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Turnover Contagion: Diagnosing, Causes, Possible Effects

“Will the last team member to leave please turn out the lights”

New year, new job.

January is the most popular month for employees to look for new opportunities. But that doesn’t have to mean starting the year with an epidemic of departures.

People leave their jobs for all sorts of reasons.

  • Personal – for instance when a family member needs to relocate.
  • Professional – to get more pay, a promotion, or make a career change.
  • And of course,
  • Organisational – when they are no longer required or suitable for their job.

Any thriving business will want to see a healthy level of turnover in its staff. But what if your people are leaving simply because their colleagues are leaving?

We call this the Turnover Contagion Effect (TCE) and it’s something that every business should care about.

Diagnosing Turnover Contagion

You may have experienced Turnover Contagion yourself. It’s that growing sense that “everyone” in your team is job hunting, and it’s been around for as long as people have worked together.

Your colleagues may not have told you directly that they’re searching. But when there’s a sudden spate of funerals, urgent repair visits or caring for holidaying parents’ goats (all true stories) you may get a sense that something’s up.

Then there are the colleagues who are cagey about letting you see their screens. And of course the ones who quite blatantly tell the rest of the team that it’s only a matter of time before they leave.

However confident and secure you may feel in your role and the organisation, it’s only natural to begin to question your position.

Have your colleagues spotted some major flaw in the business that you’ve overlooked? Do they know something you don’t? Should you put some feelers out there, just in case?

But if you’re observing that disintegrating team from the Human Resources department, you’re probably asking rather different questions.
How did TCE start? Can you stop it spreading further? And how can you prevent it from happening in the first place?

What causes the Turnover Contagion Effect?

Turnover contagion stems from co-workers sharing how they’re feeling and how they’re valued at work. When it’s positive it contributes to more productive working environments and more engaged workers. But when workers are looking around it breeds unrest – it becomes contagious. And once TCE starts it can be hard to stop.

And it seems to be getting worse nowadays, for a variety of reasons;

  • Lower unemployment rates globally make it much easier for your employees to find a new job, and feel more confident in looking for one. There’s also some evidence that the current political climate is discouraging people from looking outside their home countries. So once an employee starts to look, they may find that they are up against far fewer competitors on the shortlist.
  • Social media, and the web in general, have made it amazingly easy to browse for new jobs, even for those who are “not really” looking. LinkedIn is the most obvious place, but there’s a wealth of job sites and careers advice sites that can stir up job dissatisfaction. Social media also spreads the contagion. It’s always been obvious when an unexpectedly large number from one team leave, but now any employee who has reasonable internal connections can spot a trend.
  • Lack of job satisfaction also contributes. Just a few little shared problems in the magic combination that includes pay satisfaction, team relationships and support, communication across, up and down the organisation, the demands of the job, and opportunities for growth and training can add to the spread of TCE.
  • Poor job embeddedness in your company makes things even worse. Studies (1) show that a highly embedded employee is less likely to leave, and very likely to motivate co-workers to stay. A well-embedded employee has many connections within the organisation and the local community, and their job fits with other aspects in their life. The stronger those links, the more committed a worker is to the organisation. Leaving their job would mean sacrificing more than salary. They also risk the loss of friendships, community links and their sense of belonging. So a company where many workers are strongly embedded is less susceptible to TCE. When workers are poorly embedded, far more are ready to leave. They’ll be updating their resumes, watching job postings, applying for new positions, and that inevitably causes an increased individual turnover.

Add these together and you may also experience a fifth factor.

  • Damaged employer reputation. As awareness of increasing staff turnover grows, your reputation as an employer may take a hit. And from there it can become a downward spiral. Your employees notice that more people are on the move. They start to think there’s something wrong with the organisation. They conclude there’s something wrong with anyone who chooses to stay, and they start their own job hunts. The internal damage spreads rapidly over social and traditional media to the local community and across your industry, making it harder to persuade new people to work with you, as well as increasing turnover. It can even start to damage the reputation of the products or services you provide.

Why does Turnover Contagion Effect matter?

When your business starts to suffer from TCE you might think there’s an upside. A long-awaited clear out of rotten wood. A way to make savings on employee costs. A chance for re-organising a dysfunctional department. And yes, all those can be somewhat true.

