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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


You can try out Sapia’s FirstInterview right now, or leave us your details to book a personalised demo


 


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New Research Proves the Value of AI Hiring

A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.

Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.

We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.

The problem with traditional psychometric tests

While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.

For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.

This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.

Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.

The Rise of Chat-Based Interviews

Enter conversational AI.

Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach. 

Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.

The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.

Importantly, every candidate receives the same questions, evaluated against the same objective, explainable frameworkThese interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.

Key Findings from the Latest Research

The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.

One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.

The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.

And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.

Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.

Why Trust and Candidate Agency Win

Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.

Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.

This signals respect from the employer. It says: We trust you to show us who you are.

Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.

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AI Maturity in the Enterprise

Barb Hyman, CEO & Founder, Sapia.ai

 

It’s time for a new way to map progress in AI adoption, and pilots are not it. 

Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.

And here’s what struck me:

Despite the hype, we still don’t have a shared language for AI maturity in business.

Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.

You don’t need more pilots. You need a maturity model.

The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.

It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.

Here’s an early sketch based on what I’ve seen:

The 5 Stages of AI Maturity (for real enterprises)
  1. Curious
    • Awareness is growing across leadership
    • Experimentation led by innovation teams
    • Risk is unclear, appetite is cautious
    • AI is seen as “tech”
  2. Reactive
    • Gen AI introduced via vendors or tools (e.g., copilots, agents)
    • Some pilots show promise, but with limited scale or guardrails
    • Data privacy and sovereignty questions begin to surface
    • Risk is siloed in legal/IT
  3. Capable
    • Clear policies on privacy, bias, and governance
    • Dedicated AI leads or councils exist
    • Internal use cases scale (e.g., summarisation, scoring, chat)
    • LLMs integrated with guardrails, safety reviewed
  4. Strategic
    • AI embedded in workflows, not layered on
    • LLM/data infrastructure is regionally compliant
    • AI outcomes measured (accuracy, equity, productivity)
    • Teams restructured around AI capability — not just tech enablement
  5. AI-Native
    • AI informs and transforms core decisions (hiring, pricing, customer service)
    • Enterprise builds proprietary intelligence
    • FAIR™/RAI principles deeply operationalised
    • Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders

AI is a capability.And like any capability, it needs time, structure, investment, and a map.

If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.

Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”

I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.

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Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever

For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.

That era is ending.

“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”

At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.

Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.

Transparency Is the New Talent Advantage

Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.

Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.

“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”

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Metrics That Make Transparency Real (and Actionable)

 

🕒 Time to Hire

 

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What it is: The median time between application and hire.

Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.

Faster time to hire = faster access to revenue-driving talent.

 

💬 Candidate Sentiment, Advocacy & Verbatim Feedback

 

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What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.

Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.

And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.

You don’t just measure experience. You understand it in the candidates’ own words.

 

🔍 Drop-Off Rates, Funnel Visibility & Automation That Works

 

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What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.

Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.

Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.

Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.

 

📈 Hiring Yield (Hired / Applied)

 

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What it is: The percentage of completed applications that result in a hire.

Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.

Hiring yield signals funnel health, recruiter performance, and candidate-process fit.

 

🧠 AI Effectiveness: Score Distribution & Answer Originality

 

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What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.

Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.

Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.

From Metrics to Momentum

To effectively lead, you need more than simply tracking; you need insights enabling action.

When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.

Learn more about Discover Insights here

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