Measuring local competitiveness
Comparing and integrating two methods PCA and AHP
Bibliographic Data
| ID | 21378080 |
|---|---|
| Authors | Katarzyna A Kurek (0000-0002-6153-1441, Wageningen University & Research, corresponding author), Wim Heijman (0000-0003-0514-3855, Czech University of Life Sciences Prague), Johan Van Ophem (0000-0002-6940-5626, Wageningen University & Research), Stanisław Gędek (0000-0002-4712-2836, Rzeszów University of Technology), Jacek Strojny (0000-0002-0577-377X, Rzeszów University of Technology) |
| Year | 2022 |
| Volume | 56 |
| Issue | 3 |
| Pages | 1371-1389 |
| Publication date | 2022-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Quality & Quantity (JOURNAL) |
| Journal identifiers | ISSN: 0033-5177 • E-ISSN: 1573-7845 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11135-021-01181-z |
| OpenAlex | W3174160896 |
| Language | EN |
| Citations received | 6 |
| References cited | 59 |
This article discusses two methods to measure the concept of local competitiveness: Principal Component Analysis (PCA) and Analytical Hierarchy Process (AHP). The goal of this analysis is to determine whether these two methods used in social sciences research lead to comparable model results. By non-parametric tests we show that there is a significant correlation between the PCA and AHP local competitiveness indexes. Thereafter, a developed mixed method examination of whether the methods can be used interchangeably is presented and illustrated with detailed examples of two mixed approaches. The mixed method confirms the correlation between the PCA and AHP models. However, the mixed modelling results indicate the utility of the PCA in the situation of a multicriteria local competitiveness data examination
Analytic hierarchy process · Data mining · Econometrics · Operations research · Parametric statistics · Principal component analysis · Statistics · Artificial Intelligence · Computer Science · Global Trade and Competitiveness · Mathematics
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| Citations per year | 2 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |