Contributions in Merlin

Incorporating Pedagogical Conversational Agents (PCAs) into learning environments has shown promising benefits for learning. However, most of the research has been performed in the field of academia. Building on a previous paper, this study focuses on the application of PCAs for organisational workplace learning. Having a strong Corporate Identity (CI) has become a competitive advantage for companies. In sectors, like public administrations that have a distinct service side, training staff on CI can be beneficial for the organisation. This thesis is part of a bigger research project that designed and implemented a CI Training for two public administrations in Southern Germany. Using Action Design Research, this thesis focused on designing a learning platform and learning videos that aimed to train staff on external communication, based on existing theories about multimedia learning and practice-inspired research. The main role of this thesis in the project was to design the usability of the learning platform, the experience of the learners, as well as the learning videos. So far, no uniform guidelines for the design of learning platforms that include a Pedagogical Agent (PA) could be found. The aim of this thesis was therefore to investigate the interaction of the agent, the platform, and the learner, thus contributing towards answering the question how a successful workplace training environment, that is supported by a PCA, can be designed. The results showed that incorporating a PCA into the learning environment increased the overall usability of the platform, especially the perceived control that the learners have over the interactions with the platform. The agent initially motivated the participants, however, this motivation declined as the agent did not perform to their expectations. No effect of the PCA-supported learning treatments on self-efficacy or self-determination could be found, in comparison to traditional e-learning without a PCA. Based on the results and on the qualitative feedback from the participants, design principles for PCA-supported learning environments were formulated. The data and results of this thesis can be used for further iterations of the overarching research project. Simultaneously, it can serve as a source of information for the design of other PCA-supported learning environments.

Detecting and solving sleep disorders can significantly impact society and the economy in general. The polysomnogram is the gold standard exam for diagnosing sleep disorders. Manually annotating the patient's sleep has limitations, including its time-consuming and tedious nature, lack of reliability, sensitivity to the setup of different clinics, and motion noise. This work tests the ability of neural network models to be faster and more reliable than manual scoring by incorporating temporal information in the training setting and changing the model architecture. The study concentrates on algorithms that are robust to the setup of different clinics and fair to diverse populations, using an intelligent combination of the most used datasets in experimental settings: the Sleep-EDF and the MASS datasets. We first analyze the ability of the automated classifier to handle data from different sleep centers and patient groups by experimentally testing loss functions and other crucial model parameters across datasets. Then, we incorporate temporal context in the data samples by concatenating previous sleep epochs to the current sample. We show that our model trained on longer temporal context performs equally to many of the analyzed manual sleep stage scoring conducted by expert technicians and is superior to some state-of-the-art models we analyzed.

This paper investigates the relationship between stock liquidity and the corresponding ADR shares discount using a sample of cross-listed firms in both the A shares market in China and the ADR market in the USA. The liquidity hypothesis is examined by introducing the market depth variables. The result of this study indicates that a smaller difference in stock liquidity between A shares and ADR is related to a lower corresponding discount in the ADR market. Such an effect is validated by the subperiod check. Overall, this study highlights that market liquidity of cross-listed stocks explains a proportion of the variation in price disparity that long existed in the ADR market. Meanwhile, a stock-specific factor for the price disparity is also proposed in this paper.

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Media analysis reflects the most important facts about public opinion and the actors involved in the most current topics. It is beneficial for the government and the public administration to identify the information gaps in the public media to intervene and assist in specific areas concerning the reception and acclimatization process of Ukrainian refugees in Switzerland. This study aims to explore the potential of combining the three Natural Language Processing (NLP) techniques, Named Entity Recognition (NER), Sentiment Analysis (SA), and Topic Modeling (TM), with Machine Learning (ML) models to facilitate decision-making by the authorities. The data used for this study consist of German- and English-speaking articles retrieved from Swiss news media related to the Russian-Ukrainian war. The data includes articles between February 2022 and January 2023. The research approach is split into three main parts, data exploration, the modeling phase, and the evaluation of the models. During data exploration, the data was preprocessed and filtered in relation to Ukrainian refugees. In the modeling phase, the used ML models were introduced, fine-tuned, and applied to the data, leading to the final results. Using the three abovementioned NLP techniques, the most common topics, the article's sentiment over time, and the most common entities in the data could be identified. SA reveals a change of 5% from positive to negative articles regarding the total sum. TM presents the most common topics related to the Ukrainian refugee crisis in Switzerland. NER uncovers the most affected actors, locations, and organizations impacted by the crisis. In the evaluation phase, the model's performances were analyzed, which resulted in a remarkable accuracy score of 96.5% for the NER model, an average accuracy score of 62.5% for the SA model, and a coherence score of 0.65 for BERTopic, the TM model. To conclude, the research shows the potential of using ML-based NLP techniques on news media data to extract beneficial facts from a huge amount of data regarding the case of the Russian-Ukrainian war.

