SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities
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| Format: | Preprint |
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2025
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| _version_ | 1866913788786114560 |
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| author | Bočková, Nina Volná, Barbora Dohnal, Mirko |
| author_facet | Bočková, Nina Volná, Barbora Dohnal, Mirko |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_08493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities Bočková, Nina Volná, Barbora Dohnal, Mirko General Economics Economics Optimization and Control 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. |
| title | SME Gender-Related Innovation: A Non-Numerical Trend Analysis Using Positive, Zero, and Negative Quantities |
| topic | General Economics Economics Optimization and Control |
| url | https://arxiv.org/abs/2504.08493 |