@techreport{32106,
  abstract     = {{We study the consequences of modeling asymmetric bargaining power in two-person bargaining problems. Comparing application of an asymmetric version of a bargaining solution to an upfront modification of the disagreement point, the resulting distortion crucially depends on the bargaining solution concept. While for the Kalai-Smorodinsky solution weaker players benefit from modifying the disagreement point, the situation is reversed for the Nash bargaining solution. There, weaker players are better off in the asymmetric bargaining solution. When comparing application of the asymmetric versions of the Nash and the Kalai-Smorodinsky solutions, we demonstrate that there is an upper bound for the weight of a player, so that she is better off with the Nash bargaining solution. This threshold is ultimately determined by the relative utilitarian bargaining solution. From a mechanism design perspective, our results provide valuable information for a social planner, when implementing a bargaining solution for unequally powerful players.}},
  author       = {{Haake, Claus-Jochen and Streck, Thomas}},
  keywords     = {{Asymmetric bargaining power, Nash bargaining solution, Kalai-Smorodinsky bargaining solution}},
  pages        = {{17}},
  title        = {{{Distortion through modeling asymmetric bargaining power}}},
  volume       = {{148}},
  year         = {{2022}},
}

@article{31881,
  author       = {{Hoyer, Britta and De Jaegher, Kris}},
  journal      = {{International Journal of Game Theory}},
  publisher    = {{Springer}},
  title        = {{{Network Disruption and the Common-Enemy Effect}}},
  doi          = {{10.1007/s00182-022-00812-5}},
  year         = {{2022}},
}

@article{33250,
  author       = {{Szopinski, Daniel and Massa, Lorenzo and John, Thomas and Kundisch, Dennis and Tucci, Christopher}},
  journal      = {{Communications of the Association for Information Systems}},
  pages        = {{774--841}},
  title        = {{{Modeling Business Models: A cross-disciplinary Analysis of Business Model Modeling Languages and Directions for Future Research}}},
  volume       = {{51}},
  year         = {{2022}},
}

@article{13147,
  abstract     = {{Employing a unique and hand-collected sample of 648 true sale loan securitization transactions issued by 57 stock-listed banks across the EU-12 plus Switzerland over the period from 1997 to 2010, this paper empirically analyzes the relationship between true sale loan securitization and the issuing banks’ non-performing loans to total assets ratios. Overall, we provide evidence for a negative impact of securitization on NPL exposures suggesting that banks predominantly used securitization as an instrument of credit risk transfer and diversification. In addition, the analysis at hand reveals a time-sensitive relationship between securitization and NPL exposures. While we observe an even stronger NPL-reducing effect through securitization during the non-crisis periods, the effect reverses during and after the global financial crisis suggesting that banks were forced to provide credit enhancement and employ securitization as a funding management tool. Along with the results from a variety of sensitivity analyses our study provides important implications for the recent debate on reducing NPL exposures of European banks by revitalizing the European securitization market.}},
  author       = {{Wengerek, Sascha Tobias and Hippert, Benjamin and Uhde, André}},
  journal      = {{The Quarterly Review of Economics and Finance}},
  keywords     = {{European Banking, Non-performing Loans, Securitization}},
  pages        = {{48--64}},
  publisher    = {{Elsevier}},
  title        = {{{Risk allocation through securitization – Evidence from non-performing loans}}},
  doi          = {{https://doi.org/10.1016/j.qref.2022.06.005}},
  volume       = {{Vol. 86 (11)}},
  year         = {{2022}},
}

