KNOWLEDGE NETWORKS AND AMBIDEXTROUS LEARNING: WHAT IS THE IMPACT ON INNOVATION PERFORMANCE?

Li, Xiaoli; Li, Kun

doi:10.1590/S0034-759020230602

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Revista de Administração de Empresas

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ARTICLES • Rev. adm. empres. 63 (6) • 2023 • https://doi.org/10.1590/S0034-759020230602 copy

KNOWLEDGE NETWORKS AND AMBIDEXTROUS LEARNING: WHAT IS THE IMPACT ON INNOVATION PERFORMANCE?

Redes de conocimiento y aprendizaje ambidiestro: ¿cuál es el impacto en el rendimiento de la innovación?

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Knowledge networks have become a critical factor in the development of innovation. However, most studies focus on the innovation benefits derived from network embedding, and there are fewer studies on firms’ knowledge networks from the perspective of feature attributes. This study analyzes the direct and interactive effects of knowledge diversity and the combination of knowledge potential on innovation performance. The research also explores how the complementarity of ambidextrous learning affects the relationship between knowledge networks and innovation performance. The empirical analysis is based on panel data from 116 firms in China’s automotive manufacturing industry from 2010-2018. The results processed by the fixed effects negative binomial regression model indicate that the combinatorial potential of knowledge has an inverted U-shaped relationship with firm innovation performance, and knowledge diversity has a positive effect on firm innovation performance. There is an interactive effect between knowledge combination potential and knowledge diversity, and their mutual coordination improves firm innovation performance. The complementarity of ambidextrous learning significantly and positively moderates the positive relationship between the combination potential of knowledge and firm innovation performance.

Keywords:
knowledge networks; the complementarity of ambidextrous learning; combination potential of knowledge; knowledge diversity; automotive manufacturing

Patent	Classification number
1	G01M、G01B、G01B
2	G01M、G01F
3	B23K、G01B
4	B23K

	Mean	S.D.	1	2	3	4	5	6	7	8	VIF
IP	201.74	449.71	1								-
CPK	2.65	2.47	0.709^***	1							1.60
KD	0.81	0.16	0.315^***	0.201^***	1						1.46
AMB	2.54	1.17	0.633^***	0.543^***	0.517^***	1					2.24
ROE	0.08	0.12	0.043	0.003	-0.044	0.039	1				1.06
AGE	18.18	4.95	0.086^***	0.112^***	0.071^*	0.115^***	-0.028	1			1.10
SIZE	9.51	0.67	0.644^***	0.497^***	0.443^***	0.661^***	0.138^***	0.290^***	1		2.26
RD	4.32	1.93	0.025	0.140^***	0.109^***	0.102^***	-0.184^***	-0.053	-0.150^***	1	1.18

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
SIZE	0.2696^* ***** p < 0. 01. (4.27)	0.2304^* ***** p < 0. 01. (3.59)	0.2036^* ***** p < 0. 01. (3.07)	0.1309^* * p < 0. 1, (1.87)	-0.2069^* ***** p < 0. 01. (-3.88)	-0.2463^* ***** p < 0. 01. (-4.19)
RD	0.1472^* ***** p < 0. 01. (6.04)	0.1042^* ***** p < 0. 01. (4.01)	0.1313^* ***** p < 0. 01. (5.27)	0.0762^* ***** p < 0. 01. (2.85)	0.0072 (0.47)	0.0116 (0.73)
AGE	0.3323^* ***** p < 0. 01. (7.18)	0.2756^* ***** p < 0. 01. (5.83)	0.3303^* ***** p < 0. 01. (7.19)	0.2607^* ***** p < 0. 01. (5.55)	0.2090^* ***** p < 0. 01. (5.93)	0.3251^* ***** p < 0. 01. (10.64)
ROE	0.0713^* ***** p < 0. 01. (2.69)	0.0890^* ***** p < 0. 01. (3.39)	0.0723^* ***** p < 0. 01. (2.78)	0.0921^* ***** p < 0. 01. (3.64)	0.0286^* ***** p < 0. 01. (2.88)	0.0268^ p < 0. 05, (2.56)
CPK		0.4254^* ***** p < 0. 01. (5.63)		0.4190^* ***** p < 0. 01. (5.55)	0.2500^* ***** p < 0. 01. (3.60)
CPK²		-0.2824^* ***** p < 0. 01. (-4.61)		-0.2000^* ***** p < 0. 01. (-3.39)	-0.2460^* ***** p < 0. 01. (-2.63)
KD			0.1057^* ***** p < 0. 01. (3.08)	0.1382^* ***** p < 0. 01. (3.44)		0.0130 (0.44)
CPK^* * p < 0. 1, KD				0.1220^* * p < 0. 1, (1.78)
CPK2^* * p < 0. 1, KD				-0.1946^* ***** p < 0. 01. (-3.20)
AMB					0.9249^* ***** p < 0. 01. (34.79)	0.9159^* ***** p < 0. 01. (32.55)
CPK^* * p < 0. 1, AMB					0.0686^* ***** p < 0. 01. (2.97)
KD^* * p < 0. 1, AMB						0.0032 (0.12)
Constant	1.2657^* ***** p < 0. 01. (20.24)	1.3058^* ***** p < 0. 01. (21.01)	1.2776^* ***** p < 0. 01. (20.45)	1.3458^* ***** p < 0. 01. (21.37)	2.5859^* ***** p < 0. 01. (36.11)	2.5466^* ***** p < 0. 01. (36.01)
Log Likelihood	-2798.4801	-2783.1798	-2793.1616	-2770.3887	-2192.2899	-2210.4528
Wald chi2	175.27^* ***** p < 0. 01.	240.18^* ***** p < 0. 01.	181.61 ^* ***** p < 0. 01.	273.29^* ***** p < 0. 01.	1956.70^* ***** p < 0. 01.	1602.00^* ***** p < 0. 01.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
SIZE	0.4698^* ***** p < 0. 01. (8.26)	0.4511^* ***** p < 0. 01. (8.04)	0.4013^* ***** p < 0. 01. (6.73)	0.3331^* ***** p < 0. 01. (5.48)	0.1601^* ***** p < 0. 01. (5.20)	0.0952^ p < 0. 05, (1.97)
RD	0.1828^* ***** p < 0. 01. (8.05)	0.1366^* ***** p < 0. 01. (5.70)	0.1648^* ***** p < 0. 01. (7.07)	0.1019^* ***** p < 0. 01. (4.13)	0.0286^ p < 0. 05, (2.30)	0.0463^* ***** p < 0. 01. (3.20)
AGE	0.2151^* ***** p < 0. 01. (5.02)	0.1313^* ***** p < 0. 01. (3.03)	0.2132^* ***** p < 0. 01. (5.04)	0.1112^* ***** p < 0. 01. (2.65)	-0.0068 (-0.34)	0.1519^* ***** p < 0. 01. (5.60)
ROE	0.0574^ p < 0. 05, (2.27)	0.0748^* ***** p < 0. 01. (3.04)	0.0582^ p < 0. 05, (2.36)	0.0819^* ***** p < 0. 01. (3.48)	0.0097 (1.12)	0.0086 (0.87)
CPK		0.5093^* ***** p < 0. 01. (7.52)		0.5062^* ***** p < 0. 01. (7.38)	0.4546^* ***** p < 0. 01. (8.65)
CPK²		-0.3348^* ***** p < 0. 01. (-6.12)		-0.2443^* ***** p < 0. 01. (-4.51)	-0.4236^* ***** p < 0. 01. (-5.82)
KD			0.1288^* ***** p < 0. 01. (3.65)	0.1941^* ***** p < 0. 01. (4.81)		0.0208 (0.71)
CPK^* * p < 0. 1, KD				0.1686^ p < 0. 05, (2.53)
CPK2^* * p < 0. 1, KD				-0.2261^* ***** p < 0. 01. (-3.91)
AMB					1.0009^* ***** p < 0. 01. (41.81)	0.9795^* ***** p < 0. 01. (35.08)
CPK^* * p < 0. 1, AMB					0.0641^* ***** p < 0. 01. (3.46)
KD^* * p < 0. 1, AMB						0.0178 (0.64)
Constant	1.2338^* ***** p < 0. 01. (19.76)	1.2602^* ***** p < 0. 01. (20.16)	1.2416^* ***** p < 0. 01. (19.91)	1.2819^* ***** p < 0. 01. (20.16)	2.4509^* ***** p < 0. 01. (34.93)	2.4182^* ***** p < 0. 01. (33.36)
Log Likelihood	-3647.8037	-3621.1659	-3640.1426	-3601.4826	-2897.9752	-2958.8797
Wald chi2	229.47^* ***** p < 0. 01.	353.45^* ***** p < 0. 01.	235.77^* ***** p < 0. 01.	401.93^* ***** p < 0. 01.	3463.07^* ***** p < 0. 01.	2036.95^* ***** p < 0. 01.

