HRE Column: On Recruiting and the Technology Transformation

Here is my semi-frequent reminder and pointer for blog readers that I also write a monthly column at Human Resource Executive Online called Inside HR Tech that can be found here.

I kind of liked this month's column, (I suppose I like all of them, after all I wrote them), but felt like sharing this one on the blog because it touches upon what has been in the past a pretty popular topic with readers here - the kinds of transformations that organizations can drive via the application of modern HR technologies.

Here is an excerpt from the column, The Recruiting Technology Transformation:

Technology continues to fundamentally transform how, where and when work gets done, and the ways that HR leaders can drive improved business performance. In the HR tech world, recruiting technology is helping to drive that transformation and is making the recruiting function perhaps the most transformed of all HR disciplines.

This phenomenon was on display at a recent event I attended, HireVue’s Digital Disruption in Park City, Utah. At the event, numerous HireVue customers—spanning a wide variety of industries, including banking and finance, airlines, publishing and national retail chains—shared how technology has impacted and, in some cases, radically altered their talent-acquisition efforts.

Just a few of the examples from the event reveal the potential benefits of adopting modern talent-acquisition technologies for organizations of all types and sizes:

Educational and general-purpose publishing company Houghton Mifflin Harcourt adopted a three-pronged approach to improving its ability to identify, engage and hire inside sales staff. That approach resulted in increased quality of hire, faster time to fill their open sales roles and improved business results, which were measured in how many new sales-team members were attained.

By redesigning the process to better identify the candidates most likely to succeed through a combination of a statistically validated online assessment, video interviews that replaced the former recruiter phone screens and consistently applied behavioral-interviewing techniques for the candidates who passed the assessment and video screen, HMH was able to show top-line and bottom-line ROI.

The general lesson from this story is this: Applying modern tools and technologies to the talent-acquisition process, particularly for revenue-generating roles, provides HR and recruiting leaders with one of the best ways to help drive organizational results. In this example, HR could show how it was a significant contributor to sales and profits, and not just an administrative cost center....

Read the rest over at HRE Online

Good stuff, right? Humor me...

If you liked the piece you can sign up over at HRE to get the Inside HR Tech Column emailed to you each month. There is no cost to subscribe, in fact, I may even come over and wash your car or cut the grass for you if you do sign up for the monthly email.

Have a great Wednesday!

CHART OF THE DAY: Which job candidate gets the most attention from hiring managers?

Quick answer - It is Candidate #4.

Some back story on that conclusion...

Recently researchers at Old Dominion University published a study called 'How quickly do interviewers reach decisions? An examination of interviewers' decision-making time across applicants' in the Journal of Occupational and Organizational Psychology. They found that hiring manager decision-making takes closer to five minutes for the first interviewee, and reaches closer to eight minutes by the fourth applicant. After this, however, the time hiring managers take to reach a decision begins to decrease with each additional interview.

Here's a chart from the study:

From the researcher's conclusions on this data:

Interviewers tend to take longer to evaluate applicants near the beginning of their interview schedule and take less time to evaluate applicants near the end of their schedule. This may prevent applicants who appear later in the schedule from having a full opportunity to perform. Organizations may benefit from limiting the number of interviews an interviewer conducts in immediate succession to around four, which may decrease reliance on more automatic information processing strategies.

What conclusions can we draw from this data, and what changes might we need to consider to make sure we are not falling into the 'Candidate #4' trap?

Well, the first step is just being aware of this potential tendency. If you have to set up an interviewer or a hiring manager for a day-long set of candidate interviews, make sure you schedule some breaks such that they are not seeing a dozen people in a three-hour block. Chances are everyone after Candidate #4 are not getting a fair look, and we are wasting hiring manager time as well. 

Next, if you are brining in a smaller set of short listed candidates for a second round of interviews, don't slate them in the same order with every interviewer they have to meet. Mix up the order across the interviewing team to try and reduce the effects of 'interview fatigue' adversely impacting any single candidate.

And last, keeping this data in mind should make us be more careful about tracking more data around interviewing and interviewers - how much time they spend per candidate, how much does the 'Candidate #4' efffect exist in the organization, and how can we use data on these processes to get better.

Data is our friend. Use wisely.

A different view of 'Top' talent, namely that it is mostly a myth

Caught this piece, The programming talent myth', over the weekend and if you are in the technology space at all (as a techie yourself, someone who has to attract and recruit tech talent, or simply just someone who is concerned/interested with the 'state' of technology today (particularly when it comes to issues of diversity and inclusion)), then you should carve out 15 or so minutes today or soon and give the piece a read.

It is essentially a summary of a recent keynote speech at a developer's event called PyCon given by Jacob Kaplan-Moss, a well-known contributor to the programming language Django and the director of security at Heroku.

In the speech Kaplan-Ross took square aim at the concept of 'Top' technical talent, (although I would argue his logic would apply to other disciplines as well), and how the dangerous myth of the 'Rock Star' programmer and the terrible programmer (with nothing really in between these extremes), is detrimental on all kinds of levels. It drives people out of technical careers and studies - if you are not a 'Rock Star' you might as well not even bother. It continues to foster and support less-than-healthy norms and lifestyles - 'Rock Star' programmers work 80+ hours a week and don't think of anything other than programming. And finally, it feeds in to what can easily develop into that 'Bro culture' that is common in many smaller startups and tech companies.

Here is a little piece from the talk:

Programmers like to think they work in a field that is logical and analytical, but the truth is that there is no way to even talk about programming ability in a systematic way. When humans don't have any data, they make up stories, but those stories are simplistic and stereotyped. So, we say that people "suck at programming" or that they "rock at programming", without leaving any room for those in between. Everyone is either an amazing programmer or "a worthless use of a seat".

