What Drives Employee Turnover? Part 2 (2024)

In our previous blog on turnover, part 1, we showed a business case for employee turnover. In this blog, we will take a closer look at the science behind turnover and ask ourselves the question: “Why do people quit?”

In 2000, three scientists combined all existing literature on employee turnover. This resulted in a meta-analysis of over 60.000 employees! Their findings are presented in an infographic. We will explain the infographic’s different categories in more detail below. Some elements are self-explanatory, while others are more complex. We will explain all of them anyway, so don’t be afraid to skip a few lines.

In our next blog, we will give a step-by-step explanation of how employee turnover can be predicted usingR. R is software that is very well suited for analysis, and with this blog, you can analyze turnover yourself.

However, first we want to give you an idea of the relevant variables in this analysis – and of what it is that makes them relevant. In this blog, we will explain all the variables we mention in the infographic below. Some variables are practical and available from a company’s systems. Others are much harder to measure and/or more theoretical.

Turnover Predictor: Demographic

Demographic variables act as strong predictors of turnover intentions. They are easy to measure as well.

• Marital status: people who are married are less likely to switch jobs compared to people who are not married. This is most likely due to extra responsibilities that marriage brings along.
• Kinship responsibilities: the level of kinship responsibilities people experience influence their willingness to switch. This is probably because higher responsibilities discourages taking risk.
• Children: in line with the previous two factors, children bring a greater responsibility as well. People with children are less likely to switch jobs.
• Age: we have all read that Millennials are hard to please and engage and switch jobs frequently. However, this is not necessarily specific to this age group. In general, age is negatively associated to turnover intention. This means that younger people tend to leave their jobs more frequently than older people.
• Tenure: the single biggest indicator of turnover is tenure. People are for example much more likely to leave in their fourth or fifth year, compared to their very first year. People simply do not want to be seen as job-hoppers, so they tend to work at least a few years at the company. When people work for a company a very, very long time, they are less likely to ever work for another company.

How to measure demographic variables: Demographic information is, in general, easily accessible through an organization’s HR Information System.

Interaction

Before we continue with stress, let’s go into the interaction of variables. Nothing is as simple as it seems. Variables often interact with each other. Depending on this interaction, some effects will be enhanced while others will be reduced.

For example, marriage will play a more important factor when people are younger than later in life. I have seen this at big.

When a man and a woman, both with busy jobs, get married in their early 30s and plan to have children, they make different choices. The woman is more likely to quit when her workload is high than the man. The woman wants a more relaxed and flexible work environment that offers the possibility to take extra time off to raise the kids. Indeed, she may want to stop working for a few years to devote extra attention to the kids. The man, however, will be less likely to quit. He will be more likely to experience the responsibility of providing the financial stability to raise a family.

In other words, one variable can act very differently depending on another variable. This is called ‘interaction’ and is important to keep in mind when you want to predict something.

In part 3 of this series, we will get more practical and look into how you can build a predictive model on turnover (read our blog on predictive analytics to learn more). If you haven’t already, check our previous blog on turnover. In this blog, we explored the business case of predicting (and preventing) employee turnover – which is huge!

Turnover Predictor: Stress

Stress makes people leave their jobs. Highly stressful environments usually involve more turnover than environments with less stress. Stress factors explain up to 9% of turnover. Indicators are:

• Role clarity: clearly defined roles give people more support and lead to less stress.
• Role conflict: a role conflict happens when a person is expected to fulfill the duties of two contradictory positions.
• Role overload: employees need a set of resources (e.g. time, autonomy, budget, coaching, career opportunities) to cope with his/her role in a company. Role overload happens when the employee has insufficient resources and a taxing role. This causes stress and prompts people to leave.
• Overall stress.

How to measure stress: Job descriptions may give information about role clarity. In addition, when someone temporarily takes over the position of his/her manager, it is likely that there will be a role conflict. This information is difficult to measure, but it is not impossible. Role overload and overall stress are particularly hard to observe. Even more so since stress is often subjective. In order to measure them you need to use surveys.

Turnover Predictor: Job content

Job content is all about how people experience their job.

• Routinization: nobody likes to do the same thing day after day. A high degree of routinization is associated with an increase in productivity, and thus turnover.
• Promotional chances: people are less willing to quit when they think they can get a promotion within the next few months.
• Instrumental communication: the way people communicate within the company impacts turnover too. Instrumental communication is goal-oriented and focuses on the sender. Two examples are: “Can you walk the dog?”, “Can you pick up the kids?”. This kind of communication helps in defining goals and targets, and is beneficial to effective and goal-oriented tasks.

How to measure job content: There is no system that offers the possibility of entering job content or level of routinization, so surveys are the way to go. Instrumental communication can be analyzed through certain text mining techniques, which analyze written internal communication.

Turnover Predictor: External environment

People constantly compare their situation with that of others. This also holds true for people’s jobs.

