Theory of reasoned action is a psychological model that explains how our attitudes and social influences shape our intentions and, in turn, our behavior.
It suggests that people do things because they intend to, and those intentions come from what they personally believe about the behavior and what they think others expect of them.
It’s a key theory for understanding decision-making in health, relationships, and everyday life.
Key Takeaways
- Definition: The Theory of Reasoned Action (TRA) explains how people’s intentions lead to their behaviors, emphasizing that actions are guided by rational thought rather than impulse. Developed by Martin Fishbein and Icek Ajzen, it focuses on the link between beliefs, intentions, and actions.
- Components: The theory highlights two main factors that shape behavioral intentions — personal attitudes toward the behavior and perceived social pressures, known as subjective norms. Together, these predict whether someone is likely to act.
- Process: TRA proposes that behavior starts with intention, which is formed through evaluating outcomes and considering others’ expectations. If both attitude and social approval align, the person is more likely to follow through.
- Applications: The model is widely used in health campaigns, marketing, and social psychology to predict actions such as exercising, quitting smoking, or purchasing products. It helps design interventions that change beliefs to shift behavior.
- Limitations: TRA assumes people have full control over their actions, which isn’t always true in real-life situations. This led to the development of the Theory of Planned Behavior, which adds perceived behavioral control as a third factor.
- Modern evidence: Meta-analyses since 2015 confirm the model but re-rank its parts: self-efficacy-like control and affective attitude now predict best, and intention still only moderately predicts behavior.
Formally, the TRA is a mathematical (expectancy-value) model as well as a psychological theory, closely related to the Theory of Planned Behavior.
Theoretical Origins
The TRA, along with its later version, the Theory of Planned Behavior (TPB), belongs to a group of ideas known as social cognition models.
These models suggest that our actions are guided by what we believe about a behavior, how we think others will view it, and the social situations we’re in.
This way of thinking is closely related to social learning theory, developed by psychologists like Albert Bandura. Thought and social environment shape behavior together.
Origins and Early Development
The Theory of Reasoned Action was created by Martin Fishbein and Icek Ajzen.
Their early publications appeared in the late 1960s and early 1970s, notably, Fishbein’s work in 1967 and joint papers by Ajzen and Fishbein around 1970.
These studies laid the foundation for modern research into attitudes, intentions, and decision-making.
The Attitude-Behavior Gap: LaPiere’s Study
Long before TRA existed, one classic field study had already exposed why broad attitudes fail to predict specific behavior. It is the problem TRA was built to solve.
- Aim: to test whether people’s stated attitudes toward a minority group actually predicted how they treated that group in person (LaPiere, 1934).
- Method: over two years, LaPiere traveled about 10,000 miles across the US with a Chinese couple, visiting 251 hotels and restaurants. Six months later he mailed those same places a questionnaire, and 128 replied.
- Results: in person the couple was refused service only once in 251 visits. Yet 92% of hotels and restaurants that replied said, in writing, they would not serve Chinese guests.
- Conclusion: a stated attitude and real behavior can point in completely different directions, so a broad attitude is a poor guide to one specific act.
The study has a flaw: the person who answered the letter may not have been the one who served the couple in person. Even so, it remains the classic demonstration of the attitude-behavior gap that TRA was built to close.
Commitment to Goals
A key idea behind TRA is that people are more likely to commit to a goal when they believe it is both worthwhile and achievable.
This idea comes from early motivation research, summarized in the simple formula:
Commitment = Value of the goal × Expectation of success
This idea captures how motivation and belief combine to shape our intentions and behavior.
How the Model Works
The Theory of Reasoned Action has four main terms: Belief, Attitude, Subjective Norms, and Intention (Fishbein and Ajzen, 1975):
1. Behavioral Intention
Your intention is basically your plan or motivation to do something – it’s the best single predictor of whether you’ll actually do it.
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It shows how much effort you’re willing to put in.
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The stronger your intention, the more likely you’ll act on it.
For example, if you’re determined to start exercising three times a week, that intention is a strong sign you probably will.
However, your intention depends on two things: how you feel about exercising (your attitude) and what others think about it (the subjective norm).
In short:
If people have time to think about their choices, their intentions are usually the best clue to what they’ll actually do.
2. Attitude Toward the Behavior
Your attitude toward a behavior is your personal evaluation of it – whether you think it’s good, bad, enjoyable, or worthwhile.
