The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Determinants of Customers’ Responses to Customized Offers: Conceptual Framework and Research Propositions
Abstract
Marketers have been challenged by proponents of individual (one-to-one) marketing to shift from focusing on market segments to making individually customized offers. Building on current knowledge regarding the construction of customers’ preferences, the author examines the basic assumptions underlying individual marketing and presents a process model of customers’ responses to customized offers. The model addresses (1) preference development, (2) evaluation of customized offers, (3) likelihood of purchasing the customized offers, and (4) maintenance of relationships with one-to-one marketers. The analysis leads to specific propositions regarding determinants of customers’ responses to customized offers. The author discusses future research directions and managerial implications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The commitment-trust theory of relationship marketing
Morgan, R. M., & Hunt, S. D. (1994) · Journal of Marketing · doi:10.1177/002224299405800302 · Cited by 22,355
Telling more than we can know: Verbal reports on mental processes.
Nisbett, R. E., & Wilson, T. D. (1977) · Psychological Review · doi:10.1037/0033-295x.84.3.231 · Cited by 11,317 · cited 1×
“That is, because customers often do not have good insight into their preferences (e.g., Nisbett and Wilson 1977), they may fail to recognize a good match to their measured preferences.”
BackgroundThe Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships
Garbarino, E., & Johnson, M. S. (1999) · Journal of Marketing · doi:10.1177/002224299906300205 · Cited by 4,899
Lord, C. G., Ross, L., & Lepper, M. R. (1979) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.37.11.2098 · Cited by 4,583
The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811
When choice is demotivating: can one desire too much of a good thing?
Iyengar, S. S., & Lepper, M. R. (2000) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.79.6.995 · Cited by 2,878
Moorman, C., Zaltman, G., & Deshpande, R. (1992) · Journal of Marketing Research · doi:10.1177/002224379202900303 · Cited by 1,901
Levin, I. P., & Gaeth, G. J. (1988) · Journal of Consumer Research · doi:10.1086/209174 · Cited by 1,172
Choice under Conflict: The Dynamics of Deferred Decision
Tversky, A., & Shafir, E. (1992) · Psychological Science · doi:10.1111/j.1467-9280.1992.tb00047.x · Cited by 1,013
A Framework for Customer Relationship Management
Winer, R. S. (2001) · California Management Review · doi:10.2307/41166102 · Cited by 868
Cited by
Personalization in personalized marketing: Trends and ways forward
Chandra, S., Verma, S., Lim, W. M., Kumar, S., & Donthu, N. (2022) · Psychology & Marketing · doi:10.1002/mar.21670 · Cited by 595
Intelligent systems in tourism
Gretzel, U. (2011) · Annals of Tourism Research · doi:10.1016/j.annals.2011.04.014 · Cited by 468
Sutanto, J., Palme, E., Tan, C.-H., & Phang, C. W. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.4.07 · Cited by 412
Getting Too Personal: Reactance to Highly Personalized Email Solicitations
White, T. B., Zahay, D. L., Thorbjørnsen, H., & Shavitt, S. (2008) · Marketing Letters · doi:10.1007/s11002-007-9027-9 · Cited by 365
Customization of online advertising: The role of intrusiveness
van Doorn, J., & Hoekstra, J. C. (2013) · Marketing Letters · doi:10.1007/s11002-012-9222-1 · Cited by 283
Disclosure Antecedents in an Online Service Context: The Role of Sensitivity of Information
Mothersbaugh, D. L., Foxx, W. K., Beatty, S. E., & Wang, S. (2012) · Journal of Service Research · doi:10.1177/1094670511424924 · Cited by 257
Decision difficulty in the age of consumer empowerment
Broniarczyk, S. M., & Griffin, J. G. (2014) · Journal of Consumer Psychology · doi:10.1016/j.jcps.2014.05.003 · Cited by 256
The consumer production journey: marketing to consumers as co-producers in the sharing economy
Dellaert, B. G. C. (2019) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-018-0607-4 · Cited by 224
Exploring how consumers cope with online behavioral advertising
Ham, C.-D. (2017) · International Journal of Advertising · doi:10.1080/02650487.2016.1239878 · Cited by 184
Shen, A., & Dwayne Ball, A. (2009) · Journal of Services Marketing · doi:10.1108/08876040910946341 · Cited by 105
VeriPlan: Integrating Formal Verification and LLMs into End-User Planning
Lee, C. P., Porfirio, D., Wang, X. J., Zhao, K. C., & Mutlu, B. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3714113 · Cited by 21
Metadata
- Authors
- Simonson, I.
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- Published
- 2005-01
- Volume
- 69
- Issue
- 1
- Pages
- 32-45
- ISSN
- 0022-2429 (ISSN-L)
- 1547-7185
- OpenAlex
- W3021891781
- Cited by
- 523
- References
- 10
- FWCI
- 29.41
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.
