Institut für Marktangebote und Konsumentscheidungen
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Publikation Who thinks COVID-19 is a hoax? Psychological correlates of beliefs in conspiracy theories and attitudes towards anti-Coronavirus measures at the end of the first lockdown in Germany(Ubiquity Press, 2022) Hudecek, Matthias; Fischer, Peter; Gaube, Susanne; Lermer, Eva12 - PatentPublikation Insights on the current state and future outlook of AI in health care: expert interview study(JMIR Publications, 2023) Hummelsberger, Pia; Koch, Timo K.; Rauh, Sabrina; Dorn, Julia; Lermer, Eva; Raue, Martina; Hudecek, Matthias; Schicho, Andreas; Colak, Errol; Ghassemi, Marzyeh; Gaube, SusanneBackground Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. Objective This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. Methods For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Results Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology’s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Conclusions Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Surfing in the streets: How problematic smartphone use, fear of missing out, and antisocial personality traits are linked to driving behavior(Public Library of Science, 2023) Hudecek, Matthias; Lemster, Simon; Fischer, Peter; Cecil, Julia; Frey, Dieter; Gaube, Susanne; Lermer, EvaSmartphone use while driving (SUWD) is a major cause of accidents and fatal crashes. This serious problem is still too little understood to be solved. Therefore, the current research aimed to contribute to a better understanding of SUWD by examining factors that have received little or no attention in this context: problematic smartphone use (PSU), fear of missing out (FOMO), and Dark Triad. In the first step, we conducted a systematic literature review to map the current state of research on these factors. In the second step, we conducted a cross-sectional study and collected data from 989 German car drivers. A clear majority (61%) admitted to using the smartphone while driving at least occasionally. Further, the results showed that FOMO is positively linked to PSU and that both are positively associated with SUWD. Additionally, we found that Dark Triad traits are relevant predictors of SUWD and other problematic driving behaviors––in particular, psychopathy is associated with committed traffic offenses. Thus, results indicate that PSU, FOMO, and Dark Triad are relevant factors to explain SUWD. We hope to contribute to a more comprehensive understanding of this dangerous phenomenon with these findings.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Early and later perceptions and reactions to the COVID-19 pandemic in Germany: on predictors of behavioral responses and guideline adherence during the restrictions(Frontiers Research Foundation, 2021) Lermer, Eva; Hudecek, Matthias; Gaube, Susanne; Raue, Martina; Batz, FalkIn March 2020, the German government enacted measures on movement restrictions and social distancing due to the COVID-19 pandemic. As this situation was previously unknown, it raised numerous questions about people’s perceptions of and behavioral responses to these new policies. In this context, we were specifically interested in people’s trust in official information, predictors for self-prepping behavior and health behavior to protect oneself and others, and determinants for adherence to social distancing guidelines. To explore these questions, we conducted three studies in which a total of 1,368 participants were surveyed (Study 1 N=377, March 2020; Study 2 N=461, April 2020; Study 3 N=530, April 2021) across Germany between March 2020 and April 2021. Results showed striking differences in the level of trust in official statistics (depending on the source). Furthermore, all three studies showed congruent findings regarding the influence of different factors on the respective behavioral responses. Trust in official statistics predicted behavioral responses in all three studies. However, it did not influence adherence to social distancing guidelines in 2020, but in 2021. Furthermore, adherence to social distancing guidelines was associated with higher acceptance rates of the measures and being older. Being female and less right-wing orientated were positively associated with guidelines adherence only in the studies from 2020. This year, political orientation moderated the association between acceptance of the measures and guideline adherence. This investigation is one of the first to examine perceptions and reactions during the COVID-19 pandemic in Germany across 1year and provides insights into important dimensions that need to be considered when communicating with the public.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays(Nature, 2023) Gaube, Susanne; Suresh, Harini; Raue, Martina; Lermer, Eva; Koch, Timo K.; Hudecek, Matthias; Ackery, Alun D.; Grover, Samir C.; Coughlin, Joseph F.; Frey, Dieter; Kitamura, Felipe C.; Ghassemi, Marzyeh; Colak, ErrolArtificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Explainability does not mitigate the negative impact of incorrect AI advice in a personnel selection task(Nature, 2024) Cecil, Julia; Lermer, Eva; Hudecek, Matthias; Sauer, Jan; Gaube, SusanneDespite the rise of decision support systems enabled by artificial intelligence (AI) in personnel selection, their impact on decision-making processes is largely unknown. Consequently, we conducted five experiments (N = 1403 students and Human Resource Management (HRM) employees) investigating how people interact with AI-generated advice in a personnel selection task. In all pre-registered experiments, we presented correct and incorrect advice. In Experiments 1a and 1b, we manipulated the source of the advice (human vs. AI). In Experiments 2a, 2b, and 2c, we further manipulated the type of explainability of AI advice (2a and 2b: heatmaps and 2c: charts). We hypothesized that accurate and explainable advice improves decision-making. The independent variables were regressed on task performance, perceived advice quality and confidence ratings. The results consistently showed that incorrect advice negatively impacted performance, as people failed to dismiss it (i.e., overreliance). Additionally, we found that the effects of source and explainability of advice on the dependent variables were limited. The lack of reduction in participants’ overreliance on inaccurate advice when the systems’ predictions were made more explainable highlights the complexity of human-AI interaction and the need for regulation and quality standards in HRM.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Fine for others but not for me: The role of perspective in patients’ perception of artificial intelligence in online medical platforms(Elsevier, 2024) Hudecek, Matthias; Lermer, Eva; Gaube, Susanne; Cecil, Julia; Heiss, Silke F.; Batz, Falk01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Patient:innen und KI: Eine Frage der Perspektive bei der Bewertung von KI bei medizinischen Online-Diensten(Frankfurt University of Applied Sciences, 2024) Lermer, Eva; Gaube, Susanne; Cecil, Julia; Kleine, Anne-Kathrin; Kokje, Eesha; Frey, Dieter; Hudecek, Matthias; Klein, Barbara; Rägle, Susanne; Klüber, Susanne04A - Beitrag Sammelband