Marc Chesney, The Smell Of Hypocrisy In Dubai, In: The London Economic Newspaper, p. online, 19 December 2023. (Newspaper Article)
Professor Marc Chesney of the University of Zurich says that the foul stench oil, gas, and coal emanated from global climate summit COP28. |
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Sandra Andraszewicz, Dániel Kaszás, Stefan Zeisberger, Christoph Hölscher, The influence of upward social comparison on retail trading behaviour, Scientific Reports, Vol. 13 (1), 2023. (Journal Article)
Online investing is often facilitated by digital platforms, where the information of peer top performers can be widely accessible and distributed. However, the influence of such information on retail investors’ psychology, their trading behaviour and potential risks they may be prone to is poorly understood. We investigate the impact of upward social comparison on risk-taking, trading activity and investor satisfaction using a tailored experiment with 807 experienced retail investors trading on a dynamically evolving simulated stock market, designed to systematically measure various facets of trading activity. We find that investors presented with an upward social comparison take more risk and trade more actively, and they report significantly lower satisfaction with their own performance. Our findings demonstrate the pitfalls of modern investment platforms with peer information and social trading. The broad implications of this study also provide guidelines for improving retail investor satisfaction and protection. |
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Saeid Ashraf Vaghefi, Dominik Stammbach, Veruska Muccione, Julia Bingler, Jingwei Ni, Mathias Kraus, Simon Allen, Chiara Colesanti-Senni, Tobias Wekhof, Tobias Schimanski, Glen Gostlow, Tingyu Yu, Qian Wang, Nicolas Webersinke, Christian Huggel, Markus Leippold, ChatClimate: Grounding conversational AI in climate science, Communications Earth & Environment, Vol. 4, 2023. (Journal Article)
Large Language Models have made remarkable progress in question-answering tasks, but challenges like hallucination and outdated information persist. These issues are especially critical in domains like climate change, where timely access to reliable information is vital. One solution is granting these models access to external, scientifically accurate sources to enhance their knowledge and reliability. Here, we enhance GPT-4 by providing access to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain (refer to the ’Data Availability’ section). We present our conversational AI prototype, available at www.chatclimate.ai, and demonstrate its ability to answer challenging questions in three different setups: (1) GPT-4, (2) ChatClimate, which relies exclusively on IPCC AR6 reports, and (3) Hybrid ChatClimate, which utilizes IPCC AR6 reports with in-house GPT-4 knowledge. The evaluation of answers by experts show that the hybrid ChatClimate AI assistant provide more accurate responses, highlighting the effectiveness of our solution. |
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Irem Erten, Steven Ongena, Environmental risk and bank lending, VoxEU, CEPR Policy Portal, London, https://cepr.org/voxeu/columns/environmental-risk-and-bank-lending, 2023-12-14. (Scientific Publication In Electronic Form)
Belief in the effects of climate change remains stubbornly regionally specific. This column discusses how banks assess environmental risks in syndicated loan markets in the US. The deregulation following US withdrawal from the Paris Agreement in 2017 prompted banks to reduce the environmental-risk sensitivity of their loan pricing in Republican states, while lenders charged higher rates to borrowers causing severe environmental damage only in states where climate denial is low. The price of environmental risk in bank lending, the authors suggest, is driven by local beliefs and regulatory enforcement practices. |
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Benjamin Kraner, Nicolo Vallarano, Claudio Tessone, Tokenization of the Common: An Economic Model of Multidimensional Incentives, In: Middleware '23: 24th International Middleware Conference, ACM Digital library, 2023-12-11. (Conference or Workshop Paper published in Proceedings)
The concept of the tragedy of the commons, originally rooted in economics, describes the depletion of shared resources due to self-interested actions by individuals. This work proposes a novel solution to address this economic challenge by leveraging tokens to capture its multidimensional nature. By utilising blockchain and DLTs, this decentralised approach aims to achieve a social optimum while promoting self-regulation. The paper presents a mathematical treatment of the tragedy of the commons, incorporating multi-dimensional tokens and exploring the divergence from the classic optimal solution, highlighting the potential of tokenisation in shaping a sustainable and efficient economy. |
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Dario Staehelin, Gianluca Miscione, Mateusz Dolata, From Solution Trap to Solution Patchwork: Tensions in Digital Health in the Global Context, In: 2023 International Conference on Information Systems, Association for Information Systems, 2023-12-10. (Conference or Workshop Paper published in Proceedings)
