@article{63401, author = {{Thommes, Kirsten and Mehic, Miro}}, issn = {{0965-8564}}, journal = {{Transportation Research Part A: Policy and Practice}}, publisher = {{Elsevier BV}}, title = {{{The persistence of default effects: Evidence from CO2 offsetting in cargo transportation}}}, doi = {{10.1016/j.tra.2025.104838}}, volume = {{204}}, year = {{2026}}, } @misc{64904, author = {{Kombert, Sounia and Sureth-Sloane, Caren}}, booktitle = {{Frankfurter Allgemeine Zeitung}}, number = {{51}}, pages = {{16, Sp. 1--4}}, title = {{{Schocks durch Zölle und Steuern. Handelskonflikte und Abkommen werden zur zentralen Frage für Unternehmen. Wie bewältigen sie die neue Unsicherheit?}}}, year = {{2026}}, } @article{64903, author = {{Hoppe, Thomas and Schanz, Deborah and Sturm, Susann and Sureth-Sloane, Caren}}, journal = {{Schmalenbach IMPULSE}}, number = {{6}}, pages = {{1--12}}, title = {{{Steuerkomplexität: Wie lässt sie sich messen und welche Folgen hat sie?}}}, doi = {{10.54585/IEZK8936}}, year = {{2026}}, } @article{65054, author = {{Jenert, Tobias and Kremer, H.-Hugo and Kückmann, Marie-Ann and Sänger, Niklas and Schmid, Leonie and Wilde, Stephanie}}, journal = {{bwp@ Spezial 23}}, title = {{{Kontextualisierung von Lehren und Lernen: Didaktische Einbettung und Implikationen eines virtuellen Berufskollegs zur Förderung von Professionalisierungsprozessen in der beruflichen Lehrkräftebildung}}}, year = {{2026}}, } @inbook{64735, author = {{Jenert, Tobias and Kremer, H.-Hugo and Kückmann, Marie-Ann and Sänger, Niklas and Schmid, Leonie and Wilde, Stephanie}}, booktitle = {{Handlungsorientierung in der Ausbildung von Lehrkräften und pädagogischen Fachkräften - Konzeptionen und Forschungsperspektiven}}, editor = {{Vogelsang, Christoph and Grotegut, Lea and Bruns, Julia and Fechner, Sabine}}, publisher = {{Waxmann}}, title = {{{Professionelle Entwicklung für das Lehramt an Berufskollegs. Theoretische Analysen besonderer Kompetenzanforderungen und Konsequenzen für die Studienganggestaltung}}}, doi = {{https://doi.org/10.31244/9783818851057}}, volume = {{2}}, year = {{2026}}, } @article{65066, abstract = {{Abstract We investigate whether the recently approved reforms of the apportionment of parliamentary seats to parties in the German Bundestag affects the parties’ political influence measured by power indices. We find that under neither reform the underlying simple game, which describes the possibilities to form governments, remains unchanged and as a result the Shapley-Shubik and the Banzhaf index are unaltered. As a consequence, the major change resulting from the reforms is the reduction of the Bundestag’s size to 630 seats.}}, author = {{Duman, Papatya and Haake, Claus-Jochen}}, issn = {{0948-5139}}, journal = {{Review of Economics}}, keywords = {{Bundestag reform, Banzhaf power index, Shapley-Shubik power index}}, number = {{3}}, pages = {{241--270}}, publisher = {{Walter de Gruyter GmbH}}, title = {{{A Note on the Size Reduction Reform in the German Parliament: A Game Theoretic Analysis of Power Indices}}}, doi = {{10.1515/roe-2024-0048}}, volume = {{76}}, year = {{2026}}, } @inbook{65090, abstract = {{Abstract If XAI are to become social XAI, XAI methods must have capabilities enabling them to ‘extract’ information about the underlying AI model and to generate explanatory content based on that information. In a dialog between explainer and explainee, the explanans presented in every explanation move have to relate to each other understandably and coherently in order to remain trustworthy. This signifies that the generated explanantia have to be consistent—independently of what question is answered by each explanans, in what modality, in what vocabulary, and at what level of abstraction. Moreover, it is advantageous to be able to provide a rich palette of different kinds of explanantia in order to be able to have a fluent dialog in which the explanantia can be generated and adapted to the context, the explainee, feedback, reactions during the interaction with the explainee, and so forth. This chapter attempts to identify relevant questions that an explainee might ask during an explanatory dialog, and it assesses to what extent different XAI methods are capable of addressing these questions in a coherent way. The Contextual Importance and Utility (CIU) method is used to illustrate how an XAI method can generate explanantia for most of the identified questions. CIU also provides a flexibility in how explanatory content is generated that makes it possible to create a meaningful dialog with the explainee.