Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
Artificial Intelligence (AI) technologies such as machine learning (ML), natural language processing (NLP), and image recognition, are being incorporated into a wide variety of applications. These AI-enabled applications (AIapps) promise to reshape people's lives. However, despite the proliferation of AI-related research, very little research has focused on how AIapps' unique characteristics affect an individual's adoption behavior. This study examines factors influencing an individual's intention to use AIapps with a proposed research model based on the Task-Technology Fit (TTF) as the underlying theoretical framework. The research model is empirically evaluated using the survey data and SEM method. Theoretically, this study focuses on how the unique characteristics of AIapps influence the task-technology fit and drive the intention of use. The findings are expected to help AIapp developers to evaluate the relative importance of AIapp features which can provide insights into the technology characteristics and identify priorities for further research and development.
Recommended Citation
Zhao, Yu (Gary); El-Gayar, Omar; and Tu, Zhiling, "Is Artificial Intelligence Attractive? An Empirical Study on User’s Intention to Use AI-Enabled Applications" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/st/metaverse/2
Is Artificial Intelligence Attractive? An Empirical Study on User’s Intention to Use AI-Enabled Applications
Online
Artificial Intelligence (AI) technologies such as machine learning (ML), natural language processing (NLP), and image recognition, are being incorporated into a wide variety of applications. These AI-enabled applications (AIapps) promise to reshape people's lives. However, despite the proliferation of AI-related research, very little research has focused on how AIapps' unique characteristics affect an individual's adoption behavior. This study examines factors influencing an individual's intention to use AIapps with a proposed research model based on the Task-Technology Fit (TTF) as the underlying theoretical framework. The research model is empirically evaluated using the survey data and SEM method. Theoretically, this study focuses on how the unique characteristics of AIapps influence the task-technology fit and drive the intention of use. The findings are expected to help AIapp developers to evaluate the relative importance of AIapp features which can provide insights into the technology characteristics and identify priorities for further research and development.
https://aisel.aisnet.org/hicss-56/st/metaverse/2