Page 250 - Social Enterprise A New Business Paradigm for Thailand

▪ Net profit margin (NPM) is calculated by dividing net profit by total income and multiplying
the result by 100, yielding a percentage. A higher NPM signifies stronger profitability.

▪ Current ratio is determined by dividing current assets by current liabilities. The resulting
figure, expressed as a multiple, reflects the enterprise’s ability to meet short-term

obligations, the higher the ratio, the better the liquidity.

▪ Debt-to-equity ratio (D/E) is calculated by dividing total liabilities by shareholders’ equity.
Expressed as a multiple, a lower D/E ratio indicates a more favorable capital structure,
suggesting that the enterprise is better positioned to repay its debts.

▪ Return on assets (ROA) is calculated by dividing net profit by total assets and multiplying
by 100 to yield a percentage. ROA reflects how efficiently a social enterprise utilizes its
assets to generate profit. Higher values indicate more effective asset use.

▪ Return on equity (ROE) is calculated by dividing net profit by shareholders’ equity and
multiplying by 100. This ratio measures the enterprise’s ability to generate returns for its

shareholders, with higher ROE values signaling stronger financial performance.

3) The third analytical approach focuses on identifying predictors of financial performance by
examining various characteristics of social enterprises, such as legal registration type,
business nature, social objectives, enterprise size, tax registration status, and organizational
structure, that may influence all seven financial indicators. This analysis uses a statistical
method known as Multiple Classification Analysis (MCA).
Multiple Classification Analysis (MCA) is a statistical tool used to analyze the patterns and
nature of relationships between dependent and independent variables. The dependent

variable must be measured on an interval scale and should follow a normal distribution. If
the dependent variable is a dummy variable used to classify two groups, the distribution
should be relatively even. Independent variables may be measured on a nominal or ordinal
scale. They may be interrelated (correlated), and the relationship between independent and
dependent variables can be non-linear (Mingsan Santikarn, 1980). However, two key
assumptions must be met: (1) the variance within each population group must be constant
(homogeneity of variance), and (2) each independent variable must have no interaction

effects with others, that is, the model must be additive. Therefore, before conducting MCA,
an analysis of variance (ANOVA) must be performed to ensure the data satisfies both
assumptions (Petchnoi Singchangchai, 2005). MCA enables researchers to assess the
influence of each independent variable on the dependent variable, as reflected in the
coefficients representing deviations from the grand mean. It also accounts for the influence
of other independent variables simultaneously. In essence, MCA is an adaptation of dummy
regression that presents the size of each subgroup’s influence in an easily interpretable form
by comparing them against the grand mean.

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