“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.
- You lose the training you’ve invested in that person.
- You lose their knowledge of your business and all the relationships they’ve built up, internal and external.
- 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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Neuroinclusion by design. Not by exception.
Why neuroinclusion can’t be a retrofit and how Sapia.ai is building a better experience for every candidate.
In the past, if you were neurodivergent and applying for a job, you were often asked to disclose your diagnosis to get a basic accommodation – extra time on a test, maybe the option to skip a task. That disclosure often came with risk: of judgment, of stigma, or just being seen as different.
This wasn’t inclusion. It was bureaucracy. And it made neurodiverse candidates carry the burden of fitting in.
We’ve come a long way, but we’re not there yet.
Shifting from retrofits to inclusive-by-design
Over the last two decades, hiring practices have slowly moved away from reactive accommodations toward proactive, human-centric design. Leading employers began experimenting with:
- Sharing interview questions in advance
- Replacing group exercises with structured simulations
- Offering a variety of assessment formats
- Co-designing assessments with neurodiverse candidates
But even these advances have often been limited in scope, applied to special hiring programs or specific roles. Neurodiverse talent still encounters systems built for neurotypical profiles, with limited flexibility and a heavy dose of social performance pressure.
Hiring needs to look different.
Insight 1: The next frontier of hiring equity is universal design
Truly inclusive hiring doesn’t rely on diagnosis or disclosure. It doesn’t just give a select few special treatment. It’s about removing friction for everyone, especially those who’ve historically been excluded.
That’s why Sapia.ai was built with universal design principles from day one.
Here’s what that looks like in practice:
- No time limits — Candidates answer at their own pace
- No pressure to perform — It’s a conversation, not a spotlight
- No video, no group tasks — Just structured, 1:1 chat-based interviews
- Built-in coaching — Everyone gets personalised feedback
It’s not a workaround. It’s a rework.
Insight 2: Not all “friendly” methods are inclusive
We tend to assume that social or “casual” interview formats make people comfortable. But for many neurodiverse individuals, icebreakers, group exercises, and informal chats are the problem, not the solution.
When we asked 6,000 neurodiverse candidates about their experience using Sapia.ai’s chat-based interview, they told us:
“It felt very 1:1 and trustworthy… I had time to fully think about my answers.”
“It was less anxiety-inducing than video interviews.”
“I like that all applicants get initial interviews which ensures an unbiased and fair way to weigh-up candidates.”
Insight 3: Prediction ≠ Inclusion
Some AI systems claim to infer skills or fit from resumes or behavioural data. But if the training data is biased or the experience itself is exclusionary, you’re just replicating the same inequity with more speed and scale.
Inclusion means seeing people for who they are, not who they resemble in your data set.
At Sapia.ai, every interaction is transparent, explainable, and scientifically validated. We use structured, fair assessments that work for all brains, not just neurotypical ones.
Where to from here?
Neurodiversity is rising in both awareness and representation. However, inclusion won’t scale unless the systems behind hiring change as well.
That’s why we built a platform that:
- Doesn’t rely on disclosure
- Removes ambiguity and pressure
- Creates space for everyone to shine
- Measures what matters, fairly
Sapia.ai is already powering inclusive, structured, and scalable hiring for global employers like BT Group, Costa Coffee and Concentrix. Want to see how your hiring process can be more inclusive for neurodivergent individuals? Let’s chat.
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Skills Measurement vs Skills Inference – What’s the Difference and Why Does It Matter?
There’s growing interest in AI-driven tools that infer skills from CVs, LinkedIn profiles, and other passive data sources. These systems claim to map someone’s capability based on the words they use, the jobs they’ve held, and patterns derived from millions of similar profiles. In theory, it’s efficient. But when inference becomes the primary basis for hiring or promotion, we need to scrutinise what’s actually being measured and what’s not.
Let’s be clear: the technology isn’t the problem. Modern inference engines use advanced natural language processing, embeddings, and knowledge graphs. The science behind them is genuinely impressive. And when they’re used alongside richer sources of data, such as internal project contributions, validated assessments, or behavioural evidence, they can offer valuable insight for workforce planning and development.
But we need to separate the two ideas:
- Skills Measurement: Directly observing or quantifying a skill based on evidence of actual performance.
- Skills Inference: Estimating the likelihood that someone has a skill, based on indirect signals or patterns in their data.
The risk lies in conflating the two.
The Problem Isn’t Inference of Skills. It’s the Data Feeding It
CVs and LinkedIn profiles are riddled with bias, inconsistency, and omission. They’re self-authored, unverified, and often written strategically – for example, to enhance certain experiences or downplay others in response to a job ad.
And different groups represent themselves in different ways. Ahuja (2024) showed, for example, that male MBA graduates in India tend to self-promote more than their female peers. Something as simple as a longer LinkedIn ‘About’ section becomes a proxy for perceived competence.
Job titles are vague. Skill descriptions vary. Proficiency is rarely signposted. Even where systems draw on internal performance data, the quality is often questionable. Ratings tend to cluster (remember the year everyone got a ‘3’ at your org?) and can often reflect manager bias or company culture more than actual output.
Sophisticated ≠ Objective
The most advanced skill inference platforms use layered data: open web sources like job ads and bios, public databases like O*NET and ESCO, internal frameworks, even anonymised behavioural signals from platform users. This breadth gives a more complete picture, and the models powering it are undeniably sophisticated.
