@article{54910,
  author       = {{Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@article{54908,
  author       = {{Heid, Stefan and Hanselle, Jonas and Fürnkranz, Johannes and Hüllermeier, Eyke}},
  issn         = {{0888-613X}},
  journal      = {{International Journal of Approximate Reasoning}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning decision catalogues for situated decision making: The case of scoring systems}}},
  doi          = {{10.1016/j.ijar.2024.109190}},
  volume       = {{171}},
  year         = {{2024}},
}

@book{54972,
  editor       = {{Thommes, Kirsten and Iseke, Anja and Schneider, Martin}},
  isbn         = {{9783662688373}},
  issn         = {{2523-3637}},
  publisher    = {{Springer Berlin Heidelberg}},
  title        = {{{Digitales und prädiktives Kompetenzmanagement}}},
  doi          = {{10.1007/978-3-662-68838-0}},
  year         = {{2024}},
}

@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     = {{<jats:sec><jats:title content-type="abstract-subheading">Purpose</jats:title><jats:p>The 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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Design/methodology/approach</jats:title><jats:p>We report a quasi-experiment analyzing the impact of past, present and future orientation variation in dyads (<jats:italic>N</jats:italic> = 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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Findings</jats:title><jats:p>Results 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.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Practical implications</jats:title><jats:p>This study provides insights for organizations on composing individuals regarding their temporal focus for collaborative tasks that should be executed rapidly and require creative solutions.</jats:p></jats:sec><jats:sec><jats:title content-type="abstract-subheading">Originality/value</jats:title><jats:p>Our 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.</jats:p></jats:sec>}},
  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{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}},
}

@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}},
}

@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{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{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}},
}

@inproceedings{48280,
  author       = {{Schütze, Christian and Lammert, Olesja and Richter, Birte and Thommes, Kirsten and Wrede, Britta}},
  booktitle    = {{Artificial Intelligence in HCI}},
  title        = {{{Emotional Debiasing Explanations for Decisions in HCI}}},
  doi          = {{10.1007/978-3-031-35891-3_20}},
  year         = {{2023}},
}

@inproceedings{47972,
  author       = {{Gutt, Jana Kim and Mehic, Miro and Thommes, Kirsten}},
  booktitle    = {{Academy of Management Proceedings}},
  title        = {{{Oh my Goodness: Investigating the Goodness of Performance Appraisal Formats Between and Within Teams}}},
  doi          = {{https://doi.org/10.5465/AMPROC.2023.214bp}},
  year         = {{2023}},
}

@inproceedings{51209,
  author       = {{Hanselle, Jonas Manuel and Kornowicz, Jaroslaw and Heid, Stefan and Thommes, Kirsten and Hüllermeier, Eyke}},
  booktitle    = {{LWDA’23: Learning, Knowledge, Data, Analysis. }},
  editor       = {{Leyer, M and Wichmann, J}},
  issn         = {{1613-0073}},
  title        = {{{Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain}}},
  year         = {{2023}},
}

@article{32266,
  author       = {{Hoppe, Julia Amelie and Melkas, Helinä and Pekkarinen, Satu and Tuisku, Outi and Hennala, Lea and Johansson-Pajala, Rose-Marie and Gustafsson, Christine and Thommes, Kirsten}},
  issn         = {{1044-7318}},
  journal      = {{International Journal of Human–Computer Interaction}},
  keywords     = {{Computer Science Applications, Human-Computer Interaction, Human Factors and Ergonomics}},
  pages        = {{1--17}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Perception of Society’s Trust in Care Robots by Public Opinion Leaders}}},
  doi          = {{10.1080/10447318.2022.2081283}},
  year         = {{2022}},
}