M.Sc. Philipp Reinhard

Wissenschaftlicher Mitarbeiter

Telefon: +49 561 804-6021
E-Mail: philipp.reinhard[at]uni-kassel[dot]de

Standort: Pfannkuchstraße 1

Raum: ITeG, Raum 1170, 1. Etage

Sprechstunde: Mi, 13:00-14:00 Anmeldung per E-Mail erforderlich. Weitere Termine nach Vereinbarung.

Lebenslauf

Ausbildung

(10/2019 – 05/2022) Studium M.Sc. in Wirtschaftsinformatik an der Technischen Universität Darmstadt

(04/2021 – 05/2022) Studium M.Sc. in "Entrepreneurship and Innovation Management” an der Technischen Universität Darmstadt

Thema der Masterarbeit: „Customer Education in Smart Grids: Facilitating the adoption of innovation for energy efficiency and flexibility“

(08/2020 – 06/2021) Erasmus-Programm an der Chalmers University of Technology in Göteborg, Schweden

(10/2015 – 03/2019) Studium B.Sc.in Wirtschaftsinformatik an der Technischen Universität Darmstadt

Thema der Bachelorarbeit: „Analyse und Abbildung von hybriden Ansätzen im Projekt- und Programmmanagement am Beispiel von cloudbasierten Microsoft Lösungen“

Arbeitserfahrung

(06/2016 – 03/2017) Werkstudent im Online Marketing (SEO) bei Re7Consulting UG (haftungsbeschränkt) in Fulda 

(04/2016 – 03/2018) Studentischer Unternehmensberater bei Junior Comtec in Darmstadt mit Fokus auf IT-getriebene Prozessoptimierungen (insgesamt 5 Projekte, davon 3 als Projektleiter)  

Projekte:

Supplier Relationship Management (Procurement 4.0)
Re-Engineering von Geschäftsprozessen
Optimierung und Automatisierung des Zielvereinbarungsprozesses

(04/2018 – 10/2018) Bachelorand im Enterprise Project Management (insbesondere “hybride Projektmanagement-Ansätze“) bei Campana & Schott in Frankfurt 

(11/2018 – 11/2020) Werkstudent im Enterprise Project Management bei Campana & Schott in Frankfurt

(08/2021 – 04/2022) Studentischer Mitarbeiter am IÖW im Forschungsfeld Umweltökonomie und Umweltpolitik in Berlin Projekt: SustAIn - Sustainability Index for Artificial Intelligence 

Forschungsinteressen

Hybride Intelligenz (Human-AI Collaboration)
(Nachhaltige) Künstliche Intelligenz
Chatbots, Voicebots und Conversational AI
Service Science, Service Engineering (insbesondere IT Service Management)
Digitale Arbeit und agile Organisation
IT Projektmanagement & Softwareentwicklung

Publikationen

2024[ to top ]

Reinhard, P.; Li, M. M.; Peters, C.; & Leimeister, J. M. (2024): Generative AI in Customer Support Services: A Framework for Augmenting the Routines of Frontline Service Employees. In: Hawaii International Conference on System Sciences (HICSS). Waikiki, Hawaii, USA.
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@inproceedings{ls_leimeister, abstract = {Customer support service employees are facing an increased workload, while artificial intelligence (AI) appears to possess the potential to change the way we work. With the advent of modern types of generative AI, new opportunities to augment frontline service employees have emerged. However, little is known about how to integrate generative AI in customer support service organizations and purposefully change service employee work routines. Following multi-method qualitative research, we performed a literature review, conducted workshops, and interviewed IT support agents, managers, and AI experts. Thereby, we examine AI augmentation for frontline service employees in the context of IT support to carve out where and how GenAI can be leveraged to develop more efficient and higher-quality customer support. Our resulting framework reveals that especially adapting solutions and retaining knowledge is subject to a high degree of AI augmentation.}, address = {Waikiki, Hawaii, USA}, author = {Reinhard, Philipp and Li, Mahei Manhai and Peters, Christoph and Leimeister, Jan Marco}, booktitle = {Hawaii International Conference on System Sciences (HICSS)}, keywords = {itegpub}, title = {Generative AI in Customer Support Services: A Framework for Augmenting the Routines of Frontline Service Employees}, year = 2024 }
%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Li, Mahei Manhai %A Peters, Christoph %A Leimeister, Jan Marco %B Hawaii International Conference on System Sciences (HICSS) %C Waikiki, Hawaii, USA %D 2024 %T Generative AI in Customer Support Services: A Framework for Augmenting the Routines of Frontline Service Employees %U https://pubs.wi-kassel.de/wp-content/uploads/2023/12/JML_956.pdf %X Customer support service employees are facing an increased workload, while artificial intelligence (AI) appears to possess the potential to change the way we work. With the advent of modern types of generative AI, new opportunities to augment frontline service employees have emerged. However, little is known about how to integrate generative AI in customer support service organizations and purposefully change service employee work routines. Following multi-method qualitative research, we performed a literature review, conducted workshops, and interviewed IT support agents, managers, and AI experts. Thereby, we examine AI augmentation for frontline service employees in the context of IT support to carve out where and how GenAI can be leveraged to develop more efficient and higher-quality customer support. Our resulting framework reveals that especially adapting solutions and retaining knowledge is subject to a high degree of AI augmentation.

