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Online Recommendation Systems: Factors Influencing Use in E-Commerce

Author

Listed:
  • Juan-Pedro Cabrera-Sánchez

    (Business Administration and Marketing Department, Universidad de Sevilla, 41018 Sevilla, Spain)

  • Iviane Ramos-de-Luna

    (Economics and Business Studies Department, Universitat Oberta de Catalunya, 08035 Barcelona, Spain)

  • Elena Carvajal-Trujillo

    (Business Administration and Marketing Department, Universidad de Huelva, 21071 Huelva, Spain)

  • Ángel F. Villarejo-Ramos

    (Business Administration and Marketing Department, Universidad de Sevilla, 41018 Sevilla, Spain)

Abstract

The increasing use of artificial intelligence (AI) to understand purchasing behavior has led to the development of recommendation systems in e-commerce platforms used as an influential element in the purchase decision process. This paper intends to ascertain what factors affect consumers’ adoption and use of online purchases recommendation systems. In order to achieve this objective, the Unified Theory of Adoption and Use of Technology (UTAUT 2) is extended with two variables that act as an inhibiting or positive influence on intention to use: technology fear and trust. The structural model was assessed using partial least squares (PLS) with an adequate global adjustment on a sample of 448 users of online recommendation systems. Among the results, it’s highlighted the importance of the inhibiting role of technology fear and the importance that users attach to the level of perceived trust in the recommendation system are highlighted. The performance expectancy and hedonic motivations have the greatest influence on intention to use these systems. Based on the results, this work provides a relevant recommendation to companies for the design of their e-commerce platforms and the implementation of online purchase recommendation systems.

Suggested Citation

  • Juan-Pedro Cabrera-Sánchez & Iviane Ramos-de-Luna & Elena Carvajal-Trujillo & Ángel F. Villarejo-Ramos, 2020. "Online Recommendation Systems: Factors Influencing Use in E-Commerce," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8888-:d:435093
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    4. Jun Wu & Xinyu Song & Xiaxia Niu & Li Shi & Lu Gao & Liping Geng & Dan Wang & Dongkui Zhang, 2022. "IT-PMF: A Novel Community E-Commerce Recommendation Method Based on Implicit Trust," Mathematics, MDPI, vol. 10(14), pages 1-16, July.
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