How do institutional innovation systems affect open innovation?

Figures & data

Table 1. Explanation of the variables used in the analysis.

Variables	References	Explanation
Dependent variable innovation	(OECD, 2005)	Whether a firm introduced a radically new or significantly improved product in the previous three years (1/0)
Independent variables
search_breadth	(Laursen & Salter, 2006)	Number of sources (from 0 to 9) that firms deemed to be important or very important for innovation
R&D cooperation	(Lee et al., 2010)	Number of research and development (R&D) cooperation activities (from 0 to 3)
government_funds	(Lee et al., 2010)	Whether firms receive financial support from government agencies (1/0)
private_funds	(Lee et al., 2010)	Whether firms receive financial support from commercial banks (1/0)
training	(Lee et al., 2010)	Whether firms engage in training activities (1/0)
Control variables small	(Rammer & Schubert, 2018)	If firms have fewer than 250 employees (1/0)
age	(Brunswicker & Vanhaverbeke, 2015)	Number of years in business
manufacturing	(West & Bogers, 2014)	If firms operate in manufacturing industries (1/0)
service	(West & Bogers, 2014)	If firms operate in service industries (1/0)
market_local	(Kang & Kang, 2009)	If firms sell products/services to local markets (1/0)
market_national	(Kang & Kang, 2009)	If firms sell products/services to national markets (1/0)
market_international	(Kang & Kang, 2009)	If firms sell to overseas markets (1/0)
family	(Chrisman et al., 2015)	If firms are family owned (1/0)
country		If firms are located in Australia or China (1/0)

Table 2. Descriptive statistics.

			Observations	Mean	SD
1	innovation	China	119	0.597	0.493
		Australia	223	0.645	0.477
		Sample	342	0.635	0.482
2	search_breadth	China	119	8.202	1.078
		Australia	223	6.798	2.589
		Sample	342	7.287	2.283
3	R&D cooperation	China	119	0.731	0.909
		Australia	223	0.475	0.924
		Sample	342	0.564	0.926
4	government_funds	China	119	0.672	0.471
		Australia	223	0.099	0.299
		Sample	342	0.298	0.458
5	private_funds	China	119	0.664	0.474
		Australia	223	0.175	0.381
		Sample	342	0.345	0.476
6	training	China	119	0.513	0.502
		Australia	223	0.170	0.377
		Sample	342	0.289	0.454
7	small	China	119	0.975	0.157
		Australia	223	0.839	0.369
		Sample	342	0.886	0.318
8	age	China	119	2.378	2.029
		Australia	223	3.570	1.851
		Sample	342	3.155	1.995
9	manufacturing	China	119	0.361	0.482
		Australia	223	0.215	0.412
		Sample	342	0.266	0.443
10	service	China	119	0.067	0.251
		Australia	223	0.619	0.487
		Sample	342	0.427	0.495
11	market_local	China	119	0.563	0.498
		Australia	223	0.933	0.251
		Sample	342	0.804	0.397
12	market_national	China	119	0.580	0.496
		Australia	223	0.574	0.496
		Sample	342	0.576	0.495
13	market_international	China	119	0.235	0.426
		Australia	223	0.408	0.493
		Sample	342	0.348	0.477
14	family	China	119	0.185	0.390
		Australia	223	0.117	0.322
		Sample	342	0.140	0.348
15	country	Sample	342	0.652	0.477

Table 3. Correlation matrix.

