@inproceedings{55177, author = {{Thommes, Kirsten and Lammert, Olesja and Schütze, Christian and Richter, Birte and Wrede, Britta}}, booktitle = {{Communications in Computer and Information Science}}, isbn = {{9783031638022}}, issn = {{1865-0929}}, publisher = {{Springer Nature Switzerland}}, title = {{{Human Emotions in AI Explanations}}}, doi = {{10.1007/978-3-031-63803-9_15}}, year = {{2024}}, } @article{53610, abstract = {{PurposeThe relationship between variation in time perspectives and collaborative performance is scarcely explored, and even less is known about the respective mechanisms that lead to varying task performance. Thus, we aim to further the literature on time perspectives and collaborative performance, shedding light on the underlying behavioral patterns.Design/methodology/approachWe report a quasi-experiment analyzing the impact of past, present and future orientation variation in dyads (N = 76) on their quantitative and qualitative performance when confronted with a simple incentivized creative task with constraints. Subsequently, we offer a qualitative analysis of comments given by the participants after the task on the collaboration.FindingsResults indicate that a dyad's elevation of past orientation and diversity in future orientation negatively affect collaborative performance. At the same time, there is a positive effect of elevation of future orientation. The positive effect is driven by clear communication and agreement during the task, while the negative effect arises from work sharing and complementation.Practical implicationsThis study provides insights for organizations on composing individuals regarding their temporal focus for collaborative tasks that should be executed rapidly and require creative solutions.Originality/valueOur study distinguishes by considering the composition of past, present and future time perspectives in dyads and focuses on a creative task setting. Moreover, we explore the mechanisms in the dyads with a substantial elevation of/diversity in future orientation, leading to their stronger/weaker performance.}}, author = {{Auer, Thorsten Fabian and Hoppe, Julia Amelie and Thommes, Kirsten}}, issn = {{2051-6614}}, journal = {{Journal of Organizational Effectiveness: People and Performance}}, keywords = {{Organizational Behavior and Human Resource Management}}, number = {{4}}, pages = {{1023--1042}}, publisher = {{Emerald}}, title = {{{Time perspectives and collaborative performance in creative tasks}}}, doi = {{10.1108/joepp-07-2023-0285}}, volume = {{11}}, year = {{2024}}, } @inproceedings{57290, author = {{Kürpick, Christian and Schreiner, Nick and Krauß-Kodytek, Laura and Plaß, Sabrina and Scholz, Thorben and Kühn, Arno}}, location = {{Riga Technical University}}, pages = {{1--6}}, title = {{{Capabilities for the Strategic Alignment of Sustainability and Digitalization in Manufacturing: Insights from Theory and Practice}}}, year = {{2024}}, } @article{57461, abstract = {{This study empirically examines the "Evaluative AI" framework, which aims to enhance the decision-making process for AI users by transitioning from a recommendation-based approach to a hypothesis-driven one. Rather than offering direct recommendations, this framework presents users pro and con evidence for hypotheses to support more informed decisions. However, findings from the current behavioral experiment reveal no significant improvement in decision-making performance and limited user engagement with the evidence provided, resulting in cognitive processes similar to those observed in traditional AI systems. Despite these results, the framework still holds promise for further exploration in future research. }}, author = {{Kornowicz, Jaroslaw}}, journal = {{arXiv}}, title = {{{An Empirical Examination of the Evaluative AI Framework}}}, doi = {{10.48550/ARXIV.2411.08583}}, year = {{2024}}, } @inproceedings{62155, author = {{Radermacher, Katharina and Horsthemke, Johanna and Täuber, Mona }}, booktitle = {{Herbstworkshop WK Pers}}, title = {{{Unveiling the Impact of Flexible Work on Employer Attractiveness: Examining the Role of Work Experience}}}, year = {{2024}}, } @inproceedings{57250, author = {{Schütze, Christian and Richter, Birte and Lammert, Olesja and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{HAI '24: Proceedings of the 12th International Conference on Human-Agent Interaction}}, isbn = {{9798400711787}}, pages = {{141--149}}, publisher = {{ACM}}, title = {{{Static Socio-demographic and Individual Factors for Generating Explanations in XAI: Can they serve as a prior in DSS for adaptation of explanation strategies?