Adjusting Your HR Strategy When Your Company Decides to Train For Basic Job Skills

There is a presumption that the US education system will provide employers with workers that possess requisite job skills.  Companies are then responsible for providing more advanced ones through apprenticeships, job training, and leadership development.  But, what if job seekers do not possess the skills for tech jobs?

This article describes what lengths some employers are going to get people in their talent pipeline.  In many ways, there is nothing new here.  It comes down to searching for talent where they previously hadn’t and providing training rather than expecting people to come with skills.  It’s the latter that I find most interesting.

When designing selection programs, particularly for entry level positions, we tend to focus on what knowledge or skills the candidates needs on the first day.  Those expectations are higher if we expect someone to come with experience than if we are going to be providing a lot of training.  This has important impacts on how we select candidates, including:

  1. Use of aptitude tests rather than knowledge tests.  Aptitude tests are terrific measures of basic skills and are quite valid.  However, speeded ones can lead to adverse impact, so they require good validation studies, meaningful passing scores, and adverse impact analyses.
  2. Alter interview questions so that a wide variety of experiences can be used to answer them.  If you are hiring people who don’t have experiences in your industry, you should be asking valid questions that people with little or no job experience can answer.  For instance, instead of, “Tell me about a time when you led a team project at work and…” use “Tell me about a time when you had to influence a group of friends and…”
  3. Focus on reducing turnover.  Training is EXPENSIVE, so hiring mistakes in a boot camp environment are very costly.  Take special care in developing realistic job previews and other ways that allow candidates to decide if they are not a good fit.  Collect information (previous experiences, referral sources, school majors, etc.) that may be indicative of future turnover and validate them.  These can be part of very useful pre-employment processes.

What this approach really presents is a change in HR strategy from one that relies on people to be able to start on day one to taking time to get them up to speed.  By having recruitment, selection, and development leaders involved in the execution, organizations can adapt their tactics for identifying and selecting talent and have a smoother transition.

Overcoming Selection Buzzwords

When doing a job analysis or writing job descriptions, one of the terms that comes up that makes my hair stand on end is “multi-tasking.”  While our bodies can perform automated functions simultaneously (e.g., driving and talking), our brains cannot consciously do two things at once.  Rather, when I’m “multi-tasking” (like checking my phone and looking up data), what I am really doing is switching quickly (hopefully) between two tasks.  Oh, and recent research shows that men and woman are equally bad at it.

When seeking to understand what managers really want people to do, it is important that we challenge them on vague terms like “multi-task” and “empower.”  When we really get to the meaning of these terms, multi-tasking is being able to handle several projects at once and empower is delegating effectively.  Those are behaviors selection specialists can work with in designing assessments and interviews.

As buzzwords find their way into our conversations about what employees do, our job is to determine the behaviors behind them.  Doing so assigns meaning to the words and starts us on the path of objectively measuring them as part of validated selection systems.

What are your least favorite job description buzzwords?

Who Should Identify and Develop the Non-College Workforce?

On some occasions I have mentioned that companies that need blue-collar workers are in a tough spot. Their jobs are not very sexy to the millennial or Gen Z workforces who prefer tech jobs.  Also, because lifetime wages are significantly higher for jobs that require a bachelor’s degree, parents and high school students tend to have a much more favorable attitude towards going to college than training in a vocation, which is reflected in college application statistics.  We are currently in the midst of low unemployment which makes recruitment for blue collar jobs even more difficult.

Companies should think about this as a long-term, rather than an immediate, issue.  This article talks about how some firms are dipping into high schools to begin identifying students who might not desire (or be qualified for) 4 additional years of school and providing them with what used to be called vocational training.

Of course, if a specific company or industry designs the career education programs (read: vocational), there is a danger of the training being too narrow.  However, no public school in its right mind would ever turn down private money that helps kids get jobs.  And we don’t seem to have a problem with it at the college level where business schools take money (and input) from big employers and provide the students with internships.

