Artificiell intelligens data – att dela eller inte dela?
PublikationArtikel (utan peer review)
Sammanfattning
Long, V. & Bjuggren, P-O. (2022). Artificiell intelligens data – att dela eller inte dela? Ekonomisk debatt, 2022(6).
Long, V. & Bjuggren, P-O. (2022). Artificiell intelligens data – att dela eller inte dela? Ekonomisk debatt, 2022(6).
2022
Journal of Smart Cities and Society, (Preprint), 1-23.
Smart Cities have been around as a concept for quite some time. However, most examples of Smart Cities (SCs) originate from megacities (MCs), despite the fact that most people live in Small and Medium-sized Cities (SMCs). This paper addresses the contextual setting for smart cities from the perspective of such small and medium-sized cities. It starts with an overview of the current trends in the research and development of SCs, highlighting the current bias and the challenges it brings. We follow with a few concrete examples of projects which introduced some form of “smartness” in the small and medium cities context, explaining what influence said context had and what specific effects did it lead to. Building on those experiences, we summarise the current understanding of Smart Cities, with a focus on its multi-faceted (e.g., smart economy, smart people, smart governance, smart mobility, smart environment and smart living) nature; we describe mainstream publications and highlight the bias towards large and very large cities (sometimes even subconscious); give examples of (often implicit) assumptions deriving from this bias; finally, we define the need of contextualising SCs also for small and medium-sized cities. The aim of this paper is to establish and strengthen the discourse on the need for SMCs perspective in Smart Cities literature. We hope to provide an initial formulation of the problem, mainly focusing on the unique needs and the specific requirements. We expect that the three example cases describing the effects of applying new solutions and studying SC on small and medium-sized cities, together with the lessons learnt from these experiences, will encourage more research to consider SMCs perspective. To this end, the current paper aims to justify the need for this under-studied perspective, as well as to propose interesting challenges faced by SMCs that can serve as initial directions of such research.
2022
Bjuggren, P.O. & Long, V.
This paper decomposes the factors that govern the access and sharing of machine-generated industrial data in the artificial intelligence era. Through a mapping of the key technological, institutional, and firm-level factors that affect the choice of governance structures, this study provides a synthesised view of AI data-sharing and coordination mechanisms. The question to be asked here is whether the hitherto de facto control—bilateral contracts and technical solution-dominating industrial practices in data sharing—can handle the long-run exchange needs or not.
2021
Industrial transformation is a research and teaching field with a focus on the phenomenon and mechanisms of industrial development and renewal. It concerns changes in economic activities caused by innovation, competition and collaboration, and has a rich heritage of evolutionary economics, institutional economics, industrial dynamics, technology history and innovation studies. It borrows concepts and models from the social sciences (sociology, history, political sciences, business/management, economics, behavioural sciences) and also from technology and engineering studies.
In this book, the authors present the key theories, frameworks and concepts of industrial transformation and use empirical cases to describe and explain the causes, processes and outcomes of transformation in the context of digitalization and sustainability. They stress that industrial transformation consists both of Darwinian “survival of the fittest” selection, and of intentional pursuits of innovation, and of industrial capabilities creation. The work argues that managing the global trends of transformation is not only about new technology and innovation: existing institutional settings and dynamic interactions between technological change, organizational adaptation and economic activities also have a profound impact on future trajectories.
The areas under investigation are of great relevance for strategic management decisions and industrial and technology policies, and understanding the mechanisms underlying transformation and sustainable growth.