}}}, doi = {{10.1145/3687272.3688300}}, year = {{2024}}, } @inbook{54623, author = {{Papenkordt, Jörg}}, booktitle = {{Artificial Intelligence in HCI}}, isbn = {{9783031606052}}, issn = {{0302-9743}}, publisher = {{Springer Nature Switzerland}}, title = {{{Navigating Transparency: The Influence of On-demand Explanations on Non-expert User Interaction with AI}}}, doi = {{10.1007/978-3-031-60606-9_14}}, year = {{2024}}, } @inproceedings{55178, author = {{Thommes, Kirsten and Lammert, Olesja and Schütze, Christian and Richter, Birte and Wrede, Britta}}, title = {{{Human Emotions in AI Explanations}}}, year = {{2024}}, } @inproceedings{57645, author = {{Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas Manuel and Hüllermeier, Eyke and Thommes, Kirsten}}, booktitle = {{PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE}}, pages = {{233}}, title = {{{Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems}}}, volume = {{21}}, year = {{2024}}, } @inbook{48387, author = {{Lebedeva, Anastasia and Protte, Marius and van Straaten, Dirk and Fahr, René}}, booktitle = {{Advances in Information and Communication}}, location = {{Berlin}}, pages = {{178–204}}, publisher = {{Springer, Cham}}, title = {{{Involvement of domain experts in the AI training does not affect adherence – An AutoML study}}}, doi = {{https://doi.org/10.1007/978-3-031-53960-2_13}}, volume = {{919}}, year = {{2024}}, } @article{58511, abstract = {{We investigate differences in bribing decisions among two generations from East and West Germany in a bribery game conducted as an online study (N=168). This way, we aim to explore moral considerations of individuals influenced by two formerly different institutional systems. We find a higher propensity to bribe among young Germans compared to the older generation. Young East Germans even reveal a slightly greater inclination to bribe than their West German counterparts. We conclude that preferences for personal favors may be induced among young East Germans given the tense relationship between market opportunities and conveyed cultural traits of a socialist imprint.}}, author = {{Auer, Thorsten Fabian and Berg, Timo and Hoffmann, Christin}}, issn = {{1824-2979}}, journal = {{European Journal of Comparative Economics}}, keywords = {{Moral behavior, Corruption, Intra- and intergenerational study, Institutional transformation, Reunification}}, number = {{2}}, pages = {{211--264}}, title = {{{Inter- and intragenerational differences in corrupt behavior: The development of morals after German reunification}}}, doi = {{10.25428/1824-2979/032}}, volume = {{21}}, year = {{2024}}, } @inproceedings{55403, abstract = {{In this paper we consider the interactive processes by which an explainer and an explainee cooperate to produce an explanation, which we refer to as co-construction. Explainable Artificial Intelligence (XAI) is concerned with the development of intelligent systems and robots that can explain and justify their actions, decisions, recommendations, and so on. However, the cooperative construction of explanations remains a key but under-explored issue. This short paper proposes an architecture for intelligent systems that promotes a co-constructive and interactive approach to explanation generation. By outlining its basic components and their specific roles, we aim to contribute to the advancement of XAI computational frameworks that actively engage users in the explanation process.}}, author = {{Buschmeier, Hendrik and Cimiano, Philipp and Kopp, Stefan and Kornowicz, Jaroslaw and Lammert, Olesja and Matarese, Marco and Mindlin, Dimitry and Robrecht, Amelie Sophie and Vollmer, Anna-Lisa and Wagner, Petra and Wrede, Britta and Booshehri, Meisam}}, booktitle = {{Proceedings of the 2024 Workshop on Explainability Engineering}}, location = {{Lisbon, Portugal}}, pages = {{20--25}}, publisher = {{ACM}}, title = {{{Towards a Computational Architecture for Co-Constructive Explainable Systems}}}, doi = {{10.1145/3648505.3648509}}, year = {{2024}}, } @techreport{65911, author = {{Radermacher, Katharina and Schneider, Martin}}, title = {{{HR-Praktiken als Ressourcenmanager: Wie die Schwächen flexibler und unflexibler Arbeitsmodelle kompensiert werden können. Studie Industrieverband Büro und Arbeitswelt e.V..}}}, year = {{2024}}, } @inproceedings{48285, author = {{Lebedeva, Anastasia and Kornowicz, Jaroslaw and Lammert, Olesja and Papenkordt, Jörg}}, booktitle = {{Artificial Intelligence in HCI}}, title = {{{The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks}}}, doi = {{10.1007/978-3-031-35891-3_9}}, year = {{2023}}, } @inproceedings{47976, author = {{Papenkordt, Jörg and Ngonga-Ngomo, Axel-Cyrille and Thommes, Kirsten}}, booktitle = {{Academy of Management Proceedings}}, title = {{{Are Numbers or Words the Key to User Reliance on AI?