When 5,000 resumes don’t get you what they used to: How to avoid “motivated hiring” in a demotivated labor market

As businesses reopen nearly as fast as they shut down due to the pandemic, the demand for workers has skyrocketed thereby creating risk for what I term "Motivated hiring." A reasonable assumption would be that most workers displaced by the pandemic will return to their previous employer and job. That’s not what’s happening. In fact, the reverse is.

Workers are quitting.

According to the Bureau of Labor Statistics, four and a half million people quit their jobs in November 2021 in what has been called, "The Great Resignation." This stubborn labor market isn’t just unmotivated, it’s actively demotivated. Estimates are that one in seven individuals comprising current unemployment numbers is not seeking (traditional) employment at all. Those 4.5M signing off in November? They didn’t just quit jobs, they quit work.

That’s A problem.

When people are voluntarily out of the workforce, traditional models of supply and demand falter. The things that used to work to find and employ talent don’t work as well. Five thousand resumes don’t get you what they used to (apologies for the sarcasm). So, what do organizations do when standard practices aren’t working?

They “double-down.”

Bigger signing bonuses, higher salaries, free beer (I am not making this up), organizations are doing “whatever it takes” to hire the talent they need to keep the business going. But in times of “whatever it takes,” sometimes “whatever" will do.

That’s THE problem.

And don’t think this is someone else’s problem. Not all 4.5M that quit left jobs in hospitality and healthcare, although the effects of the pandemic have been especially devastating to these sectors. Many more are simply NOT going back to the job they quit, or that quit them.

This isn't called, “The Great Resignation” for a couple of front line industry sectors. The quits span industry sectors, and all levels.

The Brutal Truth.

When people flat out quit without another job or plan, they quit to escape bad. Fixing bad is much harder for organizations than maintaining, or even adding, good.

When fixing wages, benefits, hours, etc. doesn’t work, hiring managers and organizations “fix” themselves. Without knowing it.

That's THE REALLY BIG problem.

Cognitive dissonance, or motivated reasoning, occurs when irrational thinking, or behavior is justified by adding more illogical thinking or behavior. Here’s an example of what that sounds like: “Sure, plastic is bad for the environment, but burning gas is worse and I drive a Prius -- besides, I planted a tree the other day.” Neither your Prius nor that tree you planted has anything to do with the plastic – it only makes it SEEM less bad. And you drive away in your toy.

“Motivated hiring” is the term I use for the organization- or individual-level hiring equivalent of cognitive dissonance. It’s not the same as panic hiring or working harder to hire. It’s the opposite.

Adding resources to your recruiting and hiring efforts is logical, if not necessarily productive in today’s labor market. It’s what happens when you can’t, or don’t want to fix things anymore that motivated hiring enters the situation. Without knowing it, individuals and organizations start to justify loose hiring practices. They start making excuses for hiring candidates that aren’t “fully” qualified. Hiring what you want to believe instead of what it is and being okay with that -- that’s motivated hiring.

At the individual level it may sound like, “… I can coach them.” At the organization level, it might be changing job titles to feel like hiring for something different, and differently.

It’s mind games, fooling ourselves, and it happens to the best of us when we’re not at our best.

It's a business decision.

Without flight attendants, planes don’t fly. Without teachers, schools don’t teach. Sure, that's business.

Interim hires, substitutes, contract hires – right?

These are the decisions that keep business going when there’s no better option. And it feels okay. Actually, it may even be a good “business decision.” Sometimes “business decisions” are a convenient way of passing blame.  “I know better, but you know, it's a 'business decision'.”

It's time to get real.

At what point in “reality hiring” does “better than nothing,” “bridge hiring,” or the ultimate smokescreen of “business decisions,” belie the truth that “nothing, is better”? (i.e., to not hire is better)

Who knows? Answer: Everyone, at the unconscious, or almost unconscious level. It doesn’t matter. That’s the ‘fix’ of motivated hiring.

