On the reliability and predictive validity of purchase intention measures

By Manohar U. Kalwani, A. Silk. 2011

142 citations recorded by the source corpus.

This paper reports some further analyses and applications of Morrison's model of the predictive relationship between measures of intentions and subsequent purchasing behavior. A review of published studies bearing on the threats to predictive validity of intention scales represented in Morrison's model is presented. Findings from a test-retest study of intention ratings for concept stimuli are shown to be consistent with the levels of reliability expected under the model's assumptions of beta binomial distributed scores. Evidence of the predictive validity of intention measures is found in a re-analysis of several sets of relevant data but a different form of predictive relationship is shown to hold for generic durable goods as compared to branded packaged goods. Whereas a linear relationship is supported in the case of durable goods, the presence of a threshold phenomenon in the branded packaged goods data suggests the use of a piecewise linear model. There is reason to believe that the nature and sources of systematic error present in intentions ratings are different for these two types of purchases.

Subfields: Applied Psychology, Cognitive Neuroscience, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/1b93ed0b81326722ca091026dfa3413f0e07513f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Applied Psychology

  1. Jody L. Crosno
  2. Traci H. Freling
  3. D. Henard
  4. Vanessa Apaolaza-Ibáñez
  5. Vicki G. Morwitz
  6. Patrick Hartmann

In Cognitive Neuroscience

  1. Jody L. Crosno
  2. Traci H. Freling
  3. D. Henard
  4. Glen L. Urban
  5. Vicki G. Morwitz

In General Decision Sciences

  1. Jody L. Crosno
  2. Traci H. Freling
  3. D. Henard
  4. Glen L. Urban
  5. Vicki G. Morwitz
  6. Patrick Hartmann

Citations in the corpus