Turnover factors revisited: a longitudinal study of Taiwan-based staff nurses

The relationship between employees' intent to stay/leave a position and the actual turnover of employees merits further investigation. Most previous studies of this relationship have utilized cross-sectional designs to examine nurse turnover from a fixed point in time. Research using a longitudinal design could increase the ability to predict who will leave, and to identify factors that cause turnover behavior.

To investigate whether the same mechanisms and factors that affect employee's turnover intentions can be applied to actual turnover in a longitudinal way in an effort to expose causal relationships.

After a review of existing literature, we collected baseline data on turnover determinants as well as two intervening variables: job satisfaction and intent to stay. Three years later, hospital personnel records were used to identify the actual turnover of nurses who responded in the first wave.

With its 600 beds and metropolitan site, the target hospital located in Taichung, Taiwan is representative of Taiwan's general hospitals.

The 412 registered staff nurses (managers excluded) at work in this hospital were reached by a mail questionnaire in the first wave. Three years later, the turnover data collected in wave two had divided the wave one's 308 respondents (74.8%) into 132 leavers (42.9%, coded as "1") and 176 stayers (57.1%, coded as "0"). The data were then processed by descriptive statistics, exploratory factor analysis, multiple regression, and logistic regression.

As in previous studies of this type, distributive justice, workload, resource inadequacy, supervisory support, kinship support, and job satisfaction were again proven to be highly associated with intent to stay/leave. Nevertheless, with the exception of workload, these indicators worked poorly when predicting the actual turnover.

The study confirms earlier findings on the relationships among turnover determinants, job satisfaction, and intent to stay, and suggests a more comprehensive selection of turnover factors must be taken into account when attempting to explain variations in actual turnover.