@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}},
}

@article{35722,
  author       = {{Kengelbach, Jens and Friedman, Daniel and Keienburg, Georg and Degen, Dominik and Söllner, Tobias and Wang, Yiran and Sievers, Sönke}},
  journal      = {{BCG M&A Report 2022}},
  title        = {{{Do Green Deals Create Value? }}},
  year         = {{2022}},
}

@article{35740,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>While the Information Systems (IS) discipline has researched digital platforms extensively, the body of knowledge appertaining to platforms still appears fragmented and lacking conceptual consistency. Based on automated text mining and unsupervised machine learning, we collect, analyze, and interpret the IS discipline’s comprehensive research on platforms—comprising 11,049 papers spanning 44 years of research activity. From a cluster analysis concerning platform concepts’ semantically most similar words, we identify six research streams on platforms, each with their own platform terms. Based on interpreting the identified concepts vis-à-vis the extant research and considering a temporal perspective on the concepts’ application, we present a lexicon of platform concepts, to guide further research on platforms in the IS discipline. Researchers and managers can build on our results to position their work appropriately, applying a specific theoretical perspective on platforms in isolation or combining multiple perspectives to study platform phenomena at a more abstract level.</jats:p>}},
  author       = {{Bartelheimer, Christian and zur Heiden, Philipp and Lüttenberg, Hedda and Beverungen, Daniel}},
  issn         = {{1019-6781}},
  journal      = {{Electronic Markets}},
  keywords     = {{Management of Technology and Innovation, Marketing, Computer Science Applications, Economics and Econometrics, Business and International Management}},
  number       = {{1}},
  pages        = {{375--396}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Systematizing the lexicon of platforms in information systems: a data-driven study}}},
  doi          = {{10.1007/s12525-022-00530-6}},
  volume       = {{32}},
  year         = {{2022}},
}

@techreport{35097,
  author       = {{Ebert, Michael and Schäfer, Ulrich and Schneider, Georg Thomas}},
  issn         = {{1556-5068}},
  title        = {{{Information Leaks and Voluntary Disclosure}}},
  doi          = {{10.2139/ssrn.4168084}},
  year         = {{2022}},
}

@article{47920,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Integrated thinking (IT) is a managerial mindset increasingly discussed in the context of value creation. Through the lens of systems theory, this study examines how the degree to which IT is embedded in a firm's strategy and day‐to‐day business processes is associated with the firm's social and environmental value creation. Using a broad international dataset, we find strong evidence that our measure of IT is positively related to a firm's sustainability performance (SP), which we use to operationalize social and environmental value creation (or erosion). Our results also reveal that the increase in a firm's SP might come at the cost of a short‐term decrease in financial performance (FP). We find no indication, however, that IT induces a trade‐off between SP and long‐term FP. Integrated thinking appears to stipulate long‐term financial value creation instead. We further explore moderating factors within the organizational and institutional context of our sample firms and highlight implications for society, corporate practice, and policymaking.</jats:p>}},
  author       = {{Reimsbach, Daniel and Braam, Geert}},
  issn         = {{0964-4733}},
  journal      = {{Business Strategy and the Environment}},
  keywords     = {{Management, Monitoring, Policy and Law, Strategy and Management, Geography, Planning and Development, Business and International Management}},
  number       = {{1}},
  pages        = {{304--320}},
  publisher    = {{Wiley}},
  title        = {{{Creating social and environmental value through integrated thinking: International evidence}}},
  doi          = {{10.1002/bse.3131}},
  volume       = {{32}},
  year         = {{2022}},
}