reduce calculation errors.

Perceived Ease of Use (PEOU)

Perceived ease of use indicates the extent to which an individual believes that a technology can be used without requiring excessive effort.

Technology that has a simple interface, clear navigation, and is easy to learn tends to gain better acceptance.

For example, a digital banking application with an easy-to-understand menu can increase user comfort in performing transactions.

In Davis's research (1989), the testing of PU and PEOU instruments was conducted through two studies involving a total of 152 users and four application programs.

The results showed that both constructs have high reliability and validity. Perceived usefulness was also found to have a stronger relationship with technology usage compared to perceived ease of use.

In its simplified form, the relationship between TAM constructs can be described as follows:

Perceived Ease of Use → Perceived Usefulness → Behavioral Intention → Actual System Use.

However, the relationships in the TAM model are not limited to that single path. Perceived ease of use can also influence attitude or usage intention, while perceived usefulness can have a direct influence on intention.

The advantage of TAM lies in the simplicity of its structure. Researchers can use the model to measure user acceptance without having to include too many variables.

Nevertheless, this simplicity is also a limitation because technology acceptance in real life can be influenced by various factors not fully explained in the initial model.

4. Development of TAM in the 1990s

Throughout the 1990s, TAM began to be used more widely in information systems research.

This model was not only applied to personal computer usage but also to organizational software, communication systems, and various information technology applications.

During that period, research began to pay attention to external factors that could shape perceived usefulness and ease of use.

Researchers no longer only questioned whether a technology was considered beneficial, but also how that perception was formed.

For example, user training can help improve perceived ease of use. Experience using technology can also make an individual feel more confident when interacting with a new system.

In an organizational environment, management support and the compatibility of technology with work needs also influence user assessment.

These research developments show that technology acceptance is not entirely an individual decision. The social and organizational environment also plays a role in shaping user perceptions.

This condition then became one of the foundations for the development of TAM2.

5. TAM2: Incorporating Social Influence and Cognitive Processes

In 2000, Venkatesh and Davis introduced TAM2 as an extension of the initial model.

TAM2 aims to provide a broader explanation of the factors that shape perceived usefulness and an individual's intention to use technology.

This model incorporates two main groups of factors: social influence processes (social influence processes) and cognitive instrumental processes (cognitive instrumental processes).

Social Influence Processes

This group consists of three main components.

1. Subjective norm: An individual's perception regarding the expectations of important people around them regarding the use of technology.

2. Voluntariness: The extent to which the use of technology is considered a voluntary decision or an obligation.

3. Image: The perception that the use of technology can improve an individual's status or image in a social environment.

In a work environment, for example, an employee may be encouraged to use a new system due to management directives or the influence of colleagues.

Cognitive Instrumental Processes

This group explains how an individual assesses the benefits of technology based on job characteristics and expected outcomes.

Its main components include:

1. Job relevance: The degree of compatibility between the technology and job requirements.

2. Output quality: The assessment of the quality of results produced by the technology.

3. Result demonstrability: The extent to which the benefits of the technology can be observed and explained.

4. Perceived ease of use: Perception regarding the ease of using the system.

Venkatesh and Davis (2000) tested TAM2 through longitudinal research in four organizations with 156 respondents.

The results showed that social factors and cognitive instrumental processes play a role in explaining technology acceptance.

Thus, TAM2 expands the understanding that system acceptance is not only influenced by the user's personal perception but also by the social environment and the relevance of the technology to the job.

6. TAM3: Integration of Technology Acceptance Determinants

The next development occurred in 2008 when Venkatesh and Bala introduced TAM3.

This model integrates TAM2 with research on factors that shape perceived ease of use.

TAM3 pays greater attention to how user experience, individual ability, and environmental support influence technology acceptance.

According to Venkatesh and Bala (2008), technology acceptance research also needs to pay attention to the forms of intervention that organizations can implement to increase system utilization.

Some important constructs included in TAM3 include:

1. Computer Self-Efficacy

Refers to an individual's belief in their ability to use computer technology.

Users who feel they have adequate ability tend to be more confident when using a new system.

2. Perception of External Control

Describes the user's belief that the necessary facilities, resources, and support are available to use the technology.

Such support can be in the form of devices, internet networks, technical assistance, or training.

3. Computer Anxiety

Indicates an individual's worry or fear when they have to use computer technology.

Users with high levels of anxiety may experience difficulty accepting a system even if the technology is actually beneficial.

