Strategic Unification of Artificial Intelligence in Foreign Direct Investment Application Forms
Keywords:
foreign direct investment, artificial intelligence, machine learning, neural network, robotics, expert systems, fuzzy logic, natural language processing
Abstract
A foreign direct investment (FDI) is a very popular method of investing overseas but different from a stock investment in a foreign company. It could be purchasing of an interest in a company by an investor located outside its borders and in most cases, governments pay special interest on them. This is a business decision to acquire a substantial stake in a foreign business or to buy it outright as to expand its operations to a new region. Embedding artificial intelligence (AI) across the business requires significant investment and a change in overall approach. It is highly constructive and productive transformation that should be planned professionally, applied systematically, and managed strategically. AI drives meaningful value to business through better decision-making and consumer-facing applications. The general perception about filling a FDI application is a cumbersome job. Some countries manage this stage very methodically and investors always give priority for them as they can commence the production/business activities within a short period. Those countries who fail to gain this competitive advantage tend to lose the FDI opportunities even if they own various other advantages of resources to attract investors. This paper attempts to evaluate the potential of embedding a strategic unification of artificial intelligence in the application forms used to fill by investors at the time of starting foreign direct investment projects.
References
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McQueen, R. J., Gamer, S. R., Nevill-Manning, C. G., & Witten, I. H. (1995). Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 1(12), 275-293. https://doi.org/10.1016/0168-1699(95)98601-9
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O'Leary, D. E. (2013). Artificial Intelligence and Big Data. IEEE Intelligent Systems, 28(2), 96-99. https://doi.org/10.1109/MIS.2013.39
Ridwan, W. M., Sapitang, M., Aziz, A., Kushiar, K. F., Ahmed, A. N., & El-Shafie, A. (2021). Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia. Ain Shams Engineering Journal, 12(2), 1651-1663. https://doi.org/10.1016/j.asej.2020.09.011
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Zupan, B., Demsar, J., Kattan, M. W., Beck, J. R., & Bratko, I. (2000). Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artificial Intelligence in Medicine, 20(1), 59–75. https://doi.org/10.1016/S0933-3657(00)00053-1
Bangladesh Investment Development Authority. (2021, Novemeber 20). Bangladesh Investment Development Authority. Retrieved Novemeber 20, 2021, from https://fid.portal.gov.bd/sites/default/files/files/fid.portal.gov.bd/forms/87c6a6ad_1092_4417_a6dd_df798085d1ce/registration-of-industrial-invesment-project-1.pdf
Board of Investment of Pakistan. (2021, November 20). Board of Investment of Pakistan. Retrieved November 20, 2021, from https://invest.gov.pk/sites/default/files/inline-files/Investment%20Guide%20.pdf
Board of investment of Sri Lanka. (2021, November 20). Board of investment of Sri Lanka. Retrieved November 20, 2021, from https://investsrilanka.com/wp-content/uploads/2020/04/Section-17.pdf
business-standard. (2021). What is Foreign Direct Investment(FDI). (www.business-standard.com/) Retrieved 10 02, 2021, from https://www.business-standard.com/about/what-is-fdi
Campisi-Pinto, S., Adamowski, J., & Oron, G. (2012). Forecasting UrbanWater Demand ViaWavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy. Water Resour Manage, 26(1), 3539–3558. https://doi.org/10.1007/s11269-012-0089-y
CCG. (2021, 09 28). Market Overview. (International Trade Administration) Retrieved November 1, 2021, from https://www.trade.gov/country-commercial-guides/sri-lanka-market-overview
Chang, P. C., Liu, C.-H., & Fan, C. Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(1), 344–355. https://doi.org/10.1016/j.knosys.2009.02.005
Chopra, A., Prashar, A., & Sain, C. (2013). Natural Language Processing. International Journal of Technology Enhancements and Emerging Engineering Research, 1(4), 131-134.
Chui, M., Henke, N., & Miremad, M. (2018, 07 20). Most of AI’s Business Uses Will Be in Two Areas. (Harvard Business Review) Retrieved November 12, 2021, from https://hbr.org/2018/07/most-of-ais-business-uses-will-be-in-two-areas
Dombaycı, Ö. A., & Gölcü, M. (2009). Daily means ambient temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy, 34(1), 1158–1161. https://doi.org/10.1016/j.renene.2008.07.007
Edirisinghe, H., & Koggalage, D. (2021). The Role of Entrepreneurs in Business Development. International Journal of Scientific & Engineering Research, 12(5), 768-774.