			1	2	3	4	5	6	7	8	9	10	11	12	13	14
1	innovation
2	search_breadth	China	0.231*
		Australia	0.250***
		Sample	0.216***
3	R&D cooperation	China	0.239**	0.399***
		Australia	0.151*	0.085
		Sample	0.166**	0.195***
4	government_funds	China	0.009	0.084	−0.062
		Australia	0.177**	0.078	0.200**
		Sample	0.044	0.178**	0.176**
5	private_funds	China	−0.005	−0.173	−0.230*	0.223*
		Australia	0.185**	0.008	0.213**	0.046
		Sample	0.066	0.044	0.013	0.387***
6	training	China	0.261**	0.478***	0.535***	−0.036	−0.374***
		Australia	0.129	0.125	0.763***	0.210**	0.231**
		Sample	0.150**	0.277***	0.629***	0.274***	0.134*
7	small	China	−0.023	0.086	0.065	−0.112	−0.114	0.165
		Australia	−0.165*	−0.036	0.052	−0.018	−0.087	0.069
		Sample	−0.139*	0.029	0.080	0.093	0.028	0.148**
8	age	China	0.124	0.069	0.127	0.185	−0.061	0.130	0.106
		Australia	−0.018	−0.111	−0.081	−0.037	0.074	−0.029	−0.038
		Sample	0.047	−0.109*	−0.064	−0.116*	−0.120*	−0.067	−0.072
9	manufacturing	China	−0.059	0.031	−0.053	−0.071	−0.168	0.069	0.009	−0.226*
		Australia	0.082	0.114	0.130	−0.027	0.017	0.140*	−0.037	−0.085
		Sample	0.017	0.114*	0.069	0.056	0.022	0.156**	0.008	−0.179***
10	service	China	−0.258**	−0.103	−0.196	0.045	0.049	−0.141	0.043	0.224	−0.202
		Australia	−0.065	−0.109	−0.073	0.074	0.045	−0.086	0.057	0.106	−0.667***
		Sample	−0.057	−0.202***	−0.143**	−0.278***	−0.229***	−0.264***	−0.062	0.249***	−0.520***
11	market_local	China	−0.034	0.112	0.108	−0.038	0.055	−0.046	0.183*	−0.038	−0.043	−0.034
		Australia	0.031	0.094	0.067	0.089	−0.018	0.027	0.077	−0.087	0.054	−0.063
		Sample	0.023	−0.018	0.014	−0.258***	−0.200***	−0.172**	−0.015	0.071	−0.07	0.203***
12	market_national	China	0.098	0.054	0.050	0.204*	−0.029	0.090	0.080	−0.055	0.144	−0.179	−0.407***
		Australia	0.118	0.104	0.022	0.011	0.230**	0.149*	−0.058	0.007	0.098	0.015	−0.087
		Sample	0.111*	0.077	0.031	0.081	0.112*	0.117*	−0.029	−0.017	0.115*	−0.025	−0.215***
13	market_international_ international	China	0.173	0.005	0.114	−0.077	0.059	0.105	−0.164	0.040	−0.087	−0.070	0.090	−0.130
		Australia	0.162*	0.191**	0.077	0.093	0.122	0.085	−0.107	−0.084	0.009	0.051	−0.105	0.512***
		Sample	0.172**	0.099*	0.059***	−0.087	−0.001	0.021	−0.144**	0.009	−0.051	0.114*	0.067	0.304***
14	family	China	−0.050	−0.032	−0.339***	−0.036	−0.074	−0.099	0.077	−0.085	0.047	0.218*	0.114	−0.209*	0.093
		Australia	0.029	−0.087	0.084	−0.027	0.127	0.058	0.083	0.011	−0.02	0.026	0.042	0.059	0.011
		Sample	−0.008	−0.059	−0.065	0.031	0.079	0.021	0.092	−0.050	0.023	0.009	0.03	−0.045	0.023
15	country	Sample	0.057	−0.202***	−0.132*	−0.597***	−0.490***	−0.359***	−0.204***	0.281***	−0.158**	0.531***	0.444***	−0.006	0.173**	−0.094

Spearman correlation, *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 4. Variance inflation factor (VIF) values.

	VIF
innovation	1.16
search_breadth	1.23
R&D cooperation	1.78
government_funds	1.64
private_funds	1.41
training	2.04
small	1.11
age	1.14
manufacturing	1.47
service	1.98
market_local	1.39
market_national	1.25
market_international	1.21
family	1.05
country	3.25

Table 5. Estimation of the impact of search breadth on innovation.

	Innovation: China	Innovation: Australia	Innovation (combined sample)
	(2)	(3)	(1)
Explanatory variables
search_breadth	−0.799**	0.307**	0.409***
	(0.079)	(0.041)	(0.045)
Control variables
small	0.566	−0.532	−0.419
age	0.086	0.038	0.052
family	0.123	0.238	0.100
manufacturing	−0.110	0.026	−0.164
service	−1.016	−0.021	−0.209
country			0.693
X^2	67.71***	28.15***	69.32***
Observation	119	223	342

Average marginal effects in parentheses. Instrumental variables were: small, manufacturing, service, market_local, market_national, and market_international. *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 6. Estimation of the moderation impact of research and development (R&D) cooperation on the relationship between search breadth and innovation.

