Conceptualizing Responsible Adoption of Artificial Agents in the Workplace: A Systems Thinking Perspective

Ivan Ðula (), Tabea Berberena, Ksenia Keplinger and Maria Wirzberger
Additional contact information
Ivan Ðula: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”
Tabea Berberena: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”
Ksenia Keplinger: Max Planck Institute for Intelligent Systems, Organizational Leadership & Diversity
Maria Wirzberger: University of Stuttgart, Cluster of Excellence EXC 2075 “Data-Integrated Simulation Science”

A chapter in Navigating Digital Transformation, 2024, pp 229-249 from Springer

Abstract: Abstract Following recent technological developments, organizations and businesses seek to improve their effectiveness by increasing the use of artificial agents in the workplace. Previous research suggests that humans react to the adoption of artificial agents in three ways: 1) some humans appreciate algorithmic advice (algorithm appreciation); 2) some humans oppose algorithmic advice (algorithm aversion); and 3) some humans fully relinquish control to artificial agents (automation bias). Using tools and methods form the field of systems thinking, we analyze the existing literature on human-machine interactions in organizational settings and develop a conceptual model that provides an underlying structural explanation for the emergence of algorithm appreciation, algorithm aversion, and automation bias in various contexts. In doing so, we create a powerful visual tool that can be used to ground discussions about the responsible adoption of artificial agents in the workplace and the long-term impact they cause for organizations and humans within them. We use the model to hypothesize possible behavioral outcomes produced by the proposed structure.

Keywords: Artificial agents; Artificial intelligence; Systems thinking; Algorithm appreciation; Algorithm aversion; Automation bias (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-76970-2_15

Ordering information: This item can be ordered from
http://www.springer.com/9783031769702

DOI: 10.1007/978-3-031-76970-2_15

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().