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 -
TY - CONF
AB - The humanitarian crisis resulting from the Russian invasion of Ukraine has led to millions of displaced individuals across Europe. Addressing the evolving needs of these refugees is crucial for hosting countries and humanitarian organizations. This study leverages social media analytics to supplement traditional surveys, providing real-time insights into refugee needs by analyzing over two million messages from Telegram, a vital platform for Ukrainian refugees in Germany. We employ Natural Language Processing techniques, including language identification, sentiment analysis, and topic modeling, to identify well-defined topic clusters such as housing, financial and legal assistance, language courses, job market access, and medical needs. Our findings also reveal changes in topic occurrence and nature over time. To support practitioners, we introduce an interactive web-based dashboard for continuous analysis of refugee needs.
AU - Reimann, Raphael
AU - Caron, Matthew
ID - 50437
T2 - Wirtschaftsinformatik
TI - Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine Learning Approach
ER -
TY - GEN
AU - Knorr, Lukas
AU - Jungeilges, André
AU - Pfeifer, Florian
AU - Burmeister, Sascha Christian
AU - Meschede, Henning
ID - 48335
TI - Regenerative Energien für einen effizienten Betrieb von Presshärtelinien
ER -
TY - CONF
AB - Digital technologies have made the line of visibility more transparent, enabling customers to get deeper insights into an organization’s core operations than ever before. This creates new challenges for organizations trying to consistently deliver high-quality customer experiences. In this paper we conduct an empirical analysis of customers’ preferences and their willingness-to-pay for different degrees of process transparency, using the example of digitally-enabled business-to-customer delivery services. Applying conjoint analysis, we quantify customers’ preferences and willingness-to-pay for different service attributes and levels. Our contributions are two-fold: For research, we provide empirical measurements of customers’ preferences and their willingness-to-pay for process transparency, suggesting that more is not always better. Additionally, we provide a blueprint of how conjoint analysis can be applied to study design decisions regarding changing an organization’s digital line of visibility. For practice, our findings enable service managers to make decisions about process transparency and establishing different levels of service quality.
AU - Brennig, Katharina
AU - Müller, Oliver
ID - 37058
KW - Digital Services
KW - Line of Visibility
KW - Process Transparency
KW - Customer Preferences
KW - Conjoint Analysis
T2 - Hawaii International Conference on System Sciences
TI - More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process Transparency
ER -
TY - CONF
AB - Organizations employ process mining to discover, check, or enhance process models based on data from information systems to improve business processes. Even though process mining is increasingly relevant in academia and organizations, achieving process mining excellence and generating business value through its application is elusive. Maturity models can help to manage interdisciplinary teams in their efforts to plan, implement, and manage process mining in organizations. However, while numerous maturity models on business process management (BPM) are available, recent calls for process mining maturity models indicate a gap in the current knowledge base. We systematically design and develop a comprehensive process mining maturity model that consists of five factors comprising 23 elements, which organizations need to develop to apply process mining sustainably and successfully. We contribute to the knowledge base by the exaptation of existing BPM maturity models, and validate our model through its application to a real-world scenario.
AU - Brock, Jonathan
AU - Löhr, Bernd
AU - Brennig, Katharina
AU - Seger, Thilo
AU - Bartelheimer, Christian
AU - von Enzberg, Sebastian
AU - Kühn, Arno
AU - Dumitrescu, Roman
ID - 50459
T2 - European Conference on Information Systems
TI - A Process Mining Maturity Model: Enabling Organizations to Assess and Improve their Process Mining Activities
ER -
TY - CHAP
AU - Brennig, Katharina
AU - Benkert, Kay
AU - Löhr, Bernd
AU - Müller, Oliver
ID - 50450
SN - 1865-1348
T2 - Business Process Management Workshops
TI - Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?
ER -
TY - JOUR
AU - Yahyaoui, Y.
AU - Jakob, E.A.
AU - Steinmetz, Holger
AU - Wehner, M.C.
AU - Isidor, R.
AU - Kabst, Rüdiger
ID - 50461
IS - 4
JF - Nonprofit Management & Leadership
TI - The Equivocal Image of Young Social Enterprises - How Self- vs. Other-Oriented Values Influence External Perceptions
VL - 33
ER -
TY - CONF
AU - Schütze, Christian
AU - Lammert, Olesja
AU - Richter, Birte
AU - Thommes, Kirsten
AU - Wrede, Britta
ID - 48280
T2 - Artificial Intelligence in HCI
TI - Emotional Debiasing Explanations for Decisions in HCI
ER -
TY - CONF
AU - Küpper, K.
AU - Garnefeld, I.
AU - Steinhoff, Lena
ID - 50978
TI - Evaluation of product testing programs as an effective marketing tool - Negative and positive effects of rejections in product testing programs
ER -
TY - CONF
AU - Alberternst, B.
AU - Giesler, M.
AU - Steinhoff, Lena
ID - 50975
TI - The Consumerization of Care: How Capitalism Is Co-Opting Solidarity
ER -
TY - JOUR
AB - This study examines GAAP effective tax rate (ETR) visibility as a distinct disclosure choice in firms’ financial statements. By applying a game-theory disclosure model for the voluntary disclosure strategies of firms, in a tax setting, we argue that firms face a trade-off in their ETR disclosure decisions. On the one hand, firms have an incentive to enhance their ETR disclosure when the ratio offers shareholders ‘favourable conditions’, for example, higher expected after-tax cash flows. On the other hand, the disclosure of a favourable low ETR could attract the attention of tax auditors and the public and ultimately result in disclosure costs. We empirically test disclosure behaviour by examining the relation between disclosure visibility and different ETR conditions that reflect different stakeholder-specific costs and benefits. While we find that unfavourable ETR conditions are not highlighted, we observe higher disclosure visibility for favourable ETRs (smooth, close to the industry average, and decreasing ETRs). Additional analyses reveal that this high visibility is characteristic of firm years with only moderately decreasing ETRs at usual ETR levels, while extreme ETRs are not highlighted. Interestingly and in contrast to our main results, a subsample of family firms does not seem to highlight favourable ETRs.
AU - Flagmeier, Vanessa
AU - Müller, Jens
AU - Sureth-Sloane, Caren
ID - 29050
IS - 1
JF - Accounting and Business Research
TI - When Do Firms Highlight Their Effective Tax Rate?
VL - 53
ER -