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Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA

Author

Listed:
  • Manu Sharma

    (Graphic Era Deemed to Be University
    London Metropolitan University)

  • Sudhanshu Joshi

    (Doon University
    University of Technology Sydney)

  • Sunil Luthra

    (Ch. Ranbir Singh State Institute of Engineering & Technology (CRSSIET))

  • Anil Kumar

    (London Metropolitan University)

Abstract

The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA.

Suggested Citation

  • Manu Sharma & Sudhanshu Joshi & Sunil Luthra & Anil Kumar, 2024. "Impact of Digital Assistant Attributes on Millennials’ Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA," Information Systems Frontiers, Springer, vol. 26(3), pages 943-966, June.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-022-10339-5
    DOI: 10.1007/s10796-022-10339-5
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