Vorträge im Sommersemester 2022

Punishing Firms for Their Manager's Misreporting

Upon discovery of misreporting, not only managers but also firms incur penalties. We develop a model to study the effects of managers' and corporate penalties for financial reporting quality, firm value, the ex ante cost of capital, and the asymmetry in market reactions to positive and negative earnings news. Our model features a myopic manager who engages in accounting manipulation, a benevolent board that exerts costly oversight effort to prevent misreporting, and a perfectly competitive capital market. Corporate penalties have two immediate implications: they provide oversight incentives to the board, and investors assign a discount to the firm's stock price, which is detrimental to the firm's cost of capital. We derive the following main results. (i) Corporate penalties unambiguously improve financial reporting quality and reduce the asymmetry in market reactions, whereas managers' penalties can impair quality and increase asymmetry if corporate penalties are large. (ii) Firm value and the ex ante cost of capital can increase or decrease in both corporate and managers' penalties, where the effect again depends on the size of corporate penalties. (iii) Increasing the ex post misreporting detection likelihood (e.g., by increasing public enforcement intensity) improves financial reporting quality and market reaction asymmetry in most cases but has an ambiguous effect on firm value and the cost of capital. We discuss empirical and regulatory implications of our results.

Lightening the dark side of customer participation – The mitigating role of relationship performance in business-to-business project contexts

Customer participation offers significant opportunities for B2B suppliers in contexts where parties interact over an extended period of time. However, a growing body of research highlights concerns about the darker side of customer participation. Integrating literature on the dark side, reactance theory, and social exchange, this research contends that suppliers aim to preserve their autonomy. The authors forward a conceptual framework examining the effects of customer participation on two types of supplier commitment. A survey of 105 managers of business and industrial projects suggests that customer participation reduces suppliers’ affective and calculative commitment. Furthermore, relationship performance positively moderates the negative effect of customer participation on calculative commitment thereby alleviating the detrimental effects of customer participation. The present research advances the literature by highlighting the potential of relational approaches in lightening the dark side of customer participation and mitigating its possible detrimental effects.

In the AI of the beholder – A qualitative study of HR professionals' beliefs about AI based chatbots and decision support in candidate pre-selection

Despite the fast technological development and the high potential of artificial intelligence (AI), the actual adoption of AI in recruiting is low. Explanations for this discrepancy are scarce. Hence, this paper presents an exploratory interview study investigating HR professionals' beliefs about AI to examine their impact on use cases and barriers and to identify the reasons that lead to non-adoption of AI in recruiting. Semi-structured interviews were conducted with 25 HR professionals from 21 companies. The results revealed that HR professionals' beliefs about AI can be categorized along two dimensions: (1) scope of AI, and (2) definition of instruction. "Scope of AI" describes the perceived technical capabilities of AI and determines the use cases that HR professionals imagine, while "definition of instruction" describes the perceived effort to enable an AI to take on a task and determines how HR professionals perceive barriers to AI. Our findings suggest that HR professionals' beliefs base on vague knowledge about AI leading to non-adoption and that training and awareness campaigns could be a suitable measure.

Die Frage der Einkommensgerechtigkeit in der Zeit der Corona-Krise

Die Corona-Krise hat deutlich gemacht, welche Berufe für die Aufrechterhaltung der Grundfunktionen in der österreichischen Gesellschaft von besonderer Bedeutung sind. Die so genannten Systemerhalter*innen im Sozial-, Gesundheits- und Pflegebereich sowie im Handel ernteten vor allem zu Beginn der Krise viel Lob vonseiten der österreichischen Politik. Im Zuge dessen wurden vermehrt Stimmen laut, die für eine entsprechende finanzielle Honorierung der Arbeitsleistung dieser Berufsgruppen eintraten.

Vor diesem Hintergrund wird im Vortrag anhand von repräsentativen Umfragedaten des International Social Survey Programme (2009, 2021) der Frage nachgegangen, welche Einkommenshöhen die österreichische Bevölkerung für Verkäufer*innen, ungelernte Arbeiter*innen, Ärzt*innen, Manger*innen und Minister*innen vor und in Zeiten der Corona-Krise als gerecht empfindet. Ergänzend dazu werden zentrale Befunde zur Akzeptanz der Einführung eines bedingungslosen Grundeinkommens vorgestellt, das zu einer sozialen Absicherung von Personen in statusniedrigen Berufen führen sollte.

Die Ergebnisse zeigen, dass sich die Befragten sowohl vor als auch in der Krise für eine erhebliche Reduktion der Einkommen von Eliteberufen (Manager*innen und Politiker*innen) aussprechen, während die Einkommen von statusniedrigen Berufen im Verkauf und in der Industrie ihrer Ansicht nach „moderat“ erhöht werden sollten. Auffallend ist, dass im Vergleich zu statusniedrigen Berufen insbesondere Mediziner*innen und Minister*innen etwas höhere Einkommen zugestanden werden als vor der Krise. Gleichzeitig ist die Befürwortung eines bedingungslosen Grundeinkommens in der Krise etwas höher als davor, wobei die Bevölkerung in dieser Frage nach wie vor gespalten ist und sich zunehmend polarisierte.