This attitude is shaped by two things:
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Outcome beliefs – what you think will happen if you do it (e.g., “If I quit smoking, I’ll be healthier” or “If I quit smoking, I might gain weight.”)
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Outcome value – how much those outcomes matter to you (e.g., “Being healthier is really important to me.”)
The theory shows that specific attitudes, such as “I plan to use birth control pills in the next two years,” predict behavior far better than vague, general ones. A vague attitude like “I support birth control” predicts little.
A classic study tracked married women’s birth-control attitudes and their actual pill use two years later (Davidson & Jaccard, 1979). The correlation rose from about r ≈ 0.08, for a general attitude, to about r ≈ 0.57 once the attitude matched the specific behavior.
3. Subjective Norms (Social Pressure)
Subjective norms refer to the social pressure you feel about a behavior – what you think the important people in your life believe you should do.
This depends on two parts:
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Beliefs about others’ opinions – what you think people close to you (like parents, friends, or partners) expect of you.
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Motivation to comply – how much you care about meeting those expectations.
For instance, someone might not personally like the idea of dieting. But if their friends and family strongly encourage it, and their opinions matter to them, they may still intend to diet.
Putting It All Together
The TRA suggests that your attitude (how you feel about the behavior) and your subjective norm (social expectations) combine to shape your intention, which in turn predicts your behavior.
Research shows:
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Attitudes and subjective norms are strongly linked to intentions (around 0.67 correlation).
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Intentions and actual behavior are moderately linked (around 0.53–0.62 correlation).
This means the theory explains intentions well. It does not guarantee follow-through. Time, opportunity, or confidence can still get in the way.
- Aim: to test TRA’s predictive validity across the accumulated literature, including cases the theory was not originally designed for (Sheppard, Hartwick, & Warshaw, 1988).
- Method: a meta-analysis of 87 studies (156 separate tests), computing average correlations for two paths: attitude and norm to intention, and intention to behavior.
- Results: attitude and subjective norm together predicted intention strongly (R ≈ 0.66). Intention predicted behavior only more modestly (R ≈ 0.53).
- Conclusion: TRA predicts well within, and somewhat beyond, its intended domain. The weak link is intention-to-behavior, not belief-to-intention.
As a meta-analysis, this evidence sits high in the evidence hierarchy. It still mostly pools cross-sectional, self-report correlations, so it shows association rather than proof of cause.
A Worked Example
Consider a student deciding whether to exercise three times a week for the next month. Each belief is rated from -3 (very unlikely or very bad) to +3 (very likely or very good).
Attitude toward the behavior: each belief is multiplied by its evaluation.
- "Will improve my fitness": +3 belief × +3 evaluation = +9
- "Will improve my mood": +2 × +2 = +4
- "Will take up study time": +2 × -2 = -4
Added together, the attitude score totals +9 points.
Subjective norm: each normative belief is multiplied by motivation to comply.
- "My partner thinks I should": +3 belief × +2 motivation = +6
- "My GP thinks I should": +3 × +1 = +3
Added together, the norm score also totals +9 points.
If attitude carries somewhat more weight than the norm for this person, the resulting intention score is strongly positive, raising the probability the person actually exercises. Weakening any single belief lowers the intention, and the predicted behavior, in turn.
Each of these terms is often treated by behavioral scientists as a factor in an equation intended to predict human behavior. As such, they are all related — factors that ultimately contribute to behavior.
Fishbein and Ajzen (1975), who originated the theory of reasoned action, created a diagram to describe the relationship between the main components of their model.
Attitudes, norms, and perceived control each lead to intentions — the readiness to do a behavior. These intentions can then lead, albeit imperfectly, to behaviors.
After Fishbein and Ajzen’s (1975) original work, other researchers tried to map the background factors more precisely. These factors shape the beliefs behind attitude, subjective norms, and perceived behavioral control.
These environmental factors could include the following:
- Personal factors: traits, locus of control, emotions, and health concerns.
- Demographic factors: Age, gender, race, ethnicity, education, income, and religion.
- Environmental factors: diagnosis, stress, and media exposure.
The Principle of Compatibility
Fishbein and Ajzen set one strict rule for TRA to work: the attitude measured and the behavior predicted must match in specificity. They called this the principle of compatibility.
