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作者

Robert Wilhelm Siegfried Ruhlandt;Raymond Levitt;Rishee Jain;Daniel Hall

来源

CITIES,Vol.104,Issue1,Article 102800

语言

英文

关键字

fsQCAFuzzy-set Qualitative Comparative Analysis;Smart city;Data and analytics;Causal pathways;fsQCA

作者单位

Stanford University, Civil and Environmental Engineering Department, Global Projects Center, 473 Via Ortega, Stanford, CA 94305, United States of America;Stanford University, Civil and Environmental Engineering Department, 473 Via Ortega, Stanford, CA 94305, United States of America;ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Stefano-Franscini-Platz 5, Zurich CH-8093, Switzerland;Stanford University, Civil and Environmental Engineering Department, Global Projects Center, 473 Via Ortega, Stanford, CA 94305, United States of America;Stanford University, Civil and Environmental Engineering Department, 473 Via Ortega, Stanford, CA 94305, United States of America;ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Stefano-Franscini-Platz 5, Zurich CH-8093, Switzerland

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.