But whenever you lose a team member there are costs, apart from the obvious ones of losing their production and having to recruit and train a replacement. And these costs far outweigh the benefits.

  1. You lose the training you’ve invested in that person.
  2. You lose their knowledge of your business and all the relationships they’ve built up, internal and external.
  3. You may have to ask other team members to take on their workload while you recruit and then get the new hire up to full productivity – with potential detriment to their normal work.

And as you lose more and more from a team you also risk the engagement and morale of all of their former colleagues. In fact, that’s the greatest risk of the Turnover Contagion Effect – that it spreads further.

As our recent White Paper says (2), “… failing to monitor and moderate turnover can result in leaver behaviour becoming a cultural mainstay of a particular role type, or an accepted norm in the business as a whole.”

Here are 11 Essential Things to Know About Employee Turnover

A Possible Cure for Turnover Contagion Effect

Like most infectious diseases, TCE is easier to prevent than it is to cure. But if you do find that you’re already suffering from TCE, there are a few dos and don’ts.

Don’t

Reduce Social Communication

It’s certainly NOT effective to apply one commentator’s suggestion of trying to “…combat the social environment that stimulates turnover”.

That social side of work may be spreading the contagion, but it’s also the foundation of the strong sense of belonging to a business and a community that encourages people to stay.

Trying to move desks further apart, ban Tweets and Facebook posts or prevent canteen gossip will cause more problems than it solves.

Do

Instead, it may be more productive to consider the root cause of the lack of organisational commitment.

You should be asking:

  • Are supervisors and managers actively supporting the teams experiencing Turnover Contagion?
  • Should you be finding ways to make your business feel a true part of your local community or your industry?
  • Are there working practices and benefits that could be flexed to make workers’ life and work more balanced?
  • Could community engagement or social responsibility programmes help?

… and Probable Prevention for Turnover Contagion Effect

But as mentioned, it’s easier to prevent than cure, so better still is to start at the beginning.

Think about who you hire and how you look after them when they start work.

Are you hiring people who align well with your company culture and values? Are you hiring people with the personality and behavioural traits that make them more likely to stay and perform in your company?

If you’re unsure, that’s where you should start. Try to find out what makes people stay with your organisation. What do your long tenure employees have in common? With your newfound knowledge of your ideal candidate, identify the applicants that fit the bill and prioritise them in your shortlist.

This may sound like a difficult task, but nowadays there are even analytics and technology solutions that can do this for you.

Once you’ve found the right people you still need to look after them and help them commit to your organisation. Introducing each new hire to your company in a motivating induction
process, where they get to know other workers, will give them a strong start.

As they become truly embedded they’re your best hope for preventing future outbreaks of Turnover Contagion.

At Sapia, we help you find your shortlist of candidates who are more likely to stay in your specific business. We combine your data with our workforce and data science to scientifically screen your applicants and predict who is more likely to succeed. And that can also include how well those candidates will fit into your team, your organisation and your community.

References

(1) Felps et al. “TURNOVER CONTAGION: HOW COWORKERS’ JOB EMBEDDEDNESS AND JOB SEARCH BEHAVIORS INFLUENCE QUITTING” © Academy of Management Journal 2009, Vol. 52, No. 3, 545–561


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Sapia.ai Wrapped 2024

It’s been a year of Big Moves at Sapia.ai. From welcoming groundbreaking brands to achieving incredible milestones in our product innovation and scale, we’re pushing the boundaries of what’s possible in hiring.

And we’re just getting started 🚀

Take a look at the highlights of 2024 

All-in-one hiring platform
This year, with the addition of Live Interview, we’re proud to say our platform now covers screening, assessing and scheduling.
It’s an all-in-one volume hiring platform that enables our customers to deliver a world-leading experience from application through to offer.

Supercharging hiring efficiency
Every 15 seconds, a candidate is interviewed with Sapia.ai.
This year, we’ve saved hiring managers and recruiters hours of precious time that can now be used for higher-value tasks. 

See why our users love us 

Giving candidates the best experience
Our platform allows candidates to be their best selves, so our customers can find the people that truly belong with them. They’re proud to use a technology that’s changing hiring, for good.