Four elements must line up: action, target, context, and time (often shortened to TACT). An attitude toward “exercising at the gym three times a week this month” predicts that exact behavior far better than a general attitude toward “exercise.” That is because it matches on all four elements.
Most apparent failures of the theory are not really failures of the model. They come from mismatched measurement: pairing a broad attitude with a narrow behavior, or the reverse.
Health Applications
Peer Influence and Addiction
Neighbors, Foster, and Fossos (2013) outlined several models of addiction. One was built on the Theory of Reasoned Action and its successor, the Theory of Planned Behavior.
The norm they describe is narrower than usual. It concerns only what a person’s valued others think about that person’s own behavior, not behavior in general.
Consider an adolescent who believes people who matter to them would disapprove of smoking. Their intention to smoke should weaken. That should make them less likely to actually smoke.
This narrows TRA’s subjective norms twice over. They concern only people who matter to the individual, and only that person’s own behavior, not behavior in general (Neighbors, Foster, & Fossos, 2013).
This may seem like a small distinction. It matters in practice. The theory does not ask how much someone’s friends approve of smoking in general.
It asks how much they approve of that person’s own smoking in particular.
For instance, parents with moderate or favorable views on marijuana legalization may still disapprove of marijuana use by their own child (Neighbors, Foster, & Fossos, 2013).
Breakfast Consumption
One of the most popular applications of the theory of reasoned action is assessing whether a group will adopt a health behavior. Breakfast is a good example.
One study tested whether the theory could increase breakfast consumption among secondary-school students in Iran (Hosseini et al., 2015).
Researchers ran an information program to promote breakfast consumption, then had students complete a questionnaire before and after it. The questionnaire covered breakfast knowledge first, then the Theory of Reasoned Action’s own factors.
The results were clear. According to the data, subjective norms were the best predictor of breakfast consumption (Hosseini et al., 2015).
Whether someone believed others ate breakfast, and wanted them to, mattered more than personal attitude.
Fast Food Consumption
Marketers have also used the theory to explain many other behaviors: buying cars, banking, software, coupons, detergents, and soft drinks. Fast food is one example.
Bagozzi, Wong, Abe, and Bergami (2000) tested the theory across cultures, comparing fast-food consumption in the United States, Italy, China, and Japan.
Subjective norms shaped choices when people ate with friends, but not when they ate alone, regardless of whether the culture was individualistic or collectivist. Company mattered more than culture.
Attitudes, norms, and past behavior mattered more for Americans than for Italians, Chinese, or Japanese consumers.
Overall, Western consumers showed more explainable variance in behavior than Eastern consumers did (Bagozzi, Wong, Abe, & Bergami, 2000).
TRA’s predictive power reaches beyond these single studies. Meta-analyses confirm attitudes and subjective norms also predict intentions for condom use (Albarracín, Johnson, Fishbein, & Muellerleile, 2001) and physical activity (Hagger, Chatzisarantis, & Biddle, 2002). In both areas, intention again predicts actual behavior only moderately.
Technology Acceptance
TRA is the theoretical ancestor of the Technology Acceptance Model (TAM), a specialized model that predicts whether people will adopt new technology. It is one of the most widely used frameworks in information-systems research.
Davis (1989) adapted reasoned-action logic to technology use. He replaced TRA’s general belief structure with two specific beliefs: perceived usefulness and perceived ease of use.
- Aim: to build and test belief measures that carry reasoned action into technology adoption (Davis, 1989).
- Method: across two studies of 152 users, Davis tested two six-item scales and related them to current and predicted future use.
- Results: both scales were highly reliable (α ≈ 0.98 for usefulness, 0.94 for ease of use). Usefulness correlated more strongly with actual use (r ≈ 0.63 to 0.85) than ease of use did (r ≈ 0.45 to 0.59).
- Conclusion: usefulness carries the most weight, but both beliefs help carry the reasoned-action pathway into technology adoption.
The evidence here is cross-sectional and self-reported. Even so, careful scale validation made TAM one of the most replicated specializations of TRA.
Critical Evaluation
The TRA can be seen as a blueprint for a bridge that connects thoughts to actions.
The main critique is that this blueprint only works perfectly if the bridge is short (a simple, fully controllable action) and built in ideal conditions (perfectly rational deliberation).
