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- Publisher Website: 10.1108/ECAM-04-2020-0262
- Scopus: eid_2-s2.0-85124196435
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Article: Measuring competitiveness with data-driven principal component analysis: a case study of Chinese international construction companies
Title | Measuring competitiveness with data-driven principal component analysis: a case study of Chinese international construction companies |
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Authors | |
Keywords | China Competitive advantage Competitiveness Firm competitiveness International construction Principal component analysis |
Issue Date | 1-Feb-2022 |
Publisher | Emerald |
Citation | Engineering, Construction and Architectural Management, 2022, v. 30, n. 4, p. 1558-1577 How to Cite? |
Abstract | PurposeDefining and measuring competitiveness has been a major focus in the business and competition literature over the past decades. The paper aims to use data-driven principal component analysis (PCA) to measure firm competitiveness. Design/methodology/approachA “3Ps” (performance, potential, and process) firm competitiveness indicator system is structured for indicator selection. Data-driven PCA is proposed to measure competitiveness by reducing the dimensionality of indicators and assigning weights according to the endogenous structure of a dataset. To illustrate and validate the method, a case study applying to Chinese international construction companies (CICCs) was conducted. FindingsIn the case study, 4 principal components were derived from 11 indicators through PCA. The principal components were labeled as “performance” and “capability” under the two respective super-components of “profitability” and “solvency” of a company. Weights of 11 indicators were then generated and competitiveness of CICCs was finally calculated by composite indexes. Research limitations/implicationsThis study offers a systematic indicator framework for firm competitiveness. The study also provides an alternative approach to better solve the problem of firm competitiveness measurement that has long plagued researchers. Originality/valueThe data-driven PCA approach alleviates the difficulties of dimensionality and subjectivity in measuring firm competitiveness and offers an alternative choice for companies and researchers to evaluate business success in future studies. |
Persistent Identifier | http://hdl.handle.net/10722/329144 |
ISSN | 2023 Impact Factor: 3.6 2023 SCImago Journal Rankings: 0.896 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Guo, Hui | - |
dc.contributor.author | Lu, Weisheng | - |
dc.date.accessioned | 2023-08-05T07:55:38Z | - |
dc.date.available | 2023-08-05T07:55:38Z | - |
dc.date.issued | 2022-02-01 | - |
dc.identifier.citation | Engineering, Construction and Architectural Management, 2022, v. 30, n. 4, p. 1558-1577 | - |
dc.identifier.issn | 0969-9988 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329144 | - |
dc.description.abstract | <h3>Purpose</h3><p>Defining and measuring competitiveness has been a major focus in the business and competition literature over the past decades. The paper aims to use data-driven principal component analysis (PCA) to measure firm competitiveness.</p><h3>Design/methodology/approach</h3><p>A “3Ps” (performance, potential, and process) firm competitiveness indicator system is structured for indicator selection. Data-driven PCA is proposed to measure competitiveness by reducing the dimensionality of indicators and assigning weights according to the endogenous structure of a dataset. To illustrate and validate the method, a case study applying to Chinese international construction companies (CICCs) was conducted.</p><h3>Findings</h3><p>In the case study, 4 principal components were derived from 11 indicators through PCA. The principal components were labeled as “performance” and “capability” under the two respective super-components of “profitability” and “solvency” of a company. Weights of 11 indicators were then generated and competitiveness of CICCs was finally calculated by composite indexes.</p><h3>Research limitations/implications</h3><p>This study offers a systematic indicator framework for firm competitiveness. The study also provides an alternative approach to better solve the problem of firm competitiveness measurement that has long plagued researchers.</p><h3>Originality/value</h3><p>The data-driven PCA approach alleviates the difficulties of dimensionality and subjectivity in measuring firm competitiveness and offers an alternative choice for companies and researchers to evaluate business success in future studies.</p> | - |
dc.language | eng | - |
dc.publisher | Emerald | - |
dc.relation.ispartof | Engineering, Construction and Architectural Management | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | China | - |
dc.subject | Competitive advantage | - |
dc.subject | Competitiveness | - |
dc.subject | Firm competitiveness | - |
dc.subject | International construction | - |
dc.subject | Principal component analysis | - |
dc.title | Measuring competitiveness with data-driven principal component analysis: a case study of Chinese international construction companies | - |
dc.type | Article | - |
dc.identifier.doi | 10.1108/ECAM-04-2020-0262 | - |
dc.identifier.scopus | eid_2-s2.0-85124196435 | - |
dc.identifier.volume | 30 | - |
dc.identifier.issue | 4 | - |
dc.identifier.spage | 1558 | - |
dc.identifier.epage | 1577 | - |
dc.identifier.eissn | 1365-232X | - |
dc.identifier.isi | WOS:000751500400001 | - |
dc.identifier.issnl | 0969-9988 | - |