2023[ to top ]

Langes, B.; Peters, C.; Reinhard, P.; Li, M. M.; Hermann, S.; Katzenberger, R.; … Leimeister, J. M. (2023): Hybrid Intelligence Service Support. In: Langes, B., Peters, C., Boes, A., & Leimeister, J. M. (Eds.), Innovationen für datenbasierte Wertschöpfung. Mit internetbasierten Dienstleistungen die Zukunft der Wirtschaft gestalten (pp. 37–49). München: ISF München.
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@incollection{ls_leimeister, address = {München}, author = {Langes, Barbara and Peters, Christoph and Reinhard, Philipp and Li, Mahei Manhai and Hermann, Sophia and Katzenberger, Rebecca and Boes, Andreas and Leimeister, Jan Marco}, booktitle = {Innovationen für datenbasierte Wertschöpfung. Mit internetbasierten Dienstleistungen die Zukunft der Wirtschaft gestalten}, editor = {Langes, Barbara and Peters, Christoph and Boes, Andreas and Leimeister, Jan Marco}, keywords = {Hybride_Intelligenz}, pages = {37-49}, publisher = {ISF München}, title = {Hybrid Intelligence Service Support}, year = 2023 }
%0 Book Section %1 ls_leimeister %A Langes, Barbara %A Peters, Christoph %A Reinhard, Philipp %A Li, Mahei Manhai %A Hermann, Sophia %A Katzenberger, Rebecca %A Boes, Andreas %A Leimeister, Jan Marco %B Innovationen für datenbasierte Wertschöpfung. Mit internetbasierten Dienstleistungen die Zukunft der Wirtschaft gestalten %C München %D 2023 %E Langes, Barbara %E Peters, Christoph %E Boes, Andreas %E Leimeister, Jan Marco %I ISF München %P 37-49 %R 10.36194/ING_2023 %T Hybrid Intelligence Service Support %U https://pubs.wi-kassel.de/wp-content/uploads/2023/07/JML_941.pdf
Li, M. M.; Reinhard, P.; Oeste-Reiss, S.; Peters, C.; & Leimeister, J. M. (2023): A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study. In: Information Systems (IS), Erscheinungsjahr/Year: 2023. p. 102311.
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@article{ls_leimeister, abstract = {The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. The improved labels can be utilized to continuously enhance the AI system as rewards showed increasing growth with decreasing marginal returns. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems.}, author = {Li, Mahei Manhai and Reinhard, Philipp and Oeste-Reiss, Sarah and Peters, Christoph and Leimeister, Jan Marco}, journal = {Information Systems (IS)}, keywords = {IT_support}, pages = 102311, title = {A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study}, year = 2023 }
%0 Journal Article %1 ls_leimeister %A Li, Mahei Manhai %A Reinhard, Philipp %A Oeste-Reiss, Sarah %A Peters, Christoph %A Leimeister, Jan Marco %D 2023 %J Information Systems (IS) %P 102311 %R 10.1016/j.is.2023.102311 %T A Value Co-Creation Perspective on Data Labeling in Hybrid Intelligence Systems: A Design Study %U https://doi.org/10.1016/j.is.2023.102311 %X The adoption of innovative technologies confronts IT-Service-Management (ITSM) with an increasing volume and variety of requests. Artificial intelligence (AI) possesses the potential to augment customer service employees. However, the training data for AI systems are annotated by domain experts with little interest in labeling correctly due to their limited perceived value. Ultimately, insufficient labeled data leads to diminishing returns in AI performance. Following a design science research approach, we provide a novel human-in-the-loop (HIL) design for ITSM support ticket recommendations by incorporating a value co-creation perspective. The design incentivizes ITSM agents to provide labels during their everyday ticket-handling procedures. We develop a functional prototype based on 17,120 support tickets provided by a pilot partner as an instantiation and evaluate the design through accuracy metrics and user evaluations. Our evaluation revealed that recommendations after label improvement showed increased user ratings, and users are willing to contribute their domain knowledge. The improved labels can be utilized to continuously enhance the AI system as rewards showed increasing growth with decreasing marginal returns. Overall, our results emphasize agents' need for value-in-use by providing better results if they improve the labeling of support tickets pre-labeled by AI. Thus, we provide prescriptive knowledge of a novel HIL design that enables efficient and interactive labeling in the context of diverse applications of reinforcement learning systems.
Reinhard, P.; Wischer, D.; Verlande, L.; Neis, N.; & Li, M. M. (2023): Towards designing an AI-based conversational agent for on-the-job training of customer support novices. In: International Conference on Design Science Research (DESRIST). Pretoria, South Africa.
