Increasing sample size compensates for data problems in segmentation studies

Dolnicar, Sara, Grun, Bettina and Leisch, Friedrich (2016) Increasing sample size compensates for data problems in segmentation studies. Journal of Business Research, 69 2: 992-999. doi:10.1016/j.jbusres.2015.09.004

Author Dolnicar, Sara
Grun, Bettina
Leisch, Friedrich
Title Increasing sample size compensates for data problems in segmentation studies
Journal name Journal of Business Research   Check publisher's open access policy
ISSN 0148-2963
Publication date 2016-02-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.jbusres.2015.09.004
Open Access Status DOI
Volume 69
Issue 2
Start page 992
End page 999
Total pages 8
Place of publication New York, NY, United States
Publisher Elsevier
Language eng
Formatted abstract
Survey data frequently serve as the basis for market segmentation studies. Survey data, however, are prone to a range of biases. Little is known about the effects of such biases on the quality of data-driven market segmentation solutions. This study uses artificial data sets of known structure to study the effects of data problems on segment recovery. Some of the data problems under study are partially under the control of market research companies, some are outside their control. Results indicate that (1) insufficient sample sizes lead to suboptimal segmentation solutions; (2) biases in survey data have a strong negative effect on segment recovery; (3) increasing the sample size can compensate for some biases; (4) the effect of sample size increase on segment recovery demonstrates decreasing marginal returns; and—for highly detrimental biases—(5) improvement in segment recovery at high sample size levels occurs only if additional data is free of bias.
Keyword Market segmentation
Response bias
Sample size
Survey data
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
UQ Business School Publications
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Citation counts: TR Web of Science Citation Count  Cited 1 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 3 times in Scopus Article | Citations
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