}}}, doi = {{10.5465/AMPROC.2023.12946}}, year = {{2023}}, } @article{49213, author = {{Schneider, Martin and Radermacher, Katharina}}, issn = {{0032-3446}}, journal = {{Die Politische Meinung}}, number = {{580}}, pages = {{63--67}}, title = {{{Employer Branding - Wie Arbeitgeber strategisch gegen den Arbeitskräftemangel vorgehen. }}}, year = {{2023}}, } @article{49446, author = {{Diederich, Sarah and Iseke, Anja and Pull, Kerstin and Schneider, Martin}}, issn = {{0958-5192}}, journal = {{The International Journal of Human Resource Management}}, keywords = {{Management of Technology and Innovation, Organizational Behavior and Human Resource Management, Strategy and Management, Business and International Management, Industrial relations}}, pages = {{1--29}}, publisher = {{Informa UK Limited}}, title = {{{Role (in-)congruity and the Catch 22 for female executives: how stereotyping contributes to the gender pay gap at top executive level}}}, doi = {{10.1080/09585192.2023.2273331}}, year = {{2023}}, } @inbook{49469, abstract = {{Today, it is possible to collect and connect large amounts of digital data from various sources and life domains. This chapter examines the potential and the risks of this development from an interdisciplinary perspective. It defines the ‘global digital twin’ of a human being as the sum of all digitally stored information and predictive knowledge about a person. It points out that, compared to the digital twin of a machine, the human global digital twin is far more complex because it comprises the genetic code and the biographic code of a person. The genetic code contains not only a simple ‘construction plan’ but also hereditary information, in a form that is difficult to read. The biographic code contains all other information that can be assembled about a person, which is obtained via data from cameras, microphones, or other sensors, as well as general personal information. When the growing wealth of information concerning the genetic code and the biographical code is properly utilised, insights from biology and the behavioural sciences may be used to predict personal events such as health problems, job resignations, or even crimes. Because our own interests and those of private firms are partly in conflict over the use of this powerful knowledge, it is still unclear whether the global digital twins of humans will become a liberating or disciplining force for citizens. On the one hand, human beings are not machines: They are aware of their digital twin and therefore are able to influence it throughout their lives. Because of their free will, human beings are in general difficult to predict. Dystopias of full control over individual behaviour are therefore unlikely to materialise. On the other hand, private firms are beginning to take advantage of the available digital twins of humans by monopolising data access and by commercialising predictive knowledge. This is problematic because, unlike machines, human beings cannot only benefit from but also suffer due to their digital twins as they attempt to shape their own lives. We illustrate these issues with some examples and arrive at two conclusions: It is in the public interest for people to be granted more property rights over their personal global digital twins, and publicly funded research needs to become more interdisciplinary, much like private firms that have already begun to perform interdisciplinary research.}}, author = {{Hellweg, Talea Davina and Schneider, Martin and Rückert, Ulrich and Harteis, Christian and Pilz, Sarah}}, booktitle = {{The Digital Twin of Human}}, title = {{{Who Will Own Our Global Digital Twin: The Power of Genetic and Biographic Information to Shape Our Lives}}}, year = {{2023}}, } @article{47953, author = {{Kornowicz, Jaroslaw and Thommes, Kirsten}}, isbn = {{9783031358906}}, issn = {{0302-9743}}, journal = {{Artificial Intelligence in HCI}}, publisher = {{Springer Nature Switzerland}}, title = {{{Aggregating Human Domain Knowledge for Feature Ranking}}}, doi = {{10.1007/978-3-031-35891-3_7}}, year = {{2023}}, } @article{44639, author = {{Hoppe, Julia Amelie and Tuisku, Outi and Johansson-Pajala, Rose-Marie and Pekkarinen, Satu and Hennala, Lea and Gustafsson, Christine and Melkas, Helinä and Thommes, Kirsten}}, issn = {{2451-9588}}, journal = {{Computers in Human Behavior Reports}}, keywords = {{Artificial Intelligence, Cognitive Neuroscience, Computer Science Applications, Human-Computer Interaction, Applied Psychology, Neuroscience (miscellaneous)}}, publisher = {{Elsevier BV}}, title = {{{When do individuals choose care robots over a human caregiver? Insights from a laboratory experiment on choices under uncertainty}}}, doi = {{10.1016/j.chbr.2022.100258}}, volume = {{9}}, year = {{2023}}, }