Foreign direct investment performance drivers at the country level: a robust compromise multi-criteria decision-making approach

    Peter Wanke Affiliation
    ; Yong Tan   Affiliation
    ; Jorge Antunes Affiliation
    ; Ali Emrouznejad Affiliation


This paper focuses on the performance drivers of Foreign Direct Investment (FDI) at the country level, exploring the socio-demographic specifics of donor and receiver countries. To this end, a novel Robust Compromise (RoCo) Multi-Criteria Decision-Making (MCDM) model is proposed using non-linear programming solved by genetic algorithms. The model builds upon established traditional models for alternative ranking and criteria weighting. Subsequently, a stochastic robust regression is performed, building upon previously computed bootstrapped Tobit, Simplex, and Beta regressions to handle performance scores ranging between 0 and 1. The goal is to test FDI performance against a set of contextual variables. The findings suggest that the performance of FDI is relatively low, and relevant improvements should be made. Our second stage analysis reports that higher GDP per capita and good social welfare, including lower infant mortality and higher life expectancy, contribute to the improvement in FDI performance. Furthermore, it is found that a large percentage of women in the total population, wealth concentration in the destination country, as well as the degree of urbanization, are helpful to improve FDI performance. Finally, we find that FDI performance is mainly concentrated on industries that are high-tech and high value-added.

Keyword : multiple criteria analysis, FDI, robust analysis, social welfare, economic development, socio-demographic drivers

How to Cite
Wanke, P., Tan, Y., Antunes, J., & Emrouznejad, A. (2024). Foreign direct investment performance drivers at the country level: a robust compromise multi-criteria decision-making approach. Technological and Economic Development of Economy, 30(1), 148–174.
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Feb 12, 2024
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Abramo, L., & Valenzuela, M. (2005). Women’s labour force participation rates in Latin America. International Labor Review, 144(4), 369–400.

Antunes, J., Hadi-Vencheh, A., Jamshidi, A., Tan, Y., & Wanke, P. (2023a). TEA-IS: A hybrid DEA-TOPSIS approach for assessing performance and synergy in Chinese health care. Decision Support Systems, 171, Article 113916.

Antunes, J., Tan, Y., Wanke, P., & Jabbour, C. J. C. (2023b). Impact of R&D and innovation in Chinese road transportation sustainability performance: A novel trigonometric envelopment analysis for ideal solutions (TEA-IS). Socio-Economic Planning Sciences, 87(A), Article 101544.

Barros, C. P., & Wanke, P. (2015). An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. Journal of Air Transport Management, 44–45, 90–102.

Beyer, J. (2002). “Please invest in our country”– how successful were the tax incentives for foreign investment in transition countries? Communist and Post-Communist Studies, 35(2), 191–211.

Branstetter, L. (2006). Is foreign direct investment a channel of knowledge spillovers? Evidence from Japan’s FDI in the United States. Journal of International Economics, 68(2), 325–344.

Carr, D. L., Markusen, J. R., & Maskus, K E. (2001). Estimating the knolwedge captial model pf the multinational enterprises. American Economic Review, 91(3), 693–708.

Chen, C., Chang, L., & Zhang, Y. (1995). The role of foreign direct investment in China’s post-1978 economic development. World Development, 23(4), 691–703.

Chen, M.-Y. (2012). Entry mode choice and performance: Evidence from Taiwanese FDI in China. Emerging Markets Finance and Trade, 48(3), 31–51.

Contractor, F. J., Dangol, R., Nuruzzaman, N., & Raghunath, S. (2020). How do country regulations and business environment impact foreign direct investment (FDI) inflows? International Business Review, 29(2), Article 101640.

Cribari-Neto, F., & Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software, 34(2), 1–24.

Cuadros, A., Orts, V., & Alguacil, M. (2004). Openness and growth: Re-examining foreign direct investment, trade and output linkages in Latin America. Journal of Development Studies, 40(4), 167–192.

Dahooie, J. H., Zavadskas, E. K., Abolhasani, M., Vanaki, A., & Turskis, Z. (2018). A novel approach for evaluation of projects using an interval-valued fuzzy Additive Ratio Assessment (ARAS) method: A case study of oil and gas well drilling projects. Symmetry, 10(2), Article 45.

Dehshiri, H. M., Sameti, M., & Sameti, M. (2012). Impact of human development index and rule of law to attract foreign direct investment in selected developing countries (MPRA Paper No. 81479). Retrieved August 28, 2023, from

Dimitrova, A., Triki, D., & Valentino, A. (2022). The effects of business- and non-business-targeting terrorism on FDI to the MENA region: The moderating role of political regime. International Business Review, 31(6), Article 101976.

