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Consumer behavior on cashback websites: Network strategies

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  • Ballestar, María Teresa
  • Grau-Carles, Pilar
  • Sainz, Jorge

Abstract

The size of cashback sites, both in terms of users and business, has grown considerably over the last decade. This article presents a complete analysis of the behavior of the users of the webs both in terms of transactions, and navigation and registration on cashback sites by using a large sample of one of the largest European sites. The study also presents a first analysis on the structure of the sites. An analysis using Partial Least Squares Structural Equation Modelling shows that the volume of the user's network, the diversification of the navigation, and the size of the transactions are relevant to the decision of the consumer and to his or her engagements on the affiliate merchants. These results represent a first step on the understanding of these marketing strategies and open new areas of research.

Suggested Citation

  • Ballestar, María Teresa & Grau-Carles, Pilar & Sainz, Jorge, 2016. "Consumer behavior on cashback websites: Network strategies," Journal of Business Research, Elsevier, vol. 69(6), pages 2101-2107.
  • Handle: RePEc:eee:jbrese:v:69:y:2016:i:6:p:2101-2107
    DOI: 10.1016/j.jbusres.2015.12.015
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Afonso Vieira, Valter & Agnihotri, Raj & de Almeida, Marcos Inácio Severo & Lopes, Evandro Luiz, 2022. "How cashback strategies yield financial benefits for retailers: The mediating role of consumers’ program loyalty," Journal of Business Research, Elsevier, vol. 141(C), pages 200-212.
    2. María Teresa Ballestar & Pilar Grau-Carles & Jorge Sainz, 2019. "Predicting customer quality in e-commerce social networks: a machine learning approach," Review of Managerial Science, Springer, vol. 13(3), pages 589-603, June.
    3. Xu, Lina & Roy, Abhijit, 2022. "Cashback as cash forward: The serial mediating effect of time/effort and money savings," Journal of Business Research, Elsevier, vol. 149(C), pages 30-37.
    4. Ata Allah Taleizadeh & Alireza Mahmoudzade Varzi & Alireza Amjadian & Mahsa Noori-daryan & Ioannis Konstantaras, 2023. "How cash-back strategy affect sale rate under refund and customers’ credit," Operational Research, Springer, vol. 23(1), pages 1-69, March.
    5. Christino, Juliana Maria Magalhães & Silva, Thaís Santos & Cardozo, Erico Aurélio Abreu & de Pádua Carrieri, Alexandre & de Paiva Nunes, Patricia, 2019. "Understanding affiliation to cashback programs: An emerging technique in an emerging country," Journal of Retailing and Consumer Services, Elsevier, vol. 47(C), pages 78-86.
    6. Ballestar, María Teresa & Grau-Carles, Pilar & Sainz, Jorge, 2018. "Customer segmentation in e-commerce: Applications to the cashback business model," Journal of Business Research, Elsevier, vol. 88(C), pages 407-414.
    7. Ye Qiu & Ram C. Rao, 2020. "Increasing Retailer Loyalty Through the Use of Cash Back Rebate Sites," Marketing Science, INFORMS, vol. 39(4), pages 743-762, July.

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