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The Financial Information Content of Perceived Quality
Abstract
The authors investigate whether movement in a firm's stock price, that is, a measure of firm value, is associated with information contained in perceived quality measures. In a model that also allows for the effect of economywide factors and a firm's return on investment, they find a positive relationship between stock return and changes in quality perceptions. These results imply that the quality measure contains information, incremental to that reflected by current-term accounting measures, about future-term business performance. They suggest that managers should convey information to the stock market, such as the brand's quality image, useful in depicting the long-term prospects of the business. By doing so, the stock market will rely less on short-term measures of business performance, and managers will be freer to undertake strategies necessary for ensuring the long-term viability of their firms.
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Anderson, E. W., & Fornell, C. (2000) · Total Quality Management · doi:10.1080/09544120050135425 · Cited by 553
Path analysis of perceived service quality, satisfaction and loyalty in Greek insurance
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Metadata
- Authors
- Aaker, D. A., & Jacobson, R.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 1994-05
- Volume
- 31
- Issue
- 2
- Pages
- 191-201
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W1975908777
- Cited by
- 788
- References
- 0
- FWCI
- 41.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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- Aaker, D. A., & Jacobson, R. (1994). The Financial Information Content of Perceived Quality. Journal of Marketing Research. https://doi.org/10.1177/002224379403100204
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.