Paper Type
ERF
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.
Recommended Citation
Huang, Tzu-Ling and Teng, Ching-I, "AI Design to Innovation" (2020). AMCIS 2020 Proceedings. 1.
https://aisel.aisnet.org/amcis2020/cognitive_in_is/cognitive_in_is/1
AI Design to Innovation
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.
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