This paper problematizes underlying assumptions in Design Science Research – and Information Systems Research more broadly by conceptualizing the „solution trap“. The solution trap is caused by the incompatibility of co-existing solutions in complex socio-technical contexts. Information systems bring diverse cultures and theories together, causing tensions in the different institutional logics. We emphasize the need for a nuanced understanding of context unevenness and propose solution patchwork as a coordination approach to evade the solution trap. Substantiating the preliminary insights and propositions with a literature review and further empirical grounding will transition this research-in-progress to a full paper. |
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Sven Eckhardt, Andreas Bucher, Madlaina Kalunder, Mateusz Dolata, Doris Agotai, Gerhard Schwabe, Secondary Mental Models: Introducing Conversational Agents in Financial Advisory Service Encounters, In: Forty-Fourth International Conference on Information Systems, Association for Information Systems, 2023. (Conference or Workshop Paper published in Proceedings)
When introducing unfamiliar Artificial Intelligence (AI)-based systems, such as conversational agents (CAs), one needs to ensure that users interact with them according to their design. While past research has studied single-user environments, many practical settings involve multiple parties. This study addresses this gap and focuses on financial advisory service encounters and how mental models evolve in multi-party contexts. A multimodal interactive CA is developed and tested in financial consultations with 24 clients. The observations of these consultations and subsequent interviews provide insights into the challenges of using CAs in unfamiliar contexts. The clients have difficulties effectively using the system. This is linked to the institutional setting of financial advisory service encounters and a mismatch between the designer’s conceptual model and the client’s mental model, which we call secondary mental model. |
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Dzmitry Katsiuba, Tannon Kew, Mateusz Dolata, Matej Gurica, Gerhard Schwabe, Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management, In: International Conference on Information Systems, ICIS 2023, Association for Information Systems, 2023-12-09. (Conference or Workshop Paper published in Proceedings)
Online customer feedback management plays an increasingly important role for businesses. Yet providing customers with good responses to their reviews can be challenging, especially as the number of reviews grows. This paper explores the potential of using generative AI to formulate responses to customer reviews. Using advanced NLP techniques, we generated responses to reviews in different authoring configurations. To compare the communicative effectiveness of AI-generated and human-written responses, we conducted an online experiment with 502 participants. The results show that a Large Language Model performed remarkably well in this context. By providing concrete evidence of the quality of AI-generated responses, we contribute to the growing body of knowledge in this area. Our findings may have implications for businesses seeking to improve their customer feedback management strategies, and for researchers interested in the intersection of AI and customer feedback. This opens opportunities for practical applications of NLP and for further IS research. |
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Florian Ruosch, Cristina Sarasua, Abraham Bernstein, DREAM: Deployment of Recombination and Ensembles in Argument Mining, In: 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, 2023-12-06. (Conference or Workshop Paper published in Proceedings)
Current approaches to Argument Mining (AM) tend to take a holistic or black-box view of the overall pipeline. This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions. To that end, it presents the Deployment of Recombination and Ensemble methods for Argument Miners (DREAM) framework that allows for the (automated) combination of AM components. Using ensemble methods, DREAM combines sets of AM systems to improve accuracy for the four tasks in the AM pipeline. Furthermore, it leverages recombination by using different argument miners elements throughout the pipeline. Experiments with five systems previously included in a benchmark show that the systems combined with DREAM can outperform the previous best single systems in terms of accuracy measured by an AM benchmark. |
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Jan von der Assen, Alberto Huertas Celdran, Janik Luechinger, Pedro Miguel Sánchez Sánchez, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller, RansomAI: AI-powered Ransomware for Stealthy Encryption, In: IEEE Global Communications Conference, Institute of Electrical and Electronics Engineers, Kuala Lumpur, Malaysia, 2023-12. (Conference or Workshop Paper published in Proceedings)
Cybersecurity solutions have shown promising performance when detecting ransomware samples that use fixed algorithms and encryption rates. However, due to the current explosion of Artificial Intelligence (AI), sooner than later, ransomware (and malware in general) will incorporate AI techniques to intelligently and dynamically adapt its encryption behavior to be undetected. It might result in ineffective and obsolete cybersecurity solutions, but the literature lacks AI-powered ransomware to verify it. Thus, this work proposes RansomAI, a Reinforcement Learning-based framework that can be integrated into existing ransomware samples to adapt their encryption behavior and stay stealthy while encrypting files. RansomAI presents an agent that learns the best encryption algorithm, rate, and duration that minimizes its detection (using a reward mechanism and a fingerprinting intelligent detection system) while maximizing its damage function. The proposed framework was validated in a ransomware, Ransomware-PoC, that infected a Raspberry Pi 4, acting as a crowdsensor. A pool of experiments with Deep Q-Learning and Isolation Forest (deployed on the agent and detection system, respectively) has demonstrated that RansomAI evades the detection of Ransomware-PoC affecting the Raspberry Pi 4 in a few minutes with >90% accuracy. |
