EPISODE SUMMARY The third episode of Research Chat features Peter Fisher, PhD in management in the Organizational Behaviour and Human Resource program at Lazaridis School of Business and Economics at Laurier. He will speak about his research into how to avoid bias and improve corporate hiring practices.
The episode features:
o an interview with Peter Fisher, PhD in management in the Organizational Behaviour and Human Resource program at Lazaridis School of Business and Economics at Laurier. He shares his experiences at Laurier and how he came to study at Laurier.
o Next he explains his on-going research into how organizations can find the right person for a particular job.
o His interview concludes with an discussion of interviewing processes including using AI for hiring.
o Additional information about the research and transcript (with relevant links) available from wlu.ca/ResearchChat
people, hiring, laurier, bias, decisions, research, based, company, job, organisations, behaviour, human, question, tests, determining, intuition, stereotypes, employees, hiring practices, hiring process
Peter Fisher, Shawna Reibling
Shawna Reibling 00:04
Welcome to Research Chat, a podcast where students share their experiences at Wilfrid Laurier University and their current research findings. I'm your guide Shawna Reibling, a Knowledge Mobilization Officer at Laurier and in each episode, I will interview a Laurier student who is exploring a specific research passion through their graduate research work.
Shawna Reibling 00:35
Please tell me your name and the program you completed at Laurier.
Peter Fisher 00:39
Peter Fisher. I'm actually just finishing up the PhD in management in the Organizational Behaviour and Human Resource departments at Laurier.
Shawna Reibling 00:48
So can you just explain the programme you studied at Laurier?
Peter Fisher 00:52
Sure. So the management department is pretty big. It's all of business and think about all the different aspects of business. Everything's covered under there in there. Organizational Behaviour and Human Resources department that has to do with I guess you can think about today, like psychology at work, how to get people to, for example, be happier at work, how to fix issues regarding injustice, or unfairness stuff that I do is hiring and onboarding people trying to be as unbiased as possible, just all the different aspects of people, relations, business. And then you can contrast that with, for example, other departments and management department like marketing, sales and things like that, or accounting or operation.
Shawna Reibling 01:34
Who is your supervisor?
Peter Fisher 01:36
I worked with Chet Robie. He's full professor at Laurier and does all the similar stuff that I do. I really enjoyed working with him.
Shawna Reibling 01:45
Why did you choose your doctoral program at Laurier?
Peter Fisher 01:47
So, I actually started with Laurier in the undergrad. I did the joint program between Laurier and the University of Waterloo, granted the business degree and Laurier and the computer science degree at Waterloo. I started out in that undergrad program thinking I'm going to graduate, get a job in finance, investment banking work 100 hours a week, make my millions by the time 35 retire, and sail off into the sunset. After a couple of years of undergrad I found that I didn't really like that idea as much. I like the people aspect of business, not necessarily just crunching numbers and killing myself to get more money. And then it was in my third year, I took the Intro to Human Resources class with one of the faculty members. She introduced the grad program Laurier, the management program and she mentioned that most business students who, I mean she was right, most business students at Laurier wouldn't have the opportunity to study in a research based grad program because you just don't have that exposure to research.
I took that as a challenge and decided to look into the research because it seemed like something that was interesting. And then the more that I got into it, the more I realised, we really don't know that - a lot of the questions I had were unanswered. So I just got deeper and deeper into the the organizational behaviour and human resources topics from a research perspective. And I stuck around Laurier, because they were, the faculty at Laurier, were doing the research I wanted to do, which is personnel selection and maintaining a bias free environment when you're selecting your employees. That was exactly what I wanted to do. And I had a good thing going from my undergrad into my masters and then I just kept that going into the PhD program.
Shawna Reibling 03:37
Looking back to your first days at Laurier, and that could be in your undergrad or your doctoral program, what advice would you give yourself?
Peter Fisher 03:46
I got some advice, I guess towards the end of my first year, but I think it's something that every grad student should hear on their first day. Don't bother with any sort of upward comparisons. Don't look at other students in the program, to see how they're doing, comparing yourself to the, all the students who are ahead of you in the program, don't look at students in other programs, arguably students in other programmes and other schools. Even if they say they're in the same department doing the same stuff as you, really don't have that much in common with them. And it's ingrained in us to try to figure out what we're doing versus someone else, what we're doing right what they're doing right and trying to improve ourselves like that and make sense for a lot of different situations.
