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“The Role of AI-Generated Real-Time Product Recommendations in Impulse Buying Among Centennials”

In: Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025)

Author

Listed:
  • Yashawant Pathak

    (Amity University)

  • Bhupendra Kumar

    (Amity University)

  • Jagdish Makhijani

    (Rustamji Institute of Technology)

  • Manoj Kumar Niranjan

    (Rustamji Institute of Technology)

Abstract

This research examines how real-time, AI-based product recommendations affect impulse buying among Centennials consumers, also called Generation Z. This group is known for its digital upbringing and openness to technology-driven marketing. Online retailers now rely on machine learning tools that track browsing habits, analyze behavior, and predict future needs to provide personalized suggestions, which often result in unplanned purchases. Using the Stimulus–Organism–Response (SOR) model, the study treats AI recommendations as the stimulus, the perceived benefits as the organism, and impulse buying as the response. Data were collected through a survey of 500 young consumers in urban India and analyzed with R and Python, applying exploratory factor analysis, reliability checks, and structural equation modeling. The results indicate that personalization driven by AI strengthens impulse purchases, with perceived usefulness and enjoyment playing a mediating role. Beyond practical insights for marketers, the study also raises questions about the ethical consequences of highly targeted digital persuasion.

Suggested Citation

  • Yashawant Pathak & Bhupendra Kumar & Jagdish Makhijani & Manoj Kumar Niranjan, 2025. "“The Role of AI-Generated Real-Time Product Recommendations in Impulse Buying Among Centennials”," Advances in Economics, Business and Management Research, in: Preeti Sharma & Sweta Pareek & Sourav Banerjee (ed.), Proceedings of the International Conference on Artificial Intelligence in Management for Business and Industrial Growth (AIMBIG 2025), pages 303-323, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-898-1_22
    DOI: 10.2991/978-94-6463-898-1_22
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