The effect of talent management practices on employee turnover intention in the Information and Communication Technologies (ICTs) sector: case of Jordan

  • Received May 28, 2020;
    Accepted November 4, 2020;
    Published November 13, 2020
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.18(4).2020.06
  • Article Info
    Volume 18 2020, Issue #4, pp. 59-71
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This work is licensed under a Creative Commons Attribution 4.0 International License

This research aims to examine the intention of talented employees to leave an organization and discover how talent management practices could affect employee intention to leave an organization. This paper`s framework intends to outline the relationship between variables to present the idea of talent management practices and employee retention. The hypothesis was tested using a survey data set of 210 questionnaires collected from employees working in 82 ICT companies in Jordan to attain the research objectives. The collected data were analyzed using the SPSS program, and EMOS program, and basic and initial statistical techniques were applied. The results show that talent management practices significantly affect employee intention to leave an organization. Accordingly, whenever firms applied talent management practices, employee intention to leave decreases. The results demonstrated that attracting talented employees has emerged to have the strongest effect on decreasing employee intention to leave; however, developing and rewarding talented employees was revealed to have the lowest effect. Thus, the ICT firms’ managers have to generate specific training programs to reward and develop talented employees.

Acknowledgment
The publication is supported by the EU-funded Hungarian grant EFOP-3.6.3.-VEKOP-16-2017-00007 for the project entitled “From Talent to Young Researchers” – Supporting the Career-developing Activities of Researchers in Higher Education”.

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    • Figure 1. Research model
    • Table 1. Research respondents’ characteristics
    • Table 2. Descriptive statistics
    • Table 3. Normality test
    • Table 4. Cronbach’s alpha coefficients
    • Table 5. Correlations
    • Table 6. Model summary of the main hypothesis
    • Data curation
      Maha Al-Dalahmeh
    • Methodology
      Maha Al-Dalahmeh, Krisztina Dajnoki
    • Resources
      Maha Al-Dalahmeh, Mária Héder-Rima, Krisztina Dajnoki
    • Writing – original draft
      Maha Al-Dalahmeh, Mária Héder-Rima, Krisztina Dajnoki
    • Writing – review & editing
      Mária Héder-Rima
    • Conceptualization
      Krisztina Dajnoki
    • Funding acquisition
      Krisztina Dajnoki
    • Supervision
      Krisztina Dajnoki