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@inproceedings{ls_leimeister, abstract = {Due to the high drop-out rates in IT support desks, efficient onboarding of novices becomes a relevant and recurring challenge. Especially in the case of IT support, solving technical issues and service requests while the conversation with the customer is still ongoing imposes high demands on novice support agents. As artificial intelligence (AI) can already classify service requests and help find solutions, AIbased augmentation holds great potential for improving the onboarding phase and reducing time-to-performance. For this reason, we propose an AI-based conversational (co-)agent during the onboarding phase of customer support novices to reduce the time spent on service tasks and enable on-the-job training. Following action design research, we aim to develop an instantiation of an AI-based co-agent to reduce the job demand for the service center agent novices and augment problem-solving capabilities by considering cognitive load. The co-agent will be implemented with one development partner and evaluated with two different case partner organizations. In this research-in-progress project, we developed a low-fidelity prototype and derived a tentative architecture that allows for a generalized development of such conversational agents in customer service organizations.}, address = {Pretoria, South Africa}, author = {Reinhard, Philipp and Wischer, Dennis and Verlande, Lisa and Neis, Nicolas and Li, Mahei Manhai}, booktitle = {International Conference on Design Science Research (DESRIST)}, keywords = {itegpub}, title = {Towards designing an AI-based conversational agent for on-the-job training of customer support novices}, year = 2023 }
%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Wischer, Dennis %A Verlande, Lisa %A Neis, Nicolas %A Li, Mahei Manhai %B International Conference on Design Science Research (DESRIST) %C Pretoria, South Africa %D 2023 %T Towards designing an AI-based conversational agent for on-the-job training of customer support novices %U https://pubs.wi-kassel.de/wp-content/uploads/2023/06/JML_931.pdf %X Due to the high drop-out rates in IT support desks, efficient onboarding of novices becomes a relevant and recurring challenge. Especially in the case of IT support, solving technical issues and service requests while the conversation with the customer is still ongoing imposes high demands on novice support agents. As artificial intelligence (AI) can already classify service requests and help find solutions, AIbased augmentation holds great potential for improving the onboarding phase and reducing time-to-performance. For this reason, we propose an AI-based conversational (co-)agent during the onboarding phase of customer support novices to reduce the time spent on service tasks and enable on-the-job training. Following action design research, we aim to develop an instantiation of an AI-based co-agent to reduce the job demand for the service center agent novices and augment problem-solving capabilities by considering cognitive load. The co-agent will be implemented with one development partner and evaluated with two different case partner organizations. In this research-in-progress project, we developed a low-fidelity prototype and derived a tentative architecture that allows for a generalized development of such conversational agents in customer service organizations.
Reinhard, P.; Li, M. M.; Dickhaut, E.; Peters, C.; & Leimeister, J. M. (2023): Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-based Textual Data Quality Enrichment. In: Gerber, A., & Baskerville, R. (Eds.), International Conference on Design Science Research (DESRIST). Pretoria, South Africa.
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@inproceedings{ls_leimeister, abstract = {AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher pre-diction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of as-sessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems.}, address = {Pretoria, South Africa}, author = {Reinhard, Philipp and Li, Mahei Manhai and Dickhaut, Ernestine and Peters, Christoph and Leimeister, Jan Marco}, booktitle = {International Conference on Design Science Research (DESRIST)}, editor = {Gerber, Aurona and Baskerville, Richard}, keywords = {itegpub}, title = {Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-based Textual Data Quality Enrichment}, year = 2023 }