}}, author = {{Främling, Kary and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Generation of Explanatory Content and Requirements for Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_15}}, year = {{2026}}, } @inbook{65088, abstract = {{Abstract Quantitatively evaluating the benefits of eXplainable Artificial Intelligence (XAI) and social XAI for humans is not a trivial pursuit. Therefore, we categorize the potential measures in terms of subjective and objective outcomes and short- and long-term outcomes of interactive social XAI. When reviewing the current state of the art, we observed some measurement problems in the literature: (a) Researchers do not clearly state whether they want to measure the inner state of users, users’ behavioral response, or the overall AI-human collaborative performance. (b) Moreover, most measures implicitly assume that all humans either do not react or improve in attitudes or performance. Psychological reactance (feeling or doing the opposite) is usually not captured. (c) Many researchers invent their own scale when measuring psychological constructs, thereby jeopardizing the validity of their measures and slowing down progress in the field, because general evidence and subsequent learning can be achieved only by collecting many compatible pieces of evidence. (d) Most studies look into short-term outcomes and neglect that experiences in social interactions with XAI may evolve and have long-term outcomes not only for the individual but also for groups or society at large.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Measuring the Outcome of sXAI}}}, doi = {{10.1007/978-981-96-5290-7_28}}, year = {{2026}}, } @inbook{65086, abstract = {{Abstract Explainable AI (XAI) aims to make the decisions and behavior of an AI understandable to the people interacting with it and to those affected by its outcomes. To make XAI social, real-world XAI systems need to simulate not only the ways in which human explainers behave within explanatory dialogs but also the ways in which such dialogs can successfully achieve the intended understanding on the explainee’s side. This, in turn, requires an operationalization of the three core aspects of social XAI: multimodality, incrementality, and patterns. This chapter lays the ground for this goal by defining a basic operational model of social interactions that can be refined and extended to account for the specificities of any explanatory real-world setting. This serves as a basis for summarizing and discussing existing ideas from explainability research and related areas in order to operationalize each core aspect. Selected examples and case studies illustrate how to concretely realize such an operationalization, thereby serving as a starting point for future research on social interaction with XAI.}}, author = {{Wachsmuth, Henning and Thommes, Kirsten and Alshomary, Milad}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Operationalizing Social Interaction}}}, doi = {{10.1007/978-981-96-5290-7_27}}, year = {{2026}}, } @inbook{65091, abstract = {{Abstract This chapter examines key challenges and potential improvements in the areas of user interaction and dynamic explanations. It highlights the need for XAI systems to address context factors beyond their predefined scope, it points to the potential need to cocreate new concepts that are adapted to particular explainees, and it provides a clear overview of the XAI system’s underlying knowledge structure and interaction steps. Emphasis is placed on mixed-initiative interaction in which the system can lead or respond based on the context and the explainee’s reactions while asserting the importance of maintaining coherence across consecutive explanations. These advances aim to make XAI systems more flexible, interactive, and user-centric. An operationalization section outlines how such social XAI systems could be implemented based on the XAI capabilities provided by the Contextual Importance and Utility XAI method described in the previous chapter.}}, author = {{Främling, Kary and Wrede, Britta and Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Exploration of Explaining Content}}}, doi = {{10.1007/978-981-96-5290-7_16}}, year = {{2026}}, } @inbook{65087, abstract = {{Abstract Much research in XAI focuses on single, one-shot interactions, implicitly assuming that interactions have no past, no future, and no surroundings. Although this assumption may be necessary for many empirical research settings, it is overly simplifying and unrealistic. Whereas empirical research focuses on a world in which no social context exists, real applications are embedded in a temporal (past and future) and social context. Social science research shows that repeated interactions and secondhand knowledge in the social space massively affect human attitudes and behaviors. This chapter explains how not only repeated interactions between XAI and humans but also the social space and secondhand information may affect social XAI research.}}, author = {{Thommes, Kirsten and Främling, Kary and Wrede, Britta and Kubler, Sylvain}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Interaction History in Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_17}}, year = {{2026}}, } @inbook{65089, abstract = {{Abstract In the past, there has been much research aiming to evaluate XAI practices—that is, explanations that can add to a user’s understanding of “why” or “why not.” However, because there is such a huge amount of diversity in social contexts, optimizing for the mean neglects the social dimensions of to whom, what, why, when, and where explanations are provided. Nonetheless, these dimensions matter. We give some brief examples on the accuracy of the mental model (as an example for who?), on measuring explanation practices (as an example of what?), on human motivation (as an example of why?), on repeated interactions (as an example of when), and on bystander effects (as an example of where?). Importantly, controlling for these factors (or randomizing them) is as important as attempting to perform external validations.