Vertical merger, R&D collaboration and innovation

ABSTRACT

This paper studies the effects of vertical merger and R&D collaboration activities on firms' innovation decisions and stock returns based on a continuous-time real option model under market and technological uncertainties. Our analysis confirms vertical merger's benefit in amplifying the potential gain from innovation through eliminating inefficiencies. We show that vertical merger boosts innovation incentives in two ways: it reduces the optimal innovation threshold when firms suspend the project and increases R&D investment when firms launch the project. If vertical merger is not possible, R&D collaboration can improve firms' innovation levels as an alternative decision, but inefficiencies still exist which implies less pronounced stimulation effects. Both vertical merger and R&D collaboration can reduce firms' risk when conducting innovation project and weaken the positive R&D-returns relation and financial constraints-returns relation, while these effects of vertical merger are stronger than those of R&D collaboration.

KEYWORDS:

• Vertical merger
• innovation
• R&D collaboration
• stock return

JEL CLASSIFICATIONS:

• D43
• G21
• G28

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 We assume that firms' innovation decision-making procedures and the merger process take no time.

2 We assume that the degrees of financial constraints of all firms along this production chain are the same.

3 We could introduce an another parameter p to characterize the probability of innovation success. In this case, we have two dimensions to describe the risk of innovation: (i) the probability of innovation success p, and (ii) the random completion date for successful innovation h. Detailed analysis is in Appendix 8.

4 See Appendix 1.

5 See Appendix 3.

6 The proof is similar to that of Lemma 3.1.

7 The proof is also similar to Lemma 3.1.

8 $I^{c\ast }$ is the sum of investments of the downstream firm and the upstream firm in the R&D collaboration case.

9 Here we choose the baseline parameters according to Tarsalewska (2015) and Dockner and Siyahhan (2015). In fact, though not reported here, we also observe that the baseline parameters does not qualitatively affect the results.

10 We find the impacts of α and ϵ on firms' optimal investment are consistent with those on the optimal innovation threshold. More analysis is given in Appendix 9.

Additional information

Funding

The authors acknowledge financial supports from the National Natural Science Foundation of China [grant numbers 71572203, 71501196, 71721001 and U1811462], the Innovative Research Team Project of Guangdong Province [grant number 2016WCXTD001] and the Natural Science Foundation of Guangdong Province of China [grant number 2014A030312003], respectively.