To compete is to innovate! Can you identify the competence to innovate in applicants and employees?

Shark Tank

Whoever watches Shark Tank? One of the most important assessments investors make when estimating a business’s growth potential is how quickly and easily the product can be replicated. In other words, estimating copy time.

A patent is, of course, very nice because it prevents competitors from copying for the time being. In short, if you want to remain distinctive, your organization must move much faster. The competence of innovation is key.

Copy time as a unit for competing

Copy time is a competitive unit not to be underestimated. A key driver of the ability to compete on speed is innovation strength.
At Starcheck, we see this increasingly reflected in our customers‘ assessment questions.

Especially organizations that deliberately compete on pace. They want us to investigate whether their candidates have innovation potential. We get that demand in leadership assessments for CxO positions, in assessments for managers & professionals, and in talent assessment for graduates.

How do you determine if a candidate can contribute to the innovation strength of your organization?

The most common tool organizations use is the interview. However, this often goes seriously wrong. Somehow, there is a notion that the best way to assess innovativeness and creativity is to ask your applicant a very creative question. On Glassdoor, you can find great examples of this. “What would you do if you found a penguin in the freezer?” for example. The problem with this type of question is that it never leads to an objective selection.

Randomness is not a basis for structurally high-quality selection. In our interview training, we refer to a statement by Laszlo Bock (Senior Vice President of People Operations at Google) in the New York Times. At Google, the relationship between interview scores and job performance was investigated. His conclusion: “zero relationship, a complete random mess”.

Skill innovation and meaningful interviewing

If you use the interview as a selection method, you will have to interview in a competency-oriented and structured way in order to make a meaningful assessment of a candidate’s ability to innovate. This requires an in-depth understanding of how your organization views this competency and of the candidate’s desired response patterns. You will also need to develop a scoring system and train your interviewers.

In doing so, you also need to pay explicit attention to pre-selection. If you want your hiring managers to interview professionally, the average level of candidates they interview has to be pretty good. Too many mediocre candidates, and your hiring manager is really going to use the time needed to prepare for the interview differently.

Observing innovation in a role-play and assessment exercise

Using behavioral observations seems attractive because it provides a direct means of observation. You can create an environment where candidates have the opportunity to demonstrate their competency in innovation. A common tool in the assessment industry is role-playing. Usually, an analysis presentation exercise. A candidate is instructed to analyze a problem, propose improvements, and develop creative, innovative solutions. You then see the competency in action.

A significant challenge with role plays and assessment exercises is low inter-rater reliability. A methodological problem: assessors often appear to arrive at quite different scores, even when assessing the same candidate at the same time. The remedy here, too, is standardization on all fronts, as with the interview. This only makes sense when there is volume and is therefore the domain of assessment bureaus or large organizations with their own assessment centers. This still leaves the problem that truly out-of-the-box thinking is difficult to capture in standardized systems.

Competence innovate psychometric testing

This is always an indirect way of measuring. You are measuring underlying aspects of the competency innovate, namely potential. You don’t see the competency in action. What you do measure is creative and conceptual reasoning. And whether it’s in the nature of the beast, for example, to combine creative reasoning with goal orientation.

You can create combinations based on personality structure. You can also measure whether the drives indicate innovative thinking, or perhaps give counterindications. For example, suppose a candidate has a strong need for structure and hierarchy, as well as a lack of stress. This pattern is then, for example, a counterindication to independent and free thinking.

Artificial intelligence enables measurement of innovation

The missing link in psychometrically measuring innovation potential was being able to objectively map creative reasoning. Our assessment practice has access to an ability test that uses artificial intelligence to establish objective scores across three factors of creative reasoning.

Fluent idea generation, flexibility, and originality are scored separately. And combined, this creates an overall creative reasoning score. By combining this with several other components of intelligence, personality, and motivation, we can identify the potential for innovative competence.

Questions, comments or a different take on this topic? Let us know below and engage with the author.

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Evidence-based Selection Methods.

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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