Alle Publikationen der Fakultät für Wirtschaftswissenschaften

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.

Ideational impact refers to the uptake of a paper's ideas and concepts by subsequent research. It is defined in stark contrast to total citation impact, a measure predominantly used in research evaluation that assumes that all citations are equal. Understanding ideational impact is critical for evaluating research impact and understanding how scientific disciplines build a cumulative tradition. Research has only recently developed automated citation classification techniques to distinguish between different types of citations and generally does not emphasize the conceptual content of the citations and its ideational impact. To address this problem, we develop Deep Content-enriched Ideational Impact Classification (Deep-CENIC) as the first automated approach for ideational impact classification to support researchers' literature search practices. We evaluate Deep-CENIC on 1,256 papers citing 24 information systems review articles from the IT business value domain. We show that Deep-CENIC significantly outperforms state-of-the-art benchmark models. We contribute to information systems research by operationalizing the concept of ideational impact, designing a recommender system for academic papers based on deep learning techniques, and empirically exploring the ideational impact of the IT business value domain.

The containment of COVID-19 critically hinges on individuals’ behavior. We investigate how individuals react to variations in COVID-19 reporting. Using a survey, we elicit individuals' perceived infection risk given various COVID-19 metrics (e.g., confirmed cases, reproduction rate, or case-fatality ratio). We proxy individuals' risk perception with their willingness to pay for the participation in everyday life and amusements events. We find that participants react to different COVID-19 metrics with varying sensitivity. We observe a saturation of sensitivity for several measures at critical limits used in the political discussion, making our results highly relevant for policy makers in their efforts to direct individuals to adhere to hygienic etiquette and social distancing guidelines.

Employing a unique hand-collected sample of 956 credit risk securitization transactions issued by 64 stock-listed European banks across the EU-13 plus Switzerland over the period from 1997 to 2010, this paper empirically analyzes the impact of securitization on the issuing banks’ effective tax rates. Our analysis reveals that banks may reduce their tax expense through securitization via a direct and indirect channel suggesting that tax avoidance may be a further motive for banks to engage in the securitization business. These baseline findings remain robust under various robustness checks, especially when implementing structural equation models and controlling for a reverse causality between the banks’ tax burden and their incentive to securitize. Finally, various sensitivity analyses provide further important results and implications for tax policies, banking regulation and the ongoing process of revitalizing the European securitization market.

Review papers are essential for knowledge development in IS. While some are cited twice a day, others accumulate single digit citations over a decade. The magnitude of these differences prompts us to analyze what distinguishes those reviews that have proven to be integral to scientific progress from those that might be considered less impactful. Our results highlight differences between reviews aimed at describing, understanding, explaining, and theory testing. Beyond the control variables, they demonstrate the importance of methodological transparency and the development of research agendas. These insights inform all stakeholders involved in the development and publication of review papers.

In January 2021, the GameStop stock was the epicenter of the first case of predatory trading initiated by retail investors. We use brokerage accounts to study who participated in this GameStop frenzy and how they performed. We investigate the extent to which investors’ personal and trading characteristics differ from the general population of retail investors. GameStop traders had a history of investing in speculative instruments, including stocks with lottery-like features. They were also more likely to close their positions before the peak of the bubble. At the onset of the frenzy, numerous retail investors also shorted GameStop. Overall, our results indicate that the GameStop frenzy was not a pure digital protest against Wall Street but speculative trading by a group of retail investors, in line with their prior high-risk trading behavior.

Literature reviews (LRs) play an important role in the development of domain knowledge in all fields. Yet, we observe a lack of insights into the activities with which LRs actually develop knowledge. To address this important gap, we (1) derive knowledge building activities from the extant literature on LRs, (2) suggest a knowledge-based typology of LRs that complements existing typologies, and (3) apply the suggested typology in an empirical study that explores how LRs with different goals and methodologies have contributed to knowledge development. The analysis of 240 LRs published in 40 renowned IS journals between 2000 and 2014 allows us to draw a detailed picture of knowledge development achieved by one of the most important genres in the IS field. An overarching contribution of our work is to unify extant conceptualizations of LRs by clarifying and illustrating how LRs apply different methodologies in a range of knowledge building activities to achieve their goals with respect to theory.

