A Multi-Model Approach to Assess the Relative Weights and Sensitivities of the Factors of Regional Competitiveness

Abstract

In order to prioritize the intervention to augment regional competitiveness, it is essential to assess the relative weights and sensitivities related to the factors of competitiveness. Improper assignment of relative weights is prominent in the case when multi-co-linearity exists among independent variables. The paper tests the suitability of multiple models for their capacity of assessing relative weights, and subsequently for forming a competitiveness index. The relative weights of critical components of economic infrastructure have been assessed with Zero-order correlation, Structure coefficient analysis, Beta coefficient analysis, Product measure analysis, Relative weight analysis, and Commonality analysis. Subsequently, regional competitiveness indices have been formed with relative weights as a linear combination. The most suitable technique to form an index has been identified through the Pearson correlation and Spearman rank correlation analyses. Multiple regression analysis assigns the relative weights and consecutively forms the regional competitiveness index, better than other applied techniques. Zero-order correlation and Structural coefficient analysis performed reasonably well. Commonality analysis is a very appropriate technique for the detailed investigation of unique and shared effects among variables. The result shows that the common effects of the critical components of the economic infrastructure are stronger than their unique effects. The sensitivity of competitiveness related to the variables has been assessed through Artificial Neural Network. Regional competitiveness is most sensitive to the variable of rural roads. The results indicate that better connectivity triggers capital and labor drain from the rural areas of the region.

Models of regional competitiveness: priority influencing factors

“…Appropriate techniques for index formation can be identified as Pearson correlation and Spearman rank correlation analysis. Despite the lack of thorough social research, these indicators offer a convergence in employment in the regions [14]. The level of employment reflects the sensitivity.…”

A Conceptual Framework for Innovation Product and Marketing Strategy at Precast Construction Company in Indonesia to Increase Competitiveness Using SWOT and Neural Network Model

“…The use of ANN has been widely carried out in various scientific fields, due to possessing more capabilities to perform classification, prediction, projection, and segmentation functions, which are popularly and heavily relied on for problem-solving. Based on its applications in competitive fields, the contesting power of a company to the competitiveness of the country was observed in Ülengin et al (2011Ülengin et al ( ), Önsel Ekici et al (2016, Kolkova (2020), Lou et al (2020), Rahmat and Sen (2021). The implementation of this network in the fields related to the increase in product innovation was also found by Kinne and Kinne and Lenz (2021), Wang et al (2021), Y.…”

“…Precast concrete is a new innovation widely used in construction processes, such as buildings and bridges, due to its ability to speed up processing time, save costs, and minimize waste for formwork and scaffolding (Reichenbach and Kromoser 2021). This innovation is one of the construction systems in accordance with the Industry 4.0 concept.…”