AUTHOR=Ðula Ivan , Berberena Tabea , Keplinger Ksenia , Wirzberger Maria 

TITLE=Hooked on artificial agents: a systems thinking perspective

JOURNAL=Frontiers in Behavioral Economics

VOLUME=Volume 2 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/behavioral-economics/articles/10.3389/frbhe.2023.1223281

DOI=10.3389/frbhe.2023.1223281

ISSN=2813-5296

ABSTRACT=Following recent technological developments in the artificial intelligence space, artificial agents are increasingly taking over organizational tasks typically reserved for humans. Studies have shown that humans respond differently to this, with some being appreciative of their advice (algorithm appreciation), others being averse towards them (algorithm aversion), and others still fully relinquishing control to artificial agents without adequate oversight (automation bias). Using systems thinking, we analyze the existing literature on these phenomena and develop a conceptual model that provides an underlying structural explanation for their emergence. In doing so, we create a powerful visual tool that can be used to ground discussions about the impact artificial agents have on organizations and humans within them. This is a provisional file, not the final typeset article especially in manufacturing and service industries (Müller, Buliga, and Voigt, 2020). AAs are implemented in the workplace to complement individual intelligence and thus to increase the quality and accuracy of decision-making processes (Wilkens, 2020). Previous research describes three distinct, though seemingly connected, phenomena among humans interacting with AAs in the workplace: algorithm appreciation, algorithm aversion, and automation bias. Algorithm appreciation occurs when humans appreciate the automated advice and may prefer it to human advice (Madhavan   and Weigmann, 2007;Chugunova and Sele, 2022), even in situations where the automated advice is incorrect and the human advice is correct (Dijsktra, 1999). Algorithm appreciation involves understanding how algorithms work, what they can and cannot do, and how they can be used to solve real-world problems. It also encompasses recognizing the strengths and limitations of different algorithms as well as understanding how to evaluate their performance (Jussupow et al., 2020).However, not everyone reacts the same (positive) way when it comes to AAs. Some users are growing reluctant to interact with AAs instead of human agents (