Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research
Bibliographic record
Abstract
Studies of the relationship between purchase intentions and purchase behavior have ignored the possibility that the very act of measurement may inflate the association between intentions and behavior, a phenomenon called “self-generated validity.” In this research, the authors develop a latent model of the reactive effects of measurement that is applicable to intentions, attitude, or satisfaction data, and they show that this model can be estimated with a two-stage procedure. In the first stage, the authors use data from surveyed consumers to predict the presurvey latent purchase intentions of both surveyed and nonsurveyed consumers. In the second stage, they compare the strength of the association between the presurvey latent intentions and the postsurvey behavior across both groups. The authors find large and reliable self-generated validity effects across three diverse large-scale field studies. On average, the correlation between latent intentions and purchase behavior is 58% greater among surveyed consumers than it is among similar nonsurveyed consumers. One study also shows that the reactive effect of the measurement of purchase intentions is entirely mediated by self-generated validity and not by social norms, intention modification, or other measurement effects that are independent of presurvey latent intentions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.130 | 0.387 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".