Optimization of behavioral indicators of entrepreneurial performance in technology-based startups

The aim of the present study is to optimize the behavioral indicators of entrepreneurial performance in technology-based startups. The research method of this study is a mixture (qualitative-quantitative) of the meta-synthesis method in the qualitative part and the decision tree and genetic algorithm in the quantitative part. For this purpose, the meta-synthesis method has been used to examine and identify the dimensions and influential factors, in which regard the research population is 164 valid scientific articles published between 2013 and 2023, after specialized filtering and quality control of the texts, 38 articles were selected for analysis and coding. Next, the meta-heuristic algorithm was used to optimize the identified indicators. For this purpose, in the meta-heuristic algorithm section, the genetic algorithm was used for its optimal modeling because it was the best method for optimizing this research according to experts. The findings showed that in the qualitative section, we obtained 97 open codes and 18 closed codes, which were categorized into four categories: entrepreneurial structural factors, entrepreneurial behavioral factors, entrepreneurial capital factors, and entrepreneurial external factors. In the quantitative section, for the indicators, it shows that the indicators of government programs, entrepreneurship education in elementary schools, entrepreneurship education after school, and finally business and professional infrastructure have the greatest impact on the classification of countries, respectively, and to improve the status of entrepreneurs and the ranking of countries, special focus should be placed on these four areas