TY - JOUR AU - Krämer, T. AU - Weiger, W. AU - Trang, S. AU - Trenz, M. ID - 49457 JF - Journal of Product Innovation Management TI - Deflected by the Tin Foil Hat? Word-of-Mouth, Conspiracy Beliefs, and the Adoption of Public Health Apps VL - 40 ER - TY - JOUR AU - Hengstler, S. AU - Kuehnel, S. AU - Masuch, K. AU - Nastjuk, I. AU - Trang, S. ID - 49455 JF - Computers & Security TI - Should I Really do That? Using Quantile Regression to Examine the Impact of Sanctions on Information Security Policy Compliance Behavior ER - TY - JOUR AU - Kornowicz, Jaroslaw AU - Thommes, Kirsten ID - 47953 JF - Artificial Intelligence in HCI SN - 0302-9743 TI - Aggregating Human Domain Knowledge for Feature Ranking ER - TY - CONF AU - Gutt, Jana Kim AU - Mehic, Miro AU - Thommes, Kirsten ID - 47972 T2 - Academy of Management Proceedings TI - Oh my Goodness: Investigating the Goodness of Performance Appraisal Formats Between and Within Teams ER - TY - JOUR AU - Hoppe, Julia Amelie AU - Tuisku, Outi AU - Johansson-Pajala, Rose-Marie AU - Pekkarinen, Satu AU - Hennala, Lea AU - Gustafsson, Christine AU - Melkas, Helinä AU - Thommes, Kirsten ID - 44639 JF - Computers in Human Behavior Reports KW - Artificial Intelligence KW - Cognitive Neuroscience KW - Computer Science Applications KW - Human-Computer Interaction KW - Applied Psychology KW - Neuroscience (miscellaneous) SN - 2451-9588 TI - When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty VL - 9 ER - TY - GEN AB - Informationen sind für eine erfolgreiche Klimapolitik in doppelter Hinsicht wichtig: Sie werden benötigt, wenn Potenziale zur Vermeidung von Emissionen identifiziert und klimapolitische Instrumente ausgewählt werden. Und sie sind zentral, damit Bürger/innen selbst Entscheidungen im Sinne des Klimaschutzes treffen können. AU - Frick, Marc AU - Foese, Dario AU - Von Graevenitz, Kathrine AU - Kesternich, Martin AU - Wagner, Ulrich ID - 47078 KW - General Medicine SN - 1430-8800 TI - Die Doppelwirkung von Information für klimafreundliches Handeln ER - TY - JOUR AB - The relationship between nonfinancial reporting and real sustainable change within and beyond organizations is fraught with complication. Furthermore, all facets of the relationship have not been examined equally. The contributions of this special issue made substantive progress in this regard and draw our focus to several remaining complications—in particular, the societal impacts of nonfinancial reporting. With this introduction, we seek to move the conversation forward by proposing a framework that disentangles the linkages between nonfinancial reporting and real sustainable change at multiple levels of analysis. We highlight the distinction between sustainability-related outputs and outcomes that typically materialize at the firm level, and eventually lead to sustainable impact at the societal level. Future research should advance this distinction and scrutinize the impact of real sustainable change beyond firm-level outputs, study the organizational change processes from antecedents to impacts, and examine the interrelationships between different instruments to foster real sustainable change. AU - Hahn, Rüdiger AU - Reimsbach, Daniel AU - Wickert, Christopher ID - 47921 IS - 1 JF - Organization & Environment KW - Organizational Behavior and Human Resource Management KW - General Environmental Science SN - 1086-0266 TI - Nonfinancial Reporting and Real Sustainable Change: Relationship Status—It’s Complicated VL - 36 ER - TY - JOUR AB - This year, the 7th edition of the Dutch Accounting Research Conference (DARC) was hosted by the Nijmegen School of Management at Radboud University on Thursday, March 23. In total, over 75 accounting researchers from various Dutch universities were welcomed by Frank Hartmann, chair of the accounting group and head of the Business Economics department. During the day, four keynote speakers presented their research and in a panel discussion, the current state of accounting education was debated. In the evening, participants gathered to network over dinner. This article presents a discussion of the theme of the conference, an outline of the research papers and projects presented during the conference, and a summary of the panel discussion on Accounting Education. AU - De Meyst, Karen AU - Niederkofler, Thomas AU - Reimsbach, Daniel ID - 47922 IS - 5/6 JF - Maandblad voor Accountancy en Bedrijfseconomie KW - General Arts and Humanities SN - 2543-1684 TI - DARC 2023 at Radboud University: Societal challenges in accounting research and education VL - 97 ER - TY - GEN AU - Harst, Simon AU - Schanz, Deborah AU - Siegel, Felix AU - Sureth-Sloane, Caren ID - 49549 TI - 2022 Global MNC Tax Complexity Survey ER - TY - GEN AU - Giese, Henning AU - Holtmann, Svea ID - 46044 TI - Towards Green Driving - Income Taxes Incentives for Plug-In Hybrids VL - 118 ER - TY - GEN AU - Giese, Henning AU - Koch, Reinald AU - Sureth-Sloane, Caren ID - 49873 TI - Tax Complexity, Tax Department Structure, and Tax Risk ER - TY - JOUR AB - AbstractThe ability of various policy activities to reduce the reproduction rate of the COVID-19 disease is widely discussed. Using a stringency index that comprises a variety of lockdown levels, such as school and workplace closures, we analyze the effectiveness of government restrictions. At the same time, we investigate the capacity of a range of lockdown measures to lower the reproduction rate by considering vaccination rates and testing strategies. By including all three components in an SIR (Susceptible, Infected, Recovery) model, we show that a general and comprehensive test strategy is instrumental in reducing the spread of COVID-19. The empirical study demonstrates that testing and isolation represent a highly effective and preferable approach towards overcoming the pandemic, in particular until vaccination rates have risen to the point of herd immunity. AU - Fritz, Marlon AU - Gries, Thomas AU - Redlin, Margarete ID - 44591 JF - International Journal of Health Economics and Management KW - Health Policy KW - Economics KW - Econometrics and Finance (miscellaneous) SN - 2199-9023 TI - The effectiveness of vaccination, testing, and lockdown strategies against COVID-19 ER - TY - CONF AU - Daniel-Söltenfuß, Desiree ID - 50283 TI - „Wir fahren jetzt nicht mit’m Mercedes vor, wenn man sich nachher eigentlich nur ‘n Polo leisten kann.“ Vorstellungen von Transfer in Theorie und Praxis der Beruflichen Bildung und ihre Implikationen ER - TY - CONF AU - Daniel-Söltenfuß, Desiree AU - Kückmann, Marie-Ann ID - 50281 TI - Zum Verständnis von Innovation und Transfer in einer vernetzten Berufsbildungspraxis. Ergebnisse einer übergreifenden Interviewstudie ER - TY - CONF AU - Daniel-Söltenfuß, Desiree AU - Kückmann, Marie-Ann ID - 50282 TI - „Go with the flow?!“ Einblicke in Forschungsansatz und erste Ergebnisse des Begleitforschungsprojekts ITiB ER - TY - CONF AB - Optimal decision making requires appropriate evaluation of advice. Recent literature reports that algorithm aversion reduces the effectiveness of predictive algorithms. However, it remains unclear how people recover from bad advice given by an otherwise good advisor. Previous work has focused on algorithm aversion at a single time point. We extend this work by examining successive decisions in a time series forecasting task using an online between-subjects experiment (N = 87). Our empirical results do not confirm algorithm aversion immediately after bad advice. The estimated effect suggests an increasing algorithm appreciation over time. Our work extends the current knowledge on algorithm aversion with insights into how weight on advice is adjusted over consecutive tasks. Since most forecasting tasks are not one-off decisions, this also has implications for practitioners. AU - Leffrang, Dirk AU - Bösch, Kevin AU - Müller, Oliver ID - 37312 KW - Algorithm aversion KW - Time series KW - Decision making KW - Advice taking KW - Forecasting T2 - Hawaii International Conference on System Sciences TI - Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time ER - TY - CONF AB - Many researchers and practitioners see artificial intelligence as a game changer compared to classical statistical models. However, some software providers engage in “AI washing”, relabeling solutions that use simple statistical models as AI systems. By contrast, research on algorithm aversion unsystematically varied the labels for advisors and treated labels such as "artificial intelligence" and "statistical model" synonymously. This study investigates the effect of individual labels on users' actual advice utilization behavior. Through two incentivized online within-subjects experiments on regression tasks, we find that labeling human advisors with labels that suggest higher expertise leads to an increase in advice-taking, even though the content of the advice remains the same. In contrast, our results do not suggest such an expert effect for advice-taking from algorithms, despite differences in self-reported perception. These findings challenge the effectiveness of framing intelligent systems as AI-based systems and have important implications for both research and practice. AU - Leffrang, Dirk ID - 50121 IS - 10 KW - Artificial Intelligence KW - Algorithm Appreciation KW - Framing KW - Advice-taking KW - Expertise T2 - International Conference on Information Systems TI - AI Washing: The Framing Effect of Labels on Algorithmic Advice Utilization ER - TY - CONF AB - Despite the widespread use of machine learning algorithms, their effectiveness is limited by a phenomenon known as algorithm aversion. Recent research concluded that unobserved variables can cause algorithm aversion. However, the impact of an unobserved variable on algorithm aversion remains unclear. Previous