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 -