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A Customer Loyalty Model for E-Commerce Recommendation Systems

Author

Listed:
  • Leila Ebrahimi

    (Department of Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran)

  • Vahid Reza Mirabi

    (Department of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran)

  • Mohammad Hossein Ranjbar

    (Department of Accounting and Management, Faculty of Humanities, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran)

  • Esmaeil Hassan Pour

    (Department of Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran)

Abstract

Customer loyalty is one of the important issues of e-commerce. In the context of e-commerce recommender systems, there is small number of studies investigating this issue. The main objective of this research is to provide a customer loyalty model for e-commerce recommender systems. The proposed model is developed using Delone and McLean Information System success model and a set of factors which are identified from the literature. To test the research hypotheses of the developed model, a questionnaire survey is conducted and the data is collected from 384 customers in a B2C website. We used SPSS and SmartPLS software for descriptive statistics and path analyses and to verify the proposed model. Our analysis on the collected data showed the acceptance of all hypotheses in the model. The result of the Structural Equations Modelling showed that trust has a significant relationship with the customers’ satisfaction in the e-commerce recommendation systems. In addition, the results revealed that satisfaction with the recommended products can improve the customers’ loyalty in the B2C recommendation systems. The proposed model will help the e-commerce managers to improve their website recommendation systems and increase the sale of the products by achieving the customers’ loyalty in the online shopping websites.

Suggested Citation

  • Leila Ebrahimi & Vahid Reza Mirabi & Mohammad Hossein Ranjbar & Esmaeil Hassan Pour, 2019. "A Customer Loyalty Model for E-Commerce Recommendation Systems," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 18(03), pages 1-33, September.
    • Handle: RePEc:wsi:jikmxx:v:18:y:2019:i:03:n:s0219649219500369
    DOI: 10.1142/S0219649219500369
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    1. Blut, Markus & Ghiassaleh, Arezou & Wang, Cheng, 2023. "Testing the performance of online recommendation agents: A meta-analysis," Journal of Retailing, Elsevier, vol. 99(3), pages 440-459.

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