Measuring urban digitalization using cognitive mapping and the best worst method (BWM)

Fabiana C. Vieira, Fernando A.F. Ferreira, Kannan Govindan, Neuza C.M.Q.F. Ferreira and Audrius Banaitis

Technology in Society, 2022, vol. 71, issue C

Abstract: Rapid population growth and urbanization have a strong impact on individuals’ well-being. These trends are currently important topics in city management. As a significant driver of digitalization, technological development has been found to have great potential as a mitigator of a wide range of issues that cities face. To ensure the available technologies are being correctly used, municipalities need to measure their degree of digitalization. This assessment is also important to improve city officials’ understanding of areas that need interventions so that cities can be managed better and provide their residents with a better quality of life. This study applies the baseline principles of the multiple-criteria decision analysis (MCDA) approach to develop a decision-support system that facilitates urban digitalization evaluations. The results rely on insights provided by a panel of experts in this area, who used cognitive mapping and the best worst method (BWM) to identify important evaluation criteria within urban digitalization and to analyze their cause-and-effect relationships. The final outcomes were validated by the panel members and representatives of the Associação Nacional de Municípios Portugueses (i.e., National Association of Portuguese Municipalities) and the Agência National de Inovação (i.e., National Innovation Agency). These specialists confirmed that the evaluation system developed facilitates city differentiation in key areas of urban digitalization. The advantages and limitations of the proposed procedures are also discussed.

Keywords: Best worst method (BWM); Cognitive mapping; Multiple-criteria decision analysis (MCDA); Smart city; Urban digitalization (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:71:y:2022:i:c:s0160791x2200272x

DOI: 10.1016/j.techsoc.2022.102131

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