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This study investigates the impact of AI tools (chatbots, virtual assistants, augmented reality, visual search, and voice assistants) on online shopping behavior across Malaysia, UAE, Russia, and Brazil. Utilizing machine learning approaches including Random Forest, Gradient Boosting, and Decision Trees, we analyzed data from 2,613 respondents to predict purchase intention, customer satisfaction, and loyalty. Our findings reveal significant variation in AI tool awareness and effectiveness across countries, with Malaysia and UAE demonstrating higher adoption rates compared to Russia and Brazil. The predictive models achieved accuracies ranging from 67% to 99%, with particular strength in predicting purchase intention (79–84%) and customer loyalty (83–90%). Feature importance analysis identified ease of use, trust, and perception as primary drivers in Russia and Malaysia, while demographic factors like age and income were more influential in UAE and Brazil. This research provides valuable insights for businesses implementing AI strategies across different markets, highlighting the need for contextual adaptation to maximize consumer engagement and loyalty.

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AI‑Based Algorithmic Predictions of Purchase Intention, and Loyalty: A Multi‑country Study

  • 会议论文

  • 发表日期：

    2026年

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  • Kannan Ramakrishnan,
  • Rathimala Kannan,
  • Ayse Begum Ersoy,
  • Davide Contu,
  • Aagata Stachowicz-Stanusch,
  • Leonardo Mataruna

摘要

This study investigates the impact of AI tools (chatbots, virtual assistants, augmented reality, visual search, and voice assistants) on online shopping behavior across Malaysia, UAE, Russia, and Brazil. Utilizing machine learning approaches including Random Forest, Gradient Boosting, and Decision Trees, we analyzed data from 2,613 respondents to predict purchase intention, customer satisfaction, and loyalty. Our findings reveal significant variation in AI tool awareness and effectiveness across countries, with Malaysia and UAE demonstrating higher adoption rates compared to Russia and Brazil. The predictive models achieved accuracies ranging from 67% to 99%, with particular strength in predicting purchase intention (79–84%) and customer loyalty (83–90%). Feature importance analysis identified ease of use, trust, and perception as primary drivers in Russia and Malaysia, while demographic factors like age and income were more influential in UAE and Brazil. This research provides valuable insights for businesses implementing AI strategies across different markets, highlighting the need for contextual adaptation to maximize consumer engagement and loyalty.