科学学研究

变量	模型1	模型2	模型3	模型4
Assignee	0.041	0.039	0.042	0.041
	(1.373)	(1.297)	(1.416)	(1.381)
Team	0.129	0.137	0.127	0.133
	(0.864)	(0.919)	(0.849)	(0.894)
Gexperience	-0.294^***	-0.292^***	-0.295^***	-0.290^***
	(-4.882)	(-4.813)	(-4.852)	(-4.712)
Scale	2.399^***	2.441^***	2.398^***	2.426^***
	(4.603)	(4.721)	(4.433)	(4.603)
Patcites	0.188^***	0.184^***	0.185^***	0.182^***
	(6.881)	(6.708)	(6.735)	(6.507)
Npatcites	1.151^***	1.154^***	1.132^***	1.151^***
	(8.790)	(8.786)	(8.508)	(8.576)
Claims	2.163^***	2.201^***	2.136^***	2.198^***
	(6.111)	(6.156)	(6.150)	(6.316)
Family	-12.524^***	-12.480^***	-12.647^***	-12.542^***
	(-3.231)	(-3.206)	(-3.264)	(-3.244)
Distance		1.092^**		1.515^***
		(2.201)		(2.729)
Distance²		-1.030^*		-1.383^**
		(-1.826)		(-2.256)
Diversity			1.448^*	1.782^**
			(1.798)	(2.204)
Diversity²			-1.378^*	-1.964^**
			(-1.829)	(-2.528)
Year dummies	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included
Constant	-3.846^***	-3.910^***	-4.096^***	-4.153^***
	(-7.209)	(-7.274)	(-7.459)	(-7.547)
Observations	8,485	8,485	8,485	8,485
Pseudo R²	0.102	0.103	0.103	0.105
Wald chi²	307.6	306.7	314.8	317.2
log likelihood	-1488	-1486	-1487	-1482

变量	模型1	模型2	模型3	模型4
Assignee	0.041	0.039	0.042	0.041
	(1.373)	(1.297)	(1.416)	(1.381)
Team	0.129	0.137	0.127	0.133
	(0.864)	(0.919)	(0.849)	(0.894)
Gexperience	-0.294^***	-0.292^***	-0.295^***	-0.290^***
	(-4.882)	(-4.813)	(-4.852)	(-4.712)
Scale	2.399^***	2.441^***	2.398^***	2.426^***
	(4.603)	(4.721)	(4.433)	(4.603)
Patcites	0.188^***	0.184^***	0.185^***	0.182^***
	(6.881)	(6.708)	(6.735)	(6.507)
Npatcites	1.151^***	1.154^***	1.132^***	1.151^***
	(8.790)	(8.786)	(8.508)	(8.576)
Claims	2.163^***	2.201^***	2.136^***	2.198^***
	(6.111)	(6.156)	(6.150)	(6.316)
Family	-12.524^***	-12.480^***	-12.647^***	-12.542^***
	(-3.231)	(-3.206)	(-3.264)	(-3.244)
Distance		1.092^**		1.515^***
		(2.201)		(2.729)
Distance²		-1.030^*		-1.383^**
		(-1.826)		(-2.256)
Diversity			1.448^*	1.782^**
			(1.798)	(2.204)
Diversity²			-1.378^*	-1.964^**
			(-1.829)	(-2.528)
Year dummies	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included
Constant	-3.846^***	-3.910^***	-4.096^***	-4.153^***
	(-7.209)	(-7.274)	(-7.459)	(-7.547)
Observations	8,485	8,485	8,485	8,485
Pseudo R²	0.102	0.103	0.103	0.105
Wald chi²	307.6	306.7	314.8	317.2
log likelihood	-1488	-1486	-1487	-1482

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.034	0.042	0.045	0.049^*	0.037	0.047
	(1.068)	(1.426)	(1.547)	(1.686)	(1.191)	(1.632)
Team	0.149	0.138	-0.148	-0.159	-0.144	-0.153
	(0.998)	(0.924)	(-0.963)	(-1.033)	(-0.932)	(-0.993)
Gexperience	-0.296^***	-0.296^***	-0.412^***	-0.413^***	-0.414^***	-0.414^***
	(-4.884)	(-4.873)	(-6.555)	(-6.394)	(-6.561)	(-6.397)
Scale	2.463^***	2.422^***	0.076	0.078	0.085	0.114
	(4.704)	(4.462)	(0.093)	(0.100)	(0.103)	(0.146)
Patcites	0.192^***	0.186^***	0.166^***	0.167^***	0.171^***	0.167^***
	(6.663)	(6.717)	(6.073)	(6.083)	(6.039)	(6.039)
Npatcites	1.143^***	1.129^***	1.019^***	0.996^***	1.009^***	0.991^***
	(8.700)	(8.466)	(7.361)	(7.079)	(7.296)	(7.029)
Claims	2.181^***	2.139^***	2.090^***	2.043^***	2.070^***	2.044^***
	(6.084)	(6.079)	(5.645)	(5.670)	(5.614)	(5.632)
Family	-12.237^***	-12.479^***	-11.867^***	-12.069^***	-11.713^***	-11.967^***
	(-3.133)	(-3.214)	(-3.145)	(-3.206)	(-3.081)	(-3.165)
Distance	1.220^**		1.067^**		1.172^**
	(2.454)		(2.087)		(2.294)
Distance²	-1.257^**		-0.997^*		-1.200^**
	(-2.204)		(-1.721)		(-2.055)
Diversity		1.530^*		1.527^*		1.633^**
		(1.878)		(1.862)		(1.964)
Diversity²		-1.410^*		-1.547^**		-1.606^**
		(-1.867)		(-2.019)		(-2.088)
Interdensity	-0.480^**	-0.323			-0.332	-0.199
	(-1.990)	(-1.356)			(-1.474)	(-0.847)
Distance*Interdensity	5.405^***				5.198^***
	(3.210)				(3.273)
Distance²*Interdensity	-7.785^***				-7.598^***
	(-3.110)				(-3.116)
Diversity*Interdensity		1.689				2.010
		(0.446)				(0.516)
Diversity²*Interdensity		-0.098				-0.482
		(-0.036)				(-0.172)
Intradensity			0.159^***	0.158^***	0.158^***	0.157^***
			(7.294)	(7.484)	(7.202)	(7.398)
Distance*Intradensity			-0.109		-0.085
			(-0.798)		(-0.621)
Distance²*Intradensity			0.082		0.052
			(0.562)		(0.356)
Diversity*Intradensity				-0.120		-0.124
				(-0.622)		(-0.643)
Diversity²*Intradensity				0.104		0.114
				(0.524)		(0.574)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.860^***	-3.848^***	-3.357^***	-3.391^***	-3.404^***	-3.684^***
	(-7.170)	(-7.228)	(-6.270)	(-6.401)	(-6.313)	(-6.663)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.106	0.104	0.119	0.118	0.121	0.119
Wald chi²	317.1	322.6	348.9	362.8	358.5	369.1
log likelihood	-1481	-1485	-1461	-1461	-1456	-1460

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.034	0.042	0.045	0.049^*	0.037	0.047
	(1.068)	(1.426)	(1.547)	(1.686)	(1.191)	(1.632)
Team	0.149	0.138	-0.148	-0.159	-0.144	-0.153
	(0.998)	(0.924)	(-0.963)	(-1.033)	(-0.932)	(-0.993)
Gexperience	-0.296^***	-0.296^***	-0.412^***	-0.413^***	-0.414^***	-0.414^***
	(-4.884)	(-4.873)	(-6.555)	(-6.394)	(-6.561)	(-6.397)
Scale	2.463^***	2.422^***	0.076	0.078	0.085	0.114
	(4.704)	(4.462)	(0.093)	(0.100)	(0.103)	(0.146)
Patcites	0.192^***	0.186^***	0.166^***	0.167^***	0.171^***	0.167^***
	(6.663)	(6.717)	(6.073)	(6.083)	(6.039)	(6.039)
Npatcites	1.143^***	1.129^***	1.019^***	0.996^***	1.009^***	0.991^***
	(8.700)	(8.466)	(7.361)	(7.079)	(7.296)	(7.029)
Claims	2.181^***	2.139^***	2.090^***	2.043^***	2.070^***	2.044^***
	(6.084)	(6.079)	(5.645)	(5.670)	(5.614)	(5.632)
Family	-12.237^***	-12.479^***	-11.867^***	-12.069^***	-11.713^***	-11.967^***
	(-3.133)	(-3.214)	(-3.145)	(-3.206)	(-3.081)	(-3.165)
Distance	1.220^**		1.067^**		1.172^**
	(2.454)		(2.087)		(2.294)
Distance²	-1.257^**		-0.997^*		-1.200^**
	(-2.204)		(-1.721)		(-2.055)
Diversity		1.530^*		1.527^*		1.633^**
		(1.878)		(1.862)		(1.964)
Diversity²		-1.410^*		-1.547^**		-1.606^**
		(-1.867)		(-2.019)		(-2.088)
Interdensity	-0.480^**	-0.323			-0.332	-0.199
	(-1.990)	(-1.356)			(-1.474)	(-0.847)
Distance*Interdensity	5.405^***				5.198^***
	(3.210)				(3.273)
Distance²*Interdensity	-7.785^***				-7.598^***
	(-3.110)				(-3.116)
Diversity*Interdensity		1.689				2.010
		(0.446)				(0.516)
Diversity²*Interdensity		-0.098				-0.482
		(-0.036)				(-0.172)
Intradensity			0.159^***	0.158^***	0.158^***	0.157^***
			(7.294)	(7.484)	(7.202)	(7.398)
Distance*Intradensity			-0.109		-0.085
			(-0.798)		(-0.621)
Distance²*Intradensity			0.082		0.052
			(0.562)		(0.356)
Diversity*Intradensity				-0.120		-0.124
				(-0.622)		(-0.643)
Diversity²*Intradensity				0.104		0.114
				(0.524)		(0.574)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.860^***	-3.848^***	-3.357^***	-3.391^***	-3.404^***	-3.684^***
	(-7.170)	(-7.228)	(-6.270)	(-6.401)	(-6.313)	(-6.663)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.106	0.104	0.119	0.118	0.121	0.119
Wald chi²	317.1	322.6	348.9	362.8	358.5	369.1
log likelihood	-1481	-1485	-1461	-1461	-1456	-1460

y=Forwardcite
变量	模型1	模型2	模型3
Assignee	0.019^**	0.020^**	0.019^**
	(2.33)	(2.49)	(2.39)
Unique	0.034	0.033	0.033
	(1.20)	(1.14)	(1.16)
Gexperience	-0.018^***	-0.018^***	-0.018^***
	(-2.90)	(-2.79)	(-2.86)
Scale	0.128	0.105	0.126
	(0.71)	(0.58)	(0.70)
Patcites	0.112^***	0.112^***	0.111^***
	(11.24)	(11.14)	(11.10)
Npatcites	0.473^***	0.465^***	0.469^***
	(10.86)	(10.64)	(10.72)
Claims	1.513^***	1.504^***	1.509^***
	(14.27)	(14.17)	(14.22)
Family	-1.383^***	-1.409^***	-1.402^***
	(-7.29)	(-7.42)	(-7.39)
Distance	0.322^***		0.456^***
	(3.01)		(3.89)
Distance²	-0.382^***		-0.492^***
	(-3.20)		(-3.88)
Diversity		0.474^***	0.532^***
		(3.25)	(3.59)
Diversity²		-0.467^***	-0.587^***
		(-3.30)	(-3.97)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	2.049^***	1.976^***	1.980^***
	(17.30)	(16.24)	(16.25)
Observations	8,485	8,485	8,485
Adjusted R²	0.298	0.298	0.299
F	106.8	106.8	101.5

y=Forwardcite
变量	模型1	模型2	模型3
Assignee	0.019^**	0.020^**	0.019^**
	(2.33)	(2.49)	(2.39)
Unique	0.034	0.033	0.033
	(1.20)	(1.14)	(1.16)
Gexperience	-0.018^***	-0.018^***	-0.018^***
	(-2.90)	(-2.79)	(-2.86)
Scale	0.128	0.105	0.126
	(0.71)	(0.58)	(0.70)
Patcites	0.112^***	0.112^***	0.111^***
	(11.24)	(11.14)	(11.10)
Npatcites	0.473^***	0.465^***	0.469^***
	(10.86)	(10.64)	(10.72)
Claims	1.513^***	1.504^***	1.509^***
	(14.27)	(14.17)	(14.22)
Family	-1.383^***	-1.409^***	-1.402^***
	(-7.29)	(-7.42)	(-7.39)
Distance	0.322^***		0.456^***
	(3.01)		(3.89)
Distance²	-0.382^***		-0.492^***
	(-3.20)		(-3.88)
Diversity		0.474^***	0.532^***
		(3.25)	(3.59)
Diversity²		-0.467^***	-0.587^***
		(-3.30)	(-3.97)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	2.049^***	1.976^***	1.980^***
	(17.30)	(16.24)	(16.25)
Observations	8,485	8,485	8,485
Adjusted R²	0.298	0.298	0.299
F	106.8	106.8	101.5

