@techreport{45668,
  abstract     = {{<jats:p>Im Rahmen dieser Studie wird der Status Quo des KI-Einsatzes in der industriellen Arbeitswelt in der Region OstWestfalenLippe erfasst und beschrieben. Dadurch wird eine Grundlage geschaffen, um eine zielführende Unterstützung der Gestaltung von durch Künstliche Intelligenz (KI) gestützter Arbeitsprozesse in Unternehmen zu ermöglichen, indem beispielsweise bedarfsbezogene Maßnahmen entwickelt und durchgeführt sowie weiterer Forschungsbedarf aufgezeigt wird.  Die Befragung wurde im Jahr 2021 von dem Kompetenzzentrum Arbeitswelt.Plus sowie dem Spitzencluster it’s OWL initiiert. Dabei sind drei Zielgruppen – Unternehmensleitung, Personalabteilung (HR) sowie Arbeitnehmer*innen – adressiert worden. Insgesamt nahmen 317 Personen aus 89 verschiedenen Unternehmen bzw. Organisationen an der Befragung teil – zu 38 % Unternehmer*innen, zu 13 % Personaler*innen und zu 49 % Arbeitnehmer*innen. Die meisten der Teilnehmenden stammten aus der Elektroindustrie, dem Maschinenbau sowie dem Informations- und Kommunikationstechnologie (IKT)-Sektor.  Die Befragungsergebnisse zeigen, dass sich die meisten Unternehmen in der Anfangsphase der KI-Nutzung befinden. Zwischen einzelnen Unternehmensbereichen und verschiedenen Branchen zeigen sich gewisse Unterschiede in der Nutzungsphase. Die Befragten stehen aktuell vor der Nutzung von vor allem teilautonomen KI-Systemen, die ausführende und analytische menschliche Tätigkeitenbeispielsweise durch Informationsbereitstellungen unterstützen. Wesentliche Ziele der KI-Nutzung sind die Effizienzsteigerung, Qualitätsverbesserung, Entscheidungsoptimierung sowie Unterstützung der Arbeitnehmer*innen. Allerdings werden in allen Unternehmen die fehlende Expertise sowie insgesamt die Komplexität des Themenfelds als Hinderungsgründe identifiziert.  In allen Unternehmen und allen Unternehmensbereichen werden hohe Auswirkungen durch KI erwartet. Auf die Arbeitsgestaltung werden insgesamt eher positive Auswirkungen erwartet. Die Befragten schätzen die Bedeutung von KI, ihre Aufgeschlossenheit sowie ihr Vertrauen gegenüber KI als insgesamt hoch ein, ihr Verständnis von KI dagegen eher als gering. Tendenziell zeigt sich eine große Diskrepanz zwischen Selbst- und Fremdbild mit einer teils deutlich negativeren Wahrnehmung anderer. Die Befragten erwarten außerdem steigende Kompetenzanforderungen sowie einen hohen Weiterbildungsbedarf, insbesondere bezüglich des grundlegenden Verständnisses über KI. In den wenigsten Unternehmen existiert jedoch ein gezieltes Weiterbildungsangebot.  Die Erkenntnisse aus der Befragung fließen im Rahmen des Kompetenzzentrums Arbeitswelt.Plus in die gezielte Gestaltung und Einführung KI-gestützter Arbeitsformen sowie bedarfsgerechter Unterstützungsangebote ein. Die hohe Komplexität der KI-Einführung sowie die sowohl technischen als auch mitarbeiterbezogenen Herausforderungen verdeutlichen den Bedarf für eine soziotechnische Perspektive und ein systematisches Vorgehen bei der Gestaltung dieses vielschichtigen Themenfelds.</jats:p>}},
  author       = {{Papenkordt, Jörg and Gabriel, Stefan and Thommes, Kirsten and Dumitrescu, Roman}},
  publisher    = {{Kompetenzzentrum Arbeitswelt.Plus}},
  title        = {{{Künstliche Intelligenz in der industriellen Arbeitswelt}}},
  doi          = {{10.55594/tmao3234}},
  year         = {{2022}},
}

@article{44637,
  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         = {{2022}},
}

