@inproceedings{b43e9044b25d4ab5ba1ef4d8a5c9c060,
title = "AI design to innovation",
abstract = "Artificial intelligence (AI) is expected to create various innovations for changing human workplaces. AI is characterized by features of learning and self-growth. Efficient AI learning should depend on human inputs, particularly from human professionals (e.g., doctors and nurses). Hence, professionals' intention to facilitate AI innovation is critical. However, little is known about how to design AI to strengthen such intention, warranting our research to answer this question. We use expectancy-value theory to identify three potential AI design elements and examine how they enhance the perception that AI enhances professionals' capabilities and their intention to facilitate AI innovation. These elements are contextual-specific features of AI, extending the expectancy-value theory to the novel AI technologies. We will test our model by using two-wave data of nursing professionals' responses. The results are expected to assist AI designs that effectively motivate professionals to facilitate AI innovations.",
keywords = "AI design, Artificial intelligence (AI), Expectancy value, Innovation, Professional",
author = "Huang, {Tzu Ling} and Teng, {Ching I.}",
note = "Publisher Copyright: {\textcopyright} 2020 26th Americas Conference on Information Systems, AMCIS 2020. All rights reserved.; 26th Americas Conference on Information Systems, AMCIS 2020 ; Conference date: 10-08-2020 Through 14-08-2020",
year = "2020",
language = "???core.languages.en_GB???",
series = "26th Americas Conference on Information Systems, AMCIS 2020",
publisher = "Association for Information Systems",
booktitle = "26th Americas Conference on Information Systems, AMCIS 2020",
}