	Innovation: China	Innovation: Australia	Innovation (combined sample)
	(4)	(5)	(6)
Explanatory variables
search_breadth	1.040***	0.248	0.249
	(0.051)	(0.045)	(0.034)
R&D_cooperation	−0.771	−0.362	−0.717
	(−0.069)	(0.057)	(−0.002)
search_breadth*R&D_cooperation	0.568*	0.099	0.112
	(−0.033)	(0.013)	(−0.011)
Control variables
small	−1.128	−0.668**	−0.682**
age	−0.038	−0.012	−0.019
family	0.152	0.522	0.739
manufacturing	0.009	0.277	0.026
service	0.306	0.248	0.138
country			0.328
X^2	199.88***	23.76**	76.30***
Observation	119	223	342

Average marginal effects in parentheses. Instrumental variables were: small, manufacturing, service, market_local, market_national, and market_international. *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 7. Estimation of the moderation impact of government funds on the relationship between search breadth and innovation.

	Innovation: China	Innovation: Australia	Innovation (combined sample)
	(7)	(8)	(9)
Explanatory variables
search_breadth	−1.404***	0.708**	0.934***
	(0.050)	(0.076)	(0.058)
government_funds	0.430	0.033	−0.371
	(0.075)	(0.174)	(0.070)
search_breadth*government_funds	−1.302***	0.663*	0.903***
	(−0.048)	(0.046)	(0.017)
Control variables
small	0.225	−0.403	−0.099
age	−0.002	−0.010	0.010
family	0.054	0.450	0.284
manufacturing	0.073	−0.048	−0.264
service	−0.422	−0.231	−0.490**
country			0.604***
X^2	122.85***	53.05***	148.41***
Observation	119	223	342

Average marginal effects in parentheses. Instrumental variables were: small, manufacturing, service, market_local, market_national, and market_international. *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 8. Estimation of the moderation impact of private funds on the relationship between search breadth and innovation.

	Innovation: China	Innovation: Australia	Innovation (combined sample)
	(10)	(11)	(12)
Explanatory variables
search_breadth	−1.157***	0.341	−0.666***
	(0.072)	(0.055)	(0.044)
private_funds	0.165	−0.087	0.797
	(−0.015)	(0.247)	(0.102)
search_breadth*private_funds	−0.898*	0.270	−0.801***
	(−0.015)	(0.022)	(−0.003)
Control variables
small	0.398	−0.635	0.024
age	0.025	0.040	−0.105**
family	0.009	0.389	−0.323
manufacturing	−0.018	0.210	−0.162
service	−0.230	0.100	−0.254
country			−0.241
X^2	140.35***	24.24**	191.21***
Observation	119	223	342

Average marginal effects in parentheses. Instrumental variables were: small, manufacturing, service, market_local, market_national, and market_international. *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 9. Estimation of the moderation impact of training on the relationship between search breadth and innovation.

	Innovation: China	Innovation: Australia	Innovation (combined sample)
	(13)	(14)	(15)
Explanatory variables
search_breadth	1.216***	−0.233	0.409
	(0.067)	(0.057)	(0.047)
training	−1.788*	−1.714**	−0.732
	(−0.157)	(0.060)	(−0.059)
search_breadth*training	1.401***	−0.390	0.348
	(0.014)	(0.039)	(0.002)
Control variables
small	−0.888	−0.476	−0.694*
age	−0.009	−0.111*	−0.016
family	0.189	0.158	0.247
manufacturing	0.028	0.323	−0.136
service	0.254	0.142	−0.130
country			0.461*
X^2	125.21***	68.76***	47.32***
Observation	119	223	342

Marginal effects. Instrumental variables were: small, manufacturing, service, market_local, market_national, and market_international. *, **, *** denote p < 0.05, p < 0.01, and p < 0.001, respectively.

