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Decoding willingness to buy in live-streaming retail: The application of stimulus organism response model using PLS-SEM and SEM-ANN

Author

Listed:
  • Hameed, Irfan
  • Zainab, Bibi
  • Akram, Umair
  • Ying, Woo Jia
  • Chan Xing, Chesney
  • Khan, Kamran

Abstract

This study aims to explore the factors influencing consumer engagement in live-streaming retail. Using the Stimulus-Organism-Response (SOR) framework, the research examines the impact of relational bonds, trust, internal perceptual processes, and product uncertainty on consumer behavior. A structured questionnaire was used to collect data from live-streaming shoppers following the purposive sampling technique. The respondents were approached through social media platforms, and 437 responses were received. Partial least squares structural equation modeling (PLS-SEM) and artificial neural networks (ANN) analysis have been performed to extract the findings. The results showed that financial, social, and structural bonds positively influence trust. Trust has an encouraging influence on the internal perception process, leading to purchase intention. Additionally, trust reduces product uncertainty, which ultimately diminishes purchase intention. Influencers and companies interested in live-streaming should focus on relational bonds, consumers' internal perception processes, and reducing product uncertainty. Additionally, the ANN's non-linear output provided further insight into the significance of cognitive drivers. The findings benefit emerging influencers and companies striving to make their marks in the varying online sphere.

Suggested Citation

  • Hameed, Irfan & Zainab, Bibi & Akram, Umair & Ying, Woo Jia & Chan Xing, Chesney & Khan, Kamran, 2025. "Decoding willingness to buy in live-streaming retail: The application of stimulus organism response model using PLS-SEM and SEM-ANN," Journal of Retailing and Consumer Services, Elsevier, vol. 84(C).
  • Handle: RePEc:eee:joreco:v:84:y:2025:i:c:s0969698925000153
    DOI: 10.1016/j.jretconser.2025.104236
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