The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Increasing sample size compensates for data problems in segmentation studies
Open accessAbstract
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.
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“Insufficient sample size and other data-quality issues can influence the validity of segmentation solutions and thereby misguide the practical implications for commercial purposes (Dolnicar & Grün, 2017;Dolnicar, Grün, & Leisch, 2016).”
From Relationships between functional food consumption and individual traits and values: A segmentation approach (2021) · cites this paper as Dolnicar et al., 2016
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R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Objective Criteria for the Evaluation of Clustering Methods
Rand, W. M. (1971) · Journal of the American Statistical Association · doi:10.1080/01621459.1971.10482356 · Cited by 6,493
Measurement and Control of Response Bias
Paulhus, D. L. (1991) · Elsevier eBooks · doi:10.1016/b978-0-12-590241-0.50006-x · Cited by 3,867
The C-OAR-SE procedure for scale development in marketing
Rossiter, J. R. (2002) · International Journal of Research in Marketing · doi:10.1016/s0167-8116(02)00097-6 · Cited by 2,478
Krosnick, J. A. (1999) · Annual Review of Psychology · doi:10.1146/annurev.psych.50.1.537 · Cited by 1,852
Measures of Personality and Social Psychological Attitudes:
Jordan, R. P. (1992) · Behavioral & Social Sciences Librarian · doi:10.1300/j103v11n02_05 · Cited by 1,359
Response Styles in Marketing Research: A Cross-National Investigation
Baumgartner, H., & Steenkamp, J.-B. E. M. (2001) · Journal of Marketing Research · doi:10.1509/jmkr.38.2.143.18840 · Cited by 1,262
An Examination of the Effect of Six Types of Error Perturbation on Fifteen Clustering Algorithms
Milligan, G. W. (1980) · Psychometrika · doi:10.1007/bf02293907 · Cited by 1,176
Natural Scales in Geographical PatternsPreprint
Menezes, T., & Roth, C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01036 · Cited by 1,126
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Metadata
- Authors
- Dolnicar, S., Grün, B., & Leisch, F.
- Affiliations
- Johannes Kepler University of Linz (AT)
- BOKU University (AT)
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2016-02
- Volume
- 69
- Issue
- 2
- Pages
- 992-999
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2177427102
- Cited by
- 131
- References
- 72
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 20 · 2023: 17 · 2022: 12 · 2021: 14 · 2020: 14 · 2019: 17 · 2018: 12 · 2017: 6 · 2016: 3
- FWCI
- 17.37
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Business Research (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- WU Research (submittedVersion)
- Funders
- Australian Research Council (ARC)
- Austrian Science Fund (FWF) (DP110101347, LX0881890, V170-N18)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Physical Sciences › Mathematics › Statistics and Probability › Survey Sampling and Estimation Techniques
- Keywords
- market segmentation, sample size, data quality, artificial datasets, survey sampling, data-driven segmentation
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