But sophistication doesn’t equal accuracy.
These systems rely heavily on proxies and correlations, rather than observed behaviour. They estimate presence, not proficiency. And when used in high-stakes decisions, that distinction matters.
Transparency (or Lack Thereof)
In many inference systems, it’s hard to trace where a skill came from. Was it picked up from a keyword? Assumed from a job title? Correlated with others in similar roles? The logic is rarely visible, and that’s a problem, especially when decisions based on these inferences affect access to jobs, development, or promotion.
Presence ≠ Proficiency
Inferred skills suggest someone might have a capability. But hiring isn’t about possibility. It’s about evidence of capability. Saying you’ve led a team isn’t the same as doing it well. Collecting or observing actual examples of behaviour allows you to evaluate someone’s true competence at a claimed skill.
Some platforms try to infer proficiency, too, but this is still inference, not measurement. No matter how smart the model, it’s still drawing conclusions from indirect data.
By contrast, validated assessments like structured interviews, simulations, and psychometric tools are designed to measure. They observe behaviour against defined criteria, use consistent scoring frameworks (like Behaviourally Anchored Rating Scales, or BARS), and provide a transparent, defensible basis for decision-making. In doing this, the level or proficiency of a skill can be placed on a properly calibrated scale.
But here’s the thing: we don’t have to choose one over the other.
A Smarter Way Forward: The Hybrid Model
The real opportunity lies in combining the rigour of measurement with the scalability of inference.
Start with measurement
Define the skills that matter. Use structured tools to capture behavioural evidence. Set a clear standard for what good looks like. For example, define Behaviourally Anchored Rating Scales (BARS) when assessing interviews for skills. Using a framework like Sapia.ai’s Competency Framework is critical for defining what you want to measure.
Layer in inference
Apply AI to scale scoring, add contextual nuance, and detect deeper patterns that human assessors might miss, especially when reviewing large volumes of data.
Anchor the whole system in transparency and validation
Ensure people understand how inferences are made by providing clear explanations. Continuously test for fairness. Keep human oversight in the loop, especially where the stakes are high. More information on ensuring AI systems are transparent can be found in this paper.
This hybrid model respects the strengths and limits of both approaches. It recognises that AI can’t replace human judgement, but it can enhance it. That inference can extend reach, but only measurement can give you higher confidence in the results.
The Bottom Line
Inference can support and guide, but only measurement can prove. And when people’s futures are on the line, proof should always win.
References
Ahuja, A. (2024). LinkedIn profile analysis reveals gender-based differences in self-presentation among Indian MBA graduates. Journal of Business and Psychology.
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Making Healthcare Hiring Human with Ethical AI
Hiring for care is unlike any other sector. Recruiters are looking for people who can bring empathy, resilience, and energy to the most demanding human roles. Whether it’s dental care, mental health, or aged care, new hires are charged with looking after others when they’re most vulnerable. The stakes are high.
Hiring for care is exactly where leveraging ethical AI can make the biggest impact.
Hiring for the traits that matter
The best carers don’t always have the best CVs.
That’s why our chat-based AI interview doesn’t screen for qualifications. It screens for the the skills that matter when caring for others. The traits that define a brilliant care worker, things like:
Empathy, Self-awareness, Accountability, Teamwork, and Energy.
The best way to uncover these traits is through structured behavioural science, delivered through an experience that allows candidates to open up. Giving candidates space to give real-life, open-text answers. With no time pressure or video stress. Then, our AI picks up the signals that matter, free from any demographic data or bias-inducing signals.
Candidates’ answers to our structured interview questions aren’t simply ticking boxes. They’re a window into how someone shows up under pressure. And they’re helping leading care organisations hire people who belong in care and those who stay.
Inclusion, built in
Inclusivity should be a core foundation of any talent assessment, and it’s a fundamental requirement for hirers in the care industry.
When healthcare hirers use chat-based AI interviews, designed to be inclusive for all groups, candidates complete their interviews when and where they choose, without the bias traps of face-to-face or phone screening. There are no accents to judge, no assumptions, just their words and their story.
And it works:
- 91.8% of carer candidates complete their interviews
- Carer candidates report 9/10 Candidate Satisfaction with their interview experience
- 80% of candidates would recommend others to apply
- Every candidate receives personalised feedback, regardless of the outcome
Drop-offs are reduced, and engagement & employer brand advocacy go up. Building a brand that candidates want to work for includes providing a hiring experience that candidates want to complete.
Real outcomes in care hiring
Our smart chat already works for some of the most respected names in healthcare and community services. Here’s a sample of the outcomes that are possible by leveraging ethical AI, a validated scientific assessment, wrapped in an experience that candidates love:
Anglicare – a leading provider of aged care services
- Time-to-offer dropped from 40+ days to just 14
- Candidate pool grew by 30%
- Turnover dropped by 63%
Abano Healthcare – Australasia’s largest dental support organisation
- 1,213 recruiter hours saved in the first month (67 hours per individual hiring team member)
- $25,000 saved in screening and interviewing time
Berry Street – a not for profit family & child services organisation
- Time-to-hire down from 22 to 7 days
- 95.4% of candidates completed their chat interviews
A smarter way to hire
The case study tells the full story of how Sapia.ai helped Anglicare, Abano Healthcare, and Berry Street transform their hiring processes by scaling up, reducing burnout, and hiring with heart.
Download it here: