Cognitive Analysis and Modeling of Innovation Potential

Aim: The main aim of the research is to develop cognitive model for innovation potential evaluation.

Study Design: Empirical research of financial, statistic and management reports of enterprises dated 2012 and 2015, analysis of expert findings for the same periods.

Place and Duration of Study: Accounting Department of Simon Kuznets National University of Economics, between May 2016 and April 2017.

Methodology: Content analysis has been used as the primary method to reveal the nature of innovation potential. Research proceeds from the fact that enterprise innovative potential is the integral subsystem of its innovation capacity and consists of material, labor, financial and information resources. The inherent characteristics of innovative potential were captured by applying cognitive analysis and modeling methods.

Results: Based on the content analysis it was ground, that innovation potential is one of the main components of enterprise’s innovation capacity, which is formed in the periods preceding the period of innovation implementation. As for empirical part of research, sample included 81 (based on data of the year 2012) and 93 (based on 2015 year) companies of engineering, metallurgy and chemical industry of four regions of Ukraine, expert analysis involved 27 top-managers. Developed cognitive map and method of evaluating of innovation potential allowed to reveal, that metallurgical enterprises have high innovation potential, however in modern realities of the Ukrainian economy and the world market situation, their innovative activity is restrained; the engineering companies have low innovation potential and hardly implement organizational innovations.

Conclusion: Cognitive methods of analysis and modeling of innovation potential allow to ensure accuracy of evaluation, which is achieved by combining quantitative data of financial reports and qualitative information of expert groups; comparability of results for enterprises of different industries and regions; possibility of quantitative generalization for conclusions at different levels of management.