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Predicting and Explaining Intentions and Behavior: How Well Are We Doing?
Author(s) -
Sutton Stephen
Publication year - 1998
Publication title -
journal of applied social psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.822
H-Index - 111
eISSN - 1559-1816
pISSN - 0021-9029
DOI - 10.1111/j.1559-1816.1998.tb01679.x
Subject(s) - variance (accounting) , theory of planned behavior , theory of reasoned action , psychology , explained variation , social psychology , measure (data warehouse) , econometrics , action (physics) , statistics , mathematics , control (management) , computer science , economics , artificial intelligence , data mining , physics , accounting , quantum mechanics
Meta‐analyses of research using the theory of reasoned action (TRA) and the theory of planned behavior (TPB) show that these models explain on average between 40% and 50% of the variance in intention, and between 19% and 38% of the variance in behavior. This paper evaluates the performance of these models in predicting and explaining intentions and behavior. It discusses the distinction between prediction and explanation, the different standards of comparison against which predictive performance can be judged, the use of percentage of variance explained as a measure of effect size, and presents 9 reasons why the models do not always predict as well as we would like them to do.