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As an extension of the export-led growth of national economy overseas, direct investment by Korean multinationals has been rapidly increasing. This article explores the development of the overseas manufacturing direct investment by Korean firms and the relationship between parent firms and overseas affiliates. Three main issues are discussed: the ownership-specific assets, location-specific factors, and government policies are the important sets of variables that help to explain the internationalization of the Korean firms in the manufacturing sector, the nature of technology transfer by Korean multinationals to their subsidiaries, and the benefits as a result of its overseas investment to Korea. The data for this study were gathered through structured interviews with the parent companies of 18 out of 21 manufacturing overseas subsidiaries and joint ventures formed by Korean firms. * The Republic of Korea (henceforth called Korea) is no longer only a recipient of foreign direct investment (FDI). It is also emerging, slowly but steadily, as a source. The country now occupies a prominent place among a small group of Third World nations (Argentina, Brazil, Hong Kong, India, Mexico, Singapore, and Taiwan) whose firms have been establishing foreign direct investment, thereby earning the label of "multinational."1 The total number of overseas joint ventures and subsidiaries established by Korean multinationals was 298 by the middle of 1980, and their total volume of overseas investment was $246 million (U.S.).2 Korean multinationals have invested in trading, warehousing, transportation, mining, forestry, and construction. Manufacturing accounts for only 12 percent of the overseas projects and of the total volume of FDI by Korean multinationals. The authorized Korean FDI in manufacturing was $31,266,000 by June 1980.3 This volume of investment is not a large sum considering the size of overseas investments by firms from industrialized nations, but is also not an insignificant amount in the context of the size, resources, and state of economic development of Korea. The Korean government has authorized 24 overseas manufacturing investments in a wide range of industries including garment, cement, electric cables, motors and diesel engines, paper, plywood, artificial chemicals, and shoes. Three of the 24 have been abandoned for various reasons. The details of the existing projects (at the time of interview) are given in Table 1. The overwhelming majority of these projects are located in developing countries, particularly Asia and the Middle East. The main purpose of this paper is to discuss overseas manufacturing direct INTRODUCTION *Krishna Kumar received his M.A. and Ph.D. from Michigan State University. He has held many academic and administrative positions and has published extensively in professional journals. His University, Seoul, Korea. He holds a B.A. from Yonsei University; an M.B.A. and a Ph.D. from Washington University; and was at MIT and the East-West Center as a research fellow. His published books include Pr...

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Concrete is the most commonly used construction material. The physical properties of concrete vary with the type of concrete, such as high and ultra-high-strength concrete, fibre-reinforced concrete, polymer-modified concrete, and lightweight concrete. The precise prediction of the properties of concrete is a problem due to the design code, which typically requires specific characteristics. The emergence of a new category of technology has motivated researchers to develop mechanical strength prediction models using Artificial Intelligence (AI). Empirical and statistical models have been extensively used. These models require a huge amount of laboratory data and still provide inaccurate results. Sometimes, these models cannot predict the properties of concrete due to complexity in the concrete mix design and curing conditions. To conquer such issues, AI models have been introduced as another approach for predicting the compressive strength and other properties of concrete. This article discusses machine learning algorithms, such as Gaussian Progress Regression (GPR), Support Vector Machine Regression (SVMR), Ensemble Learning (EL), and optimized GPR, SVMR, and EL, to predict the compressive strength of Lightweight Concrete (LWC). The simulation approaches of these trained models indicate that AI can provide accurate prediction models without undertaking extensive laboratory trials. Each model’s applicability and performance were rigorously reviewed and assessed. The findings revealed that the optimized GPR model (R = 0.9803) used in this study had the greatest accuracy. In addition, the optimized SVMR and GPR model showed good performance, with R-values 0.9777 and 0.9740, respectively. The proposed model is economic and efficient, and can be adopted by researchers and engineers to predict the compressive strength of LWC.