Spatio-temporal dynamics and influencing factors of overseas R&D greenfield investments by multinational corporations
Received date: 2023-01-09
Accepted date: 2023-06-15
Online published: 2023-11-07
As one of the manifestations of economic globalization, R&D globalization of multinational corporations (MNCs) has become an important way for innovation resources to flow in the world. Overseas R&D investment by MNCs is the main manifestation of R&D globalization. Meanwhile, overseas R&D greenfield investment can reduce the cost and risk brought by knowledge differences, while being able to transfer tacit knowledge more effectively and help promote knowledge sharing and technology integration in the internal network of MNCs, which is one of the better ways to invest in overseas R&D. This paper analyzes the temporal and spatial dynamics and influencing factors of MNCs' R&D globalization from 2003 to 2020 based on MNCs' overseas R&D greenfield investment data, with the help of GIS spatial method and statistical models. The main findings are as follows. (1) The overall growth trend of overseas R&D greenfield investment by MNCs from 2003 to 2020 is non-linear and unstable, and accompanied by cyclical fluctuations of the stage characteristics, forming two stages of gentle rise and drastic fluctuation changes. (2) MNCs' overseas R&D greenfield investments have always been dominated by manufacturing industries, with stable industry structure development, and have not shown obvious changing characteristics, but the country composition of overseas R&D greenfield investments in different industries varies significantly. (3) The spatial scope of overseas R&D greenfield investment by MNCs gradually spreads, and the spatial distribution of source countries basically locks in the "dual-core" pattern led by North America and Western Europe, while the spatial distribution of destination countries shows a triple pattern of India, China and North America. (4) Although still dominated by the "grand triangle" pattern formed by North America, EU and East Asia, the spatial linkage of overseas R&D greenfield investments by MNCs has gradually spread, tending to be complicated and networked, but has been promoted by the Asia-Pacific region with China and India as the core. The development trend of global multipolarity has emerged. (5) Economic scale, R&D resources, technology level and other attributes, as well as language, history and culture and economic proximity are the important driving forces of overseas R&D greenfield investment by MNCs, while the effect of geographical proximity is not obvious.
LI Tingzhu , DU Debin , YANG Wenlong , HUANG Xiaodong . Spatio-temporal dynamics and influencing factors of overseas R&D greenfield investments by multinational corporations[J]. GEOGRAPHICAL RESEARCH, 2023 , 42(11) : 2881 -2898 . DOI: 10.11821/dlyj020230030
图2 2003—2020年跨国公司海外R&D绿地投资的行业分布Fig. 2 Industry distribution of overseas R&D greenfield investments by MNCs from 2003 to 2020 |
表1 2003—2020年跨国公司海外R&D绿地投资影响因素的负二项回归结果Tab. 1 Regression result of the negative binomial models from 2003 to 2020 |
变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 |
---|---|---|---|---|---|
GDPi | 0.340***(0.010) | 0.288***(0.023) | |||
GDPj | 0.339***(0.010) | 0.353***(0.019) | |||
per_GDPi | 0.094***(0.018) | 0.131**(0.056) | |||
per_GDPj | 0.238***(0.018) | 0.348***(0.036) | |||
patentsi | 0.011(0.011) | 0.002(0.011) | |||
patentsj | 0.066***(0.014) | 0.034**(0.013) | |||
papersi | 0.022***(0.001) | 0.041**(0.016) | |||
papersj | 0.017***(0.001) | 0.002*0.018) | |||
intensityi | 0.663***(0.038) | 0.244***(0.039) | |||
intensityj | 0.450***(0.038) | -0.040(0.039) | |||
researchersi | -0.252***(0.052) | -0.206**(0.079) | |||
researchersj | 0.368***(0.042) | 0.106**(0.042) | |||
geo_proij | 0.578***(0.049) | 0.041(0.042) | 0.514***(0.061) | 0.085**(0.045) | 0.201***(0.053) |
lan_proij | 0.908***(0.680) | 0.474***(0.054) | 1.154***(0.084) | 0.563***(0.062) | 0.670***(0.073) |
cul_proij | 0.311***(0.074) | 0.650***(0.060) | 0.374***(0.105) | 0.139**(0.068) | 0.531***(0.093) |
eco_proij | -0.250***(0.037) | 0.190***(0.051) | -0.154***(0.066) | -0.307***(0.036) | 0.296***(0.069) |
常数项 | 3.886***(0.044) | 13.812***(0.431) | 6.458***(0.511) | 3.492***(0.041) | 11.934***(0.999) |
Log likelihood | -28585.001 | -27622.007 | -19450.472 | -28022.972 | -18943.859 |
Prob > chi2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
注:***、**、*分别表示在1%、5%、10%的显著水平下通过显著性检验;括号内为标准误差。 |
真诚感谢匿名评审专家在论文评审中所付出的时间和精力,评审专家对本文的理论背景、方法选择、结果分析、图表展示等方面的修改意见,使本文获益匪浅。
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