Before the Technology Acceptance Model (TAM) provided a focused lens for information systems research, the study of why people choose to use a new technology was grounded in a broader and more diverse set of foundational theories. These “grandparent” models, drawn from sociology, social psychology, and management, provided the essential intellectual DNA for everything that followed. To understand the evolution of adoption research, we must first appreciate this bedrock—the core constructs and perspectives that early researchers adapted to the specific challenge of technology use.
This article analyzes the key pre-TAM theories, explaining their core constructs and how they collectively laid the groundwork for the more specialized models that now define the field.
Diffusion of Innovations (DOI) Theory
Originating from a sociological perspective, Everett Rogers’ Diffusion of Innovations Theory [1] is one of the oldest and most influential theories in the field. It is not focused on a single adoption decision but on the macro-level process of how an innovation spreads through a social system over time. DOI describes the process by which individuals and organizations adopt new ideas, products, or practices.
Its most enduring contribution to the field is the identification of five key perceived attributes of an innovation that influence its rate of adoption:
- Relative Advantage: The degree to which an innovation is perceived as better than the idea it supersedes.
- Compatibility: The degree to which an innovation is perceived as consistent with existing values, past experiences, and needs.
- Complexity: The degree to which an innovation is perceived as difficult to understand and use.
- Trialability: The degree to which an innovation may be experimented with on a limited basis.
- Observability: The degree to which the results of an innovation are visible to others.
Beyond these attributes, DOI is renowned for categorizing adopters based on their propensity to adopt innovations. When plotted over time, these categories form a bell curve representing the percentage of a population, and an S-shaped curve representing the cumulative adoption. The categories are:
- Innovators (2.5%): Venturesome risk-takers who are the very first to adopt an innovation.
- Early Adopters (13.5%): Respected social leaders and opinion makers who adopt early but with more discretion than innovators.
- Early Majority (34%): Deliberate individuals who adopt new ideas just before the average member of a system.
- Late Majority (34%): Skeptical individuals who adopt an innovation only after a majority of people have tried it.
- Laggards (16%): Traditionalists who are the last to adopt an innovation, often with suspicion.
These five factors and the adopter categories provided a rich, context-specific vocabulary that adoption researchers would later formalize. While DOI theory provides a lens on how innovations spread, other foundational theories from social psychology focused on the cognitive processes of the individual adopter.
Theory of Reasoned Action (TRA)
Developed by Fishbein and Ajzen [2], the Theory of Reasoned Action was a landmark general theory in social psychology designed to explain the specifics of individual behavior. Its central premise is that the most immediate predictor of a person’s behavior is their behavioral intention—their subjective probability that they will perform the behavior in question.
This intention, in turn, is determined by two key factors:
- Attitude Toward the Behavior: The individual’s positive or negative feelings about performing the behavior. This is shaped by their behavioral beliefs (e.g., “Using this new software will make me more productive”).
- Subjective Norm: The individual’s perception of the social pressure to perform or not perform the behavior. This is shaped by their normative beliefs (e.g., “My manager and respected colleagues think I should use this new software”).
TRA’s primary contribution was its elegant causal chain: beliefs influence attitudes and subjective norms, which together shape intentions, which in turn lead to behavior. It established the critical role of intention as a mediator and provided the basic two-pronged structure—personal attitude and social influence—that would become a staple of future adoption models.
Theory of Planned Behavior (TPB)
A decade and a half after TRA, Ajzen extended his own model to address a significant limitation: its assumption that behaviors are under a person’s complete volitional control. The resulting Theory of Planned Behavior [3] kept the core structure of TRA but added a critical third determinant of behavioral intention:
- Perceived Behavioral Control (PBC): An individual’s perception of the ease or difficulty of performing the behavior. This construct accounts for the presence or absence of requisite resources and opportunities (e.g., “I have the time, training, and technical support needed to learn this system”).
PBC influences behavior directly (if one lacks control, intention alone is insufficient) and indirectly by influencing behavioral intention (one is less likely to intend to do something they believe is too difficult). This addition was profoundly important for technology adoption research, as it formally introduced the concepts of self-efficacy and facilitating conditions—barriers and enablers—into the dominant theoretical model.
Social Cognitive Theory (SCT)
While TRA and TPB focused on a linear path from belief to behavior, Bandura’s Social Cognitive Theory [4] proposed a more dynamic model of triadic reciprocal determinism. SCT posits that behavior is the result of a continuous interaction between three factors:
- Personal Factors: An individual’s cognitive, affective, and biological attributes (e.g., self-efficacy, knowledge).
- Environmental Factors: The external social and physical environment (e.g., social norms, access to resources).
- Behavior: The individual’s actions.
A key contribution of SCT to technology adoption was its emphasis on self-efficacy—an individual’s belief in their own capability to execute the actions required to achieve a specific goal. This concept is a more refined version of TPB’s Perceived Behavioral Control and became a cornerstone construct in many subsequent models, explaining why two individuals with the same objective skills might exhibit vastly different adoption behaviors.
Motivational Model (MM)
While many theories focused on instrumental drivers (i.e., achieving a subsequent outcome), the Motivational Model, as applied by Davis, Bagozzi, and Warshaw [5], highlighted a different set of drivers. Drawing from Self-Determination Theory, the MM argues that behavior is also influenced by:
- Extrinsic Motivation: The drive to perform a behavior because it is perceived to be instrumental in achieving valued outcomes that are distinct from the activity itself (e.g., “Using this system will get me a raise”). This aligns closely with the concept of usefulness.
- Intrinsic Motivation: The drive to perform a behavior for its own sake, simply for the pleasure and satisfaction derived from the process (e.g., “Using this system is fun and engaging”).
The MM’s crucial contribution was the formal introduction of intrinsic motivation, or perceived enjoyment, as a direct determinant of technology use. This helped explain why people might use technologies that are not strictly necessary for their job, laying the groundwork for understanding adoption in hedonic or voluntary contexts.
Model of PC Utilization (MPCU)
The Model of PC Utilization [6] represents a key early attempt to move from general theories to a specific model of information systems use. Thompson, Higgins, and Howell synthesized constructs from several of the theories mentioned above, including DOI and TPB, to create a more comprehensive model. It posited that PC utilization was influenced by factors such as job-fit (similar to relative advantage), complexity, long-term consequences, affect toward use (similar to attitude), and social factors.
The MPCU is significant not for its dominance—it was soon overshadowed—but for its role as a conceptual bridge. It demonstrated the value of integrating multiple theoretical perspectives and tailoring them to the specific context of computer use, setting the stage for the parsimonious and powerful model that would come to define the field: the Technology Acceptance Model.
Series Navigation
- Article 1: Branch Introduction – The User’s Journey
- Article 1.1: The Bedrock – Foundational Theories That Shaped Tech Acceptance
- Article 1.2: The Game Changer – A Deep Dive into the Technology Acceptance Model (TAM)
- Article 1.3: Expanding the Classic – The Evolution to TAM 2, TAM 3, and C-TAM-TPB
- Article 1.4: The Grand Unification – The Unified Theory of Acceptance and Use of Technology (UTAUT)
- Article 1.5: Beyond the Office – UTAUT2, Consumer Context, and Modern Syntheses
- Article 1.6: Context is King – Specialized Individual Adoption Models
- Article 1.7: Are you Ready? The Role of Technology Readiness (TRI & TRAM)
References
[1] Rogers, E. M. (1962). Diffusion of Innovations. Free Press of Glencoe.
[2] Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
[3] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
[4] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
[5] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
[6] Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443