Contributions in Merlin

Purpose: Previous research indicates gender discrimination in leadership positions. However, performance and not gender should be the key indicator when evaluating a leader. We examine the performance effect of changing from a female to a male coach and vice versa. Methodology: We analyze 1,093 Billie Jean King Cup singles matches from 2006 to 2016, with the match result as the dependent variable. First, we examine the very short-term effects arising from the change of a coach with a regression discontinuity design. Second, we evaluate the short-, medium-, and long-term performances. Findings: The results show that the gender of the new coach has no significant effect on performance. However, when a female coach succeeds another female coach, performance improves. This provides an argument in favor of female leadership. Practical Implications: Team managers should primarily focus on the quality of the coach instead of gender. The results also suggest that a continuum of female leadership is likely advantageous. Research Contribution: This paper contributes to the debate regarding the misrepresentation of women as head coaches and offers an avenue for further research.

While second-tier stars lack popularity compared to superstars, their marginal contribution to team performance on the pitch relative to that of superstars is unknown. Relying on league-specific preseason market value distributions to define superstars and second-tier stars, we compare the marginal contributions of superstars and second-tier stars to team performance on the pitch in the top five European football leagues. Examining the impact of unexpected injury-related absences, we find that second-tier stars’ marginal contribution is at least equal to that of superstars. Thus, the players with arguably the highest costs for clubs do not contribute accordingly to short-run sportive success.

As part of the 6th Lifelog Search Challenge, this paper presents an approach to arrange Lifelog data in a multi-modal knowledge graph based on cluster hierarchies. We use multiple sequence clustering approaches to address the multi-modal nature of Lifelogs in relation to temporal, spatial, and visual factors. The resulting clusters, along with semantic metadata captions and augmentations based on OpenCLIP, provide for the semantic structure of a graph including all Lifelogs as entries. Textual queries on this hierarchical graph can be expressed to retrieve individual Lifelogs, as well as clusters of Lifelogs.

The war in Ukraine and the following refugee crisis have recently again highlighted the need for effective refugee management across European countries. Refugee management contemporarily mostly relies on top-down management approaches by governments. These often lead to suboptimal policies for refugees and highlight a need to better identify and integrate refugee needs into management. Here, we show that modern applications of Natural Language Processing (NLP) allow for the effective analysis of large text corpora linked to refugee needs, making it possible to complement top-down approaches with bottom-up knowledge centered around the current needs of the refugee population. By following a Design Science Research Methodology, we utilize 58 semi-structured stakeholder interviews within Switzerland to develop design requirements for NLP applications for refugee management. Based on the design requirements, we developed R2G – “Refugees to Government”, an application based on state-of-the-art topic modeling to identify refugee needs bottom-up through Telegram data. We evaluate R2G with a dedicated workshop held with stakeholders from the public sector and civil society. Thus, we contribute to the ongoing discourse on how to design refugee management applications and showcase how topic modeling can be utilized for data-driven governance during refugee crises.

Multi-task learning (MTL) aims at achieving a better model by leveraging data and knowledge from multiple tasks. However, MTL does not always work – sometimes negative transfer occurs between tasks, especially when aggregating loosely related skills, leaving it an open question when MTL works. Previous studies show that MTL performance can be improved by algorithmic tricks. However, what tasks and skills should be included is less well explored. In this work, we conduct a case study in Financial NLP where multiple datasets exist for skills relevant to the domain, such as numeric reasoning and sentiment analysis. Due to the task difficulty and data scarcity in the Financial NLP domain, we explore when aggregating such diverse skills from multiple datasets with MTL can work. Our findings suggest that the key to MTL success lies in skill diversity, relatedness between tasks, and choice of aggregation size and shared capacity. Specifically, MTL works well when tasks are diverse but related, and when the size of the task aggregation and the shared capacity of the model are balanced to avoid overwhelming certain tasks.

In this study, we explore the susceptibility of financial sentiment analysis to adversarial attacks that manipulate financial texts. With the rise of AI readership in the financial sector, companies are adapting their language and disclosures to fit AI processing better, leading to concerns about the potential for manipulation. In the finance literature, keyword-based methods, such as dictionaries, are still widely used for financial sentiment analysis due to their perceived transparency. However, our research demonstrates the vulnerability of keyword-based approaches by successfully generating adversarial attacks using the sophisticated transformer model, GPT-3. With a success rate of nearly 99% for negative sentences in the Financial Phrase Bank, a widely used database for financial sentiment analysis, we highlight the importance of incorporating robust methods, such as context-aware approaches such as BERT, in financial sentiment analysis.

Switzerland has become the latest country to put words into action working towards more sustainable finance. During the Point Zero Forum in Zurich, the three-day event connecting policy with technology, the new Swiss Green Fintech Network (GFN) was launched, aiming to boost the green digital finance ecosystem.

This paper explores the public value outcomes generated by collaborative platforms in public services through a comparative case study of 25 privately-owned digital platforms across various public service areas in Italy. The study confirms that collaborative digitally-enabled endeavors can have both positive and negative effects on public value, highlighting the challenges associated with multi-actor dynamics and the delicate balance between public interest and private gain in public services. The findings reveal implications related to user-centric platforms, including concerns about inclusivity, autonomy, decision-making abilities, and privacy infringement. Additionally, the study suggests that collaborative platforms alone do not enhance collaboration in public services unless accompanied by strong public governance that promotes interoperability through standardized frameworks, common templates, and data reuse.

This thesis aims to address a key challenge in time-series data analysis - efficiently identifying correlations between various data streams. As the prominence of sensor networks rises, the analysis of time-series data has become increasingly crucial. The insights derived from these data streams hold significant intelligence, but extracting them efficiently remains a complex task. This thesis brings into focus the dimensionality-reduction filter-and-refine techniques, designed to expedite the process of identifying correlations. Despite their utility, these techniques lack a comprehensive comparative analysis over streaming systems, and this thesis seeks to fill this gap. The core objective is to implement these techniques within a streaming platform, enabling benchmarking under realistic conditions. The thesis is divided into several chapters that provide a comprehensive overview of the problem, delve into the dimensionality reduction algorithms, and discuss their implementation within a streaming system. Particular emphasis is placed on the Filter-and-Refine Algorithm on the Kafka Streaming Platform. Two distinct design approaches, one based on Kafka Streams and another simpler Producer-Consumer design, are implemented, compared, and evaluated. The thesis culminates with an exhaustive series of experiments assessing the performance of the implemented algorithms. The ultimate goal is to provide a framework that not only implements the techniques but also evaluates their performance, aiming to contribute to the field of time-series data analysis.

The number of people suffering from chronic diseases worldwide is rapidly increasing, leading to rising costs and decreased quality of life. Lifestyle changes are an effective measure in the treatment of chronic diseases such as obesity. However, treatment adherence is consistently low. This thesis aims to analyse how the initial phases of the Digital Companion influence patient empowerment as well as self-perceived and actual adherence to the treatment plan. We analyse 18 patient interviews conducted during the field study of the Digital Companion. The results indicate that the structural empowerment provided by the Health Care Professional (HCP) together with the app have a positive effect on patients’ psychological empowerment. Patients report high levels of self-perceived empowerment and predicted adherence to the treatment plan which they planned with their HCP. The evaluation of patient-generated data recorded in the app shows a very high average actual adherence of 80%.