Daniel Fasnacht, D. Proba, Inter-Organizational Agility as Driving Force for Innovation, SAGE Open , Vol. forthcoming, 2023. (Journal Article)

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Nadine Kammerlander, Jochen Menges, Dennis Herhausen, Petra Kipfelsberger, Heike Bruch, How family CEOs affect employees’ feelings and behaviors: A study on positive emotions, Long Range Planning, Vol. 56 (5), 2023. (Journal Article)
 
Research suggests that firms with family CEOs differ from other types of businesses, yet surprisingly little is known about how employees in these firms feel and behave compared to those working in other firms. We draw from family science and management research to suggest that family CEOs, because of their emotion-evoking double role as family members and business leaders, are, on average, more likely to infuse employees with positive emotions, such as enthusiasm and excitement, than hired professional CEOs. We suggest that these emotions spread through firms by way of emotional contagion during interactions with employees, thereby setting the organizational affective tone. In turn, we hypothesize that in firms with family CEOs the voluntary turnover rate is lower. In considering structural features as boundary conditions, we propose that family CEOs have stronger effects in smaller and centralized firms, and weaker effects in formalized firms. Multilevel data from 41,200 employees and 2,246 direct reports of CEOs from 497 firms with and without family CEOs provide support for our model. This research suggests that firms managed by family CEOs, despite often being criticized as nepotistic relics of the past, tend to offer pleasant work environments. |
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Ernesto de León, Mykola Makhortykh, Teresa Gil-Lopez, Aleksandra Urman, Silke Adam, News, Threats, and Trust: How COVID-19 News Shaped Political Trust, and How Threat Perceptions Conditioned This Relationship, International Journal of Press/Politics, Vol. 28 (4), 2023. (Journal Article)

This study explores shifts in political trust during the outbreak of the COVID-19 pandemic in Switzerland, examining the role that media consumption and threat perceptions played in individuals’ trust in politics. We combine panel surveys taken before and during the first nation-wide lockdown with webtracking data of participants' online behaviour to paint a nuanced picture of media effects during the crisis. Our work has several findings. First, political trust, an attitude known for its stability, increased following lockdown. Second, consumption of mainstream news on COVID-19 directly hindered this increase, with those reading more news having lower over-time trust, while the relatively minor alternative news consumption had no direct effect on political trust. Third, threat perceptions a) to health and b) from the policy response to the pandemic, have strong and opposite effects on political trust, with threats to health increasing trust, and threats from the government policy response decreasing it. Lastly, these threat perceptions condition the effect of COVID-19 news consumption on political trust: perceptions of threat had the power to both exacerbate and mute the effect of media consumption on government trust during the pandemic. Notably, we show that the expected negative effect of alternative news on political trust only exists for those who did not think COVID-19 posed a threat to their health, while public service news consumption reduced the negative effect produced by government threat perceptions. The paper therefore advances our understanding of the nuanced nature of media effects, particularly as relates to alternative media, especially during moments of crisis. |
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Igor Letina, Shuo Liu, Nick Netzer, Optimal contest design: tuning the heat, Journal of Economic Theory, Vol. 213, 2023. (Journal Article)
 
We consider the design of contests when the principal can choose both the prize profile and how the prizes are allocated as a function of a possibly noisy signal about the agents' efforts. We provide sufficient conditions that guarantee optimality of a contest. Optimal contests have a minimally competitive prize profile and an intermediate degree of competitiveness in the contest success function. Whenever observation is not too noisy, the optimum can be achieved by an all-pay contest with a cap. When observation is perfect, the optimum can also be achieved by a nested Tullock contest. We relate our results to a recent literature which has asked similar questions but has typically focused on the design of either the prize profile or the contest success function. |
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Mateusz Dolata, Gerhard Schwabe, Moving beyond privacy and airspace safety: Guidelines for just drones in policing, Government Information Quarterly, Vol. 40 (4), 2023. (Journal Article)
 
The use of drones offers police forces potential gains in efficiency and safety. However, their use may also harm public perception of the police if drones are refused. Therefore, police forces should consider the perception of bystanders and broader society to maximize drones' potential. This article examines the concerns expressed by members of the public during a field trial involving 52 test participants. Analysis of the group interviews suggests that their worries go beyond airspace safety and privacy, broadly dis-cussed in existing literature and regulations. The interpretation of the results indicates that the perceived justice of drone use is a significant factor in acceptance. Leveraging the concept of organizational justice and data collected, we propose a catalogue of guidelines for just operation of drones to supplement the existing policy. We present the organizational justice perspective as a framework to integrate the concerns of the public and bystanders into legal work. Finally, we discuss the relevance of justice for the legitimacy of the police's actions and provide implications for research and practice. |
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Reto Eberle, Kurt Speck, «Am Beginn der Lernkurve»: Der Professor an der Universität Zürich und Partner bei KPMG zu den Herausforderungen der Branche, In: Handelszeitung, p. 51, 28 September 2023. (Newspaper Article)
 
