@inproceedings{SmeetsRoetzel2024, author = {Smeets, Mario and R{\"o}tzel, Peter}, title = {The Moderating Role of Non-Monetary Gamification in Reducing Algorithm Aversion in the Adoption of AI-based Decision Support Systems}, series = {ECIS - European Conference on Information Systems}, volume = {2024}, booktitle = {ECIS - European Conference on Information Systems}, number = {1}, pages = {1}, year = {2024}, abstract = {Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, "algorithm aversion", can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @inproceedings{SmeetsRoetzel2024, author = {Smeets, Mario and R{\"o}tzel, Peter}, title = {The Moderating Role of Relative Performance Information in Reducing Algorithm Aversion In The Adoption Of AI-Based Decision Support Systems in Insolvency Prediction Tasks}, series = {4th ENEAR Conference at Erasmus University Rotterdam}, volume = {4}, booktitle = {4th ENEAR Conference at Erasmus University Rotterdam}, number = {1}, pages = {1 -- 18}, year = {2024}, abstract = {The integration of Artificial Intelligence (AI) into decision-making processes emerges as a pivotal strategy for enhancing organizational performance. The paper delves into the criticality of trust in AI-based Decision Support Systems (DSSs), similar to the trust required for other (Accounting) information systems to integrate them efficiently. We explore the disruptive phenomenon known as "algorithm aversion" - a significant barrier to the trust and acceptance of AI. Although AI recommendations outperform human recommendations in different decision-making fields, there exists a tendency among individuals to underweight AI-based DSSs recommendations relative to those from human decision-makers. This underutilization is attributed to the lack of trust in AI. We conducted a laboratory experiment designed to investigate the role of AI recommendations in a workplace-related task in the field of financial accounting. The study is twofold: firstly, it examines how AI trust mediates and algorithm aversion adversely impacts decision-making performance, while also considering the moderating role of technical competence. Secondly, it investigates the potential of gamification by using means of Relative Performance Information (RPI) as a strategy to mitigate the effects of algorithm aversion. Through this experiment, we provide empirical evidence on methods to enhance decision-making performance in the context of AI recommendations. Additionally, we identify and propose counterstrategies to combat algorithm aversion, thereby facilitating the broader adoption and integration of AI-based DSSs in accounting and auditing settings. This study contributes to the accounting and auditing research community by offering insights into how AI can be more effectively incorporated into decision-making processes, addressing both psychological and technical barriers to its acceptance.}, subject = {K{\"u}nstliche Intelligenz}, language = {en} } @article{Roetzel2024, author = {R{\"o}tzel, Peter}, title = {The role of supplier-induced demand on the occurrence of information overload in managerial reporting environments}, series = {PLOS ONE}, volume = {19}, journal = {PLOS ONE}, number = {7}, doi = {https://doi.org/10.1371/journal.pone.0307671}, pages = {e0307671 -- e0307671}, year = {2024}, abstract = {This article develops a model showing how information reporters influence information load among decision makers and generate supplier-induced information demand (SID). The intra-corporate information-providing process is an expert market with information asymmetry. I show that information overload occurs as an SID and is the result of informational overconsumption deliberately caused by the supplying reporter. My analysis highlights that the information overload depends on the specificity of information. It also shows that the decision maker may face a hold-up situation in light of switching costs. The more specific the information needed, the higher the threat of information overload. The strategic content of information tempts reporting managers to overload the decision maker for the purpose of increasing their reporting transfer price and to discourage the decision maker from getting the information from another reporting manager. Although the decision maker knows a part of the information demand, information overload involves the cost of using unnecessary inputs, information overload occurs as an SID of information, even if other competing reporting managers exist. My analysis demonstrates that information overload can occur due to uncertainty and opportunism of both the decision maker and reporting managers.