My goal is to raise awareness of the most likely and consequential risks to good hiring practices in today’s job market. I also provide practical recommendations for how best to manage these risks.

How to avoid motivated hiring in a demotivated labor market

Let's review:

1. A lot of people are not in jobs, many aren't looking.

2. A lot of organizations are aggressively looking for workers to fill a lot of open jobs, it isn't going well.

It doesn’t take an economist to see that conditions are ripe for panic hiring. It may take a psychologist to help avoid the perils of motivated hiring.

What follow are some specific facts and rationale for why the risk of motivated hiring (and bad decisions) is now especially high.

{Please note that although I write in first person and do use examples of conveniently daft managers and organizations, I don’t mean to be flippant nor personally critical. The subject is serious, and my points and recommendations are evidence-backed, even if presented colloquially or sarcastically. Science isn’t incompatible with entertainment. You know, ratings.}


1. Today’s labor market isn’t playing “right.” Its size is deceptive, its contents unknown, and its behavior unpredictable.

The number of people out of work is big and largely the result of easily counted layoffs. Usually, the assumption in such layoffs is that when the market recovers, displaced workers will return to the jobs they left. Not this time. This is no “round trip.” You can probably stop leaving messages for your old payroll.

While you’re at it, you can ditch any old resumes by the same logic. Folks are not going back to their last job.

But this isn’t the whole story, and why you aren’t exempt. The same thing appears to be affecting any of a wide range of jobs. They don’t call it, “The Great Resignation” because of a couple singularly impacted jobs or even industries.

Nothing is certain except that this is an exceptionally hard time to hire.

Engineers have a simple term for situations like our current labor market -- noise. Everything is harder in noisy environments, but we also have a noisy (fickle, picky, undetermined) target hire – with first right of refusal.

What do you think happens when a candidate or two show up for interview?

It takes more than even good interviewers to not pick one. {Business decisions}

You need to be more than aware of this market, you need to be ready for it.

2. You’ve gotten worse at hiring due to,

a) disuse – like any complex skill, interviewing gets rusty without frequent use. So does negotiating, which is also becoming more important, and

b) sagging and lagging definitions of “good hires” – those qualities that constituted top talent the last time you were hiring aren’t the same as they are now. How about the bar? And

c) technology bias, over dependence, and FOMO – similar to the “halo effect” that assumes a good person does no wrong, people assume (buy, use, believe) technology is infallible (wrong), the newer (less tested?) the better. You have to keep pace with the market and competition (still wrong). As I’ll detail in my recommendations, technology is a must have for recruiting. It is not good for making specific hiring decisions. But I know some are using it this way and this generates FOMO. Unless you, someone in your organization, or someone you trust, understands how that cutting edge technology works, you shouldn’t be using it. And

d) overconfidence – Funny, I’ve never met a bad interviewer. For years I trained (calibrated) assessors to observe, record and rate various well-defined behaviors exhibited by candidates on display for at least three days. All were surprised by how inaccurate their initial efforts at objective evaluation were. And that’s professionals who assess up to 12 candidates per week – all year. Think about this, who gets more practice interviewing, you, or your candidates?

3. Studies show that hiring managers tend to make really bad decisions when,

a) they have few candidates and many jobs to fill - cognitive biases are more frequent and disruptive when people are under stress. Managers deprived of hires are highly stressed, many having to perform several jobs due to short staff. It's open season for any and all cognitive biases. But they also make poor decisions when,

b) there are many candidates to review – more choices lead to less scrutiny and worse decisions. Technology has made sifting through thousands of resumes a speedy task, it’s also made applications easy. Pull a random resume or two; is more really better? They also make bad decisions,

c) when they’re thinking about themselves (e.g., “my hire”, “I need”, etc.) – self-oriented thinking is common in any social environment, especially when one is stressed or insecure. Instead of assessing candidates against the job demands, hiring managers assess candidates in terms of how they’d work for them. This is a power-based form of “dynasty-building” aimed at elevating the status or power of the hiring manager. Of course, none will admit to this, but have you noticed how rare it is for a manager to hire someone equal to or more capable than they are? This point of view also precludes considering the candidate’s potential for someone else in the organization – a charitable, real possibility good for the company.

