Using machine learning for evaluating global expansion location decisions: An analysis of Chinese manufacturing sector

Da Huo and Hassan Rauf Chaudhry

Technological Forecasting and Social Change, 2021, vol. 163, issue C

Abstract: It is now an acknowledged fact that the Fourth Industrial Revolution, and the advent of other associated technologies, mainly machine learning, are drastically changing the evolutionary framework of corporate decision making. Therefore, this research studies the location decision of Chinese companies in the global network, by using novel machine learning based framework and techniques. These include the 3D vision of mode network, heat map and the hierarchical cluster analysis, with the following support of neural network, and by incorporating the internet intensity as a proxy of the Fourth Industrial Revolution. These machine learning based algorithms reaffirm the relevance of classical variables, such as financial leverage and wage level, for the expansion decisions by Chinese companies. Our results assert that financial leverage has a negative effect on the location decision of companies in the global network. However, these connotations can be mitigated through the interaction of leverage with the firm size that yields a positive effect on the location decision. Moreover, the wage level, through its interaction with financial leverage, is able to exert a negative effect on the location decision. Finally, the effect of the probability, of the involvement in different behavioral clusters on diversification of internet intensity, is further analyzed by machine learning that is based on the neural network.

Keywords: Location decision; Machine learning; Financial leverage; Neural network; Fourth industrial revolution (search for similar items in EconPapers)
JEL-codes: F17 F23 L14 L86 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:163:y:2021:i:c:s0040162520312622

DOI: 10.1016/j.techfore.2020.120436

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