变量	模型1	模型2	模型3
Assignee	0.047	0.039	0.045
	(1.616)	(1.299)	(1.543)
Team	0.116	0.132	0.121
	(0.775)	(0.885)	(0.809)
Gexperience	-0.322^***	-0.299^***	-0.325^***
	(-5.275)	(-4.925)	(-5.328)
Scale	2.353^***	2.448^***	2.401^***
	(4.538)	(4.380)	(4.362)
Patcites	0.167^***	0.172^***	0.152^***
	(5.867)	(6.344)	(5.348)
Npatcites	1.104^***	1.083^***	1.040^***
	(8.277)	(8.138)	(7.696)
Claims	2.203^***	2.091^***	2.136^***
	(6.384)	(6.020)	(6.311)
Family	-11.850^***	-12.536^***	-11.937^***
	(-3.099)	(-3.254)	(-3.131)
Distance_sem	0.298^***		0.294^***
	(4.317)		(4.292)
Distance_sem²	-0.011^***		-0.011^***
	(-2.609)		(-2.608)
Diversity_sem		5.133^*	5.446^**
		(1.899)	(1.997)
Diversity_sem²		-26.954^**	-26.912^**
		(-2.528)	(-2.513)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-4.610^***	-3.937^***	-4.729^***
	(-8.177)	(-7.223)	(-8.240)
Observations	8,485	8,485	8,485
Pseudo R²	0.116	0.105	0.118
Wald chi²	352	322.2	366.4
log likelihood	-1465	-1484	-1461

变量	模型1	模型2	模型3
Assignee	0.047	0.039	0.045
	(1.616)	(1.299)	(1.543)
Team	0.116	0.132	0.121
	(0.775)	(0.885)	(0.809)
Gexperience	-0.322^***	-0.299^***	-0.325^***
	(-5.275)	(-4.925)	(-5.328)
Scale	2.353^***	2.448^***	2.401^***
	(4.538)	(4.380)	(4.362)
Patcites	0.167^***	0.172^***	0.152^***
	(5.867)	(6.344)	(5.348)
Npatcites	1.104^***	1.083^***	1.040^***
	(8.277)	(8.138)	(7.696)
Claims	2.203^***	2.091^***	2.136^***
	(6.384)	(6.020)	(6.311)
Family	-11.850^***	-12.536^***	-11.937^***
	(-3.099)	(-3.254)	(-3.131)
Distance_sem	0.298^***		0.294^***
	(4.317)		(4.292)
Distance_sem²	-0.011^***		-0.011^***
	(-2.609)		(-2.608)
Diversity_sem		5.133^*	5.446^**
		(1.899)	(1.997)
Diversity_sem²		-26.954^**	-26.912^**
		(-2.528)	(-2.513)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-4.610^***	-3.937^***	-4.729^***
	(-8.177)	(-7.223)	(-8.240)
Observations	8,485	8,485	8,485
Pseudo R²	0.116	0.105	0.118
Wald chi²	352	322.2	366.4
log likelihood	-1465	-1484	-1461

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.055	0.039	0.052^*	0.045	0.057^*	0.043
	(1.619)	(1.342)	(1.823)	(1.548)	(1.798)	(1.520)
Team	0.129	0.148	-0.123	-0.160	-0.111	-0.150
	(0.868)	(0.991)	(-0.796)	(-1.038)	(-0.716)	(-0.972)
Gexperience	-0.324^***	-0.301^***	-0.416^***	-0.412^***	-0.413^***	-0.413^***
	(-5.312)	(-4.935)	(-6.331)	(-6.579)	(-6.302)	(-6.553)
Scale	2.351^***	2.461^***	0.255	0.063	0.274	0.077
	(4.518)	(4.407)	(0.298)	(0.074)	(0.319)	(0.090)
Patcites	0.167^***	0.170^***	0.157^***	0.149^***	0.158^***	0.147^***
	(5.798)	(6.253)	(5.426)	(5.434)	(5.358)	(5.308)
Npatcites	1.094^***	1.081^***	0.965^***	0.916^***	0.956^***	0.911^***
	(8.184)	(8.120)	(6.632)	(6.432)	(6.557)	(6.391)
Claims	2.215^***	2.102^***	2.134^***	1.989^***	2.143^***	1.996^***
	(6.145)	(5.952)	(6.067)	(5.480)	(5.899)	(5.451)
Family	-11.584^***	-12.381^***	-11.382^***	-12.060^***	-11.181^***	-11.957^***
	(-3.047)	(-3.210)	(-3.057)	(-3.196)	(-3.019)	(-3.158)
Distance_sem	0.368^***		0.332^***		0.386^***
	(4.442)		(3.898)		(4.090)
Distance_sem²	-0.018^***		-0.015^**		-0.021^**
	(-3.014)		(-2.135)		(-2.546)
Diversity_sem		6.181^**		5.194^*		6.218^**
		(2.318)		(1.900)		(2.304)
Diversity_sem²		-32.962^***		-26.546^**		-32.222^***
		(-3.128)		(-2.464)		(-3.032)
Interdensity	-0.629^*	-0.710^**			-0.499	-0.553^*
	(-1.894)	(-2.265)			(-1.574)	(-1.860)
Distance_sem*Interdensity	1.347^**				1.229^*
	(1.972)				(1.836)
Distance_sem²*Interdensity	-0.134^**				-0.121^*
	(-1.961)				(-1.803)
Diversity_sem*Interdensity		25.798^***				25.766^**
		(2.578)				(2.491)
Diversity_sem²*Interdensity		-136.112^***				-132.450^***
		(-3.243)				(-3.212)
Intradensity			0.127^***	0.158^***	0.123^***	0.157^***
			(4.247)	(7.362)	(4.057)	(7.230)
Distance_sem*Intradensity			0.013		0.018
			(0.587)		(0.766)
Distance_sem²*Intradensity			-0.000		-0.001
			(-0.310)		(-0.515)
Diversity_sem*Intradensity				1.408^*		1.498^*
				(1.663)		(1.721)
Diversity_sem²*Intradensity				-4.833		-5.248
				(-1.342)		(-1.416)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.613^***	-3.860^***	-3.196^***	-3.361^***	-3.266^***	-3.367^***
	(-6.776)	(-7.224)	(-6.036)	(-6.295)	(-6.106)	(-6.286)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.118	0.106	0.128	0.121	0.130	0.122
Wald chi²	350.7	345.6	373.7	360.6	374.7	383.3
log likelihood	-1462	-1481	-1444	-1457	-1442	-1455

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.055	0.039	0.052^*	0.045	0.057^*	0.043
	(1.619)	(1.342)	(1.823)	(1.548)	(1.798)	(1.520)
Team	0.129	0.148	-0.123	-0.160	-0.111	-0.150
	(0.868)	(0.991)	(-0.796)	(-1.038)	(-0.716)	(-0.972)
Gexperience	-0.324^***	-0.301^***	-0.416^***	-0.412^***	-0.413^***	-0.413^***
	(-5.312)	(-4.935)	(-6.331)	(-6.579)	(-6.302)	(-6.553)
Scale	2.351^***	2.461^***	0.255	0.063	0.274	0.077
	(4.518)	(4.407)	(0.298)	(0.074)	(0.319)	(0.090)
Patcites	0.167^***	0.170^***	0.157^***	0.149^***	0.158^***	0.147^***
	(5.798)	(6.253)	(5.426)	(5.434)	(5.358)	(5.308)
Npatcites	1.094^***	1.081^***	0.965^***	0.916^***	0.956^***	0.911^***
	(8.184)	(8.120)	(6.632)	(6.432)	(6.557)	(6.391)
Claims	2.215^***	2.102^***	2.134^***	1.989^***	2.143^***	1.996^***
	(6.145)	(5.952)	(6.067)	(5.480)	(5.899)	(5.451)
Family	-11.584^***	-12.381^***	-11.382^***	-12.060^***	-11.181^***	-11.957^***
	(-3.047)	(-3.210)	(-3.057)	(-3.196)	(-3.019)	(-3.158)
Distance_sem	0.368^***		0.332^***		0.386^***
	(4.442)		(3.898)		(4.090)
Distance_sem²	-0.018^***		-0.015^**		-0.021^**
	(-3.014)		(-2.135)		(-2.546)
Diversity_sem		6.181^**		5.194^*		6.218^**
		(2.318)		(1.900)		(2.304)
Diversity_sem²		-32.962^***		-26.546^**		-32.222^***
		(-3.128)		(-2.464)		(-3.032)
Interdensity	-0.629^*	-0.710^**			-0.499	-0.553^*
	(-1.894)	(-2.265)			(-1.574)	(-1.860)
Distance_sem*Interdensity	1.347^**				1.229^*
	(1.972)				(1.836)
Distance_sem²*Interdensity	-0.134^**				-0.121^*
	(-1.961)				(-1.803)
Diversity_sem*Interdensity		25.798^***				25.766^**
		(2.578)				(2.491)
Diversity_sem²*Interdensity		-136.112^***				-132.450^***
		(-3.243)				(-3.212)
Intradensity			0.127^***	0.158^***	0.123^***	0.157^***
			(4.247)	(7.362)	(4.057)	(7.230)
Distance_sem*Intradensity			0.013		0.018
			(0.587)		(0.766)
Distance_sem²*Intradensity			-0.000		-0.001
			(-0.310)		(-0.515)
Diversity_sem*Intradensity				1.408^*		1.498^*
				(1.663)		(1.721)
Diversity_sem²*Intradensity				-4.833		-5.248
				(-1.342)		(-1.416)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.613^***	-3.860^***	-3.196^***	-3.361^***	-3.266^***	-3.367^***
	(-6.776)	(-7.224)	(-6.036)	(-6.295)	(-6.106)	(-6.286)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.118	0.106	0.128	0.121	0.130	0.122
Wald chi²	350.7	345.6	373.7	360.6	374.7	383.3
log likelihood	-1462	-1481	-1444	-1457	-1442	-1455

变量	模型1	模型2	模型3
Assignee	-0.019	-0.015	-0.004
	(-0.674)	(-0.435)	(-0.137)
Gexperience	-0.255^***	-0.251^***	-0.278^***
	(-3.796)	(-3.834)	(-4.452)
Scale	2.337^***	2.427^***	2.355^***
	(4.187)	(4.293)	(4.782)
Patcites	0.202^***	0.221^***	0.179^***
	(7.032)	(7.784)	(6.121)
Npatcites	0.303^***	0.344^***	1.309^***
	(6.191)	(6.632)	(9.351)
Claims	1.600^***	1.719^***	2.031^***
	(4.975)	(5.660)	(6.260)
Family	-1.173^***	-12.636^***	-11.966^***
	(-6.567)	(-2.716)	(-2.853)
Distance	0.995^*		1.243^**
	(1.685)		(2.168)
Distance²	-1.243^*		-1.166^*
	(-1.828)		(-1.810)
Diversity		2.398^**	1.685^*
		(2.171)	(1.827)
Diversity²		-2.558^***	-2.100^**
		(-2.766)	(-2.569)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-3.248^***	-4.353^***	-3.674^***
	(-5.536)	(-6.720)	(-6.608)
Observations	6,891	6,891	6,891
Pseudo R²	0.152	0.123	0.112
Wald chi²	270.4	254.4	299.7
log likelihood	-990.5	-1024	-1247

变量	模型1	模型2	模型3
Assignee	-0.019	-0.015	-0.004
	(-0.674)	(-0.435)	(-0.137)
Gexperience	-0.255^***	-0.251^***	-0.278^***
	(-3.796)	(-3.834)	(-4.452)
Scale	2.337^***	2.427^***	2.355^***
	(4.187)	(4.293)	(4.782)
Patcites	0.202^***	0.221^***	0.179^***
	(7.032)	(7.784)	(6.121)
Npatcites	0.303^***	0.344^***	1.309^***
	(6.191)	(6.632)	(9.351)
Claims	1.600^***	1.719^***	2.031^***
	(4.975)	(5.660)	(6.260)
Family	-1.173^***	-12.636^***	-11.966^***
	(-6.567)	(-2.716)	(-2.853)
Distance	0.995^*		1.243^**
	(1.685)		(2.168)
Distance²	-1.243^*		-1.166^*
	(-1.828)		(-1.810)
Diversity		2.398^**	1.685^*
		(2.171)	(1.827)
Diversity²		-2.558^***	-2.100^**
		(-2.766)	(-2.569)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-3.248^***	-4.353^***	-3.674^***
	(-5.536)	(-6.720)	(-6.608)
Observations	6,891	6,891	6,891
Pseudo R²	0.152	0.123	0.112
Wald chi²	270.4	254.4	299.7
log likelihood	-990.5	-1024	-1247

