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
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.
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In the citing paper
“Additionally, rewarding referral behavior increases the perceived social costs of making referrals, due to doubt about the recommender ' s motives (Ryu and Feick 2007).”
From Unintended reward costs: the effectiveness of customer referral reward programs for innovative products and services (2019) · cites this paper as Ryu and Feick 2007
In the citing paper
“On the other hand, rewards may prevent referrals with raising social impression concerns (Wirtz and Chew, 2002; Ryu and Feick, 2007; Wirtz et al., 2013, 2019; Orsingher and Wirtz, 2018).”
From Reward Design for Customer Referral Programs: Reward–Product Congruence Effect and Gender Difference (2021) · cites this paper as Ryu and Feick, 2007
In the citing paper
“existing research shows that CRPs stimulate referral behavior (Ryu and Feick 2007)”
From Unintended reward costs: the effectiveness of customer referral reward programs for innovative products and services (2019) · cites this paper as Ryu and Feick 2007
In the citing paper
“For example, Ryu and Feick (2007) find that offering referral rewards increases referral likelihood.”
From Unintended reward costs: the effectiveness of customer referral reward programs for innovative products and services (2019) · cites this paper as Ryu and Feick 2007
In the citing paper
“Ryu and Feick (2007) show that rewarding both parties reduces the perceived social costs of a recommendation.”
From Unintended reward costs: the effectiveness of customer referral reward programs for innovative products and services (2019) · cites this paper as Ryu and Feick (2007)
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“Ryu and Feick (2007) found that simply recommending positive consumption experiences can boost consumers’ postevaluation satisfaction and future brand loyalty; they also found that the mental processes of sharing experiences provide extensive benefits that are derived from “letting it out”…”
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“They can also run “seeding programs,” in which they offer various rewards (such as vouchers or gifts) to seed customers, expecting that their adoption will initiate a contagion (Ryu & Feick, 2007).”
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“For example, as only one experience is able to trigger recommendations, service managers should consider more effective tools for customer acquisition such as referral reward programs (Ryu & Feick, 2007).”
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Kumar, V., Petersen, J. A., & Leone, R. P. (2010) · Journal of Marketing · doi:10.1509/jmkg.74.5.1 · Cited by 238
Targeting Revenue Leaders for a New Product
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The Role of Metaperception on the Effectiveness of Referral Reward Programs
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“The giver’s concerns about the impression she makes on others might be most important in weak tie relationships, such as acquaintances and work colleagues, where a relatively minor single action like a referral can shape evaluations more strongly (Ryu and Feick 2007; Tuk et al. 2009).”
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“How much should rewards cost to maximize revenue (Ryu and Feick 2007) and how should she balance these costs with the number of customers who receive the reward (Schlereth et al. 2013, Stonedahl et al. 2010)?”
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“This may, for example, be the case when services are of an inconsistent quality or when the firm’s brand equity is low (Ryu and Feick 2007).”
BackgroundResultInfluentialShare experiences: receiving word of mouth and its effect on relationships with donors
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“This calls into question the idea of whether or not WOM contains a social pressure component in addition to an informational one (Ryu and Feick, 2007).”
BackgroundInfluentialQi, T., Wang, T., & Yan, J. (2021) · Internet Research · doi:10.1108/intr-08-2020-0445 · Cited by 30
Wang, X., & Ding, Y. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.052 · Cited by 29
“We Earned the Coupon Together”: The Missing Link of Experience Cocreation in Shared Coupons
Fang, E., Dong, B., Zhuang, M., & Cai, F. (2022) · Journal of Marketing · doi:10.1177/00222429221126990 · Cited by 28
“Study 4 shows that economic and social incentives substitute for each other in the context of experience cocreation (Ryu and Feick 2007).”
BackgroundInfluentialCustomer Referral Behavior: Do Switchers and Stayers Differ?
Stein, A., & Ramaseshan, B. (2014) · Journal of Service Research · doi:10.1177/1094670514563495 · Cited by 32
“In recognition of this, marketing scholars have begun to examine customer referral behavior (e.g., de Matos and Rossi 2008; Ryu and Feick 2007; Schmitt, Skiera, and Van den Bulte 2011; Wentzel, Tomczak, and Henkel 2014).”
BackgroundCustomer Engagement in International Markets
Steinhoff, L., Liu, J., Li, X., & Palmatier, R. W. (2022) · Journal of International Marketing · doi:10.1177/1069031x221099211 · Cited by 35
“Specifically, customers’ referral likelihood increases when rewards are provided through the referral program, especially when recommending to weak ties and advocating weaker brands (Ryu and Feick 2007).”
BackgroundZhang, D., Zhang, F., Liu, S., & Du, H. S. (2019) · Information Technology & People · doi:10.1108/itp-06-2017-0188 · Cited by 26
It pays off to be authentic: An examination of direct versus indirect brand mentions on social media
Hu, M., Chen, J., Chen, Q., & He, W. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.05.032 · Cited by 28
Social Referral Programs for Freemium Platforms
Belo, R., & Li, T. (2022) · Management Science · doi:10.1287/mnsc.2022.4301 · Cited by 32
Metadata
- Authors
- Ryu, G., & Feick, L.
- Affiliations
- Korea University (KR)
- University of Pittsburgh (US)
- Corresponding author
- Ryu, Gangseog
- Feick, Lawrence
- Venue
- Journal of Marketing
- Publisher
- SAGE Publications
- 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 ›
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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.