The Effects of Leader Communication on a Worker's Intent to Stay: An Investigation Using Structural Equation Modeling

Abstract

This study presents findings on the link between leader motivating language (ML) use and worker intent to stay. Structural equation modeling indicated that ML use significantly improves worker intent to stay—with a 10% increase in ML leading to an approximate 5% increase in worker intent to stay. Also, analysis showed that the full ML model better describes the data than any partial model based on a subset of the ML components, and this outcome helps advance researchers' understanding of the ML theory. Results indicate that proper leader language use can substantially improve the critical organizational outcome of worker retention. As such, this study identifies potential new paths for requisite leader communication research, training, and development.