The economy goes in cycles and it is not a matter of if, but when, the economy slows and there won’t be the same worker shortage.  However, the trend towards more interest in college and tech jobs will continue for the foreseeable future.  This means that employers of skilled, but not college educated, workers will have to find more ways to create a larger labor pool to find the talent they need.  They can do this by:

  • Aligning with local high schools and community colleges to create curriculum that is broad enough that provides students with career options, but specific enough to allow for an easy transfer from school to the employer.
  • Gauge the interests of students as they enter the program. Interest inventories are an under-utilized selection tool.  This is especially true for entry level employees.  If I’m not interested in social activities, I probably should not be on the wait staff at a restaurant, even if I need the money.  But, if I’d rather work with things than people, then becoming a welder might be up my alley. Validating these types of tests can be a good way to predict potential success by placing students in areas where they are more likely to do well.
  • Provide lifetime learning programs. One thing we know about millennials and Gen Z is that rewarding them for learning is a powerful incentive. Companies should show new recruits all of the opportunities they could potentially have, not just the ones in their trade.

Companies that need skilled blue-collar workers can no longer passively expect a deep talent pool to be available.  Rather, they should take action to identify and develop potential employees.  This will require partnerships, better pre-employment screening, and having developmental programs.  It may not solve the immediate problem, but it will ensure that they have the necessary talent in the future.

Adapting to Changes in Job Duties

I wrote a couple of months ago about how McDonald’s is changing the cognitive requirements of some of its jobs by adding channels for customers to order food. I argued that such a development should get them thinking about who they hire and how they train new employees.

If you have recently wandered into one of their stores, you probably noticed that, if it is not too busy, a McDonald’s employee may bring you your order. OK, this is not particularly revolutionary. But, to quote a franchisee in an article, “We’re bringing the employees from behind the counter out front to engage, in a more personal way, with our customers.” Maybe I am making more out of this particular example than it warrants, but this strikes me a really upping the customer service requirements of a McDonald’s employee. And I am guessing that a fair amount of the employees are not going to meet it. It’s just not what they signed up for.

This is not about whether McDonald’s employees are capable of providing the additional service or whether their ability to do it well affects the customer experience and/or sales. Rather, it appears to be an example of company changing job requirements and then assuming that people hired using a process that does not account for the new skills will be able to carry out the new duties.

Changing skills requirements is a good thing. It shows adaptation to technology and customer needs and makes the work experience more interesting for people in repetitive jobs. But, companies cannot assume that the incumbents can magically adapt without training and revised performance expectations.

This change also requires updating validation selection processes. Whether it means increasing the weight given to certain aspects or validating a new test, we must adapt our workforce to new job requirements on the front end. As jobs change, hiring practices should as well.

Technology and customers are big drivers of change in the skills, abilities, and personality characteristics required of employees. Smart companies not only redesign work to account for this, but they also update how they train and hire to help their workforce adapt.

Selection When There Are More Jobs Than People

As the economy adds new jobs, some sectors are having a problem finding enough workers for them, including construction. This is regardless of the pay and benefits associated with the jobs. However, the same is true in other blue-collar sectors. This is not a shock to those of you who have been trying to hire people for these types of positions in companies that were not hit by the great recession. For instance, utility companies have been having a difficult time recruiting lineman (sic) for years, and these jobs pay into the six-figures will full benefits.

While the reasons for the hiring shortage are numerous (“You can’t pay me enough to do that kind of work,” “I’d rather work in tech,” “I want to set my own hours,” etc.), these businesses do have a significant challenge. There are some things that you cannot use technology to replace (yet).

In this situation, HR should take the long view. With low unemployment, it’s unlikely that you can just hire your way out this. The labor pool won’t support it. Rather, companies need to engage with high schools and trade colleges to develop candidates. But, they also need to promote and market these jobs in a way that will make them more appealing because right now. This is because many more young people (and their parents) would rather code than swing a hammer.

To avoid the expense of high turnover when hiring for these positions, companies need to do a very good job of validating good selection tools with tenure in mind (as well as performance). They include:

1) Modified versions of Interest inventories (what are someone’s likes and dislikes).

2) Biographical information (do candidates enjoy physically difficult hobbies) surveys (also known as biodata) are very useful ways to determine whether a person is likely to stay in a specific area of work.

I have had good success in validating these for hard to fill positions in manufacturing. This is especially true where giving physical ability tests are either expensive, have a risk of injury, or may lead to high levels of adverse impact against women.

