Drivers of Data and Analytics Utilization within (Smart) Cities: A Multimethod Approach

Abstract

Data and analytics can be a facilitator and driver of growth for cities. Their significance will likely continue to grow and be amplified by new technological developments. However, research on cities’ utilization of data and analytics has been comparatively vague and imprecise and requires a more holistic and systematic perspective. Therefore, this study examines the potential condition variables that could drive cities’ utilization of data and analytics, employing a multimethod approach that includes comparative case studies, content analysis, and the Delphi method. This hybrid research approach allows the authors to combine the strengths of various research methods and is, therefore, among the first that uses this kind of approach in such a research context. The authors identify several indicators or drivers (structures, processes, leadership, strategy, culture, data infrastructure, data governance, skills, training, capacities, budgets) that are essential to build a theory around a city’s utilization of data and analytics. In addition, a conceptual model classifies these potential drivers into six broad (superordinate) categories: organization, procedures, direction, data, competencies, and resources. For scholars, the study contributes to the growing body of knowledge by identifying potential drivers of cities’ utilization of data and analytics. For practitioners, the study provides insights through the formation of a standardization tool (appropriate measurement techniques for each potential driver) for assessing cities’ data and analytics utilization. In addition, the authors suggest directions for further research.