But it doesn't make sense for grad school, you learn so much so quickly. Comparing yourself to someone who's been in the program for even one year longer than you is insane, they're in a completely different state of mind to the experience that you are and even other students who are in the same year, same point of the program as you they'll have completely different backgrounds so things that they're doing are not necessarily applicable to you and vice versa.
Really just care about what you're doing and how you can improve yourself. I found that hugely beneficial to hear it when I was early on and I think hearing it right away, it is super important.
Shawna Reibling 05:03
Is your research part of a larger study? Or are you pursuing an individual research question?
Peter Fisher 05:08
So the research that I'd like to talk about today, it's kind of, it kind of sums up a lot of what I'm interested in. I guess you could say it's a larger series of studies, a more broad research question. We want to figure out what people are doing when it comes to hiring because we have all of this evidence that says people are not falling research, best practice best practices, but we don't really have a whole lot of evidence for what they actually are doing, and how we can improve that. We've got decades of evidence to suggest what the best way to hire people is and we're finding more about it every single day. And for the most part, it's being largely ignored in industry, when you go out and talk to people, but what they're doing in hiring, they're not learning from the experience that academics have looked into for decades now.
The stuff I'm going to be talking about today is specific to high tech selection environments. So picking people in very small startups, you're picking the first person to work on project, any project, whatever it is, that's very specific, context specific there. But in general, I'm interested in how we can get people to use these research best practices, which are inherently less biased than a lot of the things that are going on in the world right now.
Shawna Reibling 06:29
What qualities or practices have helped you so far on your educational journey?
Peter Fisher 06:35
I think going back to that keeping that individual mindset. Grad school is inherently lonely, there are other people doing very similar things to you, they're doing the same classes as you, perhaps or working with the same people as you. But your research question is unique to you. And just from a pragmatic point of view, if you want to get something published, it has to be at least somewhat unique.
Nobody wants to look at the exact same stuff someone else has done and just say, yeah, it's the same. So it is uniquely a lonely process, but I think keeping in mind that what you are doing is unique is helpful and trying to avoid that upward comparison, or that outward comparison. Because, like I said, what you're doing is unique, you're the only person working on what you're working on. So if someone else's publishing five times a year, that's maybe they wouldn't be publishing five times a year, if they were looking at the same thing you were looking at. Or if you're doing really well and someone else isn't doing so well. Maybe they're working on something that's just the nature of their idea. So keeping focused on what you're doing, and not concerning ourselves with other people has helped me a lot.
Shawna Reibling 07:50
Thank you. I'd like to ask you about your research now. Your research explores how to help organisations find the right person for a particular job. Could you tell me what led you to explore this research question?
Peter Fisher 08:02
Sure. I mentioned before I started out my undergrad in computer science degree. And I hadn't noticed at first and perhaps this is, this is definitely emblematic of other problems we have in society but, having grown up myself in a relatively privileged position, like growing up in Canada, in a rather, a relatively well off family, like I'm a white male, by definition, and I'm in a privileged position. I didn't notice at first, the the fact that the computer science programme was almost exclusively men, it's one of those things, I think a common thing people have started to realise now is it's really hard to see your own privilege and I just didn't recognise that at first. And then, as I moved through the program, and I started to get a bit more involved with research from an academic perspective, I kind of was made aware of the gender imbalance, in particular in computer science, the program that I was in, but in more broadly, science, technology, engineering and math STEM fields. Once I started getting into that I started seeing that racism and sexism aren't just topics that existed in the past or exist elsewhere, but they exist right now in Canada, and people experience that every single day of their lives.
When I kind of learned about that it was certainly a little bit depressing. I wanted to believe that the world was fair, I wanted to believe that everyone, if they worked hard enough, would be successful. And I think that's kind of a common belief, it's like the definition of the American Dream, which is something that a lot of people like to believe in. And then finding out that that's not how the world works. You can stumble into positions of power without having any sort of background or education or experience to give you a reason to be there or, you can And you could be successful just based on luck. It bugs me and it's been bugging me since then.
