Interesting that these two stories were posted on the same day as they demonstrate the gaping chasm between hiring practices.
What would you do if you went for a job, and the HR person said one of the criteria for selection was a favourable analysis of your handwriting?
Personally I would refuse to work for such a company in the same way that I would refuse to work for a company that included tea leaf reading in their recruitment process. I would not trust a company or organisation that puts faith in pseudo-science with my career and my future. I’d worry that they would also use mediums to dictate their business strategy.
And to be clear, graphology has failed under numerous attempts to prove it’s worth scientifically. The prevailing theory (apart from in France, apparently) is that graphologists are using the content of what’s provided to them to judge candidates rather than the style of hand writing.
And across the pond, US recruiters are being forced in to increasingly desperate approaches to find top class talent in the IT market.
Of late, growing numbers of academics and entrepreneurs are applying Big Data to human resources and the search for talent, creating a field called work-force science. Gild is trying to see whether these technologies can also be used to predict how well a programmer will perform in a job. The company scours the Internet for clues: Is his or her code well-regarded by other programmers? Does it get reused? How does the programmer communicate ideas? How does he or she relate on social media sites?
It’s a really interesting approach, but one that seems fraught with danger. As alluded to in the article, people with good ratings in the community can be difficult to work with day-to-day. And with an increasing emphasis on team working with agile approaches such as Scrum putting a heavy emphasis on the team, getting a good fit might be more important than getting the right skills for many companies.
It’s interesting to me that on the one hand you’ve got French recruiters reverting to faux science and American recruiters resorting to data analysis. Could these two approaches be more different?