Real-world complexity strains this blueprint. It lacks components like Perceived Behavioral Control, and supports like implementation planning, needed to reliably support complex human behavior.
Before turning to its limitations, TRA also has real strengths worth noting.
- Clear and testable: TRA specifies a small set of measurable factors, which makes it easy to test and falsify.
- Strong prediction of intention: Attitude and subjective norm jointly predict intention well, with correlations around 0.66 to 0.67 in meta-analysis (Sheppard, Hartwick, & Warshaw, 1988).
- Widely useful in practice: The model has guided real interventions in health promotion, marketing, and technology design by showing practitioners which beliefs to target.
- A productive foundation: By exposing its own limits, TRA led directly to the Theory of Planned Behavior and the wider reasoned action approach.
1. Limited to Volitional (Controllable) Behavior
The most significant limitation of the original TRA is its restriction to fully volitional behaviors. These are actions entirely under a person’s conscious control.
- Inapplicability to Non-Volitional Actions: Many important behaviors, like addiction or negotiating condom use, are not fully under a person’s control.
- Need for Expansion: Ajzen (1985, 1991) added Perceived Behavioral Control (PBC) to address this gap, creating the Theory of Planned Behavior (TPB).
2. The Intention-Behavior Gap
A consistent critique applied to the TRA (and often to its successor, the TPB) is the significant drop in predictive ability when moving from intention to actual behavior.
While the components of the TRA (attitudes and subjective norms) correlate strongly with intention, their correlation with the actual performance of behavior is noticeably weaker.
- Intention-Behavior Gap: Sheeran’s (2002) review documents this shortfall directly: attitudes and norms predict intention strongly, but intention predicts actual behavior only moderately (r ≈ 0.53–0.62).
- Experimental Evidence: Webb and Sheeran’s (2006) meta-analysis found that experimentally changing intention by a medium-to-large amount produced only a small-to-medium change in behavior.
- Need for "Action" Variables: The gap has spurred research into concepts like Implementation Intentions, concrete "when, where, and how" plans that help bridge intention to action.
3. Oversimplification of Human Decision-Making (The Rational Agent)
The TRA, as a social cognition model, assumes that individuals are generally rational decision makers who deliberately weigh the perceived implications of their actions before making a decision.
This strong assumption about human rationality has been subject to criticism in psychology generally, as well as specific limitations within the model:
- Neglect of Affective Variables: The model focuses on cognitive factors like beliefs and norms, often failing to capture emotional (affective) influences on behavior.
- Bounded Rationality: People try to weigh costs and benefits rationally, but limited memory, time, and attention prevent them from being fully rational decision-makers.
- Automatic, Non-Conscious Processes: Many behaviors happen quickly and unintentionally, outside conscious awareness. By focusing only on reasoned action, TRA overlooks this automatic side of behavior.
4. Methodological and Structural Issues
The way the TRA defines and measures its components also presents limitations:
- Lack of Causal Specificity: Support for the intention-behavior link relies heavily on cross-sectional studies, which cannot separate cause from effect over time.
- Neglect of Reciprocal Relationships: The TRA assumes attitudes shape behavior, but behavior can also reshape attitudes afterward. This reciprocal path is not part of the model.
- Issue of Specificity: A more specific attitude predicts a behavior better, but measuring subjective norms is trickier. It depends on whether researchers assess individually salient beliefs or generalized modal beliefs.
- Weakest Predictor: The subjective norm is usually TRA’s weakest predictor; attitude typically carries more weight (Armitage & Conner, 2001). Later models split norms into injunctive and descriptive types.
Contemporary Research
Since 2015 the reasoned-action tradition has kept being tested. The picture is now more precise, mostly through meta-analyses of the RAA (the model’s modern, more granular successor).
McEachan et al. (2016) ran the highest-value test.
- Aim: to compare the RAA’s paired subcomponents, such as experiential versus instrumental attitude and injunctive versus descriptive norm, as predictors of intention and behavior.
- Method: a meta-analysis of correlational tests of every subcomponent, with regressions predicting intention and behavior.
- Results: capacity (a self-efficacy-like facet) and experiential (affective) attitude showed the largest links to intention. Intention, capacity, experiential attitude, and descriptive norm all independently predicted behavior.
- Conclusion: the RAA’s constructs predict health behavior, but their weights are markedly unequal. The affective side of attitude and self-efficacy-like control do most of the work.
