Temporal multi-criteria decision making approach on sustainable competitiveness of countries: Science and technology innovation indicators

Abstract

This research introduces a novel Temporal MCDM (Multi-Criteria Decision Making) method in the research domain of measuring sustainable competitiveness of countries based on Global Sustainable Competitiveness Index (GSCI) indicators. The principal innovation is the explicit incorporation of temporal analysis into esteemed BWM-WASPAS (Best-Worst Method-Weighted Aggregated Sum Product Assessment) framework, facilitating the systematic modeling and evaluation of economic, social, and environmental factors that experience dynamic performance variations over time. The application of this framework provides resulting levels or rankings of countries for analyzing relative strengths and weaknesses in specific areas of sustainable competitiveness in order to improve policy-making and strategic planning for long-term success. An empirical study on determining sustainable competitiveness scores of 15 high-economy countries in Science and Technology Innovation (STI) for the period of 2016–2020 validates the adaptability of proposed method. In which, the countries are identified from GII (Global Innovation Index) as alternatives and the STI indicators from GSCI are employed as criteria. The findings indicate that the research and development expenditure indicator achieved high significant value, and USA stands as the leader in overall innovation capability rankings. Meanwhile, Republic of Korea achieved high-value for significant improvements in innovation capabilities, whereas Switzerland and Estonia have high-value for declining innovation capability. The results further formulated recommendations for under performing countries to strengthen their innovation capability and achieve competitiveness sustainability. This decision support model guarantees appropriateness for addressing countries’ sustainability and competitiveness challenges that incorporate expert preferences, ambiguous data, and dynamic decision-making scenarios.