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<p>The increasing popularity of conversational artificial intelligence (chatbot) in retail has fuelled consumer purchase decisions. The present study measures human–bot interaction through anthropomorphism, social presence, personalization, media richness, and its role in customer engagement and continuous purchase intention for retail revenue growth. Data were collected from 366 respondents and analyzed using PLS-SEM-ANN integrated approach. To overcome the limitation of establishing only the linear relationship among variables for prediction, the study deploys nonlinear complex artificial neural network (ANN) models to determine the relative importance of the significant predictors identified through structural equation modeling (PLS-SEM). The findings of the study confirm the role of anthropomorphism, personalization, and media richness in customer engagement and customer engagement leading to continuous purchase intention. Furthermore, Chatbots in the electronic commerce space strengthen involvement, but trust and price fairness also play a positive moderating influence in shaping consumer purchase intentions. Study implications highlight the role of anthropomorphic design in the desired social presence for sustained engagement and its contribution to revenue. This study enriches the AI literature and guides organizations leveraging chatbots to create more natural and personalized interactions so retailers can better meet customer needs, foster loyalty, and stay competitive in an increasingly digital marketplace.</p>

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Examining humanized bot interaction for the retail revenue growth

  • 期刊论文

  • 发表日期：

    2025年8月3日

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  • Nishant Kumar,
  • Pratibha Garg,
  • Neha Gupta,
  • Kamal Upreti

摘要

The increasing popularity of conversational artificial intelligence (chatbot) in retail has fuelled consumer purchase decisions. The present study measures human–bot interaction through anthropomorphism, social presence, personalization, media richness, and its role in customer engagement and continuous purchase intention for retail revenue growth. Data were collected from 366 respondents and analyzed using PLS-SEM-ANN integrated approach. To overcome the limitation of establishing only the linear relationship among variables for prediction, the study deploys nonlinear complex artificial neural network (ANN) models to determine the relative importance of the significant predictors identified through structural equation modeling (PLS-SEM). The findings of the study confirm the role of anthropomorphism, personalization, and media richness in customer engagement and customer engagement leading to continuous purchase intention. Furthermore, Chatbots in the electronic commerce space strengthen involvement, but trust and price fairness also play a positive moderating influence in shaping consumer purchase intentions. Study implications highlight the role of anthropomorphic design in the desired social presence for sustained engagement and its contribution to revenue. This study enriches the AI literature and guides organizations leveraging chatbots to create more natural and personalized interactions so retailers can better meet customer needs, foster loyalty, and stay competitive in an increasingly digital marketplace.