The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Do Intentions Really Predict Behavior? Self-Generated Validity Effects in Survey Research
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Mittal, V., & Kamakura, W. A. (2001) · Journal of Marketing Research · doi:10.1509/jmkr.38.1.131.18832 · Cited by 2,256
Bolton, R. N. (1998) · Marketing Science · doi:10.1287/mksc.17.1.45 · Cited by 1,948
Cited by
Customer engagement: exploring customer relationships beyond purchase.
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679200201 · Cited by 2,089
Gray Shades of Green: Causes and Consequences of Green Skepticism
Leonidou, C. N., & Skarmeas, D. (2017) · Journal of Business Ethics · doi:10.1007/s10551-015-2829-4 · Cited by 589
The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance
Morgan, N. A., & Rego, L. L. (2006) · Marketing Science · doi:10.1287/mksc.1050.0180 · Cited by 505
Impact of hotel website quality on online booking intentions: eTrust as a mediator
Wang, L., Law, R., Guillet, B. D., Hung, K., & Fong, D. K. C. (2015) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2015.03.012 · Cited by 347
Customer satisfaction and loyalty in service: Two concepts, four constructs, several relationships
Bodet, G. (2008) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2007.11.004 · Cited by 299
A Quantum Question Order Model Supported by Empirical Tests of an A Priori and Precise Prediction
Wang, Z., & Busemeyer, J. R. (2013) · Topics in Cognitive Science · doi:10.1111/tops.12040 · Cited by 220
Microfoundations of Corporate Social Responsibility and Irresponsibility
Shea, C. T., & Hawn, O. V. (2019) · Academy of Management Journal · doi:10.5465/amj.2014.0795 · Cited by 209
A comprehensive model of customer trust in two retail stores
Guenzi, P., Johnson, M. D., & Castaldo, S. (2009) · Journal of Service Management · doi:10.1108/09564230910964408 · Cited by 191
Okazaki, S., Taylor, C. R., Vargas, P., & Henseler, J. (2019) · International Marketing Review · doi:10.1108/imr-04-2018-0158 · Cited by 26
Tudoran, A. A., & Olsen, S. O. (2017) · Journal of Consumer Behaviour · doi:10.1002/cb.1633 · Cited by 13
“Although intention is a good indicator of behavior in frequent decisions such as those involving grocery items (Chandon et al., 2005) and incrementally new products (Alexander et al., 2009), the results may be even more relevant when applied to actual purchases of new products.”
BackgroundExplaining customers’ financial service choice with loyalty and cross-buying behaviour
Estrella-Ramón, A. (2017) · Journal of Services Marketing · doi:10.1108/jsm-05-2015-0189 · Cited by 9
Metadata
- Authors
- Chandon, P., Morwitz, V. G., & Reinartz, W. J.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2005-04
- Volume
- 69
- Issue
- 2
- Pages
- 1-14
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W2116308420
- Cited by
- 656
- References
- 2
- FWCI
- 79.89
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.