Cross-border mergers and acquisitions: A double-edged sword for Chinese firms’ innovation performance

Figures & data

Table 1. Variable list and Description

Variable	Type	Description	Source
INO	Dependent	The natural logarithm of the patents granted to the specifically listed firm each year during the sample period	Zephyr
DEAL	Independent variables	The growth rate of CBM&A deals a firm has conducted in a specific year compared with the previous year	Zephyr
R&D	Independent variables	R&D expenditure for a specific firm divided by the sales in a given year	CSMAR
IND	Moderator	The proportion of deals both parties have from a related industry	Zephyr
OWN	Moderator	Dummy, 1 if the firm is an SOE	CSMAR
RQ	Moderator	Regulatory quality index	World Governance Indicators
RL	Moderator	Rules of law index	World Governance Indicators
SIZE	Control	The natural logarithm of the firm’s total assets	CSMAR
AGE	Control	Firm’s age	CSMAR

Table 2. Information on the sample firms

Variable	N	% of sample		N	% of sample
Acquirers’ industry			Target Country
Mining Manufacturing Transportation and communications Wholesale and retails Finance Service Total	25 91 36 12 35 40 239	10.5% 38.1% 15.1% 5.0% 14.6% 16.7%	Europe Asia/Pacific North America Africa Others Total	52 77 36 65 9 239	21.8% 32.2% 15.1% 27.2% 3.8%
Ownership status of acquirers			R&D intensity of target firms
State-owned Private Total	167 72 239	69.9% 30.1%	High R&D intensity Low R&D intensity	128 111	53.6% 46.4%

Table 3. Correlations matrix for CBM&A by Chinese MNEs, 2014–2018

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
INO	1.00
DEAL	−0.01**	1.00
IND	0.07	0.53	1.00
SOE	0.02	0.02***	0.01**	1.00
R&D	0.09*	0.01	−0.02	−0.04	1.00
RL	0.01	0.25	0.13**	−0.03	−0.02*	1.00
RQ	0.05**	0.56	0.48	−0.01	0.04***	0.15	1.00
SIZE	0.41	−0.06	0.10***	0.15	−0.21	0.04	0.00*	1.00
AGE	0.08***	0.05***	0.05**	0.08*	0.09**	0.02**	0.02	0.17	1.00

Table 4. Results of quasi-Poisson analysis for CBM&A by Chinese MNEs, 2014–2018

VARIABLES	MODEL 1	MODEL2	MODEL3	MODEL4
	Controls Only			Negative binomial model
DEAL		−0.01 (0.04)	−0.07* (0.04)	−0.03* (0.02)
AGE	0.01 (0.01)	0.01 (0.01)	0.01 (0.01)	0.01 (0.01)
IND	0.04 (0.05)	0.03 (0.07)	−0.56*** (0.001)	−0.44** (0.005)
OWN	−0.001 (0.002)	−0.001 (0.002)	−0.001 (0.002)	−0.003 (0.002)
R&D	0.03*** (0.001)	0.03*** (0.0008)	0.03*** (0.001)	0.04** (0.0006)
SIZE	0.24*** (0.03)	0.24*** (0.0003)	0.23*** (0.0003)	0.23** (0.007)
RQ		0.41 (0.29)	−1.24** (0.06)	−0.99* (0.04)
RL		−0.38 (0.29)	1.42*** (0.0004)	−1.02*** (0.0008)
DEAL * IND			0.61*** (0.0005)	0.44*** (0.0006)
DEAL * OWN			0.0003 (0.002)	0.0001 (0.003)
DEAL * RQ			1.48*** (0.0004)	1.94** (0.006)
DEAL * RL			−1.59*** (0.0004)	−1.19*** (0.001)
R^2	0.23	0.31	0.25	0.28

*p<0.05, **p<0.01 and ***p<0.001 (standard errors in parenthesis).

Table 5. Robustness check for CBM&A by Chinese MNEs, 2014–2018

VARIABLES	MODEL 1	MODEL2	MODEL3
	Controls Only
DEAL		−0.02* (0.03)	−0.04* (0.04)
AGE	0.02 (0.01)	0.02 (0.01)	0.02 (0.01)
IND	0.06 (0.11)	0.05 (0.11)	−0.17* (0.02)
OWN	−0.002 (0.001)	−0.002 (0.001)	−0.003 (0.001)
R&D	0.04*** (0.0002)	0.04*** (0.0002)	0.04*** (0.0002)
SIZE	0.36** (0.009)	0.36** (0.009)	0.36** (0.009)
GE		0.31 (0.32)	−1.04** (0.002)
PS		−0.48 (0.31)	1.12** (0.005)
DEAL * IND			0.41** (0.005)
DEAL * OWN			0.001 (0.001)
DEAL * GE			1.33*** (0.0004)
DEAL * PS			−1.08*** (0.0008)
R^2	0.25	0.29	0.22

*p<0.05, **p<0.01 and ***p<0.001 (standard errors in parenthesis).