A Co-evolutionary Perspective on Data-driven Organization: Highlights from Smart Cities in the Covid-19 Era

Abstract

The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.

Citation

Loia, F. (2023), "A Co-evolutionary Perspective on Data-driven Organization: Highlights from Smart Cities in the Covid-19 Era", Visvizi, A., Troisi, O. and Grimaldi, M. (Ed.) Big Data and Decision-Making: Applications and Uses in the Public and Private Sector (Emerald Studies in Politics and Technology), Emerald Publishing Limited, Leeds, pp. 181-201. https://doi.org/10.1108/978-1-80382-551-920231012