Taxonomy of Holistic Performance of Current Creative Cities: Empirical Study

Rodrigues, Margarida; Franco, Mário

doi:10.1061/(asce)up.1943-5444.0000548

Cited by 5 publications

(4 citation statements)

References 118 publications

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“…Shen et al (2018) applied two methods, i.e., the 'entropy' and 'technique for order preference by similarity to ideal solution' (TOPSIS) to holistically evaluate China's smart cities' performances. Rodrigues & Franco (2020) accommodated the 'quantitative research method' using the two multivariate statistical techniques (i.e., EFA, PCA) to assess creative cities' performance. Fan et al (2019) used a 'multiple case analysis approach' to evaluate the status of Cluj-Napoca, Romania's regional innovation cluster.…”

Section: Innovation District Performance Assessmentmentioning

confidence: 99%

“…the "entropy" and "technique for order preference by similarity to ideal solution" (TOPSIS) to holistically evaluate China's smart cities' performances. Rodrigues and Franco (2020) accommodated the "quantitative research method" using the two multivariate statistical techniques (i.e. exploratory factor analysis, principal component analysis) to assess creative cities' performance.…”

Section: Introductionmentioning

confidence: 99%

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How can innovation district performance be assessed? Insights from South East Queensland, Australia

Adu-McVie

Yigitcanlar²,

Erol³

et al. 2022

JPMD

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Purpose Many cities across the world are actively investing in ways to excel in the innovation economy through the development of innovation districts as one of the most popular policy options. While innovation districts are among the leading drivers of innovation activities in cities, they are also high-cost and high-risk investments. Besides, holistic approaches for assessing these districts’ multifaceted performances are scarce. Bridging this knowledge gap is critical, hence, this paper aims to explore how innovation district performance can be assessed through a classification framework. Design/methodology/approach The paper introduces a multidimensional innovation district classification framework and applies it into Australian innovation districts with divergent features, functions, spatial and contextual characteristics. The study places 30 innovation districts from South East Queensland under the microscope of the framework to assess the multifaceted nature of innovation district performance. It uses qualitative analysis method to analyse both the primary and secondary data, and descriptive analysis with basic excel spreadsheet calculations to analyse the validity of the data. Findings The data analysis clusters 30 innovation districts from South East Queensland under three performance levels – i.e. desired, acceptable and unsavoury – concerning their form, feature and function characteristics. Originality/value The results disclose that the framework is a practical tool for informing planners, developers and managers on innovation district performances, and it has the capability to provide guidance for policymakers on their policy and investment decisions regarding the most suitable innovation district types and characteristics to consider.

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Section: Innovation District Performance Assessmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How can innovation district performance be assessed? Insights from South East Queensland, Australia

Adu-McVie

Yigitcanlar²,

Erol³

et al. 2022

JPMD

View full text Add to dashboard Cite

show abstract

“…These are spatial groupings formed by those regions displaying similar values (high or low) in terms of the territorial clusters and which are identified thanks to their geographical proximity and the existence of spatial autocorrelation between regions, based on the creativity indicator. This is a different approach for determining spatial clusters to the one used by Rodrigues and Franco [82], who employ hierarchical clustering for this purpose. Figure 7 shows the spatial patterns of the EIRC_PCA indicator and its statistically significant hotspots (spatial clusters with high values) and cold spots (spatial clusters with low values), based on the Getis-Ord Gi* statistic.…”

Section: Spatial Analysis Of the Eirc In European Regionsmentioning

confidence: 99%

“…These are spatial groupings formed by those regions displaying similar values (high or low) in terms of the territorial clusters and which are identified thanks to their geographical proximity and the existence of spatial autocorrelation between regions, based on the creativity indicator. This is a different approach for determining spatial clusters to the one used by Rodrigues and Franco [82], who employ hierarchical clustering for this purpose. Regions shaded in red evidence a strong likelihood of spatial dependence in the indicator's high value; in other words, better performance in the creative economy, and they are mainly located in Germany, Belgium, Denmark, northern France and the south of the Nordic countries.…”

Section: Spatial Analysis Of the Eirc In European Regionsmentioning

confidence: 99%

Reliability of Creative Composite Indicators with Territorial Specification in the EU

Miguel

Prieto

2020

Sustainability

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There is wide consensus regarding the capacity of the creative economy as a factor in economic development and sustainability in regions in the medium term, and it is an issue that has aroused interest in measuring the creative potential of areas by estimating composite indicators. Most works posit indicators by countries or cities and normally employ a single aggregation method. This work proposes the construction of a synthetic indicator of creativity with regional disaggregation for a wide sample of European countries, comparing different aggregation methods under the hypothesis of the benefit of the doubt (BoD). We use Eurostat data and also analyse spatial dependence processes. Findings point to an inequality in the regional distribution of creativity in Europe and this reveals creative gap models in areas, suggesting that creativity triggers greater spatial disparities wherever it is found. The principal creative clusters are located in regions in central and northern Europe, particularly in regions with national capitals. The regional distribution of the indicator evidences positive spatial dependence, thereby revealing the existence of spatial externalities that provide feedback for the concentration process and, therefore, a widening of the disparities.

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Digital entrepreneurship in local government: Case study in Municipality of Fundão, Portugal

Rodrigues

Franco

2021

Sustainable Cities and Society

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Taxonomy of Holistic Performance of Current Creative Cities: Empirical Study

Cited by 5 publications

References 118 publications

How can innovation district performance be assessed? Insights from South East Queensland, Australia

How can innovation district performance be assessed? Insights from South East Queensland, Australia

Reliability of Creative Composite Indicators with Territorial Specification in the EU

Digital entrepreneurship in local government: Case study in Municipality of Fundão, Portugal

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