A new approach to validate customer satisfaction multi-item measures: the case of shopping goods. Issue 4 (8th August 2018)

• Record Type: Journal Article
• Authors: Bassi, Francesca
• Is Part Of: International review of retail, distribution and consumer research. Volume 28:Issue 4(2018)
• Page Start: 380
• Page End: 396
• Publication Date: 2018-08-08
• Subjects: Measurement scales -- satisfaction -- shopping goods -- validity -- reliability; Retail trade -- Periodicals; Physical distribution of goods -- Periodicals; Consumers -- Research -- Periodicals; 381.1
• DOI: 10.1080/09593969.2018.1470996
• Languages: English
• ISSNs: 0959-3969

Abstract

Abstract: In the field of marketing many objects of interest exist that are not directly observable, nevertheless they can be measured through multi-item measurement scales. These instruments are extremely useful and their importance requires accurate development and validation procedures. The traditional marketing literature highlights specific protocols along with statistical instruments and techniques to be used for achieving this goal. For example, correlation coefficients, univariate and multivariate analysis of variance and factorial analysis are widely employed with this purpose. However, these statistical tools are suitable for metric variables but they are adopted even when the nature of the observed variables is different, as it often occurs, since in many cases the items of which the scale is made up are ordinal. Latent class analysis takes explicitly into account the ordinal nature of the observed variables and also the fact that the object of interest is unobservable. The aim of this paper is to show how latent class analysis can improve the procedures for developing and validating a multi-item measurement scale for measuring customer satisfaction with reference to a shopping good, that is a good characterized by a high level of involvement and an emotional learning, linked to the lifestyle of the customer. The latent class approach explicitly considers both the ordinal nature of the observed variables and the fact that the construct to be measured is not directly observable. Applying appropriate latent class models, important features such as scale dimensionality, criterion and construct validity can be better assessed while evaluating the scale.