Predicting turnover based on relationship diagnosis - lessons from marital research
RIS ID
94722
Link to publisher version (URL)
Abstract
Decades of turnover research have identified sets of factors that lead to voluntary employee turn-over. However, existing models predict employee separation from organisations with an ex-tremely limited predictive power, which rarely goes far beyond 30%. In contrast, marital re-search has identified a method for predicting separations which has an accuracy of over 90%, based on a couples' reflections on their past. This paper presents preliminary findings from a pi-lot study, which applied this method to predicting employee turnover. The study identifies some indicators of distressed and non-distressed marriage that are transferrable to employment context, and indicators that were only identified in organisational context. The paper concludes with an expectation that this method can extend methods of enquiry used in turnover research.
Publication Details
Alony, I., Hasan, H. & Sense, A. (2014). Predicting turnover based on relationship diagnosis - lessons from marital research. In E. Cohen & E. Boyd (Eds.), Proceedings of Informing Science & IT Education Conference (InSITE) 2014 (pp. 25-37). United States: Informing Science Institute.