Journal of Strategic Management Studies

Journal of Strategic Management Studies

Providing a Model for Predicting the Successful Implementing of Talent Management in Knowledge-based Companies Using Fuzzy Inference System

Document Type : Research

Authors
Saied Sehhat 1
Mohammad Taghi TaghaviFard 2
Reza salami 1
Mojtaba Afsharian 3
1 Assistant Professor atAllameh Tabataba'i University
2 Allameh Tabataba'i University
3 Ph.D. student, Allameh Tabataba'i University
Abstract
The main purpose of this research is to identify the factors affecting the successful of implementing talent management in Knowledge-based Companies. The present research is an applied research in terms of purpose, and in terms of the research design, it is a descriptive-survey research. Also, it is a cross-sectional, in term of time and in term of the nature of data is quantitative. In order to analyze the data and modeling, the fuzzy inference system was used and the fuzzy inference model was designed in MATLAB software.Statistical population of the study include experts that consisting of managers, senior executives and HR managers knowledge-based companies in Rasht Which their opinions were used to formulate fuzzy inference rules and the construction of these rules was based on the opinions of 25 experts. The findings of this research indicate that three factors, such as employer brand, organizational culture and transformational leadership style, have been identified as the underlying and effective factors on the successful implementation of talent management in knowledge-based companies. Based on the results of this study, among these factors, the transformational leadership style has the greatest impact on the successful implementation of talent management in knowledge based companies. Similarly, the employer brand and organizational culture were ranked accordingly.
Keywords
Talent Management
Employer Brand
Organizational Culture
Transformational Leadership Style
Knowledge-based Companies
Fuzzy Inference System
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Journal of Strategic Management Studies
Volume 10, Issue 37
Spring 2019
Pages 41-60

  • Receive Date 26 November 2018
  • Revise Date 04 July 2019
  • Accept Date 06 April 2019