Fuzzy Recognition Map of Drivers of Sustainable Human Resources

The emergence of the concept of sustainable human resource management has evolved as a field of study to facilitate the understanding of the relationship between the organization and its stakeholders. Therefore, the aim of this research is to prepare a fuzzy recognition map of drivers of sustainable human resources.

This research is applied in terms of purpose, and in terms of qualitative and quantitative method. The statistical population includes professors and experts familiar with the subject of human resources in Lorestan University, and the samples were selected using the snowball method and based on the principle of theoretical data saturation. In the qualitative part, interviews have been used to identify drivers of sustainable human resources. The validity and reliability of the interview was tested using the relative content validity index and Cohen's kappa reliability index. In the quantitative part, the tool for collecting information is a questionnaire. The identified drivers were provided to the sample members in the form of a questionnaire based on the matrix of paired comparisons, based on the fuzzy recognition map method. The validity and reliability of the questionnaire in the quantitative part was also confirmed using content validity and retesting. And data analysis has been done in the qualitative part of the research using MaxQuda software and in the quantitative part using the fuzzy recognition map method.

The findings showed that job satisfaction is the most important factor affecting the creation of sustainable human resources, and after that, job attitude, support environment, turnover goals and talent management are other drivers of sustainable human resources in order of importance.

Based on the results, with increasing job satisfaction, employees consider themselves more committed to work and are more eager to accept more responsibilities.