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	-0.033	-0.008	-0.017	0.001	-0.040	0.001
	(-1.130)	(-0.331)	(-0.778)	(0.037)	(-1.267)	(0.011)
Gexperience	-0.264^***	-0.253^***	-0.400^***	-0.371^***	-0.393^***	-0.372^***
	(-3.996)	(-3.858)	(-5.887)	(-5.622)	(-5.922)	(-5.052)
Scale	2.512^***	2.449^***	-0.422	-0.318	-0.231	-0.300
	(4.128)	(4.300)	(-0.612)	(-0.450)	(-0.335)	(-0.362)
Patcites	0.224^***	0.222^***	0.358^***	0.193^***	0.372^***	0.192^***
	(7.350)	(7.744)	(7.187)	(6.703)	(6.700)	(6.147)
Npatcites	0.330^***	0.344^***	0.174^***	0.308^***	0.182^***	0.308^***
	(6.511)	(6.630)	(3.644)	(5.886)	(3.456)	(6.555)
Claims	1.705^***	1.715^***	1.532^***	1.646^***	1.397^***	1.643^***
	(5.457)	(5.645)	(4.535)	(5.097)	(4.072)	(4.889)
Family	-12.088^***	-12.485^***	-12.326^***	-11.401^**	-11.016^**	-11.346^***
	(-2.607)	(-2.684)	(-2.708)	(-2.546)	(-2.474)	(-4.309)
Distance	0.985^*		1.155^*		1.090^*
	(1.663)		(1.826)		(1.711)
Distance²	-1.345^**		-1.320^*		-1.308^*
	(-1.962)		(-1.763)		(-1.742)
Diversity		2.385^**		2.216^**		2.242^*
		(2.174)		(1.991)		(1.937)
Diversity²		-2.544^***		-2.492^***		-2.514^**
		(-2.769)		(-2.635)		(-2.513)
Interdensity	-0.627^**	-0.233			-0.396	-0.092
	(-2.073)	(-1.185)			(-1.487)	(-0.451)
Distance*Interdensity	7.025^***				6.199^***
	(3.174)				(3.142)
Distance²*Interdensity	-9.690^***				-8.862^***
	(-3.196)				(-3.036)
Diversity*Interdensity		-0.102				0.994
		(-0.029)				(0.193)
Diversity²*Interdensity		1.094				0.082
		(0.423)				(0.021)
Intradensity			0.181^***	0.176^***	0.168^***	0.174^***
			(7.958)	(7.463)	(7.254)	(6.515)
Distance*Intradensity			-0.151		-0.126
			(-0.982)		(-0.829)
Distance²*Intradensity			0.107		0.105
			(0.629)		(0.624)
Diversity*Intradensity				0.543^*		0.572
				(1.747)		(1.080)
Diversity²*Intradensity				-0.451^*		-0.459
				(-1.661)		(-1.185)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-4.113^***	-4.190^***	-4.193^***	-3.935^***	-4.574^***	-3.946^***
	(-6.899)	(-7.159)	(-7.334)	(-6.734)	(-7.549)	(-5.740)
Observations	6891	6891	6891	6891	6891	6891
Pseudo R²	0.125	0.124	0.143	0.147	0.158	0.148
Wald chi²	253.8	259.8	316	319.4	354.6	345.1
log likelihood	-1022	-1023	-1004	-995.7	-982.9	-995.3

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	-0.033	-0.008	-0.017	0.001	-0.040	0.001
	(-1.130)	(-0.331)	(-0.778)	(0.037)	(-1.267)	(0.011)
Gexperience	-0.264^***	-0.253^***	-0.400^***	-0.371^***	-0.393^***	-0.372^***
	(-3.996)	(-3.858)	(-5.887)	(-5.622)	(-5.922)	(-5.052)
Scale	2.512^***	2.449^***	-0.422	-0.318	-0.231	-0.300
	(4.128)	(4.300)	(-0.612)	(-0.450)	(-0.335)	(-0.362)
Patcites	0.224^***	0.222^***	0.358^***	0.193^***	0.372^***	0.192^***
	(7.350)	(7.744)	(7.187)	(6.703)	(6.700)	(6.147)
Npatcites	0.330^***	0.344^***	0.174^***	0.308^***	0.182^***	0.308^***
	(6.511)	(6.630)	(3.644)	(5.886)	(3.456)	(6.555)
Claims	1.705^***	1.715^***	1.532^***	1.646^***	1.397^***	1.643^***
	(5.457)	(5.645)	(4.535)	(5.097)	(4.072)	(4.889)
Family	-12.088^***	-12.485^***	-12.326^***	-11.401^**	-11.016^**	-11.346^***
	(-2.607)	(-2.684)	(-2.708)	(-2.546)	(-2.474)	(-4.309)
Distance	0.985^*		1.155^*		1.090^*
	(1.663)		(1.826)		(1.711)
Distance²	-1.345^**		-1.320^*		-1.308^*
	(-1.962)		(-1.763)		(-1.742)
Diversity		2.385^**		2.216^**		2.242^*
		(2.174)		(1.991)		(1.937)
Diversity²		-2.544^***		-2.492^***		-2.514^**
		(-2.769)		(-2.635)		(-2.513)
Interdensity	-0.627^**	-0.233			-0.396	-0.092
	(-2.073)	(-1.185)			(-1.487)	(-0.451)
Distance*Interdensity	7.025^***				6.199^***
	(3.174)				(3.142)
Distance²*Interdensity	-9.690^***				-8.862^***
	(-3.196)				(-3.036)
Diversity*Interdensity		-0.102				0.994
		(-0.029)				(0.193)
Diversity²*Interdensity		1.094				0.082
		(0.423)				(0.021)
Intradensity			0.181^***	0.176^***	0.168^***	0.174^***
			(7.958)	(7.463)	(7.254)	(6.515)
Distance*Intradensity			-0.151		-0.126
			(-0.982)		(-0.829)
Distance²*Intradensity			0.107		0.105
			(0.629)		(0.624)
Diversity*Intradensity				0.543^*		0.572
				(1.747)		(1.080)
Diversity²*Intradensity				-0.451^*		-0.459
				(-1.661)		(-1.185)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-4.113^***	-4.190^***	-4.193^***	-3.935^***	-4.574^***	-3.946^***
	(-6.899)	(-7.159)	(-7.334)	(-6.734)	(-7.549)	(-5.740)
Observations	6891	6891	6891	6891	6891	6891
Pseudo R²	0.125	0.124	0.143	0.147	0.158	0.148
Wald chi²	253.8	259.8	316	319.4	354.6	345.1
log likelihood	-1022	-1023	-1004	-995.7	-982.9	-995.3

知识重组视角下突破性技术形成机理研究

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郑世鑫 , 汪雪锋 , 陈虹枢 , 雷鸣

科学学研究 | 理论与方法 2026,44(9): 1833-1851

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科学学研究 |理论与方法 2026 , 44 (9) : 1833 -1851

知识重组视角下突破性技术形成机理研究

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郑世鑫(2001-),女,满族,博士研究生。

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郑世鑫(2001-),女,满族,博士研究生。

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雷鸣(1994-),男,博士研究生。

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雷鸣(1994-),男,博士研究生。

郑世鑫, 汪雪锋, 陈虹枢, 雷鸣

作者信息

北京理工大学管理学院, 北京 100081

通讯作者:

汪雪锋(1977-),男,教授、博士生导师,E-mail: wxf5122@bit.edu.cn。

作者简介:

郑世鑫(2001-),女,满族,博士研究生。

陈虹枢(1986-),女,满族,长聘副教授、博士生导师。

雷鸣(1994-),男,博士研究生。

Research on the formation of breakthrough technology from the perspective of knowledge recombination

Shi-xin ZHENG, Xue-feng WANG, Hong-shu CHEN, Ming LEI

Affiliations

School of Management, Beijing Institute of Technology, Beijing 100081, China

出版时间: 2026-09-15

文章导航

摘要

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作为实现高水平科技自立自强和推动我国经济社会高质量发展的内在要求和重要着力点,新质生产力的培育和形成面临亟待破解的理论与现实难题,迫切需要技术革命性突破为其发展提供动力支撑。因此,把握技术形成机理,促进突破性技术涌现成为我国创新发展的重要问题。本文基于知识重组理论,从知识和发明人两个层面出发,分析知识重组特征(知识距离、知识多样性)对突破性技术形成的影响,以及发明人合作强度(组织间合作、组织内合作)的调节作用。本研究以2004-2021年美国合成生物学领域专利数据为样本进行实证检验,结果表明:知识距离、知识多样性均与突破性技术形成呈倒U型关系,且组织间合作强度强化了知识重组与突破性技术形成间的倒U型关系,但组织内合作强度不起作用。本研究丰富了突破性技术形成来源相关研究,为研发人员通过合作实现技术突破提供理论支持和实践启示。

关键词

突破性技术 / 知识重组 / 知识距离 / 知识多样性 / 组织合作

Abstract

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As an intrinsic requirement and a crucial focal point for achieving high-level technological self-reliance and driving high-quality economic and social development in China, the cultivation and formation of new quality productive forces represent a complex process confronted with multifaceted theoretical and practical challenges that require systematic investigation and urgent resolution. This process fundamentally depends on the emergence of revolutionary technological breakthroughs to provide sustained momentum and structural support for its advancement. Within this context, understanding the foundational mechanism of technological formation and fostering the emergence of breakthrough technologies have become critical priorities for China's innovation-driven strategy.

Building upon the theoretical foundation of knowledge recombination, this study systematically investigates how specific characteristics of knowledge recombination—primarily knowledge distance and knowledge diversity—influence the formation of breakthrough technologies. The study further examines the moderating role of inventor collaboration intensity, making a critical distinction between inter-organizational and intra-organizational cooperation. To empirically validate the proposed theoretical framework, we constructed a comprehensive dataset comprising patent records from the United States synthetic biology sector spanning the period from 2004 to 2021. This sector constitutes an ideal empirical setting due to its inherently interdisciplinary nature, rapid knowledge obsolescence, and high-stakes innovation environment. These characteristics collectively create an amenable context for examining the dynamics between knowledge recombination and breakthrough technological outcomes.

The empirical analysis yields several significant findings that contribute to both theoretical understanding and practical application. First, the research confirms that both knowledge distance and knowledge diversity maintain inverted U-shaped relationships with the formation of breakthrough technologies. This nonlinear pattern suggests that while moderate levels of knowledge heterogeneity stimulate novel connections and creative synthesis, excessive cognitive distance or overwhelming diversity may exceed organizational absorption capacities and impede meaningful knowledge integration. Second, regarding contextual moderators, the intensity of inter-organizational collaboration significantly strengthens these observed inverted U-shaped relationships between knowledge recombination characteristics and technological breakthroughs. Conversely, intra-organizational collaboration demonstrates no statistically significant moderating effect. This differential impact highlights the unique value of cross-organizational partnerships in introducing complementary knowledge stocks, diverse problem-solving approaches, and alternative interpretive frameworks that collectively enhance the innovative potential of knowledge recombination processes.

This research makes substantive contributions to the theoretical discourse surrounding the origins and drivers of breakthrough technologies by providing nuanced insights into the complex interplay between knowledge structures, collaborative contexts, and innovative outcomes. From a practical perspective, the findings offer valuable guidance for scholars, innovation policymakers, and organizational leaders—particularly within China's ongoing pursuit of technological sovereignty and quality-driven development. By strategically configuring knowledge portfolios, optimizing collaboration patterns, and implementing governance mechanisms that foster productive cross-boundary exchanges, organizations and nations can systematically enhance their capacity to generate transformative technologies and accelerate the cultivation of new quality productive forces in strategic emerging sectors.

This study has theoretical and empirical contributions, but it also has limitations that point to future research. First, the generalizability of the findings, derived from synthetic biology, requires further validation. Second, the underlying mechanisms for the distinct effects of technologically-measured versus semantically-measured knowledge recombination on breakthrough technology remain unclear. Finally, while we examine the macro-level moderating role of collaboration, future work should investigate its micro-level foundations.

