Design and Validation of Organizational Symbolism Model in Government Organizations of Ilam Province

The current research was conducted with the aim of designing and validating the model of organizational symbolism in government organizations of Ilam province. This research is exploratory in terms of practical purpose and in terms of data collection method, which has been analyzed with a mixed (qualitative-quantitative) approach. In the first stage, qualitative method and in-depth interviews were used to identify the components of the model. The participants in the qualitative section were 12 university experts and specialists from executive bodies of Ilam province who were selected in a targeted way. In the second stage, quantitative method and structural equation modeling approach were used to fit the model designed in the qualitative part. The statistical population of this department included managers and experts in the administrative and human resources fields of government agencies of Ilam province, and 188 people were selected by stratified random method. The data collection tool in this section was a researcher-made questionnaire with 71 questions, whose validity and reliability were confirmed. Qualitative part data analysis was done by coding method using Max Quda software and quantitative part data analysis was done with SmartPLS software. Based on the findings of the research, 71 primary codes and 12 subcategories were identified in the form of 6 main categories, which are: the central phenomenon, including the organizational symbolism component; Causal factors, including managerial, legal, academic and personal components; Background factors, including the components of organizational culture and leadership style; Intervening factors, including the multiplicity and contradiction of organizational rules; Strategies, including symbolic strategies and consequences, include the components of improving organizational performance, individual and organizational development, and public trust in the organization. The results of the quantitative part of the designed model have an acceptable fit.