4. One bad hire is worse than five good ones are good. {This is no guesstimate.}

a) Despite the “positivity bias” of most selection frameworks (i.e., emphasis on the “good traits” of people with terms like “Conscientious”, “Agreeable” and “Openness”), studies reveal what you’ve undoubtedly experienced: bad outweighs good – by far, as I’ll explain, below. But it’s worse than just that. Bad managers aren't the opposite of good managers. In fact,

b) Bad hires are frequently the BEST interviewers. And they have everything in common with leaders dubbed as having “high potential” (i.e., smart, ambitious, socially gifted, etc.) so they get off to a great start post hire. But that which makes them great ultimately fails them in advancing their maligned agenda. Strengths become weaknesses (McCall & Lombardo, 1983) and “dark side” behaviors emerge when these terrors become stressed, distracted, or unconcerned about their impression (Hogan et al., 2021).

c) In fact, studies, as well as my own experience and research have found that removing a bad leader improves employee morale, whereas allocating more recognition, pay, etc. to employees has no effect. Specifically, exit interviews conducted by an outside agency found that bad leaders were the primary cause of employee stress and turnover accounting for half of all resignations. Pay and career opportunities accounted for only 8% each.

There are more, but these are the main factors contributing to the risks (and, potential rewards) of selection decisions in these times.

Getting better.

What follow are my recommendations for how best to address our current, but not changing anytime soon, workforce hiring conditions, and avoid motivated hiring:

  1. Be absolutely clear about what you want.

I know this sounds elementary, but I can’t overstate the importance of a truly prescriptive job description to include the key responsibilities and qualities of the successful hire. Share it with others and invite their critical review. They should be able to define for one another exactly what you seek. This can help to minimize the risk of:

a) Convenient hires that simply, “pop up” like a gift even when you’re not looking.

b) Impulse hires that are “too good to pass up” – never mind you were looking for something completely different.

c) Window shopping, aka “I’ll know it when I see it” interviewing. Other examples include, “best available talent”, “better than nothing”, and “for goodness sake, just pick one.”

A lack of clear success criteria can also work in reverse by rejecting qualified candidates. And there couldn't be a worse time for this mistake.

  1. Be even more clear and disciplined about who you interview and how you do it.

Again, this sounds like “wash behind your ears” wisdom, but no one is immune to:

Distracted interviewing – when either the interviewer or candidate digress deliberately, or not, for outside interruptions, or not. Before you know it, time’s up and you only learned that you both like the same bands. I.e., zilch.

Premature judgment – first impressions are a real thing, and they happen exceptionally fast – like millisecond fast. “You had me at hello” isn’t just a movie clip or love song. Neither is “Be afraid ... be very afraid.”

Rogue interviewing – deviating from the plan, i.e., “HR gave me this interview transcript, but it’s for amateurs. My way is better.” This is related to the risk of overconfidence, or belief that you’re actually a very good interviewer, but is more problematic for another reason. Research is clear here; a structured interview outperforms “my keen insight” interviewing. You simply aren’t as good as you think – perhaps there’s solace in knowing you’re not alone, very not alone.

The fix.

1. Stick to the script – no matter how awkward it may make you feel. It's as close to calibration as most get.

2. Get familiar with at least these common psychological errors:

“Like me” bias -- the variably narcissistic belief that candidates who resemble yourself are necessarily good. This flawed logic is based on two psychological effects, 1) we’re drawn toward the familiar and we believe we know ourselves, and 2) we (especially leaders) do like ourselves and that must be good.

Think for just one moment: would you really want to hire someone exactly like your “self”? Have you ever been told that you're your own harshest critic?