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Peter Kuhn, Liudmila Zavolokina, Dian Balta, Florian Matthes, Toward Government as a Platform: An Analysis Method for Public Sector Infrastructure, In: 18th International Conference on Wirtschaftsinformatik, AIS Electronic Library (AISeL), Paderborn, Germany, 2023-09-18. (Conference or Workshop Paper published in Proceedings)
 
Government as a Platform (GaaP) is a promising approach to the digital transformation of the public sector. In practice, GaaP is realized by platform-oriented infrastructures. However, despite successful examples, the transformation toward platform-oriented infrastructures remains challenging. A potential remedy is the analysis of existing public infrastructure regarding its platform orientation. Such an analysis can identify the gaps to an ideal platform-oriented infrastructure and, thus, support the transformation toward it. We follow the design science research methodology to develop a four-dimensional analysis method. We do so in three iterations, and, after each iteration, evaluate the method by its application to infrastructures in practice. With regard to theory, our results suggest extending GaaP conceptualizations with a specific emphasis on platform principles. With regard to practice, we contribute an analysis method that creates proposals for the improvement of infrastructures and, thus, supports the transformation toward GaaP. |
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Stefania Gavrila-Ionescu, Aniko Hannak, Nicolo Pagan, Group Fairness for Content Creators: the Role of Human and Algorithmic Biases under Popularity-based Recommendations, In: RecSys '23: 17th ACM Conference on Recommender Systems, ACM Digital library, 2023-09-18. (Conference or Workshop Paper published in Proceedings)
 
The Creator Economy faces concerning levels of unfairness. Content creators (CCs) publicly accuse platforms of purposefully reducing the visibility of their content based on protected attributes, while platforms place the blame on viewer biases. Meanwhile, prior work warns about the “rich-get-richer” effect perpetuated by existing popularity biases in recommender systems: Any initial advantage in visibility will likely be exacerbated over time. What remains unclear is how the biases based on protected attributes from platforms and viewers interact and contribute to the observed inequality in the context of popularity-biased recommender systems. The difficulty of the question lies in the complexity and opacity of the system. To overcome this challenge, we design a simple agent-based model (ABM) that unifies the platform systems which allocate the visibility of CCs (e.g., recommender systems, moderation) into a single popularity-based function, which we call the visibility allocation system (VAS). Through simulations, we find that although viewer homophilic biases do alone create inequalities, small levels of additional biases in VAS are more harmful. From the perspective of interventions, our results suggest that (a) attempts to reduce attribute-biases in moderation and recommendations should precede those reducing viewers’ homophilic tendencies, (b) decreasing the popularity-biases in VAS decreases but not eliminates inequalities, (c) boosting the visibility of protected CCs to overcome viewers’ homophily with respect to one fairness metric is unlikely to produce fair outcomes with respect to all metrics, and (d) the process is also unfair for viewers and this unfairness could be overcome through the same interventions. More generally, this work demonstrates the potential of using ABMs to better understand the causes and effects of biases and interventions within complex sociotechnical systems. |
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Xingzhen Zhu, Markus Lang, Helmut Max Dietl, Content Quality Assurance on Media Platforms with User-Generated Content, Journal of Theoretical and Applied Electronic Commerce Research, Vol. 18 (3), 2023. (Journal Article)
 
This paper develops a duopoly model for user-generated content (UGC) platforms, which compete for consumers and content producers in two-sided markets characterized by network externalities. Each platform has the option to invest in a content quality assurance (CQA) system and determine the level of advertising. Our model reveals that network effects are pivotal in shaping the platforms’ optimal strategies and user behavior, specifically in terms of single vs. multi-homing. We find that when network effects for producers are weak, consumers tend to engage in multi-homing while producers prefer single-homing. Conversely, strong network effects lead to the opposite behavior. Furthermore, our model demonstrates that user behavior and network effects dictate whether a platform is incentivized to incorporate advertisements and/or invest in CQA. Generally, weak network effects prompt a platform to invest in a CQA system, unless both consumers and producers engage in multi-homing. Our model’s results highlight the importance for platform companies to evaluate the extent of network effects on their platform in order to anticipate user behavior, which subsequently informs the optimal CQA and advertising strategy. |
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Dietmar Harhoff, Patrick Lehnert, Curdin Pfister, Uschi Backes-Gellner, Innovation effects and knowledge complementarities in a diverse research landscape, In: International Conference on Technical and Vocational Education and Training. 2023. (Conference Presentation)

