Faking is a common issue with traditional self-report assessments in personnel selection (Levashina et al., 2014).
The major concern with faking is that it may affect construct and criterion-related validity (Tett & Simonet, 2021). Concerningly, some research reports the prevalence of self-report faking to be as high as 30-50% depending on the assumed faking severity (Griffith et al., 2007).
In this paper, we examine a parallel adversarial input type in modern text/chat-based interviews: plagiarism. Plagiarism poses a threat similar to faking in self-reports impacting construct and criterion-related validity. Furthermore, both plagiarism and faking impact fairness. The rank order of applicants may be altered by both practices, thereby changing the hiring decisions (Levashina et al., 2014).
While not studied exclusively in the selection space, plagiarism has been a major concern for the education sector and extensively studied in the literature (Park, 2003). One aspect that has received considerable attention is gender differences in plagiarism. Results remain inconclusive, with some evidence that men are more likely to plagiarize than women (Jereb, et al, 2018; Negre et al., 2015).
We also explore differences in plagiarism rates across different job families and device types (i.e., mobile vs. desktop).
Data from over 200,000 candidates (56% female) who applied to various organizations across the world. Candidates participated in an online chat-based structured interview, answering 5-7 open-ended questions on the Sapia Chat Interview™ platform. Over 1 million individual textual answers were checked against answers from past candidates (over 6.4 million answers) for plagiarism. Plagiarism detection calculates the Jaccard similarity coefficient between the new submission and all existing answers, and answers resulting in a Jaccard coefficient (Wang et al., 2013) over 0.75 were marked as plagiarized and flagged for hiring manager review.
Results show that 3.28% of candidates plagiarized at least one answer, which is significantly lower than the up-to 30-50% of candidates estimated to be faking self-report measures (Griffith et al., 2007).
Consistent with previous findings on self-report faking, males plagiarized significantly more than females. Plagiarism rates also differed significantly across role families, with the highest level of plagiarism observed among candidates who applied to ‘Call center sales’ roles and the lowest plagiarism rates observed for ‘Graduate’ roles. Additionally, we found candidates answering on a mobile phone plagiarized significantly higher than those using a desktop computer.
This work represents an important first step in investigating plagiarism detection in online, open-text chat interviews. While the prevalence is much lower than faking in self-reports, there are still fairness implications, especially given men are more likely to plagiarism than women. This is why it is so important to flag candidates who plagiarize so the hiring manager is made aware and can manually review their responses.
References:
Griffith, R. L., Chmielowski, T., & Yoshita, Y. (2007). Do applicants fake? An examination of the frequency of applicant faking behavior. Personnel Review, 36(3), 341–355.
Jereb, E., Urh, M., Jerebic, J., & Šprajc, P. (2018). Gender differences and the awareness of plagiarism in higher education. Social Psychology of Education : An International Journal, 21(2), 409–426.
Levashina, J., Weekley, J. A., Roulin, N., & Hauck, E. (2014). Using Blatant Extreme Responding for Detecting Faking in High-stakes Selection: Construct validity, relationship with general mental ability, and subgroup differences. International Journal of Selection and Assessment, 22(4), 371–383.
Negre, J. S., Forgas, R. C., & Trobat, M. F. O. (2015). Academic Plagiarism among Secondary and High School Students: Differences in Gender and Procrastination. Comunicar. Media Education Research Journal, 23(1).
Park, C. (2003). In Other (People’s) Words: Plagiarism by university students–literature and lessons. Assessment & Evaluation in Higher Education, 28(5), 471–488.
Tett, R., & Simonet, D. (2021). Applicant Faking on Personality Tests: Good or Bad and Why Should We Care? Personnel Assessment and Decisions, 7(1).
Wang, S., Qi, H., Kong, L., & Nu, C. (2013). Combination of VSM and Jaccard coefficient for external plagiarism detection. 2013 International Conference on Machine Learning and Cybernetics, 04, 1880–1885.
Related
Blog
Mirrored diversity: why retail teams should look like their customers
Walk into any store this festive season and you’ll see it instantly. The lights, the displays, the products are all crafted to draw people in. Retailers spend millions on campaigns to bring customers through the door.
But the real moment of truth isn’t the emotional TV ad, or the shimmering window display. It’s the human standing behind the counter. That person is the brand.
The missing link in retail hiring
Most retailers know this, yet their hiring processes tell a different story. Candidates are often screened by rigid CV reviews or psychometric tests that force them into boxes. Neurodiverse candidates, career changers, and people from different cultural or educational backgrounds are often the ones who fall through the cracks.
And yet, these are the very people who may best understand your customers. If your store colleagues don’t reflect the diversity of the communities you serve, you create distance where there should be connection. You lose loyalty. You lose growth.
We call this gap the diversity mirror.
What mirrored diversity looks like
When retailers achieve mirrored diversity, their teams look like their customers:
- A grocery store team that reflects the cultural mix of its neighbourhood.
