Presenting a model of data-driven intelligent human resources management with a comparative approach in the free and public universities of Isfahan province

Subject Areas :

mostafa toghiyani pozveh ¹ , محمدرضا دلوی ^{2 *} , Seyed Rasool Aghadavood ³

1 - دانشجوی دکتری گروه مدیریت، واحد دهاقان، دانشگاه آزاد اسلامی، دهاقان، ایران
2 - عضو هیان علمی دانشگاه دهاقان
3 - management department

Received: 2024-04-26 Accepted : 2024-11-19 Published : 2024-11-26

Keywords: Human resource management , Data-Driven intelligent management , Azad universities, , public universities,

Abstract :

This research has been prepared with the aim of presenting a data-oriented intelligent human resources management model with a comparative approach in the open and public universities of Isfahan province. The statistical population of the research includes specialists and experts in the field of human resources in the qualitative part and managers, planners and experts in this field in the quantitative part. In the qualitative part, the sample was made using the stratified random method and through the available sample of 12 people. In the quantitative part, the results were analyzed using the simple random sampling method with a volume of 182 people.In order to analyze the information, in the qualitative part, using the Brody approach while comparing the dimensions of the subject in the existing studies, in order to identify and categorize the factors from the two approaches of reviewing the articles using the meta-combination approach and combining the desired results with interviews with experts. was used After that, validity was calculated and confirmed through Lauche coefficient and reliability through kappa. In the quantitative section, confirmatory factor analysis was used to examine the results in the research field. The analyzes were done using smartPLS and SPSS version 26 software.Data validity was calculated through face value and reliability through Cronbach's alpha(0.88).The findings have shown that there are 22 common components between data-driven human resource management between public and private universities

References:

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