This study explores the significance of task-technology fit (TTF) and social-technology fit (STF) in generative AI-based shopping platforms. Examination of this integration can assume importance for generative AI-based shopping platforms that can drive consumer intentions to continue using, ensuring long-term engagement and platform success. The study evaluates how these alignments influence users' satisfaction, perceived usefulness, and intention to continue using the platform. A mixed methodology approach was used in the study, and exploratory and confirmatory analyses were conducted. In the exploratory study, 27 respondents provided their responses; in study 2, which is confirmatory, 472 participants answered the questions. Generative AI can handle complex tasks and accommodate various user needs to enhance the platform's efficiency and overall user experience. In addition, the study focuses on social factors such as trust and community engagement, which influence user satisfaction and the effectiveness of the platform being used. Gender differences are also considered in the study by examining how these affect users' interactions with AI features. Gender differences significantly influence satisfaction and continued use of generative AI-based shopping platforms, highlighting the need for personalized and diverse AI features to cater to varied user preferences. These results provide detailed suggestions and worthwhile practices for developing AI-based shopping platforms that would appeal to their users in the long run and satisfy their emerging needs and preferences. Practical implications show the importance of deploying AI tasks that fit most business needs in order to promote scalability, community needs, personalization based on the gender of the user, and ethical considerations in order to promote the proper use of AI in business. These findings offer practical guidance for enhancing user engagement through tailored AI features.
Exploring consumer intentions to continue: Integrating task technology fit and social technology fit in generative AI based shopping platforms
Troise, Ciro
Co-first
;Bresciani, Stefano
2025-01-01
Abstract
This study explores the significance of task-technology fit (TTF) and social-technology fit (STF) in generative AI-based shopping platforms. Examination of this integration can assume importance for generative AI-based shopping platforms that can drive consumer intentions to continue using, ensuring long-term engagement and platform success. The study evaluates how these alignments influence users' satisfaction, perceived usefulness, and intention to continue using the platform. A mixed methodology approach was used in the study, and exploratory and confirmatory analyses were conducted. In the exploratory study, 27 respondents provided their responses; in study 2, which is confirmatory, 472 participants answered the questions. Generative AI can handle complex tasks and accommodate various user needs to enhance the platform's efficiency and overall user experience. In addition, the study focuses on social factors such as trust and community engagement, which influence user satisfaction and the effectiveness of the platform being used. Gender differences are also considered in the study by examining how these affect users' interactions with AI features. Gender differences significantly influence satisfaction and continued use of generative AI-based shopping platforms, highlighting the need for personalized and diverse AI features to cater to varied user preferences. These results provide detailed suggestions and worthwhile practices for developing AI-based shopping platforms that would appeal to their users in the long run and satisfy their emerging needs and preferences. Practical implications show the importance of deploying AI tasks that fit most business needs in order to promote scalability, community needs, personalization based on the gender of the user, and ethical considerations in order to promote the proper use of AI in business. These findings offer practical guidance for enhancing user engagement through tailored AI features.
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