Dissertation/Thesis Abstract

US Knowledge Worker Augmentation versus Replacement with AI Software: Impact on Organizational Returns, Innovation, and Resistance
by Boggan, Chad M., D.Engr., The George Washington University, 2019, 96; 10979025
Abstract (Summary)

This praxis studies the effects on organizations of replacing US knowledge workers with artificial intelligence software (automation) and enhancing US knowledge workers with artificial intelligence software (augmentation). The effects on organizational innovation, resistance, and return on investment (ROI) are studied.

The main purpose of this study is to confirm the relationships between automation/augmentation, innovation, resistance, and ROI. This study is also meant to aid researchers, policy makers, executives, and others that have influence over automation and augmentation decisions. The implications of these decisions will reverberate through the multi-billion-dollar US job market in the coming years.

Quantitative methods were used to look at researched examples of both automation and augmentation. Data from 1993 to 2018 was gathered and assessed on innovation, resistance, and ROI from a number of different industries and a number of different types of firms based on size and ownership structure (public or private). Statistical methods were then used to compare the effects of automation and augmentation on organizations.

Research data was gathered to study the relationship between innovation and ROI, as well as the relationship between resistance and ROI. These relationships were used to combine ROI, innovation, and resistance using Monte Carlo simulations. This combination of ROI, innovation, and resistance was then used to compare the combined effects of automation and augmentation on organizations over time.

Indexing (document details)
Advisor: Malalla, Ebrahim, Etemadi, Amirhossein
Commitee: Blackburn, Timothy D., Etemadi, Amirhossein, Malalla, Ebrahim
School: The George Washington University
Department: Engineering Management
School Location: United States -- District of Columbia
Source: DAI-B 80/03(E), Dissertation Abstracts International
Subjects: Business administration, Engineering, Artificial intelligence, Computer science
Keywords: Augmentation, Automation, Innovation, Resistance, Return on investment
Publication Number: 10979025
ISBN: 978-0-438-68161-3
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