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EvaCRC: Evaluating Code Review Comments, In: ESEC/FSE '23: 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Association for Computing Machinery, 2023-12-03. (Conference or Workshop Paper published in Proceedings)
In code reviews, developers examine code changes authored by peers and provide feedback through comments. Despite the importance of these comments, no accepted approach currently exists for assessing their quality. Therefore, this study has two main objectives: (1) to devise a conceptual model for an explainable evaluation of review comment quality, and (2) to develop models for the automated evaluation of comments according to the conceptual model. To do so, we conduct mixed-method studies and propose a new approach: EvaCRC (Evaluating Code Review Comments). To achieve the first goal, we collect and synthesize quality attributes of review comments, by triangulating data from both authoritative documentation on code review standards and academic literature. We then validate these attributes using real-world instances. Finally, we establish mappings between quality attributes and grades by inquiring domain experts, thus defining our final explainable conceptual model. To achieve the second goal, EvaCRC leverages multi-label learning. To evaluate and refine EvaCRC, we conduct an industrial case study with a global ICT enterprise. The results indicate that EvaCRC can effectively evaluate review comments while offering reasons for the grades. |
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Jakub Lokoč, Stelios Andreadis, Werner Bailer, Aaron Duane, Cathal Gurrin, Zhixin Ma, Nicola Messina, Thao-Nhu Nguyen, Ladislav Peška, Luca Rossetto, Loris Sauter, Konstantin Schall, Klaus Schoeffmann, Omar Shahbaz Khan, Florian Spiess, Lucia Vadicamo, Stefanos Vrochidis, Interactive video retrieval in the age of effective joint embedding deep models: lessons from the 11th VBS, Multimedia Systems, Vol. 29 (6), 2023. (Journal Article)
This paper presents findings of the eleventh Video Browser Showdown competition, where sixteen teams competed in known-item and ad-hoc search tasks. Many of the teams utilized state-of-the-art video retrieval approaches that demonstrated high effectiveness in challenging search scenarios. In this paper, a broad survey of all utilized approaches is presented in connection with an analysis of the performance of participating teams. Specifically, both high-level performance indicators are presented with overall statistics as well as in-depth analysis of the performance of selected tools implementing result set logging. The analysis reveals evidence that the CLIP model represents a versatile tool for cross-modal video retrieval when combined with interactive search capabilities. Furthermore, the analysis investigates the effect of different users and text query properties on the performance in search tasks. Last but not least, lessons learned from search task preparation are presented, and a new direction for ad-hoc search based tasks at Video Browser Showdown is introduced. |
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Tobias Schlegel, Uschi Backes-Gellner, The role of fields of study for the effects of higher education institutions on regional firm location, Small Business Economics, Vol. 61 (4), 2023. (Journal Article)
The literature on knowledge spillovers provides evidence that higher education institutions (HEIs) positively affect regional firm location (i.e., start-ups or firms located in a region). However, less is known about how HEIs in different fields of study impact regional firm location in different industries. To investigate this question, we exploit the establishment of universities of applied sciences (UASs)—bachelor’s degree-granting three-year HEIs in Switzerland. We find that the effects of UASs are heterogeneous across fields of study and industries. UASs specializing in “chemistry and the life sciences” and “business, management, and services” are the only UASs that positively affect regional firm location across several industries. Positive effects emerge in service industries characterized by radical service, incremental product, or process innovations. Thus, UASs are not a one-size-fits-all solution for increasing regional firm location. Instead, only UASs specializing in particular fields of study positively influence firm location in certain industries. |
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Markus Leippold, Hanlin Yang, Mixed-Frequency Predictive Regressions, Journal of Forecasting, Vol. 42 (8), 2023. (Journal Article)
We explore the performance of mixed-frequency predictive regressions for stock returns from the perspective of a Bayesian investor. We develop a constrained parameter learning approach for sequential estimation allowing for belief revisions. Empirically, we find that mixed-frequency models improve predictability, not only because of the combination of predictors with different frequencies but also due to the preservation of high-frequency features such as time-varying volatility. Temporally aggregated models misspecify the evolution frequency of the volatility dynamics, resulting in poor volatility timing and worse portfolio performance than the mixed-frequency specification. These results highlight the importance of preserving the potential mixed-frequency nature of predictors and volatility in predictive regressions. |
