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PublicationGovernment-university collaboration on smart city and smart government projects: What are the success factors?( 2024-01)
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PublicationA social cognition perspective on autonomous technology(Elsevier, 2021-09)The last several years have been characterized by an increasing autonomization of technology. Advanced technologies are now capable of learning and interacting. These capabilities expand the scope in which technology can be used. As a result, these increasingly autonomous machines and programs are completing an ever-growing number of tasks in collaboration with humans. This is why it is important to determine whether increasing levels of autonomy affect how technical systems are perceived by their human counterparts. An online survey experiment using vignettes to describe technical systems with varying levels of autonomy was employed to test the effects of technical autonomy on the dimensions of social cognition (i.e., warmth and competence) and mind perception (i.e., experience and agency). The findings show that increasing levels of technical autonomy impacted how the technical systems were socially perceived. More specifically, the findings suggest that the more autonomous a technical system is, the more competence and agency it will be ascribed. Conversely, increasing levels of technical autonomy had no effect on the ascription of warmth and experience. These results highlight that technical autonomy is relevant to and effectively shapes social judgements regarding technology.Type:Journal:Issue:
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PublicationLegitimizing the Smart City Idea: The Case of the #Smarthalle( 2020-11-09)
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PublicationA taxonomy of human-machine collaboration: capturing automation and technical autonomyDue to the ongoing advancements in technology, socio-technical collaboration has become increasingly prevalent. This poses challenges in terms of governance and accountability, as well as issues in various other fields. Therefore, it is crucial to famil- iarize decision-makers and researchers with the core of human–machine collaboration. This study introduces a taxonomy that enables identification of the very nature of human–machine interaction. A literature review has revealed that automation and technical autonomy are main parameters for describing and understanding such interaction. Both aspects must be carefully evaluated, as their increase has potentially far-reaching consequences. Hence, these two concepts comprise the taxonomy’s axes. Five levels of automation and five levels of technical autonomy are introduced below, based on the assumption that both automation and autonomy are gradual. The levels of automation were developed from existing approaches; those of autonomy were carefully derived from a review of the literature. The taxonomy’s use is also explained, as are its limitations and avenues for further research.
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