Discipline: Business Economics
The study focuses on generating the paths that would model the effects of six predictors on competitiveness. These predictors are innovation, human development, Information and Communications Technology(ICT), Research and Development (R&D), population and education. Data on competitiveness are based on Global Competitiveness Index (GCI). Data on predictors are Global Innovation Index (GII), Human Development Index (HDI), Total Population (TPO), ICT Development Index (IDI), Expenditure on Education(EOE) and Expenditure on R & D (ERD). Multiple regression and path analysis are the statistical tools used to investigate the effects of these six predictors on competitiveness. Sample data are from fifty-eight countries. Stepwise regression and multiple regression have screened out education and the total population, leaving four predictors which are innovation, human development, ICT, and R&D. From these, an initial model was established. It was a simplified model that assumed that all predictors have symmetric relationship that has direct effect on competitiveness. The initial model showed that innovation and human development have more dominant direct effects on competitiveness. It also showed that ICT and R&D have larger indirect effects over the other predictors. Since the initial model lacks a more in-depth relationship among the predictors, an improved design was drawn out of the findings of the first model. The improved model proved that innovation was the most potent predictor and its effect on competitiveness is direct. The second most powerful predictor is ICT, however its effect on competitiveness is indirect. Results also showed the need for ICT to drive innovation and human development to improve competitiveness. R&D can only be seen to contribute to global competitiveness provided that it will drive ICT. Among all predictors, human development has the least effect on competitiveness among the four, but it has direct impact.
ISSN 2244-6710 (Print)
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