Introduction: Entrepreneurial behavioral performance in Iran is presented as a key indicator for explaining the success of tech-enabled startups; because the combination of entrepreneurs’ personal characteristics with team behaviors and operational responses constitutes the core of creating sustainable competitive advantage. Attention to these behavioral dimensions can lead to a deeper understanding of growth cycles, investor attraction, and the sustainability of business models in the country’s digital economy, and provides a framework for measuring and improving team performance and investment efficiency. The aim of this study, in the first stage, is to identify the dimensions and components of entrepreneurial behavior in tech-enabled startups, and in the second stage develop an efficient model to deliver guidelines and optimal behavioral rules for entrepreneurship based on the components identified in the previous section.

Methodology: This study follows a mixed-methods approach (qualitative-quantitative). In the qualitative section, using bibliometric and text-mining tools as well as RapidMiner 9.10 and Excel 2016, the period 2013–2023 and a corpus of 1,200 articles are examined for a systematic literature review; subsequently, the dimensions and components that constitute entrepreneurial performance are clustered and identified. In the quantitative sectio, the genetic metaheuristic algorithm was used using MATLAB software, which is one of the best and most effective optimization algorithms for finding the best subset of a set of strings or graphs.

Findings: The findings are presented in two sections. First, in the qualitative section, 143 records were reviewed and analyzed; using a text-mining approach, seven main clusters were identified: competitiveness and risk acceptance, investment, linkage with the system, growth, innovation and initiative, skills, capabilities and competencies, learning of knowledge and the perception of discoverability. The reliability and validity of the extracted constructs were assessed with Cronbach’s alpha of 0.862 at the 0.01 significance level, indicating satisfactory reliability. The dimensions and components identified in the qualitative analysis were used as inputs to the quantitative model with Global Entrepreneurship Monitordata and the evolutionary algorithm to determine optimal weights and the relationships among the dimensions. This arrangement provides flexibility and operability of the model across technology-driven environments and different countries. The Iran status from 2014 to 2023 shows that over this decade, Iran experienced an adverse condition only in 2020, while in other years Iran fell into the category of relatively to very adverse countries regarding entrepreneurship and the startup sector.

Conclusion / Contributions: The findings show that the dimensions and components identified from the qualitative analysis using the Global Entrepreneurship Monitor data and the Genetic Metaheuristic Algorithm were integrated into the quantitative model to optimally determine the weights, effects, and relationships between the dimensions. This approach leads to the presentation of a set of optimal rules of entrepreneurial behavior in technology-based startups. The overall conclusion is that the combination of qualitative and quantitative data using the metaheuristic approach provides an efficient and practical model for startup managers and policymakers to facilitate the path of entrepreneurial development with optimal decisions.