Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research 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.
onsumers’ self-reported intentions have been used widely in academic and commercial research because they represent easy-to-collect proxies of behavior. For example, most academic studies of satisfaction use consumers’ intentions to repurchase as the criterion variable (for an exception, see Bolton 1998), and most companies rely on consumers’ purchase intentions to forecast their adoption of new products or the repeat purchase of existing ones (Jamieson and Bass 1989). However, it is well known that consumers’ self-reported purchase intentions do not perfectly predict their future purchase behavior, nor do these differences cancel each other out when intentions and behavior are aggregated across consumers. In a metaanalysis of 87 behaviors, Sheppard, Hartwick, and Warshaw (1988) find a frequency-weighted average correlation between intentions and behavior of .53, with wide varia-
Pierre Chandon is Assistant Professor of Marketing, INSEAD, and currently he is Visiting Assistant Professor of Marketing, Kellogg School of Management, Northwestern University (e-mail: pierre.chandon@insead. edu). Vicki G. Morwitz is Associate Professor of Marketing and Robert Stansky Faculty Research Fellow, Stern School of Business, New York University (e-mail: email@example.com). Werner J. Reinartz is Associate Professor of Marketing, INSEAD (e-mail: werner.reinartz@insead. edu). The authors acknowledge the helpful input of the anonymous JM reviewers, John Lynch, Gilles Laurent, and Albert Bemmaor, as well as those who participated when the authors presented this research at INSEAD; at the Association for Consumer Research Conference, Portland; at the Marketing Science Conference, College Park, Md.; and at the ESSEC-HEC-INSEAD conference. In addition, the authors thank the French online grocer Prodigy Services Company and Allison Fisher for providing data and INSEAD for its financial assistance.
tions across measures of intentions and types of behavior (for a review, see Morwitz 2001). To improve the ability to forecast behavior from intentions, researchers have tested alternative scales (Reichheld 2003; Wansink and Ray 2000) and have developed models that account for biases in the measurement and reporting of intentions, the heterogeneity across customers, changes in true intentions between the time of the survey and the time of the behavior, and the stochastic and nonlinear nature of the relationship between intentions and behavior (Bemmaor 1995; Hsiao, Sun, and...