REGRESSION ANALYSIS AS A TOOL FOR EVALUATING THE EFFICIENCY OF MANAGEMENT PROCESSES IN SMALL AND MEDIUM-SIZED ENTERPRISES.

Abstract

The article aims to systematise the theoretical and methodological foundations of regression analysis as an instrument for quantitative assessment of management process efficiency in small and medium-sized enterprises (SMEs). The study employs methods of logical generalisation, scientific abstraction, structural analysis, and classification. The conditions of applicability of the Ordinary Least Squares (OLS) estimator are systematised in accordance with the six classical assumptions MLR.1-MLR.6 and the Gauss-Markov theorem, which guarantees optimal properties of OLS estimators only when assumptions MLR.1-MLR.5 are satisfied. Three groups of structural challenges specific to SME econometric modelling are identified: limited sample sizes that restrict the number of predictors and require normality testing; informational incompleteness arising from non-systematic record-keeping and the prevalence of informal business practices; and endogeneity caused by high concentration of ownership and managerial functions in a single person, which creates a bidirectional causal relationship between management quality and financial performance. A four-group classification of variables is proposed, covering financial performance indicators (ROA, ROE, labour productivity), organisational factors, human capital characteristics, digitalisation level, and process parameters. A step-by-step model-building procedure is described, including conceptualisation, sample formation, preliminary correlation analysis, OLS estimation, goodness-of-fit assessment, and residual diagnostics. A structured diagnostic framework is presented, covering multicollinearity detection (VIF), heteroskedasticity testing (Breusch-Pagan; White), autocorrelation testing (Durbin-Watson; Breusch-Godfrey), endogeneity testing (Hausman test), specification testing (Ramsey RESET), and structural stability testing (Chow test; CUSUM). Alternative model specifications for panel data are characterised, including fixed-effects models and Arellano-Bond GMM estimation. Application of the proposed methodological procedure will enable researchers and practitioners to use regression analysis correctly for assessing SME management processes and to avoid common specification and interpretation errors. Prospects for further research include developing sector-specific regression specifications for Ukrainian SMEs based on open registry data, testing panel models with fixed effects accounting for wartime shocks of 2022-2025, and adapting GMM estimation to address endogeneity between managerial investments and financial performance.