%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Li, Mahei Manhai %A Dickhaut, Ernestine %A Peters, Christoph %A Leimeister, Jan Marco %B International Conference on Design Science Research (DESRIST) %C Pretoria, South Africa %D 2023 %E Gerber, Aurona %E Baskerville, Richard %T Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-based Textual Data Quality Enrichment %U https://pubs.wi-kassel.de/wp-content/uploads/2023/06/JML_932.pdf %X AI-based recommendation systems to augment working conditions in the field of IT service management (ITSM) have attracted new attention. However, many IT support organizations possess high volumes of tickets but are confronted with low quality, to which they train the underlying models of their AI systems. In particular, support tickets are documented insufficiently due to time pressure and lack of motivation. Following design science research, we design and evaluate an analytics pipeline to address the data quality issue. The pipeline can be applied to assess and extract high-quality support tickets for subsequent model training and operation. Based on a data set of 60.000 real-life support tickets from a manufacturing company, we develop the artifact, instantiate a recommender system and achieve a higher pre-diction performance in comparison to naïve enrichment methods. In terms of data management literature, we contribute to the understanding of as-sessing textual ticket data quality. By deriving a pipeline reference model, we move towards a general approach to designing machine learning-driven data quality analytics pipelines for attached recommender systems.
Reinhard, P.; Li, M. M.; Dickhaut, E.; Reh, C.; Peters, C.; & Leimeister, J. M. (2023): A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-Creation Perspective. In: Gerber, A., & Baskerville, R. (Eds.), International Conference on Design Science Research (DESRIST). Pretoria, South Africa.
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@inproceedings{ls_leimeister, abstract = {Artificial intelligence (AI) possesses the potential to augment customer service employees e.g. via decision support or solution recommendations. Still, its underlying data for training and testing the AI systems is provided by human annotators through human-in-the-loop configurations. However, due to the high effort for annotators and lack of incentives, AI systems face low underlying data quality. That in turn results in low prediction performance and limited acceptance by the targeted user group. Faced with the enormous volume and increasing complexity of service requests, IT service management (ITSM) especially, relies on high data quality for AI systems and in-corporating domain-specific knowledge. By analyzing the existing labeling process in that specific case, we design a revised to-be process and develop a conceptual model from a value co-creation perspective. Finally, a functional prototype as an instantiation in the ITSM domain is implemented and evaluated through accuracy metrics and user evaluation. The results show that the new process increases the perceived value of both labeling quality and the perceived prediction quality. Thus, we contribute a conceptual model that supports the systematic design of efficient and interactive labeling processes in diverse applications of reinforcement learning systems.}, address = {Pretoria, South Africa}, author = {Reinhard, Philipp and Li, Mahei Manhai and Dickhaut, Ernestine and Reh, Cornelius and Peters, Christoph and Leimeister, Jan Marco}, booktitle = {International Conference on Design Science Research (DESRIST)}, editor = {Gerber, Aurona and Baskerville, Richard}, keywords = {Interactive_labeling}, title = {A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-Creation Perspective}, year = 2023 }
%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Li, Mahei Manhai %A Dickhaut, Ernestine %A Reh, Cornelius %A Peters, Christoph %A Leimeister, Jan Marco %B International Conference on Design Science Research (DESRIST) %C Pretoria, South Africa %D 2023 %E Gerber, Aurona %E Baskerville, Richard %T A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-Creation Perspective %U https://pubs.wi-kassel.de/wp-content/uploads/2023/03/JML_923.pdf %X Artificial intelligence (AI) possesses the potential to augment customer service employees e.g. via decision support or solution recommendations. Still, its underlying data for training and testing the AI systems is provided by human annotators through human-in-the-loop configurations. However, due to the high effort for annotators and lack of incentives, AI systems face low underlying data quality. That in turn results in low prediction performance and limited acceptance by the targeted user group. Faced with the enormous volume and increasing complexity of service requests, IT service management (ITSM) especially, relies on high data quality for AI systems and in-corporating domain-specific knowledge. By analyzing the existing labeling process in that specific case, we design a revised to-be process and develop a conceptual model from a value co-creation perspective. Finally, a functional prototype as an instantiation in the ITSM domain is implemented and evaluated through accuracy metrics and user evaluation. The results show that the new process increases the perceived value of both labeling quality and the perceived prediction quality. Thus, we contribute a conceptual model that supports the systematic design of efficient and interactive labeling processes in diverse applications of reinforcement learning systems.

