SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities

Abstract

This paper addresses gender-related aspects of innovation processes in Small and Medium Enterprises (SMEs). Classical analytical and statistical approaches often struggle with the high complexity and insufficient data typical of gender-related innovation studies. We propose a trend-based modelling framework that requires minimal information and uses non-numerical quantifiers: increasing, constant, and decreasing. This approach enables the analysis of ten-dimensional models including variables such as Gender, Product Innovation, Process Innovation, and High-Risk Tolerance. Using trend-based artificial intelligence methods, we identify 13 distinct scenarios and all possible transitions between them. This allows for the evaluation of queries like: Can exports increase while gender parameters remain constant? Two versions of the GASI trend model are presented: the original and an expert-modified version addressing critiques related to scenario transitions. The final model confirms stability and supports the assumption that "no tree grows to heaven." Trend-based modelling offers a practical, interpretable alternative for complex, data-scarce systems.