System-analytical typology of predictors of strategic decision effectiveness in construction companies

Abstract

The article substantiates a system-analytical approach to identifying and classifying factors capable of predicting the performance of strategic decisions in construction companies. The aim is to construct a typology of predictors that integrates macro-, meso- and micro-levels with financial, resource, institutional and innovation determinants, and provides validation of indicators for forecasting the performance of strategic alternatives in a volatile market environment. The methodology combines the system approach and econometric verification of the magnitude and direction of predictor effects, enabling the integration of quantitative indicators (profitability, liquidity, cost of capital, NPV, IRR) and qualitative characteristics (organizational resilience, investor trust, reputational risks) into a single diagnostic toolkit. A chronological overview of value-oriented forecasting models in development is provided: from book value and DCF to the shareholder-value paradigm (EVA/MVA), integrated BSC frameworks, scenario planning and real options, risk-oriented models based on CVaR, and contemporary digital-analytic solutions (BIM, Big Data, AI, ESG). It is shown that innovation and economic determinants form a synergistic cycle: technological renewal raises productivity and value outcomes, while financial stability creates room for further innovation. To select representative indicators, the use of correlation-regression, factor and econometric analysis, MCDA methods and the entropy approach is substantiated; an example demonstrates the formation of a “core” set of highly informative metrics. The scientific novelty lies in aligning the multi-level typology of predictors with a procedure for their econometric validation; the practical significance is improved accuracy of economic diagnostics, forecasting the effectiveness of investment-construction projects, and decision support for investment policy, financial planning, and risk management in the industry.