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In the light of the information age, information overload research in new areas (e.g., social media, virtual collaboration) rises rapidly in many fields of research in business administration with a variety of methods and subjects. This review article analyzes the development of information overload literature in business administration and related interdisciplinary fields and provides a comprehensive and overarching overview using a bibliometric literature analysis combined with a snowball sampling approach. For the last decade, this article reveals research directions and bridges of literature in a wide range of fields of business administration (e.g., accounting, finance, health management, human resources, innovation management, international management, information systems, marketing, manufacturing, or organizational science). This review article identifies the
major papers of various research streams to capture the pulse of the information overload-related research and suggest new questions that could be addressed in the future and identifies concrete open gaps for further research. Furthermore, this article presents a new framework for structuring information overload issues which extends our understanding of influence factors and effects of information overload in the decision-making process.
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.
Erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements im Unternehmen
Dieser kompakte Band geht auf zentrale Prinzipien und Konzepte ein, die Unternehmen helfen, ein Nachhaltigkeitsmanagement aufzubauen und weiterzuentwickeln. Darüber hinaus bietet er einen Überblick über die verschiedenen Ansätze und Instrumente des Nachhaltigkeitsmanagements, damit Unternehmen ihre Aktivitäten analysieren, bewerten und verbessern können, um ökologische und soziale Auswirkungen zu reduzieren und langfristige Wertschöpfung zu ermöglichen.
Diese grundlegende Einführung richtet sich an die Führungskräfte in den Unternehmen, die sich mit Fragen des Umwelt- und Nachhaltigkeitsmanagements beschäftigen. Zunächst werden die relevanten normativen und regulativen Anforderungen an das unternehmerische Nachhaltigkeitsmanagement vorgestellt, bevor konkret erste Schritte zur Implementierung eines Nachhaltigkeitsmanagements entwickelt werden. Die beiden letzten Kapitel stellen ein geeignetes Steuerungssystem sowie die Grundlagen der Nachhaltigkeitskommunikation eines Unternehmens vor.
Chaoticity Versus Stochasticity in Financial Markets: Are Daily S&P 500 Return Dynamics Chaotic?
(2021)
In this study, we present a combinatory chaos analysis of daily wavelet-filtered (denoised) S&P 500 returns (2000–2020) compared with respective surrogate datasets, Brownian motion returns and a Lorenz system realisation. We show that the dynamics of the S&P 500 return series consist of an almost equally divided combination of stochastic and deterministic chaos. The strange attractor of the S&P 500 return system is graphically displayed via Takens’ embedding and by spectral embedding in combination with Laplacian Eigenmaps. For the field of nonlinear and financial chaos research, we present a bibliometric analysis paired with citation network analysis. We critically discuss implications and 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.
Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.
Abstract: Like many service industries, the financial industry is largely characterized
by administrative and back-office processes and distinguished by a broad systems
landscape with a high proportion of legacy systems. Missing interfaces between information
systems, user interfaces, or web applications often require many manual
activities. As banks are often functionally organized into traditional departments, a
process-oriented organizational structure is rarely in place. The financial industry
therefore offers enormous potential for the use of robotic process automation (RPA)
and the raising of potential benefits such as process-related cost savings, time reductions,
and quality improvements.
The aim of this chapter is to describe the tremendous opportunities that the use of
RPA technology offers to the financial industry and to explain how these opportunities
can be realized. Therefore, we start by explaining the challenges that progressive digitalization
poses to the industry and how RPA, but also more advanced technologies
(that work not only rule-based but also define own rules), such as artificial intelligence,
can help to overcome them. As well as providing an overview of the various
applications of RPA in the financial industry, we also provide a comprehensive case
study of a relevant practical application