2022[ to top ]

Reinhard, P.; Li, M. M.; & Leimeister, J. M. (2022): Towards an Employee-Centered Design for Human-AI Collaboration: How Work Design Theory Informs the Design of AI Systems. In: Pre-ICIS Workshop on the Changing Nature of Work (SIG 11th CNoW). Copenhagen, Denmark.
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@inproceedings{ls_leimeister, abstract = {IT support is under growing pressure to ensure efficient, flexible, and scalable use of digital technologies (Kumbakara, 2008). As a result, technical support staff is affected by monotonous work and work overload (Schmidt et al., 2022). Our research aims to augment the precarious workplace of support agents with artificial intelligence (AI). To incorporate an employee-centered perspective a priori and ensure positive impacts, we propose a framework for combining work design theory (e.g. Demerouti et al., 2001) and design science theory (Peffers et al., 2007, Niehaves & Ortbach 2016). The advances in AI promise to leverage large potential in optimizing and enhancing service processes and workplaces (Huang & Rust, 2018, de Keyser, 2019). Introducing AI into service processes, imply effects on work characteristics (Larivière et al., 2017). By combining human and artificial intelligence we propose hybrid intelligence (Dellermann et al., 2019) as a suitable solution for mitigating the persistent issues of support workers and the possible negative impacts of AI. To a great extent, IS research emphasizes the implied impacts of AI use in workplaces (Verma & Singh 2022, Wang et al., 2020). As such, work design models are widely used to empirically evaluate the impacts of AI design (Sturm & Peters, 2020), but are rarely utilized to substantiate the design of AI-augmented work systems. Only Poser et al. (2022) and Zschech et al. (2021) recently applied such models. The goal of this paper is to overcome the lack of work design in design science research (DSR) for AI-based systems and to steer the development into desired socio-technological configurations. The here presented work is expected to answer : How can work design theory inform the design of AI-augmented workplaces? [RQ1] How should a hybrid intelligence system be designed to augment IT support agents’ workplaces by incorporating work design theory? [RQ2] To systematically design the integration of AI, we make use of the DSR paradigm (Peffers et al., 2007). We first interview support agents and utilize the organizing move theory of Pentland (1992) and the technical support work theory of Das (2003) to ensure relevance. Based on a review of the IS literature on work design theories, we then derive a preliminary theoretical framework (Paul & Benito, 2018) [RQ1]. The framework represents a kernel theory for the development of meta design requirements. Contributing to the second research question, we design and subsequently evaluate the augmentation based on work-related outcomes [RQ2].}, address = {Copenhagen, Denmark}, author = {Reinhard, Philipp and Li, Mahei Manhai and Leimeister, Jan Marco}, booktitle = {Pre-ICIS Workshop on the Changing Nature of Work (SIG 11th CNoW)}, keywords = {itegpub}, title = {Towards an Employee-Centered Design for Human-AI Collaboration: How Work Design Theory Informs the Design of AI Systems}, year = 2022 }
%0 Conference Paper %1 ls_leimeister %A Reinhard, Philipp %A Li, Mahei Manhai %A Leimeister, Jan Marco %B Pre-ICIS Workshop on the Changing Nature of Work (SIG 11th CNoW) %C Copenhagen, Denmark %D 2022 %T Towards an Employee-Centered Design for Human-AI Collaboration: How Work Design Theory Informs the Design of AI Systems %U https://pubs.wi-kassel.de/wp-content/uploads/2023/02/JML_921.pdf %X IT support is under growing pressure to ensure efficient, flexible, and scalable use of digital technologies (Kumbakara, 2008). As a result, technical support staff is affected by monotonous work and work overload (Schmidt et al., 2022). Our research aims to augment the precarious workplace of support agents with artificial intelligence (AI). To incorporate an employee-centered perspective a priori and ensure positive impacts, we propose a framework for combining work design theory (e.g. Demerouti et al., 2001) and design science theory (Peffers et al., 2007, Niehaves & Ortbach 2016). The advances in AI promise to leverage large potential in optimizing and enhancing service processes and workplaces (Huang & Rust, 2018, de Keyser, 2019). Introducing AI into service processes, imply effects on work characteristics (Larivière et al., 2017). By combining human and artificial intelligence we propose hybrid intelligence (Dellermann et al., 2019) as a suitable solution for mitigating the persistent issues of support workers and the possible negative impacts of AI. To a great extent, IS research emphasizes the implied impacts of AI use in workplaces (Verma & Singh 2022, Wang et al., 2020). As such, work design models are widely used to empirically evaluate the impacts of AI design (Sturm & Peters, 2020), but are rarely utilized to substantiate the design of AI-augmented work systems. Only Poser et al. (2022) and Zschech et al. (2021) recently applied such models. The goal of this paper is to overcome the lack of work design in design science research (DSR) for AI-based systems and to steer the development into desired socio-technological configurations. The here presented work is expected to answer : How can work design theory inform the design of AI-augmented workplaces? [RQ1] How should a hybrid intelligence system be designed to augment IT support agents’ workplaces by incorporating work design theory? [RQ2] To systematically design the integration of AI, we make use of the DSR paradigm (Peffers et al., 2007). We first interview support agents and utilize the organizing move theory of Pentland (1992) and the technical support work theory of Das (2003) to ensure relevance. Based on a review of the IS literature on work design theories, we then derive a preliminary theoretical framework (Paul & Benito, 2018) [RQ1]. The framework represents a kernel theory for the development of meta design requirements. Contributing to the second research question, we design and subsequently evaluate the augmentation based on work-related outcomes [RQ2].

