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Local context of local government participation in the innovation networks: Evidence from Poland

Malgorzata Godlewska
Malgorzata Godlewska
 and 
Marta Mackiewicz
Marta Mackiewicz
   | Sep 30, 2023
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Article Category: Empirical Paper
Published Online: Sep 30, 2023
Page range: 243 - 263
Received: Jun 03, 2022
Accepted: Feb 28, 2023
DOI: https://doi.org/10.2478/ijme-2023-0007
Keywords
© 2023 Malgorzata Godlewska et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Stages of the study process. Source: The authors’ own compilation based on Gorzelany-Dziadkowiec et al. [2019]. LGUs, local government units.
Stages of the study process. Source: The authors’ own compilation based on Gorzelany-Dziadkowiec et al. [2019]. LGUs, local government units.

Variables in the equation

B S.E. Wald df Sig. Exp (B)
Step 1a SLI 2.178 0.439 24.555 1 −0.001 8.826
BEI 0.001 0.001 0.822 1 0.365 1.001
NRE 0.005 0.004 1.209 1 0.272 1.005
ENE 0 0 0.118 1 0.731 1
DEB −0.001 0.001 1.174 1 0.279 0.999
TEP 0 0 0 1 0.991 1
TLG 0.225 0.254 0.788 1 0.375 1.252
Constant −2.986 1.236 5.833 1 0.016 0.051

Model summary

Step −2 Log likelihood Cox & Snell R square Nagelkerke R square
1 170.781a 0.206 0.3

List of independent variables used in the empirical analysis

No. Variable Explanation of the variable The type of factor The expected direction of impact on the participation of LGUs in innovation networks
1 SLI Cooperation with SLI such as technology transfer centers, BEI, science and technology parks, technology incubators, academic business incubators, regional and local loan funds, seed capital funds, advisory and training centers, business angel networks, or chambers of commerce (no – 0; yes – 1). Institutional Positive
2 BEI BEI per 10,000 ENE such as technology transfer centers, BEI, science and technology parks, technology incubators, academic business incubators, regional and local loan funds, seed capital funds, advisory and training centers, business angel networks, or chambers of commerce (up to 150 BEI – 1; from 151 to 250 – 2; from 251 to 350 – 3; from 351 to 450 – 4; from 451 to 550 – 5; from 551 to 650 – 6; from 651 to 750 – 7; over 751 – 8). Institutional Positive
3 NRE NRE per 10,000 working-age population such as newly registered public and private enterprises (up to 100 – 1; from 101 to 150 – 2; from 151 to 200 – 3; from 201 to 250 – 4; from 251 to 300 – 5; over 300 – 6). Economic Positive
4 ENE ENE in total such as public and private enterprises (up to 500 – 1; from 501 to 1,000 – 2; from 1,001 to 1,500 – 3; from 1,501 to 2,000 – 4; over 2,000 – 5). Economic Positive
5 DEB Debt of LGU per capita (from PLN –2,000 to –1,000 [–2]; from –999 to –0.01 [–1]; PLN 0 [0]; from 0.01 to 999 [1]; from 1,000 to 2,000 [2]). Economic Negative
6 TIP Total income of LGU per capita (up to PLN 4,500 – 1; from 4,500.01 to 5,000 – 2; from 5,000.01 to 5,500 – 3; from 5,500.01 to 6,000 – 4; from 6,000.01 to 6,500 – 5; over PLN 6,500.01). Economic Positive
7 TEP Total expenditure of LGU per capita (up to PLN 4,500 – 1; from 4,500.01 to 5,000 – 2; from 5,000.01 to 5,500 – 3; from 5,500.01 to 6,000 – 4; from 6,000.01 to 6,500 – 5; over PLN 6,500.01). Economic Negative
8 NCI Amount of cultural infrastructure such as libraries, cinemas, theaters, and cultural centers, i.e., (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11). Cultural Positive
9 PDE Population density of LGU territory per 1 km2 (up to 100 per 1 km2 – 1; from 101 to 250 – 2; from 251 to 500 – 3; from 501 to 1,000 – 4; over 1,000 – 5) Demographic Positive
10 NIN Numbers of LGU inhabitants (up to 5,000 inhabitants – 1; from 5,001 to 10,000 – 2; from 10,001 to 15,000 – 3; from 15,001 to 20,000 – 4; from 20,001 to 50,000 – 5; from 50,001 to 100,000 – 6; over 100,000 – 7). Demographic Positive
11 ALM Age of LGU manager (aged over 60 years – 1; from 56 years to 60 years – 2; from 51 years to 55 years – 3; from 46 years to 50 years – 4; from 41 years to 45 years – 5; from 36 years to 40 years – 6; from 31 years to 35 years – 7; age up to 30 years – 8). Managerial Positive
12 GLM GLM (men – 0; female – 1; diverse – 2). Managerial Neutral
13 ELM ELM (technical secondary school – 1; secondary school – 2; higher education – 3; higher administrative education – 4; higher law education – 5; higher economic education – 6). Managerial Positive
14 ILM Political ILM (politically dependent – 0; politically independent – 1). Political Negative
15 TLG TLG (rural municipalities – 1; urban-rural municipalities – 2; urban municipalities – 3; cities on district rights – 4). Administrative Positive
16 VOI Voivodeship (seat of LGU in one of the following voivodeships, ordered by the level of economic development from the least economically developed to the most economically developed according to Michoń [2017]: Warmińsko-Mazurskie – 1; Świętokrzyskie – 2; Lubelskie – 3; Podlaskie – 4; Kujawsko-Pomorskie – 5; Opolskie – 6; Podkarpackie – 7; Łódzkie – 8; Lubuskie – 9; Zachodniopomorskie – 10; Śląskie – 11; Małopolskie – 12; Pomorskie – 13; Wielkopolskie – 14; Dolośląskie – 15; Mazowieckie – 16). Geographical Positive