Testing the Matthew Effect in peer-review

We study how author prominence affects the peer-review process. More specifically, we test the hypotheses that i) reviewers are more likely to accept review invitations for manuscripts written by prominent authors than by relatively less known ones; and ii) that publishing recommendations are more favorable for articles written by prominent authors than relatively less known ones. To test these hypotheses, we conduct a pre-registered field experiment and invite more than 3000 researchers to review a manuscript written by a Nobel laureate and an early-career research associate. Across multiple treatments we vary the designated corresponding author and whether author names are shown in the review invitation and on the manuscript. The results are in line with a strong status bias: Researchers are more likely to accept the review invitation for the manuscript written by the prominent author. Publishing recommendations are significantly more favorable for the prominent author.

What drives willingness to pay for repair services?

Utilizing repair services for repairing defective products has not only positive (economic) implications for repair companies but also eliminates waste, saves natural resources, creates local added value and contributes to a Circular Economy. However, too high repair costs and inconvenient repair services are often the main reason against using repair services. Therefore, an Adaptive Choice-Based Conjoint analysis was made to identify willingness to pay (WTP) for repair services and to evaluate if the WTP for repair services is higher, if certain repair service (convenience) characteristics are improved. The focus is on repair companies providing a smartphone and washing machine repair with different purchase prices (higher and lower price segment). In the study the repair price is combined with attributes like trust-indicators, store hours, travel time to the repair company, waiting time and guarantee for the repair carried out. With the help of the conjoint technique, consumer’s utilities for service-characteristics of repairing, the relative importance of repair service attributes and WTP values were identified. Based on data of repair companies, suggestions are made on how to increase demand for repair services by improving certain repair service characteristics.

Learning Analytics – Datengesteuerte Subjektivierungsprozesse von Studierenden

Innerhalb der letzten 10 Jahre hat die Rolle von Learning Analytics (LA) im Hochschulbereich zunehmend an Bedeutung gewonnen. Obwohl es bis dato wenig empirische Belege für die Lernwirksamkeit von LA-Systemen gibt, nehmen gegenwärtig die Bemühungen zu, LA-Prototypen und LA-Modelle zu konzeptualisieren, sowie systemweite Umsetzungsstrategien zu erarbeiten. Die vorhandene Kritik an LA richtet sich dabei insbesondere auf Datenschutzbedenken, mangelnde ethische Standards und die häufig fehlende pädagogische Perspektive bei der Gestaltung von LA-Anwendungen. Weitgehend ignoriert wird allerdings die Tatsache, dass der Einsatz von Bildungstechnologien kein wertneutraler Prozess ist, der Daten objektiv analysiert, misst und präsentiert, sondern selbst aktiv an der Erzeugung sozialer Realität beteiligt ist. LA-Elemente wie Dashboards produzieren und vermitteln normative Vorstellungen und Erwartungen über Lernende, Lernumgebungen und Lern- und Bildungsprozesse und strukturieren somit die Denk- und Handlungsweisen von Lehrenden und Lernenden. Bis dato ist wenig darüber bekannt, wie sich die Nutzung von LA auf die Verhaltensweisen der Lernenden auswirkt und welche Subjektivierungsweisen dadurch befördert werden. Das vorliegende Forschungsprojekt widmet sich diesem Forschungsdesiderat und betrachtet den Einsatz von Learning Analytics im Rahmen eines konkreten Anwendungsszenarios im Masterstudium Wirtschaftspädagogik der Universität Graz aus einer Foucault-inspirierten Perspektive. Anhand des Konzepts der algorithmischen Gouvernementalität und mittels problemzentrierter Interviews wird untersucht, inwiefern die Nutzung von LA-Elementen die Subjektivierungsprozesse von Studierenden prägt.

Taxation, Board Monitoring, and Management Incentives

We analyze the interplay between management incentives, board monitoring, and corporate taxes. In doing so, we illustrate a novel theoretical link between monitoring incentives and corporate taxation which is of similar quality as previous literature has demonstrated for dividend taxation (e.g. Chetty and Saez, 2010). We study the implications of this link on performance measurement and the design of managerial incentives. Moreover, we analyze how taxation interacts with board monitoring commitment problems. Using a continuous economic model, we show that board composition of corporate monitoring boards is influenced by corporate taxation. More specifically, we show that the proportion of corporate insiders necessary to facilitate optimal board monitoring intensity is moderated by corporate taxes.

Predicting Voter Ideology using Machine Learning

Studies on voter ideology usually rely on surveys where respondents place themselves on a left-right scale. This approach has three apparent problems: First, respondents may not understand the meaning of “left” and “right”. Second, they may have a biased view of their own position. Third, a unidimensional axis may not suffice to describe a given ideology coherently. I tackle all three problems by investigating how experts would perceive the ideology of each voter by applying machine learning and classical regression analysis to data from the Chapel Hill Expert Survey and the European Voter Study. Random forest regression outperforms other approaches in terms of i) in-sample fit, ii) out-of-sample forecast and iii) predicted ideological difference to the party voted for in the last national elections. My analysis suggests that there is a significant and sizeable “center bias”, i.e. voters are much more likely to place themselves at the political center than experts are predicted to do. Nevertheless, the predicted level of ideological fragmentation is lower than the fragmentation based on self-reported ideology. Departing from a unidimensional ideological axis, I show that voters tend to be more left-wing economically than generally, and that the ideological fragmentation along the economic axis is lower than along an “authoritarian-libertarian” axis. This paper shows that machine learning can be a fruitful tool to predict the political landscape and points to directions for future research.