These companies also need to embrace the investment in training and accelerating wages as new hires gain more skills. I have seen this put to effective use in reducing turnover.

There will not be a silver-bullet for creating enough workers for physically demanding jobs in the near term. However, employers who think long term may find viable solutions that will serve them well.

Adapting Selection Systems After the Robots Take Over

I am not sure that any HR futurist can tell us how many jobs will be displaced by automation over the next 5, 10, or 20 years. The answer is clearly more than zero. The latest example of this can be read here. The theme of the article is, “Really, a formula can make predictions better than a person’s intuition?” In psychology (well, industrial psychology), we have only known this since the mid-1950s (see this book), so I can see why the idea is just catching on.

Any kind of judgment that is made based on accumulating data will ALWAYS be more accurate over time when done by a machine than a person. This is because the machine is not biased by what has happened most recently, how impacted it is by the decision, how attractive the others who are involved are, etc. While this type of analysis is somewhat difficult for people to do consistently well, it is simple math for a computer. There is really no reason, besides stroking someone’s ego, to have humans do it.

As computers continue to remove the computational portions of jobs, such as analyzing trends, making buying decisions, they will impact HR in the following ways:

• Fewer customer facing jobs to manage, but more IT related ones.

• Many of the remaining jobs will require less cognitive ability and more interpersonal skills. This is because these employees could potentially spend more time meeting specific customer needs and being the interface between end users and the algorithms.

• The key predictors of job success would potentially become conscientiousness, agreeableness, and customer service orientation rather than problem solving ability.

• Developing a validating a different set of pre-employment tests.

• Recruiters will need to source people with very specific skills (cognitive ability for programmers and willingness to get along with others for many other jobs).

The challenge to industrial psychology continues to be developing more valid measures of personality. Tests of cognitive ability predict job performance about twice as well as those of “soft” skills, even in those that already have a high personality component (such as customer service). This also means developing better measures of performance (e.g., how interpersonal skills impact business outcomes).

Or, maybe the robots will do it for us.

People–Can’t Profit With Them, Can’t Profit Without Them

So, in the same week that Tesla says that lack of people is a problem in their business (too many robots!), Starbucks comes to the conclusion that people are biased and are hurting its business, everyone gets training. So, which one is right?

Let’s start with Tesla. Their statement is not as much about how wonderful people are as it is that they haven’t quite (yet) gotten the engineering down for their new cars to be built completely by robots. So, it is not exactly an “Up with people” moment as a “Well, we guess we have to put up with them for a bit longer” one.

The Starbucks situation is a bit stickier. On one hand, they clearly felt as if they had to do something after a horrible incident involving African-American customers to maintain their brand image. But, I think they are setting themselves up for failure. Implicit bias training is well meaning, but correcting a lifetime of assumptions about people in a ½ day seminar is a pretty tall order. What will they do next time a racially tinged incident occurs? Do a full day of training? Validate a test that predicts levels of implicit bias?

Where I think the training will have the most impact is on their new hires. It sets a cultural norm of what is and is not OK. Yes, this will require management support and some way of recognizing employees for being decent human beings. But, in reading the comments on their social media pages after the announcement that may not matter as a lot of people were pretty bent out of shape of having to go one whole afternoon without their Starbucks. Ah, the downsides of selling a legal, but addicting, product.

Service sector organizations will always face the challenge of directing the activities of people in a way that is consistent with their values. Manufacturers are always challenged with introducing technology (which improves efficiency), but also understanding its limits (for now). We are not quite at a point where people can be engineered out of business. So, we still need to lead them in productive ways.

Should Employers Embrace the Push for GEDs?

The U.S. has a lot of people who do not get a high school diploma. This can lead to significant barriers in employment and future opportunities in college. As a result, in 2013, over 500,000 people took and passed a high school equivalency exam (GED). This was a 20% increase over 2012. The Bureau of Labor Statistics accepts a diploma and GED as being the same. But, should employers?

The idea behind the GED is that some people are unable to complete high school for a variety of reasons and by passing the test they show that they have acquired the same amount of knowledge. That may be true, but there is little high school knowledge, except perhaps some math, that employers find valuable. What is valuable is the skill of being able to navigate something for 4 years. But, you don’t have to take my word for it. This report outlines in detail that the career and economic trajectories for those with a GED more closely resemble high school dropouts without a GED than those who complete high school. From a public policy perspective, this leads me to believe that that the proponents of the test are selling snake oil.

