One approach does not fit all (smart) cities: Causal recipes for cities' use of 'data and analytics'

摘要

What causes cities to incorporate data and analytics into their operations? So far, this question has been left largely unanswered. Research on smart cities and data needs a more holistic approach to better capture the separate and joint effects of certain causal ("condition") variables (e.g., structures, processes) on cities' use of data and analytics (the "outcome variable"). This study adopts the Qualitative Comparative Analysis (QCA) research approach to investigate the condition variables' influence on the outcome variable to systematically detect the various causal relationships ("recipes"). The study identifies the existence of several different plausible causal pathways and a set of necessary and sufficient values of the condition variables to enhance cities' utilization of data and analytics. Building on these initial results, we offer recommendations for future research on the drivers of data and analytics utilization in cities.