نقشه راه خط‌مشی‌گذاری دولتی برای تولید دانش‌بنیان و اشتغال‌آفرین براساس منویات مقام معظّم رهبری: رهیافتی آمیخته

Purpose: One of the most significant issues in the economic sector is the emphasis on knowledge-based or knowledge-focused companies, which are established to align with the development of a knowledge-based economy and to achieve the country's objectives across various fields.
Firms that can foster sustainable development and create a competitive advantage for the country by leveraging knowledge and technology in the realm of enhancing economic power and expanding commercial opportunities. In this context, the objective of the present study is to develop a roadmap for government policymaking that promotes knowledge-based and job-creating production, in accordance with the directives of the Supreme Leader, utilizing both qualitative and quantitative approaches.
Method: This research is exploratory in purpose and descriptive in nature, and it was conducted using a field method. The statistical population in the qualitative phase of this study consisted of 30 experts and professors from universities in Ardabil Province. These individuals were selected using a purposive sampling method (theoretical sampling) and a chain referral method (snowball sampling). After reaching theoretical saturation during the interviews, the coding process was initiated. In the quantitative phase, all employees of knowledge-based companies and the Science and Technology Park in Ardabil Province were included. According to the Morgan table, a statistical sample of 155 individuals was determined and studied using a stratified random sampling method. Additionally, data collection for this research was conducted in the initial stage through in-depth and semi-structured interviews. The data collection tool used in the second stage was a researcher-designed questionnaire featuring closed-ended responses on a Likert scale. This questionnaire was developed based on the indicators outlined in the government policy roadmap for knowledge-based and employment-generating production, which were identified in the first stage. To analyze the questions in the first stage, structural codes were collected using a data-driven approach, employing three coding methods: open, axial, and selective coding. These codes were examined using MaxQDA software. In the second stage, the generalized least squares method, along with bootstrapping or jackknife resampling, was utilized in Smart PLS software to assess the fit of the model derived from the first stage. Finally, the fit indices—NPAR, DF, P, CMIN (Chi-Square), AGFI, GFI, Tucker-Lewis Index (TLI), Bentler-Bonnet Index (NFI), CFI, PNFI, PCFI, RMSEA, and CMIN/DF—were employed to validate the model, ensuring a good fit for the presented framework.
Findings: The dataset obtained through a continuous process of open, axial, and selective coding, utilizing MaxQDA software, was organized into 104 open codes, 62 concepts, 14 categories, and 6 axial categories (intervening conditions, causal conditions, contextual conditions, strategies, and consequences) based on a systematic approach. According to the results obtained for the fit indices, the Comparative Fit Index (CFI), Incremental Fit Index (IFI), Goodness of Fit Index (GFI), and Normed Fit Index (NFI) demonstrate a good fit. Additionally, the root mean square error of estimation was 0.066, which is below the threshold of 0.08. Consequently, the model's fit was assessed as favorable, indicating that the results obtained from it can be considered reliable.
Conclusion: Based on the research findings and the output from the Smart PLS software, the index "Identifying Potential and Actual Issues and Problems Related to Knowledge-Based Companies, with a path coefficient of 0.892, was identified as the most significant and influential index in the developed roadmap. Among the indicators related to causal factors, the index "Increasing Human Resource and Production Productivity (el7) was identified as the most significant and influential index, with a path coefficient of 0.804. The most important index within the category-oriented component is "Facing Unexpected Challenges and Having a Plan a path coefficient of 0.850. The most significant index within the contextual categories was the "Existence of Expert Human Resources, Digital Platform, and Digital Customer a path coefficient of 0.809. In the strategies category, the most critical indicator was the "Identification of General Issues in Knowledge-Based Production by the Responsible Institutions with a path coefficient of 0.865. Finally, based on the research findings, the most important indicator in the outcomes category was the "Improvement of General Satisfaction with Knowledge-Based Production a path coefficient of 0.868.