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Redaktion, UNECE Co-Develops AI Platform for Resilient Energy Systems, In: null, , 13 September 2023. (Newspaper Article)

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Raluca Ioana Gui, Markus Meierer, Patrik Schilter, René Algesheimer, REndo: Internal Instrumental Variables to Address Endogeneity, Journal of Statistical Software, Vol. 107 (3), 2023. (Journal Article)
 
Endogeneity is a common problem in any causal analysis. It arises when the independence assumption between an explanatory variable and the error in a statistical model is violated. The causes of endogeneity are manifold and include response bias in surveys, omission of important explanatory variables, or simultaneity between explanatory and response variables. Instrumental variable estimation provides a possible solution. However, valid and strong external instruments are difficult to find. Consequently, internal instrumental variable approaches have been proposed to correct for endogeneity without relying on external instruments. The R package REndo implements various internal instrumental variable approaches, i.e., latent instrumental variables estimation (Ebbes, Wedel, Boeckenholt, and Steerneman 2005), higher moments estimation (Lewbel 1997), heteroscedastic error estimation (Lewbel 2012), joint estimation using copula (Park and Gupta 2012) and multilevel generalized method of moments estimation (Kim and Frees 2007). Package usage is illustrated on simulated and real-world data. |
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Susanne Müller-Zantop , Markus Leippold, ESG: Game Over?, In: In$ide Paradeplatz, 7 September 2023. (Media Coverage)

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Nick Sidorenko, Hui-Kuan Chung, Marcus Grüschow, Boris B Quednow, Helen Hayward-Könnecke, Alexander Jetter, Philippe Tobler, Acetylcholine and noradrenaline enhance foraging optimality in humans, Proceedings of the National Academy of Sciences of the United States of America, Vol. 120 (36), 2023. (Journal Article)
 
Foraging theory prescribes when optimal foragers should leave the current option for more rewarding alternatives. Actual foragers often exploit options longer than prescribed by the theory, but it is unclear how this foraging suboptimality arises. We investigated whether the upregulation of cholinergic, noradrenergic, and dopaminergic systems increases foraging optimality. In a double-blind, between-subject design, participants (N = 160) received placebo, the nicotinic acetylcholine receptor agonist nicotine, a noradrenaline reuptake inhibitor reboxetine, or a preferential dopamine reuptake inhibitor
methylphenidate, and played the role of a farmer who collected milk from patches with different yield. Across all groups, participants on average overharvested. While methylphenidate had no effects on this bias, nicotine, and to some extent also reboxetine, significantly reduced deviation from foraging optimality, which resulted in better performance compared to placebo. Concurring with amplified goal-directedness and excluding heuristic explanations, nicotine independently also improved trial initiation and time perception. Our findings elucidate the neurochemical basis of behavioral flexibility and decision optimality and open unique perspectives on psychiatric disorders affecting these functions. |
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Anton Fedosov, Lisa Ochsenbein, Ivan Mele, Robin Oster, Maude Rivière, Ronny Gisin, Züri teilt: Facilitating Resource Sharing Practices in Neighborhoods, In: Mensch Und Computer 2023, ACM Digital library, New York, NY, USA, 2023-09-03. (Conference or Workshop Paper published in Proceedings)
 
In the non-profit sharing economy context, an increasing number of resource sharing collectives and organizations (e.g., libraries of things) and peer-to-peer grassroots sharing initiatives leverage underutilized household resources (e.g., tools) to optimize their shared use for the benefit of their local communities. However, a number of social-technical challenges prevent the endurance and growth of such initiatives. Prior research highlighted the specific difficulties related to poor visibility of members’ activities and often high social barriers that hinder interactions among neighbors and strangers. In our prior work, stemming from our continuous engagement with one local sharing community in Switzerland over several years, through fieldwork, interviews, and co-creation studies, we elicited a set of design opportunities to address the emergent community’s challenges. Based on these design considerations, we developed Züri teilt, a mobile application to facilitate resource sharing practices among neighbors aligning with the slow, temporal, and gradual nature of their relationships. |
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Yufeng Xiao, Analyzing the Impact of Occlusion on the Quality of Semantic Segmentation Methods for Point Cloud Data, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
 