4. Computer Playfulness

Describes an individual's tendency to interact with technology spontaneously, creatively, and with curiosity.

5. Perceived Enjoyment

Refers to the extent to which the use of technology is considered an enjoyable activity, regardless of its direct benefits to the job.

6. Objective Usability

Describes the level of system ease based on observable measures, such as the time and effort required to complete a task.

Through these constructs, TAM3 provides a more comprehensive understanding of the factors that shape user perception.

This model also provides practical benefits for organizations as it can help determine strategies for training, mentoring, technical support, and the design of more user-friendly systems.

7. Relationship of TAM with UTAUT and UTAUT2

In addition to the development of TAM2 and TAM3, technology acceptance research has also expanded through an approach of integrating various theories.

In 2003, Venkatesh, Morris, Davis, and Davis introduced the Unified Theory of Acceptance and Use of Technology (UTAUT).

UTAUT was developed through the study and integration of eight technology acceptance models, including TAM and TPB.

The model has four main constructs, namely:

1. Performance expectancy: The belief that technology can improve performance.

2. Effort expectancy: Perception regarding the level of ease of use of the technology.

3. Social influence: The influence of the social environment on technology usage.

4. Facilitating conditions: The availability of facilities and support to use the technology.

UTAUT also considers moderator variables such as age, gender, experience, and voluntariness of use.

In 2012, Venkatesh, Thong, and Xu introduced UTAUT2, which was designed to explain technology acceptance in a consumer context.

UTAUT2 adds three main constructs, namely hedonic motivation, price value, and habit.

Hedonic motivation relates to the pleasure derived from using technology. Price value refers to the user's assessment of the benefits of the technology compared to the costs incurred. Meanwhile, habit describes the habits formed from previous usage experience.

It should be understood that UTAUT and UTAUT2 are not merely TAM4 or a direct continuation of TAM3. Both are integrative frameworks that evolved from a number of technology acceptance theories.

Although they have different approaches, TAM, TAM2, TAM3, UTAUT, and UTAUT2 all strive to explain the factors that influence the intention and behavior of technology usage.

8. Application of TAM in Digital Technology Development

The development of digital technology has expanded the application of TAM to various fields.

The model, which was initially used to understand the acceptance of computer systems, is now applied in research regarding digital banking, e-commerce, online learning, health applications, digital government services, and internet-based technology.

In an educational context, for example, TAM can be used to measure student acceptance of a Learning Management System (LMS).

Students tend to accept the use of an LMS if the system facilitates access to materials, assignment submission, communication with lecturers, and learning activities.

In the business sector, TAM can be used to understand consumer acceptance of online shopping or digital payment applications.

Meanwhile, in organizations, TAM can help evaluate employee acceptance of management information systems.

Ilmi et al. (2020) showed that TAM has been used in various studies in Indonesia with diverse contexts, methods, and research objects.

Huda and Utomo (2026) also explained the importance of TAM constructs in measuring technology acceptance, including through statistical analysis approaches such as Partial Least Squares Structural Equation Modeling (PLS-SEM).

These developments show that TAM still has practical value in research, although every context of technology usage requires variable adjustments.

9. TAM in the Era of Artificial Intelligence until 2026

Entering the era of artificial intelligence, technology acceptance research faces increasingly complex challenges.

Unlike conventional software that generally works based on predetermined functions, AI technology can generate recommendations, predictions, decisions, and content based on data processing.

This capability opens opportunities to increase productivity but also raises various questions regarding the accuracy of information, transparency, security, and responsibility for usage.

In systematic studies on AI acceptance, the extended TAM is one of the approaches widely used to understand the factors influencing user intention and behavior.

AI acceptance research shows that perceived usefulness and ease of use are still relevant. However, various additional factors are needed to provide a more comprehensive explanation.

One important development is seen in the research by Shenoy and Saarela (2026) regarding AI acceptance by teachers.

The research reviewed 38 empirical studies involving 15,919 teachers from 19 countries. The results showed a positive relationship between perceived usefulness, ease of use, and the intention to use AI.

However, the research also found that the relationships between constructs can differ significantly according to context. AI literacy, trust, pedagogical beliefs, institutional norms, and ethical risks are important factors in understanding AI acceptance in an educational environment.

Some variables that are increasingly relevant in the development of TAM in the AI era are as follows.

1. Trust in Artificial Intelligence

Trust is one of the important factors because users need to believe that AI technology can provide reliable results.

Users might assess an AI system as easy to use and beneficial, but still be reluctant to use it if they doubt the accuracy of its results.