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Evaluation Principles}}}, doi = {{10.1007/978-981-96-5290-7_26}}, year = {{2026}}, } @article{65105, author = {{zur Heiden, Philipp and Halimeh, Haya and Hansmeier, Philipp and Vorbohle, Christian and Althaus, Maike and Beverungen, Daniel and Kundisch, Dennis and Müller, Oliver}}, journal = {{Communications of the Association for Information Systems}}, title = {{{Data Spaces for Heterogeneous Data Ecosystems – Findings from a Design Study in the Cultural Sector}}}, year = {{2026}}, } @article{65182, abstract = {{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.}}, 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 = {{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.}}, 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{65311, abstract = {{Information Systems (IS) is rooted in systems theory. Systems theory offers powerful concepts to address challenges of growing system complexity and non-systemic design approaches in information systems. Despite its systemic origins, systems theory remains a peripheral topic in IS. The study addresses this gap by introducing a comprehensive framework of 52 systems-theoretical concepts to guide the design of complex IS artifacts. We synthesize scattered systems knowledge from diverse disciplines to provide a unified level of abstraction for complex information system design. We apply the framework to a use case of business reputation systems to show how the systems lens informs the design of a novel, complex information system. We make three key contributions to the literature. First, the framework provides a common ground for interdisciplinary research in information system design. Second, it offers a unified level of abstraction grounded in systems theory that serves as a coherent basis for artifact design. Third, it demonstrates the potential of systems theory as a foundational justificatory knowledge base. Furthermore, we provide guidance on applying the framework across multiple modes of reasoning, alongside further application guidelines. The study thus serves as a bridge between the body of systems knowledge and contextual design in IS.}}, author = {{Ibrahimli, Ulvi and Hemmrich, Simon and Winkelmann, Axel}}, journal = {{Communication of the Association for Information Systems}}, keywords = {{Information Systems Research, Systems Theory, System Complexity, System Design, Design Science}}, publisher = {{AIS}}, title = {{{Bridging Systems Theory and Information Systems: A Framework for Designing Complex Information Systems}}}, doi = {{https://aisel.aisnet.org/cais/vol58/iss1/37/}}, year = {{2026}}, } @inbook{65310, abstract = {{Trust between client and consultant is perhaps the most important asset in con-sulting, as this is a highly intangible knowledge-intensive business that concerns is-sues of outstanding strategic and operational importance for the customers. Cli-ents who have not worked with a particular consultancy face considerable risk when they place an order while lacking reliable information about the service quality they can expect. There is a strong link between trust and reputation, as the positive reputation of a consultancy can act as a substitute for a new client’s missing individual experience with the provider, fostering trust in the service quali-ty. Thus, creating, maintaining, and demonstrating a good reputation is of signifi-cant importance for consultancies in a very competitive industry. To facilitate trustworthy signals, we design and implement a novel reputation mechanism that carries a monetary weight stored on a blockchain network as an immutable, decentralized, and transparent ledger. Based on an implementation in the Ethereum network and subsequent evaluation, we conclude that the reputation mechanism can contribute to leveling information asymmetry and reducing risk while increasing reputation and trust. The mechanism lends itself to being used in other business-to-business scenarios that suffer from similar information asymmetries.}}, author = {{Hemmrich, Simon and Nissen, Volker}}, booktitle = {{ Advanced Studies in Consulting Research and Digitalization – A Scientific Update on the Digital Transformation of the Consulting Industry. Springer.}}, editor = {{Nissen, Volker}}, keywords = {{Reputation Systems, Consulting, Design Science Invention, Incentive, Blockchain, Monetary ratings, building trust, reduce information asymmetry consulting, B2B reputation system, consulting risk reduction, supplier evaluation system}}, title = {{{A blockchain-based reputation system for consulting}}}, year = {{2026}}, } @inproceedings{65313, author = {{Ibrahimli, Ulvi and Hemmrich, Simon and Winkelmann, Axel}}, location = {{Münster}}, title = {{{Reputation as a Sociotechnical Design Problem: A Social Systems Theory Lens for Business Reputation Systems}}}, year = {{2026}}, } @article{63577, author = {{Eberhartinger, Eva and Speitmann, Raffael and Sureth-Sloane, Caren}}, journal = {{Journal of International Accounting, Auditing and Taxation (JIAAT)}}, title = {{{Banks' tax disclosure, financial secrecy, and tax haven heterogeneity}}}, doi = {{10.1016/j.intaccaudtax.2026.100759}}, volume = {{60}}, year = {{2026}}, }