Dries, L., & Swinnen, J. F. M. (2004). Foreign direct investment, vertical integration, and local suppliers: Evidence from the Polish diary sector. World Development, 32(9), 1525–1544.

Dupasquier, C., & Osakwe, P. N. (2006). Foreign direct investment in Africa: Performance, challenges, and responsibilities. Journal of Asian Economics, 17(2), 241–260.

Fernandes, A. M., & Paunov, C. (2012). Foreign direct investment in services and manufacturing productivity: evidence from Chile. Journal of Development Economics, 97(2), 305–321.

Gorg, H., & Strobl, E. (2007). The effect of R&D subsidies on private R&D. Economica, 74(294), 215–234.

Gul, M., & Guneri, A. F. (2016). A fuzzy multi criteria risk assessment based on decision matrix technique: A case study for aluminum industry. Journal of Loss Prevention in the Process Industries, 40, 89–100.

Hadi-Vencheh, A., Tan, Y., Wanke, P., & Loghmanian, S. M. (2021). Air pollution assessment in China: A novel group multiple criteria decision making model under uncertain information. Sustainability, 13(4), Article 1686.

Hsieh, T. Y., Lu, S. T., & Tzeng, G. H. (2004). Fuzzy MCDM approach for planning and design tenders selection in public office buildings. International Journal of Project Management, 22(7), 573–584.

Ilgin, M. A., Gupta, S. M., & Battaïa, O. (2015). Use of MCDM techniques in environmentally conscious manufacturing and product recovery: State of the art. Journal of Manufacturing Systems, 37(3), 746–758.

Jørgensen, B. (1997). The theory of dispersion models. Chapman and Hall.

Keršulienė, V., & Turskis, Z. (2011). Integrated fuzzy multiple criteria decision making model for architect selection. Technological and Economic Development of Economy, 17(4), 645–666.

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (Swara). Journal of Business Economics and Management, 11(2), 243–258.

Lim, S.-H. (2005). Foreign investment impact and incentive: A strategic approach to the relationship between the objectives of foreign investment policy and their promotion. International Business Review, 14(1), 61–76.

Liu, X., & Zou, H. (2008). The impact of greenfield FDI and mergers and acquisitions on innovation in Chinese high-tech industries. Journal of World Business, 43(3), 352–364.

Ly, A., Esperanca, J., & Davcik, N. S. (2018). What drives foreign direct investment: The role of language, geographical distance, information flows and technological similarity. Journal of Business Research, 88, 111–122.

Lyles, M., Li, D., & Yan, H. (2014). Chinese outward foreign direct investment performance: The role of learning. Management and Organization Review, 10(3), 411–437.

Makino, S., Beamish, P. W., & Zhao, N. B. (2004). The characteristics and performance of Japanese FDI in less developed and developed countries. Journal of World Business, 39(4), 377–392.

Manjappa, D. H., & Mahesha, M. (2008). Productivity performance of selected capital-intensive and labor-intensive industries in India during reform period: An empirical analysis. ICFAI Journal of Industrial Economics, 5, 57–65.

Maredza, A., Wanke, P., Antunes, J., Pimenta, R., & Tan, Y. (2022). Social welfare and bank performance: Evidence from a stochastic neural hybrid MCDM approach. Journal of Economic Studies, 49(7), 1137–1158.

McDonald, J., & Moffitt, R. (1980). The uses of Tobit analysis. The Review of Economics and Statistics, 62(2), 318–321.

McKinsey & Company. (2015). Women matter: An Asian perspective.

Nitsch, D., Beamish, P., & Makino, S. (1995). Characteristics and performance of Japanese foreign direct investment in Europe. European Management Journal, 13(3), 276–285.

Nitsch, D., Beamish, P., & Makino, S. (1996). Entry mode and performance of Japanese FDI in Western Europe. MIR: Management International Review, 36(1), 27–43.

Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.

Opricovic, S., & Tzeng, G. H. (2007). Extended VIKOR method in comparison with out ranking methods. European Journal of Operational Research, 178(2), 514–529.

Pangarkar, N., & Lim, H. (2003). Performance of foreign direct investment from Singapore. International Business Review, 12(5), 601–624.

Paramati, S. R., Alam, M. S., Hammondeh, S., & Hafeez, K. (2021). Long-run relationship between R&D investment and environmental sustainability: Evidence from the European Union member countries. International Journal of Finance and Economics, 26(4), 5775–5792.

Paul, J., & Feliciano-Cestero, M. M. (2021). Five decades of research on foreign direct investment by MNEs: An overview and research agenda. Journal of Business Research, 124, 800–812.