2. Perceived Risk

The use of AI can pose risks related to misinformation, data security, privacy, and decisions that are not in accordance with user needs.

These risks can influence an individual's willingness to use the technology.

3. Transparency and Explainability

AI systems sometimes generate recommendations or decisions through processes that are difficult for users to understand.

Therefore, transparency and the ability to explain the decision-making process are important aspects in building trust.

4. Ethical Concerns

The use of AI is related to issues of algorithmic bias, fairness, responsibility, academic integrity, and the potential for technology misuse.

In an educational context, for example, AI can help students understand materials. However, its use also raises issues regarding the originality of work and academic honesty.

5. Human-AI Interaction

AI acceptance is not only related to whether the technology can be used, but also how users interact and work with the system.

This relationship can involve the process of giving instructions, evaluating results, correcting information, and final decision-making by humans.

6. Perceived Intelligence

Perceptions regarding AI's ability to understand instructions, solve problems, adapt, and generate information can influence user assessment.

The higher the perceived capability of the technology, the greater the likelihood that users will consider it beneficial. Nevertheless, the perception of that capability still needs to be balanced with an assessment of accuracy and risk.

Thus, the development of AI shows that technology acceptance research is not sufficient if it only measures usefulness and ease of use.

Researchers also need to consider the human relationship with technology, trust in the system, organizational context, and the social and ethical impacts of AI usage.

10. Limitations of TAM and Directions for Research Development

Although TAM has been used widely, this model still has a number of limitations.

First, TAM emphasizes individual perception and intention, so it is not always able to explain the actual conditions of technology usage.

An individual may have a high intention to use a technology but not do so due to limitations in devices, access, costs, or organizational support.

Second, the use of the two main constructs in TAM is not enough to explain the acceptance of technology that carries high risk.

In the use of AI in healthcare, for example, user decisions are not only determined by benefits and ease, but also by system reliability, data security, and professional responsibility.

Third, a number of technology acceptance studies still rely on questionnaires and subjective user reports.

This approach can describe perception or intention, but it does not necessarily show how the technology is actually used in daily activities.

Fourth, the development of AI presents technological characteristics that are not fully covered in the initial TAM, including the system's ability to adapt, generate content, and influence decision-making processes.

Therefore, future TAM research needs to consider several directions for development.

Researchers can combine TAM with variables of trust, risk, digital literacy, privacy, social influence, and ethical factors.

In addition, longitudinal research can be used to understand changes in user perception from the initial stage of use to the formation of habits.

The use of actual behavioral data is also important so that research results do not only describe the desire to use technology but also the actual utilization of technology.

In the context of AI, research needs to distinguish between initial acceptance, continuous use, and the user's ability to evaluate technology output critically.

Alemayehu and Dhaliwal (2023) emphasized that each technology acceptance model has its advantages and limitations, so its selection needs to consider the characteristics of the technology and the research context.

Thus, the development of TAM should not only focus on increasing the number of variables but also on the accuracy of the conceptual framework in explaining the phenomena being studied.

C. Conclusion

The Technology Acceptance Model (TAM) introduced by Davis in 1989 has undergone significant development for more than three decades.

The initial model placed perceived usefulness and perceived ease of use as two important constructs in explaining technology acceptance. Both constructs became the foundation for various information systems studies and are still used in contemporary research.

The development of TAM2 in 2000 expanded the model by incorporating social influence and cognitive instrumental processes. Subsequently, TAM3 in 2008 paid greater attention to the factors that shape perceived ease of use and organizational intervention strategies.

On the other hand, the development of UTAUT in 2003 and UTAUT2 in 2012 showed an effort to integrate various technology acceptance theories into a more comprehensive framework.

Entering the era of digital technology and artificial intelligence, technology acceptance is increasingly influenced by diverse factors, including trust, risk, data security, transparency, user experience, and ethical considerations.

Research until 2026 shows that TAM is still relevant, including in studies of AI acceptance in education. However, the increasingly complex characteristics of AI require an approach that does not only focus on benefits and ease of use.

Thus, the main strength of TAM lies in its simplicity and its ability to be developed according to the characteristics of the technology.

In the future, TAM research needs to pay greater attention to actual use, continuous use, trust, risk, and human interaction with technology that is increasingly intelligent and autonomous.

This approach is expected to provide a more thorough understanding of technology acceptance while helping organizations, system developers, and users utilize technological developments effectively, responsibly, and sustainably.

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