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This thesis examines how financial institutions can address outcomes of the global financial crisis (GFC) such as mistrust of customers and ambitious requirements of regulations such as Basel III. Moreover, the market is facing a dramatic sociocultural change which creates the necessity to realign retail banks’ strategies. It will be outlined why social media is an important trend for the financial sector and which methods can be used to achieve a competitive advantage through customer-centricity. On the one hand the financial sector will be analysed to determine which of Porter’s five forces are shaping it. On the other hand, it will be shown how social media can be used to achieve a competitive advantage. This thesis will find out that social media is an important medium for retail banks to gain back customers’ trust in financial institutions and to focus on the customers’ needs. By engaging customers on social media platforms such as Facebook or Twitter, financial institutions can even create value beyond financial products and increase their innovative drive. Best practice examples of Banks such as CBA, ING Direct, Wells Fargo, and ICICI prove that. The implications are that social media gives customers the power to shape the bank of tomorrow and in turn banks can gain valuable insights into customers’ needs. Financial products and services will make more use of social media platforms and increase customers’ engagement through sharing, commenting and liking.
As technology advances, the services offered by libraries and the roles of
librarians continue being reconsidered. This paper describes the traditional
model and development of liaison and embedded librarians, examines the
online visibility of liaison librarians and their services in an embedded sense,
especially regarding instruction, of selected Canadian Academic Libraries, and
provides a short view to German libraries and their subject librarians.
It has been shown that even if not clearly a development of liaison librarians to
certain embedment is emphasized for each library, it at least evolves to a usercentered
approach and a preference to stronger collaborations. The selected
libraries seek to broaden their scope of partnerships. In which deepness it is
realized, lastly depends on the willingness of both participators and capacities.
Libraries have stated their flexibility in various ways and are ready to step in at
the point of need. The one closest to embedded services is the instruction of
information literacy as its effectiveness requests a longer relationship in order to
flourish. Nevertheless research support in Health Sciences obviously has
become an integral part.
As a key part of human-computer interaction(HCI) and usability testing, the capturing and recording of key user interaction plays a center role for ensuring a reliable post-hoc analysis of collected user interaction data, thus improving the odds of insightful HCI and usability testing cycles for use cases such as the evaluation of interactive information retrieval Systems(IRR). As such, the practice of logging is of significant importance for multiple fields of study such as IIR, HCI and most recently also Living Lab approaches. Living lab approaches represent a user-centered research methodology with a focus on user involvement, experimental approaches and extensive collaboration for the sake of co-production of knowledge and as such, has a dire need for robust and easy to use logging solutions.
With past logging solutions being either expensive, hard to use or error-prone, recent conferences gave rise to new logging solutions using contemporary web technologies, which aim to improve the logging landscape within the research community. Over the course of this paper, two of these recent logging solutions, LogUI and Big Brother, are to be inspected for their key features and then evaluated, whether they are suitable logging solutions for living lab and IIR environments. Results and research indicate, that both logging solutions offer significant benefits for research using living lab and IIR approaches, with LogUI embracing many of the experimental paradigms that guide the living lab approach.
With the growing scientific output that is produced, its getting more important to automate the extraction of knowledge from articles. This bachelor thesis will describe an approach doing exactly this. Scientific articles will be obtained from a database.
These articles will be preprocessed to gain a set of training data, to update a language model that already exists for Python library spaCy. The model will be trained to recognize different sorts of entities regarding to the virus rabies. After this process the model will be used for ten articles and the extracted knowledge will be used to extend the Open Research Knowledge Graph.
The goal of this work is to detect "gender biases" in the communication of users of Subreddits on the platform Reddit. The analysis is carried out for eleven selected Subreddits. Furthermore, an attempt is made to identify different user types with the help of a k-means clustering and also to analyze "gender biases" in their communication. Based on the aggregated datasets, fasttext Word Embedding models are trained to identify terms that show high semantic relatedness in terms of cosine similarity of their word vectors with selected feminine and masculine terms.
To this end, the terms are analyzed for sentiment using the NRC-VAD Lexicon and tested for statistically significant differences. In addition, the Word Embedding Association Test (WEAT) is performed to check for subliminal associations. In relation to the considered text corpus, it is essentially observed that women are frequently associated with adjectives that associate them with appearances,
childbearing abilities or adaptability also in relation to the family. In contrast, men are associated with and measured by adjectives that refer to their prestige, strengths and weaknesses, career or physical characteristics.
This paper examines different business models of companies dealing with (earmarked) remittances and sheds light on the associated challenges of the industry, specifically, remittances for health, based on the model of the fintech startup GloryHealthCare. The work "Business Model Generation" by Osterwalder and Pigneur (2010) is used as a method for the analysis, as this is often used as a basis for the business models of startups. The study focuses regionally on Europe and Africa, as Germany and Ghana are the start-up's first target markets. Among other things, the industry's processes, pricing, and existing competition are examined. The SWOT analysis methodology clarifies the individual companies' opportunities and risks and makes a competitive position visible. Meanwhile, network effects of the diverse business models are made visible based on the paper "Digital Economy and Network Effects" by Frank Linde (2020). Network effects play a crucial role in the reach, influence, and competitiveness of existing and new businesses in the remittance industry. The study also emphasizes the importance of knowledge and networks, which are more important than financial resources. The previous aspects considered a basis for developing a new concept as an alternative to the Business Model Canvas: the iBusiness Model. The results of this study provide insights into the design of efficient business models and support companies in the remittance industry in developing strategies to overcome challenges and take advantage of opportunities.
This thesis aims to extend an existing Open Educational Resource (OER), which is available as a GitHub repository, and provide an organized introduction to basic machine learning (ML) concepts and algorithms. Further models, followed by structured metadata for each object, will be included while adhering to the contribution guidelines of the OER and following the CC license. The Machine-Learning-OER Basics repository intends to provide a wide range of benefits by enabling diverse users to apply and distribute machine learning algorithms. The goal of this digital collection is to fill the existing gap for instructional material on using machine learning in OER as well as make it easier to learn ML concepts effectively. These ML models are developed using the programming language Python and the library scikit-learn, among other standard libraries. Jupyter Notebook will make it straightforward for the user to explore the code. In order to apply the models to various practical scenarios, a non-specific data set is selected. This work is considered a solution approach in that it includes adding classification models.
A performance comparison of the models is conducted. This comparative analysis evaluates the efficiency of each model. The examination includes various metrics for measurement. This work serves as a written extension, providing comprehensive background information on the algorithms utilized within the repositories and the performance comparison.