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Enhancing Marketing Personalized Shopping Recommendations in the UAE: Leveraging Logic Mining and Advanced Technologies

Author

Listed:
  • SHWEDEH Fanar

    (City University Ajman, Ajman, UNITED ARAB EMIRATES)

  • DABASH Amro

    (The British University in Dubai, Dubai, UNITED ARAB EMIRATES)

  • DABBAGH Tamadher AL

    (City University Ajman, Ajman, UNITED ARAB EMIRATES)

  • ABURAYYA Ahmad

    (City University Ajman, Ajman, UNITED ARAB EMIRATES)

Abstract

This study investigates how personalized recommendation systems in e-commerce operate, using Social Exchange Theory as its theoretical basis. Through empirical research, we analyze the effects of improving consumer experiences, enhancing product suggestions, and building trust and satisfaction on the effectiveness of personalized recommendation systems. Our study combines practical techniques with theoretical knowledge to improve user engagement and build long-lasting brand loyalty. This research provides valuable insights for e-commerce stakeholders, suggesting that individualized recommendations might enhance consumer satisfaction in the digital marketplace.

Suggested Citation

  • SHWEDEH Fanar & DABASH Amro & DABBAGH Tamadher AL & ABURAYYA Ahmad, 2024. "Enhancing Marketing Personalized Shopping Recommendations in the UAE: Leveraging Logic Mining and Advanced Technologies," Foundations of Management, Sciendo, vol. 16(1), pages 345-358.
    • Handle: RePEc:vrs:founma:v:16:y:2024:i:1:p:345-358:n:1021
    DOI: 10.2478/fman-2024-0021
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    More about this item

    Keywords

    Personalized Recommendation Systems; Consumer Challenges; Trust and Satisfaction; User Engagement; Digital Marketplace;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General

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