Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 267Digital Marketing and Social MediaCustomer Service Quality and LoyaltyConsumer Market Behavior and Pricing

The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.

A Penny for Your Thoughts: Referral Reward Programs and Referral Likelihood

Ryu, Gangseog, Feick, Lawrence·2007·Journal of Marketing

Abstract

Because referral reward programs reward existing customers and build the customer base, firms use them to encourage customers to make recommendations to others. The authors report on four experiments in which they find that rewards increase referral likelihood. More specifically, they find that rewards are particularly effective in increasing referral to weak ties and for weaker brands. It is also important who receives the reward. Overall, for weak ties and weaker brands, giving a reward to the provider of the recommendation is important. For strong ties and stronger brands, providing at least some of the reward to the receiver of the referral seems to be more effective. The authors discuss the implications of the results for the design of reward programs.

Overview

Integrity signals

Registry

Crossref · 10.1509/jmkg.71.1.084

Cites

42 of 42 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

6 more claims about this paper

Create a free account to see every claim other papers make about this one.

See all claims — free account

Citations

Cites

Show all 42 references

Research works printed but not linked: 5

  1. Chen Yuxin, 2001, “The Design and Implications of Customer Recommendation Programs,”
  2. Kelly Harold H., 1979, Close Relationships: Their Structures and Processes.
  3. Mummert Hallie, 2000, Target Marketing, 23, 3
  4. Reingen Peter H., 1986, Journal of Marketing Research, 13, 449
  5. Wiener Joshua L., 1986, Advances in Consumer Research, 13, 306

Cited by

Metadata

Authors
Ryu, G., & Feick, L.
Affiliations
  • Korea University (KR)
  • University of Pittsburgh (US)
Corresponding author
  • Ryu, Gangseog
  • Feick, Lawrence
Venue
Journal of Marketing
Published
2007-01
Volume
71
Issue
1
Pages
84-94
ISSN
  • 0022-2429 (ISSN-L)
  • 1547-7185
OpenAlex
W4253164461
Cited by
257
References
42
Citations by year
2026: 9 · 2025: 25 · 2024: 18 · 2023: 26 · 2022: 23 · 2021: 25 · 2020: 28 · 2019: 17 · 2018: 17 · 2017: 14 · 2016: 10 · 2015: 9 · 2014: 9 · 2013: 11 · 2012: 2
FWCI
7.88
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
Journal of Marketing (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
  • Social Sciences › Business, Management and Accounting › Marketing › Customer Service Quality and Loyalty
  • Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
Keywords
referral reward programs, word-of-mouth recommendation, weak ties, brand strength, reward allocation, referral rewards, customer referral programs

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.