1: All turnover is the same, and it is all bad

Misconception of "Knowledge of underlying principles and cause-effect relationships"

2: People quit because of pay

Developing the ability to diagnose and adapt

Turnover analysis to diagnose the extent to which turnover is a problem

View turnover analysis through organizational context

Collect data to diagnose and adapt

Put into practice

Future Directions

Issues: direct costs, loss of org. memory, and mentors

High layoff rates also increases high turnover rates of survivors.

Different types of turnover exists

Some turnover is functional

Turnover costs vary

Pay level and pay satisfaction are relatively weak predictors of ind. turnover rates

Turnover intentions and job search are among the strongest predictors of turnover decisions

Job satisfaction & org. commitment are strong predictors

Mgmt/Supervision, work design, & relationships with others are consistent predictors

Job dissatisfactions is driving force in fewer than half of individual turnover decisions

Multiple paths to turnover decisions

Different paths have different retention implications

Why do people stay?

Evidence-based human resource practices associated with turnover

Recruitment, selection, and socialization practices during organizational entry affect subsequent retention

Managers can influence the work environment and turnover decisions through training, rewards, and supervisory practices

Context-specific evidence-based strategies are more effective

Turnover analysis helps diagnose the extent to which turnover is problematic

Org context matters for interpreting turnover data

Multiple data collection strategies enable more targeted and effective retention strategies

Conduct more research on boundary conditions specifically around talent retention

More research involving interventions and experimental/quasi-environmental designs

Research on effectiveness of evidence-based management