The Global Competitiveness Index: an alternative measure with endogenously derived weights

Petrarca, Francesca; Terzi, Silvia

doi:10.1007/s11135-017-0655-8

Cited by 9 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors studied whether the new methodology is able to capture better the real competitiveness of nations operating in an exceedingly complex global economy. Petrarca and Terzi (2018) presented an alternative method to compute the GCI by means of a partial least squares path model. Using the GCI, through a regression analysis on a dataset made by 140 countries, Di Fatta et al (2018) analyzed the relationships among public-sector performance, ethics and corruption.…”

Section: Literature Reviewmentioning

confidence: 99%

The evaluation of competitive position of EU-28 economies with using global multi-criteria indices

Kiseľáková

Šofranková

Onuferová

et al. 2019

View full text Add to dashboard Cite

Research background: Under the current conditions of increasing competitiveness and interdependence, national economies are more influenced by the global business environment and its development. Constantly changing economic, social, political aspects, and many other factors, cause the differences in the global competitiveness of economies, so the economies are forced to analyze their competitive level more complexly. Despite that, there is a lack of research studies analyzing the international competitiveness of EU-28 economies from the point of view various multi-criteria indices. Purpose of the article: The paper investigates the relations between the Global Competitiveness Index (GCI) and other selected multi-criteria indices, namely the Global Innovation Index (GII), the Doing Business Index (DBI), the Economic Freedom Index (EFI) and the Corruption Perceptions Index (CPI) in the case of EU–28 economies. Methods: In order to investigate the relations between the global competitiveness and selected multi-criteria indices affecting the EU–28 economies, the multiple linear regression analyses were applied. The multiple regression model was quantified for every single year, as well as, the regression model using the average score of all analyzed indices. The secondary data concerning the scores of individual indices were collected based on annually published online reports over the period of 2014–2018. Findings & Value added: The research confirmed that there is a statistically significant dependence between the global competitiveness, corruption and the level of innovation potential within the EU–28 economies. Besides, we identified the worst results in the context of competitiveness evaluation especially in the area of corruption and innovation activities. In this regard, the issue of insufficient innovation development and inappropriate corruption perception is considered to be key determinants influencing the assessment of the global competitiveness of the EU–28 member states. In our opinion, to improve the competitiveness of these countries, targeted activities should be implemented in the frame of national competitive strategies, programs, and policies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

The evaluation of competitive position of EU-28 economies with using global multi-criteria indices

Kiseľáková

Šofranková

Onuferová

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The pillars are grouped into three sub-indexes (basic requirements; efficiency improvements; innovation and sophistication factors) and the GCI is defined as the weighted average of the sub-indexes. [21].…”

Section: Analysis Stages Of Regional Development Indexmentioning

confidence: 99%

Component Driven of Regional Competitiveness for Urban Development in Central Java Province Indonesia.

Wijaya,

Sakdi

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

Economic growth in Central Java Province is currently being driven by the rapid acceleration of investment, leading to the development of cities and districts. However, the measurement of regional competitiveness is currently limited to analysing regional capabilities in development. This condition creates a situation where regencies and cities compete with each other without considering the functions and roles of interconnected regions. Therefore, there is an urgent need to understand and utilize regional competitiveness within the framework of the regional system in order to support the policies of the Central Java Province. This study adopts a quantitative approach to analyse the stages of regional development, which are identified based on competitiveness starting from basic requirements as a factor-driven, efficiency-driven, and innovation factor. Using this approach, we aim to classify the functions and roles of cities and districts within the regional system in Central Java. The data for this study was obtained through the distribution of questionnaires in 35 districts and cities in Central Java Province, as well as interviews with stakeholders from both the regions and the provinces. The findings of this study reveal that cities and regencies have different stages of competitiveness, with some focusing on driving innovation and strengthening driving factors, while neglecting efficiency. Spatially, cities exhibit a higher level of competitiveness; however, most urban districts have not incorporated the role of the region in the planning of a sustainable regional system.

show abstract

“…The method of biclustering is most widespread in bioinformatics. It also has much potential within the social sciences, as it can be used to define leagues, for example, for countries based on their competitiveness indicators Petrarca and Terzi (2018) or Dolnicar et al (2012). Within the subject of university rankings (as previously mentioned concerning the last two lines of Table 3), Raponi et al (2016) applied this method to the data of 55 Italian faculty of economics concerning the academic years 2009-2010.…”

Section: Leagues or Rankingsmentioning

confidence: 99%

University leagues alongside rankings

et al. 2022

View full text Add to dashboard Cite

University rankings can both orient and disorient potential students. In rankings, universities with very different characteristics are compared, which makes interpretation difficult. We propose the application of a clustering method, which creates groups of universities that are close to each other with respect to a subset of indicators, but the indicators also show homogeneity with respect to the universities in that group. We call such groups leagues. These leagues are defined by the data themselves and are not based on subjective criteria. We demonstrate our proposition using one member of the family of the two-way clustering method, namely, biclustering. The case we present is based on the Round University Ranking (RUR) 2020 dataset. The use of leagues could provide better guidance not only for potential applicants but also for university funding organizations and policy-makers. Our case study led to a somewhat surprising observation. In the top league (based on the RUR data and indicators), the three most important indicators measure reputation, not scientific or educational performance.

show abstract

The Global Competitiveness Index: an alternative measure with endogenously derived weights

Cited by 9 publications

References 13 publications

The evaluation of competitive position of EU-28 economies with using global multi-criteria indices

The evaluation of competitive position of EU-28 economies with using global multi-criteria indices

Component Driven of Regional Competitiveness for Urban Development in Central Java Province Indonesia.

University leagues alongside rankings

Contact Info

Product

Resources

About