Designing and Validating a local Model for Managing Natural Disasters in Villages (Case study: Ardabil province); A Mixed Approach

The severity of natural disasters in many rural parts of the country is such that the continuation of the social and economic life of the residents of these areas always faces a crisis. Villages are more vulnerable to natural disasters and hazards due to their traditional structure and texture compared to cities, and every year, natural disasters cause a lot of damage to the country's villages. Therefore, the current research was conducted with the aim of designing and validating a local model for managing natural disasters and accidents in villages in two qualitative (Delphi method) and quantitative stages.

This research is exploratory in terms of purpose and descriptive in terms of type, which was carried out by field method. The sample population of the research, in the qualitative stage, 30 people from the managers of the crisis headquarters of Ardabil province, who were selected purposefully and by the method of chain referral sampling (snowball method). Also in the second stage, based on the Morgan table, there were 384 people from the residents of the villages of Ardabil province with at least A diploma was chosen. The data collection tool in the qualitative stage was an in-depth and semi-structured interview with experts, which was conducted in three rounds using the Delphi method, and in the quantitative stage, the redesigned questionnaire resulting from the first stage was used.

In this research, the extracted indicators, based on the studied variables as well as the results of the third round of Delphi, were categorized into 7 more effetive factors (preparedness, reaction, backlash, evaluation, physical, managerial, restoration and reconstruction) and 32 concepts (open codes).

The results of the research showed that the "evaluation" dimension and the "physical" dimension with a path coefficient of 0.859 have had the greatest impact on the management model of accidents and natural disasters in villages. Finally, by using Smart PLS software, the fit of the obtained model was ensured and all significant relationships were detected.