Chinese influence in Africa by integrated regret theory and multi-criteria decision analysis

This study illuminates the multifaceted influence of Chinese in Africa, driven by the imperative to understand the strategic and economic ramifications of this rapidly evolving relationship. Motivated by the critical role Africa plays in global geopolitics and resource dynamics, alongside Chinese expanding international influences, the research aims to quantitatively and psychologically assess the decision-making processes underpinning this engagement. Adopting a regret theory-based Multiple Criteria Decision Making (MCDM) framework, the study evaluates Chinese impact across 49 African countries from 2018 to 2022, employing six economic indicators to capture the breadth of Chinese activities. Through meticulous normalization, regret utility computation, and total gap analysis, the methodology affords a systematic ranking that reflects the varying degrees of Chinese economic influence. The findings uncover pronounced variances in the level of Chinese engagement across the continent, with countries like Nigeria and Egypt showcasing substantial influence convergence with the theoretical model of ideal economic partnership, whereas others like Cape Verde indicate minimal influence. Contributing to academic and practical discourse, this study not only provides a methodological blueprint for analyzing geopolitical influences but also offers insights that policymakers can leverage to optimize their engagement strategies with Chinese. It foregrounds the interplay between empirical economic data and behavioral economics within international relations research. The study acknowledges limitations, primarily in data availability, which may not capture the full scope of informal economic interactions. It proposes further research to enrich the understanding of the Chinese-Africa nexus through longitudinal studies, integration of qualitative data, and expansion of the analytical model to encompass broader socio-economic impacts and more diverse indicators.