Economics and Econometrics, Management of Technology and Innovation, Public Administration
24
Scopus Publications
Scopus Publications
Development of a Methodology Based on Fuzzy Logic for Solving the Problem of Evaluating a Startup Team Under Uncertainty Oleksandr Dorokhov, Kadri Ukrainski, Hanna Kanep, Liudmyla Dorokhova Organizacija, 2026 Aim/Purpose The purpose of the article is to develop a methodology for evaluating startup teams and to create a corresponding computer model based on multicriteria analysis and fuzzy-logic decision-making. Particular attention is paid to determining both qualitative and quantitative characteristics of the team, and obtaining a generalized integral assessment of the startup team under uncertainty. Design/methodology/approach An integrated evaluation method is proposed that combines the principles of the fuzzy set approach and expert evaluation and is implemented as a fuzzy inference system in MATLAB. The developed model used different initial characteristics of the startup team as input parameters. For this, formulas were identified, described, and utilized to calculate the values of these evaluation parameters. The set of linguistic variables and a system of rules for processing fuzzy data were defined. Literature data, expert and investor assessments, and case studies of real startup projects served as the empirical basis for the study. Findings The results demonstrate that the proposed approach enables a fairly objective and comprehensive assessment of a startup team’s quality, considering multiple assessment criteria, their interrelationships, and the combination of qualitative and quantitative input data, all within the context of significant uncertainty. The methodology ensures the objectivity and repeatability of the assessment, making it a valuable decision-support tool for various situations and participants within the startup community. Research implications/limitations The study is limited by the amount of data on real startup teams for model verification, which leaves much to be desired, as well as the need for further empirical substantiation and adjustment of the fuzzy model as a whole, including formulas for input parameters, linguistic variables, and decision rules, based on expert opinions. Possible areas for further research include adapting the method to different stages of startup development, taking into account their field of activity, size, and other specific features, and enabling more accurate model adjustment across various practical cases. Originality/value/contribution The article’s originality lies in integrating fuzzy logic with multicriteria analysis to assess the human factor in startups. A useful contribution involves creating a practice-oriented tool that enhances the accuracy and reliability of team analysis, which is essential for startups themselves, business angels, venture funds, accelerators, and other participants in the startup community.
Business Analytics and Digitalization as Drivers of Startup Evaluation: The Experience of the Baltic States Valeriia Shcherbak, Oleksandr Dorokhov, Kadri Ukrainski, Deniss Djakons, Olha Kovalyova, et al. Organizacija, 2025 Purpose This study is motivated by the importance of startups in economic growth and the need for methods to evaluate their success, considering risk and uncertainty. The objective is to analyze factors that influence startups, using factor and cluster analysis. The hypothesis that advanced business analytics in startup evaluation can enhance the quality of investment decision-making was tested. Methods The combination of quantitative and qualitative techniques was used. Statistics about 20 startups from Latvia, Lithuania, and Estonia over five years were processed to identify success drivers and to group startups by similarity. Machine learning and social media sentiment analysis were applied to assess non-financial indicators. Results The results showed that indicators such as projected profitability, social media activity, and innovativeness are significant for startup ranking. The share of traditional methods in the Baltic states was 55%, while modern tools were 45%, highlighting the role of digitalization in risk assessment. Startups with high clustering coefficients and positive mention sentiment demonstrated superior performance. Conclusions The study demonstrated that integrating business analytics and digitalization enhances startup evaluation. The model combines financial metrics with network and sentiment analysis, offering a comprehensive framework for investors. It confirms that data-driven methods improve decision-making, reducing investment risks.
Transformative change and policy-making: the case of bioeconomy policies in the EU frontrunners and lessons for latecomers Margit Kirs, Erkki Karo, Kadri Ukrainski Innovation the European Journal of Social Science Research, 2022 European Union (EU) policies and initiatives have played an increasingly crucial role in the strategic development of bioeconomy across Europe, albeit in a widely uneven manner. This paper provides an overview of the system and policy changes that allowed some countries to become bioeconomy frontrunners in the EU and derives potential lessons for latecomers from the Central and Eastern European (CEE) region. Theoretically, we rely on the conceptual ideas of sustainability transitions and transformative innovation policy to highlight the nature and challenges of a transition towards bioeconomy. Our results show that the requirements of systemic and policy-supported transitions towards bioeconomy are threefold: stable and long timeframes in policy-making and dynamic change agents (fitting particular systems); participatory processes in policy co-design to co-develop visions; and the societal legitimacy of and commitment to bioeconomy.
Segregation of EU13 countries in EU framework programmes illuminates important challenges for cohesion policy Cesifo Forum, 2018
Fostering innovation in Estonia: The view from the governance framework of the national innovation system Cesifo Forum, 2015
The contribution of R&D to production efficiency in OECD countries: Econometric analysis of industry-level panel data Margo Liik, Jaan Masso, Kadri Ukrainski Baltic Journal of Economics, 2015 While research and development expenditures are considered a key to productivity growth and development, the question remains whether their contribution could depend on the particular countries’ and industries’ actual development levels and positions in global value chains. In this paper we analyse the relative contribution of R&D to the efficiency (productivity) on the industry and sector level in OECD countries using industry-level panel data and the stochastic frontier production function approach. The results indicate that R&D capital productivity enhancing effect increases with the level of technology; physical capital shows the opposite effect. The distribution of efficiency across industries shows remarkably different variances, reflecting different degrees of competition and the structure of value chains. Among different external factors, the share of labour with tertiary education at the national level showed a strong positive correlation with efficiency, while for other external factors the effect varied across the industries. The findings imply that in the design of R&D policy measures the structure of the industries needs to be considered.
Innovation-related knowledge flows: Comparative analysis of finnish and estonian wood sectors Innovation in Forestry Territorial and Value Chain Relationships, 2011
Networks and local milieus as a furniture industry innovation platform Innovation in Forestry Territorial and Value Chain Relationships, 2011
Role of policies and national programmes on innovations in timber-frame construction Innovation in Forestry Territorial and Value Chain Relationships, 2011
