English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

employee voluntary turnover factors using logistic regression and forecasts employee tenure using a decision tree for four research and development departments in a large U.S organization. Company job title, gender, ethnicity, age and years of service significantly affect employee voluntary turnover behavior determined by logistic regression. The findings assist managers and human resource departments in specific employee retention strategies to reduce R&D departments’ voluntary turnover rate. The decision tree method built a five-level depth tree model with 17 nodes. This model has the lowest AIC value and the best performance in the validation dataset. Age at hire, jobtitle, division, and race are statistically significant factors to predict employee tenure. The most important variable is age at hire located in the decision tree’s first, third, and fourth nodes. Classification rules assist managers and human resource departments in quickly predicting employee tenure and in making hiring decisions.

Determinates of Employee Voluntary Turnover and Forecasting in R&D Departments: A Case Study