Publicação
Consumer Behaviour: Consumer’s Perception of AI Chatbots and Purchase Intention
| Resumo: | This research investigates the impact of various factors on consumer trust in retail chatbots, focusing on the pre-purchase context. With the rise of artificial intelligence and its integration into customer service, understanding the dynamics of trust in chatbot interactions has become critical to improving user experience and increasing purchase intent. The study examines seven independent variables: expertise, perceived risk, competence, credibility, anthropomorphism, trust in chatbots, and trust in the seller, as well as one dependent variable: purchase intention. Data was collected through an online questionnaire distributed through multiple platforms, such as Instagram and WhatsApp, to a diverse sample of respondents, all over 18, who had interacted with retail chatbots in the past 12 months. The data was analyzed using the SmartPLS tool, and multiple partial least squares structural equation modeling (PLS-SEM) was performed to evaluate the measurement and structural models. The study's results contribute to a broader understanding of consumer behavior in AI-driven interactions and provide practical insights for retailers looking to optimize their chatbot features to build trust and drive purchases. The research highlights the importance of tailored chatbot design strategies to meet the varying expectations of different consumer segments. |
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| Autores principais: | Carvalho, Inês Mota da Silva |
| Assunto: | Chatbot Consumer Behavior Artificial Intelligence Customer Experience Consumer Perception Customer Engagement SDG 9 - Industry, innovation and infrastructure |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This research investigates the impact of various factors on consumer trust in retail chatbots, focusing on the pre-purchase context. With the rise of artificial intelligence and its integration into customer service, understanding the dynamics of trust in chatbot interactions has become critical to improving user experience and increasing purchase intent. The study examines seven independent variables: expertise, perceived risk, competence, credibility, anthropomorphism, trust in chatbots, and trust in the seller, as well as one dependent variable: purchase intention. Data was collected through an online questionnaire distributed through multiple platforms, such as Instagram and WhatsApp, to a diverse sample of respondents, all over 18, who had interacted with retail chatbots in the past 12 months. The data was analyzed using the SmartPLS tool, and multiple partial least squares structural equation modeling (PLS-SEM) was performed to evaluate the measurement and structural models. The study's results contribute to a broader understanding of consumer behavior in AI-driven interactions and provide practical insights for retailers looking to optimize their chatbot features to build trust and drive purchases. The research highlights the importance of tailored chatbot design strategies to meet the varying expectations of different consumer segments. |
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