Data, analytics and governance in (smart) cities
Abstract/Contents
- Abstract
- Opportunities to ameliorate the living standards of billions of people are scarce. However, supporting cities to deliver on their responsibilities as custodians of the public good and advancing the manner in which they operate may provide a valuable opportunity to do so. Data now fall under cities' most valuable possessions, referred to as the new oil of the digital era. If appropriately deployed, data offer the potential to make a significant contribution to overcome some of the current and upcoming urbanization challenges within cities, predominantly caused by population growth together with rural-to-urban migration. However, many cities underutilize this crucial asset. Not surprisingly, a city's path to appropriate data collection, management, analysis and utilization, referred to hereafter as data and analytics, is demanding and can be marked by substantial hurdles. For example, organizational, strategic and technical challenges have made it difficult for cities to capture the benefits of becoming or being "smart". This dissertation addresses some of the long-standing challenges to smart cities by focusing on the search for condition variables that drive cities' levels of utilization of data and analytics. In Chapter 2 (the "first" paper), a systematic literature review gathers, summarizes, and structures the diverse conceptualizations of "smart city governance" (SCG) in the extant academic literature. The review highlights the status of research in this area and identifies research gaps. Specifically, the analysis reveals that various perspectives exist regarding the different contextual factors affecting SCG, the metrics to measure the components and the envisaged outcomes of SCG. The chapter presents a comprehensive and integrated SCG scheme to create a clearer and consistent understanding of the concept of SCG among both scholars and practitioners. Chapter 3 (the "second" paper) of this dissertation employs a multi-method approach (incl. comparative case studies, content analysis, and the Delphi method) to identify and operationalize potential condition variables that could have an independent or (joint) impact on cities' levels of utilization of data and analytics. The findings of this study indicate a variety of different potential condition variables and appropriate measurement techniques. In Chapter 4 (the "third" paper), this dissertation uses a targeted application of the "fuzzy set Qualitative Comparative Analysis" (fsQCA) method to investigate the condition variables' independent or joint impacts on cities' levels of utilization of data and analytics to systematically detect the various causal relationships ("recipes"). The outcome of this fsQCA analysis allows us to derive a set of propositions about different causal pathways and reveals necessary and sufficient condition variables to enhance cities' utilization of data and analytics. For scholars, the dissertation contributes to the growing body of knowledge by creating a comprehensive taxonomy of SCG, by identifying the drivers of cities' utilization of data and analytics and by promoting the research methodology fsQCA to analyze the drivers of cities' utilization of data and analytics. For practitioners, the thesis provides valuable insights through the formation of a comprehensive conceptualization of SCG, a standardization tool for assessing cities' data and analytics utilization and a prioritization of factors that can drive cities' levels of utilization of data and analytics. I propose that future research attempts can build on or exploit certain techniques (e.g. fsQCA) or findings (e.g. causal pathways) from this dissertation in order to generate additional insights in the area of smart cities use of data and analytics. In conclusion, this dissertation can be viewed as an attempt to advance the theoretical and practical knowledge in the fields of smart cities and data and analytics. This appears crucial at a time when great significance is ascribed to the SC concept, and thus on data and analytics, to tackle and overcome the significant challenges and obstacles lying ahead for cities.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ruhlandt, Robert Wilhelm Siegfried |
|---|---|
| Degree supervisor | Levitt, Raymond E |
| Thesis advisor | Levitt, Raymond E |
| Thesis advisor | Hall, Daniel |
| Thesis advisor | Jain, Rishee |
| Degree committee member | Hall, Daniel |
| Degree committee member | Jain, Rishee |
| Associated with | Stanford University, Civil & Environmental Engineering Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Robert Wilhelm Siegfried Ruhlandt. |
|---|---|
| Note | Submitted to the Civil & Environmental Engineering Department. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Robert Wilhelm Siegfried Ruhlandt
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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