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Determinants of growth of small high-tech companies in transition economies. / Kravchenko, Nataliya; Goryushkin, Anton; Ivanova, Anastasiya et al.

In: Model Assisted Statistics and Applications, Vol. 12, No. 4, 01.01.2017, p. 399-412.

Research output: Contribution to journalArticlepeer-review

Harvard

Kravchenko, N, Goryushkin, A, Ivanova, A, Khalimova, S, Kuznetsova, S & Yusupova, A 2017, 'Determinants of growth of small high-tech companies in transition economies', Model Assisted Statistics and Applications, vol. 12, no. 4, pp. 399-412. https://doi.org/10.3233/MAS-170407

APA

Vancouver

Kravchenko N, Goryushkin A, Ivanova A, Khalimova S, Kuznetsova S, Yusupova A. Determinants of growth of small high-tech companies in transition economies. Model Assisted Statistics and Applications. 2017 Jan 1;12(4):399-412. doi: 10.3233/MAS-170407

Author

Kravchenko, Nataliya ; Goryushkin, Anton ; Ivanova, Anastasiya et al. / Determinants of growth of small high-tech companies in transition economies. In: Model Assisted Statistics and Applications. 2017 ; Vol. 12, No. 4. pp. 399-412.

BibTeX

@article{484cf6c0ea1b4f3792480ec1a617ae7c,
title = "Determinants of growth of small high-tech companies in transition economies",
abstract = "High-technology business acts as an important driver of any economy. Microeconomic factors influencing employment in small high-technology companies are identified and assessed in the paper. The case of high-technology manufacturing and knowledge-intensive services in Russian transition economy is discussed. The empirical part of this research is based on data provided by Business Environment and Enterprise Performance Survey (BEEPS). A two-step assessment procedure was applied in order to determine and estimate the factors of growth. Significant factors were selected with the help of best subsets regression and then these factors were further analyzed using OLS. Such an approach enables an increased explanatory power of the obtained results. It was found that younger companies have greater influence on job creation than older ones. Significant differences in growth factors between companies in high-technology manufacturing and knowledge-intensive services were demonstrated. This difference constitutes a new result in the research of growth of high-technology companies in transition economies. The suggested model could help to construct the companies' rating, which would be useful for investors in the emerging markets with high volatility of assets' prices and lack of information for investment analysis.",
keywords = "best subsets approach JEL Classification Codes: C10, determinants of growth, High-technology firms, multiple regression, O14, O30, Russia",
author = "Nataliya Kravchenko and Anton Goryushkin and Anastasiya Ivanova and Sofia Khalimova and Svetlana Kuznetsova and Almira Yusupova",
year = "2017",
month = jan,
day = "1",
doi = "10.3233/MAS-170407",
language = "English",
volume = "12",
pages = "399--412",
journal = "Model Assisted Statistics and Applications",
issn = "1574-1699",
publisher = "IOS Press",
number = "4",

}

RIS

TY - JOUR

T1 - Determinants of growth of small high-tech companies in transition economies

AU - Kravchenko, Nataliya

AU - Goryushkin, Anton

AU - Ivanova, Anastasiya

AU - Khalimova, Sofia

AU - Kuznetsova, Svetlana

AU - Yusupova, Almira

PY - 2017/1/1

Y1 - 2017/1/1

N2 - High-technology business acts as an important driver of any economy. Microeconomic factors influencing employment in small high-technology companies are identified and assessed in the paper. The case of high-technology manufacturing and knowledge-intensive services in Russian transition economy is discussed. The empirical part of this research is based on data provided by Business Environment and Enterprise Performance Survey (BEEPS). A two-step assessment procedure was applied in order to determine and estimate the factors of growth. Significant factors were selected with the help of best subsets regression and then these factors were further analyzed using OLS. Such an approach enables an increased explanatory power of the obtained results. It was found that younger companies have greater influence on job creation than older ones. Significant differences in growth factors between companies in high-technology manufacturing and knowledge-intensive services were demonstrated. This difference constitutes a new result in the research of growth of high-technology companies in transition economies. The suggested model could help to construct the companies' rating, which would be useful for investors in the emerging markets with high volatility of assets' prices and lack of information for investment analysis.

AB - High-technology business acts as an important driver of any economy. Microeconomic factors influencing employment in small high-technology companies are identified and assessed in the paper. The case of high-technology manufacturing and knowledge-intensive services in Russian transition economy is discussed. The empirical part of this research is based on data provided by Business Environment and Enterprise Performance Survey (BEEPS). A two-step assessment procedure was applied in order to determine and estimate the factors of growth. Significant factors were selected with the help of best subsets regression and then these factors were further analyzed using OLS. Such an approach enables an increased explanatory power of the obtained results. It was found that younger companies have greater influence on job creation than older ones. Significant differences in growth factors between companies in high-technology manufacturing and knowledge-intensive services were demonstrated. This difference constitutes a new result in the research of growth of high-technology companies in transition economies. The suggested model could help to construct the companies' rating, which would be useful for investors in the emerging markets with high volatility of assets' prices and lack of information for investment analysis.

KW - best subsets approach JEL Classification Codes: C10

KW - determinants of growth

KW - High-technology firms

KW - multiple regression

KW - O14

KW - O30

KW - Russia

UR - http://www.scopus.com/inward/record.url?scp=85039157395&partnerID=8YFLogxK

U2 - 10.3233/MAS-170407

DO - 10.3233/MAS-170407

M3 - Article

AN - SCOPUS:85039157395

VL - 12

SP - 399

EP - 412

JO - Model Assisted Statistics and Applications

JF - Model Assisted Statistics and Applications

SN - 1574-1699

IS - 4

ER -

ID: 10065552