Industry-University-Research collaboration networks: the identification and driving factors of key technologies

Peng, Qining; Zhang, Xian; Fu, Zhenkang

doi:10.2478/jdis-2025-0058

Figures & Tables

The analytical framework for IUR Key Technology Identification and Evolutionary.

Distribution of Patent Applications in I-U-R Cooperation Applicants.

The parameters of the ERGM for 2017-2018_

Variable	Feature	Estimated coefficient	Significance test value	P-value
Control variables	edges	-66.90	-6.561	< 0.001*** (10.19)
	Applicant Degree	0.89	6.55	< 0.001*** (0.13)
	Key technology Degree	0.12	0.23	0.81725 (0.56)
	Applicant BC	-0.35	-3.01	0.0026** (0.11)
	Key technology BC	0.004	0.18	0.8527 (0.02)
	Applicant CC	21.74	3.42	0.0006*** (6.35)
	Key technology CC	116.17	4.12	< 0.001*** (28.17)
Independent variables	Applicant KD	-0.46	-5.63	< 0.001*** (0.08)
	Applicant KW	0.002	0.02	0.9815 (0.12)
	Applicant KC	0.001	0.001	< 0.001*** (0.001)

The parameters of the ERGM for 2019-2020_

Variable	Feature	Estimated coefficient	Significance test value	P-value
Control variables	edges	-75.72	-6.43	< 0.001*** (11.76)
	Applicant Degree	0.92	6.28	< 0.001*** (0.14)
	Key technology Degree	-0.52	-0.65	0.5111 (0.80)
	Applicant BC	-0.47	-3.77	0.0001*** (0.12)
	Key technology BC	0.002	0.13	0.8909 (0.02)
	Applicant CC	27.77	4.56	< 0.001*** (6.08)
	Key technology CC	134.40	4.01	< 0.001*** (33.49)
Independent variables	Applicant KD	-0.40	-5.03	< 0.001*** (0.08)
	Applicant KW	0.74	3.73	0.0001*** (0.19)
	Applicant KC	0.001	0.001	< 0.001*** (0.001)

The concept and formula of characteristic variables_

Variable	Feature	Equation	Concept
	Degree	Degreei=∑jAijDegre{e_i} = \sum\nolimits_j {{A_{ij}}}	The number of other nodes directly connected to a node, where A_ij is an indicator variable representing whether there is an edge between node i and node j (if there is an edge, A_ij=1; otherwise, A_ij=0).
Control variables	Betweenness Centrality	BCi=∑s≠i≠tσst(i)σstB{C_i} = \sum\nolimits_{s \ne i \ne t} {{{{{\rm{\sigma }}_{st}}(i)} \over {{{\rm{\sigma }}_{st}}}}}	The measure of a node’s ability to control information flow in the network, where σ_st represents the number of shortest paths from node s to node t, and σ_st(i)是 represents the number of those shortest paths that pass through node i.
	Closeness Centrality	CCc(v)=1∑u∈V{ V }d(v,u)C{C_c}(v) = {1 \over {\sum\nolimits_{u \in V\left\{ V \right\}} {d(v,u)} }}	A measure used to assess the importance of a node, reflecting the degree of closeness between a particular node and all other nodes in the network.
	Knowledge Depth	knowledgedepthi=Σ(countNP)2knowledg{e_{dept{h_i}}} = \Sigma {\left( {{{count} \over {NP}}} \right)^2}	The extent, complexity, and systematic nature of an individual’s or organization’s knowledge in a specific field. It reflects the professional level and mastery of a person or organization in a particular domain.
Independent variables	Knowledge Width	knowledgewidh =1−Σ( count NPi)2knowledg{e_{widh }} = 1 - \Sigma {\left( {{{{\rm{ }}count{\rm{ }}} \over {N{P_i}}}} \right)^2}	A measure used to describe the scope and diversity of the knowledge possessed by an organization or system across different fields or subjects.
	Knowledge Combination	Clustering Coefficient−1=12tripletsk(k−1)Clustering Coefficien{t^{ - 1}} = {1 \over {{{{2^}triplets} \over {{k^}(k - 1)}}}}	The behavior of individuals or organizations in the innovation process of establishing and altering the connections between knowledge units to form new combinations or change existing ones.