{Sometimes when I’m addressing a group on the topic of personality, I get asked, “When it comes to romance, do opposites attract, or do birds of a feather flock together?” The first couple times I got this question I was a bit surprised and gave the academic answer, “there’s a slight preference for similarity in early attraction, over time the effect is insignificant – neither prevails.” Now when I’m asked this, I do what a good coach should do – throw the question back, “Would you marry yourself if you could?” and ask for a show of hands. About 15% indicate that they would. These 15% tend to be very high on conscientiousness and they identify with the description of a couple composed of two perfectionists, “You’d always know where everything is” they firmly agree. With a smile, I go on with the hypothesis, “When two people are very similar – but not identical – could the slightest difference generate friction?” After a few seconds most agree, they’d be open to dating someone else.}

“Self-fulfilling prophesy” bias -- a common form of cognitive bias, and a form of cognitive dissonance, which I've already discussed. In this case it's a behavioral decision that triggers other, seemingly supported behavior. Because this is a cognitive bias that affects behavior, I provide the subtitles: “I flew them in from halfway across the globe. Only a reasonable person would do that and only for a really good candidate. I’m reasonable, therefore, they must be really good.” Wrong. You flew them in to interview, not to court, at least not before the interview. As stated, this is a very common bias and probably the source most psychological errors because it’s broadly applicable and nearly impossible to avoid or overcome. To break free of this bias is basically psychological disarmament.

The priming effect – a bias in which preliminary external information influences subsequent perceptions of the candidate. It’s very similar to the self-fulfilling prophesy bias, the distinction being in the source of origin; “self” being key here. The priming effect is precipitated by or attributed to a real or presumed external stimulus. Historically, the most common stimulus has been another’s opinion (shared or presumed). Now, it’s the internet. Like a playground secret, the more privileged or covert the "intel" is perceived to be – especially relative to the candidate -- the more influence vis-à-vis the priming effect.

{In this section I realize that I have stepped into a highly controversial topic, using the internet to inform hiring decisions – or any personnel decisions. This was not on my mind when I started this article, and I have not done adequate research to address the issue here. However, I recognize that I can’t simply side-step such a powerfully important topic, so I do acknowledge the issue with some, but minimal, elaboration. Any more is beyond the scope of this article. My comments are mine only and should not be presumed definitive. These follow immediately.

With the evolution of data-intensive techniques such as AI, internet scraping is a foregone conclusion and soon to be a common practice. Keen minds are working to manage issues of ethical and procedural fairness and I will be the first to endorse and adopt AI in hiring once these and other related issues are settled. But they’re not sorted out yet (see below for my recommendations regarding AI).

I abstain completely from ‘searching’ individuals, or organizations in the universe of my clientele. Without exception. I also do not personally participate in any but professional social media. I accept that ‘professional’ is subject to debate.

My practices may be austere, but I offer no apology; it’s the way I work. I expect my colleagues do the same or similar for the same reasons as I.

Delving into all the reasons for why or why not to leverage data from the internet is practically an insurmountable task, and this, I do ‘step around.’ However, with the stakes as high as they are for hiring, I can understand why others do tap this resource. I can only caution anyone against the use of unverifiable data, which the internet is full of. Like any other information, cross checking may mitigate the risk of illegitimate data, and I’m sure AI will help here. For now, there’s more noise on the internet than can be estimated or cleaned. If such data are to be used, and I know they are, it should be done with ‘eyes wide open’ to the risks, only some of which I’ve mentioned here.}

Well, that was a grind.

Appropriately, I resume with the last psychological error to watch for,

Interviewer fatigue – This isn’t strictly a psychological error, but it is a common mistake. When hiring managers quit instead of finishing, i.e., “I’ve seen enough,” bad happens. A hire is made in the likeness of Malcom Gladwell’s best seller, “The Tipping Point” (Gladwell, 2000) where something as trivial, and stupid, as when a candidate is seen can make all the difference.

