Measuring competitiveness with data-driven principal component analysis: A case study of chinese international construction companies

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Research limitations/implications: 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. Originality/value: 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.