Chiara Zisler, Damiano Pregaldini, Uschi Backes-Gellner, Opening doors for immigrants: The role of occupational skills and workplace-based cultural skills for a successful labor market entry, In: SASE Annual Conference. 2023. (Conference Presentation)

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Thadeu Gasparetto, Helmut Max Dietl, Cornel Nesseler, Cristina Muñiz, When a woman replaces a man: evaluating coach dismissal in professional tennis, Managing Sport and Leisure, 2023. (Journal Article)
 
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. |
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Redaktion, Steven Ongena, Domestic Climate Policy and Cross-Border Lending, In: Easy Branches Network, 17 July 2023. (Media Coverage)

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Fabienne Jedelhauser, Raphael Flepp, Egon Franck, Overshadowed by Popularity: The Value of Second-Tier Stars in European Football, Journal of Sports Economics, 2023. (Journal Article)
 
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. |
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Martin Sterchi, Lorenz Hilfiker, Rolf Grütter, Abraham Bernstein, Active querying approach to epidemic source detection on contact networks, Scientific Reports, Vol. 13 (1), 2023. (Journal Article)
 
The problem of identifying the source of an epidemic (also called patient zero) given a network of contacts and a set of infected individuals has attracted interest from a broad range of research communities. The successful and timely identification of the source can prevent a lot of harm as the number of possible infection routes can be narrowed down and potentially infected individuals can be isolated. Previous research on this topic often assumes that it is possible to observe the state of a substantial fraction of individuals in the network before attempting to identify the source. We, on the contrary, assume that observing the state of individuals in the network is costly or difficult and, hence, only the state of one or few individuals is initially observed. Moreover, we presume that not only the source is unknown, but also the duration for which the epidemic has evolved. From this more general problem setting a need to query the state of other (so far unobserved) individuals arises. In analogy with active learning, this leads us to formulate the active querying problem. In the active querying problem, we alternate between a source inference step and a querying step. For the source inference step, we rely on existing work but take a Bayesian perspective by putting a prior on the duration of the epidemic. In the querying step, we aim to query the states of individuals that provide the most information about the source of the epidemic, and to this end, we propose strategies inspired by the active learning literature. Our results are strongly in favor of a querying strategy that selects individuals for whom the disagreement between individual predictions, made by all possible sources separately, and a consensus prediction is maximal. Our approach is flexible and, in particular, can be applied to static as well as temporal networks. To demonstrate our approach’s practical importance, we experiment with three empirical (temporal) contact networks: a network of pig movements, a network of sexual contacts, and a network of face-to-face contacts between residents of a village in Malawi. The results show that active querying strategies can lead to substantially improved source inference results as compared to baseline heuristics. In fact, querying only a small fraction of nodes in a network is often enough to achieve a source inference performance comparable to a situation where the infection states of all nodes are known. |
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Luca Rossetto, Oana Inel, Svenja Lange, Florian Ruosch, Ruijie Wang, Abraham Bernstein, Multi-Mode Clustering for Graph-Based Lifelog Retrieval, In: ICMR '23: International Conference on Multimedia Retrieval, ACM Digital library, New York, NY, USA, 2023-07-12. (Conference or Workshop Paper published in Proceedings)
 
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. |
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Florian Spiess, Ralph Gasser, Heiko Schuldt, Luca Rossetto, The Best of Both Worlds: Lifelog Retrieval with a Desktop-Virtual Reality Hybrid System, In: ICMR '23: International Conference on Multimedia Retrieval, ACM, New York, NY, USA, 2023. (Conference or Workshop Paper published in Proceedings)
 
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Kilian Sprenkamp, Liudmila Zavolokina, Mario Angst, Mateusz Dolata, Data-Driven Governance in Crises: Topic Modelling for the Identification of Refugee Needs, In: 24th Annual International Conference on Digital Government Research, ACM Digital library, New York, NY, USA, 2023-07-11. (Conference or Workshop Paper published in Proceedings)
 
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. |
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Carlos Gomez Gonzalez, Helmut Max Dietl, David Berri, Cornel Nesseler, Gender information and perceived quality: An experiment with professional soccer performance, Sport management review, 2023. (Journal Article)
 
Whether one looks at revenue, investment or coverage, men’s sports do better than women’s. Many assume that absolute differences in quality of athletic performance are the driving force. However, the existence of stereotypes should alert us to another possibility: gender information might influence perceived quality. We perform an experiment in which 613 participants viewed clips of elite female and male soccer players. In the control group, participants evaluated unmodified videos where the gender of the players is clear to see. In the treatment group, participants evaluated the same videos but with gender obscured by blurring. Using a regression analysis, we find that participants rate men’s videos higher – but only when they know they are watching men. When blurring obscures the gender, ratings for female and male athletes do not differ. We discuss implications for research and the sports industry. |
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Markus Leippold, Jingwei Ni, Zhijing Jin, Qian Wang, Mrinmaya Sachan, When does aggregating multiple skills with multi-task learning work? A case study in financial NLP, In: 61st Annual Meeting of the Association for Computational Linguistics (ACL’23), Association for Computational Linguistics, Toronto, Canada, 2023-07-09. (Conference or Workshop Paper published in Proceedings)
 
