The Nexus Between Artificial Intelligence and the Behavioral Theory of the Firm
Type
conference contribution
Date Issued
2020-08
Author(s)
;
;
Bettis, Richard A.
;
Piezunka, Henning
;
Posen, Hart E.
;
Puranam, Phanish
;
;
Teodoridis, Florenta
Abstract
The Behavioral Theory of the Firm (BTF; Cyert & March, 1963) has, essentially since its inception, been intimately linked with AI. However, since the original pioneering work by behavioral theorists on the importance of AI for our understanding of the functioning of organizations, great strides have been made in AI technologies. Hence, the time is ripe to reevaluate both the central assumptions of the behavioral theory of the firm and its key implications. Consequently, this panel symposium intends to open up a dialogue about whether and how our understanding of the BTF has changed and, perhaps, how it should still change in response to and in step with advancements in artificial intelligence. The main purpose of this Symposium is to engage a group of panelists in a moderated and interactive discussion on the questions pertaining to the nexus between AI and the BTF. More specifically, the panelists will (1) discuss how – in the light of the technological advances of AI – the BTF has changed and should be further reevaluated, (2) identify challenges and opportunities researchers face in linking AI to behavioral theory, and (3) discuss ways forward. Moreover, this symposium aims to build a network of scholars from different career stages and divisions who share an interest in this topic.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
Global Center for Entrepreneurship + Innovation
Publisher
AOM Academy of Management
Publisher place
Briarcliff Manor, NY
Event Title
Academy of Management Annual Meeting (AOM) 2020
Event Location
Vancouver, British Columbia (CA)
Event Date
07.-11.08.2020
Subject(s)
Eprints ID
260216
File(s)![Thumbnail Image]()
Name
AI and BTF 2020 AOM Panel Symposium.pdf
Size
182.37 KB
Format
Adobe PDF
Checksum (MD5)
f91c43c04c52919f7eb66fd1f9273161