Foreign Direct Investment Decisions of Multinational Companies
Bibliographic record
Abstract
(ProQuest: ... denotes formulae omitted.)INTRODUCTIONIn the last three decades, globalization and international investment by both companies and individual investors have increased tremendously. Although investing in new projects by foreigners is evaluated favourably by local governments and public, acquisitions of local companies by foreign companies and individuals are generally evaluated as bad news. It is very timely to examine how inward and outward foreign direct investments affect local economy also how these investments affect performance of multinational companies that make these decisions. OCO Monitor, FDI Markets database reports that 4.49 trillion U.S dollar has been invested in foreign direct investment projects in the entire world and 13,490,875 new jobs have been created since 2003. At the same period 58,204 total projects have been recorded.This paper examines what factors affect multinational companies in their foreign direct investment projects. What will be the effect on profits of the companies? Do they manage the projects effectively and increase their return on investment after these projects? The paper is organized as follows: Section II shows the previous studies, Section III investigates the characteristics of the all foreign direct investment projects between 2003 and 2008. In Section IV, we show how different factors affect size of the company foreign direct investments. In Section V, we examine the important variables that influence foreign direct investment decisions. Effects of foreign direct investment on company performances before, during and after the foreign direct investment are analyzed in Section VI. We conclude the paper in Section VII.PREVIOUS STUDIESWe can classify foreign direct investment research into four different areas: those papers that concentrate on the motivations of foreign direct investment, studies that examine the location choices of the foreign direct investment, technology spillovers of inward foreign direct investment and finally individual country studies.Market access, access to high technological environment, cheap labour and resources are the most cited factors that motivate companies for foreign direct investment (Makino, Lau and Yeh (2002), Chan, Makino and Isobe (2006), Cheng (2006)).There exist many country-level studies that directly examine the foreign direct investments in a specific country or in a region. Contractor (1984) finds strategy and entry choices by U.S. multinationals are influenced by both country factors and industry characteristics. Grosse and Trevino (1996) find that bilateral trade, home country GDP and the exchange rate affect foreign direct investment into USA.Baneiji and Sambharya (1996) show that keiretsu affiliation, previous experience and higher dependence of core firms on affiliate firms, contributed to foreign direct investment decision of Japanese firms. Buckley, Clegg and Wang (2007) study both the inward and outward foreign direct investment of China to conclude that it is very important to reform the state-owned enterprises to obtain full benefits of inward foreign direct investments. Hejazi and Pauly( 2003) conclude that Canadian foreign direct investment is motivated by market access, price differences, and intra-firm trade.Harris and Ravenscraft (1991) find in their study that the majority of cross-border takeovers are in the research and development industries, whereby almost three fourths of the cross-border transactions between buyer and seller are in the same industry. Furthermore, their studies conclude that higher wealth gains are made by purchasing foreign firms than American firms. Chang and Chang (2012) examine the performance of international greenfield investments by U.S. firms and conclude that these investments can create value when they involve entering a host country for the first time or entering a developing country.Foreign direct investment in the financial industry is investigated by Moshirian (2001) by developing a model for banking industry in the US, UK and Germany. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".