Key words

breakthrough technology / knowledge recombination / knowledge distance / knowledge diversity / organizational collaboration

引用本文

郑世鑫, 汪雪锋, 陈虹枢, 雷鸣. 知识重组视角下突破性技术形成机理研究. 科学学研究, 2026 , 44 (9) : 1833 -1851 .

Shi-xin ZHENG, Xue-feng WANG, Hong-shu CHEN, Ming LEI. Research on the formation of breakthrough technology from the perspective of knowledge recombination[J]. Studies in Science of Science, 2026 , 44 (9) : 1833 -1851 .

正文

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党的二十大报告指出,“实现高水平科技自立自强,进入创新型国家前列”是我国现阶段发展总目标。为更好适应高质量发展阶段的新要求,2023年9月,习近平总书记首次提出“整合科技创新资源,引领发展战略性新兴产业和未来产业,加快形成新质生产力”。在催生新质生产力的诸因素中,科技创新是起主导作用的核心要素,技术突破是开启新的“技术—经济轨道”、实现生产力跃升的重要途径。因此,探索突破性技术形成机理与规律,实现技术革命性突破,有力支撑新质生产力发展,成为我国技术创新领域关注的焦点问题。

在突破性技术相关研究中,专利是技术的重要表征,也是衡量技术突破的直接途径,其被引量反映该技术对后续技术发展的贡献程度和影响力^[1]。Singh和Fleming提出了具体量化标准,将某一技术领域在一年中所有专利被引数排名前5%的专利界定为突破性技术。这种方法强调了在特定技术领域内的相对优势,能够更好地识别出在该领域内具有显著影响力的专利^[2]。

创新领域学者对于如何实现技术突破展开了一系列讨论。1939年,Schumpeter提出知识重组理论,认为创新是以新的方式组合现有生产要素或者包含新的组合^[3]。随后,知识重组理论受到普遍认可和广泛应用。有学者指出,更多的知识投入可以产生更多新颖的知识重组,催生出有影响力的创新产出^[4,5]。但也有学者表明,引致突破性技术的知识重组存在边界条件,往往体现为现有知识的非传统组合或是现有知识与新颖性知识的新组合^[6,7]。但也有学者认为引致突破性技术的知识重组存在边界条件,要求以前很少组合过或者新颖的知识进行重组^[7]。

然而,已有研究对于知识如何重组实现技术突破尚存争议。一方面,大量研究表明,远距离和多样化知识重组增加重组潜力^[6,8],正向促进技术新颖性^[4],但也有研究表明知识本地重组利于深入理解领域内部知识属性和结构关系,识别异常知识,产生新发现^[9];并且知识远距离重组可能会降低生产率,从而阻碍创新成果的产生^[10]。先前研究单独地考察知识远程重组和本地重组与突破性技术形成之间的关系,缺乏从整体视角讨论两种知识重组方式在整个创新过程中所起作用。

在以往的研究中,知识常被视作一种独立且客观存在的实体,学者多探讨其内在属性、结构特征以及在不同情境中的表现形式^[9,11]。然而,这一视角忽视了发明人在知识创造与传播过程中所发挥的主观能动性,而这种主观能动性对知识的生成、演化以及广泛传播具有极为重要的影响。在知识重组过程中,发明人作为关键的知识载体,承载着丰富的知识资源。发明人凭借自身对知识的理解和研究习惯,通过知识搜索对知识元素进行编码、解码、开发和探索,将不同的知识元素耦合连接,形成“最佳知识组合”^[11,12],从而推动技术创新和突破。

因此,本研究从知识层面出发,首先阐明知识距离和知识多样性对突破性技术形成的影响,揭示重组知识特征在突破性技术形成过程中的作用。然后,本文引入发明人层面特征,阐释发明人在组织间和组织内的合作强度分别对知识距离、知识多样性与突破性技术形成关系的调节作用。本文基于2004-2021年合成生物学领域美国授权发明专利数据进行实证检验,从知识和发明人两个维度系统揭示突破性技术形成机理,为组织和发明人面向突破性技术战略选择提供理论支持和经验借鉴,为新质生产力培育和形成提供理论支撑。

1 影响机理与研究假设

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1.1 知识重组与突破性技术形成

创新源自知识元素的重新组合成为创新领域被广泛认可的观点^[13],技术知识构成了创新的基本组成部分^[4]。在以往文献中,学者对知识搜索和知识重组概念不多做区分,但从主体角度看,知识搜索行为依托于发明人,具有较强主观性,受发明人属性特征影响,知识重组是知识搜索过程中对知识的整合与重构,是知识搜索行为在知识层面的体现。

关于何种知识重组方式更有利于突破性技术的形成,现有研究存在两种不同观点,分别是知识重组“张力观”(tension view)和“基础观”(foundational view)。知识重组“张力观”强调各种形式的广泛重组是产生技术突破的主要机制。研究表明,如果重组知识距离较远,由此产生的新技术往往具有更高的被引率^[14]。Dahlin和Behrens研究表明,重组相距较远技术知识的发明更具新颖性和突破性^[15]。陈傲和柳卸林同样发现突破性技术与已有知识存在密切关联,与非突破性技术相比,更需要对已有知识的吸收与集成^[16]。除考察重组知识距离外,重组知识多样性也广受关注。大多学者认为跨界重组的知识来源于更广泛的知识领域,知识多元化、异质化程度更高,重组这类知识有助于提高突破性技术形成概率^[17]。Audia和Goncalo提出桥接遥远或多样化的知识有助于实现知识重组,从而提升创造力^[18]。相反地,在单一或者少数领域的深度知识可能会使发明者陷入熟悉陷阱,认知僵化,倾向于将现有的、范式性的解决方案应用到所有问题中^[19],大幅削弱发明者创新突破的能力。例如,Fleming研究表明,知识组合熟悉度的增加会导致发明成功的数量减少,从而降低技术突破的概率^[4]。

知识重组“基础观”认为,专注某一特定技术领域进行深度挖掘更有利于触碰到前沿知识并识别创新机会,因此聚焦于较窄技术领域对熟悉的知识元素进行深度搜索和重组将更有利于突破性技术形成^[20]。Arts和Veugelers认为只有当熟悉的技术以前所未有的方式重新组合时,才会产生突破^[21]。不同的科学领域在学科特有的概念体系、隐喻表达、研究工具等方面具有“认知文化”差异性。这种“认知文化”具有高度的自我指涉性(self-referentiality)和学科封闭性特征,形成相对独立的学术话语体系,重组知识间较远的距离使得知识的共享和整合变得困难^[22]。

Kaplan和Vakili提出将知识重组“基础观”和知识重组“张力观”看作影响突破性技术形成的“双刃剑”,表明这两种效应同时存在、共同起作用^[17]。综合这两种观点并基于已有研究结论,本研究认为知识重组和突破性技术形成可能存在一种非线性关系。基于跨领域研究中知识吸收和整合的思想^[23],知识重组过程可以看作不同知识要素通过吸收和整合产生新的概念和思想的过程。结合Haans等对于U型关系的研究^[24],知识重组与突破性技术形成的关系中可能存在正向的“知识吸收”和负向的“知识整合”(如图1所示)。当知识距离和知识多样性处于较低水平时,知识吸收所带来的知识组合机会的收益显著超过知识整合难度的成本,随着知识距离和知识多样性的增加,知识吸收带来的积极影响占上风,因此突破性技术形成概率随之增加。当知识距离和知识多样性水平继续上升到某点后,由于人类认知程度的限制^[25],知识整合困难的成本高于吸收新知识带来的效益,研发人员对知识缺乏深度理解,信息融合困难程度直线增加,表现为突破性技术形成概率显著下降。因此,起初知识距离和知识多样性水平的增加会带来突破性技术形成概率的上升,支持知识重组“张力观”;随着知识距离和知识多样性水平的持续增加,突破性技术形成概率反而下降(如图2所示),支持知识重组“基础观”。因此,本文提出以下假设:

假设1a:知识距离与突破性技术形成呈倒U型关系。

假设1b:知识多样性与突破性技术形成呈倒U型关系。

1.2 组织间合作强度的调节作用

由于知识在组织间的分布呈现出显著的异质性、互补性与非对称性等特征,相同组织的发明人与不同组织的发明人所接触的信息存在本质差异^[26]。因此,发明人在组织内部合作情境与组织间合作情境下,对信息的理解、加工以及创新产出等方面均呈现出显著差异。

组织间合作强度对知识整合与吸收具有复杂的调节机制,具体表现为以下两个方面:第一,提高远距离、多样化知识吸收效率。组织间合作强度的提升,能够促进组织间信息与专业知识的频繁交换,有助于打破组织边界,实现不同领域知识的汇聚与集成^[2],进而有效提高发明人对这些知识的吸收效率,增强知识距离和知识多样性对突破性技术形成的正向边际效应。第二,降低远距离、多样化知识整合效率。组织间合作强度的提升也会带来一定挑战,由于人类认知能力存在局限^[25],当发明人面临组织间密切联系带来大量不同领域的知识时,往往难以有效整合利用这些跨领域的知识,使得知识整合效率受到抑制,加剧了知识距离和知识多样性对突破性技术形成的负向边际效应。

综上所述,组织间合作强度在知识吸收与整合过程中扮演着双重角色。一方面,它通过促进信息交换,提高了知识吸收的效率,放大了突破性技术形成概率的正边际效应;另一方面,当知识距离和知识多样性水平过高时,又因认知能力的限制,降低了知识整合的效率,放大了突破性技术形成概率的负边际效应。因此,本文提出以下假设:

假设2a:组织间合作强度强化知识距离与突破性技术形成之间的倒U型关系。

假设2b:组织间合作强度强化知识多样性与突破性技术形成之间的倒U型关系。

1.3 组织内合作强度的调节作用

在组织内部,发明人之间的合作强度同样对知识吸收与整合过程产生显著影响,其作用机制可从以下两个方面进行阐述。第一,降低远距离、多样化知识吸收效率。组织内发明人合作强度高,往往导致专利团队内部形成较为紧密的子群结构^[27],使得团队成员主要在子群内部进行知识交流与共享,限制了对外部知识的获取。同时,由于团队成员的知识来源相对单一,集中在组织内部已有知识体系中,对远距离、多样化知识的吸收能力会受到显著抑制,知识距离和知识多样性对突破性技术形成的正向边际效应被削弱。第二,提高远距离、多样化知识整合效率。组织内发明人间较强的合作有助于提高对本领域知识的理解和掌握程度,将本领域知识应用到不同的环境或与其他领域的知识进行整合时,能够有效降低知识整合过程中的错误概率^[28]。这种深度理解使得发明人在面对跨领域知识整合任务时,能够更加准确地识别知识之间的关联性,从而提高知识整合效率,削弱知识距离和知识多样性对突破性技术形成的负向边际效应。

综上所述,研发人员组织内合作强度在知识吸收与整合过程中呈现出复杂的调节效应。一方面,较强的组织内合作强度通过强化子群边界,限制了远距离、多样化知识的吸收效率,抑制了突破性技术形成概率的正边际效应;另一方面,当知识距离和知识多样性超过某临界点时,组织内合作强度又通过提高对本领域知识的深度理解,提升了知识整合效率,抑制了突破性技术形成的负边际效应。因此,本文提出以下假设:

假设3a:组织内合作强度弱化知识距离与突破性技术形成之间的倒U型关系。

假设3b:组织内合作强度弱化知识多样性与突破性技术形成之间的倒U型关系。

具体理论模型如图3所示。

2 研究设计

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2.1 变量测度

(1)被解释变量

突破性技术(Breakthrough):在技术创新研究中,学者普遍认为被后续专利高频引用的发明具备更强的技术突破性,专利被引数量作为专利质量的衡量指标已形成广泛共识^[2]。突破性发明通常呈现显著的右偏分布特征,即影响力指标位于其技术领域影响力顶端^[2]。Singh和Fleming^[2]从相对排名的视角出发,将某一技术领域在一年内所有专利中被引数排名前5%的专利界定为突破性技术。同样地,Chan等^[29]考察1985-2009年专利,将位于同领域专利被引频次的前5%的专利定义为突破性技术,据此构建0-1二元变量。

本研究据此构建二元变量Breakthrough,当目标专利被引数量位于所在领域同年专利被引数量的前5%时,Breakthrough取值为1,否则Breakthrough取值为0^[29]。

(2)解释变量

基于Kaplan和Vakili^[17]的研究,本研究基于知识距离和知识多样性等特征维度对知识重组进行测度,较高知识距离和知识多样性水平表征远程重组(即知识重组“张力观”),而较低水平则表征本地重组(即知识重组“基础观”)。本研究基于IPC分类对知识距离进行测度。