Successful design of human-in-the-loop control sys- tems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings. This paper addresses the following two key questions: 1) How do behavioral biases affect decision making? 2) What is the role played by feedback in human-in-the-loop control systems? Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of – or even because of – receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a “hot hand fallacy”. We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.

<jats:p>Peer-to-peer markets are especially suitable for the analysis of online ratings as they represent two-sided markets that match buyers to sellers and thus lead to reduced scope for opportunistic behavior. We decompose the online ratings by focusing on the customer’s decision-making process in a leading peer-to-peer ridesharing platform. Using data from the leading peer-to-peer ridesharing platform BlaBlaCar, we analyze 17,584 users registered between 2004 and 2014 and their online ratings focusing on the decomposition of the explicit determinants reflecting the variance of online ratings. We find clear evidence to suggest that a driver’s attitude towards music, pets, smoking, and conversation has a significantly positive influence on his received online ratings. However, we also show that the interaction of female drivers and their attitude towards pets has a significantly negative effect on average ratings.</jats:p>

After the introduction of CbCR – pursuant to the BEPS Project (Action 13) in 2015 –, which was established to reduce the information asymmetry between MNEs and tax authorities of the countries they operate in, now public CbCR – as suggested by the EU Commission in 2016 – is discussed as a next step. Here, the objective is to overcome information asymmetries between MNEs and the general public of the countries they operate in. Starting from the assumption that regulators care about the legitimacy of tax laws, this article evaluates pros and cons of public CbCR. The authors find that from the perspective of information asymmetries, public CbCR increases tax transparency only marginally at best. Accordingly, it is concluded that democracies that are based on the rule of law seem to rely on pillories in terms of public CbCR to enforce fair tax payments.

<jats:p>We experimentally compare the consequences for group cooperation of two decision mechanisms involving the extension of group membership. We analyze an exogenous decision (random draw) and an endogenous decision (made by a particular group member) mechanism to extend a temporary agent’s group membership. Our results reveal that the prospect of group membership extension affects not only the temporary but also the permanent group members’ contributions with an endogenous mechanism.</jats:p>

Today, organizations must deal with a plethora of IT security threats and to ensure smooth and uninterrupted business operations, firms are challenged to predict the volume of IT security vulnerabilities and allocate resources for fixing them. This challenge requires decision makers to assess which system or software packages are prone to vulnerabilities, how many post-release vulnerabilities can be expected to occur during a certain period of time, and what impact exploits might have. Substantial research has been dedicated to techniques that analyze source code and detect security vulnerabilities. However, only limited research has focused on forecasting security vulnerabilities that are detected and reported after the release of software. To address this shortcoming, we apply established methodologies which are capable of forecasting events exhibiting specific time series characteristics of security vulnerabilities, i.e., rareness of occurrence, volatility, non-stationarity, and seasonality. Based on a dataset taken from the National Vulnerability Database (NVD), we use the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) to measure the forecasting accuracy of single, double, and triple exponential smoothing methodologies, Croston's methodology, ARIMA, and a neural network-based approach. We analyze the impact of the applied forecasting methodology on the prediction accuracy with regard to its robustness along the dimensions of the examined system and software package "operating systems", "browsers" and "office solutions" and the applied metrics. To the best of our knowledge, this study is the first to analyze the effect of forecasting methodologies and to apply metrics that are suitable in this context. Our results show that the optimal forecasting methodology depends on the software or system package, as some methodologies perform poorly in the context of IT security vulnerabilities, that absolute metrics can cover the actual prediction error precisely, and that the prediction accuracy is robust within the two applied forecasting-error metrics.

This study examines the gender gap in competitiveness in an educational setting and tests whether this gap depends on the difficulty of the task at hand. For this purpose, we administered a series of experiments during the final exam of a university course. We confronted three cohorts of undergraduate students with a set of bonus questions and the choice between an absolute and a tournament grading scheme for these questions. To test the moderating impact of task difficulty, we (randomly) varied the difficulty of the questions between treatment groups. We find that, on average, women are significantly less likely to select the tournament scheme. However, the results show that the gender gap in tournament entry is sizable when the questions are relative easy, but much smaller and statistical insignificant when the questions are difficult.