And so this research question of finding the right person for the right job, is kind of my taking a few steps back, and how do we how did we get to that point? Why are some people super successful when they have no reason to be? Why? How do people get that lucky? A lot of it comes down to these individual decisions that are made on hiring. Like if you're being hired at a major investment banking company, I talked to those my interests before, often times those decisions aren't necessarily made on who's right for the job, but they're made on do you get along with the other people in that organisation? And people tend to get along with people that are similar to them, that comes down to race and gender, two major factors that you can base similarity on. Finding the right person for the right job has to do with eliminating the biases that you can have in these decisions that are being made. That's where I'm at right now.
I would like, broadly, my ideal society is based on merit, like we're everybody, if you work hard, you can be successful. Because that's not the case, I have to start somewhere and hiring people as unbiased as possible is my solution, what I can do, what I can contribute to help make that a little bit more a reality.
Shawna Reibling 11:28
Why is it so hard to get the right fit between a job, a candidate, and an organisation beyond these biases?
Peter Fisher 11:36
So there's a couple factors. One is, it's an imperfect science. There's no way to perfectly predict human behaviour down the road, I think the short term perhaps you can make short term assessments if that person is bringing their arm up with the hammer, and if they're probably going to bring it down to hit the nail on the head. But humans have freewill, just, I think, by definition, we can change our behaviour at whim and you can't predict that stuff very far in advance. So even when people say they have a really good solid selection system, I mean if even if it's based on science, and you've done everything right, you're still facing the problem of freewill, there's just no way you can really get around that. And even beyond that, there are other factors that you just can't perceive. Everyone goes into these things, with their own biases, things that affect your behaviour that even you're not aware of. So saying that you can perfectly predict someone's performance down the road, it's just impossible.
On top of that, there's this belief that people can predict who's going to be a good worker, who's not going to be a good worker, this belief that you are, you I say you, in general, are really good judge of people. And I think that almost every single case, humans are just terrible at determining who's lying and who's not like, just as an easy example of why we're so bad at this. In 99.9% of our interactions, day to day, people are telling the truth. If you're asked, how's the weather? I say, Oh, it's actually pretty nice today. Then you just can't function as a human if you're second guessing every single thing anyone else is telling you if you don't believe that the weather's nice today, is it really nice, does he really think it's nicer today, you can't possibly function. And so we've just had no reason as humans evolutionarily, to develop the sense of just telling when someone's telling the truth. And that's just one example of it.
Beyond that, we've developed all of these kinds of things like intuition, it's really good at determining short term, for example, safety. Your ancestors, for example, heard some rustle in the bushes, and their intuition told them "that's a jaguar maybe should avoid those bushes", then they probably survived versus someone who has intuition told them "oh, that should be fine", they got eaten, they didn't survive, they didn't pass on those genes. So intuition is really useful in a less complex society. And it's even really useful in our society today.
If you think about any time that you've been driving, and you might have seen someone just out of the corner of your eye, you notice that they are about to merge into your lane and possibly hit you and you avoided that without even realising how you knew that there are going to be they're going to be doing that. There's parts of our brains that are hardwired to react based on this short term, really quick input. And it's really useful for making short term decisions and determining how you think people are going to going to react in the immediate future. But as soon as we start basing our long term, for example, hiring decisions on that, it really starts to break down. Humans inherently try to categorise things. If every time I walked into a room, and I saw a different style of chair, there are millions of styles of chairs, and I had to re establish, okay, that is a chair, by determining everything about it, then I would spend all my time figuring out what is a chair and what is the chair. So I'm stereotyping chairs, it's I just categorise it in my brain, and it helps me get through the day, I'm not spending all my time determining what is a chair.
But we naturally extend that to humans. And the number of times I've been asked, "Do you play basketball?", just because I'm tall, it would blow your mind. And I find it frustrating but it makes sense from a stereotyping point of view, tall people are probably basketball players, might enjoy basketball. I'm the worst basketball player on Earth by the way. So one hard to find the right fit between a job candidate and an organisation. We're just naturally bad at it, despite the beliefs that we are good at it. So when we're given tools that tell us this will help you make a better decision, we don't want to use them. Because we we believe that we're good at making these decisions and in general, we're very, very bad at it,
Shawna Reibling 16:26
I guess, stereotypes and that decision making fatigue are the reason why people try and simplify parts of their lives to leave space for making those harder decisions.
