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PublicationNAVIGATING RESOURCE CONSTRAINTS IN CONVERSATIONAL AI IMPLEMENTATIONS: THE ROLE OF INSTITUTIONAL LOGICS AND ORGANIZATIONAL RESPONSES( 2024-06-16)
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PublicationIlluminating Smart City Solutions -A Taxonomy and Clusters( 2023-12)With urban problems intensifying, Smart City solutions are recognized by researchers and practitioners as one of the most promising solutions to make urban areas economically, environmentally, and socially sustainable. While many elements of Smart City solutions have been explored, existing works either treat Smart City solutions as technical black boxes or focus exclusively on Smart City solutions' technical or nontechnical characteristics. Therefore, to conceptualize the unique characteristics of Smart City solutions currently available, we developed a multi-layer taxonomy based on Smart City solution literature and a sample of 106 Smart City solutions. Moreover, we identified three clusters, each covering a typical combination of characteristics of Smart City solutions. We evaluated our findings by applying the Q-sort method. The results contribute to the descriptive knowledge of Smart City solutions as a first step for a theory for analyzing and enable researchers and practitioners to understand Smart City solutions more holistically.
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PublicationChallenges and Good Practices in Conversational AI-Driven Service Automation( 2023-12)
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PublicationDemystifying Industrial Internet of Things start-ups -A multi-layer taxonomy( 2022-02)Described as a fundamental paradigm shift by researchers, the Industrial Internet of Things (IIoT) is credited with massive potential. In the context of emerging technologies, such as the IIoT, start-ups occupy a crucial role, as new technologies are often first commercialized by start-ups. Because of the rising importance of IIoT start-ups as drivers of industrial innovation, IIoT solutions demand deepened theoretical insights. As existing classification schemes in the industrial context do not sufficiently account for the ever more critical role of IIoT start-ups, we present a multi-layer taxonomy of IIoT start-up solutions. Building on state-of-the-art literature and a sample of 78 real-world IIoT start-up solutions, the taxonomy comprises ten dimensions and related characteristics structured along the three layers solution, data, and business model. The taxonomy contributes to the descriptive knowledge on the IIoT and enables researchers and practitioners to better understand IIoT start-up solutions.