@article{21571,
  abstract     = {{The paper investigates the impact of individual attention on investor risk-taking. We analyze a large sample of trading records from a brokerage service that allows its customers to trade contracts-for-differences (CFD), and sends standardized push messages on recent stock performance to its client investors. The advantage of this sample is that it allows us to isolate the "push" messages as individual attention triggers, which we can directly link to the same individuals' risk-taking. A particular advantage of CFD trading is that it allows investors to make use of leverage, which provides us a pure measure of investors' willingness to take risks that is independent of the decision to purchase a particular stock. Leverage is a major catalyst of speculative trading, as it increases the scope of extreme returns, and enables investors to take larger positions than what they can afford with their own capital. We show that investors execute attention-driven trades with higher leverage, compared to their other trades, as well as those of other investors who are not alerted by attention triggers.}},
  author       = {{Arnold, Marc and Pelster, Matthias and Subrahmanyam, Marti G.}},
  journal      = {{Journal of Financial Economics}},
  number       = {{2}},
  pages        = {{ 846--875}},
  title        = {{{Attention triggers and investors' risk-taking}}},
  doi          = {{10.1016/j.jfineco.2021.05.031}},
  volume       = {{143}},
  year         = {{2022}},
}

@article{23415,
  author       = {{Sperling, Martina and Schryen, Guido}},
  journal      = {{European Journal of Operational Research (EJOR)}},
  number       = {{2}},
  pages        = {{690 -- 705}},
  title        = {{{Decision Support for Disaster Relief: Coordinating Spontaneous Volunteers}}},
  volume       = {{299}},
  year         = {{2022}},
}

@inproceedings{29539,
  abstract     = {{Explainable Artificial Intelligence (XAI) is currently an important topic for the application of Machine Learning (ML) in high-stakes decision scenarios. Related research focuses on evaluating ML algorithms in terms of interpretability. However, providing a human understandable explanation of an intelligent system does not only relate to the used ML algorithm. The data and features used also have a considerable impact on interpretability. In this paper, we develop a taxonomy for describing XAI systems based on aspects about the algorithm and data. The proposed taxonomy gives researchers and practitioners opportunities to describe and evaluate current XAI systems with respect to interpretability and guides the future development of this class of systems.}},
  author       = {{Kucklick, Jan-Peter}},
  booktitle    = {{Wirtschaftsinformatik 2022 Proceedings}},
  keywords     = {{Explainable Artificial Intelligence, XAI, Interpretability, Decision Support Systems, Taxonomy}},
  location     = {{Nürnberg (online)}},
  title        = {{{Towards a model- and data-focused taxonomy of XAI systems}}},
  year         = {{2022}},
}

@article{32857,
  author       = {{Gutt, Jana Kim and Thommes, Kirsten}},
  issn         = {{0065-0668}},
  journal      = {{Academy of Management Proceedings}},
  keywords     = {{Microbiology}},
  number       = {{1}},
  publisher    = {{Academy of Management}},
  title        = {{{Speaking of Performance: Evaluating Team Members’ Performance with Open-Ended Audio Comments}}},
  doi          = {{10.5465/ambpp.2022.16394abstract}},
  volume       = {{2022}},
  year         = {{2022}},
}

@article{32866,
  author       = {{Shollo, Arisa and Hopf, Konstantin and Thiess, Tiemo and Müller, Oliver}},
  issn         = {{0963-8687}},
  journal      = {{The Journal of Strategic Information Systems}},
  keywords     = {{Information Systems and Management, Information Systems, Management Information Systems}},
  number       = {{3}},
  publisher    = {{Elsevier BV}},
  title        = {{{Shifting ML value creation mechanisms: A process model of ML value creation}}},
  doi          = {{10.1016/j.jsis.2022.101734}},
  volume       = {{31}},
  year         = {{2022}},
}

@article{33221,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Non-pharmaceutical interventions are an effective strategy to prevent and control COVID-19 transmission in the community. However, the timing and stringency to which these measures have been implemented varied between countries and regions. The differences in stringency can only to a limited extent be explained by the number of infections and the prevailing vaccination strategies. Our study aims to shed more light on the lockdown strategies and to identify the determinants underlying the differences between countries on regional, economic, institutional, and political level. Based on daily panel data for 173 countries and the period from January 2020 to October 2021 we find significant regional differences in lockdown strategies. Further, more prosperous countries implemented milder restrictions but responded more quickly, while poorer countries introduced more stringent measures but had a longer response time. Finally, democratic regimes and stronger manifested institutions alleviated and slowed down the introduction of lockdown measures.</jats:p>}},
  author       = {{Redlin, Margarete}},
  issn         = {{0922-680X}},
  journal      = {{Journal of Regulatory Economics}},
  keywords     = {{Economics and Econometrics}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Differences in NPI strategies against COVID-19}}},
  doi          = {{10.1007/s11149-022-09452-9}},
  year         = {{2022}},
}