3.  Take a close look at the recruiting and job management system you use. Trace a resume, candidate, or job from entry to endpoint. Don’t assume it has to be that way if you’re paying for a private system or service.

Job/candidate management platforms have been the norm for years. They’re not all the same and this isn’t a review. I’m generalizing and only make a few comments.

Here's what you need to know about the candidate/job management system:

a) What data are collected and how?

b) How the data are managed (analyzed, retained, scrubbed, filtered, shared, etc.)?

Data in these databases are highly unreliable and variable, especially the ones that are mostly filled with aimless resumes.  If you think candidates 'touch up' their resumes when they know it's you that will review them, what do you think they do when they're putting them into a massive database with unknown, if any, reviewers? These questions, above, are intended to reduce some uncertainty about the data and its quality.

If you have an “in-house” system, these questions will be easy to answer. If you’re using an open, “free” system, or even if you pay for the service, you won’t learn much more than you can see as an end user.

These platforms are where you will find “Artificial Intelligence”, or the mighty, AI, being used and claimed by all. They aren’t wrong or bending the truth. But AI is very poorly defined, and the term tells you almost nothing. A Coke machine uses AI.

So, the first thing to do is to figure out what someone means when they claim, “AI.” From data and computer science experts to salespeople, definitions vary from the use of a few algorithms (as simple as “if/then” processing) to systems that approximate “learning” by their ability to construct algorithms on top of algorithms without human intervention. The ability to construct algorithms based on the results of others or simply patterns in data is the more commonly accepted, and higher-level definition of AI.

AI, as defined above, needs a ton of data (more than any ‘cloud storage’ can manage) and a lot of training. The training consists of modeling expert decisions (predictions, constraints, criteria) and ‘tweaking’ algorithms or parameters to effectively predict a given, typically desirable, outcome.

These systems need to be closely managed. With the amount of data AI systems can process, “surprises” tend to pop up. Neither scientists nor lawyers like surprises, especially when they’re illegal or unethical. Early iterations of AI made errors such as these and had to be “unplugged” for refinement.

As AI systems evolve, they will be able to avoid issues if an expert makes these corrections. The real risk now is what to do when AI becomes a virtual “black box” for which no one knows exactly how it’s functioning. At this point, errors might not be caught, and answers may not be understood.

How far can we trust AI?

We can trust AI as an advanced form of data analysis much more than those who use it - at least for now.

The fact is nobody knows exactly where and how AI is being used. For example, the 2016 US presidential election was “touched” by AI used by the consultancy, Cambridge Analytica, with data made available to them from Facebook.

I do NOT support using AI to make specific, high stakes hiring decisions.

AI is essentially very efficient – it can process huge amounts of highly dynamic data. And it’s very good for many situations. But it has limits. AI should not be used with any more confidence than we can be clear about what a successful decision ‘looks like’ and what errors are possible or permissible.

Why so cautious?

AI is used in medicine and such data intense technology like CT scans can detect certain illnesses better than medical experts. But the medical experts can ‘vouch’ for what AI finds. It’s not blind faith – it’s a good tool. The accuracy of prediction or evaluation of people’s behavior isn’t the same. There’s much more error. Statistical error is random, "noise", and generally not a problem when minor. But sometimes there's "systematic error" which more accurately is a mistake or mystery. That's what happens when something is predictable (technically) but unexpected or unknown. Systematic error is very problematic because it can lead to mistaken assumptions of what causes what. Both types of error have been found in AI.

What do you do when AI converges on a solution, and you don’t know how?

You stop. Until you do know.

This is the conundrum of AI. It’s intended to solve difficult problems, but when those solutions are the result of “magic” (unknown processes), what you don’t know can hurt you, and others. There are several well-known cases where organizations testing AI for hiring decisions had to stop because of accidental discriminatory algorithms and outcomes. This isn’t something to simply monitor and fix after the fact. You need to understand how a result, in this case a discriminatory outcome, happened.

My recommendations for using AI in hiring.