Share the candidate love

Leading the way in AI for hiring 

We’ve continued to push the boundaries in leveraging ethical AI for hiring, with new products on the way for Coaching, Internal Mobility & Interview Builders. 

Join us in celebrating an incredible 2024

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Situational Judgement Tests vs. AI Chat Interviews: A Modern Perspective on Candidate Assessment

Choosing the right tool for assessing candidates can be challenging. For years, situational judgement tests (SJTs) have been a common choice for evaluating behaviour and decision-making skills. However, they come with limitations that can make the hiring process less effective and less inclusive.

AI-enabled chat-based interviews, such as Sapia.ai, provide organisations with a modern alternative. They focus on understanding candidates as individuals and creating a hiring experience that is both fair and insightful while enabling efficient screening and selection. 

This shift raises important questions: Are SJTs still a tool that should be considered for volume hiring? And what do AI assessments offer in comparison?

1. The Static Nature of SJTs

Traditional SJTs use predefined multiple-choice questions to assess behavioural tendencies and situational knowledge. While useful for screening, these static frameworks lack the flexibility to adapt based on real-world performance data or evolving role requirements. 

Once created, SJTs don’t adapt to new data or evolving organisational needs. They rely on fixed scenarios and responses that may not fully reflect the dynamic realities of modern workplaces, and as a result, their relevance may diminish over time.

AI-enabled chat interviews, on the other hand, are inherently adaptive. Using machine learning, these tools can continuously refine their models based on feedback from real-world outcomes such as hiring or turnover data. This ability to evolve ensures the assessments align with organisations’ needs.

2. Richer Data Through Open-Ended Responses

One of the main critiques of SJTs is their reliance on multiple-choice responses. While structured and straightforward, these options may not capture the full scope of a candidate’s thinking, communication skills, or problem-solving ability. The approach is often limiting, reducing complex human behaviour to a few predefined choices.

AI-enabled chat interviews work more holistically and dynamically. These tools provide a more complete picture of a person by allowing candidates to answer questions in their own words. Natural language processing (NLP) analyses their responses, offering insights into personality traits, communication skills, and behavioural tendencies. This open-ended format lets candidates express themselves authentically, giving employers a deeper understanding of their potential.

3. The Candidate Experience: Stressful or Supportive?

SJTs often include time constraints and rigid formats, which can create pressure for candidates. This is especially true when candidates feel forced to choose options that don’t fully reflect how they would actually behave. The process can feel impersonal, even transactional.

In contrast, chat-based interviews are designed to be conversational and low-pressure for candidates. By removing time limits and adopting a familiar chat interface, these tools help candidates feel more at ease. They also frequently include personalised feedback, turning the assessment into a valuable experience for the candidate, not just the employer.

4. Addressing Bias and Fairness

Traditional SJTs are prone to transparency issues, as candidates can often identify and select the “best practice” answers without revealing their true tendencies. Additionally, static test designs can unintentionally embed bias; due to the nature of the timed test, SJTs have been found to disadvantage some groups. 

AI chat interviews, when developed ethically within a framework like Sapia.ai’s FAIR Hiring Framework, eliminate explicit bias by relying solely on the content of a candidate’s responses. Their machine learning models are continuously validated for fairness, ensuring that hiring decisions are free from subjective judgments or irrelevant demographic factors.

5. An Assessment That Improves Over Time

Workplaces are constantly changing, and hiring tools need to keep up. SJTs’ fixed nature can make them less effective as roles evolve or organizational priorities shift. They provide a snapshot but not a dynamic view of what’s needed.

AI-enabled chat interviews are built to adapt. With feedback loops and continuous learning, they incorporate real-world hiring outcomes—like retention and performance data—into their models. This ensures that assessments stay relevant and effective over time.

Rethinking Candidate Assessment

As hiring demands grow more complex, so does the need for tools that can capture the whole person, not just their response to hypothetical scenarios. While SJTs have played an important role in hiring practices, they are increasingly being replaced by tools like AI-enabled chat interviews.

These modern approaches provide richer data, adapt to changing needs, and create a richer and more engaging experience for candidates. Perhaps most importantly, they emphasise fairness and inclusivity, aligning with the growing demand for unbiased hiring practices.

