The Intention-Behaviour Gap and What It Means for Measurement

Most commercial behavioral research measures intention. Purchase intent scales, likelihood-to-recommend questions, concept tests, stated preference surveys. Intention is easy to collect and it feels like a forecast. It is a weak one, and the weakness is structural rather than a matter of sample quality.

The size of the gap

Webb and Sheeran (2006) conducted a meta-analysis of experimental studies that successfully changed intentions and asked what happened to behavior. A medium-to-large change in intention produced only a small-to-medium change in behavior. Intention change is necessary for some behavior change and far from sufficient.

Sheeran and Webb (2016) reviewed the mechanisms behind the gap: competing goals, absent opportunity, failures of self-regulation, and — importantly — the fact that intentions are formed in a state that does not resemble the state in which the behavior must occur.

Why the state mismatch matters

A person answering a survey is calm, unhurried and cognitively unloaded. The same person making the actual decision is distracted, time-pressured, and subject to social and situational forces the survey did not contain.

Loewenstein's work on the hot-cold empathy gap describes the general problem: people in a cold state systematically mispredict their behavior in a hot state, and vice versa. Stated intention is a cold-state prediction of hot-state behavior, which is exactly the case where the prediction is least reliable.

The specific case of privacy and ethics questions

The gap widens for socially loaded topics. Stated willingness to pay more for sustainable products, stated privacy concern, stated intolerance of advertising — all show large discrepancies with behavior, partly through social desirability and partly because the trade-off is absent from the question. Surveys rarely make the respondent give something up.

This is why the privacy paradox is better understood as a measurement artefact than as evidence of inconsistent consumers.

What improves prediction

Several design choices narrow the gap measurably. Making the question concrete and situational rather than general. Including the actual trade-off — price, time, alternatives foregone. Asking about recent past behavior, which predicts future behavior better than intention does. And using implementation-intention formats that specify when, where and how, which have shown reliable effects on follow-through (Gollwitzer, 1999).

The stronger option is to measure behavior directly. Field experiments, small live pilots and behavioral proxies cost more per observation and are worth it for consequential decisions, because they sample the state the behavior will actually occur in.

The organizational problem

Intention data has an institutional advantage that has nothing to do with validity: it is fast, cheap, quantitative and arrives before the decision has to be made. Behavioral data is slow, expensive and often arrives after commitments are set.

Research functions that do not acknowledge this pressure will keep producing intention data and keep being surprised. The realistic response is to be explicit about what a given measure can support — intention data is adequate for screening and directional comparison, and inadequate for forecasting volumes or sizing an opportunity.

What this means in practice

Label every study by what it measured. Where a decision depends on how many people will actually do something, find a way to observe behavior at small scale before committing, and treat intent scores as a filter rather than a forecast.

References

1. Webb, T. L., & Sheeran, P. (2006). Does changing behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249–268.
2. Sheeran, P., & Webb, T. L. (2016). The intention-behavior gap. Social and Personality Psychology Compass, 10(9), 503–518.
3. Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493–503.
4. Loewenstein, G. (2005). Hot-cold empathy gaps and medical decision making. Health Psychology, 24(4S), S49–S56.
5. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.