AI and other forms of machine learning are already ubiquitous. But so far, what AI can do is very much in human hands - a tool. What’s done with AI is a matter of human decision and discretion. Because of this, my following recommendations aren’t moot. At least, not yet.

a) Use AI with quality control measures for simple tasks and stay on top of what data are being analyzed and how. Don’t depend on AI any more than you can accept its risk. You will be accountable for outcomes.

b) Get as much information as you can about how anyone, not just your employees, is using AI for your purposes (i.e., vendors, services, search agencies, etc.).

c) Use experts to understand, train, and manage how it’s being used for you. Do not use AI without knowing how it works.

d) Do not use AI to make sensitive, high stakes, individual or limited candidate decisions except in very well controlled situations.

e) When using AI to inform hiring, be careful to avoid all of the ‘errors’ mentioned in this article – particularly the biases.

Early stages of recruiting can generally meet these conditions and I do endorse AI for recruiting purposes. Late stages of selection increasingly include data that are more nuanced to specific job needs and candidate merit. You should not let AI make decisions such as these where what distinguishes the final candidates may not be data the AI system has or is suitable for its analyses.

For all the reasons, risks, and recommendations I’ve made to this point, I make my simplest and most encompassing recommendation last:

  1. Use an expert in industrial/organizational psychology.

Here, again, I make no apology for putting forth an admittedly self-serving recommendation. But that’s not why I make it. I can provide several good referrals, too.

Hiring is not a “non-regrettable” decision, it’s among the most consequential of all. The decision to bring someone into your team is a big deal, and no guarantee. But your odds can be improved with the risks and recommendations I’ve described here. The probability of a great hire (and avoiding a tragic one) can be increased more, and more quickly, with the help of an I/O psychologist.

Here's why that matters more than ever.

In today’s market you won’t get as many chances to hire a star as in the not too distant, but long gone, past. You need to be ready with your best pitch as to why they should join you and your organization, including your offer. Emboldened by the insight of an expert in assessment, you can make your most aggressive offer with confidence.

Be sure to understand what processes and tools your psychologist will use. There is no substitute for a strong psychological interview. Nearly all I/O psychologists use interviews for assessment, but this is where the biggest differences are among I/O psychologists. Practices differ and so does accuracy of insight.

There isn’t much I can share to help you evaluate the quality of an I/O psychologist’s interviewing skill – that falls under the condition, “it takes one to know one.” At minimum, I can state that psychological interviewing is quite complex and isn’t adequately taught in academic programs, it can only be learned with practice and mentoring from an experienced, applied I/O psychologist.

Tests and personality inventories are great, but not enough. And that’s the good ones. These can help to describe and predict behavior, but they don’t answer questions about “why” a person is or behaves a certain way. To understand “why”, which is very important to analysis and prediction, one needs to have contextual information which can’t always be known without the conditional nature of an interview.

The type of interview I’m describing for high stakes, high functioning candidates for the purpose of hire (or development) requires extensive training and a thorough understanding of many disciplines of psychology. Be sure to use a psychologist that not only understands but is experienced applying psychology in the workplace.

As expertise in psychology at work has increased in demand, many (psychologists) have entered the market. Not all are qualified.

SIOP (Society for Industrial/Organizational Psychology) is a good source to help understand the qualifications of a trained I/O psychologist. There’s even a consultant locator that can help to identify applied I/O psychologists by expertise, location, and other considerations.


Exceptionally high demand for workers as businesses recover is faced with a labor market of unknown number, quality, and motivation. Primarily, but not only, because of a fickle labor market, hiring parties need to take extra measures to avoid compromised selection practices and poor decisions driven by motivated hiring. Specific risks and recommendations for careful hiring in today’s market are identified.

As a “friend of mine” once claimed, “Selection is a lot like shaving, the line between hiring who's exceptional and who's tragic is razor thin, but cuts deep.”

May your hiring practices be sharp and your hires exceptional.

Psychways is owned and produced by Talentlift, LLC.