知识距离(Distance):借鉴Trajtenberg等^[14]与Kaplan和Vakili^[17]的研究方法,构建目标专利i与其引用专利j的距离指标techdistance_i,j。如果目标专利i与其引用专利j属于相同的小类,techdistance_i,j取值为0;如果目标专利与其引用专利仅属于相同的大类,techdistance_i,j取值为0.33;如果目标专利与其引用专利仅属于相同的部,techdistance_i,j取值为0.66;如果目标专利与其引用专利属于不同部,或者目标专利没有引用专利,techdistance_i,j取值为1。有学者指出,目标专利的引用专利在重要性上存在差异^[30],距目标专利距离更近的引用专利与其处于更细化的技术分类中,在领域知识和研究内容上与目标专利更加接近,对目标专利的影响更大。因此,本研究采用目标专利与其所有引用专利间距离techdistance_i的第一四分位数(25%)表征知识距离(Distance)。

知识多样性(Diversity):利用赫芬达尔指数(Herfindahl index)测度目标专利的引用专利的类别集中度,以此表征构成目标专利的知识多样性程度。知识多样性程度越高,构成目标专利的知识来源越多样化。

(1)

$Diversit{y}_{i}=\frac{numberofpatent{s}_{i}}{numberofpatent{s}_{i}-1}*\left(Technologicaldiversit{y}_{i}\right)$

(2)

$Technologicaldiversit{y}_{i}=1-\sum _{k}^{{n}_{i}}{s}_{ik}^{2}$

s_ik表示目标专利i的引用专利所属类别k占所有引用专利的比例,n_i为引用专利类别数量,本研究采用大组作为专利类别判断标准。

(3)调节变量

组织内合作强度(Intraintensity):基于Vestal和Danneels^[27]的研究,统计目标专利i研发者团队中隶属于相同组织机构的任意两个发明人先前合作次数$C{o}_{h}^{intra}$(不算本次合作),加总后除以专利团队内相同组织机构研发人员所有可能的合作次数,消除发明团队规模影响,N为专利团队中相同组织机构内发明人数量。若专利团队内不存在组织内合作,则合作强度取0;若专利团队内存在多个组织机构,合作强度取这些组织机构的均值,合作模式如图4所示。

(3)

$Intradensity={\sum }_{h}^{mi}\frac{C{o}_{h}^{intra}}{N(N-1)/2}$

组织间合作强度(Interintensity):统计目标专利i研发者团队中隶属于不同组织机构的任意两个发明人先前合作次数$C{o}_{g}^{inter}$(不算本次合作),加总后除以专利团队内不同组织机构研发人员所有可能的合作次数,消除发明团队规模影响。若专利团队内不存在组织间合作,则合作强度取0,合作模式如图4所示。

(4)控制变量

本文对其他可能影响突破性技术形成的因素进行控制,专利层面控制变量包括:引用专利数量(Patcites),研究发现引用更多专利时,目标专利被引数量也会随之增加^[31];引用非专利数量(Npatcites),非专利引用数量也会正向影响专利被引数量^[31];权利要求数量(Claims),Singh和Fleming指出专利范围越大,在未来被引用的几率越高^[2];专利族数量(Family),Cockburn和Henderson认为专利族规模越大,曝光度增加,未来被引用可能性越高^[32]。发明人层面的控制变量包括:专利权人数量(Assignee),多专利权人合作可能通过知识技术共享促进创新,但也有可能因为决策效率低下导致创新动力不足,产出成果影响力下降^[33];是否团队合作(Team),有学者认为突破性技术主要来源于团队合作^[2,27],但也有学者认为独立发明人由于不隶属于任何组织,不受外部环境的局限,拥有更高水平的知识和研发自由,因此可以提出更加新颖的想法,更易创造出突破性技术^[29]。因此本研究控制是否团队合作变量,Team取值为1时代表团队合作,取值为0时代表独立发明人;团队合作经验(Gexperience),整个团队先前合作次数^[27];合作网络规模(Scale),Singh和Fleming发现合作网络规模会影响技术突破性^[2],本研究以发明团队先前平均直接合作者数量来测度合作网络规模^[27]。模型控制了年份效应和技术效应以控制未观察到的影响因素。

2.2 样本与数据

本研究选取合成生物学领域专利作为实证对象,原因在于合成生物学领域深度整合生命科学、工程学与信息科学的知识体系,并通过工程化设计突破传统技术边界,引领生物技术革命,兼具学科交叉融合的特性与技术革命性突破的潜力^[34]。

专利数据来源于IncoPat数据库,基于Shapira等^[35]的研究,确定合成生物学专利的检索式为:TIAB=(syntheticbiology OR synthetic gene* OR artificial gene* OR synthetic DNA OR DNA synthesis OR synthetic cell* OR synthetic genome* OR artificial cell* OR synthetic tissue* OR synthetic* nucleotide OR synthetic promoter* OR synthetic amino acid OR synthetic base pair OR synthetic sequence OR synthetic nucleic acids OR artificial genome OR artificial nucleic acids OR artificial nucleotide OR artificial sequence OR synthetic bio* system OR synthetic bio* device OR synthetic bio* part* OR artificial bio’ system OR artificial bio* device OR artificial bio* part* OR synthetic chassis OR artificial chassis OR synthetic element* OR artificial element* OR synthetic communities OR synthetic yeast OR synthetic base pair OR artificialbase pair OR synthetic protocell OR dna assembly OR artificial mammalian gene OR synthetic mammalian gene* OR artificial gene circuit* OR synthetic gene circuit* OR synthetic shRNA OR artificial shRNA OR synthetic enzyme OR artificial enzyme* OR bio brick) NOT (TIAB = (cell*phone OR logic cell*OR fuel cell*OR battery cell*OR load cell* OR geo-synthetic-cell* OR memory cell OR cellular network OR ram cell* OR rom cell* OR maximum cell* OR electrochemical cell* OR solar cell* OR photosynthe* OR photo-synthe*))。经筛选、清洗、同族专利合并后获得1994-2024年美国授权专利10279条(检索时间为2024年11月3日)。合成生物学专利申请总体趋势经过缓慢发展期和快速增长期,从2004年开始步入平稳发展期^[36]。样本数据选取2004年之后的专利申请作为研究对象,因为技术发展进入成熟阶段后,专利布局往往呈现系统性特征,更有利于探究技术形成机理。同时,本文核心解释变量和部分控制变量测度基于引文分析,对引用时间窗口具有敏感性^[37]。Wang关于生物医学领域的研究表明,采用3-5年的观测窗口能够有效平衡引文指标的全面性与时效性^[37]。因此,本研究设定了一个为期3年的引用时间窗口,并以2004至2021年间美国授权的发明专利作为初始样本。在此基础上,重点考察组织层面行为如何影响知识重组与突破性技术形成间关系,剔除了申请人为个人的专利数据,最终得到8485条专利样本作为实证分析对象。

3 实证结果与讨论

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3.1 描述性统计与相关性分析

本研究采用描述性统计分析和相关性分析方法初步统计数据特征。表1报告了所有变量的描述性统计结果和相关系数。自变量的方差膨胀因子(VIF)都低于10,表明自变量间不存在多重共线性问题。

3.2 回归结果分析

(1)基准回归分析

本文采用混合截面数据,在专利层面开展实证研究。由于被解释变量是0-1二元变量,选择Logit二值选择模型进行参数估计。表2报告了知识重组特征(知识距离、知识多样性)与突破性技术形成概率的回归结果,模型1检验所有控制变量对突破性技术形成概率的影响,其影响在之后的模型中几乎一致。

模型2检验知识距离与突破性技术形成概率的关系,结果显示,知识距离一次项系数与突破性技术显著正相关(β=1.092, p<0.05),二次项系数与突破性技术显著负相关(β=-1.030, p<0.1),模型拐点在知识距离取值范围内。同时,采用Lind和Mehlum^[38]研发的U-test检验,结果支持知识距离与突破性技术形成之间的倒U型关系(p<0.1, t-value=1.42)。因此,假设1a得到支持,如图5a所示。

模型3检验知识多样性与突破性技术形成概率的关系,结果显示,知识多样性一次项系数与突破性技术显著正相关(β=1.448, p<0.1),二次项系数与突破性技术显著负相关(β=-1.378, p<0.1),模型拐点在知识多样性取值范围内。同时,U-test检验结果支持知识多样性与突破性技术间的倒U型关系(p<0.05, t-value=1.71)。因此,假设1b得到支持,如图5b所示。

模型4同时引入知识距离与知识多样性变量,结果表现出一定稳健性,假设1a、1b均得到支持。

(2)调节效应检验

为避免解释变量和调节变量分别与其交乘项的多重共线性问题,本研究对解释变量与调节变量做去中心化处理,回归结果如表3所示。

模型1中交乘项Distance²*Interdensity系数显著为负(β=-7.785, p<0.01),强化了知识距离与突破性技术形成之间倒U型关系,验证假设2a(如图6a所示)。模型2中交乘项Diversity²*Interdensity不显著(β=-0.098, p>0.1),不支持假设2b(如图6b所示)。模型3中交乘项Distance²*Intradensity系数虽然为正但不显著(β=0.082, p>0.1),模型4中交乘项Diversity²*Intradensity系数同样为正,但不显著(β=0.104, p>0.1)(如图6c、6d所示),说明组织内合作在知识重组影响突破性技术形成过程中不起作用。模型5和模型6同时加入两个调节变量,结果保持不变。

分析上述实证结果可得,跨组织合作通过知识元素再配置对知识重组与突破性技术形成之间的倒U型关系产生显著正向调节效应,提高突破性技术涌现概率;而组织内部合作不影响知识重组与突破性技术形成之间关系。这一发现表明,组织内部发明人合作既无法优化知识重组效果的最佳平衡点,也不能提高技术突破的可能性。

(3)稳健性检验

替换被解释变量。为了检验知识重组与突破性技术形成关系实证结果的稳健性,本研究将因变量突破性技术替换为连续变量被引频次(Forwardcite),进行OLS回归分析,如表4所示。结果与主检验保持一致,支持假设1a和假设1b。

替换解释变量。为了确保研究结论的稳健性,本研究进一步采用文本挖掘方法,从语义维度测度知识距离(Distance_sem)和知识多样性(Diversity_sem)指标^[39,40]。

基准回归实证结果如表5所示,结果进一步验证了知识重组特征与突破性技术形成间的倒U型关系,支撑了假设1a和假设1b。表6呈现了组织间合作强度和组织内合作强度的调节作用,结果显示,组织间合作强度强化了知识距离与突破性技术形成之间的倒U型关系,支持假设2a,同时组织间合作强度也增强了知识多样性与突破性技术形成之间的倒U型关系,支持假设2b。组织内合作强度不影响知识距离或知识多样性与突破性技术形成之间关系,与调节效应检验结果一致,不支持假设3a和3b。

在本研究中,基于语义维度测度的知识距离和知识多样性与突破性技术形成之间关系与主检验结果基本一致,侧面验证了本研究从语义维度设计指标的合理性。

基于子样本的稳健性检验。为了进一步研究发明人在组织间和组织内的合作情况如何影响突破性技术形成,剔除样本中1954个独立发明人专利,保留6891个团队合作样本,重复主检验和调节效应检验步骤,结果如表7和表8所示,与上述结论保持一致,实证研究结果基本稳健。

4 结论与展望

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突破性技术是催生新质生产力的关键动力,深入探究其形成机理,有助于揭示新质生产力的生成与演化机制,为其培育与发展提供理论支撑和实践指引。本研究基于知识重组理论,从知识层面特征出发,考察了知识距离和知识多样性对突破性技术形成的影响;同时引入发明人层面特征,揭示了发明人组织间合作和组织内合作的调节作用。基于IncoPat数据库2004-2021年合成生物学领域美国授权发明专利数据的实证检验,本研究主要得到以下结论:

第一,知识距离和知识多样性均与突破性技术形成呈倒U型关系,即在一定范围内,随着构成专利技术的知识间距离和多样性水平的增加,技术突破概率提升。然而突破性技术产生概率不会无限逼近于1,而是在知识距离和多样性越过某个水平后开始下降,在理论上产生技术突破的“最佳知识搜索范围”。

第二,组织间合作强度强化了知识距离与突破性技术形成之间的倒U型关系。具体而言,较高的组织间合作强度提高知识距离积极影响突破性技术形成的正斜率,并降低知识距离消极影响突破性技术形成的负斜率,使倒U型曲线变得陡峭。同时,随着组织间合作强度的增加,知识距离的最佳平衡点会向更低水平移动,这意味着在高强度合作下,技术突破所需的重组知识距离在缩小。知识多样性水平虽然在整体上没有明显变动,但其对突破性技术形成的促进作用在高强度合作下得到增强,表现为突破性技术形成概率的上限得到显著提高。这表明组织间合作不仅优化了知识重组的效率,还提升了技术创新的潜力,为突破性技术的产生创造了更有利的条件。

