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Serious games to study the management of paradoxes in family firms: Introducing a research agenda
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How do nonfamily employees judge the legitimacy of family-internal successors? Insights from an experimental study
No full textEngagement für den Frieden oder Ausbildung künftiger Putschisten? Ambivalenzen in der journalistischen Online-Berichterstattung über die UN-Friedensmission MINUSMA
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Transparent (Support) Structures: On Visibility and Social Reproduction in Socially Engaged Art : Transparente (Unterstützungs-)Strukturen: Über Sichtbarkeit und soziale Reproduktion in sozial engagierter Kunst
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From Solution Trap to Solution Patchwork: Tensions in Digital Health in the Global Context
No full textThis paper problematizes underlying assumptions in Design Science Research – and Information Systems Research more broadly by conceptualizing the „solution trap“. The solution trap is caused by the incompatibility of co-existing solutions in complex socio-technical contexts. Information systems bring diverse cultures and theories together, causing tensions in the different institutional logics. We emphasize the need for a nuanced understanding of context unevenness and propose solution patchwork as a coordination approach to evade the solution trap. Substantiating the preliminary insights and propositions with a literature review and further empirical grounding will transition this research-in-progress to a full paper
Artificially Human: Examining the Potential of Text-Generating Technologies in Online Customer Feedback Management
No full text“Garbage In, Garbage Out”: Mitigating Human Biases in Data Entry by Means of Artificial Intelligence
No full textCurrent HCI research often focuses on mitigating algorithmic biases. While such algorithmic fairness during model training is worthwhile, we see fit to mitigate human cognitive biases earlier, namely during data entry. We developed a conversational agent with voice-based data entry and visualization to support financial consultations, which are human-human settings with information asymmetries. In a pre-study, we reveal data-entry biases in advisors by a quantitative analysis of 5 advisors consulting 15 clients in total. Our main study evaluates the conversational agent with 12 advisors and 24 clients. A thematic analysis of interviews shows that advisors introduce biases by “feeling” and “forgetting” data. Additionally, the conversational agent makes financial consultations more transparent and automates data entry. These findings may be transferred to various dyads, such as doctor visits. Finally, we stress that AI not only poses a risk of becoming a mirror of human biases but also has the potential to intervene in the early stages of data entry