Behaviour-based Job Matching

Behaviour-based Job Matching

Avoid the classic… get them in based on CV and be disappointed because of behaviour

The beginning; job gets posted and candidates react.

You are looking for reinforcement in a team and start a recruitment process to select the best candidate. You are looking for reinforcement in a team and start a recruitment process to select the best candidate. A job profile is drawn up, in which not only the "hard" skills such as education and work experience, but also a number of soft skills such as individual attributes and desired behaviour is included.

Conform the current trend, this job will also be posted online. The online channels are well chosen and focused on the target group (not only LinkedIn, Facebook, but also by community platforms and job banks) (see "Recruitment in the digital age: growing, higher quality requirements and online" (1) ).

There are also other channels for recruiting candidates, such as open applications and through efforts of own employees referring future colleagues. The latter turns out to be extremely effective. (see "State of recruiting 2015" (2)).

Candidates apply online, via a portal or via e-mail.

How do you achieve a good (pre)selection? ?

Pre-selection; do we follow the old, familiar way ?

A standard solution (even if cost intensive and time consuming) is to select every candidate based on his/her cover letter who passes the check on "firm criteria" (education and work experience) and a first glimpse on "soft criteria" (personality or behavioural criteria) for a first interview. This takes time and chances are real that good candidates are missed and the wrong were invited. Only during the interview it becomes clearer that personality, behaviour or attitude are not fitting. How come? where the "soft criteria" not known at the time of pre selection?

From a candidates point of view one does occasionally present himself differently just to better fit the (presumed) expectations or even does not address the requested aspects at all. On the other hand, it is possible that the job profile is not distinctly enough formulated or the search is for the "sheep with the 5 legs". Maybe there are candidates among the pre-selected who fit less on the hard criteria but are, given their appearance, behaviour or attitude, fitting well to successfully do the job. Experience has shown that a certain mismatch at the "hard criteria" can be more than offset by a good match at the "soft criteria". Candidates are looking for growth opportunities and can soon feel overqualified with a 100% match on the "hard criteria".)

Consequence: a lot of time is invested in preselection. leading to unnecessary first interviews due to lack of detailed info/criteria or leave good candidates undiscovered.

Source: Presentation Belbin Associates (Belbin Certification)
Remark: In Belbin terminology somebody fulfilling the hard criteria well is "Eligible", and somebody fitting well on behaviour is called "Suitable". Somebody scoring on Eligible and Suitable is at that moment the ideal match, but also somebody who probably only stays for a short time while feeling overqualified with little chances to grow in this job. Somebody fulfilling barely the hard criteria ("Barely eligible") but fully the soft criteria ("Suitable") is for Belbin a probable "surprise fit" and somebody feeling challenged and motivated. Thus a "long stayer"!

The final selection: Late discovery

If little information about the "soft" attributes of the candidate is available in the preliminary selection , then rejection of the unfit candidate takes place in the final round instead much earlier in the process. Once in the final round, time and money is invested to get a picture of the candidate\'s personality maybe even through Assessment-Centres.

This final stage can be conducted more efficiently if candidates undergo an online test showing their preferred behaviour prior to entering the pre-selection pool. Similar for a job, for which, next to hard requirements like education, subject matter expertise and experience, the behaviour required to be successful in this job can be tested up front. A test that shows the required behaviour in for of a profile that can be compared with those from the tested candidates. For the final selection and the consequent interview not only hard facts but also corresponding soft facts including a matching analysis are available. This facilitates the final interview and its preparation considerably.

The above mentioned approach to (pre)select the right candidates from the very beginning is more efficient and effective. Unnecessary time and money spending for non-fitting candidates can be avoided. A welcome side effect: With this approach a personal preference of the recruiter does not influence the selection, increasing the diversity of candidates within the behaviour related job- and company-requirements.

Online Options

  • There are some online tools that predict on the basis of big data, to what extent a candidate meets the job requirements. Both for the hard and the soft characteristics. For this, all available information in the social media is analysed (CV, letters, publications, LinkedIn, XING - and Facebookprofile, Tweets, and many more), possibly supplemented with phone, Skype or video interviews. With the help of algorithms the best fit for a job is highlighted. Examples include Seedlink (Rina Joosten, (3) ) and the Erasmus University (Colin Lee, (4)). These are forerunners, in various stages of development. Both claim success.
  • A proven and more accessible method is the inclusion of preferred behaviour. As is known, displayed behavior is a good indication of personal skills and allows conclusions on future behaviour. The Belbin Teamroletests supports such a method (see TRiNGiNE (5)). The Belbin-Model is scientifically tested for its accuracy and the standardised team roles through its simplicity are perfectly suitable for online-tests of candidates as well as jobs.

The added value of TRiNGiNE:
online matching of behaviour related job- and advanced candidate profiles

Based on Belbin\'s model a person-related test was developed, including a self - and observer assessment and a job-related test. Both result in a team role profile. By comparing the two, conclusions can be drawn, whether and to what extent the profile of the candidates correspond to that of the job. TRiNGiNE automates this comparison in which profiles of various candidates are compared with that of a job and ranks the matching result in sequence of the best fit. The more people contribute to the assessment of a person and that of a job (360° view), the more relevant, meaningful and distinctive is the information available for the matching.

It is, for example, important to determine the required behaviour for the job profile from different angles:

  • from the point of view of the person that performed this job already
  • from the perspective of the team in which this job will be placed (in order to be able to work effectively with this team)
  • But also from the point of view of the work environment such as: supervisors, colleagues, employees, (internal or external) customers or suppliers.

Of course, so many inputs require also a coordinated process, to distil a single view from the various inputs, which ultimately forms the desired job profile. TRiNGiNE supports this consolidation process by gathering the different views, providing the overview up to the output of the eventual result. This gives all participants the opportunity to discuss and clarify the various aspects of the job and to assemble commonly the final job requirements.

This by TRiNGiNE (5) developed function supports the job review (consolidation) process and is included in its online product.

TRiNGiNE (5) compares in its Matching module in addition to the traditional candidate profiles the profiles resulting from additional tests such as: Jobpreference test, Organisationpreference test, Organisation test and the above mentioned Jobtest.

(1)TRiNGiNE, Recruitment in the digital age
((2)State of recruiting 2015 overview also in (1) )
(3)Rina Joosten, seedlink, and
((4)Colin Lee, Erasmus University, The algorithm says: suitable!