Fundação Getulio Vargas, Escola de Administração de Empresas de S.Paulo Av 9 de Julho, 2029, 01313-902 S. Paulo - SP Brasil, Tel.: (55 11) 3799-7999, Fax: (55 11) 3799-7871 - São Paulo - SP - Brazil
E-mail: rae@fgv.br

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[1] *Corresponding author

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
SIZE	0.2696^* ***** p < 0. 01. (4.27)	0.2304^* ***** p < 0. 01. (3.59)	0.2036^* ***** p < 0. 01. (3.07)	0.1309^* * p < 0. 1, (1.87)	-0.2069^* ***** p < 0. 01. (-3.88)	-0.2463^* ***** p < 0. 01. (-4.19)
RD	0.1472^* ***** p < 0. 01. (6.04)	0.1042^* ***** p < 0. 01. (4.01)	0.1313^* ***** p < 0. 01. (5.27)	0.0762^* ***** p < 0. 01. (2.85)	0.0072 (0.47)	0.0116 (0.73)
AGE	0.3323^* ***** p < 0. 01. (7.18)	0.2756^* ***** p < 0. 01. (5.83)	0.3303^* ***** p < 0. 01. (7.19)	0.2607^* ***** p < 0. 01. (5.55)	0.2090^* ***** p < 0. 01. (5.93)	0.3251^* ***** p < 0. 01. (10.64)
ROE	0.0713^* ***** p < 0. 01. (2.69)	0.0890^* ***** p < 0. 01. (3.39)	0.0723^* ***** p < 0. 01. (2.78)	0.0921^* ***** p < 0. 01. (3.64)	0.0286^* ***** p < 0. 01. (2.88)	0.0268^ p < 0. 05, (2.56)
CPK		0.4254^* ***** p < 0. 01. (5.63)		0.4190^* ***** p < 0. 01. (5.55)	0.2500^* ***** p < 0. 01. (3.60)
CPK²		-0.2824^* ***** p < 0. 01. (-4.61)		-0.2000^* ***** p < 0. 01. (-3.39)	-0.2460^* ***** p < 0. 01. (-2.63)
KD			0.1057^* ***** p < 0. 01. (3.08)	0.1382^* ***** p < 0. 01. (3.44)		0.0130 (0.44)
CPK^* * p < 0. 1, KD				0.1220^* * p < 0. 1, (1.78)
CPK2^* * p < 0. 1, KD				-0.1946^* ***** p < 0. 01. (-3.20)
AMB					0.9249^* ***** p < 0. 01. (34.79)	0.9159^* ***** p < 0. 01. (32.55)
CPK^* * p < 0. 1, AMB					0.0686^* ***** p < 0. 01. (2.97)
KD^* * p < 0. 1, AMB						0.0032 (0.12)
Constant	1.2657^* ***** p < 0. 01. (20.24)	1.3058^* ***** p < 0. 01. (21.01)	1.2776^* ***** p < 0. 01. (20.45)	1.3458^* ***** p < 0. 01. (21.37)	2.5859^* ***** p < 0. 01. (36.11)	2.5466^* ***** p < 0. 01. (36.01)
Log Likelihood	-2798.4801	-2783.1798	-2793.1616	-2770.3887	-2192.2899	-2210.4528
