IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v7y2015i4p516-529d60964.html
   My bibliography  Save this article

Optimal Referral Reward Considering Customer’s Budget Constraint

Author

Listed:
  • Dan Zhou

    (School of Economics and Management, Beihang University, Beijing 100080, China)

  • Zhong Yao

    (School of Economics and Management, Beihang University, Beijing 100080, China)

Abstract

Everyone likes Porsche but few can afford it. Budget constraints always play a critical role in a customer’s decision-making. The literature disproportionally focuses on how firms can induce customer valuations toward the product, but does not address how to assess the influence of budget constraints. We study these questions in the context of a referral reward program (RRP). RRP is a prominent marketing strategy that utilizes recommendations passed from existing customers to their friends and effectively stimulates word of mouth (WoM). We build a stylized game-theoretical model with a nested Stackelberg game involving three players: a firm, an existing customer, and a potential customer who is a friend of the existing customer. The budget is the friend’s private information. We show that RRPs might be optimal when the friend has either a low or a high valuation, but they work differently in each situation because of the budget. Furthermore, there are two budget thresholds, a fixed one and a variable one, which limit a firm’s ability to use rewards.

Suggested Citation

  • Dan Zhou & Zhong Yao, 2015. "Optimal Referral Reward Considering Customer’s Budget Constraint," Future Internet, MDPI, vol. 7(4), pages 1-14, December.
    • Handle: RePEc:gam:jftint:v:7:y:2015:i:4:p:516-529:d:60964
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/7/4/516/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/7/4/516/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. David J. Salant, 1997. "Up in the Air: GTE's Experience in the MTA Auction for Personal Communication Services Licenses," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(3), pages 549-572, September.
    2. Kuester, Madlen & Benkenstein, Martin, 2014. "Turning dissatisfied into satisfied customers: How referral reward programs affect the referrer׳s attitude and loyalty toward the recommended service provider," Journal of Retailing and Consumer Services, Elsevier, vol. 21(6), pages 897-904.
    3. Tuk, M.A. & Verlegh, P.W.J. & Smidts, A. & Wigboldus, D.H.J., 2008. "Sales and Sincerity: The Role of Relational Framing in Word-of-Mouth Marketing," ERIM Report Series Research in Management ERS-2008-056-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    4. Xiao, Ping & Tang, Christopher S. & Wirtz, Jochen, 2011. "Optimizing referral reward programs under impression management considerations," European Journal of Operational Research, Elsevier, vol. 215(3), pages 730-739, December.
    5. Eyal Biyalogorsky & Eitan Gerstner & Barak Libai, 2001. "Customer Referral Management: Optimal Reward Programs," Marketing Science, INFORMS, vol. 20(1), pages 82-95, August.
    6. Laura J. Kornish & Qiuping Li, 2010. "Optimal Referral Bonuses with Asymmetric Information: Firm-Offered and Interpersonal Incentives," Marketing Science, INFORMS, vol. 29(1), pages 108-121, 01-02.
    7. Hauser, John R & Urban, Glen L, 1986. "The Value Priority Hypotheses for Consumer Budget Plans," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 12(4), pages 446-462, March.
    8. Che, Yeon-Koo & Gale, Ian, 2000. "The Optimal Mechanism for Selling to a Budget-Constrained Buyer," Journal of Economic Theory, Elsevier, vol. 92(2), pages 198-233, June.
    9. Arijit Sen, 1998. "Seller Financing of Consumer Durables," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 7(3), pages 435-460, September.
    10. Xiaoqing Jing & Jinhong Xie, 2011. "Group Buying: A New Mechanism for Selling Through Social Interactions," Management Science, INFORMS, vol. 57(8), pages 1354-1372, August.
    11. Jin, Liyin & Huang, Yunhui, 2014. "When giving money does not work: The differential effects of monetary versus in-kind rewards in referral reward programs," International Journal of Research in Marketing, Elsevier, vol. 31(1), pages 107-116.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lili Wang & Zoey Chen, 2022. "The effect of incentive structure on referral: the determining role of self-construal," Journal of the Academy of Marketing Science, Springer, vol. 50(5), pages 1091-1110, September.