第三,组织内合作强度在知识重组影响突破性技术形成过程中不起作用,可能是正面效应和负面效应相抵消的结果。当知识距离和知识多样性水平较低时,与较低的组织内合作强度相比,组织内部高强度合作造成知识冗余和研发人员认知锁定带来的负面作用与研发人员深度思考挖掘狭窄领域知识产生新观点的正面效应相抵消;当知识距离与多样性水平较高时,较强的组织内合作虽有助于专利团队整合知识、更新知识库并抑制信息过载,从而提升创新;但其同时也强化了子群边界与局部搜索惯性,产生抵消性的负面效应。因此,组织内合作强度未呈现显著的调节作用。

本文理论贡献主要集中在以下两点。第一,本研究从知识重组理论出发解构突破性技术形成机理,通过探索知识在距离和多样性维度上各阶段重组特征对突破性技术影响,进而寻找最大可能产生突破性技术的知识配置。已有研究多基于知识重组“张力观”开展量化研究,而知识重组“基础观”多停留在理论层面^[1,9,19]。本研究调和了知识重组“张力观”和知识重组“基础观”间矛盾,明确两个观点各自适用场景,加深了对以往研究的理解,为后续研究探究知识重组影响提供实证支撑。第二,本研究从发明人层面出发探讨组织合作特征对知识重组与突破性技术形成的影响。已有研究大多从网络视角出发,单独探讨组织内部合作模式或管理策略对于技术创新的影响^[28],少数同时关注组织内部合作和外部合作的研究大多从企业层面出发^[17],将公司内部合作视为知识本地搜索,将公司外部合作视为知识远程搜索,忽略研发人员个体知识储备和知识搜索倾向带来的异质性影响。本研究考虑发明人作为知识载体的作用,探讨发明人主观能动性对知识创造和传播的重要影响,探究跨组织合作强度与组织内部合作强度对技术突破的差异化作用机制,是对已有研究的深化与发展。

为促进突破性技术涌现,进而推动新质生产力发展,本研究提出以下三点管理启示:科研机构应设计知识共享激励机制,以充分激发科研人员的创新活力与合作意愿,并鼓励其拓展外部合作渠道。研究表明,跨组织合作有助于打破组织间知识壁垒,促进异质性知识融合,从而显著提高技术突破的可能性,并为科研机构的可持续发展提供重要动力。建议通过建立联合实验室、专利池等多元化合作平台,为科研人员提供更广泛的合作空间与资源支持。同时,应引导研发人员主动突破“舒适圈”,积极寻求与具有互补性知识结构的跨组织伙伴开展深度合作,以增强知识重组的积极效果,实现知识资源的优化配置与创新融合,共同推动技术突破。第二,科研机构不应该过度依赖内部合作来推动技术创新,而应通过组织内部改革来优化合作效果。研究发现,组织内合作强度对知识重组影响技术突破过程无调节效应,可能由于专利团队内明显子群边界和局部搜索惯性带来的负面影响抵消了知识整合过程中避免信息过载等正向影响。科研机构可以通过破除部门间知识壁垒,通过轮岗制、跨职能团队等弱化子群边界,增强远距离、多样化知识整合能力,提升组织内部合作创新水平。第三,研发人员在知识搜索时,应灵活调整知识搜索的距离和多样性,以实现知识的有效重组和创新。研究发现知识距离和知识多样性存在“最佳搜索范围”,需要平衡知识距离引致的知识可获取性和相关性不足与知识同质化和局限性等问题;以及知识多样性引致的信息过载和资源浪费与知识重组机会不足等问题。首先,在知识搜索过程中建议研发人员结合自身研究方向和创新需求,合理选择知识距离适中的知识源,避免过度依赖本地知识或盲目追求遥远的知识领域。其次,研发人员应根据项目目标和资源限制,合理规划知识搜索的范围,避免因知识多样性过高而导致的信息过载和资源浪费。

本研究在理论构建与实证分析方面取得了一定成果,但仍存在局限性,有待在未来研究中进一步完善。首先,本研究以合成生物学领域为研究对象,其结论的普适性尚需进一步验证。其次,研究发现基于技术测度的知识重组特征与基于语义测度的知识重组特征对突破性技术形成的影响存在差异,复杂的作用机制有待深入阐释。最后,本文仅从宏观层面考察了组织内、组织间合作强度对知识重组影响技术突破的调节作用,未来有必要深入微观层面进一步剖析其影响机制。

基金

收起

国家自然科学基金资助项目(72074020)
北京市自然科学基金(9252016)

参考文献

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文献

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2026年第44卷第9期

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接收时间：2025-07-28
首发时间：2026-10-15
出版时间：2026-09-15

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收稿日期：2025-07-28
修回日期：2025-10-21

基金

国家自然科学基金资助项目(72074020)

北京市自然科学基金(9252016)

作者信息

北京理工大学管理学院, 北京 100081

通讯作者:

汪雪锋(1977-),男,教授、博士生导师,E-mail: wxf5122@bit.edu.cn。

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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变量	模型1	模型2	模型3	模型4
Assignee	0.041	0.039	0.042	0.041
	(1.373)	(1.297)	(1.416)	(1.381)
Team	0.129	0.137	0.127	0.133
	(0.864)	(0.919)	(0.849)	(0.894)
Gexperience	-0.294^***	-0.292^***	-0.295^***	-0.290^***
	(-4.882)	(-4.813)	(-4.852)	(-4.712)
Scale	2.399^***	2.441^***	2.398^***	2.426^***
	(4.603)	(4.721)	(4.433)	(4.603)
Patcites	0.188^***	0.184^***	0.185^***	0.182^***
	(6.881)	(6.708)	(6.735)	(6.507)
Npatcites	1.151^***	1.154^***	1.132^***	1.151^***
	(8.790)	(8.786)	(8.508)	(8.576)
Claims	2.163^***	2.201^***	2.136^***	2.198^***
	(6.111)	(6.156)	(6.150)	(6.316)
Family	-12.524^***	-12.480^***	-12.647^***	-12.542^***
	(-3.231)	(-3.206)	(-3.264)	(-3.244)
Distance		1.092^**		1.515^***
		(2.201)		(2.729)
Distance²		-1.030^*		-1.383^**
		(-1.826)		(-2.256)
Diversity			1.448^*	1.782^**
			(1.798)	(2.204)
Diversity²			-1.378^*	-1.964^**
			(-1.829)	(-2.528)
Year dummies	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included
Constant	-3.846^***	-3.910^***	-4.096^***	-4.153^***
	(-7.209)	(-7.274)	(-7.459)	(-7.547)
Observations	8,485	8,485	8,485	8,485
Pseudo R²	0.102	0.103	0.103	0.105
Wald chi²	307.6	306.7	314.8	317.2
log likelihood	-1488	-1486	-1487	-1482

变量

模型1

模型2

模型3

模型4

Assignee

0.041

0.039

0.042

0.041

(1.373)

(1.297)

(1.416)

(1.381)

Team

0.129

0.137

0.127

0.133

(0.864)

(0.919)

(0.849)

(0.894)

Gexperience

-0.294^***

-0.292^***

-0.295^***

-0.290^***

(-4.882)

(-4.813)

(-4.852)

(-4.712)

Scale

2.399^***

2.441^***

2.398^***

2.426^***

(4.603)

(4.721)

(4.433)

(4.603)

Patcites

0.188^***

0.184^***

0.185^***

0.182^***

(6.881)

(6.708)

(6.735)

(6.507)

Npatcites

1.151^***

1.154^***

1.132^***

1.151^***

(8.790)

(8.786)

(8.508)

(8.576)

Claims

2.163^***

2.201^***

2.136^***

2.198^***

(6.111)

(6.156)

(6.150)

(6.316)

Family

-12.524^***

-12.480^***

-12.647^***

-12.542^***

(-3.231)

(-3.206)

(-3.264)

(-3.244)

Distance

1.092^**

1.515^***

(2.201)

(2.729)

Distance²

-1.030^*

-1.383^**

(-1.826)

(-2.256)

Diversity

1.448^*

1.782^**

(1.798)

(2.204)

Diversity²

-1.378^*

-1.964^**

(-1.829)

(-2.528)

Year dummies

Included

Technology dummies

Included

Constant

-3.846^***

-3.910^***

-4.096^***

-4.153^***

(-7.209)

(-7.274)

(-7.459)

(-7.547)

Observations

8,485

Pseudo R²

0.102

0.103

0.105

Wald chi²

307.6

306.7

314.8

317.2

log likelihood

-1488

-1486

-1487

-1482

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.034	0.042	0.045	0.049^*	0.037	0.047
	(1.068)	(1.426)	(1.547)	(1.686)	(1.191)	(1.632)
Team	0.149	0.138	-0.148	-0.159	-0.144	-0.153
	(0.998)	(0.924)	(-0.963)	(-1.033)	(-0.932)	(-0.993)
Gexperience	-0.296^***	-0.296^***	-0.412^***	-0.413^***	-0.414^***	-0.414^***
	(-4.884)	(-4.873)	(-6.555)	(-6.394)	(-6.561)	(-6.397)
Scale	2.463^***	2.422^***	0.076	0.078	0.085	0.114
	(4.704)	(4.462)	(0.093)	(0.100)	(0.103)	(0.146)
Patcites	0.192^***	0.186^***	0.166^***	0.167^***	0.171^***	0.167^***
	(6.663)	(6.717)	(6.073)	(6.083)	(6.039)	(6.039)
Npatcites	1.143^***	1.129^***	1.019^***	0.996^***	1.009^***	0.991^***
	(8.700)	(8.466)	(7.361)	(7.079)	(7.296)	(7.029)
Claims	2.181^***	2.139^***	2.090^***	2.043^***	2.070^***	2.044^***
	(6.084)	(6.079)	(5.645)	(5.670)	(5.614)	(5.632)
Family	-12.237^***	-12.479^***	-11.867^***	-12.069^***	-11.713^***	-11.967^***
	(-3.133)	(-3.214)	(-3.145)	(-3.206)	(-3.081)	(-3.165)
Distance	1.220^**		1.067^**		1.172^**
	(2.454)		(2.087)		(2.294)
Distance²	-1.257^**		-0.997^*		-1.200^**
	(-2.204)		(-1.721)		(-2.055)
Diversity		1.530^*		1.527^*		1.633^**
		(1.878)		(1.862)		(1.964)
Diversity²		-1.410^*		-1.547^**		-1.606^**
		(-1.867)		(-2.019)		(-2.088)
Interdensity	-0.480^**	-0.323			-0.332	-0.199
	(-1.990)	(-1.356)			(-1.474)	(-0.847)
Distance*Interdensity	5.405^***				5.198^***
	(3.210)				(3.273)
Distance²*Interdensity	-7.785^***				-7.598^***
	(-3.110)				(-3.116)
Diversity*Interdensity		1.689				2.010
		(0.446)				(0.516)
Diversity²*Interdensity		-0.098				-0.482
		(-0.036)				(-0.172)
Intradensity			0.159^***	0.158^***	0.158^***	0.157^***
			(7.294)	(7.484)	(7.202)	(7.398)
Distance*Intradensity			-0.109		-0.085
			(-0.798)		(-0.621)
Distance²*Intradensity			0.082		0.052
			(0.562)		(0.356)
Diversity*Intradensity				-0.120		-0.124
				(-0.622)		(-0.643)
Diversity²*Intradensity				0.104		0.114
				(0.524)		(0.574)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.860^***	-3.848^***	-3.357^***	-3.391^***	-3.404^***	-3.684^***
	(-7.170)	(-7.228)	(-6.270)	(-6.401)	(-6.313)	(-6.663)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.106	0.104	0.119	0.118	0.121	0.119
Wald chi²	317.1	322.6	348.9	362.8	358.5	369.1
log likelihood	-1481	-1485	-1461	-1461	-1456	-1460

变量

模型1

模型2

模型3

模型4

模型5

模型6

Assignee

0.034

0.042

0.045

0.049^*

0.037

0.047

(1.068)

(1.426)

(1.547)

(1.686)

(1.191)

(1.632)

Team

0.149

0.138

-0.148

-0.159

-0.144

-0.153

(0.998)

(0.924)

(-0.963)

(-1.033)

(-0.932)

(-0.993)

Gexperience

-0.296^***

-0.412^***

-0.413^***

-0.414^***

(-4.884)

(-4.873)

(-6.555)

(-6.394)

(-6.561)

(-6.397)

