Refine
Year of publication
- 2023 (167) (remove)
Document type
- Conference Proceeding (50)
- Bachelor Thesis (46)
- Article (peer-reviewed) (34)
- Master's Thesis (16)
- Other (6)
- Part of a Book (5)
- Report (3)
- Working Paper (3)
- Book (2)
- Doctoral Thesis (2)
Language
- English (167) (remove)
Has full text
- No (167) (remove)
Keywords
- Online-Ressource (7)
- Poster (6)
- Electrical impedance tomography (4)
- Generation Z (3)
- Poster Presentation (3)
- Preprint (3)
- Psychophysiology (3)
- Circular economy (2)
- Customer support (2)
- Emotion induction (2)
Analysis of Fintech Markets
(2023)
The technological advancement in the business sector, in particular in the banking world, forces the financial sector to adapt its services. The Fintech companies aim to cope with these advancements and change the money management for clients. The question that arises is how the FinTech companies have developed and if they are profitable. That is the reason for the research of the development of the Fintech companies theoretically and practically, focusing on their financial performance. The methodology used in this paper is for the first chapter gathering existing knowledge of previous studies and including law directives to underline the development and in the second chapter using the information available from the companies itself. One can conclude the development of the acceptance of digitalized banks have increased after the economic crisis in 2008. Furthermore, the regulatory systems have problems coping with the velocity of change and the income of new products and services. The practical analysis of the FinTech companies show, that they do have certain advantages for clients, but are not immune against economic volatility. Having analyzed existing companies is beneficiary for the research field, as for now no practical analysis of the companies have been done. Nevertheless, the limitation of this research is due to the fact of probable biased information from the companies.
As machine learning becomes increasingly pervasive, its resource demands and financial implications escalate, necessitating energy and cost optimisations to meet stakeholder demands. Quality metrics for predictive machine learning models are abundant, but efficiency metrics remain rare. We propose a framework for efficiency metrics, that enables the comparison of distinct efficiency types. A quality-focused efficiency metric is introduced that considers resource consumption, computational effort, and runtime in addition to prediction quality. The metric has been successfully tested for usability, plausibility, and compensation for dataset size and host performance. This framework enables informed decisions to be made about the use and design of machine learning in an environmentally responsible and cost-effective manner.
Phenomena like talent shortage, war for talents, and demographic change – Organizations are facing many challenges and changes these days. To compete in a contested market space the issue of employer attractiveness is becoming increasingly important. It seems that prioritizing the provision of attractive working conditions becomes prominent, while the importance of locus of control is underestimated. The research question addressed in this study is: What are the effects of working conditions and locus of control on performance? For this
investigation, a quasi-experiment with a 2x2 factorial design was conducted. Participants were assigned to either an attractive or unattractive working condition while simultaneously experiencing internal or external locus of control. Subjects were asked to perform cognitive tasks and performance was measured by the total number of points reached.
The results of the study did not yield statistical significance. However, it was observed that the group experiencing unattractive working conditions and internal locus of control had the highest performance. When also considering relevant research literature that highlights the psychological importance of control, it is suggested to conduct further research in this area of
interest to gain a deeper understanding of the effects of control and their impact on various job outcomes like performance.
Gamification has become an innovative marketing tool in the tourism industry. It can potentially increase engagement and brand awareness and enhance overall tourist experiences. This thesis presents the theory behind gamification and its application in travel marketing. It discusses extrinsic and intrinsic motivational factors, fundamental concepts, practical examples, and the role of mobile applications in driving these strategies forward. By understanding how gamification can amplify tourist engagement, marketers could take advantage of this strategy and create campaigns that leave a long-lasting and positive impression. In addition, real-world examples demonstrate that tourism marketing can benefit from this innovative technique. Based on the literature's most critical findings, a model for implementing gamified marketing strategies to potentially increase tourist numbers during off-season traveling in less popular destinations is proposed.
This chapter introduces the technology Non-Intrusive Load Monitoring, a method for detecting individual devices from an overall signal. Non-Intrusive Load Monitoring is the research area and technology behind the third word in Smart Meter Inclusive. Using a smart meter as a basis and recognizing devices from the power profile is not a new idea but is now a common practice in Non-Intrusive Load Monitoring. However, the approach to creating such a measurement system that classifies appliances in real-time and visualizes the results directly on the same hardware has not been existing yet. Smart Meter Inclusive wants to leave the data where it originates, namely with the customer. This book chapter provides a general overview of non-intrusive load monitoring to be able to understand the basics and approaches for such a Smart Meter Inclusive.
This Thesis analyses the difference in the impact of the issuance of a green bond on the stock prices of the issuing entity by country, using an event study approach of 135 green bonds, by comparing the actual daily returns of the event window with the expected returns calculated from the estimation window. With only a slight influence of the country found, further factors effecting the impact are considered. While we conclude that German issuers tend to see a stronger impact than most issuers from other European countries, factors such as the risk of greenwashing, time of issuance, industry, and firm fundamentals appear to skew this impact so that it is not possible to make a definitive statement about the impact of the country of origin on the effectiveness of green bond issuances at providing excess stock returns.