@article{33220,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>We provide a partial equilibrium model wherein AI provides abilities combined with human skills to provide an aggregate intermediate service good. We use the model to find that the extent of automation through AI will be greater if (a) the economy is relatively abundant in sophisticated programs and machine abilities compared to human skills; (b) the economy hosts a relatively large number of AI-providing firms and experts; and (c) the task-specific productivity of AI services is relatively high compared to the task-specific productivity of general labor and labor skills. We also illustrate that the contribution of AI to aggregate productive labor service depends not only on the amount of AI services available but on the endogenous number of automated tasks, the relative productivity of standard and IT-related labor, and the substitutability of tasks. These determinants also affect the income distribution between the two kinds of labor. We derive several empirical implications and identify possible future extensions.</jats:p>}},
  author       = {{Gries, Thomas and Naudé, Wim}},
  issn         = {{2510-5019}},
  journal      = {{Journal for Labour Market Research}},
  keywords     = {{General Medicine}},
  number       = {{1}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Modelling artificial intelligence in economics}}},
  doi          = {{10.1186/s12651-022-00319-2}},
  volume       = {{56}},
  year         = {{2022}},
}

@article{33219,
  author       = {{Gries, Thomas and Müller, Veronika and Jost, John T.}},
  issn         = {{1047-840X}},
  journal      = {{Psychological Inquiry}},
  keywords     = {{General Psychology}},
  number       = {{2}},
  pages        = {{65--83}},
  publisher    = {{Informa UK Limited}},
  title        = {{{The Market for Belief Systems: A Formal Model of Ideological Choice}}},
  doi          = {{10.1080/1047840x.2022.2065128}},
  volume       = {{33}},
  year         = {{2022}},
}

@phdthesis{32856,
  author       = {{Endres-Fröhlich, Angelika Elfriede}},
  title        = {{{Essays on Industrial Organization and Networks: Retail Bundling, Exclusive Dealing, and Network Disruption}}},
  doi          = {{10.17619/UNIPB/1-1581}},
  year         = {{2022}},
}

@article{33692,
  abstract     = {{<jats:title>Abstract</jats:title>
               <jats:p>An individual’s relation to time may be an important driver of pro-environmental behaviour. We studied whether young individual’s gender and time-orientation are associated with pro-environmental behaviour. In a controlled laboratory environment with students in Germany, participants earned money by performing a real-effort task and were then offered the opportunity to invest their money into an environmental project that supports climate protection. Afterwards, we controlled for their time-orientation. In this consequential behavioural setting, we find that males who scored higher on <jats:italic>future-negative</jats:italic> orientation showed significantly more pro-environmental behaviour compared to females who scored higher on <jats:italic>future-negative</jats:italic> orientation and males who scored lower on <jats:italic>future-negative</jats:italic> orientation. Interestingly, our results are completely reversed when it comes to <jats:italic>past-positive</jats:italic> orientation. These findings have practical implications regarding the most appropriate way to address individuals in order to achieve more pro-environmental behaviour.</jats:p>}},
  author       = {{Hoffmann, Christin and Hoppe, Julia Amelie and Ziemann, Niklas}},
  issn         = {{1748-9326}},
  journal      = {{Environmental Research Letters}},
  keywords     = {{Public Health, Environmental and Occupational Health, General Environmental Science, Renewable Energy, Sustainability and the Environment}},
  number       = {{10}},
  publisher    = {{IOP Publishing}},
  title        = {{{Who has the future in mind? Gender, time perspectives, and pro-environmental behaviour}}},
  doi          = {{10.1088/1748-9326/ac9296}},
  volume       = {{17}},
  year         = {{2022}},
}

