How Would Individuals Like to Use AI-Enabled Applications? An Empirical Study Based on HCI Framework
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Paper Type
Complete
Description
The market of AI-enabled applications is growing fast and AI-enabled application has become a regular part of people’s daily lives. Applying the Human-Computer Interaction (HCI) framework, this research attempts to study how likely people would accept using AI-enabled applications and what factors affect individual decisions. An empirical study is conducted through an online survey and tested using a PLS-SEM model. Results show that User characteristics, AI-enabled application characteristics, user interaction, trust, and task-technology fit are the factors that affect users’ intention to use AI-enabled applications. Theoretically, this study improves the understanding of how the unique characteristics of AI-enabled applications affect users’ acceptance of AI-enabled applications. Practically, the findings of this study can enhance the comprehension of individual users' application usage patterns for developers of AI-powered applications.
Paper Number
1326
Recommended Citation
Tu, Cindy Zhiling; Zhao, Gary Yu; and Adkins, Joni, "How Would Individuals Like to Use AI-Enabled Applications? An Empirical Study Based on HCI Framework" (2023). AMCIS 2023 Proceedings. 9.
https://aisel.aisnet.org/amcis2023/sig_adit/sig_adit/9
How Would Individuals Like to Use AI-Enabled Applications? An Empirical Study Based on HCI Framework
The market of AI-enabled applications is growing fast and AI-enabled application has become a regular part of people’s daily lives. Applying the Human-Computer Interaction (HCI) framework, this research attempts to study how likely people would accept using AI-enabled applications and what factors affect individual decisions. An empirical study is conducted through an online survey and tested using a PLS-SEM model. Results show that User characteristics, AI-enabled application characteristics, user interaction, trust, and task-technology fit are the factors that affect users’ intention to use AI-enabled applications. Theoretically, this study improves the understanding of how the unique characteristics of AI-enabled applications affect users’ acceptance of AI-enabled applications. Practically, the findings of this study can enhance the comprehension of individual users' application usage patterns for developers of AI-powered applications.
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