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AI and Machine Learning in Inclusive Development to Personalize User Experience and Increase Digital Literacy

In: Digital Inclusiveness Bridging the Divide in the Digital Economy

Author

Listed:
  • Tatul M. Mkrtchyan
  • Aziza B. Karbekova
  • Dmitriy S. Zakharov
  • Olesya K. Pakhomova

Abstract

This research aims to determine the prospects of personalizing consumer experience and enhancing the level of digital literacy in society through the utilization of artificial intelligence and machine learning in the practice of buying and selling goods and services. The research methodology is based on correlation and regression analysis. These methods are used to model the relationship between indicators characterizing the inclusiveness of the digital consumption society and the use of artificial intelligence and machine learning in the exchange of goods and services. Drawing on the international experience of the top 15 fastest-growing digital consumption societies in 2023 and IMD statistics, it is substantiated that the inclusiveness of the digital consumption society increases as the activity of using artificial intelligence and machine learning in the practice of buying and selling goods and services grows. The scientific novelty of the obtained results is explained by the fact that they have, for the first time, revealed the causal relationships between the use of artificial intelligence and machine learning in the practice of buying and selling goods and services and the inclusiveness of the digital consumption society. The authors revealed the potential for a wider application of artificial intelligence and machine learning in the trade of goods and services. Furthermore, they offered recommendations for realizing this potential. The practical significance of these recommendations lies in their ability to be employed within digital business strategies to increase sales and foster consumer loyalty. This will lead to increased availability of goods and services, reduced transaction costs, and increased efficiency in purchasing practices.

Suggested Citation

  • Tatul M. Mkrtchyan & Aziza B. Karbekova & Dmitriy S. Zakharov & Olesya K. Pakhomova, 2024. "AI and Machine Learning in Inclusive Development to Personalize User Experience and Increase Digital Literacy," World Scientific Book Chapters, in: Elena G Popkova (ed.), Digital Inclusiveness Bridging the Divide in the Digital Economy, chapter 3, pages 25-35, World Scientific Publishing Co. Pte. Ltd..
  • Handle: RePEc:wsi:wschap:9789811270826_0003
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    More about this item

    Keywords

    Society; Inclusiveness; Digital Technologies; Digital Divide; Digital Literacy; Cybersecurity; Discrimination; Innovation; Data Management; Public Infrastructure;
    All these keywords.

    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O - Economic Development, Innovation, Technological Change, and Growth
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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