Table 10. Comparison of research and development (R&D) cooperation in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.7311	0.0833	0.9085	0.5662	0.8960
1	223	0.4753	0.0619	0.92422	0.3534	0.5973
combined	342	0.5643	0.0500	0.9255	0.4659	0.6628
diff		0.2558	0.1043		0.0506	0.4609
diff = mean(0) – mean(1)					t = 2.4521
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 0.9926		Pr(|T| > |t|) = 0.0147			Pr(T > t) = 0.0074

Table 11. Comparison of access to government funds in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.6723	0.0432	0.4714	0.5867	0.7578
1	223	0.0987	0.02	0.2989	0.0592	0.1381
combined	342	0.2982	0.0248	0.4582	0.2495	0.347
diff		0.5736	0.0418		0.4914	0.6558
diff = mean(0) – mean(1)					t = 13.7299
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 1.0000		Pr(|T| > |t|) = 0.0000			Pr(T > t) = 0.0000

Table 12. Comparison of access to private funds in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.6639	0.0435	0.4744	0.5778	0.75
1	223	0.1749	0.0255	0.3807	0.1246	0.2251
combined	342	0.345	0.0257	0.4761	0.2944	0.3957
diff		0.489	0.0472		0.3962	0.5818
diff = mean(0) – mean(1)					t = 10.3633
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 1.0000		Pr(|T| > |t|) = 0.0000			Pr(T > t) = 0.0000

Table 13. Comparison of training in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.5126	0.046	0.502	0.4215	0.6037
1	223	0.1704	0.0252	0.3768	0.1207	0.2201
combined	342	0.2895	0.0246	0.4542	0.2412	0.3378
diff		0.3422	0.0482		0.2474	0.437
diff = mean(0) – mean(1)					t = 7.1017
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 1.0000		Pr(|T| > |t|) = 0.0000			Pr(T > t) = 0.0000

Table 14. Comparison of radical innovation in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.3782	0.0446	0.487	0.2897	0.4666
1	223	0.2601	0.0294	0.4397	0.2021	0.3181
combined	342	0.3012	0.0248	0.4594	0.2523	0.35
diff		0.1181	0.0518		0.0161	0.22
diff = mean(0) – mean(1)					t = 2.2774
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 0.9883		Pr(|T| > |t|) = 0.0234			Pr(T > t) = 0.0117

Table 15. Comparison of incremental innovation in Chinese and Australian firms.

Group	Obs.	Mean	SE	SD	[95% Conf.	interval]
0	119	0.3675	0.0448	0.4842	0.2789	0.4562
1	223	0.5243	0.0349	0.5006	0.4555	0.593
combined	342	0.4675	0.0278	0.4997	0.4128	0.5222
diff		−0.1568	0.0573		−0.2694	−0.0441
diff = mean(0) – mean(1)					t = − 2.7368
Ho: diff = 0					degrees of freedom = 340
Ha: diff < 0		Ha: diff ! = 0			Ha: diff > 0
Pr(T < t) = 0.0033		Pr(|T| > |t|) = 0.0065			Pr(T > t) = 0.9967

Organisation for Economic Co-operation and Development. (2005). Oslo manual: Guidelines for collecting and interpreting innovation data (3rd ed.). OECD Publishing.

 Google Scholar

Laursen, K., & Salter, A. (2006). Open for innovation: The role of openness in explaining innovation performance among UK manufacturing firms. Strategic Management Journal, 27(2), 131–150. https://doi.org/10.1002/smj.507

 Web of Science ®Google Scholar

Lee, S., Park, G., Yoon, B., & Park, J. (2010). Open innovation in SMEs—An intermediated network model. Research Policy, 39(2), 290–300. https://doi.org/10.1016/j.respol.2009.12.009

 Web of Science ®Google Scholar

Rammer, C., & Schubert, T. (2018). Concentration on the few: Mechanisms behind a falling share of innovative firms in Germany. Research Policy, 47(2), 379–389. https://doi.org/10.1016/j.respol.2017.12.002

 Web of Science ®Google Scholar

Brunswicker, S., & Vanhaverbeke, W. (2015). Open innovation in small and medium-sized enterprises (SMEs): External knowledge sourcing strategies and internal organizational facilitators. Journal of Small Business Management, 53(4), 1241–1263. https://doi.org/10.1111/jsbm.12120

 Web of Science ®Google Scholar

West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814–831. https://doi.org/10.1111/jpim.12125

 Web of Science ®Google Scholar

Kang, K. H., & Kang, J. (2009). How do firms source external knowledge for innovation? Analysing effects of different knowledge sourcing methods. International Journal of Innovation Management, 13(1), 1–17. https://doi.org/10.1142/S1363919609002194

 Google Scholar

Chrisman, J. J., Chua, J. H., De Massis, A., Frattini, F., & Wright, M. (2015). The ability and willingness paradox in family firm innovation. Journal of Product Innovation Management, 32(3), 310–318. https://doi.org/10.1111/jpim.12207

 Web of Science ®Google Scholar