@article{34191,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>Since the seminal work of Albert and Whetten, the organizational identity concept has become ubiquitous and highly relevant in various fields. This study systematically reviews how Albert and Whetten’s concept of organizational identity has been disseminated in different research areas. It employs quantitative (topic modeling) and qualitative text analysis, as well as a network analysis to examine a sample of 1,041 papers published between 1985 and mid-2022 that cite Albert and Whetten’s seminal work. Using this method of systematic literature analysis, the current study investigates the criteria of the basic definition and hypotheses mentioned in their work that contribute to its increasing significance, and those with the potential to become substantial aspects of future organizational identity research. Accordingly, Albert and Whetten’s conceptualization of organizational identity is often partially adopted in the literature. Thus, this study contributes to organizational identity research by unveiling further research questions on the evolving character of organizational identity, research methodology, and quantitative operationalization, on the basis of Albert and Whetten’s organizational identity conceptualization.</jats:p>}},
  author       = {{Knorr, Karin and Hein-Pensel, Franziska}},
  issn         = {{2198-1620}},
  journal      = {{Management Review Quarterly}},
  keywords     = {{Strategy and Management, Business, Management and Accounting (miscellaneous)}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Since Albert and Whetten: the dissemination of Albert and Whetten’s conceptualization of organizational identity}}},
  doi          = {{10.1007/s11301-022-00311-7}},
  year         = {{2022}},
}

@article{34295,
  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}},
  year         = {{2022}},
}

@article{44636,
  author       = {{Hoppe, Julia A. 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         = {{2022}},
}

@article{32273,
  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}},
}

@article{46290,
  author       = {{Caruso, Carina and Drossel, Kerstin and Heldt, M}},
  journal      = {{Lehrerbildung auf dem Prüfstand}},
  number       = {{2}},
  pages        = {{347--361}},
  title        = {{{Zum Ausmaß und Zusammenhang der unterrichtsbezogenen Nutzung digitaler Medien mit der Medienkompetenz von Lehrkräften unter Berücksichtigung von Hintergrundmerkmalen}}},
  volume       = {{15}},
  year         = {{2022}},
}

@techreport{47094,
  author       = {{Bartels, Lara and Kesternich, Martin}},
  issn         = {{1556-5068}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  publisher    = {{ZEW Discussion Paper 22-040}},
  title        = {{{Motivate the Crowd or Crowd-Them Out? The Impact of Local Government Spending on the Voluntary Provision of a Green Public Good}}},
  doi          = {{10.2139/ssrn.4251592}},
  year         = {{2022}},
}

@techreport{47096,
  author       = {{Chlond, Bettina and Goeschl, Timo and Kesternich, Martin}},
  issn         = {{1556-5068}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  publisher    = {{ZEW Discussion Paper  22-020}},
  title        = {{{More Money or Better Procedures? Evidence From an Energy Efficiency Assistance Program}}},
  doi          = {{10.2139/ssrn.4151557}},
  year         = {{2022}},
}

@techreport{47092,
  author       = {{Kesternich, Martin and Osberghaus, Daniel and Botzen, Willem Jan Wouter}},
  issn         = {{1556-5068}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  publisher    = {{ZEW Discussion Paper 22-055}},
  title        = {{{The Intention-Behavior Gap in Climate Change Adaptation}}},
  doi          = {{10.2139/ssrn.4288341}},
  year         = {{2022}},
}

@techreport{47080,
  author       = {{Kesternich, Martin and von Graevenitz, Kathrine}},
  publisher    = {{ifo Schnelldienst 11/2022, 21-24}},
  title        = {{{Gas- statt Preisbremse: Wie die Umsetzung von Unterstützungsprogrammen zum Gassparen für Haushalte und Unternehmen gelingen kann}}},
  year         = {{2022}},
}

@techreport{47097,
  author       = {{Chlond, Bettina and Goeschl, Timo  and Kesternich, Martin}},
  publisher    = {{ZEW Policy Brief 22-01}},
  title        = {{{Wie lässt sich die Energieeffizienz in einkommensschwachen Haushalten steigern?}}},
  year         = {{2022}},
}

@inproceedings{30883,
  author       = {{Krings, Sarah Claudia and Yigitbas, Enes and Biermeier, Kai and Engels, Gregor}},
  booktitle    = {{Proceedings of the 14th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2022)}},
  title        = {{{Design and Evaluation of AR-Assisted End-User Robot Path Planning Strategies}}},
  year         = {{2022}},
}

