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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Forecasting European high-growth Firms - A Random Forest Approach
Jurij Weinblat
Journal of Industry Competition and Trade (2017) Vol. 18, Iss. 3, pp. 253-294
Closed Access | Times Cited: 24

Showing 24 citing articles:

Scaling‐up: Building, Leading and Sustaining Rapid Growth Over Time
Justin J.P. Jansen, Ciarán Heavey, Tom Mom, et al.
Journal of Management Studies (2023) Vol. 60, Iss. 3, pp. 581-604
Open Access | Times Cited: 41
Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms
Alex Coad, Stjepan Srhoj
Small Business Economics (2019) Vol. 55, Iss. 3, pp. 541-565
Closed Access | Times Cited: 104
Rural economic benefits of land consolidation in mountainous and hilly areas of southeast China: Implications for rural development
Lingxiao Ying, Zhanjie Dong, Jun Wang, et al.
Journal of Rural Studies (2020) Vol. 74, pp. 142-159
Closed Access | Times Cited: 42
Too fast to live? Effects of growth on survival across the growth distribution
Alex Coad, Julian Frankish, David Storey
Journal of Small Business Management (2019) Vol. 58, Iss. 3, pp. 544-571
Open Access | Times Cited: 26
Supervised Learning for the Prediction of Firm Dynamics
Falco J. Bargagli-Stoffi, Jan Niederreiter, Massimo Riccaboni
Springer eBooks (2021), pp. 19-41
Closed Access | Times Cited: 20
Predicting New Venture Gestation Outcomes With Machine Learning Methods
Paris Koumbarakis, Thierry Voléry
Journal of Small Business Management (2022) Vol. 61, Iss. 5, pp. 2227-2260
Open Access | Times Cited: 12
In search of gazelles: machine learning prediction for Korean high-growth firms
Ho-Chang Chae
Small Business Economics (2023) Vol. 62, Iss. 1, pp. 243-284
Closed Access | Times Cited: 6
Ex Ante Predictability of Rapid Growth: A Design Science Approach
Ari Hyytinen, Petri Rouvinen, Mika Pajarinen, et al.
Entrepreneurship Theory and Practice (2022) Vol. 47, Iss. 6, pp. 2465-2493
Open Access | Times Cited: 9
The role of cluster ecosystems and intellectual capital in achieving high-growth entrepreneurship: evidence from Germany
Yama Temouri, Ha‐Phuong Luong, Vijay Pereira, et al.
Journal of Intellectual Capital (2024)
Closed Access | Times Cited: 1
Machine Learning for Zombie Hunting. Firms' Failures and Financial Constraints.
Falco Bargagli Stoffi, Massimo Riccaboni, Armando Rungi
SSRN Electronic Journal (2020)
Open Access | Times Cited: 9
Foreign Versus Local Ownership and Performance in Eastern Versus Western EU: A Random Forest Application
Alexandra Horobeţ, Oana Cristina Popovici, Vlad-Cosmin Bulai, et al.
Engineering Economics (2023) Vol. 34, Iss. 2, pp. 123-138
Open Access | Times Cited: 3
Can we predict high growth firms with financial ratios?
Stjepan Srhoj
Financial Internet Quarterly (2022) Vol. 18, Iss. 1, pp. 66-73
Open Access | Times Cited: 5
Deep Learning Algorithm Forecasting the Unemployment Rates in the Central European Countries
Szilárd Madaras
Economics and Business (2024) Vol. 38, pp. 86-102
Open Access
Detection and prediction of anomalous behaviors of enterprise’s employees based on data-mining and optimization algorithm
Xiao Zhang, Yutong Meng
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
Forecasting Firm Growth Resumption Post-Stagnation
Darko Vuković, Vladislav Spitsin, А. Д. Брагин, et al.
Journal of Open Innovation Technology Market and Complexity (2024), pp. 100406-100406
Open Access
Unlocking high-growth potential: does intellectual capital efficiency drive European entrepreneurial ventures?
Giulia Cattafi, Francesco Pistolesi, Emanuele Teti
Journal of Small Business and Enterprise Development (2024)
Closed Access
The Good, the Better and the Challenging: Insights into Predicting High-Growth Firms using Machine Learning
Sermet Pekin, Aykut Şengül
Borsa Istanbul Review (2024)
Open Access
Prevalence and Persistence of High-Growth Entrepreneurship: Which Institutions Matter Most?
Eva Christine Erhardt
Journal of Industry Competition and Trade (2022) Vol. 22, Iss. 2, pp. 297-332
Open Access | Times Cited: 2
The role of knowledge-intense high-impact firms in city innovation systems
Thommie Burström, Juhana Peltonen
Innovation (2018) Vol. 20, Iss. 4, pp. 377-392
Closed Access | Times Cited: 2
Broadening Economics in the Era of Artificial Intelligence and Experimental Evidence
Jan Niederreiter
Italian Economic Journal (2021) Vol. 9, Iss. 1, pp. 265-294
Closed Access | Times Cited: 1
Predictive Modeling of Voice of Customers Using Random Forest Based on Semi-supervised Learning
Eriko KOGURE, Fumiaki Saıtoh, Syohei Ishizu
Transactions of Japan Society of Kansei Engineering (2018) Vol. 17, Iss. 5, pp. 537-545
Open Access
Finnish technology-intensive growth firms : individual capabilities during high-growth and internationalization
Samu Vilkki
(2019)
Closed Access
PREDICTING HIGH-GROWTH FIRMS IN KAZAKHSTAN WITH MACHINE LEARNING METHODS
Yelzhas KADYR, Azat Aituar, Saule KEMELBAYEVA
Public Administration and Civil Service (2022), Iss. 2, pp. 128-149
Open Access
Genetic Algorithm-based Variable Selection Approach for High-Growth Firm Prediction
Anna Kusetogullari, Huseyin Kusetogullari, Amir Yavariabdi, et al.
2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME) (2022), pp. 1-6
Closed Access
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