How Cities Learn: From Experimentation to Transformation

Abstract

Cities must change rapidly to address a range of sustainability challenges. While urban experimentation has prospered as a framework for innovation, it has struggled to stimulate broader transformation. We offer a novel contribution to this debate by focusing on what municipalities learn from experimentation and how this drives organisational change. The practicalities of how municipalities learn and change has received relatively little attention, despite the recognised importance of learning within the literature on urban experiments and the central role of municipalities in enabling urban transformation. We address this research gap, drawing on four years of in-depth research coproduced with European municipal project coordinators responsible for designing and implementing the largest urban research and innovation projects ever undertaken. This cohort of professionals plays a critical role in urban experimentation and transformation, funnelling billions of Euros into trials of new solutions to urban challenges and coordinating large public-private partnerships to deliver them. For our respondents, learning how to experiment more effectively and embedding these lessons into their organisations was the most important outcome of these projects. We develop the novel concept of <em>process learning</em> to<em> </em>capture the importance of experimentation in driving organisational change. Process learning is significant because it offers a new way to understand the relationship between experimentation and urban transformation and should form the focus of innovation projects that seek to prompt broader urban transformation, rather than technical performance. We conclude by identifying implications for urban planning and innovation funding.

Meaningful Transfer: Tech-Knowlogical Interdependencies in the Digital Built Environment

“…We argue that most research in the domain of digital urbanism focuses on the system or the platform for knowledge generation, rather than its transference and translation. This echoes the findings of Evans et al (2021) who indicate that, in the context of the municipal organisation, there is considerable challenge in learning, understanding and rolling out new knowledge associated with innovation (Doren et al, 2016;Dijk et al, 2018;Gopakumar, 2020).…”

“…A central argument in this paper is that knowledge production, transference and translation is a missing link between how the smart cities learn and transform into successful digital built environments. This argument is linked to the observation by Evans et al (2021) that there is a gap between the process of experimentation in the city and work focusing on learning. This argues that moving from a project to changing business as usual relies on how lessons from experimentation are captured and embedded into organisations (Evans et al, 2021: 172).…”

“…Initial learning has been considered, particularly through urban experimentation, via various formulations of urban, living, city and building labs (Hajer and Versteeg, 2019;Scholl and Kraker, 2021), and more recently at the municipal scale (Evans et al, 2021) in relation to how cities transform through adoption of initial innovation into customary practise. However, there is less engagement with the type of knowledge that is generated (either individually or as a group) within this process and how better understanding of this might aid this wider transformational process.…”

“…This type of knowledge cannot be accounted for by improved workflow documentation or better use of technology (Addis, 2016). In this sense, knowledge development is simply assumed to happen anyway, bound up within the process of innovation (Evans et al, 2021). For this reason, we favour an alternative position of knowledge co-production.…”

Local Autonomy in Temporary Organizations: The Case of Smart City Pilots

“…The general ambition is to support knowledge production and scale-up when results that are deemed fruitful (see, for example, Bulkeley et al, 2019;Sengers et al, 2020;Voytenko et al, 2016). Accordingly, a key aspect of experimentation is learning (Evans et al, 2016(Evans et al, , 2021van Winden & van den Buuse, 2017), with the notion of "learning from doing" acting as a core legitimizing logic (Kronsell & Mukhtar-Landgren, 2020;cf. Caprotti & Cowley, 2017).…”