Turnover of Staff: 33 Causes Of Staff Turnover Backed By Empirical Evidence

Turnover of Staff: 33 Causes Of Staff Turnover Backed By Empirical Evidence


The turnover of staff is a big issue for many businesses regardless of sector and size. There is consensus that turnover of staff costs money for all businesses. The extent of this cost can vary by the size and sector of the organization. Research has shown that organizations incur costs estimated to be around 200% of the target role's annual salary. The money or costs goes towards hiring and training new employees.



Some researchers argue that staff turnover is required to reduce business stagnation and improve innovation. Others contend that the impact of turnover must be interpreted in light of the context. Through their meta-analytic studies, Hausknecht and Trevor (2011) support the notion that staff turnover harms organizational performance.



Turnover of staff: 31 Reasons that lead to turnover of staff

Turnover of staff refers to a process where employees voluntarily leave the organization for various reasons. The reasons why staff leave organizations have been a topic for research for years. There is enough empirical evidence to come up with a credible list of why employees leave organizations voluntarily. Below I share the main reasons backed by empirical evidence.



Individual Predictors

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  1. Tenure - there is a statistically significant negative correlation between tenure and staff turnover. The longer employees have been with the company, the less likely they are to leave(p = - 0.27).
  2. Age – there is a statistically significant negative correlation between age and staff turnover (p= -0.21). The older the employees, the less likely they are to leave.
  3. Job security - there is a statistically significant negative correlation between job security and staff turnover. Job security is the degree to which an employee is confident of stable and future employment in the current role. The more secure employees feel in a job, the less likely they are to quit (p =-0.23).
  4. Children – This refers to the number of children, especially at home. The more children the employee stays with at home, the less likely they are to quit. There is a negative correlation between employees' number of children staying at home and staff turnover( p =-0.20).
  5. Emotional Stability – The extent to which employees are calm, stable and not subject to frequent mood swings. There is a negative correlation between the emotional stability of the employee and staff turnover( p =-0.19). The more emotionally stable the employee is, the less likely they are to leave.
  6. Conscientiousness - The extent to which individuals are dependable, diligent, organized, persistent and achievement-oriented. There is a negative correlation between employee conscientiousness and staff turnover( p = -0.16).
  7. Locus of Control - The extent to which individuals attribute success or failure either to self (internal locus of control) or to the environment (external locus of control). There is a negative correlation between locus of control and staff turnover( p = -0.10). Individuals with an internal locus of control are less likely to quit.
  8. Internal motivation – This motivation is based on internal factors such as enjoyment of the tasks, a sense of confidence and the general belief in the importance of work. There is a negative correlation between internal motivation and staff turnover( p =-0.16). Individuals with internal motivation are less likely to quit.




Predictors based on aspects of the job

  1. Job Characteristics - This dimension is based on the job characteristic model. The model looks at sub-dimensions such as variety, task identity, task significance, autonomy and feedback. The lower this is in a job, the more likely individuals are to leave( p= -0.18).
  2. Task Complexity –there is a statistically significant negative correlation between task complexity and staff turnover. The more complex the tasks, the less likely employees will leave the company, but this effect is very small( p = - 0.01).
  3. Workload - there is a statistically significant negative correlation between workload and staff turnover. The higher the workload, the less likely an individual is to quit ( p = -0.10).
  4. Pay–There is a negative correlation between pay and staff turnover. The higher the pay, the less likely an employee will quit (p = -0.17). The correlation is small when compared to other drivers of turnover.
  5. Role Ambiguity There is a statistically significant positive relationship between role ambiguity and staff turnover (p=0.15). The more ambiguous the role, the more likely an employee will quit.
  6. Role Conflict- ( the degree to which employee role expectations are conflicting or incompatible) - There is a statistically significant positive relationship between role conflict and staff turnover (p=0.15). The more role conflict an employee experiences, the more likely an employee will quit.



Traditional job attitudes predictors

  1. Job satisfaction – The more satisfied someone is with their job, the less likely they are to quit(p= -28).
  2. Commitment – The more committed employees are to the organization, the less likely they are to quit ( p = -0.29).



Emerging personal conditions predictors

  1. Work Stress – The more stressed employees are, the more likely they are to quit ( p = -0.21).
  2. Employee Coping (ability to manage internal and external demands to the job)– Employees who cope are less likely to quit(p = -0.39).
  3. Engagement – The more engaged employees are, the less likely they are to quit ( p = -0.20).



Organizational factors

  1. Organizational climate – The more positive the organizational climate, the less likely employees will quit ( p = -0.24).
  2. Organizational Support - The more organizational support employees receive, the less likely they are to quit ( p = - 0.19).
  3. Rewards – The more rewards employees receive over and above their pay, the less likely they are to quit ( p = -0.28).
  4. Organizational size(in terms of the number of employees) – The larger the organization, the more likely people are to quit ( p = -0.03).
  5. Prestige (in the organization) - The more prestigious the organization is, the less likely they are to quit( p = - 0.06).



Person context interface

  1. Met Expectations ( the extent to which an individual work encounters align with their expectations for such encounters) – The more this is present in a work situation, the less likely the individual is to quit (p = -0.12).
  2. Justice (how much individuals feel they are getting equitable treatment )- the more this is present, the less likely individuals are to quit ( p = - 0.17).



Factors present in the job market

  1. Alternatives – The perceived availability of employment opportunities increases staff turnover ( p = 0.23).



Attitudinal related factors

  1. Withdrawal cognitions (This covers thoughts about leaving and job search intentions) – The intention to leave strongly correlates with turnover ( p =0.56).



Employee behavior predictors


  1. Performance – The better someone performs, the less likely they are to leave( p = -0.21).
  2. Citizenship behavior – The more OCB, the less likely someone will quit their job ( p=-0.10).
  3. Lateness – the more someone comes to work late, the more likely for that person to quit ( p = 0.14).
  4. Absenteeism – The more time employees are absent from work, the more likely they are to quit ( p = 0.23).
  5. Job search – The more someone searches for job opportunities, the more likely they are to quit ( p = 0.40).



Conclusion

The biggest predictors of staff turnover are commitment and job satisfaction, which can be measured through surveys. In addition, there are certain behaviors that managers can observe that signal that the employee will exit soon. Such behaviors include more frequent unplanned absenteeism and job search activities. The more rewards employees receive over and above their pay has a significant impact on reducing turnover. This may mean organizations must go beyond pay when crafting strategies aimed at retaining staff.

For those who want to read more about this research's original sources, you can start here.


Memory Nguwi
Super User
This article was written by Memory a Super User at Industrial Psychology Consultants (Pvt) Ltd

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