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Zacharias Sautner, Laurence Van Lent, Grigory Vilkov, Ruishen Zhang, Pricing climate change exposure, Management Science, Vol. 69 (12), 2023. (Journal Article)
We estimate the risk premium for firm-level climate change exposure among S&P 500 stocks and its time-series evolution between 2005 to 2020. Exposure reflects the attention paid by market participants in earnings calls to a firm’s climate-related risks and opportunities. When extracted from realized returns, the unconditional risk premium is insignificant but exhibits a period with a positive risk premium before the financial crisis and a steady increase thereafter. Forward-looking expected return proxies deliver an unconditionally positive risk premium with maximum values of 0.5%–1% p.a., depending on the proxy, between 2011 and 2014. The risk premium has been lower since 2015, especially when the expected return proxy explicitly accounts for the higher opportunities and lower crash risks that characterize high-exposure stocks. This finding arises as the priced part of the risk premium primarily originates from uncertainty about climate-related upside opportunities. In the time series, the risk premium is negatively associated with green innovation; Big Three holdings; and environmental, social, and governance fund flows and positively associated with climate change adaptation programs. |
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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, Vol. 24 (8), 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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Pascal Flurin Meier, Raphael Flepp, Egon Franck, Replication: Do coaches stick with what barely worked? Evidence of outcome bias in sports, Journal of Economic Psychology, Vol. 99, 2023. (Journal Article)
We replicate the finding of Lefgren et al. (2015) showing that professional basketball coaches in the NBA discontinuously change their starting lineup more often after narrow losses than after narrow wins. This result is consistent with outcome bias because such narrow outcomes are conditionally uninformative. As our paper shows, this pattern is not restricted to the NBA; we also find evidence of outcome bias in the top women’s professional basketball league and college basketball. Finally, we show that outcome bias in coaching decisions generalizes to the National Football League (NFL). We conclude that outcome bias is credible and robust, although it has weakened over time in some instances. |
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Francesco D’Ercole, Alexander Wagner, The green energy transition and the 2023 Banking Crisis, Finance Research Letters, Vol. 58, 2023. (Journal Article)
This study examines the stock price reactions of environmentally responsible stocks during the onset of the 2023 banking crisis, triggered by the collapse of Silicon Valley Bank (SVB). Our findings indicate that stocks poised to benefit from the shift to a low-carbon economy underperformed during the 2023 crisis. This suggests that investors anticipate a slowdown in climate tech development due to distress in the banking sector. Our results underscore the significance of considering not only the influence of the climate crisis on financial stability, but also the pivotal role that financial stability plays in ensuring a successful energy transition. |
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Berenika Sztandera, Klaudia Ponikiewska, Jan Cieciuch, Predictive validity of two conceptualizations of basic temperament dimensions, Personality and Individual Differences, Vol. 215, 2023. (Journal Article)
In this paper, we aimed to compare the predictive validity of two models of temperament structure: the one proposed within Strelau's Regulative Theory of Temperament (RTT) and the other developed by Strus, Ponikiewska and Cieciuch as a reconceptualization of the RTT fundamental temperament dimensions based on the insights from the temperamental Big Two and the Circumplex of Personality Metatraits. Specifically, we compared the predictive validity of these two temperament models in relation to a set of external variables related to stress (well-being, stress, PTSD, and COVID stress). The study was conducted on a Polish sample (N = 336, age range 17–65). We found that the reconceptualized temperament dimensions allow for better predictions of well-being, stress, and PTSD than the RTT ones. |
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Ralph De Haas, Liping Lu, Steven Ongena, Close competitors? Bilateral bank competition and spatial variation in firms’ access to credit, Journal of Economic Geography, Vol. 23 (6), 2023. (Journal Article)
We interviewed 379 bank CEOs in 20 emerging markets to identify their banks’ main competitors. We show that banks are more likely to identify another bank as a main competitor in small-business lending when both banks are foreign owned or relationship oriented; when there exists a large spatial overlap in their branch networks and when the potential competitor has fewer hierarchical layers. We then construct a novel bilateral competition measure at the locality level and assess how well it explains geographic variation in firms’ credit constraints. We show that intense bilateral bank competition tightens local credit constraints, especially for small firms, as competition may impede the formation of lending relationships. |
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