A framework for optimizing the cost and performance of concept testing
Document Type
Journal article
Source Publication
Journal of Marketing Management
Publication Date
7-1-2012
Volume
28
Issue
7/8
First Page
1000
Last Page
1013
Publisher
Routledge
Keywords
concept testing, generalisability theory, cost optimisation
Abstract
To anticipate the likely market demand better and identify the best customers to target with potential new products early in their development, concept tests need to provide adequate data quality for the objectives of measurement. Generalisability theory provides a framework where a generalisability study can be conducted to identify each possible aspect of a measurement as a factor that may be a potential source of variability. When a limited budget is available, optimising measurement designs involves a trade-off between the accuracy of the data (i.e. generalisability coefficients) and cost considerations. Building on previous and ongoing research, this study presents a multivariate optimisation procedure to achieve the most cost-efficient measurement design under the pre-specified generalisability coefficient constraint. We used the available online concept testing data to illustrate how to optimise the measurement cost by sampling along the facets that contribute to the total error variance in different iso-generalisability designs. The findings may help to facilitate decision making for the sampling of respondents, concepts, items, and occasions in the design of concept test. However, the optimisation framework may be applied more generally to improve the effectiveness and efficiency of other customer tests in marketing.
DOI
10.1080/0267257X.2011.615336
Print ISSN
0267257X
E-ISSN
14721376
Publisher Statement
Copyright © 2012 Westburn Publishers Ltd
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Peng, L., Li, C., & Wan, X. (2012). A framework for optimizing the cost and performance of concept testing. Journal of Marketing Management, 28(7/8), 1000-1013. doi: 10.1080/0267257X.2011.615336