Edirisinghe, L. (2017). Strategic Marketing Management in Container Shipping: application of Ten S Model. CINEC Acadamic Journal, 2(2017 in Press), 96-104. https://doi.org/10.4038/caj.v2i0.63
Edirisinghe, L. (2017). The Influence of Logistics Competence in International Trade in Sri Lanka: A Contemporary Study. 6th International Conference of Sri Lanka Forum of University Economists (pp. 53-59). Mihintale: Rajarata University of Sri Lanka. https://doi.org/10.2139/ssrn.3305996
Edwards, R. E., New, J., & Parker, L. E. (2012). Predicting future hourly residential electrical consumption: A machine learning case study. Energy and Buildings, 49(1), 591–603. https://doi.org/10.1016/j.enbuild.2012.03.010
Guo, P., Liu, T., Zhang, Q., Wang, L., Xiao, J., Zhang, Q., . . . Ma, W. (2017). Developing a dengue forecast model using machine learning: A case study in China. PLOS Neglected Tropical Diseases, 11(10), 1 - 22. https://doi.org/10.1371/journal.pntd.0005973
Guo, Z. H., Wu, J., Lu, H. Y., & Wang, J. Z. (2011). A case study on a hybrid wind speed forecasting method using BP neural network. Knowledge-Based Systems, 24(1), 1048–1056. https://doi.org/10.1016/j.knosys.2011.04.019
Hafezi, R., Shahrabi, J., & Hadavandi, E. (2015). A Bat-Neural Network Multi-Agent System (BNNMAS) For Stock Price Prediction: Case Study of DAX Stock Price. Applied Soft Computing, 29(1), 196-210. https://doi.org/10.1016/j.asoc.2014.12.028
IBM. (2020, 07 15). Machine Learning. (www.ibm.com) Retrieved October 1, 2021, from https://www.ibm.com/cloud/learn/machine-learning
Invest Maldives. (2021, November 20). Invest Maldives. Retrieved November 20, 2021, from https://business.egov.mv/Downloads/Forms/Foreign_Investment_Application_Form.pdf
Kok, J. N., Boers, E. J., Kosters, W. A., Putten, P. V., & Poel, M. (2020). Artificial Intelligence: Definition, Trends, Techniques, and Cases. Paris: Encyclopedia of Life Support Systems. Retrieved from http://www.eolss.net/sample-chapters/c15/e6-44.pdf
Kwong, C. K., Ip, W. H., & Chan, J. W. (2002). Combining scoring method and fuzzy expert systems approach to supplier assessment: a case study. Integrated Manufacturing Systems, 13(7), 512 - 519. https://doi.org/10.1108/09576060210442671
Lee, W. S., Choi, H. S., & Sohn, S. Y. (2018). Forecasting new product diffusion using both patent citation and web search traffic. Plos One, 13(4), 1-12. https://doi.org/10.1371/journal.pone.0194723
Lin, Y. P., Chu, H. J., Wu, C. F., & Verburg, P. H. (2011). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25(1), 65-87. https://doi.org/10.1080/13658811003752332
Lingitz, L., Gallina, V., Ansari, F., Gyulai, D., Pfeiffer, A., Sihn, W., & Monostori, L. (2018). Lead time prediction using machine learning algorithms: A case study by a semiconductor manufacturer. 51st CIRP Conference on Manufacturing Systems, 72, 1051-1056. Stockholm. https://doi.org/10.1016/j.procir.2018.03.148
McCoy, J. T., & Auret, L. (2019). Machine learning applications in minerals processing: A review. Minerals Engineering, 132(1), 95-109. https://doi.org/10.1016/j.mineng.2018.12.004
McQueen, R. J., Gamer, S. R., Nevill-Manning, C. G., & Witten, I. H. (1995). Applying machine learning to agricultural data. Computers and Electronics in Agriculture, 1(12), 275-293. https://doi.org/10.1016/0168-1699(95)98601-9
Moran, T., Görg, H., Seric, A., & Krieger, Boden, C. (2017, 10 19). Attracting quality foreign direct investment in developing countries. (International Growth Centre) Retrieved October 5, 2021, from https://www.theigc.org/blog/attracting-quality-foreign-direct-investment-developing-countries/
Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544–551. https://doi.org/10.1136/amiajnl-2011-000464
Nwanji, T. I., Howell, K. E., Faye, S., Otekunrin, A. O., Eluyela, D. F., Lawal, A. I., & Eze, S. C. (2020). Impact of Foreign Direct Investment on the Financial Performance of Listed Deposit Banks in Nigeria. International Journal of Financial Research, 11(2), 323-347. https://doi.org/10.5430/ijfr.v11n2p323
OECD. (2002). Foreign Direct Investment for Development. France: Organisation for Economic Co-operation and Development. https://doi.org/10.1787/9789264199286-en
O'Leary, D. E. (2013). Artificial Intelligence and Big Data. IEEE Intelligent Systems, 28(2), 96-99. https://doi.org/10.1109/MIS.2013.39
Ridwan, W. M., Sapitang, M., Aziz, A., Kushiar, K. F., Ahmed, A. N., & El-Shafie, A. (2021). Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia. Ain Shams Engineering Journal, 12(2), 1651-1663. https://doi.org/10.1016/j.asej.2020.09.011
Saeed, K., & Lamech, R. (2003). What International Investors Look For When Investing In Developing Countries. Washington, DC: World Bank.
Schwab, K. (2019). Global Competitiveness Report. Geneva: World Economic Forum.
Singh, K. P., Basant, A., Malik, A., & Jain, G. (2009). Artificial neural network modeling of the river water quality—A case study. Ecological Modelling, 220(1), 888–895. https://doi.org/10.1016/j.ecolmodel.2009.01.004
UNCTAD. (2003). Foreign Direct Investment and Performance Requirements: New Evidence from Selected Countries. (https://unctad.org/system/) Retrieved November 10, 2021, from https://unctad.org/system/files/official-document/iteiia20037_en.pdf
UNCTAD. (2004). Investment Policy Review -Sri Lanka. Geneva: United Nations Conference on Trade and Development.
UNCTAD. (2009). UNCTAD Training Manual on Statistics for FDI and the Operations of TNCs. (https://unctad.org) Retrieved November 11, 2021, from https://unctad.org/system/files/official-document/diaeia20091_en.pdf
Yao, J., & Tan, C. L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing, 34(1), 2000. https://doi.org/10.1016/S0925-2312(00)00300-3
Zupan, B., Demsar, J., Kattan, M. W., Beck, J. R., & Bratko, I. (2000). Machine learning for survival analysis: a case study on recurrence of prostate cancer. Artificial Intelligence in Medicine, 20(1), 59–75. https://doi.org/10.1016/S0933-3657(00)00053-1
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2021-12-08
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