Bias and variability in purchase intention scales
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Although purchase intention scales are widely used, relatively little is known about bias and variability in the estimated purchase proportions. Psychometric techniques have been developed to correct for such problems, and analytical approaches have shown that most predictive errors can be explained as probabilistic variability. However, there is a lack of systematic empirical work in the area. We address this problem using two meta-analyses of published work. Our results show that purchase intention scales are empirically unbiased. Furthermore, the variability is much less than previously assumed. This finding improves the confidence researchers can have in the use of such scales. Interestingly, purchase probability scales performed even better than purchase intention scales. The greater precision of probability scales suggests that they may be more useful both as direct measures of likely behavior and as dependent variables in consumer behavior research.
KeywordsStated purchase probability Purchase intention Meta-analysis Juster scale
Thanks to the Academy of Business Research at Massey University for financial support, to Dr. Mike Brennan for assistance with the stated purchase probability literature, to Associate Professor Ron Garland, members of the Ehrenberg-Institute, Professor Pierre Chandon and the editor and three anonymous JAMS reviewers for helpful comments.
- Armstrong, J. S. (1985). Long range forecasting: From crystal ball to computer. New York: Wiley.Google Scholar
- Bird, M., & Ehrenberg, A. (1966). Intentions-to-buy and claimed brand usage. Operational Research Quarterly, 17(1), 27–46.Google Scholar
- Byrnes, J. (1964). An experiment in the measure of consumer intentions to purchase. Proceedings of the Business and Economic Statistics Section, American Statistical Association. In Juster, Thomas F. (1966). Consumer buying intentions and purchase probability: An experiment in survey design. Journal of the American Statistical Association, 61(September), 658–696.Google Scholar
- Cooper, H., & Hedges, L. V. (Eds.) (1994). The handbook of research synthesis. New York: Russell Sage Foundation.Google Scholar
- Day, D., Gan, B., Gendall, P., & Esslemont, D. (1991). Predicting purchase behaviour. Marketing Bulletin, 2, 18–30.Google Scholar
- Hsiao, C., Sun, B., & Morwitz, V. G. (2002). The role of stated intentions in new product purchase forecasting. Econometric Models in Marketing, 16, 11–28.Google Scholar
- Kalwani, M. U., & Silk, A. J. (1982). On the reliability and predictive validity of purchase intention measures. Marketing Science, 1(3), 243–286.Google Scholar
- Lipsey, M. W., & Wilson, D. B. (2001). Practical meta-analysis. Thousand Oaks, CA: Sage.Google Scholar
- Marder, E. (1997). The laws of choice: Predicting customer behavior. New York: Simon and Schuster.Google Scholar
- Morwitz, V. G. (2001). Methods for forecasting from intentions data. In J. S. Armstrong (Ed.), Principles of forecasting: A handbook for researchers and practitioners (pp. 33–56). Norwell, MA: Kluwer.Google Scholar
- Pickering, J. F., & Isherwood, B. C. (1974). Purchase probabilities and consumer durable buying behaviour. Journal of the Market Research Society, 16(3), 203–226.Google Scholar