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How does alliance network embedding affect firm innovation? Evidence from the Chinese manufacturing industry

Open Access
|May 2025

Figures & Tables

Figure 1.

Theoretical framework.
Theoretical framework.

Figure 2.

The calculation of firm innovation performance.
The calculation of firm innovation performance.

Figure 2.

Dyadic alliance network.
Dyadic alliance network.

Figure 3.

Star alliance network.
Star alliance network.

Figure 4.

Ringlike alliance network.
Ringlike alliance network.

Figure 5.

Complex alliance network.
Complex alliance network.

Crucial decision rules by replacing random seed (random_state=32)_

Network typeConditional factorCriteriaDecision factor
CBCICCCLBCSupportConfidencePE
Dyadic network-<=25.5---62.86%0.66High
->25.5---4.29%1.00Low
Star network--<=0.002<=0.367 45.39%0.70High
--->0.367-7.23%0.80Low
Ringlike network-<=2.583-<=0.367-4.21%0.65Low
-(2.583,14]-<=0.367-8.74%0.77High
->14-<=0.367-3.88%1.00Low
--->0.367-69.57%0.89Low
Complex network---<=0.339-38.54%0.69High
->2.44->0.339-8.52%0.68Low
>2.50<=2.44->0.339-20.53%0.97Low

Descriptive statistics and correlation analysis results_

MeanSthMedianMINMAXPECBCIBCCCCL
PE2.4620.7582.4360.9174.4721.000
CB2.7153.1902.0001.00020.000-0.272***1.000
CI6.78415.0002.0000.285104.28-0.067***0.115***1.000
BC0.00040.0020.0000.0000.0110.053*0.586***0.045*1.000
CC0.0350.0360.0120.0000.100-0.067**0.270***0.0020.340***1.000
CL0.3280.4380.0000.0001.000-0.547***0.205***-0.077***-0.101***0.172***1.000

Crucial decision rules by using C4_5 model_

Network typeConditional factorCriteriaDecision facor
CBCICCCLBCSupportConfidencePE
Dyadic network-<=23.5---60.16%0.70High
->23.5---5.29%0.98Low
Star network- <=0.002<=0.371 40.05%0.77High
--->0.371-8.33%0.81Low
Ringlike network-<=2.580-<=0.371-4.55%0.69Low
-(2.580,13.65]-<=0.371-6.04%0.75High
->13.65-<=0.371-3.88%0.98Low
--->0.371-68.85%0.85Low
Complex network---<=0.343-35.25%0.68High
->2.45->0.343-7.55%0.71Low
>2.50<=2.45->0.343-18.29%0.94Low

Crucial decision rules by using ID3 model_

Network typeConditional factorCriteriaDecision factor
CBCICCCLBCSupportConfidencePE
Dyadic network-<=25.5---62.86%0.66High
->25.5---4.29%1.00Low
Star network- <=0.001<=0.371 48.05%0.72High
--->0.371-7.03%0.85Low
Ringlike network-<=2.59-<=0.371-4.02%0.66Low
-(2.59,13.65]-<=0.371-9.12%0.72High
->13.65-<=0.371-3.59%1.00Low
--->0.371-60.35%0.90Low
Complex network---<=0.339-44.37%0.68High
->2.45->0.339-7.34%0.72Low
>2.50<=2.45->0.339-17.22%0.95Low

Basic network indicators of heterogeneous alliance network embedding types_

TypeCommunitiesNodesAverage cluster coefficientAverage path lengthDensityAverage degreeMaximum diameter
Dyadic network1402800.0001.0000.0040.5001.000
Star network1205530.2381.7840.0030.8555.000
Ringlike network603090.8751.5600.0111.6183.000
Complex network161,0170.6366.1160.0041.98615.000

Crucial decision rules for various alliance network types_

Network typeConditional factorCriteriaDecision factor
CBCICCCLBCSupportConfidencePE
Dyadic network-<=25.5---62.86%0.66High
->25.5---4.29%1.00Low
Star network--<=0.002<=0.367 45.39%0.70High
--->0.367-7.23%0.80Low
Ringlike network-<=2.583-<=0.367-4.21%0.65Low
-(2.583,14]-<=0.367-8.74%0.77High
->14-<=0.367-3.88%1.00Low
--->0.367-69.57%0.89Low
Complex network---<=0.339-38.54%0.69High
->2.45->0.339-8.85%0.67Low
>2.50<=2.45->0.339-20.16%0.97Low
DOI: https://doi.org/10.2478/jdis-2025-0008 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 80 - 105
Submitted on: Jun 22, 2024
Accepted on: Nov 28, 2024
Published on: May 6, 2025
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 Zhiwei Zhang, Wenhao Zhou, Hailin Li, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.