The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
De-targeting to signal quality
Open accessAbstract
Abstract It is important for firms to signal the high quality of their products to consumers in experience goods markets. Conventional wisdom suggests that a high price can be a signal of high quality. However, we argue that the role of price in signaling quality could be weakened when firms resort to the intensive use of targeting in advertising, which could attenuate the informational content of a high price. As a consequence, a high quality firm needs to distort its price more to signal its quality. However, when different levels of targeting are available, a high quality firm may find it optimal to signal its quality with a lower level of targeting.
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“Various technologies and analytics have been used for targeting, such as search engines using keywords searched and browsing history to target search consumers, and social media platforms using interests, content, and connections to target social media consumers (Liu 2020).”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Liu 2020
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Citations
Cites
Signaling Games and Stable Equilibria
Cho, I.-K., & Kreps, D. M. (1987) · The Quarterly Journal of Economics · doi:10.2307/1885060 · Cited by 3,201
Price and Advertising Signals of Product Quality
Milgrom, P., & Roberts, J. (1986) · Journal of Political Economy · doi:10.1086/261408 · Cited by 2,271
High and Declining Prices Signal Product QualityPreprint
Bagwell, K. W., & Riordan, M. (1988) · RePEc: Research Papers in Economics · Cited by 647
Iyer, G. V., Soberman, D. A., & Villas‐Boas, J. M. (2005) · Marketing Science · doi:10.1287/mksc.1050.0117 · Cited by 520
Targeting in advertising markets: implications for offline versus online media
Bergemann, D., & Bonatti, A. (2011) · The RAND Journal of Economics · doi:10.1111/j.1756-2171.2011.00143.x · Cited by 258
Raising Awareness and Signaling Quality to Uninformed Consumers: A Price-Advertising Model
Zhao, H. (2000) · Marketing Science · doi:10.1287/mksc.19.4.390.11788 · Cited by 171
Online Advertising: Heterogeneity and Conflation in Market Design
Levin, J., & Milgrom, P. (2010) · American Economic Review · doi:10.1257/aer.100.2.603 · Cited by 159
Competitive Targeted Advertising with Price Discrimination
Esteves, R. B., & Resende, J. (2016) · Marketing Science · doi:10.1287/mksc.2015.0967 · Cited by 136
Online advertising and privacy
de Cornière, A., & de Nijs, R. (2016) · The RAND Journal of Economics · doi:10.1111/1756-2171.12118 · Cited by 135
The Impact of Targeting Technology on Advertising Markets and Media Competition
Athey, S. C., & Gans, J. S. (2010) · American Economic Review · doi:10.1257/aer.100.2.608 · Cited by 119
(De)marketing to Manage Consumer Quality Inferences
Miklós‐Thal, J., & Zhang, J. (2012) · Journal of Marketing Research · doi:10.1509/jmr.11.0432 · Cited by 111
Targeted advertising as a signal
Anand, B. N., & Shachar, R. (2009) · Quantitative Marketing and Economics · doi:10.1007/s11129-009-9068-x · Cited by 109
Targeted search and the long tail effect
Yang, H. (2013) · The RAND Journal of Economics · doi:10.1111/1756-2171.12036 · Cited by 79
Overgaard, P. B. (1993) · European Journal of Political Economy · doi:10.1016/0176-2680(93)90035-s · Cited by 19
Cited by
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“Various technologies and analytics have been used for targeting, such as search engines using keywords searched and browsing history to target search consumers, and social media platforms using interests, content, and connections to target social media consumers (Liu 2020).”
Method
Metadata
- Authors
- Liu, X.
- Affiliations
- Aston University (GB)
- Corresponding author
- Liu, Xingyi
- ORCID
- Liu, Xingyi
- Venue
- International Journal of Research in Marketing
- Publisher
- Elsevier BV
- Published
- 2020-06
- Volume
- 37
- Issue
- 2
- Pages
- 386-404
- ISSN
- 0167-8116 (ISSN-L)
- OpenAlex
- W2999471735
- Cited by
- 10
- References
- 14
- Citations by year
- 2026: 1 · 2023: 5 · 2022: 2 · 2021: 1 · 2020: 1
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Locations
- International Journal of Research in Marketing (publishedVersion)
- Aston Publications Explorer (Aston University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Merger and Competition Analysis
- Social Sciences › Business, Management and Accounting › Marketing › Marketing and Advertising Strategies
- Keywords
- targeted advertising, price signaling, product quality, experience goods, price distortions, consumer information
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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.