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. |
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Markus Leippold, Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, A dataset for detecting real-world environmental claims, In: 61st Annual Meeting of the Association for Computational Linguistics (ACL’23), arxiv.org, Toronto, Canada, 2023-07-09. (Conference or Workshop Paper published in Proceedings)
 
In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies. We train and release baseline models for detecting environmental claims using this new dataset. We further preview potential applications of our dataset: We use our fine-tuned model to detect environmental claims made in answer sections of quarterly earning calls between 2012 and 2020 - and we find that the amount of environmental claims steadily increased since the Paris Agreement in 2015. |
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Markus Leippold, Sentiment spin: Attacking financial sentiment with GPT-3, Finance Research Letters, Vol. 55 (B), 2023. (Journal Article)
 
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. |
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Andrew Jack, Zacharias Sautner, Business school sustainability research: What is read most?, In: Financial Times, 6 July 2023. (Media Coverage)

Research papers on ESG themes - positive and sceptical - dominate recent downloads from the Social Science Research Network website. |
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Francis Bignell, Thomas Puschmann, Green Fintech Network Propels Switzerland’s Standing as a Leader in Green Digital Finance, In: The Fintech Times, 5 July 2023. (Media Coverage)

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. |
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Leonardo Bursztyn, Aakaash Rao, Christopher Roth, David Yanagizawa-Drott, Opinions as facts, Review of Economic Studies, Vol. 90 (4), 2023. (Journal Article)
 
The rise of opinion programs has transformed television news. Because they present anchors’ subjective commentary and analysis, opinion programs often convey conflicting narratives about reality. We experimentally document that people across the ideological spectrum turn to opinion programs over “straight news”, even when provided large incentives to learn objective facts. We then examine the consequences of diverging narratives between opinion programs in a high-stakes setting: the early stages of the COVID-19 pandemic in the US. We find stark differences in the adoption of preventative behaviours among viewers of the two most popular opinion programs, both on the same network, which adopted opposing narratives about the threat posed by the COVID-19 pandemic. We then show that areas with greater relative viewership of the program downplaying the threat experienced a greater number of COVID-19 cases and deaths. Our evidence suggests that opinion programs may distort important beliefs and behaviours. |
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Francesca Casalini, Liudmila Zavolokina, Do collaborative platforms create public value in public services? An explorative analysis of privately-owned public service platforms in Italy, In: 39th EGOS Colloquium, 39th EGOS Colloquium, 2023. (Conference or Workshop Paper published in Proceedings)
 
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. |
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Guido Schätti, Christoph Basten, Alle Macht der Nationalbank?, In: NZZ am Sonntag, 2 July 2023. (Media Coverage)

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Wenqing Chang, End-to-End lmplementation of Pair-Wise Correlation Computation in a Streaming System, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
 
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. |
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Adrian Zermin, Building a Data Analysis Platform for the EARDREAM Project, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
 
With an aging population across the world, dementia and neurological diseases, such as Alzheimer's disease (AD), are on the rise. The disproportionate rise in AD cases in developing countries gives rise to a low-cost, robust way to diagnose early-onset AD. The EARDREAM project takes up the fight against AD in these countries using low-density electroencephalography (EEG) device, with the goal of developing a digital biomarker of early-onset AD.
To make the collected health data accessible and enable large-scale analysis, there is a need for accessible, scaleable, secure solutions for EEG data analysis.
This thesis presents the design and implementation of the Wondernap platform, a novel, cloud-based system dedicated to enhancing the current process of interacting with the data generated using the EARDREAM EEG device.
The evolution of the platform through two iterative prototypes is described, highlighting the transition from an initial prototype to a scaleable, secure, cloud-based solution.
The initial prototype laid the groundwork for a scaleable, modular, and transferable architecture capable of accounting for the unique requirements of the EARDREAM project, employing state-of-the-art technologies for EEG data analysis architectures.
Deploying a Flask backend and Apache HBase for EEG data storage, with MongoDB for patient data, the first iteration validated its usability through a user evaluation, scoring 84/100 on the System Usability Scale.
Building upon user feedback and stakeholder input, the second prototype accentuated the applicability of cloud computing to the current architecture, demonstrating its scalability and portability. Using infrastructure-as-code and incorporating Apache Phoenix, this prototype showcased enhancements in fault tolerance, security, and performance.
In summary, the developed platform offers a fault-tolerant, scaleable, secure cloud architecture supporting a user-friendly frontend allowing its users to gain insights into the data generated using the EARDREAM portable EEG device.
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Linda Weber, Analysing the Effects of the Wash-in Phase and Initial Consultation on Patient Empowerment in the Treatment of Obesity, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
 
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%. |
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