This thesis aims to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Occlusion is a prevalent phenomenon in 3D scenes, where objects often overlap or obstruct each other. This can significantly compromise the quality and integrity of data, leading to inaccuracies in semantic segmentation. While the issue of occlusion has garnered attention in 3D data processing, current research on how different occlusion levels impact the quality of semantic segmentation is rare. Specifically, there is a palpable gap in understanding how to quantify occlusion in the scene and how this characteristic influence the performance of advanced semantic segmentation software like the Minkowski Engine. To bridge the research gap, we proposed a novel metric to quantify the occlusion level of a scene. We then applied this metric to analyze the impact of occlusion on the quality of semantic segmentation methods for point cloud data. Our results show that the occlusion level of a scene has limited impact to the quality of semantic segmentation. |
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Yinglun Liu, Beautiful Switzerland, unfriendly France? Country (mis)representations and stereotypes on TikTok, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
 
Many users tend to seek information about specific countries on TikTok before planning international travel. To investigate the objectivity and authenticity of national information on TikTok, we examine the national images presented on TikTok for 12 popular tourist countries from different continents. We utilize web scraping techniques to collect TikTok data for these countries, including descriptive data of videos, watermark-free video contents, comments, etc. Subsequently, we conduct separate analyses of descriptions, video contents, and comments of TikTok videos related to each country. Additionally, we design a survey questionnaire to gather user perceptions of various national images on TikTok. Ultimately, through the analysis of TikTok data and survey responses, we identify consistent trends and specific themes in the descriptions, video contents, and comments of TikTok videos related to specific countries. For instance, TikTok videos related to Argentina predominantly revolve around the theme of football, while those related to Italy and Spain primarily focus on travel and food. Furthermore, users' impressions of specific countries on TikTok closely align with the national images presented on the platform. |
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Ali Yassine, Paint-it-Gray: Modelling, Partitioning, and Analysis of User Transaction Networks in the Bitcoin Blockchain, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Master's Thesis)
 
The rise of Blockchain technology and cryptocurrencies has enabled the creation of decentralized alternative payment systems, without the need for third party financial institutions. However, this decentralization and pseudo-anonymity have also facilitated the emergence of darknet markets (DNMs) offering illicit products. This study introduces the"grayscale diffusion framework", with the aim of modeling and understanding the propagation of dark assets in the Bitcoin network. Formulated and implemented with a combination of on-chain and off-chain data, this approach utilizes address clustering, haircut tainting, and community partitioning to offer a unique analysis perspective on dark asset proliferation across the Bitcoin blockchain. The framework uncovers interesting patterns in the assortative nature of Bitcoin transactions based on the darkness level of assets, pointing to the existence of non-random clusters and communities that facilitate dark asset diffusion. Our research not only addresses key questions related to the effective modeling and tracking of tainted asset flow, but also provides valuable insights. Keywords: Bitcoin, Address Clustering, Darknet Marketplaces, Gray-scale Diffusion, User Transaction Networks. |
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Nevio Liberato, Creation and Comparative Visualization of Rankings Derived From Pairwise Comparisons, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
 
Rankings serve to structure large datasets. They are an integral part of decision-making in various fields. To address the complex task of interpreting and comparing ranking algorithms and the results they produce, we created a Visual Analytics (VA) tool called RankViz. This prototype allows users to visualize and explore the output of various ranking algorithms and includes multiple metrics to assess the quality and differences of the rankings. The pairwise comparison data we used to construct the rankings was collected in a previous study by Barth et al. This thesis reviews different ranking algorithms, details the functionality of RankViz, demonstrates its utility with usage scenarios, and discusses potential future work in this field. |
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Viachaslau Berasneu, Design and Implementation of a System for Reproducible Machine and Deep Learning Models, University of Zurich, Faculty of Business, Economics and Informatics, 2023. (Bachelor's Thesis)
 
In recent years, small and midsize enterprises (SMEs) have become increasingly reliant on technology, but lag in terms of investment into cybersecurity. This renders them vulnerable to malware attacks, which are increasingly targeting companies rather than individuals, with great economic impact. This project proposes and implements a prototype tool, which allows for machine learning models to be trained, stored, and tested within the SecBox sandbox environment. Both classification and anomaly detection models are implemented through Scikit-learn, in order to provide predictions about known malware types (binary and multiclass classification), as well as detecting the presence of unseen malware in real-time during the SecBox execution. The models are trained using the system call and resource usage file execution logs available from the SecBox, which are transformed into suitable formats using frequency-based and sequence-based data preprocessing. Model reproducibility is ensured by generating configuration files with references to the random seeds, the datasets used in training, as well as other model parameters, which can be used to re-train the same model. To evaluate and compare model performance, each model type is tested in a realistic scenario of the execution of Monti ransomware within the SecBox, creating a confusion matrix as well as calculating the accuracy, precision, recall and F1-score metrics based on the model predictions. The system call classifier models are shown to have the best performance when classifying Monti malware samples, and the project is concluded by specifying several relevant research areas to be investigated further. |
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