Compilation and validation of the national production prosperity model based on the use of a mixed approach

This research was conducted with the aim of formulating and explaining a model for national production prosperity based on an integrated approach. The research was both practically oriented and a combination of exploratory nature, employing a mixed-method sequential approach (qualitative-quantitative) to investigate the research topic. Initially, through exploratory interviews with experts and their coding using three coding techniques (open, axial, and selective), the factors and components influencing the formation of the production prosperity pattern were identified. Subsequently, based on the identified factors and components, a research questionnaire was developed. After confirming its validity and reliability, the questionnaire was utilized to gather quantitative data for testing and validating the research model. Research participants in the qualitative phase included university experts familiar with the research subject, selected through snowball sampling, while in the quantitative phase, senior managers, supervisors, production managers, and sales managers of selected active companies in Yazd province were included, utilizing convenience sampling. Given the mixed nature of the current research, data analysis methods in the qualitative section involved using coding techniques (open, axial, and selective) with the aid of MAXQDA software. In the quantitative section, data collected through the questionnaire were analyzed using SMARTPLS and SPSS software at a 95% confidence level. The research findings indicated that six factors (dimensions) and 18 associated components play a role in shaping the production prosperity pattern. These dimensions encompass economic production indicators (3 components), social production indicators (3 components), technological production indicators (3 components), environmental production indicators (3 components), political production indicators (3 components), and cultural production indicators (3 components). After forming the research model in the qualitative phase, the model underwent testing and validation in the quantitative phase. The results indicated its validity, considering the significant values obtained (with a significance level below 0.05). The research model's paths were found to be significant at the mentioned confidence level, indicating that all proposed paths in the suggested model are confirmed. Moreover, model fit indices, including NFI, SRMR, and RMS THETA, also suggested the adequacy of the research model fit.