@proceedings{41170,
  editor       = {{Hartung, Olaf}},
  location     = {{Paderborn}},
  publisher    = {{Universität Paderborn, Historisches Institut, Abt. Theorie und Didaktik der Geschichte}},
  title        = {{{Konzeptpapier zur Konferenz "Geschichte im digitalen Wandel? Geschichtskultur – Erinnerungspraktiken – Historisches Lernen"}}},
  year         = {{2022}},
}

@article{49554,
  author       = {{Steigerwald, Jörn}},
  journal      = {{Romanistische Zeitschrift für Literaturgeschichte}},
  number       = {{3/4}},
  pages        = {{355--370}},
  title        = {{{Post-rousseauistische Kulturanthropologie: Francois-René de Chateaubriands Erzählung "Atala" }}},
  volume       = {{46}},
  year         = {{2022}},
}

@inproceedings{48861,
  abstract     = {{Generating instances of different properties is key to algorithm selection methods that differentiate between the performance of different solvers for a given combinatorial optimization problem. A wide range of methods using evolutionary computation techniques has been introduced in recent years. With this paper, we contribute to this area of research by providing a new approach based on quality diversity (QD) that is able to explore the whole feature space. QD algorithms allow to create solutions of high quality within a given feature space by splitting it up into boxes and improving solution quality within each box. We use our QD approach for the generation of TSP instances to visualize and analyze the variety of instances differentiating various TSP solvers and compare it to instances generated by established approaches from the literature.}},
  author       = {{Bossek, Jakob and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-9237-2}},
  keywords     = {{instance features, instance generation, quality diversity, TSP}},
  pages        = {{186–194}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Exploring the Feature Space of TSP Instances Using Quality Diversity}}},
  doi          = {{10.1145/3512290.3528851}},
  year         = {{2022}},
}

@inproceedings{48868,
  author       = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}},
  isbn         = {{978-1-4503-9268-6}},
  pages        = {{824–842}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Evolutionary Diversity Optimization for Combinatorial Optimization: Tutorial at GECCO’22, Boston, USA}}},
  doi          = {{10.1145/3520304.3533626}},
  year         = {{2022}},
}

@inproceedings{48882,
  abstract     = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}},
  author       = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}},
  booktitle    = {{Parallel Problem Solving from Nature (PPSN XVII)}},
  editor       = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}},
  isbn         = {{978-3-031-14714-2}},
  keywords     = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}},
  pages        = {{192–206}},
  publisher    = {{Springer International Publishing}},
  title        = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}},
  doi          = {{10.1007/978-3-031-14714-2_14}},
  year         = {{2022}},
}

@inproceedings{48894,
  abstract     = {{Recently different evolutionary computation approaches have been developed that generate sets of high quality diverse solutions for a given optimisation problem. Many studies have considered diversity 1) as a mean to explore niches in behavioural space (quality diversity) or 2) to increase the structural differences of solutions (evolutionary diversity optimisation). In this study, we introduce a co-evolutionary algorithm to simultaneously explore the two spaces for the multi-component traveling thief problem. The results show the capability of the co-evolutionary algorithm to achieve significantly higher diversity compared to the baseline evolutionary diversity algorithms from the literature.}},
  author       = {{Nikfarjam, Adel and Neumann, Aneta and Bossek, Jakob and Neumann, Frank}},
  booktitle    = {{Parallel Problem Solving from Nature (PPSN XVII)}},
  editor       = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tu\v sar, Tea}},
  isbn         = {{978-3-031-14714-2}},
  keywords     = {{Co-evolutionary algorithms, Evolutionary diversity optimisation, Quality diversity, Traveling thief problem}},
  pages        = {{237–249}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem}}},
  doi          = {{10.1007/978-3-031-14714-2_17}},
  year         = {{2022}},
}

@article{48878,
  abstract     = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse\textemdash ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}},
  author       = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}},
  issn         = {{2076-3417}},
  journal      = {{Applied Sciences}},
  keywords     = {{big data, data mining, data stream analysis, machine learning, stream classification, supervised learning}},
  number       = {{18}},
  pages        = {{9094}},
  publisher    = {{{Multidisciplinary Digital Publishing Institute}}},
  title        = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}},
  doi          = {{10.3390/app12189094}},
  volume       = {{12}},
  year         = {{2022}},
}