Now showing 1 - 4 of 4
  • Publication
    Towards designing an AI-based conversational agent for on-the-job training of customer support novices
    ( 2023-06-02)
    Reinhard, Philipp
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    Wischer, Dennis
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    Verlande, Lisa
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    Neis, Nicolas
    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.
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  • Publication
    Empowering Recommender Systems in ITSM: A Pipeline Reference Model for AI-based Textual Data Quality Enrichment
    ( 2023)
    Reinhard, Philipp
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    Dickhaut, Ernestine
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    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.
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  • Publication
    A Conceptual Model for Labeling in Reinforcement Learning Systems: A Value Co-Creation Perspective
    ( 2023)
    Reinhard, Philipp
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    Dickhaut, Ernestine
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    Reh, Cornelius
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    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.
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  • Publication
    Towards an Employee-Centered Design for Human-AI Collaboration: How Work Design Theory Informs the Design of AI Systems
    ( 2022-12-14)
    Reinhard, Philipp
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    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.
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