Scale

2.463^***

2.422^***

0.076

0.078

0.085

0.114

(4.704)

(4.462)

(0.093)

(0.100)

(0.103)

(0.146)

Patcites

0.192^***

0.186^***

0.166^***

0.167^***

0.171^***

0.167^***

(6.663)

(6.717)

(6.073)

(6.083)

(6.039)

Npatcites

1.143^***

1.129^***

1.019^***

0.996^***

1.009^***

0.991^***

(8.700)

(8.466)

(7.361)

(7.079)

(7.296)

(7.029)

Claims

2.181^***

2.139^***

2.090^***

2.043^***

2.070^***

2.044^***

(6.084)

(6.079)

(5.645)

(5.670)

(5.614)

(5.632)

Family

-12.237^***

-12.479^***

-11.867^***

-12.069^***

-11.713^***

-11.967^***

(-3.133)

(-3.214)

(-3.145)

(-3.206)

(-3.081)

(-3.165)

Distance

1.220^**

1.067^**

1.172^**

(2.454)

(2.087)

(2.294)

Distance²

-1.257^**

-0.997^*

-1.200^**

(-2.204)

(-1.721)

(-2.055)

Diversity

1.530^*

1.527^*

1.633^**

(1.878)

(1.862)

(1.964)

Diversity²

-1.410^*

-1.547^**

-1.606^**

(-1.867)

(-2.019)

(-2.088)

Interdensity

-0.480^**

-0.323

-0.332

-0.199

(-1.990)

(-1.356)

(-1.474)

(-0.847)

Distance*Interdensity

5.405^***

5.198^***

(3.210)

(3.273)

Distance²*Interdensity

-7.785^***

-7.598^***

(-3.110)

(-3.116)

Diversity*Interdensity

1.689

2.010

(0.446)

(0.516)

Diversity²*Interdensity

-0.098

-0.482

(-0.036)

(-0.172)

Intradensity

0.159^***

0.158^***

0.157^***

(7.294)

(7.484)

(7.202)

(7.398)

Distance*Intradensity

-0.109

-0.085

(-0.798)

(-0.621)

Distance²*Intradensity

0.082

0.052

(0.562)

(0.356)

Diversity*Intradensity

-0.120

-0.124

(-0.622)

(-0.643)

Diversity²*Intradensity

0.104

0.114

(0.524)

(0.574)

Year dummies

Included

Technology dummies

Included

Constant

-3.860^***

-3.848^***

-3.357^***

-3.391^***

-3.404^***

-3.684^***

(-7.170)

(-7.228)

(-6.270)

(-6.401)

(-6.313)

(-6.663)

Observations

8,485

Pseudo R²

0.106

0.104

0.119

0.118

0.121

0.119

Wald chi²

317.1

322.6

348.9

362.8

358.5

369.1

log likelihood

-1481

-1485

-1461

-1456

-1460

y=Forwardcite
变量	模型1	模型2	模型3
Assignee	0.019^**	0.020^**	0.019^**
	(2.33)	(2.49)	(2.39)
Unique	0.034	0.033	0.033
	(1.20)	(1.14)	(1.16)
Gexperience	-0.018^***	-0.018^***	-0.018^***
	(-2.90)	(-2.79)	(-2.86)
Scale	0.128	0.105	0.126
	(0.71)	(0.58)	(0.70)
Patcites	0.112^***	0.112^***	0.111^***
	(11.24)	(11.14)	(11.10)
Npatcites	0.473^***	0.465^***	0.469^***
	(10.86)	(10.64)	(10.72)
Claims	1.513^***	1.504^***	1.509^***
	(14.27)	(14.17)	(14.22)
Family	-1.383^***	-1.409^***	-1.402^***
	(-7.29)	(-7.42)	(-7.39)
Distance	0.322^***		0.456^***
	(3.01)		(3.89)
Distance²	-0.382^***		-0.492^***
	(-3.20)		(-3.88)
Diversity		0.474^***	0.532^***
		(3.25)	(3.59)
Diversity²		-0.467^***	-0.587^***
		(-3.30)	(-3.97)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	2.049^***	1.976^***	1.980^***
	(17.30)	(16.24)	(16.25)
Observations	8,485	8,485	8,485
Adjusted R²	0.298	0.298	0.299
F	106.8	106.8	101.5

y=Forwardcite

变量

模型1

模型2

模型3

Assignee

0.019^**

0.020^**

0.019^**

(2.33)

(2.49)

(2.39)

Unique

0.034

0.033

(1.20)

(1.14)

(1.16)

Gexperience

-0.018^***

(-2.90)

(-2.79)

(-2.86)

Scale

0.128

0.105

0.126

(0.71)

(0.58)

(0.70)

Patcites

0.112^***

0.111^***

(11.24)

(11.14)

(11.10)

Npatcites

0.473^***

0.465^***

0.469^***

(10.86)

(10.64)

(10.72)

Claims

1.513^***

1.504^***

1.509^***

(14.27)

(14.17)

(14.22)

Family

-1.383^***

-1.409^***

-1.402^***

(-7.29)

(-7.42)

(-7.39)

Distance

0.322^***

0.456^***

(3.01)

(3.89)

Distance²

-0.382^***

-0.492^***

(-3.20)

(-3.88)

Diversity

0.474^***

0.532^***

(3.25)

(3.59)

Diversity²

-0.467^***

-0.587^***

(-3.30)

(-3.97)

Year dummies

Included

Technology dummies

Included

Constant

2.049^***

1.976^***

1.980^***

(17.30)

(16.24)

(16.25)

Observations

8,485

Adjusted R²

0.298

0.299

106.8

101.5

变量	模型1	模型2	模型3
Assignee	0.047	0.039	0.045
	(1.616)	(1.299)	(1.543)
Team	0.116	0.132	0.121
	(0.775)	(0.885)	(0.809)
Gexperience	-0.322^***	-0.299^***	-0.325^***
	(-5.275)	(-4.925)	(-5.328)
Scale	2.353^***	2.448^***	2.401^***
	(4.538)	(4.380)	(4.362)
Patcites	0.167^***	0.172^***	0.152^***
	(5.867)	(6.344)	(5.348)
Npatcites	1.104^***	1.083^***	1.040^***
	(8.277)	(8.138)	(7.696)
Claims	2.203^***	2.091^***	2.136^***
	(6.384)	(6.020)	(6.311)
Family	-11.850^***	-12.536^***	-11.937^***
	(-3.099)	(-3.254)	(-3.131)
Distance_sem	0.298^***		0.294^***
	(4.317)		(4.292)
Distance_sem²	-0.011^***		-0.011^***
	(-2.609)		(-2.608)
Diversity_sem		5.133^*	5.446^**
		(1.899)	(1.997)
Diversity_sem²		-26.954^**	-26.912^**
		(-2.528)	(-2.513)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-4.610^***	-3.937^***	-4.729^***
	(-8.177)	(-7.223)	(-8.240)
Observations	8,485	8,485	8,485
Pseudo R²	0.116	0.105	0.118
Wald chi²	352	322.2	366.4
log likelihood	-1465	-1484	-1461

变量

模型1

模型2

模型3

Assignee

0.047

0.039

0.045

(1.616)

(1.299)

(1.543)

Team

0.116

0.132

0.121

(0.775)

(0.885)

(0.809)

Gexperience

-0.322^***

-0.299^***

-0.325^***

(-5.275)

(-4.925)

(-5.328)

Scale

2.353^***

2.448^***

2.401^***

(4.538)

(4.380)

(4.362)

Patcites

0.167^***

0.172^***

0.152^***

(5.867)

(6.344)

(5.348)

Npatcites

1.104^***

1.083^***

1.040^***

(8.277)

(8.138)

(7.696)

Claims

2.203^***

2.091^***

2.136^***

(6.384)

(6.020)

(6.311)

Family

-11.850^***

-12.536^***

-11.937^***

(-3.099)

(-3.254)

(-3.131)

Distance_sem

0.298^***

0.294^***

(4.317)

(4.292)

Distance_sem²

-0.011^***

(-2.609)

(-2.608)

Diversity_sem

5.133^*

5.446^**

(1.899)

(1.997)

Diversity_sem²

-26.954^**

-26.912^**

(-2.528)

(-2.513)

Year dummies

Included

Technology dummies

Included

Constant

-4.610^***

-3.937^***

-4.729^***

(-8.177)

(-7.223)

(-8.240)

Observations

8,485

Pseudo R²

0.116

0.105

0.118

Wald chi²

352

322.2

366.4

log likelihood

-1465

-1484

-1461

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	0.055	0.039	0.052^*	0.045	0.057^*	0.043
	(1.619)	(1.342)	(1.823)	(1.548)	(1.798)	(1.520)
Team	0.129	0.148	-0.123	-0.160	-0.111	-0.150
	(0.868)	(0.991)	(-0.796)	(-1.038)	(-0.716)	(-0.972)
Gexperience	-0.324^***	-0.301^***	-0.416^***	-0.412^***	-0.413^***	-0.413^***
	(-5.312)	(-4.935)	(-6.331)	(-6.579)	(-6.302)	(-6.553)
Scale	2.351^***	2.461^***	0.255	0.063	0.274	0.077
	(4.518)	(4.407)	(0.298)	(0.074)	(0.319)	(0.090)
Patcites	0.167^***	0.170^***	0.157^***	0.149^***	0.158^***	0.147^***
	(5.798)	(6.253)	(5.426)	(5.434)	(5.358)	(5.308)
Npatcites	1.094^***	1.081^***	0.965^***	0.916^***	0.956^***	0.911^***
	(8.184)	(8.120)	(6.632)	(6.432)	(6.557)	(6.391)
Claims	2.215^***	2.102^***	2.134^***	1.989^***	2.143^***	1.996^***
	(6.145)	(5.952)	(6.067)	(5.480)	(5.899)	(5.451)
Family	-11.584^***	-12.381^***	-11.382^***	-12.060^***	-11.181^***	-11.957^***
	(-3.047)	(-3.210)	(-3.057)	(-3.196)	(-3.019)	(-3.158)
Distance_sem	0.368^***		0.332^***		0.386^***
	(4.442)		(3.898)		(4.090)
Distance_sem²	-0.018^***		-0.015^**		-0.021^**
	(-3.014)		(-2.135)		(-2.546)
Diversity_sem		6.181^**		5.194^*		6.218^**
		(2.318)		(1.900)		(2.304)
Diversity_sem²		-32.962^***		-26.546^**		-32.222^***
		(-3.128)		(-2.464)		(-3.032)
Interdensity	-0.629^*	-0.710^**			-0.499	-0.553^*
	(-1.894)	(-2.265)			(-1.574)	(-1.860)
Distance_sem*Interdensity	1.347^**				1.229^*
	(1.972)				(1.836)
Distance_sem²*Interdensity	-0.134^**				-0.121^*
	(-1.961)				(-1.803)
Diversity_sem*Interdensity		25.798^***				25.766^**
		(2.578)				(2.491)
Diversity_sem²*Interdensity		-136.112^***				-132.450^***
		(-3.243)				(-3.212)
Intradensity			0.127^***	0.158^***	0.123^***	0.157^***
			(4.247)	(7.362)	(4.057)	(7.230)
Distance_sem*Intradensity			0.013		0.018
			(0.587)		(0.766)
Distance_sem²*Intradensity			-0.000		-0.001
			(-0.310)		(-0.515)
Diversity_sem*Intradensity				1.408^*		1.498^*
				(1.663)		(1.721)
Diversity_sem²*Intradensity				-4.833		-5.248
				(-1.342)		(-1.416)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-3.613^***	-3.860^***	-3.196^***	-3.361^***	-3.266^***	-3.367^***
	(-6.776)	(-7.224)	(-6.036)	(-6.295)	(-6.106)	(-6.286)
Observations	8,485	8,485	8,485	8,485	8,485	8,485
Pseudo R²	0.118	0.106	0.128	0.121	0.130	0.122
Wald chi²	350.7	345.6	373.7	360.6	374.7	383.3
log likelihood	-1462	-1481	-1444	-1457	-1442	-1455

变量

模型1

模型2

模型3

模型4

模型5

模型6

Assignee

0.055

0.039

0.052^*

0.045

0.057^*

0.043

(1.619)

(1.342)

(1.823)

(1.548)

(1.798)

(1.520)

Team

0.129

0.148

-0.123

-0.160

-0.111

-0.150

(0.868)

(0.991)

(-0.796)

(-1.038)

(-0.716)

(-0.972)

Gexperience

-0.324^***

-0.301^***

-0.416^***

-0.412^***

-0.413^***

(-5.312)

(-4.935)

(-6.331)

(-6.579)

(-6.302)

(-6.553)