@article{23566,
  author       = {{Kundisch, Dennis and Muntermann, J. and Oberländer, A. M. and Rau, D. and Röglinger, M. and Schoormann, T. and Szopinski, Daniel}},
  journal      = {{Business & Information Systems Engineering}},
  number       = {{4}},
  pages        = {{421--439}},
  title        = {{{An update for taxonomy designers: Methodological guidance from information systems research}}},
  volume       = {{64}},
  year         = {{2022}},
}

@inproceedings{33884,
  author       = {{Laux, Florian and Kundisch, Dennis}},
  location     = {{Copenhagen, Denmark}},
  title        = {{{Judgment or Choice? An Experimental Comparison of Evaluation Approaches for External Crowdvoting}}},
  year         = {{2022}},
}

@article{34046,
  author       = {{Hoffmann, Christin and Thommes, Kirsten}},
  issn         = {{2168-2291}},
  journal      = {{IEEE Transactions on Human-Machine Systems}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, Human-Computer Interaction, Signal Processing, Control and Systems Engineering, Human Factors and Ergonomics}},
  pages        = {{1--11}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Seizing the Opportunity for Automation—How Traffic Density Determines Truck Drivers' Use of Cruise Control}}},
  doi          = {{10.1109/thms.2022.3212335}},
  year         = {{2022}},
}

@article{35620,
  abstract     = {{Deep learning models fuel many modern decision support systems, because they typically provide high predictive performance. Among other domains, deep learning is used in real-estate appraisal, where it allows to extend the analysis from hard facts only (e.g., size, age) to also consider more implicit information about the location or appearance of houses in the form of image data. However, one downside of deep learning models is their intransparent mechanic of decision making, which leads to a trade-off between accuracy and interpretability. This limits their applicability for tasks where a justification of the decision is necessary. Therefore, in this paper, we first combine different perspectives on interpretability into a multi-dimensional framework for a socio-technical perspective on explainable artificial intelligence. Second, we measure the performance gains of using multi-view deep learning which leverages additional image data (satellite images) for real estate appraisal. Third, we propose and test a novel post-hoc explainability method called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency of convolutional neural networks (CNNs) for predicting continuous outcome variables. With this, we try to reduce the accuracy-interpretability trade-off of multi-view deep learning models. Our proposed network architecture outperforms traditional hedonic regression models by 34% in terms of MAE. Furthermore, we find that the used satellite images are the second most important predictor after square feet in our model and that the network learns interpretable patterns about the neighborhood structure and density.}},
  author       = {{Kucklick, Jan-Peter and Müller, Oliver}},
  issn         = {{2158-656X}},
  journal      = {{ACM Transactions on Management Information Systems}},
  keywords     = {{Interpretability, Convolutional Neural Network, Accuracy-Interpretability Trade-Of, Real Estate Appraisal, Hedonic Pricing, Grad-Ram}},
  publisher    = {{Association for Computing Machinery (ACM)}},
  title        = {{{Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal}}},
  doi          = {{10.1145/3567430}},
  year         = {{2022}},
}

@article{35647,
  author       = {{Tawiah, Beatrice Baaba}},
  journal      = {{Applied Economics}},
  number       = {{58}},
  pages        = {{6687--6702}},
  title        = {{{Does education have an impact on patience and risk willingness?}}},
  doi          = {{10.1080/00036846.2022.2078780}},
  volume       = {{54}},
  year         = {{2022}},
}

@article{35719,
  author       = {{Kengelbach, Jens and Keienburg, Georg and Söllner, Tobias and Wang, Yiran and Sievers, Sönke and Friedmann, Daniel and Nielsen, Jesper}},
  journal      = {{BCG M&A Report 2022}},
  title        = {{{Green Deals Gain Steam }}},
  year         = {{2022}},
}