Wald chi2	175.27^* ***** p < 0. 01.	240.18^* ***** p < 0. 01.	181.61 ^* ***** p < 0. 01.	273.29^* ***** p < 0. 01.	1956.70^* ***** p < 0. 01.	1602.00^* ***** p < 0. 01.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
SIZE	0.4698^* ***** p < 0. 01. (8.26)	0.4511^* ***** p < 0. 01. (8.04)	0.4013^* ***** p < 0. 01. (6.73)	0.3331^* ***** p < 0. 01. (5.48)	0.1601^* ***** p < 0. 01. (5.20)	0.0952^ p < 0. 05, (1.97)
RD	0.1828^* ***** p < 0. 01. (8.05)	0.1366^* ***** p < 0. 01. (5.70)	0.1648^* ***** p < 0. 01. (7.07)	0.1019^* ***** p < 0. 01. (4.13)	0.0286^ p < 0. 05, (2.30)	0.0463^* ***** p < 0. 01. (3.20)
AGE	0.2151^* ***** p < 0. 01. (5.02)	0.1313^* ***** p < 0. 01. (3.03)	0.2132^* ***** p < 0. 01. (5.04)	0.1112^* ***** p < 0. 01. (2.65)	-0.0068 (-0.34)	0.1519^* ***** p < 0. 01. (5.60)
ROE	0.0574^ p < 0. 05, (2.27)	0.0748^* ***** p < 0. 01. (3.04)	0.0582^ p < 0. 05, (2.36)	0.0819^* ***** p < 0. 01. (3.48)	0.0097 (1.12)	0.0086 (0.87)
CPK		0.5093^* ***** p < 0. 01. (7.52)		0.5062^* ***** p < 0. 01. (7.38)	0.4546^* ***** p < 0. 01. (8.65)
CPK²		-0.3348^* ***** p < 0. 01. (-6.12)		-0.2443^* ***** p < 0. 01. (-4.51)	-0.4236^* ***** p < 0. 01. (-5.82)
KD			0.1288^* ***** p < 0. 01. (3.65)	0.1941^* ***** p < 0. 01. (4.81)		0.0208 (0.71)
CPK^* * p < 0. 1, KD				0.1686^ p < 0. 05, (2.53)
CPK2^* * p < 0. 1, KD				-0.2261^* ***** p < 0. 01. (-3.91)
AMB					1.0009^* ***** p < 0. 01. (41.81)	0.9795^* ***** p < 0. 01. (35.08)
CPK^* * p < 0. 1, AMB					0.0641^* ***** p < 0. 01. (3.46)
KD^* * p < 0. 1, AMB						0.0178 (0.64)
Constant	1.2338^* ***** p < 0. 01. (19.76)	1.2602^* ***** p < 0. 01. (20.16)	1.2416^* ***** p < 0. 01. (19.91)	1.2819^* ***** p < 0. 01. (20.16)	2.4509^* ***** p < 0. 01. (34.93)	2.4182^* ***** p < 0. 01. (33.36)
Log Likelihood	-3647.8037	-3621.1659	-3640.1426	-3601.4826	-2897.9752	-2958.8797
Wald chi2	229.47^* ***** p < 0. 01.	353.45^* ***** p < 0. 01.	235.77^* ***** p < 0. 01.	401.93^* ***** p < 0. 01.	3463.07^* ***** p < 0. 01.	2036.95^* ***** p < 0. 01.