Employers should strongly consider this in their applications. Why? Because there may be economic consequences of treating a GED and a high school diploma the same way. In working with a client to validate ways to help them reduce turnover, we looked at the retention rates by education level for entry level positions. What we found was that after 12 months, the retention rate of those with a high school diploma compared to those with a GED 80% vs 65%. After 24 months the retention rates were 68% vs 50%. At a hiring rate of about 1000 per year and a cost of hire a bit more than $5k per person, these are significant differences. After checking with some colleagues, these results are not unusual.

The overall picture shows that employers should not be treating those with GEDs like those with high school diplomas. Rather, you should validate the impact of education level against turnover or performance as evaluate it accordingly in your application, biodata, or training and experience scoring process.

What Do Grades Tell Us When Hiring?

Welcome to 2018! This first link actually highlights a look at valid personality testing on a largely read website. This makes me think that the year is off to a good start in the field.

Along those same lines of predicting behavior, a line of thought has always been that school grades are indicative of future success. The logic behind this makes sense. If a student applies him/herself and does well in school, then it is likely that he or she will do the same at work. Critics will say that grades measure something very specific that does not really translate to work and there are biases in how grades are given (which is why universities use standardized tests).

As always, what makes a good predictor really depends on the outcomes you are looking for. If your goal is to hire people who are good at following rules and doing lots of things pretty well, then this article suggests that school grades should be part of your evaluation process. But, if you want to hire very creative and novel thinkers, then GPA probably is not your best answer.

What also grabbed me about the article was the definition of success. The research article cited indicated that those who did very well in high school, nearly all of them were doing well in work and leading good lives. But, for the authors, this apparently is not enough. Why? Because none of them have “impressed the world,” whatever that means. And because there are lots of millionaires with relatively low GPAs (here is a suggestion: how about controlling for parents’ wealth before making that calculation?).

From an employment perspective, we need to be clear what valuable performance looks like when validating and part of the selection process. If your goal is to select people into positions that require developing unique solutions, then GPA may not be a useful predictor. However, if you expect people to follow processes and execute procedures, then GPA is likely to be a useful tool which should be used with other valid predictors.

And, if you are looking to hire people who are going to “impress the world,” good luck to you.

The Challenge in Finding Good Performance Data

In validating tests, getting a hold of good individual performance data is key.  But, it is also one of the more difficult parts of the process to get right.

Intuitively, we all think we can judge performance well (sort of like we all think we are good interviewers).  But, we also know that supervisor ratings of performance can be, well, unreliable.  This is so much the case that there is a whole scientific literature about performance appraisals, even as there is currently a movement within the business community to get rid of them.Facetime For PC

But, what about objectively measuring performance (for every new account opened you get $X)?  If the Wells Fargo imbroglio tells us anything, it’s that hard measures of performance that are incented can run amok.  Also, while they are objective, single objective measures (sales, piece work manufacturing, etc.) rarely reflect the entirety of performance.  Lastly, for jobs where people work interdependently it can be very difficult to determine exactly who did what well, even if you wanted to.

So, what’s one to do?

  • Establish multiple measures of performance. For instance, call centers can measure productivity (average call time) and quality (number of people who have to call back a second time).  Don’t rely on just one number.
  • Even when a final product is the result of a group effort, each individual is still responsible for some pieces of it. If you focus on key parts of the process, you can find those touch points which are indicative of individual performance.  Again, look for quality (was there any rework done?) and productivity (were deadlines met?) measures.
  • Objective performance measures do not have to have the same frequency as piece work or rely on one “ta-da” measure at the end. Think of meeting deadlines, whether additional resources were required to complete the work, etc.
  • Don’t get bogged down in whether or not a small percentage of people can game the system with objective measures. We seem OK with rampant errors in supervisory judgment, but then get all excited because 1 out of 100 people can make his productivity seem higher than it is.  If you dig into the data you are likely to be able to spot when this happens.

When I hear people say that you cannot measure individual performance well, I cringe.  Of course you can.  You just need to know where to look and focus on what is important.

 

 

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