Peter Fisher 16:37
To an extent, yes, I think they're common in the sense that not necessarily in the hiring practices, people just make these assumptions about others. And like I said, it's natural, we need to make assumptions about the world to simplify it because the world is complex. We can't spend all our time determining what is a chair and what isn't a chair. It makes sense that we do that, but recognizing that we do that recognizing when it's not a good time to be doing that is a little bit more difficult. And making these long term decisions about who we believe will be a good fit for our organization, or who will be a good employee, that's one of the times where we should not be relying on our intuition, or stereotypes, and categorization.
Shawna Reibling 17:20
One of the ways that granting agencies and funders are trying to get around that is by making unconscious bias training part of the process of giving things like reference letters to students. And so the referees who are providing references for students to get, say jobs or scholarships, they have to undergo an unconscious bias training module and pass it successfully before giving reference letters. So I think that there is a trend to try and make these biases more visible in the world.
Peter Fisher 17:56
Thanks for bringing up the reference stuff, non-bias training. So I don't know a lot about bias training and its effectiveness. I don't want to comment on that necessarily. But the idea of bias in reference letters is huge, hugely influential now. And it's something that's really, really important and something that is another area of research, particularly interested in. But the idea of bias in these kind of narrative letters of recommendation, that's a really important point and I'm glad that it's being addressed.
One of the things that I'm interested in is kind of the flip side of that, rather than trying to train the bias out of people, the research that I've been doing is more changing the format to make it more difficult for the bias to appear in the final scoring system, whatever that may be.
So I think that's definitely what half of the equation, is trying to get people to be less biased, but it's difficult. These are things that people evolved through centuries, millennia, these biases to categorize, to stereotype to be more comfortable with people or similar to you. It's evolutionarily advantageous in perhaps, and especially a more simplistic society that we've evolved from and our bodies and our brains still think we're in but, our complex society and making these long term hiring decisions is completely inconsistent with that.
Shawna Reibling 19:29
Do different organisations make different kinds of hiring mistakes?
Peter Fisher 19:33
I think the major issue, stuff that I've found in my research with co authors, has been that, because our focus has been on smaller companies right now, these companies try to fit people based on their cultural fit or their their values fit. Do they want the same stuff out of this company as us? And I think that is itself the a serious issue in terms of trying to make these hiring decisions because some things are extremely hard to quantify. There's almost no way to tell, like I said, we can't really tell if someone's lying or not, we're just terrible at it as humans. So there's no real way to tell if, when someone says, "Yes, I value that, too.", unless we're spending a lot of time with them, at which points, you just we don't have the resources, many of these small companies don't have the resources to spend on having someone work with them, for example, for a month or so to actually see if their values do apply.
We can't really make those judgement calls effectively. Because we don't know if someone's telling the truth or someone is lying. And values are so hard to actually pin down, whether or not other companies are making the same decision, the same errors, I think larger companies in general will have someone who's a human resources specialist, someone who knows this a little bit better. But even under those circumstances, there's this myth of expertise. People want to believe that they are good judges of others believe that, yes, I can pick out the best employees.
If you think about baseball scouts, what was the movie Moneyball? There are these scouts, they were trying to build the team based on all their intuition, their beliefs as players, and Brad Pitt came in and I guess the real live version, relative played in the movie, he came in and introduce the sabermetrics approach, which is using statistics to determine who is going to be a good contributor to the team and who's not going to be, and the team massively outperformed the expectations set by these professional scouts who had all this experience, because they were basing their decisions on what they believe to be real predictive of success or in reality, just biased decision making.
Shawna Reibling 22:01
Should organisations outsource hiring to third party firms who specialize in hiring?
Peter Fisher 22:08
That one is very tricky. The issue with outsourcing is some organisations are really not doing anything better. Some of these specialized organizations are just doing the same thing that I'm arguing against, they're basing decisions on flawed assumptions. And then there are other organisations that are basing it on science, like I've worked with a couple of companies that do this hiring process, like outsourcing, consulting companies, that are really, really good, like they're doing these things based on science, they're conducting the studies appropriately, they're doing in a mode as well that anybody can, if you work with those companies, then you're much more likely to be successful.
