AI design to innovation

Tzu Ling Huang, Ching I. Teng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publication26th Americas Conference on Information Systems, AMCIS 2020
PublisherAssociation for Information Systems
ISBN (Electronic)9781733632546
StatePublished - 2020
Event26th Americas Conference on Information Systems, AMCIS 2020 - Salt Lake City, Virtual, United States
Duration: 10 Aug 202014 Aug 2020

Publication series

Name26th Americas Conference on Information Systems, AMCIS 2020


Conference26th Americas Conference on Information Systems, AMCIS 2020
Country/TerritoryUnited States
CitySalt Lake City, Virtual


  • AI design
  • Artificial intelligence (AI)
  • Expectancy value
  • Innovation
  • Professional


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