A second meta-analysis pushes further. Hagger, Polet, and Lintunen (2018) added past behavior to the model using meta-analytic structural equation modeling.
Including past behavior weakened the model’s paths, especially the direct intention-to-behavior link. This suggests habitual, non-conscious processes play a real part in health behavior.
Theory of Reasoned Action vs. Theory of Planned Behavior
The theory of reasoned action has some limitations. One of these is a significant risk of confounding between attitudes and norms.
This happens because attitudes can often be reframed as norms and norms as attitudes.
Take attitudes toward kale as an example. Someone’s positive view of kale may simply reflect a norm held by friends, family, doctors, and social media influencers who all believe kale is healthy.
The theory of planned behavior attempts to resolve these limitations through the idea of perceived behavior control (LaCaille, 2020).
The Theory of Reasoned Action (TRA) and the Theory of Planned Behavior (TPB) are closely related social cognition models used to explain and predict deliberate human behavior.
The TRA served as the foundational model, and the TPB represents its expansion and improvement to address specific limitations of the original theory.
The primary difference between the TRA and the TPB lies in the inclusion of a third, critical conceptual component in the TPB: Perceived Behavioral Control (PBC).
Here is a detailed comparison of the differences:
1. Scope of Behavior Explained
The most substantial difference is the type of behavior each theory is designed to explain:
| Feature | Theory of Reasoned Action (TRA) | Theory of Planned Behavior (TPB) |
|---|---|---|
| Behaviors Covered | Volitional Behavior, meaning actions that are entirely under the person’s control. | Non-Volitional Behavior and deliberate behaviors in general. |
| Limitation/Improvement | The TRA was limited because much behavior, such as addictive behaviors (e.g., smoking) or complex social negotiations (e.g., negotiating condom use), is not completely volitional. | The TPB was developed to improve the model’s ability to address non-volitional behavior. |
2. The Core Components (Determinants of Intention)
Both models assert that intention is the best predictor of deliberate behavior, and this intention is determined by predictive factors.
| TRA Component | TPB Component | Difference Explained |
|---|---|---|
| Attitude toward the Specific Behavior | Attitude toward the Specific Behavior | Both models retain this element, which is the person’s specific attitude toward the behavior, not their general attitude. |
| Subjective Norms | Subjective Norms | Both retain this element, which accounts for perceived social pressure—beliefs about how important others view the behavior. |
| (Not Included) | Perceived Behavioral Control (PBC) | The TPB includes PBC, which is defined as a person’s belief that they have control over their own behavior in certain situations, even when facing barriers. |
3. Role of Perceived Behavioral Control (PBC)
The introduction of Perceived Behavioral Control (PBC) is the defining feature of the TPB.
- Definition: PBC is how easy or difficult people believe a behavior will be. It resembles self-efficacy, the belief you can master a situation and produce a good outcome.
- Influence on Intention and Behavior: PBC directly influences intention, and can also predict behavior directly when perceptions of control are accurate.
- Empirical Success: Adding PBC made the TPB more successful than the TRA at predicting behavioral change; PBC correlates with intention at around 0.71.
4. Predictive Power
While both models show strong predictive power for intentions, the TPB offers an incremental improvement in predictive success due to the inclusion of PBC:
- Intention Prediction: Attitudes and social norms correlate highly with intention under the TRA (about 0.67); the TPB’s added variables explain 40-50% of intention’s variance.
- Behavior Prediction: TRA’s intention-behavior correlation runs 0.53-0.62; TPB variables explain 19-38% of behavior’s variance. Both models predict intention far better than behavior itself, a gap that motivates concepts like Implementation Intentions.
References
Albarracín, D., Johnson, B. T., Fishbein, M., & Muellerleile, P. A. (2001). Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin, 127(1), 142–161.
Bagozzi, R. P., Wong, N., Abe, S., & Bergami, M. (2000). Cultural and situational contingencies and the theory of reasoned action: Application to fast food restaurant consumption. Journal of consumer psychology, 9 (2), 97-106.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Fishbein, M. (1979). A theory of reasoned action: some applications and implications.
Hagger, M. S., Chatzisarantis, N. L. D., & Biddle, S. J. H. (2002). A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport and Exercise Psychology, 24(1), 3–32.
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