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- Article (36) (remove)
Die vorliegende Studie untersucht Entwicklungen und Trends im Nachhaltigkeitscontrolling, insbesondere hinsichtlich der strategischen Bedeutung der fünf Stufen der Nachhaltigkeit, des Einflusses von Stakeholdergruppen, Zielsetzungen und Instrumenten anhand von drei Studien des Fachkreis Green Controlling for Responsible Business und gibt Implikationen für die Controlling- und Unternehmenspraxis.
In this article, we contribute to the longstanding debate among economists regarding the question of “nature or nurture” with respect to economics students’ attitudes toward various allocation mechanisms for a scarce resource. While previous research starts the debate by beginning with first-year economics students, we aim to evaluate pre-firstyear individuals, i.e., school pupils. Drawing on the seminal works of Haucap, J., & Just, T. (2010). Not guilty? Another look at the nature and nurture of economics students. European Journal of Law and Economics, 29(2), 239–254 and Frey, B. S., Pommerehne,W.W., & Gygi, B. (1993). Economics indoctrination or selection? Some empirical results. The Journal of Economic Education, 24(3), 271–281, we investigate a sample of pupils ranging from the 5th to the 13th grades to determine whether pupils are “born economists” (nature), develop economic thinking (nurture), or both. We find that young individuals start to think differently in early grades and that their thinking and attitudes are shaped differently throughout their school careers, thereby providing support for the effects of both nature and nurture. Our findings show that school time impacts fairness judgments, particularly regarding price mechanisms. Regarding learning or indoctrination, we find that economics-inclined pupils are positively affected by lessons in economics in school, while pupils who are economics-averse draw completely diametric conclusions from economics lessons, thereby exhibiting increased disapproval of price allocation over the course of these classes and increased approval of the first come, first served and governmental action mechanisms.Moreover, we find strong effects of gender and migration background in this context. This study is the first to elucidate the development of economic thinking in 5th–13th grade pupils. Our results are important for economists, educators,
and researchers because they can serve as a starting point for subsequent investigations in this under-researched field.
In this study, we analyse the advantageous effects of neural networks in combination with wavelet functions on the performance of financial market predictions. We implement different approaches in multiple experiments and test their predictive abilities with different financial time series. We demonstrate experimentally that both wavelet neural networks and neural networks with data pre-processed by wavelets outperform classical network topologies. However, the precision of conducted forecasts implementing neural network algorithms still propose potential for further refinement and enhancement. Hence, we discuss our findings, comparisons with “buy-and-hold” strategies and ethical considerations critically and elaborate on future prospects.
This experimental study analyzes how a key factor, information load, influences decision making in escalation situations, i.e., in situations in which decision mak- ers reinvest further resources in a losing course of action, even when accounting information indicates that the project is performing poorly and should be discontin- ued. This study synthesizes prior escalation research with information overload and investigates how different levels of information load influence the escalation of com- mitment. Our findings reveal a U-shaped effect of information load: When decision makers face negative feedback, a higher information load mitigates the escalation tendency up to a certain point. However, beyond this point, more information rein- forces the escalation tendency. Moreover, we find that the type of feedback affects self-justification, and we find a negative and significant interaction between informa- tion load and self-justification in negative-feedback cases. Thus, studies investigat- ing escalation of commitment should control for self-justification and information load when utilizing high levels of information load. Finally, in the positive-feedback condition, higher information load encourages decision makers to continue promis- ing courses of action, i.e., increases decision-making performance.
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
(2022)
In this study, we empirically show the dynamics of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) to consist of an almost equally divided combination of stochastic and deterministic chaos, rendering the series unpredictable after expiration of the Lyapunov time, resulting in futile forecasting attempts. We achieve a clear distinction of the true nature of the underlying time series dynamics by applying a novel and combinatory chaos analysis framework comparing the wavelet-filtered S&P 500 returns with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. Furthermore, we are the first to show the strange attractor of especially the daily-frequented S&P 500 return system graphically via Takens´ embedding and by spectral embedding in combination with Laplacian Eigenmaps. Finally, we critically discuss implications and future prospects in terms of financial forecasting.
