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Signaling shaped by the context: Early-stage funding in the Turkish startup ecosystem Cover

Signaling shaped by the context: Early-stage funding in the Turkish startup ecosystem

By:
Open Access
|Sep 2025

Figures & Tables

Figure 1

Interaction between returnee founder and high-tech industry.
Interaction between returnee founder and high-tech industry.

Figure 2

Interaction between the diversity of the founder’s experience and post-COVID.
Interaction between the diversity of the founder’s experience and post-COVID.

Figure 3

Interaction between female founder and women-led startups in the ecosystem.
Interaction between female founder and women-led startups in the ecosystem.

Descriptive statistics and correlations_

VariablesMean valueStd dev(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)
(1) DV: funded0.020.13
(2) Year2017.53.37−0.01
(3) VDO0.120.320.08*0.23*
(4) Outside İstanbul0.340.47−0.04*0.04*0.01
(5) Elite founder0.310.460.01−0.06*0.01−0.01
(6) Returnee founder0.130.330.02*−0.11*−0.02−0.09*−0.25*
(7) Experience (N)0.460.880.03*−0.10*−0.05*−0.12*0.03*0.07*
(8) Experience (years)6.146.360.000.09*−0.02−0.020.03*0.06*0.06*
(9) Female founder0.120.32−0.010.010.06*−0.06*0.04*0.06*−0.10*0.03*
(10) High tech0.280.45−0.010.11*0.10*0.21*0.04*−0.05*−0.07*0.05*−0.09*
(11) Post-COVID0.190.39−0.010.61*0.15*0.06*−0.03*−0.06*−0.07*0.03*0.010.10*
(12) Women-led startups38.1819.50.000.80*0.30*0.06*−0.06*−0.14*−0.14*0.10*0.02*0.16*0.45*

Regression analysis results_

Model (1)Model (2)Model (3)Model (4)Model (5)Model (6)
Year−0.06*−0.08**−0.07−0.07−0.07−0.08**
(0.03)(0.00)(0.06)(0.06)(0.06)(0.00)
VDO2.11**2.10**2.16**2.16**2.17**2.11**
(0.28)(0.44)(0.39)(0.39)(0.41)(0.42)
Outside İstanbul−1.17**−1.09**−1.11**−1.12**−1.11**−1.08**
(0.24)(0.28)(0.28)(0.28)(0.28)(0.28)
Elite founder0.47 + 0.43 + 0.44 + 0.45 + 0.45 + 0.43 +
(0.24)(0.23)(0.23)(0.23)(0.24)(0.23)
Returnee founder 0.72*0.69*0.75*0.75*0.71*
(0.32)(0.31)(0.31)(0.32)(0.31)
Experience (N) 0.24*0.25*0.21*0.25*0.25*
(0.11)(0.11)(0.12)(0.11)(0.11)
Experience (years) 0.000.000.00−0.010.00
(0.02)(0.02)(0.02)(0.02)(0.02)
Female founder −0.31−0.33−0.32−0.31−0.33
(0.31)(0.32)(0.32)(0.32)(0.66)
High tech −0.06−0.02−0.06−0.07−0.06
(0.23)(0.25)(0.24)(0.24)(0.23)
Post-COVID 0.150.160.27−0.370.15
(0.29)(0.25)(0.27)(0.34)(0.27)
Women-led startups 0.000.000.000.000.01
(0.01)(0.01)(0.01)(0.01)(0.01)
Returnee founder # high tech 0.30*
(0.11)
Post-COVID # experience (N) 0.27*
(0.11)
Post-COVID # experience (years) 0.07
(0.05)
Female founder # women-led startups 0.02 +
(0.01)
Constant119.11 + 158.10141.74138.39137.23159.86
(62.75)(0.00)(111.75)(112.52)(115.59)(0.00)
/lnsig2u1.62**1.58**1.68**1.68**1.68**1.57**
(0.34)(0.54)(0.42)(0.42)(0.45)(0.50)
Log likelihood−1178.46−1076.51−1076.42−1076.51−1073.40−1074.54
DOI: https://doi.org/10.2478/mmcks-2025-0012 | Journal eISSN: 2069-8887 | Journal ISSN: 1842-0206
Language: English
Page range: 65 - 77
Submitted on: May 20, 2025
Accepted on: Sep 4, 2025
Published on: Sep 30, 2025
Published by: Society for Business Excellence
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2025 Başak Topaler, Hamza Khan, published by Society for Business Excellence
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.