This study explores how consumers’ familiarity with e-commerce, as well as their perceived benefits and barriers related to it, influence the acceptance of AI-powered chatbots in online shopping contexts. Drawing on the Technology Acceptance Model (TAM), the research integrates external variables such as frequency of e-commerce use and perceived convenience, risk, and lack of physical interaction with chatbot adoption. A survey was conducted on 220 participants, and data were analyzed using ANOVA and Structural Equation Modeling (SEM). Results show that e-commerce usage frequency significantly shapes how users perceive the benefits and barriers of online shopping. However, it does not directly influence their perception of chatbot usefulness or ease of use. Among the factors considered, only perceived convenience was found to positively affect both TAM constructs. The model confirms that perceived usefulness and, to a lesser extent, ease of use predict attitude toward chatbots, which strongly influences behavioral intention. These findings extend the TAM framework to the domain of conversational AI, offering theoretical insights and practical guidelines for improving chatbot adoption strategies in e-commerce environments.
From E-Commerce Habits to Chatbot Acceptance: Extending the Technology Acceptance Model to Ai Conversational Agents.
Gissi V.;Savelli E.
2025
Abstract
This study explores how consumers’ familiarity with e-commerce, as well as their perceived benefits and barriers related to it, influence the acceptance of AI-powered chatbots in online shopping contexts. Drawing on the Technology Acceptance Model (TAM), the research integrates external variables such as frequency of e-commerce use and perceived convenience, risk, and lack of physical interaction with chatbot adoption. A survey was conducted on 220 participants, and data were analyzed using ANOVA and Structural Equation Modeling (SEM). Results show that e-commerce usage frequency significantly shapes how users perceive the benefits and barriers of online shopping. However, it does not directly influence their perception of chatbot usefulness or ease of use. Among the factors considered, only perceived convenience was found to positively affect both TAM constructs. The model confirms that perceived usefulness and, to a lesser extent, ease of use predict attitude toward chatbots, which strongly influences behavioral intention. These findings extend the TAM framework to the domain of conversational AI, offering theoretical insights and practical guidelines for improving chatbot adoption strategies in e-commerce environments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
