Today, artificial intelligence technologies (AI) add significant complexities to organizational learning, performance, and change, and these technologies are proliferating across all industries at rapidly increasing rates (West, 2018). However, most organizations do not understand how to make sense of AI (Brynjolfsson & McAfee, 2017), and the scholarship best suited to guide organizations’ learning with AI is at a nascent stage (von Krogh, 2018).
To address the practical challenge and scholarly gap, this case study examined one biomedical research funding organization’s learning regarding AI. The study used Weick’s (1979) enactment theory (ET) to unpack the core dynamics of the organization’s actions and reflections in learning about AI—as posited in Schwandt and Marquardt’s (2000) organizational learning systems model (OLSM). The study was guided by the following research question: How does an organization focused on funding biomedical research use action and reflection processes while learning about AI?
This study demonstrated that ET and the OLSM are useful for guiding research and practice regarding AI. Six primary conclusions emerged: (1) dialogue is the nucleus of organizational learning; (2) dialogue links the levels of learning in a collective system; (3) organizational learning includes both orderly and dynamic processes; (4) collaboration increases organizational learning efficacy; (5) reflections must be probed/ stimulated for organizational learning to occur; and (6) organizational learning produces outcomes at multiple levels of analysis and at different time intervals.
|Advisor:||Goldman, Ellen F.|
|Commitee:||Casey, Andrea, Barnes, Mary|
|School:||The George Washington University|
|Department:||Human & Organizational Learning|
|School Location:||United States -- District of Columbia|
|Source:||DAI-A 82/10(E), Dissertation Abstracts International|
|Subjects:||Organization Theory, Artificial intelligence, Information Technology|
|Keywords:||Organizational change, Organizational learning, Organizational performance|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
The supplemental file or files you are about to download were provided to ProQuest by the author as part of a
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
Copyright of the original materials contained in the supplemental file is retained by the author and your access to the
supplemental files is subject to the ProQuest Terms and Conditions of use.
Depending on the size of the file(s) you are downloading, the system may take some time to download them. Please be