Relational assets or liabilities? Competition, collaboration, and firm intellectual property breakthrough in the Chinese high-speed train sector

Aurora Liu Genin (), Justin Tan () and Juan Song ()
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Aurora Liu Genin: University of Massachusetts Amherst
Justin Tan: York University
Juan Song: Central South University

Journal of International Business Studies, 2022, vol. 53, issue 9, No 4, 1895-1923

Abstract: Abstract How does government coordination in the strategic sectors affect the impact of relational resources on firm intellectual property (IP) development in the emerging economies? We investigate this question in the Chinese high-speed train sector from 1993 to 2014. Contrary to prior findings that international joint ventures (IJVs) lead technological innovation in the emerging economies, IJVs underperform in IP development in this context, whereas government-affiliated domestic firms outperform. We argue that government coordination in the strategic sector has escalated cross-border competitive tension but facilitated domestic collaborative innovation. Hence, IJVs face relational liabilities that hinder IP breakthrough, whereas government-affiliated domestic firms can leverage relational assets for innovation. We further examine the effects of ego-network density in the innovation network, which captures the degree to which a firm relies on partners to innovate. Consistent with our theory, innovation network density hampers IP development for the IJVs but promotes that for the government-affiliated domestic firms. Our findings offer actionable insights for innovation managers and policymakers in the strategic sectors. Firm managers should consider the potential influences from government coordination when acquiring relational resources for innovation. Policymakers should keep in mind how government actions may influence both interfirm collaboration and competition when building innovation networks.

Keywords: international joint venture; IP breakthrough in emerging economies; relational liabilities; innovation network; Chinese high-speed train sector; panel data analysis (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1057/s41267-021-00482-7

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