Alle Publikationen der Fakultät für Wirtschaftswissenschaften

<jats:title>Abstract</jats:title><jats:p>Using time series data for the period 1959–2015, our empirical analysis examines the simultaneous effects of the individual components of the global carbon budget on temperature. Specifically, we explore the possible effects of carbon emissions caused by fossil fuel combustion, cement production, land-use change emissions, and carbon sinks (here in terms of land sink and ocean sink) on climate change. The simultaneous inclusion of carbon emissions and carbon sinks allows us to look at the coexistent and opposing effects of the individual components of the carbon budget and thus provides a holistic perspective from which to explore the relationship between the global carbon budget and global warming. The results reveal a significant positive effect of carbon emissions on temperature for both fossil fuels emissions and emissions from land-use change, confirming previous results concerning carbon dioxide and temperature. Further, while ocean sink does not seem to have a significant effect, we identify a temperature-decreasing effect for land sink.</jats:p>

The diagnosis of diseases is decisive for planning proper treatment and ensuring the well-being of patients. Human error hinders accurate diagnostics, as interpreting medical information is a complex and cognitively challenging task. The application of artificial intelligence (AI) can improve the level of diagnostic accuracy and efficiency. While the current literature has examined various approaches to diagnosing various diseases, an overview of fields in which AI has been applied, including their performance aiming to identify emergent digitalized healthcare services, has not yet been adequately realized in extant research. By conducting a critical review, we portray the AI landscape in diagnostics and provide a snapshot to guide future research. This paper extends academia by proposing a research agenda. Practitioners understand the extent to which AI improves diagnostics and how healthcare benefits from it. However, several issues need to be addressed before successful application of AI in disease diagnostics can be achieved.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The application of artificial intelligence (AI) in hospitals yields many advantages but also confronts healthcare with ethical questions and challenges. While various disciplines have conducted specific research on the ethical considerations of AI in hospitals, the literature still requires a holistic overview. By conducting a systematic discourse approach highlighted by expert interviews with healthcare specialists, we identified the status quo of interdisciplinary research in academia on ethical considerations and dimensions of AI in hospitals. We found 15 fundamental manuscripts by constructing a citation network for the ethical discourse, and we extracted actionable principles and their relationships. We provide an agenda to guide academia, framed under the principles of biomedical ethics. We provide an understanding of the current ethical discourse of AI in clinical environments, identify where further research is pressingly needed, and discuss additional research questions that should be addressed. We also guide practitioners to acknowledge AI-related benefits in hospitals and to understand the related ethical concerns.</jats:p>

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.

<jats:title>Abstract</jats:title><jats:p>Using data from the German Socio-Economic Panel for 1984–2018, we analyze the intergenerational education mobility of immigrants in Germany by identifying the determinants of differences in educational stocks for first- and second-generation immigrants in comparison to individuals without a migration background. Our results show that on average, first-generation immigrants have fewer years of schooling than native-born Germans and have a disproportionate share of lower educational qualifications. This gap is strongly driven by age at immigration, with immigration age and education revealing a nonlinear relationship. While the gap is relatively small among individuals who migrate at a young age, integrating in the school system at secondary school age leads to large disadvantages. Examining the educational mobility of immigrants in Germany, we identify an inter-generational catch-up in education. The gap in education between immigrants and natives is reduced for the second generation. Finally, we find that country of origin differences can account for much of the education gap. While immigrants with an ethnic background closer to the German language and culture show the best education outcomes, immigrants from Turkey, Italy, and other southern European countries and especially the group of war refugees from Syria, Afghanistan, Iraq and other MENA countries, have the lowest educational attainment.</jats:p>

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.

We experimentally consider a dynamic multi-period Cournot duopoly with a simultaneous option to manage financial risk and a real option to delay supply. The first option allows players to manage risk before uncertainty is realized, while the second allows managing risk after realization. In our setting, firms face a strategic dilemma: They must weigh the advantages of dealing with risk exposure against the disadvantages of higher competition. In theory, firms make strategic use of the hedging component, enhancing competition. Our experimental results support this theory, suggesting that hedging increases competition and negates duopoly profits even in a simultaneous setting.

<jats:title>Abstract</jats:title><jats:p>Organizations introduce virtual assistants (VAs) to support employees with work-related tasks. VAs can increase the success of teamwork and thus become an integral part of the daily work life. However, the effect of VAs on virtual teams remains unclear. While social identity theory describes the identification of employees with team members and the continued existence of a group identity, the concept of the extended self refers to the incorporation of possessions into one’s sense of self. This raises the question of which approach applies to VAs as teammates. The article extends the IS literature by examining the impact of VAs on individuals and teams and updates the knowledge on social identity and the extended self by deploying VAs in a collaborative setting. Using a laboratory experiment with N = 50, two groups were compared in solving a task, where one group was assisted by a VA, while the other was supported by a person. Results highlight that employees who identify VAs as part of their extended self are more likely to identify with team members and vice versa. The two aspects are thus combined into the proposed construct of virtually extended identification explaining the relationships of collaboration with VAs. This study contributes to the understanding on the influence of the extended self and social identity on collaboration with VAs. Practitioners are able to assess how VAs improve collaboration and teamwork in mixed teams in organizations.</jats:p>

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>