One domain of application of artificial intelligence (AI) is decision support, particularly in management. Although there are already research streams examining the interaction of AI and humans (e.g. the stream on "hybrid intelligence"), there are still numerous open research gaps – for example, a comprehensive overview of which factors favor the intention to use AI is missing. By conducting a systematic literature review, we identify the factors that potentially positively influence AI usage intentions for decision-making processes in organizations. From this, we create a framework that both provides practical implications for the successful use of AI in organizational decision-making processes and delivers further research approaches, for example, on the validity/ usability of proven IS adoption models in the present context.
This experimental study analyzes how a key factor, information load, influences decision making in escalation situations, i.e., in situations in which decision makers reinvest further resources in a losing course of action, even when accounting information indicates that the project is performing poorly and should be discontinued. This study synthesizes prior escalation research with information overload and investigates how different levels of information load influence the escalation of commitment. Our findings reveal a U-shaped effect of information load: When decision makers face negative feedback, a higher information load mitigates the escalation tendency up to a certain point. However, beyond this point, more information reinforces the escalation tendency. Moreover, we find that the type of feedback affects self-justification, and we find a negative and significant interaction between information load and self-justification in negative-feedback cases. Thus, studies investigating escalation of commitment should control for self-justification and information load when utilizing high levels of information load. Finally, in the positive-feedback condition, higher information load encourages decision makers to continue promising courses of action, i.e., increases decision-making performance.
Risikoorientiertes Regulierungscontrolling bei Stromnetzbetreibern im Kontext der Anreizregulierung
(2013)
Die Ausgestaltung des Regulierungssystems hat einen wesentlichen Einfluss auf die Geschäftsstrategie und die Funktionsbereiche von regulierten Unternehmen. Besonders deutlich zeigt sich dies bei einem vollständigen Systemwechsel, wie er kürzlich mit der Einführung der Anreizregulierung in der deutschen Stromwirtschaft vollzogen wurde. Der vorliegende Beitrag untersucht die Auswirkungen auf die regulierten Unternehmen auf Basis einer Befragung und zeigt auf, dass der Wechsel des Regulierungssystems zu deutlichen Reaktionen hinsichtlich Risikoeinschätzung, Geschäftsstrategie und Investitionsverhalten geführt hat. Besonders starken Veränderungen sind die Funktionsbereiche Regulierungsmanagement und Controlling unterworfen. Aufbauend auf diesen empirischen Ergebnissen entwickelt der Beitrag Ansätze für ein risikoorientiertes Regulierungscontrolling.
Der vorliegende Beitrag untersucht den Einfluss des öffentlichen Anteilseigners auf gemischtwirtschaftliche Public Private Partnerships, welche die staatliche Durchführung von öffentlichen Aufgaben durch die Bereitstellung von notwendigen Gütern und Dienstleistungen unterstützen. Die empirische Analyse bezieht sich auf Public Private Partnerships des Bundesministeriums der Verteidigung. Diese Public Private Partnerships sind dadurch gekennzeichnet, dass der öffentliche Anteilseigner gleichzeitig als öffentlicher Auftraggeber auftritt. Zunächst wird gezeigt, dass die staatliche Institution als öffentlicher Anteilseigner das Zielsystem in Bezug auf die monetären Ziele maßgeblich bestimmt. Für die Public Private Partnerships ist demnach die Kostenminimierung und nicht die von den privaten Anteilseignern eingeforderte Gewinnmaximierung das wichtigste monetäre Ziel. Die staatliche Institution unterbindet durch die Beeinflussung der monetären Ziele einer Public Private Partnerhip das opportunistische Verhalten in Bezug auf das Entgelt für die zu erbringende Leistung. Im Vergleich zu einer rein privatwirtschaftlichen Erfüllung von öffentlichen Aufträgen ist die Etablierung von Public Private Partnerships eine Möglichkeit, das Kapital, die Kompetenz sowie die wirtschaftliche Effizienz von privaten Wirtschaftssubjekten zu nutzen, ohne die Transaktionskosten durch eine opportunistische Entgeltgestaltung maßgeblich zu erhöhen.