Starcheck specializes in the selection of IT professionals. We design and run selection programs for small, large, and well-known IT employers (e.g., bol.com, Ordina, ABN AMRO Clearing, Intermax). In today’s AI era, requirements are shifting: standard coding work is automating, so select those targeted for potential to learn quickly, a security-by-design mindset, and AI savviness.
IT is in every organization. The demand for IT professionals is structurally high, while outflow and inflow are out of balance. At the same time, AI is changing work; pure job experience is rarely enough. The recruitment funnel is struggling to fill with direct job candidates. Now what?
| Period / characterization | Demand IT professionals | Supply of IT professionals |
|---|---|---|
| 2018-2020 large tightness |
Strong Increase, continued digitization. | Slight/Moderate Increase, training, lateral entry. |
| 2020-2021 Extreme tightness |
Acceleration, peak due to COVID-19 & remote work. | Stable to slight increase. |
| 2022-2024 Sustained tightness |
Continued High, Cyber Security, Cloud, Data/AI. | Growing too slowly, struggling to fill critical roles. |
| 2025 Tightness remains high |
High, though slight decrease/stabilization from peak. | Increase due to retraining, wage increases. |
Does your organization have a strong labor market proposition? Then you are stronger in the competition for the experienced IT professional. In recent years, hip start-ups and scale-ups, or large, appealing organizations in particular, have benefited from their propositions. In particular, employers who can prove they invest heavily in innovation with a clear vision can position themselves distinctively.
The reality is that some organizations recruit experienced job candidates more easily than others. This has to do with image, familiarity, credibility, working conditions, perceived risk, and other elements of the proposition on offer, among other things.
If your organization cannot sufficiently fill the recruitment funnel with functionally suitable candidates, you can examine whether you should recruit more among potentially suitable candidates.
You then recruit more broadly and approach alternative target groups. And you select exactly the potential you need.
In our experience, selecting for above-average potential ultimately yields a better result than selecting moderately job-ready candidates. This also depends on the structure of your organization’s workforce.
Training a non-qualified IT professional to be an employable professional is relatively expensive. You don’t want a lot of risk of dropouts here. Moreover, developments in IT demand ongoing training. This places high demands on various facets of analytical ability.
Reduce downtime by measuring in advance what matters: analytical ability, learning speed, collaboration in agile environments, and an eye for security/ethics with data and AI. This is how you increase ROI and shorten time-to-productivity.
We have key figures for the structure of your recruitment funnel and know which psychological profile works for each position and what predicts growth. We help you choose target groups, sharpen your proposition and ensure an accurate, explainable selection based on potential. Want to know more? Schedule an appointment right away.
This fact sheet provides an overview of the most commonly used (psychological) selection methods, both classical and modern. The figures are based on meta-analyses and dominant scientific literature.
| Method | Predictive validity (r) | Typical reliability |
|---|---|---|
| Cognitive ability (GMA test) | .51 | High (.85-.95) |
| Work test | .54 | High (inter-rater ≥.70) |
| Structured interview | .51 | Medium-high (.60-.75) |
| Unstructured interview | .18-.38 | Low-medium (.40-.55) |
| Integrity test | .41 | High (α ≥.80) |
| Conscientiousness (Big Five) | .31 | Medium-high (α ~.75-.85) |
| Job knowledge test | .48 | High (≥.80) |
| Years of service | .18 | Not applicable |
| Video/asynchronous interview (incl. AI) | .30-.40 | Good at structuring; algorithmically variable |
| Machine learning / algorithmic models | .20-.50 | Depends on dataset; generalizability limited |
| Serious games / game-based work samples | .35-.50 | High on objective metrics |
| Social media screening | .00-.20 | Low and variable |
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