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Optimizing referral reward programs under impression management considerations

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  • Xiao, Ping
  • Tang, Christopher S.
  • Wirtz, Jochen

Abstract

We examine referral reward programs (RRP) that are intended for a service firm to encourage its current customers (inductors) to entice their friends (inductees) to purchase the firm's service. By considering the interplay among the firm, the inductor, and the inductee, we solve a "nested" Stackelberg game so as to determine the optimal RRP in equilibrium. We determine the conditions under which it is optimal for the firm to reward the inductor only, reward the inductee only, or reward both. Also, our results suggest that RRP dominates direct marketing when the firm's current market penetration or the inductor's referral effectiveness is sufficiently high. We then extend our model to incorporate certain key impression management factors: the inductor's intrinsic reward of making a positive impression by being seen as helping a friend, the inductor's concerns about creating a negative impression when making an incentivized referral, and the inductee's impression of the inductor's credibility when an incentive is involved. In the presence of these impression management factors, we show that the firm should reward the inductee more and the inductor less. Under certain conditions, it is optimal for the firm to reward neither the inductor nor the inductee so that the optimal RRP relies purely on unincentivized word of mouth.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:215:y:2011:i:3:p:730-739
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    References listed on IDEAS

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    Cited by:

    1. Dan Zhou & Zhong Yao, 2015. "Optimal Referral Reward Considering Customer’s Budget Constraint," Future Internet, MDPI, vol. 7(4), pages 1-14, December.
    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. 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.
    4. Berman, Barry, 2016. "Referral marketing: Harnessing the power of your customers," Business Horizons, Elsevier, vol. 59(1), pages 19-28.
    5. 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.
    6. Li, Deng-Feng, 2012. "A fast approach to compute fuzzy values of matrix games with payoffs of triangular fuzzy numbers," European Journal of Operational Research, Elsevier, vol. 223(2), pages 421-429.
    7. East, Robert & Uncles, Mark D. & Romaniuk, Jenni & Hand, Chris, 2014. "The decay of positive and negative word of mouth after product experience," Australasian marketing journal, Elsevier, vol. 22(4), pages 350-355.
    8. Bazargan, Amirhossein & Karray, Salma & Zolfaghari, Saeed, 2018. "‘Buy n times, get one free’ loyalty cards: Are they profitable for competing firms? A game theoretic analysis," European Journal of Operational Research, Elsevier, vol. 265(2), pages 621-630.
    9. Söderlund, Magnus & Mattsson, Jan, 2015. "Merely asking the customer to recommend has an impact on word-of-mouth activity," Journal of Retailing and Consumer Services, Elsevier, vol. 27(C), pages 80-89.

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