Paul, S. C., Jahan, N., Nandi, A. K., & Rahman, A. (2021). Nexus between FDI, agriculture, and rural development: evidence from Asian countries. Asian Journal of Agriculture and Rural Development, 11(4), 311–319.

Saaty, T. L. (1980). The analytic hierarchy process. McGraw-Hill.

Sabir, S., Rafique, A., & Abass, K. (2019). Institutions and FDI: Evidence from developed and developing countries. Financial Innovations, 5, Article 8.

Seguino, S. (2010). Gender, distribution, and balance of payments constrained growth in developing countries. Review of Political Economy, 22(3), 373–404.

Siripaisalpipat, P., & Hoshino, Y. (2000). Firm-specific advantages, entry modes, and performance of Japanese FDI in Thailand. Japan and the World Economy, 12(1), 33–48.

Stack, M. M., Ravishankar, G., & Pentecost, E. J. (2015). FDI performance: A stochastic analysis of location and variance determinants. Applied Economics, 47(30), 3229–3242.

Tan, Y., Jamshidi, A., Hadi-Vencheh, A., & Wanke, P. (2021). Hotel performance in the UK: The role of information entropy in a novel slack-based data envelopment analysis. Entropy, 23(2), Article 184.

Tobin, J. (1958). Liquidity preference as behavior towards risk. Review of Economic Studies, 25(2), 65–86.

Tung, S., & Cho, S. (2001). Determinants of regional investment decisions in China: An econometric model of tax incentive policy. Review of Quantitative Finance and Accounting, 17, 167–185.

Wang, S., Tong, T. W., Chen, G., & Kim, H. (2009). Expatriate utilization and foreign direct investment performance: The mediating role of knowledge transfer. Journal of Management, 35(5), 1181–1206.

Wanke, P., Azad, A. K., Antunes, J., Tan, Y., & Pimenta, R. (2023a). Endogenous and exogenous performance sources in Asian Banking: A hybrid stochastic Multi-Criteria Decision-Making approach based on sign decomposition and transfer entropy. Expert Systems with Applications, 225, Article 120180.

Wanke, P., Azad, M. A. K., & Barros, C. P. (2016a). Predicting efficiency in Malaysian Islamic banks: A two-stage TOPSIS and neural networks approach. Research in International Business and Finance, 36, 485–498.

Wanke, P., Barros, C. P., & Figueiredo, O. (2016b). Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach. Utilities Policy, 41, 31–39.

Wanke, P., Rojas, F., Tan, Y., & Moreira, J. (2023b). Temporal dependence and bank efficiency drivers in OECD: A stochastic DEA-ratio approach based on generalized auto-regressive moving averages. Expert Systems with Applications, 214, Article 119120.

Wu, Y. (2000). Measuring the performance of foreign direct investment: A case study of China. Economics Letters, 66(2), 143–150.

Yazdi, A. K., Tan, Y., Spulbar, C., Birau, R., & Alfaro, J. (2022). An approach for supply chain management contract selection in the oil and gas industry: Combination of uncertainty and multi-criteria decision-making methods. Mathematics, 10(18), Article 3230.

Yazdi, A. K., Mehdiabadi, A., Wanke, P. F., Monajemzadeh, N., Correa, H. L., & Tan, Y. (2023). Developing supply chain resilience: A robust multi-criteria decision analysis method for transportation service provider selection under uncertainty. International Journal of Management Science and Engineering Management, 18(1), 51–64.

Yu, P. L. (1973). A class of solutions for group decision problems. Management Science, 19(8), 936–946.

Zavadskas, E. K., & Kaklauskas, A. (1996). Determination of an efficient contractor by using the new method of multicriteria assessment. In D. A. Langford & A. Retik (Eds.), International Symposium for “The Organization and Management of Construction”. Shaping Theory and Practice: Vol. 2. Managing the construction project and managing risk (pp. 94–104). CIB W65. London.

Zavadskas, E. K., Antuchevicienė, J., & Chatterjee, P. (2019). Multiple-Criteria Decision-Making (MCDM) techniques for business processes information management. In E. K. Zavadskas, J. Antuchevicienė, & P. Chatterjee. Multiple-Criteria Decision-Making (MCDM) techniques for business processes information management (pp. 1–7). MDPI.

Zeleny, M. (1982). Multiple Criteria Decision Making. McGraw-Hill.

Zhang, P., Qiu, Z., & Shi, C. (2016). simplexreg: An R package for regression analysis of proportional data using the simplex distribution. Journal of Statistical Software, 71(11), 1– 21.

Zhao, Y., Antunes, J., Tan, Y., & Wanke, P. (2022). Demographic efficiency drivers in the Chinese energy production chain: A hybrid neural multi-activity network data envelopment analysis. International Journal of Finance and Economics.