Applicant Nodes Feature Statistics_

Times	Key Technology nodes	Applicants	Patents
2015-2016	87	44	9,308
2017-2018	94	54	13,630
2019-2020	94	50	16,033
2021-2022	100	55	30,394
2023-2024	100	58	46,718

The parameters of the ERGM for 2015-2016_

Variable	Feature	Estimated coefficient	Significance test value	P-value
Control variables	edges	-46.21	-5.27	< 0.001*** (8.76)
	Applicant Degree	0.82	5.02	< 0.001*** (0.16)
	Key technology Degree	0.44	0.80	0.42 (0.55)
	Applicant BC	-0.36	-2.81	0.004** (0.12)
	Key technology BC	0.008	0.32	0.742 (0.02)
	Applicant CC	23.40	3.43	0.0005*** (6.80)
	Key technology CC	64.59	2.77	0.005** (23.24)
Independent variables	Applicant KD	-0.29	-4.45	< 0.001*** (0.06)
	Applicant KW	-0.43	-3.01	0.002** (0.14)
	Applicant KC	0.001	0.001	< 0.001*** (0.001)

The parameters of the ERGM for 2023-2024_

Variable	Feature	Estimated coefficient	Significance test value	P-value
Control variables	edges	-141.60	-4.85	< 0.001 *** (29.18)
	Applicant Degree	2.151	7.39	< 0.001 *** (0.29)
	Key technology Degree	-1.807	-0.84	0.4008 (2.15)
	Applicant BC	-1.747	-6.63	< 0.001 *** (0.26)
	Key technology BC	-0.005	-0.21	0.829 (0.02)
	Applicant CC	-0.57	-4.94	< 0.001 *** (0.11)
	Key technology CC	-0.15	-0.86	0.3872 (0.17)
Independent variables	Applicant KD	0.001	0.001	< 0.001 *** (0.001)
	Applicant KW	68.58	7.87	< 0.001 *** (8.70)
	Applicant KC	258.30	2.87	0.0040** (89.77)

Key Technology Information Entropy Weights_

The Period of 2015-2016		The Period of 2017-2018		The Period of 2019-2020
Node	Entropy	Node	Entropy	Node	Entropy
G06F	7.5617	G01N	8.0909	G06F	8.5610
G01N	7.5268	G06F	7.9372	G01N	8.1527
G06Q	6.9745	G06Q	7.5348	G06Q	7.7500
H02J	6.6634	H02J	6.9692	G06T	7.4453
G01R	6.6497	G01R	6.8984	G06K	7.1600
H04L	6.3043	G06T	6.8715	G01R	7.0795
C12N	6.2971	G06K	6.8715	G06V	6.9556
G05B	6.0722	H04L	6.6329	H02J	6.9385
B01J	6.0540	G05B	6.4766	H04L	6.8685
G06K	6.0449	C12N	6.4582	A61K	6.5681
The Period of 2021-2022		The Period of 2023-2024
Node	Entropy	Node	Entropy
G06F	9.4506	G06F	10.5335
G01N	9.0077	G01N	9.8302
G06Q	8.4512	G06Q	9.4255
G06T	8.4129	G06V	9.3989
G06V	8.1743	G06T	9.1705
G01R	7.4942	H02J	8.5559
G06K	7.4035	G01R	8.3108
H02J	7.3699	H04L	8.2822
H04L	7.3504	B01J	8.1335
C04B	7.2029	C04B	8.1181

The parameters of the ERGM for 2021-2022_

Variable	Feature	Estimated coefficient	Significance test value	P-value
Control variables	edges	-114.40	-5.17	< 0.001*** (22.12)
	Applicant Degree	1.34	6.94	< 0.001*** (-0.96)
	Key technology Degree	-1.92	-1.145	0.2523 (0.18)
	Applicant BC	-0.96	-5.30	< 0.001*** ()
	Key technology BC	0.0031	0.20	0.8384 (0.01)
	Applicant CC	-0.50	-5.01	< 0.001*** (0.10)
	Key technology CC	-0.77	-3.81	0.0001*** (0.20)
Independent variables	Applicant KD	0.001	0.001	< 0.001*** (0.001)
	Applicant KW	44.12	6.37	< 0.001*** (6.91)
	Applicant KC	224.50	3.28	0.001*** (68.36)

Industry-University-Research collaboration networks: the identification and driving factors of key technologies

Figures & Tables

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The parameters of the ERGM for 2017-2018_

The parameters of the ERGM for 2019-2020_

The concept and formula of characteristic variables_

Applicant Nodes Feature Statistics_

The parameters of the ERGM for 2015-2016_

The parameters of the ERGM for 2023-2024_

Key Technology Information Entropy Weights_

The parameters of the ERGM for 2021-2022_

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