• Alternative job opportunities: people are less likely to leave when there are few alternative job opportunities.
• Comparison to the present job’s alternatives: even when there is a multitude of alternatives, people are still less likely to leave when their job is better than the alternatives out there. Their job thus becomes a golden cage. However, when the alternatives are superior – and when the grass really is greener on the other side – people are more likely to leave.

How to measure external environment: Some of the specialized HR consultancy firms have access to large amounts of detailed function data. This data gives a relatively accurate description of the demand for a certain job. People with popular jobs can more easily find an alternative job, have more alternatives and are more likely to be approached by recruiters. By matching the job functions at your company with their respective databases you can estimate which employees will be most tempted to switch. Your data-miner will be in higher demand than your secretary, because the former is more sought after and harder to replace.

Turnover Predictor: Work and job satisfaction

As you can imagine, satisfaction with one’s work and job is an important indicator of turnover.

• Job satisfaction: job satisfaction is a much used measure in surveys to see how satisfied people are with their jobs.
• Job met expectations: an important element of happiness with one’s job, is whether or not the job meets the expectations people had before they started. When people expected to have more responsibilities or more freedom than they actually have, they are likely to leave. When expectations and reality are really misaligned, people might even leave within a few months. To prevent this, techniques like realistic job preview can be used.
• Job involvement: this is about a person’s level of involvement in their job. When a job matches a person’s interest and he/she feels actively involved in the job, the person is more likely to stay.
• Work satisfaction: where job satisfaction focuses more narrowly on one’s job, work satisfaction looks at a person’s work from a broader perspective. Job satisfaction and work satisfaction are very similar.

How to measure work and job satisfaction: satisfaction is subjective. In order to accurately measure work and job satisfaction, you can use surveys. Alternatively, these variables are often also measured in engagement surveys.

Turnover Predictor: Compensation

Compensation is often seen as an important predictor of why people leave. However, this is not always true. In fact, pay is a non-significant predictor of turnover intention. Pay satisfaction, on the other hand, is.

• Pay satisfaction: people love comparing things. People are more likely to switch jobs when a colleague or friend with the same job earns considerably more. In other words: it is not the de facto pay that matters, but the person’s satisfaction with this pay.
• Distributive justice: the same holds true for distributive justice. When a manager with only a few extra responsibilities earns two or three times more than other employees, a worker will get demotivated and will be more likely to leave their job.

How to measure compensation: you can benchmark payment data with market data to find a pay-comparison. When someone is underpaid, he/she will be more likely to be dissatisfied and leave. Benchmark data may give you an indication about whether you under- or overpay your employees.

Turnover Predictor: Leadership

The age-old adagio “people leave their bosses, not their jobs” holds true in research.

• Supervisory satisfaction: when a worker is happy with his/her supervisor, he/she will stay with the company longer.
• Leader-member exchange (LMX): the LMX theory focuses on whether the relationship with the leader is a two-way relationship. When your manager sees you as an individual and recognizes your individual value to the team, you will appreciate him much more than when you are just one of the many. We all know teachers who made you feel special, and teachers to whom you were just one of the 20 or 30 other faces in the class. This is what LMX is about.

How to measure leadership: Unfortunately, the leadership variables are hard to measure. However, when you have large teams you can use the team as a control variable in your analysis. If one team loses employees much more rapidly than other teams, it might indicate that there’s something about the team manager’s leadership style. A closer analysis is always needed to justify this conclusion, of course.

Turnover Predictor: co-workers

People often love their jobs because of their colleagues. Co-workers are a factor that predicts turnover.

• Work group cohesion: cohesion amongst colleagues is associated with lower rates of turnover.
• Co-worker satisfaction: how happy people are with their co-workers is also related to a decreased possibility of turnover.

How to measure co-workers: attitudes towards co-workers can only be measured through surveys.

Turnover Predictor: Indicators

There are also a number of factors that might indicate whether or not people will leave the company.

• Lateness: when people consistently arrive late at work, it could be a result of demotivation and thus an indicator for leaving the company.
• Absenteeism: people who are absent more often than others are also more likely to leave. Reason could be that they take a sick day to interview for a new job, or because of a decreased motivation. Absenteeism is the strongest indicator for turnover intentions, together with tenure.
• Performance: another important factor is performance. People with a low performance are likely to leave as people with a high performance are less likely to leave. However, when people perform exceptionally well over a longer period of time, they are again more likely to leave. Why, you ask? Because when people consistently perform at their best they likely face a lack of challenge and change.

How to measure these indicators: Absenteeism data is usually already recorded by organizations. This data can be used to predict turnover. In addition, performance data is also easy to obtain and often available through the company’s performance management system.

A final note: I think this article shows that there is a significant difference between theory and practice. All the variables studied in literature are survey based and therefore hard to back up with facts. This makes it hard to apply theory to practice. However, in our next blog we will give a step-by-step approach on how to do this using R!

In addition, we based this research on a very large meta-study. Most of the studies analyzed in this study are correlational. Some use a longitude design, but not all of them. Keep that in mind when interpreting the results of this blog: correlation does not equal causation.

Sources:

Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of management, 26(3), 463-488.