- A fashion store with colleagues who understand both style and accessibility.
- A beauty retailer whose teams reflect every skin tone, gender, and background that walks through the door.
Customers buy where they feel seen – making this a commercial imperative.
How to recruit seasonal employees with mirrored diversity
The challenge for HR leaders is that most hiring systems are biased by design. CVs privilege pedigree over potential. Multiple-choice tests reduce people to stereotypes. And rushed festive hiring campaigns only compound the problem.
That’s where Sapia.ai changes the equation: Every candidate is interviewed automatically, fairly, and in their own words.
- Bias is measured and monitored using Sapia.ai’s FAIR™ framework.
- Outcomes are validated at scale: 7+ million candidates, 52 countries, average candidate satisfaction 9.2/10.
- Diversity can be measured: with the Diversity Dashboard, you can track DEI capture rates, candidate engagement, and diversity hiring outcomes across every stage of the funnel.
With the right HR hiring tools, mirrored diversity becomes a data point you can track, prove, and deliver on. It’s no longer just a slogan.
Retail recruiting strategies in action: the David Jones example
David Jones, Australia’s premium department store, put this into practice:
- 40,000 festive applicants screened automatically
- 80% of final hires recommended by Sapia.ai
- Recruiters freed up 4,000 hours in screening time
- Candidate experience rated 9.1/10
The result? Store teams that belong with the brand and reflect the customers they serve.
Read the David Jones Case Study here 👇
Recruiting ideas for retail leaders this festive season
As you prepare for festive hiring in the UK and Europe, ask yourself:
- How much will you spend on marketing this Christmas?
- And how much will you invest in ensuring the colleagues who deliver that brand promise reflect the people you want in your stores?
Because when your colleagues mirror your customers, you achieve growth, and by design, you’ll achieve inclusion.
See how Sapia.ai can help you achieve mirrored diversity this festive season. Book a demo with our team here.
FAQs on retail recruitment and mirrored diversity
What is mirrored diversity in retail?
Mirrored diversity means that store teams reflect the diversity of their customer base, helping create stronger connections and loyalty.
Why is diversity important in seasonal retail hiring?
Seasonal employees often provide the first impression of a brand. Inclusive teams make customers feel seen, improving both experience and sales.
How can retailers improve their hiring strategies?
Adopting tools like AI structured interviews, bias monitoring, and data dashboards helps retailers hire fairly, reduce screening time, and build more diverse teams.
Blog
The Diversity Dashboard: Proving your DEI strategy is working
Why measuring diversity matters
Organisations invest heavily in their employer brand, career sites, and EVP campaigns, especially to attract underrepresented talent. But without the right data, it’s impossible to know if that investment is paying off.
Representation often varies across functions, locations, and stages of the hiring process. Blind spots allow bias to creep in, meaning underrepresented groups may drop out long before offer.
Collecting demographic data is only step one. Turning it into insight you can act on is where real change and better hiring outcomes happen.
What is the Diversity Dashboard?
The Diversity Dashboard in Discover Insights, Sapia.ai’s analytics tool, gives you real-time visibility into representation, inclusion, and fairness at every stage of your talent funnel. It helps you connect the dots between your attraction strategies and actual hiring outcomes.
Key features include:
- Demographic filters – Switch between gender, ethnicity, English as an additional language, First Nations status, disability, and veteran status. View age and ethnicity in standard or alternative formats to match regional reporting needs.
- Representation highlights – Identify the top five represented sub-groups for each demographic, plus the three fastest-growing among underrepresented groups.
- Track trends over time – See month-by-month changes in representation over the past 12 months, compare to earlier periods, and connect the data back to your EVP and attraction spend.
- Candidate experience metrics – Measure CSAT (satisfaction) and engagement rates by demographic to ensure your hiring process works for everyone. Inclusion is measurable.
- Hiring fairness – Compare representation in your applied, recommended, and hired pools to spot drop-offs. Understand not just who applies, but who progresses — and why.
From insight to action
With the Diversity Dashboard, you can pinpoint where inclusion is thriving and where it’s falling short.
- See if your EASL candidates are applying in high numbers but not progressing to live interview.
- Spot if candidates with a disability report high satisfaction but have lower offer rates.
- Track the impact of targeted campaigns month-by-month and adjust quickly when something isn’t working.
It’s also a powerful tool to tell your success story. Celebrate wins by showing which underrepresented groups are making the biggest gains, and share that progress with boards, executives, and regulators.
Built on science, backed by trust
Powered by explainable AI and the world’s largest structured interview dataset, your insights are fair, auditable, and evidence-based.
Measuring diversity is the first step. Using that data to take action is where you close the Diversity Gap. With the Diversity Dashboard, you can prove your strategy is working and make the changes where it isn’t.
Book a demo to see the Diversity Dashboard in action.
Blog
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.