Impact of AI on Consumers’ Purchase Intention Towards Online Grocery Shopping in India
DOI:
https://doi.org/10.13052/jrss0974-8024.17210Keywords:
Artificial intelligence (AI), technology acceptance model (TAM), partial least squared structural equation modelling (PLS-SEM), purchase intention, online grocery shoppingAbstract
In recent years, the rapid advancement of technology, specifically in Artificial Intelligence (AI), has considerably impacted consumer satisfaction in the e-retailing sector. The time-saving benefits and convenience of shopping in the comfort of their home with AI induce people to adopt digital technologies, reflecting a change in consumer behaviour. While existing studies have focused on examining TAM (technology acceptance model) components’ influence on technology acceptance, there is a lack of India-specific focus in studies and limited consideration of AI technologies like voice search or chatbots’ impact on purchase intention. This study extends the application of TAM components to the Indian online grocery sector.
This study bridges a critical research gap by revealing the interplay between AI technologies and consumer behavior in India’s INR 760.2billion online grocery market. Using India’s grocery sector, the contribution of the study is in the recommendation of developing a technology-based consumer experience enhancement framework for online grocery platforms to effectively target consumers, particularly in regions with similar socio-economic characteristics.
This study utilises the PLS-SEM (partial least squares structural equation modelling) model on 231 samples to analyse data and examine the impact of AI-driven technology on consumers’ online grocery shopping behaviour. PLS-SEM is instrumental in handling complex models with multiple constructs. This method is considered ideal for handling complex models with multiple constructs and primary research with smaller sample size. The application of this method also validated the conceptual framework by confirming strong construct reliability and validity. The analysis revealed that AI features like personalized recommendations, chatbots, and voice assistants improved the shopping experience by making it more efficient and easier. This enhanced user experience led to increased purchase intentions. This could be seen by the significant moderating role of AI technology and TAM components interaction on attitude towards AI.
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