studies focused on situations where humans had more variables available than algorithms. We extend this research by conducting an online experiment with 94 participants, systematically varying the number of observable variables to the advisor and the advisor type. Surprisingly, our results did not confirm that an unobserved variable had a negative effect on advice-taking. Instead, we found a positive impact in an algorithm appreciation scenario. This study provides new insights into the paradoxical behavior in which people weigh advice more despite having fewer variables, as they correct for the advisor's errors. Practitioners should consider this behavior when designing algorithms and account for user correction behavior. AU - Leffrang, Dirk ID - 50118 IS - 19 KW - Algorithm aversion KW - Data KW - Decision-making KW - Advice-taking KW - Human-Computer Interaction T2 - Wirtschaftsinformatik Conference TI - The Broken Leg of Algorithm Appreciation: An Experimental Study on the Effect of Unobserved Variables on Advice Utilization ER - TY - CONF AB - Recommender systems now span the entire customer journey. Amid the multitude of diversified experi- ences, immersing in cultural events has become a key aspect of tourism. Cultural events, however, suffer from fleeting lifecycles, evade exact replication, and invariably lie in the future. In addition, their low standardization makes harnessing historical data regarding event content or past patron evaluations intricate. The distinctive traits of events thereby compound the challenge of the cold-start dilemma in event recommenders. Content-based recommendations stand as a viable avenue to alleviate this issue, functioning even in scenarios where item-user information is scarce. Still, the effectiveness of content- based recommendations often hinges on the quality of the data representation they build upon. In this study, we explore an array of cutting-edge uni- and multimodal vision and language foundation models (VL-FMs) for this purpose. Next, we derive content-based recommendations through a straightforward clustering approach that groups akin events together, and evaluate the efficacy of the models through a series of online user experiments across three dimensions: similarity-based evaluation, comparison-based evaluation, and clustering assignment evaluation. Our experiments generated four major findings. First, we found that all VL-FMs consistently outperformed a naive baseline of recommending randomly drawn events. Second, unimodal text-based embeddings were surprisingly on par or in some cases even superior to multimodal embeddings. Third, multimodal embeddings yielded arguably more fine-grained and diverse clusters in comparison to their unimodal counterparts. Finally, we could confirm that cross event interest is indeed reliant on the perceived similarity of events, resonating with the notion of similarity in content-based recommendations. All in all, we believe that leveraging the potential of contemporary FMs for content-based event recommendations would help address the cold-start problem and propel this field of research forward in new and exciting ways. AU - Halimeh, Haya AU - Freese, Florian AU - Müller, Oliver ID - 50431 T2 - Workshop on Recommenders in Tourism, co-located with the 17th ACM Conference on Recommender Systems TI - Event Recommendations through the Lens of Vision and Language Foundation Models ER - TY - CONF AB - Clinical depression is a serious mental disorder that poses challenges for both personal and public health. Millions of people struggle with depression each year, but for many, the disorder goes undiagnosed or untreated. Over the last decade, early depression detection on social media emerged as an interdisciplinary research field. However, there is still a gap in detecting hesitant, depression-susceptible individuals with minimal direct depressive signals at an early stage. We, therefore, take up this open point and leverage posts from Reddit to fill the addressed gap. Our results demonstrate the potential of contemporary Transformer architectures in yielding promising predictive capabilities for mental health research. Furthermore, we investigate the model’s interpretability using a surrogate and a topic modeling approach. Based on our findings, we consider this work as a further step towards developing a better understanding of mental eHealth and hope that our results can support the development of future technologies. AU - Halimeh, Haya AU - Caron, Matthew AU - Müller, Oliver ID - 45270 KW - Social Media and Healthcare Technology KW - early depression detection KW - liwc KW - mental health KW - transfer learning KW - transformer architectures T2 - Hawaii International Conference on System Sciences TI - Early Depression Detection with Transformer Models: Analyzing the Relationship between Linguistic and Psychology-Based Features ER -