COMING SOON! PQDT Open is getting a new home!

ProQuest Open Access Dissertations & Theses will remain freely available as part of a new and enhanced search experience at

Questions? Please refer to this FAQ.

Dissertation/Thesis Abstract

Organizational Learning and Intelligent Machines: A Descriptive Case Study of a Biomedical Research Funding Organization’s Learning About Artificial Intelligence Technologies
by John, Kurt, Ed.D., The George Washington University, 2021, 222; 28412262
Abstract (Summary)

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.

Indexing (document details)
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
Publication Number: 28412262
ISBN: 9798597075310
Copyright © 2021 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy