@article{65182,
  abstract     = {{<jats:p>The aggregation of rating metrics in reputation systems is crucial for mitigating information overload by condensing customer rating distributions into singular valence scores. While platforms typically employ technical aggregation functions, such as the arithmetic mean to capture product quality, it remains unclear whether these functions align with customers' innate aggregation patterns. To address this knowledge gap, we designed a controlled economic decision experiment to elicit customers' aggregation principles by analyzing their product ranking decisions and contrasting these with various reference functions. Our findings indicate that, on average, customers aggregate rating information in accordance with the arithmetic mean. However, a granular analysis at the individual level reveals significant heterogeneity in aggregation behavior, with a substantial cluster exhibiting binary patterns that focus equally on negative (1-2 star) and positive (4-5 star) ratings. Additional clusters concentrate on negative feedback, particularly 1-star ratings or 1-2 star ratings collectively. Notably, these inherent aggregation patterns exhibit stability across variations in numerical information presentation and are not significantly influenced by individual characteristics, such as online shopping experience, risk attitudes, or demographics. These findings suggest that while the arithmetic mean captures average consumer behavior, platforms could benefit from offering customizable aggregation options to better cater to diverse user preferences for processing rating distributions. By doing so, platforms can enhance the effectiveness of their reputation systems and improve the overall quality of decision-making for consumers.</jats:p>}},
  author       = {{van Straaten, Dirk and Mir Djawadi, Behnud and Melnikov, Vitalik and Hüllermeier, Eyke and Fahr, René}},
  journal      = {{SSRN Electronic Journal}},
  publisher    = {{Elsevier BV}},
  title        = {{{Aggregation Processes in Customer Rating Systems - Insights from an Economic Decision Experiment}}},
  doi          = {{http://dx.doi.org/10.2139/ssrn.6201258}},
  year         = {{2026}},
}

@article{65181,
  abstract     = {{<jats:p>In many Western societies, mass immigration has been one of the most divisive policy issues in recent years. Seemingly moderate inflows of migrants can have substantial demographic consequences in the long run, due to (1) higher fertility of the migrant population, (2) its younger age distribution, and (3) the possibility of family reunification. Yet, demography hardly appears in the policy debate, even in media outlets that are critical of mass immigration. This may indicate that the mechanics of population dynamics are not widely understood. We design a laboratory experiment in which we confront subjects with 30 different migration scenarios. Subjects have to decide when to stop a given inflow of migrants to achieve a target share of migrants after 60 years. In line with all our pre-registered hypotheses, in scenarios that contain elements of usual mass immigration the growth of the migrant population is systematically underestimated. This bias is even stronger in scenarios that closely resemble the German situation since the opening of the borders during the 2015 refugee crisis.</jats:p>}},
  author       = {{Abbink, Klaus and Mir Djawadi, Behnud}},
  journal      = {{SSRN Electronic Journal}},
  publisher    = {{Elsevier BV}},
  title        = {{{Migration and Long-Term Demographic Change: Can We Control the Numbers?}}},
  doi          = {{http://dx.doi.org/10.2139/ssrn.6343618}},
  year         = {{2026}},
}

@article{63910,
  author       = {{Mir Djawadi, Behnud}},
  journal      = {{SSRN Electronic Journal}},
  publisher    = {{Elsevier BV}},
  title        = {{{Dishonesty of Parents and Children – Evidence from a Field Experiment}}},
  doi          = {{http://dx.doi.org/10.2139/ssrn.6121987}},
  year         = {{2026}},
}

@article{65666,
  author       = {{Bodenberger, Robin and Thommes, Kirsten}},
  issn         = {{1366-9877}},
  journal      = {{Journal of Risk Research}},
  pages        = {{1--21}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Words or numbers? How framing uncertainties affects risk assessment and decision-making}}},
  doi          = {{10.1080/13669877.2026.2667755}},
  year         = {{2026}},
}

@article{65909,
  abstract     = {{<jats:title>ABSTRACT</jats:title>
                  <jats:p>Creativity and innovation are often understood as the result of a complex interplay of hierarchical factors, such as national, regional and firm characteristics, or between organisational and individual factors. While recent applications of qualitative comparative analysis (QCA) have begun to model such configurational links, their hierarchical nature has received little empirical attention. As this paper demonstrates, theories that posit hierarchical configurations can and should be explored using the two‐step variant of QCA. The paper outlines the potential of the method for the field of creativity and innovation and helps to navigate key modelling decisions. An illustrative study explores the occurrence of informal employee innovation behaviour—workarounds—based on the Ability‐Motivation‐Opportunity (AMO) framework. The results of the two‐step QCA are superior in terms of reduced limited diversity and complexity to those of the conventional one‐step QCA. Overall, the method has considerable potential for empirically capturing the complex, hierarchical interactions inherent in many innovation processes.</jats:p>}},
  author       = {{Sandfort, Luc and Hellweg, Talea Davina and Radermacher, Katharina and Schneider, Martin}},
  issn         = {{0963-1690}},
  journal      = {{Creativity and Innovation Management}},
  publisher    = {{Wiley}},
  title        = {{{Modelling Hierarchical Configurations in Innovation Research With Two‐Step QCA: Methodological Recommendations and an Application to Workarounds}}},
  doi          = {{10.1111/caim.70053}},
  year         = {{2026}},
}

@article{65910,
  author       = {{Hellweg, Talea Davina and Radermacher, Katharina and Schneider, Martin and Sandfort, Luc Dana}},
  journal      = {{PersonalQuarterly 2026(01)}},
  pages        = {{50--57}},
  title        = {{{Verstecktes Innovationspotenzial: Entstehungsfaktoren von Workarounds. }}},
  year         = {{2026}},
}

@inproceedings{63585,
  author       = {{Alberternst, Benedikt and Steinhoff, Lena and Woisetschläger, David M}},
  booktitle    = {{2025 AMA Winter Academic Conference Proceedings}},
  title        = {{{Responsible Loyalty Programs: How Redemptions for a Cause Impact Customer Loyalty}}},
  year         = {{2025}},
}

@inproceedings{63584,
  author       = {{Witte, Carina and Steinhoff, Lena}},
  booktitle    = {{2025 AMA Winter Academic Conference Proceedings}},
  title        = {{{Unlocking Spiritual Value: Magical Experiences of Commercial Services}}},
  year         = {{2025}},
}

@techreport{63596,
  abstract     = {{<jats:p>Im Rahmen der vorliegenden Studie wird der Status quo sowie die Entwicklung des Einsatzes von KI in der industriellen Arbeitswelt in OstWestfalenLippe beschrieben. Ziel ist es, eine belastbare Grundlage für die Gestaltung KI-gestützter Arbeitsprozesse zu schaffen und bedarfsbezogene Maßnahmen abzuleiten.
Die Befragungen wurden in den Jahren 2021 und 2025 vom Kompetenzzentrum Arbeitswelt.Plus und dem Spitzencluster it’s OWL durchgeführt. Im Jahr 2021 nahmen 318 Personen aus 89 Unternehmen teil. Im Jahr 2025 beteiligten sich 240 Personen aus 50 Unternehmen.
Die Ergebnisse zeigen eine deutliche Weiterentwicklung. Unternehmen bewegen sich zunehmend in Diskussions- und Einführungsphasen, und die tägliche Nutzung von KI durch die Mitarbeitenden steigt. Mit dem Durchbruch generativer KI rücken text- und wissensbezogene Anwendungen in den Vordergrund. Der Autonomiegrad der Lösungen ist überwiegend Mensch-unterstützend und dient der Entscheidungsvorbereitung. Zentrales Ziel für die Einführung von KI im Unternehmen bleibt die Effizienzsteigerung. Im Jahr 2025 gewinnen zudem die Unterstützung der Mitarbeitenden im Arbeitsalltag und die Reduzierung von Belastungen an Bedeutung, was die Relevanz der Mensch-KI-Zusammenarbeit unterstreicht.
Als zentrale Herausforderungen zeigt sich die hohe Komplexität von KI, die die Einführung im Unternehmen erschwert. Weitere Herausforderungen sind fehlende Kompetenzen, die durch gezielte Qualifizierung aufgebaut werden müssen. Dabei besteht eine Diskrepanz zwischen Selbst- und Fremdwahrnehmung hinsichtlich der Schulungsangebote. Zudem muss Datensicherheit gewährleistet werden, passende Anwendungsfälle ausgewählt werden und Entscheidungsprozesse beschleunigt werden. Demgegenüber wurde herausgefunden, dass den Mitarbeitenden in den befragten Unternehmen im Jahr 2025 grundsätzlich das Ziel und der Mehrwert der Einführung von KI klar sind und sie die Stärken und Grenzen von KI verstehen.
Die gewonnenen Erkenntnisse fließen in die Gestaltung der Einführungsprozesse von KI im Unternehmen ein. Unternehmen sollten sich der genannten Herausforderungen bewusst sein, um passend zu reagieren und die Mensch-KI-Interaktion zu stärken. Eine ganzheitliche Sicht auf KI als Technologie, auf den Menschen und auf das Unternehmen ist für die Einführung von KI wichtig.</jats:p>}},
  author       = {{Dondorf, Verena and Lebedeva, Elena and Thommes, Kirsten and Dumitrescu, Roman}},
  publisher    = {{Kompetenzzentrum Arbeitswelt.Plus}},
  title        = {{{Evolution von KI in der industriellen Arbeitswelt}}},
  doi          = {{10.55594/baey2442}},
  year         = {{2025}},
}