2021[ to top ]

Rohde, F.; Wagner, J.; Reinhard, P.; Petschow, U.; Meyer, A.; Voß, M.; & Mollen, A. (2021): Nachhaltigkeitskriterien für künstliche Intelligenz - Entwicklung eines Kriterien- und Indikatorensets für die Nachhaltigkeitsbewertung von KI-Systemen entlang des LebenszyklusSchriftenreihe des IÖW. Berlin: Institut für ökologische Wirtschaftsforschung.
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@book{ls_leimeister, abstract = {Technische Dynamiken, die zunehmende Durchdringung vieler gesellschaftlicher und wirtschaftlicher Bereiche mit Informations- und Kommunikationstechnologien sowie die Verfügbarkeit großer Rechenkapazitäten zur Datenverarbeitung haben zur Entwicklung immer leistungsfähigerer Modelle des maschinellen Lernens (ML) geführt. Diese werden häufig mit dem Überbegriff „Künstliche Intelligenz“ bezeichnet, obwohl sie korrekterweise nur als schwache künstliche Intelligenz einzustufenen sind. Technologien basierend auf ML werden mittlerweile in vielen gesellschaftlichen Bereichen und Industriesektoren eingesetzt und sind mit großen Erwartungen in Bezug auf die Optimierung von Prozessen oder der Entscheidungsfindung verbunden. Weil die zugrundeliegenden Modelle für sehr unterschiedliche Optimierungsaufgaben eingesetzt werden können, werden sie auch als general purpose technology bezeichnet. Die zunehmende Nutzung dieser immer komplexer werdenden Systeme hat weltweit Debatten über diskriminierende Effekte, intransparente Entscheidungs- und Optimierungsprozesse oder die Reproduktion gesellschaftlicher Ungleichheiten aufgeworfen. Zunehmend werden auch die Energieverbräuche und Treibhausgasemissionen in der KI-Modellentwicklung und -anwendung diskutiert sowie weitreichendere Folgen auf Arbeitsmärkte, Konsummuster oder die Marktmacht großer Unternehmen. Dementsprechend sind Systeme künstlicher Intelligenz mit vielfältigen gesellschaftlichen, ökologischen und ökonomischen Herausforderungen verbunden. Dieses Diskussionspapier entwickelt eine übergreifende Nachhaltigkeitsperspektive auf künstliche Intelligenz und basiert auf den Arbeiten im Forschungsprojekt „SustAIn – Nachhaltigkeitsindex für Künstliche Intelligenz“. Das Ziel ist, aktuelle Diskussionen zu verantwortungsvoller KI aufzugreifen und zu einer übergreifenden Perspektive auf nachhaltige KI zu erweitern. Die Gestaltung dieser sozio-technischen Systeme ist dabei genauso relevant wie die Auswirkungen ihrer Anwendung. Betrachtet werden die sozialen, ökologischen und ökonomischen Auswirkungen entlang des gesamten KI-Lebenszyklus sowie die organisationale Einbettung dieser Systeme. Dafür werden zunächst die begrifflichen Grundlagen erläutert und eine Nachhaltigkeitsperspektive auf KI entwickelt. Basierend auf einer Analyse bestehender wissenschaftlicher und gesellschaftlicher Diskurse über die sozialen, ökologischen und ökonomischen Auswirkungen von KI werden dreizehn Nachhaltigkeitskriterien mit entsprechenden Nachhaltigkeitsindikatoren vorgeschlagen sowie Querschnittsindikatoren, die sich neben weiteren Indikatoren auf die organisationale Einbettung dieser Systeme beziehen. Abschließend wird skizziert, wie dieses Kriterien- und Indikatorenset im weiteren Forschungsprozess in Bewertungsinstrumente übersetzt wird. Ziel der hier vorgestellten Forschung ist es, den gesellschaftlichen Diskurs zur Nachhaltigkeit von KI-Systemen zu stärken sowie eine systematische Nachhaltigkeitsbewertung von KI-basierten Systemen zu ermöglichen, um deren Entwicklung und Nutzung im Sinne der Nachhaltigkeit zu stärken.