МАРКЕТИНГОВЕ ДОСЛІДЖЕННЯ ВПЛИВУ ВЕБ-ДИЗАЙНУ САЙТУ З ВИКОРИСТАННЯМ СТОХАСТИЧНИХ МЕТОДІВ У СФЕРІ МАРКЕТИНГУ ІННОВАЦІЙ НА ПРОМИСЛОВОМУ РИНКУ

• Datum: 2026
• Hauptverfasser: Kofanov, Oleksii, Solntsev, Sergii, Kofanova, Olena
• Format: Artikel
• Sprache: Ukrainisch
• Veröffentlicht: Zhytomyr Ivan Franko State University 2026
• Schlagworte: маркетинг інновацій; теорія ймовірностей; сайти та веб-дизайн; стартап; промисловий ринок; аналіз даних; мова програмування R; математичні методи; стохастичні методи
• Online Zugang: https://eui.zu.edu.ua/article/view/362417
• Назва журналу: Economics. Management. Innovations

In the context of the digital transformation of the B2B market, the online presence of an innovative company or startup is a key factor in business success and a critical instrument for risk mitigation among stakeholders. However, the design of corporate websites often relies on subjective preferences, creating an urgent scientific need to develop mathematically rigorous tools capable of accounting for high levels of market uncertainty. The aim of this paper is to develop a comprehensive methodology for assessing the impact of a website’s architecture and web design on the market success and investment attractiveness of innovative companies and startups in the industrial market. The proposed methodology is based on the systematic integration of deterministic and stochastic approaches. In the first stage, T. L. Saaty’s Analytic Hierarchy Process (AHP) is applied to mathematically formalize subjective expert assessments of web design parameters within the RStudio software environment. In the second stage, the obtained global weights are transformed into random variables using Monte Carlo simulation to account for high market uncertainty. The final stage involves constructing a multiple linear regression model to assess the degree of influence of specific digital interaction criteria on the commercial success of an innovative company or startup. The scientific novelty lies in the proposal of a hybrid approach for B2B innovation marketing tasks, which transforms the deterministic qualitative parameters of the web interface into stochastic variables. The study scientifically substantiates the concept wherein the static weights derived from the AHP are utilized as the expected values of stochastic variables in a Monte Carlo simulation model, and simultaneously act as integral predictors in a multiple linear regression equation. As a result of the study, a three-level hierarchical structure of determinants of digital interaction was formed (3 criteria, 9 sub-criteria, and 2 alternatives). Based on the results of processing expert matrices in RStudio, the dominant influence of the ‘Trust and Credibility’ criterion (41.0 %) was demonstrated compared to ‘Value Rationalization’ (29.9 %) and ‘User Experience (UX) and Conversion Architecture’ (29.1 %). The key sub-criterion identified is ‘Case Studies Depth’ (19.2 %). A mathematical framework has been developed for subsequent stochastic validation of the model. In terms of practical value, the resulting system of weighting coefficients and the logic of stochastic modeling provide a basis for the quantitative comparative assessment of alternatives regarding whether a company’s web resource is a ‘High Impact B2B Site’ or a ‘Low Impact B2B Site’. The implementation of the research results will allow innovative companies and startups in the B2B market to optimize marketing budgets and increase the likelihood of attracting investment through scientifically grounded web resource design.