@unpublished{63782,
  abstract     = {{Senders of messages prefer to communicate uncertainty verbally (e.g., something is likely to happen) rather than numerically (such as 75%), leaving receivers with imprecise information. While it is well established that receivers translate verbal probabilities into numerical values that systematically deviate from the intended numerical meaning, it is less clear how this discrepancy influences subsequent behavioral actions. Thus, the role of verbal versus numerical communication of uncertainty warrants additional attention, to investigate two critical questions: 1) whether differences in decision-making under uncertainty arise between these communication forms, and 2) whether such differences persist even when verbal phrases are translated accurately into the intended numerical meaning. By implementing a laboratory experiment, we show that individuals place significantly lower values on uncertain options with medium to high likelihoods when uncertainty is communicated verbally rather than numerically. This effect may lead to less rational decisions under verbal communication, particularly at high likelihoods. Those results remain consistent even if individuals translate verbal uncertainty correctly into the intended numerical uncertainty, implying that a biased behavioral response is not only induced by miscommunication. Instead, ambiguity about the exact meaning of a verbal phrase interferes with decision-making even beyond potential mistranslations. These findings tie in with previous research on ambiguity aversion, which has predominantly operationalized ambiguity through numerical ranges rather than verbal phrases. Based on our findings we conclude that managers should communicate uncertainty numerically, as verbal communication can unintentionally influence the decision-making process of employees.}},
  author       = {{Bodenberger, Robin and Thommes, Kirsten}},
  title        = {{{Words or Numbers? How Framing Uncertainties Affects Risk Assessment and Decision-Making}}},
  year         = {{2025}},
}

@unpublished{63783,
  author       = {{Thommes, Kirsten and Mehic, Miro}},
  publisher    = {{Elsevier BV}},
  title        = {{{Foreign Language Use, Attribution Error, and Newcomer Integration}}},
  year         = {{2025}},
}

@article{61377,
  author       = {{Schneider, Martin and Hemsen, Paul and Kundisch, Dennis}},
  journal      = {{management revue - Socio-Economic Studies, Special Issue “Digital Transformation of Work”.}},
  publisher    = {{Nomos Verlag}},
  title        = {{{Who are the Actively Participating Crowdworkers? A Qualitative Comparative Analysis of a German Text Creation PlatformSocio-Economic Studies, }}},
  year         = {{2025}},
}

@inbook{61820,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>A scoring list is a sequence of simple decision models, where features are incrementally evaluated and scores of satisfied features are summed to be used for threshold-based decisions or for calculating class probabilities. In this paper, we introduce a new multi-class variant and compare it against previously introduced binary classification variants for incremental decisions, as well as multi-class variants for classical decision-making using all features. Furthermore, we introduce a new multi-class dataset to assess collaborative human-machine decision-making, which is suitable for user studies with non-expert participants. We demonstrate the usefulness of our approach by evaluating predictive performance and compared to the performance of participants without AI help.</jats:p>}},
  author       = {{Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas and Thommes, Kirsten and Hüllermeier, Eyke}},
  booktitle    = {{Communications in Computer and Information Science}},
  isbn         = {{9783032083265}},
  issn         = {{1865-0929}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making}}},
  doi          = {{10.1007/978-3-032-08327-2_6}},
  year         = {{2025}},
}

@article{61819,
  author       = {{Papenkordt, Jörg and Ngonga Ngomo, Axel-Cyrille and Thommes, Kirsten}},
  issn         = {{0144-929X}},
  journal      = {{Behaviour &amp; Information Technology}},
  pages        = {{1--22}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Are numerical or verbal explanations of AI the key to appropriate user reliance and error detection?}}},
  doi          = {{10.1080/0144929x.2025.2568928}},
  year         = {{2025}},
}

@inbook{61877,
  abstract     = {{<jats:title>Abstract</jats:title>
          <jats:p>Research indicates that anger is a prevalent emotion in human-technology interactions, often leading to frustration, rejection and reduced trust, significantly impacting user experience and acceptance of technology. Particularly in high-risk or uncertain situations, where AI explanations are intended to help users make more informed decisions, decision-making is influenced by emotional factors, impairing understanding and leading to suboptimal choices. While XAI research continues to evolve, greater consideration of users’ emotions and individual characteristics remains necessary. Broadening empirical studies in this area could foster a more comprehensive understanding of decision-making processes following explanations, especially in relation to the interaction between emotions and cognition. In response, this study seeks to contribute to this area by employing an experimental design to examine the effects of AI explanations and emotion regulation on user reliance and trust of emotional users. The results provide a foundation for future human-centered research in XAI, focusing on the impact of emotions and cognition in human-technology interactions.</jats:p>}},
  author       = {{Lammert, Olesja}},
  booktitle    = {{Communications in Computer and Information Science}},
  isbn         = {{9783032083326}},
  issn         = {{1865-0929}},
  publisher    = {{Springer Nature Switzerland}},
  title        = {{{Can AI Regulate Your Emotions? An Empirical Investigation of the Influence of AI Explanations and Emotion Regulation on Human Decision-Making Factors}}},
  doi          = {{10.1007/978-3-032-08333-3_11}},
  year         = {{2025}},
}