    2. Heike M. Wolters & Christian Schulze & Karen Gedenk, 2020. "Referral Reward Size and New Customer Profitability," Marketing Science, INFORMS, vol. 39(6), pages 1166-1180, November.
    3. Charupat, Narat & Prisman, Eliezer Z., 2004. "An essay on financial innovation: The case of instalment receipts," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 129-156, January.
    4. Upender Subramanian & Ram C. Rao, 2016. "Leveraging Experienced Consumers to Attract New Consumers: An Equilibrium Analysis of Displaying Deal Sales by Daily Deal Websites," Management Science, INFORMS, vol. 62(12), pages 3555-3575, December.
    5. Kuester, Madlen & Benkenstein, Martin, 2014. "Turning dissatisfied into satisfied customers: How referral reward programs affect the referrer׳s attitude and loyalty toward the recommended service provider," Journal of Retailing and Consumer Services, Elsevier, vol. 21(6), pages 897-904.
    6. Bobkova, Nina, 2020. "Asymmetric budget constraints in a first-price auction," Journal of Economic Theory, Elsevier, vol. 186(C).
    7. Ariel BenYishay & A. Mushfiq Mobarak, 2014. "Social Learning and Communication," NBER Working Papers 20139, National Bureau of Economic Research, Inc.
    8. Inyoung Chae & Andrew T. Stephen & Yakov Bart & Dai Yao, 2017. "Spillover Effects in Seeded Word-of-Mouth Marketing Campaigns," Marketing Science, INFORMS, vol. 36(1), pages 89-104, January.
    9. Meyners, Jannik & Barrot, Christian & Becker, Jan U. & Bodapati, Anand V., 2017. "Reward-scrounging in customer referral programs," International Journal of Research in Marketing, Elsevier, vol. 34(2), pages 382-398.
    10. Zhan, Mengmeng & Huang, Minxue & Li, Aoqi & Yang, Yvmeng, 2023. "The role of impulsive behaviour and meta-perception in referral reward programs," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    11. Yuichiro Kamada & Aniko Öry, 2020. "Contracting with Word-of-Mouth Management," Management Science, INFORMS, vol. 66(11), pages 5094-5107, November.
    12. Xiao, Ping & Tang, Christopher S. & Wirtz, Jochen, 2011. "Optimizing referral reward programs under impression management considerations," European Journal of Operational Research, Elsevier, vol. 215(3), pages 730-739, December.
    13. Wang, Xia & Ding, Ying, 2022. "The impact of monetary rewards on product sales in referral programs: The role of product image aesthetics," Journal of Business Research, Elsevier, vol. 145(C), pages 828-842.
    14. Vijay Viswanathan & Sebastian Tillmanns & Manfred Krafft & Daniel Asselmann, 2018. "Understanding the quality–quantity conundrum of customer referral programs: effects of contribution margin, extraversion, and opinion leadership," Journal of the Academy of Marketing Science, Springer, vol. 46(6), pages 1108-1132, November.
    15. Amedeo Piolatto, 2015. "Online booking and information: competition and welfare consequences of review aggregators," Working Papers 2015/11, Institut d'Economia de Barcelona (IEB).
    16. BenYishay, Ariel & Mobarak, A. Mushfiq, 2013. "Communicating with Farmers through Social Networks," Center Discussion Papers 156233, Yale University, Economic Growth Center.
    17. Oksana Loginova & Andrea Mantovani, 2019. "Price competition in the presence of a web aggregator," Journal of Economics, Springer, vol. 126(1), pages 43-73, January.
    18. Carlos Fernández-Loría & Maxime C. Cohen & Anindya Ghose, 2023. "Evolution of Referrals over Customers’ Life Cycle: Evidence from a Ride-Sharing Platform," Information Systems Research, INFORMS, vol. 34(2), pages 698-720, June.
    19. Burkett, Justin, 2015. "Endogenous budget constraints in auctions," Journal of Economic Theory, Elsevier, vol. 158(PA), pages 1-20.
    20. Che, Yeon-Koo & Gale, Ian, 2000. "The Optimal Mechanism for Selling to a Budget-Constrained Buyer," Journal of Economic Theory, Elsevier, vol. 92(2), pages 198-233, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:7:y:2015:i:4:p:516-529:d:60964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.