Case processing summary

Unweighted Casesa N Percent
Selected cases Included in analysis 184 100
Missing cases 0 0
Total 184 100
Unselected cases 0 0
Total 184 100

Block 1: Method enter Omnibus tests of model coefficients

Chi-square df Sig.
Step 1 Step 42.493 7 <0.001
Block 42.493 7 <0.001
Model 42.493 7 <0.001

Classification tablea

Predicted
Observed     MIN Percentage correct
0 1
Step 1 MIN 0 123 12 91.1
1 24 25 51
Overall percentage 80.4

Descriptive statistics of quantitative variables

Mean Std. deviation N Minimum Maximum Reference to the country average
BEI 411.6995 226.25260 184 44 950 864
NRE 135.9457 53.90801 184 42 598 94.56
ENE 1,370.4511 1,757.34608 184 104 8,870 1,175
DEB 18.9948 397.11936 184 −1,200 2,000 −82
TIP 5,431.8032 972.57948 184 3,988.76 9,797.6 5,969.49
TEP 5,416.9598 1,039.39375 184 3,871.5 10,350.7 6,051.49
NCI 2.5109 5.24481 184 0 59 1.1
PDE 311.4239 568.89024 184 9 3,243 123
NIN 11,567.4674 15,782.55126 184 200 99,350 15,495.58

Dependent variable encoding

Original value Internal value
0 0
1 1

Correlation coefficient between quantitative variables

MIN BEI NRE ENE DEB TIP TEP NCI PDE NIN
Pearson correlation 0.131 0.130 0.170 −0.157 0.070 0.124 −0.012 −0.033 0.067
Sig. (one-tailed) 0.038 0.039 0.011 0.016 0.172 0.047 0.437 0.329 0.182
N 184 184 184 184 184 184 184 184 184

Contingency coefficient

MIN SLI ALM GLM ELM ILM VOI TLG
Nominal by nominal Phi 0.470 0.184 −0.049 0.233 0.150 0.272 0.256
Approximate significance 0.001 0.513 0.503 0.075 0.388 0.554 0.007
Nominal by nominal Cramér’s V 0.470 0.184 0.049 0.233 0.150 0.272 0.256
Approximate significance 0.001 0.513 0.503 0.075 0.388 0.554 0.007
N 184 184 184 184 184 184 184 184

Block 0: Beginning block

Classification Tablea,b
Observed Predicted
MIN Percentage correct
0 1
Step 0 MIN 0 135 0 100
1 49 0 0
Overall percentage 73.4

Variables not in the equation

Score df Sig.
Step 0 Variables SLI 40.6 1 <0.001
TLG   9.927 1 0.002
BEI   3.176 1 0.075
NRE   3.123 1 0.077
ENE   5.286 1 0.021
DEB   4.558 1 0.033
TEP   2.83 1 0.093
Overall Statistics 45.205 7 <0.001
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