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- Determining the Factors of the Employees’ Intention to Stay or Leave in the Slovak's SMEs
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Authors
Affiliations
1 Alexander Dubcek University of Trencín, Trencin, SK
2 University of Entrepreneurship and Law, Prague, CZ
1 Alexander Dubcek University of Trencín, Trencin, SK
2 University of Entrepreneurship and Law, Prague, CZ
Source
International Journal of Entrepreneurial Knowledge, Vol 7, No 2 (2019), Pagination: 63-72Abstract
A large number of Slovak small and medium enterprises (SMEs) are confronted with often-leaving employees. This situation can have a devastating effect; thus, the ability to predict and early detect the employees' intention to stay or leave to another organisation provides them with a competitive advantage.
Paper aims to determine how employer attractiveness influences the employees' intention to stay or leave to another organisation. The online questionnaire survey was conducted to collect data during the September and October 2019 among 357 Slovak SMEs' employees. The employees' values were described by EmpAt's five dimensions scale. Binary logistic regression was used to predict which employer attractiveness factor leads to an intention to stay in the organisation within the next 6 months. Results show that to the employee's decision to stay working for the organisation contribute Application Value (AV Odds Ratio=2.53), followed by Economic Value (EV Odds Ratio=2.36), Interest Value (IV Odds Ratio=2.23), and Social Value (SV Odds Ratio=1.48). We did not find statistically significant associations between Development Value (DV) and the employees' intention to stay or leave (ISL).
This study makes several contributions to extant human resource management literature. First, it extends the research on employer attractiveness and employee retention. Second, it expands the knowledge about the predictors of employees' intention to stay in organisations. On the managerial level, it recommends that the employees' intent to stay or leave should be regularly measured.
Keywords
Employer Attractiveness, Retention, Prediction, SMEs.Full Text
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