Toward Building Model of Business Closure Intention in SMEs: Binomial Logistic Regression

This study reframes closure intention in small- and medium-sized enterprises (SMEs) as an ex ante diagnostic signal rather than a post-mortem symptom of failure. The survey evidence from 385 Ecuadorian SMEs was analyzed in two stages; confirmatory factor analysis validated the scales capturing environmental pessimism and personal pressures, and a structural equation model confirmed that both latent constructs directly heighten exit propensity. A binomial logistic regression model correctly classified 71% of the cases and explained 30% of variance. Five variables proved decisive: low-level liquidity (OR = 0.84), a high debt-to-equity ratio (1.41), weak profitability (0.14), negative environmental perceptions (1.72), and a shorter operating tenure (0.91); the sector and the firm size were non-significant. The combined CFA-SEM-logit sequence yields practical early warning thresholds—debt-to-equity ratio > 1.4, current ratio < 1.0, and ROA < 0.15—that lenders, advisers, and entrepreneurs can embed in dashboards or credit screens. Recognizing closure intention as a rational, strategic step challenges the stigma surrounding exit and links financial distress and the strategic exit theory. Policymakers can use the findings to pair debt relief and liquidity programs with cognitive bias training that helps owners interpret risk signals realistically. For scholars, the results highlight closure intention as a dynamic learning process, especially pertinent in emerging economies characterized by informality and institutional fragility.