But that would mean that programming skill is somehow distributed on a U-shaped curve. Most people are at one end or the other, which doesn't make much sense. Presumably, people learn throughout their careers, so how would they go from absolutely terrible to wonderful without traversing the middle ground? Since there are only two narratives possible, that is why most people would place him in the "amazing programmer" bucket. He is associated with Django, which makes the crappy programmer label unlikely, so people naturally choose the other.

But, if you could measure programming ability somehow, its curve would look like the normal distribution. Most people are average at most things.

It makes sense if you think of programming as not some mystical endeavor that somehow one is innately born with the talent for or is not. If you see programming and other technical occupations as just ones consisting of a set of skills and capabilities that can be learned over time, (like just about every other skill), then the idea of programming talent and programmers existing on a more normal distribution curve seems the most likely outcome.

One last quote from the piece:

The tech industry is rife with sexism, racism, homophobia, and discrimination. It is a multi-faceted problem, and there isn't a single cause, but the talent myth is part of the problem. In our industry, we recast the talent myth as "the myth of the brilliant asshole", he said. This is the "10x programmer" who is so good at his job that people have to work with him even though his behavior is toxic. In reality, given the normal distribution, it's likely that these people aren't actually exceptional, but even if you grant that they are, how many developers does a 10x programmer have to drive away before it is a wash?

How much does the 'Rock Star' mentality and assumption play in to toxic workplaces, less inclusive workforces, and unfulfilled 'Good, but not a Rock Star' people?

It is a really interesting piece, and Kaplan-Ross' speech is also on YouTube here, and I recommend checking it out.

By the time you catch Google as a 'Top Place to Work', it may already be too late

Here's a quick note of caution for any employers chasing 'Top' of 'Best' of 'Most Amazingly Fantastic' organizations to work for lists - the kinds of lists that are almost always topped by legendary companies like Google, courtesy of a recent piece on Business Insider titled In terms of 'prestige', Google is now a 'tier-two' employer, says recent Comp-Sci grad.

A quick excerpt from the piece, then some comments from yours truly, (it is my blog after all):

When Google offered a recent grad from a top CS program a job, the new grad said no.

That despite monthly compensation of $9,000, including a housing stipend.

Why?

In an email, the engineer gave us four reasons:

• "Lower pay after tax. Housing stipend is taxed more, and several places pay more than Google. That being said, Google is still very competitive. Google's full time offer is very average (105k starting salary) and the best startups pay more."
• "Less interesting work. It's a large tech company. The impact I'd have is minimal."
• "Lower prestige. Outside of tech, and maybe within average CS students, Google is the place to go if you're one of the smartest engineers. However, within top CS students, it's not considered that great. Probably tier two in terms of prestige and difficulty to get an internship. I have lots of friends barely passing their CS courses that are interning there. Saying you intern at Google just doesn't get you that much respect."
• "Less upside. For full time specifically, you get equity at a startup. If it IPOs, you make millions if you're one of the first 100-1000 employees.

Lots to take in there but the gist is pretty clear - at least according to this Comp-Sci grad, even one of the most highly lauded top companies in the world isn't immune to being 'topped' by competitors for the best, most sought after kinds of talent. If Google, with it's history, success, mythos, and bucketfuls of cash is getting beat out (at least in the perceptions) of top recruits, it reminds everyone that while chasing companies like Google might seem like a great strategy, it eventually is a failing one, since Google can't even keep up with Google, if that makes sense.

But there is also one other nugget in that quote worth teasing out a little and that is the way this Comp-Sci grad talks about how he and his peers think about and talk about companies and workplaces. From the quote, there definitely seems to be an odd kind of peer pressure and one-upmanship going on with these recent grads. The desire not just to get a great offer and work on great tech and projects but to be able to brag to the other kids in Comp-Sci is pretty high on the list of desires for this group.

Interesting stuff it seems to me, and a great reminder that no one, not even Google, is immune to competition, changing values, and the need to constantly be moving forward and re-inventing their value proposition in order to keep their lofty status on whichever 'Wonderful' Place to Work list you subscribe to.

Have a great weekend!

In Soviet Russia, (and America), Job Finds You

For a 'don't believe anything you read on the internet' April Fool's Day, I submit for your consideration a really interesting, (and totally not made up), conclusion about how people in the United States find jobs courtesy of a recently published Economic Letter from our pals at the Federal Reserve Bank of San Francisco.

Let's start with the researcher's money line first, then we will try and unpack it a little bit:

More than three-quarters of workers who switched employers did not report active job search in the previous three months.

Did you take a second to process that statistic? 

Of all the 'new hires' that the researchers examined, 77.6% of them had not reported being in an active job search in the previous three months. And we are not talking about internal job transfer types of moves here, these are employer-to-employer job shifts. So the vast majority of job-to-job transitions do not follow the standard interpretation of a labor market that matches workers who are actively seeking out job openings with the positions that are posted by employers.

So essentially, according to this research, over three-quarters of hiring is coming from direct recruiting/poaching, referrals, and informal networks.

Probably not a great surprise/finding for experienced HR/Talent pros, but a good reminder for folks who are still out there beating down doors in an active job search. Here's a summary of the data from the research, then one last point before we sign off.

The researcher's data shows that while 77.6% of hires are coming from employed folks who were not searching for a new job, that still only constitutes about 2% of all employed people. Translated - your recruiting/poaching/referral processes are still only nabbing less than 2% of folks out there, underscoring how hard it can be to identify, engage, convince, and finally hire people out of existing jobs into new ones at your company.

Net-net: At least according to this research, most jobs find people, not the other way around.

Have a great April Fool's!