2022
DOI: 10.1111/manc.12399
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R&D rivalry with endogenous compatibility

Abstract: This paper explores R&D investment decisions in an endogenously compatible network industry. This exploration reflects that a wide variety of network industries engage in substantial research and development. Indeed, the US Federal government considers R&D in network industries sufficiently important to have routinely subsidized relevant research since 1991. Its Networking and Information Technology Research and Development program remains one of the oldest and largest formal Federal programs that coordinate a… Show more

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Cited by 3 publications
(1 citation statement)
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“…On the other hand, both process innovation and product innovation improve the competitiveness of the firm [21,22], compressing the market size of other firms' products through the substitution effect [23,24], which may shift the level of competition in the market. In addition, innovations of potential entrants also may have a significant impact on the level of competition [25][26][27]. To address the possible endogeneity problem caused by reverse causal relationships, we identify the causal link between competition and quality of innovation through a natural experiment in China and use the difference-in-differences (DID) method to estimate the causal effect, following Nunn and Qian [28].…”
Section: Identification Strategymentioning
confidence: 99%
“…On the other hand, both process innovation and product innovation improve the competitiveness of the firm [21,22], compressing the market size of other firms' products through the substitution effect [23,24], which may shift the level of competition in the market. In addition, innovations of potential entrants also may have a significant impact on the level of competition [25][26][27]. To address the possible endogeneity problem caused by reverse causal relationships, we identify the causal link between competition and quality of innovation through a natural experiment in China and use the difference-in-differences (DID) method to estimate the causal effect, following Nunn and Qian [28].…”
Section: Identification Strategymentioning
confidence: 99%