}, address = {Berlin}, author = {Rohde, Friederike and Wagner, Josephin and Reinhard, Philipp and Petschow, Ulrich and Meyer, Andreas and Voß, Marcus and Mollen, Anne}, booktitle = {Schriftenreihe des IÖW}, keywords = {Nachhaltigkeit}, number = 220, publisher = {Institut für ökologische Wirtschaftsforschung}, series = {IÖW-Schriftenreihe}, title = {Nachhaltigkeitskriterien für künstliche Intelligenz - Entwicklung eines Kriterien- und Indikatorensets für die Nachhaltigkeitsbewertung von KI-Systemen entlang des Lebenszyklus}, year = 2021 }
%0 Book %1 ls_leimeister %A Rohde, Friederike %A Wagner, Josephin %A Reinhard, Philipp %A Petschow, Ulrich %A Meyer, Andreas %A Voß, Marcus %A Mollen, Anne %B Schriftenreihe des IÖW %C Berlin %D 2021 %I Institut für ökologische Wirtschaftsforschung %N 220 %T Nachhaltigkeitskriterien für künstliche Intelligenz - Entwicklung eines Kriterien- und Indikatorensets für die Nachhaltigkeitsbewertung von KI-Systemen entlang des Lebenszyklus %U https://pubs.wi-kassel.de/wp-content/uploads/2022/08/JML_883.pdf %X Technische Dynamiken, die zunehmende Durchdringung vieler gesellschaftlicher und wirtschaftlicher Bereiche mit Informations- und Kommunikationstechnologien sowie die Verfügbarkeit großer Rechenkapazitäten zur Datenverarbeitung haben zur Entwicklung immer leistungsfähigerer Modelle des maschinellen Lernens (ML) geführt. Diese werden häufig mit dem Überbegriff „Künstliche Intelligenz“ bezeichnet, obwohl sie korrekterweise nur als schwache künstliche Intelligenz einzustufenen sind. Technologien basierend auf ML werden mittlerweile in vielen gesellschaftlichen Bereichen und Industriesektoren eingesetzt und sind mit großen Erwartungen in Bezug auf die Optimierung von Prozessen oder der Entscheidungsfindung verbunden. Weil die zugrundeliegenden Modelle für sehr unterschiedliche Optimierungsaufgaben eingesetzt werden können, werden sie auch als general purpose technology bezeichnet. Die zunehmende Nutzung dieser immer komplexer werdenden Systeme hat weltweit Debatten über diskriminierende Effekte, intransparente Entscheidungs- und Optimierungsprozesse oder die Reproduktion gesellschaftlicher Ungleichheiten aufgeworfen. Zunehmend werden auch die Energieverbräuche und Treibhausgasemissionen in der KI-Modellentwicklung und -anwendung diskutiert sowie weitreichendere Folgen auf Arbeitsmärkte, Konsummuster oder die Marktmacht großer Unternehmen. Dementsprechend sind Systeme künstlicher Intelligenz mit vielfältigen gesellschaftlichen, ökologischen und ökonomischen Herausforderungen verbunden. Dieses Diskussionspapier entwickelt eine übergreifende Nachhaltigkeitsperspektive auf künstliche Intelligenz und basiert auf den Arbeiten im Forschungsprojekt „SustAIn – Nachhaltigkeitsindex für Künstliche Intelligenz“. Das Ziel ist, aktuelle Diskussionen zu verantwortungsvoller KI aufzugreifen und zu einer übergreifenden Perspektive auf nachhaltige KI zu erweitern. Die Gestaltung dieser sozio-technischen Systeme ist dabei genauso relevant wie die Auswirkungen ihrer Anwendung. Betrachtet werden die sozialen, ökologischen und ökonomischen Auswirkungen entlang des gesamten KI-Lebenszyklus sowie die organisationale Einbettung dieser Systeme. Dafür werden zunächst die begrifflichen Grundlagen erläutert und eine Nachhaltigkeitsperspektive auf KI entwickelt. Basierend auf einer Analyse bestehender wissenschaftlicher und gesellschaftlicher Diskurse über die