}, subject = {Controlling}, language = {en} } @inproceedings{RoetzelFehrenbacher2024, author = {R{\"o}tzel, Peter and Fehrenbacher, Dennis}, title = {Information Overload in Decision Support Systems}, series = {International Symposium on Accounting Information Systems}, volume = {2024}, booktitle = {International Symposium on Accounting Information Systems}, address = {Paphos}, year = {2024}, subject = {Entscheidungsunterst{\"u}tzungssystem}, language = {en} } @incollection{Roetzel2024, author = {R{\"o}tzel, Peter}, title = {K{\"u}nstliche Intelligenz (KI) - unser bester Freund? Wie Menschen auf KI-Entscheidungsempfehlungen reagieren}, series = {Vertrauen in K{\"u}nstliche Intelligenz}, booktitle = {Vertrauen in K{\"u}nstliche Intelligenz}, publisher = {Springer Fachmedien}, address = {Wiesbaden}, doi = {10.1007/978-3-658-43816-6_2}, pages = {17 -- 31}, year = {2024}, abstract = {K{\"u}nstliche Intelligenz (KI) hat sich zu einer transformativen Kraft entwickelt, die verschiedene Aspekte der t{\"a}glichen Arbeit beeinflusst. Es stellt sich die Frage: K{\"o}nnen Menschen freundschaftliche Beziehungen zu KI-Entscheidungsunterst{\"u}tzungssystemen aufbauen oder werden diese Systeme nur als Werkzeuge betrachtet? In diesem Kapitel werden die Dynamik, die Herausforderungen und die M{\"o}glichkeiten von Mensch-KI-Interaktionen (MKI) untersucht, wobei ein besonderer Fokus auf die entscheidende Rolle des Vertrauens in dieser Interaktion gelegt wird. Das Vertrauen in KI wird durch kognitive, emotionale und soziale Faktoren beeinflusst. Zu den kognitiven Faktoren geh{\"o}ren die Transparenz und Interpretierbarkeit von KI-Systemen, zu den emotionalen Faktoren geh{\"o}ren die emotionale Bindung und das Verh{\"a}ltnis zwischen Menschen und KI-Agenten und zu den sozialen Faktoren geh{\"o}ren gesellschaftliche Normen und kulturelle Einfl{\"u}sse. Das Spannungsverh{\"a}ltnis zwischen Automatisierungs- und Algorithmusvermeidungstendenzen stellt eine komplexe Herausforderung f{\"u}r MKI dar. Automatisierungsbias bedeutet, sich unhinterfragt auf KI-Empfehlungen zu verlassen. Die Tendenz zur Algorithmusvermeidung beschreibt die Ablehnung oder das {\"U}bergehen von KI-Empfehlungen zugunsten eines menschlichen Urteils. Um dieses Spannungsfeld zu bew{\"a}ltigen, m{\"u}ssen transparente und erkl{\"a}rbare KI-Systeme entwickelt und eine effektive Zusammenarbeit zwischen Menschen und KI gef{\"o}rdert werden. Durch die Ber{\"u}cksichtigung dieser Faktoren und die St{\"a}rkung des Vertrauens kann MKI zu einer informierteren Entscheidungsfindung und einer effektiven Nutzung der KI-Funktionen f{\"u}hren}, subject = {K{\"u}nstliche Intelligenz}, language = {de} } @article{WeberPedellRoetzel2024, author = {Weber, Max M. and Pedell, Burkhard and R{\"o}tzel, Peter}, title = {Resilience-oriented management control systems: a systematic review of the relationships between organizational resilience and management control systems}, series = {Journal of Management Control}, journal = {Journal of Management Control}, publisher = {Springer Science and Business Media LLC}, issn = {2191-4761}, doi = {10.1007/s00187-024-00385-2}, year = {2024}, abstract = {Organizations regularly face serious challenges due to pandemics, recessions, and financial crises. One reason some organizations cope better than others may be that their management control systems (MCSs) more effectively foster organizational resilience. Despite considerable literature on MCSs and organizational resilience, there is a lack of research on the impact of an MCS's use on organizational resilience. This study examines and bridges the literatures on MCSs and organizational resilience to illuminate how organizations can better cope with adversity. To identify potential relationships between management controls, MCSs, and organizational resilience, we systematically review the literature and perform a content analysis. We examine the relationships between organizational resilience measures, capabilities, and management controls. We propose the use of resilience-oriented management controls and discuss whether organizations can increase their resilience by building resilience-oriented MCSs. Based on Simons's levers of control framework and Duchek's capability-based conceptualization of organizational resilience, we develop a conceptual organizational resilience/MCS framework. Our study reveals relationships and gaps between the literatures on MCSs and organizational resilience and proposes avenues for future research. Our findings suggest that resilience-oriented MCSs are beneficial to organizational resilience.}, subject = {Management}, language = {en} }