Analyzing how ease of business affects FDI using advanced models

  • Mukhtar Abubakar Yusuf John Carroll University
  • John Kalu Osiri College of Business University of Nebraska
Keywords: Ease of Doing Business, Foreign Direct Investment, Machine and Deep Learning models, Moderated-Effect, Structural Equation Modeling


The primary aim of this research is to examine the effect of Investment Facilitation on the Ease of Doing Business and its subsequent influence on Foreign Direct Investment (FDI) inflow decisions. Using SPSS-AMOS regression applications, the impact of Investment Facilitation on Ease of Doing Business indicators was determined. For prediction, Machine Learning techniques, ARIMA in R and Python were employed. Our findings suggest that the ease of doing business rating positively impacts FDI inflow decisions, especially when combined with high investment facilitation. However, the ease of doing business ratings alone might not be sufficient to lure investors, especially in contexts like Nigeria or certain Sub-Saharan African countries. Interestingly, traditional forecasting with ARIMA on R projected a decline in FDI inflow over the next decade, whereas the Python model anticipated an increase. This research highlights the value of integrating quality investment facilitation services with favorable business ratings. Furthermore, while machine learning techniques offer refined forecasts, traditional models provide contrasting insights, accentuating the need for a multifaceted approach in predicting FDI inflow decisions. These insights are crucial for policymakers and stakeholders aiming to bolster investment attractiveness in regions like Sub-Saharan Africa.


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Author Biography

John Kalu Osiri, College of Business University of Nebraska

Associate Professor, Director of Management University of Nebraska, Lincoln.


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How to Cite
Yusuf, M. A., & Osiri, J. K. (2024). Analyzing how ease of business affects FDI using advanced models. Journal of Management Science (JMAS), 7(1), 554-564.