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Incentivized reviews: Promising the moon for a few stars

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  • Petrescu, Maria
  • O’Leary, Kathleen
  • Goldring, Deborah
  • Ben Mrad, Selima

Abstract

This paper studies the motivations behind incentivized consumer reviews generated via influencer marketing campaigns. Exchange theory is applied as a theoretical framework to analyze, in a qualitative and a quantitative study, the relationship between incentivized reviews and the satisfaction ratings assigned by consumers to a product. The main contributions of the study find that incentivized campaigns can contribute to a sustained increase in the number of reviews and have the potential to lead to higher purchase potential. Moreover, this study also uncovers that incentivized electronic word-of-mouth, in the form of consumer reviews, leads to increased consumer interest and desire to find out more about the product through search engines. Our findings also show that the scope of exchange theory can be broader, from an exchange between two parties to more complex relationships, between brands, influencers, and consumers, through an emerging, specialized word-of-mouth technique.

Suggested Citation

  • Petrescu, Maria & O’Leary, Kathleen & Goldring, Deborah & Ben Mrad, Selima, 2018. "Incentivized reviews: Promising the moon for a few stars," Journal of Retailing and Consumer Services, Elsevier, vol. 41(C), pages 288-295.
    • Handle: RePEc:eee:joreco:v:41:y:2018:i:c:p:288-295
    DOI: 10.1016/j.jretconser.2017.04.005
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    Cited by:

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    9. Perez-Castro, A. & Martínez-Torres, M.R. & Toral, S.L., 2023. "Efficiency of automatic text generators for online review content generation," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    10. Chen, Ruolan & Yuan, Ruizhi & Huang, Bo & Liu, Martin J., 2023. "Feeling warm or skeptical? An investigation into the effects of incentivized eWOM programs on customers’ eWOM sharing intentions," Journal of Business Research, Elsevier, vol. 167(C).
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    13. Ben Jabeur, Sami & Ballouk, Hossein & Ben Arfi, Wissal & Sahut, Jean-Michel, 2023. "Artificial intelligence applications in fake review detection: Bibliometric analysis and future avenues for research," Journal of Business Research, Elsevier, vol. 158(C).
    14. Tim Kollmer & Andreas Eckhardt & Victoria Reibenspiess, 2022. "Explaining consumer suspicion: insights of a vignette study on online product reviews," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1221-1238, September.
    15. Li, Yuanshuo & Zhang, Zili & Pedersen, Susanne & Liu, Xudong & Zhang, Ziqiong, 2023. "The influence of relative popularity on negative fake reviews: A case study on restaurant reviews," Journal of Business Research, Elsevier, vol. 162(C).
    16. Supratim Kundu & Swapnajit Chakraborti, 2022. "A comparative study of online consumer reviews of Apple iPhone across Amazon, Twitter and MouthShut platforms," Electronic Commerce Research, Springer, vol. 22(3), pages 925-950, September.
    17. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).
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    19. Perez, Dikla & Stockheim, Inbal & Baratz, Guy, 2022. "Complimentary competition: The impact of positive competitor reviews on review credibility and consumer purchase intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    20. Anjala S. Krishen & Maria Petrescu, 2018. "Analytics from our scholarly closets: the connections between data, information, and knowledge," Journal of Marketing Analytics, Palgrave Macmillan, vol. 6(1), pages 1-5, March.

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