@article{62213,
  author       = {{Kornowicz, Jaroslaw}},
  issn         = {{1044-7318}},
  journal      = {{International Journal of Human–Computer Interaction}},
  pages        = {{1--19}},
  publisher    = {{Informa UK Limited}},
  title        = {{{An Empirical Examination of the Evaluative AI Framework}}},
  doi          = {{10.1080/10447318.2025.2581260}},
  year         = {{2025}},
}

@article{58939,
  author       = {{Kornowicz, Jaroslaw and Thommes, Kirsten}},
  journal      = {{Plos One}},
  title        = {{{Algorithm, expert, or both? Evaluating the role of feature selection methods on user preferences and reliance}}},
  doi          = {{10.1371/journal.pone.0318874}},
  year         = {{2025}},
}

@article{60280,
  author       = {{Heinovski, Julian and Ergenç, Doǧanalp and Thommes, Kirsten and Dressler, Falko}},
  issn         = {{2687-7813}},
  journal      = {{IEEE Open Journal of Intelligent Transportation Systems}},
  pages        = {{1--1}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Incentive-Based Platoon Formation: Optimizing the Personal Benefit for Drivers}}},
  doi          = {{10.1109/ojits.2025.3580464}},
  year         = {{2025}},
}

@inbook{60292,
  abstract     = {{Im Rahmen meines SoTL-Projekts habe ich mich aufgrund meiner Vorerfahrungen mit dem experimentellen Forschungsansatz für die Untersuchung der Wirksamkeit von Lernzielen mittels eines Gruppenvergleichs entschieden. In dieser Reflexion stelle ich zunächst heraus, welche Hindernisse und welche Möglichkeiten die Durchführung einer experimentellen Studie im hochschuldidaktischen Kontext mit sich bringt. Anschließend zeige ich anhand konkreter Vorgehensweisen in meinem SoTL-Projekt sowie Bezügen zur experimentellen Wirtschaftsforschung auf, wie wesentliche Punkte für ein fundiertes experimentelles Forschungsdesign in der Hochschullehre umgesetzt werden können und wie Forschende verschiedener Fachrichtungen mit einem derart gestalteten SoTL-Projekt einen Mehrwert sowohl für sich als auch für die Lehrforschung generieren können.}},
  author       = {{Auer, Thorsten Fabian}},
  booktitle    = {{Scholarship of Teaching and Learning und disziplinäre Forschung: Eine komplexe Beziehung}},
  editor       = {{Bohndick, Carla and Kordts, Robert and Leschke, Jonas and Vöing, Nerea}},
  isbn         = {{978-3-658-47907-7}},
  pages        = {{65--69}},
  publisher    = {{Springer VS}},
  title        = {{{SoTL und Forschung in den Sozialwissenschaften: Der experimentelle Ansatz in SoTL}}},
  doi          = {{10.1007/978-3-658-47908-4_11}},
  year         = {{2025}},
}

@article{61137,
  abstract     = {{Prior research shows that social norms can reduce algorithm aversion, but little is known about how such norms become established. Most accounts emphasize technological and individual determinants, yet AI adoption unfolds within organizational social contexts shaped by peers and supervisors. We ask whether the source of the norm-peers or supervisors-shapes AI usage behavior. This question is practically relevant for organizations seeking to promote effective AI adoption. We conducted an online vignette experiment, complemented by qualitative data on participants' feelings and justifications after (counter-)normative behavior. In line with the theory, counter-normative choices elicited higher regret than norm-adherent choices. On average, choosing AI increased regret compared to choosing an human. This aversion was weaker when AI use was presented as the prevailing norm, indicating a statistically significant interaction between AI use and an AI-favoring norm. Participants also attributed less blame to technology than to humans, which increased regret when AI was chosen over human expertise. Both peer and supervisor influence emerged as relevant factors, though contrary to expectations they did not significantly affect regret. Our findings suggest that regret aversion, embedded in social norms, is a central mechanism driving imitation in AI-related decision-making.}},
  author       = {{Kornowicz, Jaroslaw and Pape, Maurice and Thommes, Kirsten}},
  journal      = {{Arxiv}},
  title        = {{{Would I regret being different? The influence of social norms on attitudes toward AI usage}}},
  doi          = {{10.48550/ARXIV.2509.04241}},
  year         = {{2025}},
}