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Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce

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  • Laith T. Khrais

    (Department of Business Administration, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia)

Abstract

The advent and incorporation of technology in businesses have reformed operations across industries. Notably, major technical shifts in e-commerce aim to influence customer behavior in favor of some products and brands. Artificial intelligence (AI) comes on board as an essential innovative tool for personalization and customizing products to meet specific demands. This research finds that, despite the contribution of AI systems in e-commerce, its ethical soundness is a contentious issue, especially regarding the concept of explainability. The study adopted the use of word cloud analysis, voyance analysis, and concordance analysis to gain a detailed understanding of the idea of explainability as has been utilized by researchers in the context of AI. Motivated by a corpus analysis, this research lays the groundwork for a uniform front, thus contributing to a scientific breakthrough that seeks to formulate Explainable Artificial Intelligence (XAI) models. XAI is a machine learning field that inspects and tries to understand the models and steps involved in how the black box decisions of AI systems are made; it provides insights into the decision points, variables, and data used to make a recommendation. This study suggested that, to deploy explainable XAI systems, ML models should be improved, making them interpretable and comprehensible.

Suggested Citation

  • Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:12:p:226-:d:458606
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    References listed on IDEAS

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    1. Ren, Shuyun & Choi, Tsan-Ming & Lee, Ka-Man & Lin, Lei, 2020. "Intelligent service capacity allocation for cross-border-E-commerce related third-party-forwarding logistics operations: A deep learning approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 134(C).
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    7. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
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    Cited by:

    1. Filipe Portela, 2021. "Data Science and Knowledge Discovery," Future Internet, MDPI, vol. 13(7), pages 1-4, July.
    2. Hasan Beyari & Hatem Garamoun, 2022. "The Effect of Artificial Intelligence on End-User Online Purchasing Decisions: Toward an Integrated Conceptual Framework," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    3. Brian Pickering, 2021. "Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies," Future Internet, MDPI, vol. 13(5), pages 1-20, May.
    4. Alessandro Massaro & Daniele Giannone & Vitangelo Birardi & Angelo Maurizio Galiano, 2021. "An Innovative Approach for the Evaluation of the Web Page Impact Combining User Experience and Neural Network Score," Future Internet, MDPI, vol. 13(6), pages 1-21, May.
    5. Laith T. Khrais & Abdullah M. Alghamdi, 2021. "The Role of Mobile Application Acceptance in Shaping E-Customer Service," Future Internet, MDPI, vol. 13(3), pages 1-13, March.
    6. Biresh Kumar & Sharmistha Roy & Anurag Sinha & Celestine Iwendi & Ľubomíra Strážovská, 2022. "E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-24, December.

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