1. Introduction
The traditional smart city development process adheres to the “god-dominant” paradigm, wherein both public and private strong organizations or individuals have complete control over the design of urban context sensing and actuation [1]. This is a top down method, structuring the discourse and practices. However, in the last few years, the dynamic underpinning of smart cities has undergone a drastic shift [2,3].
The emergence of private or associated stakeholders who position themselves between citizens and traditional stakeholders in the city’s administration has caused the original smart city planning paradigm to evolve. This organizational make-up tends to move away from centralizing rationales and aim to reach an inventive balance between top-down and bottom-up approaches on the basis of field observations. The smart urban context is now more institutionally diverse and more iterative than planned.
The transport industry exemplifies it well: the use of data may optimize transit by eliminating journey connections and streamlining multi-mode transportation. Fundamentally, it is now possible to include the transport dimension as part of a broader perspective accounting for the interaction between the public transport offering, the use of other public services (e.g., childcare facilities, schools, hospitals) and people’s professional and private lives. A district with a significant concentration of shift workers, for instance, may now receive transport solutions more suited to its needs [4].
The relationship between bottom-up agenda-setting and public policy takes centre stage in the sense that the information that communities are willing to share and political will on the part of authorities are powerful contributors to a city’s smartness.
However, the quantification of human life through digital information is still dominated by economic actors for marketing or management purposes that can clash with policy objectives. This use of data for profit can arguably objectivize neighbourhoods and infrastructure with adverse social ramifications and problematics [5,6,7]. The new institutional dynamic in smart cities can harness “datafication” to offset or regulate this effect; but overall, the flow of data modifies the policy vision of stakeholders.
Developing a smart city after this shift of dynamics means to multiply synergies and this …