Scale

2.351^***

2.461^***

0.255

0.063

0.274

0.077

(4.518)

(4.407)

(0.298)

(0.074)

(0.319)

(0.090)

Patcites

0.167^***

0.170^***

0.157^***

0.149^***

0.158^***

0.147^***

(5.798)

(6.253)

(5.426)

(5.434)

(5.358)

(5.308)

Npatcites

1.094^***

1.081^***

0.965^***

0.916^***

0.956^***

0.911^***

(8.184)

(8.120)

(6.632)

(6.432)

(6.557)

(6.391)

Claims

2.215^***

2.102^***

2.134^***

1.989^***

2.143^***

1.996^***

(6.145)

(5.952)

(6.067)

(5.480)

(5.899)

(5.451)

Family

-11.584^***

-12.381^***

-11.382^***

-12.060^***

-11.181^***

-11.957^***

(-3.047)

(-3.210)

(-3.057)

(-3.196)

(-3.019)

(-3.158)

Distance_sem

0.368^***

0.332^***

0.386^***

(4.442)

(3.898)

(4.090)

Distance_sem²

-0.018^***

-0.015^**

-0.021^**

(-3.014)

(-2.135)

(-2.546)

Diversity_sem

6.181^**

5.194^*

6.218^**

(2.318)

(1.900)

(2.304)

Diversity_sem²

-32.962^***

-26.546^**

-32.222^***

(-3.128)

(-2.464)

(-3.032)

Interdensity

-0.629^*

-0.710^**

-0.499

-0.553^*

(-1.894)

(-2.265)

(-1.574)

(-1.860)

Distance_sem*Interdensity

1.347^**

1.229^*

(1.972)

(1.836)

Distance_sem²*Interdensity

-0.134^**

-0.121^*

(-1.961)

(-1.803)

Diversity_sem*Interdensity

25.798^***

25.766^**

(2.578)

(2.491)

Diversity_sem²*Interdensity

-136.112^***

-132.450^***

(-3.243)

(-3.212)

Intradensity

0.127^***

0.158^***

0.123^***

0.157^***

(4.247)

(7.362)

(4.057)

(7.230)

Distance_sem*Intradensity

0.013

0.018

(0.587)

(0.766)

Distance_sem²*Intradensity

-0.000

-0.001

(-0.310)

(-0.515)

Diversity_sem*Intradensity

1.408^*

1.498^*

(1.663)

(1.721)

Diversity_sem²*Intradensity

-4.833

-5.248

(-1.342)

(-1.416)

Year dummies

Included

Technology dummies

Included

Constant

-3.613^***

-3.860^***

-3.196^***

-3.361^***

-3.266^***

-3.367^***

(-6.776)

(-7.224)

(-6.036)

(-6.295)

(-6.106)

(-6.286)

Observations

8,485

Pseudo R²

0.118

0.106

0.128

0.121

0.130

0.122

Wald chi²

350.7

345.6

373.7

360.6

374.7

383.3

log likelihood

-1462

-1481

-1444

-1457

-1442

-1455

变量	模型1	模型2	模型3
Assignee	-0.019	-0.015	-0.004
	(-0.674)	(-0.435)	(-0.137)
Gexperience	-0.255^***	-0.251^***	-0.278^***
	(-3.796)	(-3.834)	(-4.452)
Scale	2.337^***	2.427^***	2.355^***
	(4.187)	(4.293)	(4.782)
Patcites	0.202^***	0.221^***	0.179^***
	(7.032)	(7.784)	(6.121)
Npatcites	0.303^***	0.344^***	1.309^***
	(6.191)	(6.632)	(9.351)
Claims	1.600^***	1.719^***	2.031^***
	(4.975)	(5.660)	(6.260)
Family	-1.173^***	-12.636^***	-11.966^***
	(-6.567)	(-2.716)	(-2.853)
Distance	0.995^*		1.243^**
	(1.685)		(2.168)
Distance²	-1.243^*		-1.166^*
	(-1.828)		(-1.810)
Diversity		2.398^**	1.685^*
		(2.171)	(1.827)
Diversity²		-2.558^***	-2.100^**
		(-2.766)	(-2.569)
Year dummies	Included	Included	Included
Technology dummies	Included	Included	Included
Constant	-3.248^***	-4.353^***	-3.674^***
	(-5.536)	(-6.720)	(-6.608)
Observations	6,891	6,891	6,891
Pseudo R²	0.152	0.123	0.112
Wald chi²	270.4	254.4	299.7
log likelihood	-990.5	-1024	-1247

变量

模型1

模型2

模型3

Assignee

-0.019

-0.015

-0.004

(-0.674)

(-0.435)

(-0.137)

Gexperience

-0.255^***

-0.251^***

-0.278^***

(-3.796)

(-3.834)

(-4.452)

Scale

2.337^***

2.427^***

2.355^***

(4.187)

(4.293)

(4.782)

Patcites

0.202^***

0.221^***

0.179^***

(7.032)

(7.784)

(6.121)

Npatcites

0.303^***

0.344^***

1.309^***

(6.191)

(6.632)

(9.351)

Claims

1.600^***

1.719^***

2.031^***

(4.975)

(5.660)

(6.260)

Family

-1.173^***

-12.636^***

-11.966^***

(-6.567)

(-2.716)

(-2.853)

Distance

0.995^*

1.243^**

(1.685)

(2.168)

Distance²

-1.243^*

-1.166^*

(-1.828)

(-1.810)

Diversity

2.398^**

1.685^*

(2.171)

(1.827)

Diversity²

-2.558^***

-2.100^**

(-2.766)

(-2.569)

Year dummies

Included

Technology dummies

Included

Constant

-3.248^***

-4.353^***

-3.674^***

(-5.536)

(-6.720)

(-6.608)

Observations

6,891

Pseudo R²

0.152

0.123

0.112

Wald chi²

270.4

254.4

299.7

log likelihood

-990.5

-1024

-1247

变量	模型1	模型2	模型3	模型4	模型5	模型6
Assignee	-0.033	-0.008	-0.017	0.001	-0.040	0.001
	(-1.130)	(-0.331)	(-0.778)	(0.037)	(-1.267)	(0.011)
Gexperience	-0.264^***	-0.253^***	-0.400^***	-0.371^***	-0.393^***	-0.372^***
	(-3.996)	(-3.858)	(-5.887)	(-5.622)	(-5.922)	(-5.052)
Scale	2.512^***	2.449^***	-0.422	-0.318	-0.231	-0.300
	(4.128)	(4.300)	(-0.612)	(-0.450)	(-0.335)	(-0.362)
Patcites	0.224^***	0.222^***	0.358^***	0.193^***	0.372^***	0.192^***
	(7.350)	(7.744)	(7.187)	(6.703)	(6.700)	(6.147)
Npatcites	0.330^***	0.344^***	0.174^***	0.308^***	0.182^***	0.308^***
	(6.511)	(6.630)	(3.644)	(5.886)	(3.456)	(6.555)
Claims	1.705^***	1.715^***	1.532^***	1.646^***	1.397^***	1.643^***
	(5.457)	(5.645)	(4.535)	(5.097)	(4.072)	(4.889)
Family	-12.088^***	-12.485^***	-12.326^***	-11.401^**	-11.016^**	-11.346^***
	(-2.607)	(-2.684)	(-2.708)	(-2.546)	(-2.474)	(-4.309)
Distance	0.985^*		1.155^*		1.090^*
	(1.663)		(1.826)		(1.711)
Distance²	-1.345^**		-1.320^*		-1.308^*
	(-1.962)		(-1.763)		(-1.742)
Diversity		2.385^**		2.216^**		2.242^*
		(2.174)		(1.991)		(1.937)
Diversity²		-2.544^***		-2.492^***		-2.514^**
		(-2.769)		(-2.635)		(-2.513)
Interdensity	-0.627^**	-0.233			-0.396	-0.092
	(-2.073)	(-1.185)			(-1.487)	(-0.451)
Distance*Interdensity	7.025^***				6.199^***
	(3.174)				(3.142)
Distance²*Interdensity	-9.690^***				-8.862^***
	(-3.196)				(-3.036)
Diversity*Interdensity		-0.102				0.994
		(-0.029)				(0.193)
Diversity²*Interdensity		1.094				0.082
		(0.423)				(0.021)
Intradensity			0.181^***	0.176^***	0.168^***	0.174^***
			(7.958)	(7.463)	(7.254)	(6.515)
Distance*Intradensity			-0.151		-0.126
			(-0.982)		(-0.829)
Distance²*Intradensity			0.107		0.105
			(0.629)		(0.624)
Diversity*Intradensity				0.543^*		0.572
				(1.747)		(1.080)
Diversity²*Intradensity				-0.451^*		-0.459
				(-1.661)		(-1.185)
Year dummies	Included	Included	Included	Included	Included	Included
Technology dummies	Included	Included	Included	Included	Included	Included
Constant	-4.113^***	-4.190^***	-4.193^***	-3.935^***	-4.574^***	-3.946^***
	(-6.899)	(-7.159)	(-7.334)	(-6.734)	(-7.549)	(-5.740)
Observations	6891	6891	6891	6891	6891	6891
Pseudo R²	0.125	0.124	0.143	0.147	0.158	0.148
Wald chi²	253.8	259.8	316	319.4	354.6	345.1
log likelihood	-1022	-1023	-1004	-995.7	-982.9	-995.3

变量

模型1

模型2

模型3

模型4

模型5

模型6

Assignee

-0.033

-0.008

-0.017

0.001

-0.040

0.001

(-1.130)

(-0.331)

(-0.778)

(0.037)

(-1.267)

(0.011)

Gexperience

-0.264^***

-0.253^***

-0.400^***

-0.371^***

-0.393^***

-0.372^***

(-3.996)

(-3.858)

(-5.887)

(-5.622)

(-5.922)

(-5.052)

Scale

2.512^***

2.449^***

-0.422

-0.318

-0.231

-0.300

(4.128)

(4.300)

(-0.612)

(-0.450)

(-0.335)

(-0.362)

Patcites

0.224^***

0.222^***

0.358^***

0.193^***

0.372^***

0.192^***

(7.350)

(7.744)

(7.187)

(6.703)

(6.700)

(6.147)

Npatcites

0.330^***

0.344^***

0.174^***

0.308^***

0.182^***

0.308^***

(6.511)

(6.630)

(3.644)

(5.886)

(3.456)

(6.555)

Claims

1.705^***

1.715^***

1.532^***

1.646^***

1.397^***

1.643^***

(5.457)

(5.645)

(4.535)

(5.097)

(4.072)

(4.889)

Family

-12.088^***

-12.485^***

-12.326^***

-11.401^**

-11.016^**

-11.346^***

(-2.607)

(-2.684)

(-2.708)

(-2.546)

(-2.474)

(-4.309)

Distance

0.985^*

1.155^*

1.090^*

(1.663)

(1.826)

(1.711)

Distance²

-1.345^**

-1.320^*

-1.308^*

(-1.962)

(-1.763)

(-1.742)

Diversity

2.385^**

2.216^**

2.242^*

(2.174)

(1.991)

(1.937)

Diversity²

-2.544^***

-2.492^***

-2.514^**

(-2.769)

(-2.635)

(-2.513)

Interdensity

-0.627^**

-0.233

-0.396

-0.092

(-2.073)

(-1.185)

(-1.487)

(-0.451)

Distance*Interdensity

7.025^***

6.199^***

(3.174)

(3.142)

Distance²*Interdensity

-9.690^***

-8.862^***

(-3.196)

(-3.036)

Diversity*Interdensity

-0.102

0.994

(-0.029)

(0.193)

Diversity²*Interdensity

1.094

0.082

(0.423)

(0.021)

Intradensity

0.181^***

0.176^***

0.168^***

0.174^***

(7.958)

(7.463)

(7.254)

(6.515)

Distance*Intradensity

-0.151

-0.126

(-0.982)

(-0.829)

Distance²*Intradensity

0.107

0.105

(0.629)

(0.624)

Diversity*Intradensity

0.543^*

0.572

(1.747)

(1.080)

Diversity²*Intradensity

-0.451^*

-0.459

(-1.661)

(-1.185)

Year dummies

Included

Technology dummies

Included

Constant

-4.113^***

-4.190^***

-4.193^***

-3.935^***

-4.574^***

-3.946^***

(-6.899)

(-7.159)

(-7.334)

(-6.734)

(-7.549)

(-5.740)

Observations

6891

Pseudo R²

0.125

0.124

0.143

0.147

0.158

0.148

Wald chi²

253.8

259.8

316

319.4

354.6

345.1

log likelihood

-1022

-1023

-1004

-995.7

-982.9

-995.3