@phdthesis{Daschner2024, author = {Daschner, Stefan}, title = {Essays on Trust, Behavior, and Decision-Making in the Human-Algorithm Interaction}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14749}, school = {Universit{\"a}t Passau}, pages = {XVII, 154 Seiten}, year = {2024}, abstract = {With recent progresses in the field of artificial intelligence (AI), algorithms can support more complex tasks (Frey and Osborne, 2017) that seemed safe from automation a few years ago (Brynjolfsson and McAfee, 2012). They can even outperform human decision-making in many task domains (Meehl, 1954; Dawes et al., 1989). The prominent defeat of Garry Kasparov, the world champion in chess, in 1997 is a milestone for the potentials of algorithms, increasing the value of IBM's stock increased by \$18 billion (Norvig and Russell, 2010). More recently, universal chatbots such as ChatGPT and the Bing Chatbot Sydney can influence millions of users and provide them answers to a broad range of tasks. To gain a competitive edge, companies invest heavily in algorithmic systems (Kappelman et al., 2021), which are considered as one of the most important drivers of today's economy (Brynjolfsson and McAfee, 2011; Hogharth and Makridakis, 1981; {\"O}nkal et al., 2019). Particularly in management accounting, an algorithmic data analysis offers the potential to support a high decision quality, making their usage a key factor for success. However, algorithms can not only support, but also manipulate human decision-making (Roose et al., 2023). Algorithmic advice can lead to overtrust, i.e. to the heuristic replacement of vigilant information seeking and processing in favor of the advice provided (Mosier et al., 2001). The implementation of algorithmic decision support systems is thus no panacea to improve human decision-making. In contrast, they can entail new risks, such as the uncritical use of these algorithms (Boden, 2016). Yet, this 'dark side' of algorithms is not sufficiently investigated in the field of management accounting. With advances in machine learning, algorithmic support can become even more of a black box, blurring the reference points for evaluating its advice quality and thus increasing the risk of overtrust. Stephen Hawking even warns that AI will be "either the best, or the worst thing, ever to happen to humanity" (Alex Hern in: The Guardian: Stephen Hawking: AI will be 'either best or worst thing' for humanity, 19th October 2016). Interacting with such algorithmic systems therefore requires data literacy skills for an appropriate use. They seem to be a crucial prerequisite in today's data-driven world to reap of the benefits of algorithmic systems. If no reflective and critical interaction between human and algorithm can be ensured, the trend towards a digitalized world will be a race against the algorithm (Brynjolfsson and McAfee, 2011). This dissertation takes this understudied critical perspective on the increasing implementation of algorithmic systems and sheds light on different aspects of human-algorithm interaction, ranging from the ability to use data visualization tools to trust calibrations and critical evaluations of algorithmic advices. Study I investigates an adequate information representation of subjective multiple criteria decision problems, an important task in management accounting, to support human decision-making. Study II distinguishes different designs of the human-algorithm interaction investigated in the trust in automation literature and compiles factors influencing the calibration of trust and behavior toward the true capabilities of the algorithm. Study III examines the role of performance feedback on trust and advice usage in a forecasting task for a better understanding of the recently observed phenomenon 'algorithm aversion'. Finally, Study IV focusses on the risk of overtrust in advice in repeated interactions with a forecasting advisor and investigates the calibration of advice usage. Each study thereby represents an independent contribution and contains all information relevant to the respective research questions asked in the study.}, language = {en} } @phdthesis{Ghaffar2024, author = {Ghaffar, Abdul Muqeet}, title = {Understanding Individuals' Willingness for Prosocial Data Disclosure: Exploring Antecedents and Investigating Strategies for its Promotion}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-15001}, school = {Universit{\"a}t Passau}, pages = {iv, 94 Seiten}, year = {2024}, abstract = {Privacy research has traditionally focused on understanding individuals' behavior in disclosing personal data by assuming that individuals disclose their data mainly out of self-interest. However, in many contexts, individuals disclose their data primarily for the benefit of others and society, thereby showing a form of prosocial behavior. This dissertation conceptualizes these types of data disclosure as prosocial data disclosures and argues that existing privacy research frameworks, such as the privacy calculus, have not been sufficiently applied in privacy research and need to be reevaluated to fully capture the complexities of prosocial data disclosures. Despite the growing relevance of understanding individuals' willingness for prosocial data disclosures, existing research in this field is limited. This dissertation consists of four essays that seek to deepen the understanding of the underlying decision-making processes of individuals by exploring the antecedents and investigating strategies that promote prosocial data disclosure. By employing different research methods, including systematic literature reviews, qualitative and quantitative surveys, interviews, workshops, and conjoint analysis, this dissertation contributes to the identification of the multifaceted antecedents of prosocial data disclosure which can be classified into different drivers and barriers. Furthermore, by integrating insights from different disciplines such as behavioral economics, social psychology, and information systems research, this dissertation provides empirical evidence for the use of message framing and the deliberate emphasis on impact uncertainty as two promising strategies in promoting prosocial data disclosure. The findings derived from all four essays inform privacy research and prosocial behavior research about the need for sophisticated theories that are capable of better capturing the complexities of prosocial data disclosure. Additionally, the findings offer practical implications for designing ethically responsible data disclosure practices while respecting individual privacy rights.}, language = {en} } @article{Fritz2022, author = {Fritz, Manuela}, title = {Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany}, series = {Journal of Spatial Econometrics}, volume = {2022}, journal = {Journal of Spatial Econometrics}, number = {3}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s43071-022-00027-6}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2022112821095396799160}, pages = {30 Seiten}, year = {2022}, abstract = {The Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany's 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spill over effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses.}, language = {en} }