This paper examines the impact of the government’s partial guarantee loan provision (SBA 7(a) program) on small business lending during financial constraint periods. Our goal is to determine if access to loan securitization reduces banks’ incentives to screen borrowers, potentially leading to weakened screening standards. Our regression model and matching procedure support the idea that banks with high originate-to-distribute (OTD) ratios have sufficiently screened risky borrowers, which is supported by the evidence that high OTD banks have a wider loan pricing residual distribution. After looking into capital structure, we further confirm that banks are using SBA loans and the responsive secondary market to save regulatory capital, rather than diluting screening standards. This research provides insights into understanding government-guaranteed lending programs, the role of soft information in lending decisions, and the effects of loan sales on loan performance. Keywords: Originate-to-distribute, Small business lending, Financial crisis, Screening incentive, Government-guaranteed lending

This thesis examines the impact of the interest rate increase on March 16th, 2022 on banks using a data set of 287 listed US banks. The impact is measured by using abnormal returns as a proxy. For the analysis, I set up a regression equation with the following five independent variables: duration gap, ZIP code, total assets, Tobin’s q, and beta. I used the abnormal return for a period of 24 hours as the dependent variable. The needed data was collected from the Thomson Reuters Datastream Database, the SEC filings, and the annual reports of the banks. On average, the interest rate increase had a negative impact on the stock price. Moreover, the results show that the estimated coefficients for the duration gap, total assets, and beta are statistically significant. The duration gap has a positive estimated coefficient, which indicates that banks with a higher duration gap have a relatively larger abnormal return in times of interest rate increases. The estimated coefficient of total assets and beta is negative. This indicates that banks with more total assets and a higher beta experience a more negative abnormal return when the interest rate increases. The ZIP codes and Tobin’s q have insignificant estimated coefficients. This means that these variables did not have any impact on the abnormal return in this period. This study contributes to various papers, such as those that examine the determinants of the stock price and the relationship between interest rate changes and the change in the stock price.

This study examined the portrayal of artificial intelligence (AI) in video games and explored the potential correlation between video game exposure and individuals’ level of technological acceptance of AI virtual assistants. A Qualitative Content Analysis (QCA) of several popular video games is conducted to analyze their depiction of AI characters, their roles, and interactions within game narratives. Additionally, this study used a previously validated survey instrument based on the Technological Acceptance Model (TAM) to assess how certain video game playing habits and trust of AI virtual assistants are correlated. We found that portrayal of AI characters in the games analyzed show that AI is often portrayed as humanized and more advanced than its real-world counterpart, and that it is often hostile to humans. The analysis of the survey results found that there is a moderate positive correlation between playing video games featuring AI and willingness to use AI virtual assistant technologies. The findings of this study will contribute to the growing field of AI portrayals in popular media and provide insights into the influence of video games on individuals’ perceptions and acceptance of AI technology.

This study examines how different types of users experience and perceive gamification on Stack Overflow. In addition, the game balance and users’ opinions on the level of challenging tasks for members of different skill sets are explored. The study data were obtained using a mixed method approach that combined quantitative and qualitative methods. Quantitative data were collected through a survey, and qualitative data were obtained by interviewing 10 Stack Overflow users. The results suggest that guidelines that are applied in Stack Overflow reduce the need for competence and autonomy, and as a result, discourage expert and novice users from contributing to Stack Overflow. Furthermore, the Stack Overflows’ reward system awards trendy questions over complicated and niche questions. The results indicate that novice users may feel that they cannot contribute to the platform. In addition, they struggle with finding adequately challenging tasks to solve, resulting in them being discouraged from contributing.

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