For organisations evaluating their assessment tools, the question isn’t just which method is “better.” Understanding the specific needs of your roles, teams, and candidates will help you  choose tools that help you make decisions that are both informed and equitable.

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Keeping Interviews Real with Next-Gen AI Detection

It’s our firm belief that AI should empower, not overshadow, human potential. While AI tools like ChatGPT are brilliant at assisting us with day-to-day tasks and improving our work efficiency, employers are increasingly concerned that they’re holding candidates back from revealing their true, authentic selves in online interviews.  

As an assessment technology provider, we are responsible for ensuring the authenticity and integrity of our platform. That’s why we’re thrilled to unveil the latest upgrade to our flagship Chat Interview: the AI-Generated Content Detector 2.0. With groundbreaking accuracy and a candidate-friendly design, this innovation reinforces our mission to build ethical AI for hiring that people love.

Artificially Generated Content (AGC) is content created by an AI tool, such as ChatGPT, Claude, or Pi. We initially rolled out the first version of our AGC detector last year and have continued to improve it as our data set has grown and these AI tools have evolved.

What’s New?

Our updated AGC Detector 2.0 achieves an impressive 98% detection rate for AI-assisted responses, with a false positive rate of just 1%. This gives organisations peace of mind that they’re getting the most authentic assessment of every candidate. 

This cutting-edge system builds on Sapia.ai’s proprietary dataset of over 2 billion words, derived from more than 20 million interview question-answer pairs spanning diverse roles, industries, and regions. It’s trained on real-world data collected before and after the release of tools like ChatGPT, ensuring it remains robust and reliable even as AI tools evolve.

The Challenge of AI in Chat-based Interviews

Our data shows that around 8% of candidates use tools like GPT-4 to generate responses for three or more interview questions. While these tools may offer a quick way for candidates to complete their interview, they can inadvertently hide a person’s true personality and potential – qualities our customers are most interested in understanding through our platform. In fact, research from Sapia Labs shows that these tools have their own personality traits, which may be quite different from the candidate applying for the role. 

For Candidates: Enabling Authenticity

When a response is flagged as potentially AI-generated, the system doesn’t disqualify candidates. Instead, a real-time warning pops up, allowing them to revise their answers or submit them as-is. This ensures that candidates are encouraged to present themselves authentically, reflecting their unique communication styles and sharing their genuine experiences. 

For Hiring Teams: Actionable Insights

Responses flagged as AI-generated are highlighted in the candidate’s Talent Insights profile, accessible via Sapia.ai’s Talent Hub or ATS integrations. These insights give hiring teams the transparency to make informed decisions, fostering trust while accelerating hiring timelines. 

Built on Unmatched AI Interview Expertise

“Our detection model’s strength lies in its foundation of real-world interview data collected from diverse roles and regions,” says Dr Buddhi Jayatilleke, Sapia.ai’s Chief Data Scientist. This depth of understanding enables the AGC Detector to maintain its industry-leading accuracy – even when candidates subtly modify AI-generated answers to appear more human.

Why This Matters

The AGC Detector 2.0 embodies Sapia.ai’s commitment to ethical AI that amplifies human potential. As our CEO Barb Hyman explains:

“The hiring landscape has fundamentally changed since ChatGPT, but our commitment remains clear: AI should amplify human potential, not penalise it. This breakthrough fosters authentic hiring conversations. Our real-time warning system helps candidates make better choices and gives enterprises confidence in their selection decisions.”

Testing and Validation of the AGC Detector 2.0 

The new detector has been rigorously tested on over 25,000 interview responses generated by humans and leading AI models like GPT-4, Claude-3.5, and Llama-3. The results speak for themselves, reinforcing the reliability and fairness of this game-changing technology.

Fairness & Transparency in AI-Enabled Hiring

By detecting AI-generated content while allowing candidates to correct their responses, our AGC Detector 2.0 ensures every applicant has the chance to put their best, most authentic foot forward when applying for a role powered by Sapia.ai. For enterprises, it provides confidence in the integrity of their hiring decisions and ensures they’re connecting with real candidates at scale.

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