sozialen, ökologischen und ökonomischen Auswirkungen von KI werden dreizehn Nachhaltigkeitskriterien mit entsprechenden Nachhaltigkeitsindikatoren vorgeschlagen sowie Querschnittsindikatoren, die sich neben weiteren Indikatoren auf die organisationale Einbettung dieser Systeme beziehen. Abschließend wird skizziert, wie dieses Kriterien- und Indikatorenset im weiteren Forschungsprozess in Bewertungsinstrumente übersetzt wird. Ziel der hier vorgestellten Forschung ist es, den gesellschaftlichen Diskurs zur Nachhaltigkeit von KI-Systemen zu stärken sowie eine systematische Nachhaltigkeitsbewertung von KI-basierten Systemen zu ermöglichen, um deren Entwicklung und Nutzung im Sinne der Nachhaltigkeit zu stärken. %@ 978-3-940920-24-9
Hildebrand, A.; Jakopin, N.; Reinhard, P.; & Riegel, L. (2021): Der Smart-City-Markt in Deutschland: 2021-2026 (1st ed.).
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@book{ls_leimeister, abstract = {Die Corona-Pandemie, Klimaschutzziele, die zunehmende Urbanisierung und der internationale Wettbewerb um technologische Trends und Standortvorteile für Industrie stimulieren die Digitalisierung großer Teile der Gesellschaft. Städte und Kommunen stellt diese Entwicklung vor große Herausforderungen hinsichtlich der Priorisierung und Finanzierung von Projekten rund um das Internet der Dinge (englisch Internet of Things, kurz: IoT) und die Smart City. Das Konzept der Smart City hat sich in den letzten Jahren zu einem Treiber der Automatisierung von zentralen Säulen des gesellschaftlichen und wirtschaftlichen Zusammenlebens entwickelt. Mit der Studie „Der deutsche Smart-City-Markt 2021–2026“ haben eco – Verband der Internetwirtschaft e. V. – und Arthur D. Little anhand von Analysen und Experteninterviews untersucht, wie sich der Markt und dessen Segmente in den kommenden fünf Jahren entwickeln werden, welchen Effekt die Smart City auf Klimaziele haben wird und welche Implikationen sich daraus für Städte und Unternehmen ergeben.}, author = {Hildebrand, Anna and Jakopin, Nejc and Reinhard, Philipp and Riegel, Lars}, edition = 1, keywords = {Nachhaltigkeit}, title = {Der Smart-City-Markt in Deutschland: 2021-2026}, year = 2021 }
%0 Book %1 ls_leimeister %A Hildebrand, Anna %A Jakopin, Nejc %A Reinhard, Philipp %A Riegel, Lars %D 2021 %T Der Smart-City-Markt in Deutschland: 2021-2026 %U https://pubs.wi-kassel.de/wp-content/uploads/2022/08/JML_884.pdf %X Die Corona-Pandemie, Klimaschutzziele, die zunehmende Urbanisierung und der internationale Wettbewerb um technologische Trends und Standortvorteile für Industrie stimulieren die Digitalisierung großer Teile der Gesellschaft. Städte und Kommunen stellt diese Entwicklung vor große Herausforderungen hinsichtlich der Priorisierung und Finanzierung von Projekten rund um das Internet der Dinge (englisch Internet of Things, kurz: IoT) und die Smart City. Das Konzept der Smart City hat sich in den letzten Jahren zu einem Treiber der Automatisierung von zentralen Säulen des gesellschaftlichen und wirtschaftlichen Zusammenlebens entwickelt. Mit der Studie „Der deutsche Smart-City-Markt 2021–2026“ haben eco – Verband der Internetwirtschaft e. V. – und Arthur D. Little anhand von Analysen und Experteninterviews untersucht, wie sich der Markt und dessen Segmente in den kommenden fünf Jahren entwickeln werden, welchen Effekt die Smart City auf Klimaziele haben wird und welche Implikationen sich daraus für Städte und Unternehmen ergeben. %7 1 %@ 978-3-9821487-8-6