The problem is, when you're looking at these companies to see which one you should work with, from the outside looking in, they look nearly identical in terms of whether or not you think they might be effective. So this is a spot where my bias would come into play. And I find it difficult to make recommendations, because I know what is the right thing to look for, and I know what to try to avoid. So trying to articulate what company is doing things right and what company isn't, is probably something that would take a little bit more time for me to talk through and I might need to give some examples.
In general, I think smaller companies who cannot afford to hire a permanent HR person who may be experienced with this kind of thing, should look into external support. But keep in mind that if anybody's seems too good to be true, they probably are. Like I said, you cannot predict human performance job performance way down the road with perfect accuracy. You can hardly predicted with good accuracy. There's so much variance in human behaviour and, and so many different things that can change over time. But anybody who says they are really good at picking employees is probably like, I think even the best recruiting agency, the best company that does this kind of selection thing professionally, they're still themselves going to have bad hires from time to time. It's not a perfect science, but there is science to it.
So looking for a company that is actively conducting research, and having someone you trust who can perhaps evaluate that research to some extent. So they don't have to necessarily be in the field but if they can tell you that this company's data looks pretty good, but they use a sample size of three, and obviously that's not going to be good enough so perhaps don't go to this company. I think yes, there are definitely benefits to potentially going with outside consultant tech companies to help you with your hiring practices. But don't expect a silver bullet. Anybody that's promising a silver bullet is probably either lying or themselves misled.
Shawna Reibling 25:16
You write that research has consistently found that the most effective hiring practices take as much of the human element out of the equation as possible. Can artificial intelligence eliminate bias in hiring?
Peter Fisher 25:31
I think there's potential, what we've seen so far has been kind of cool. Clearly, I think about garbage in, garbage out. Artificial intelligence systems are, their goal is to simulate human decisions, human perception as best as possible. And to do that very quickly. So if we're talking about intelligence being something that's uniquely human, we're trying to emulate that.
The problem is, if we're trying to emulate flawed decisions, then we are going to get flawed decisions out of our AI systems. So there was an example a little while back, perhaps 2018, it is famous in my circles, perhaps everybody's circles, where Amazon was looking at using AI to help them with their hiring process. They had to scrap that a couple years ago, because they found that it was the AI system was just emulating the flawed decisions and maintaining a gender imbalance process. If 90% of your software programmers or software engineers are men and then you tell your AI that these are good employees, this group is good employees, then it's probably going to spit out 90% of men when it comes to decisions that it's making. And so props to Amazon for recognizing that discontinuing it.
But I think what we need is to have human oversight and slightly more mechanical decisions. So not completely taking away the human interaction, in terms of we need to make sure that we're not introducing new types of bias that we perhaps weren't expecting and, doing these analysis of the results to make sure that system A is not discriminating against one group or other, but also removing the human decision making of who is actually considered for a position so not just basing it on their handshake or their smile, which are all easily fake-able.
Yeah, I think mixture is definitely important but, as of right now and put the data we have to train AI systems, I don't think they are the short term solution. Perhaps in the long run.
Shawna Reibling 27:57
Why do you think that the hiring tools most supported by your research, those being intelligent tests and integrity tests are among the least used and the least well understood in the hiring process?
Peter Fisher 28:10
I think part of this issue comes down to, what I mentioned earlier and what you brought up, is removing the human element. If we started making our decisions based on mechanical tests, then HR managers, HR personnel who are specializing this hiring, lose a bit of their clout in the sense that they're no longer the ones making the decisions.
I think individuals in these positions wants to believe and they want others to believe that they are the ones who are making the final decision, and that they are the key part of the process. And I think that that's perfectly natural, we all want to believe that the job we're doing is the important job in the organization, that we are the key piece of the puzzle. And basing your decisions on tests like intelligence tests, which span of an individual number, it really, really limits how much claim you can make to making the decision. So I think that's part of it.
The second half of that is, intelligence tests are just not super popular among applicants. As an example applying to work at a fast food restaurant as a cashier or the fry cook or something like that, and someone gives you an intelligence test. You are probably not going to be super pleased because, for the most part, people don't see a connection between intelligence and your ability to flip burgers.
People want to avoid those types of things. They don't want your organization to be known as the company that doesn't even take my personality to account. You don't want to be the company that's known as they only care about whether or not I'm going to steal or be a terrible employee. Integrity tests which are attempts to measure whether or not someone's going to exhibit a lot of counterproductive work behaviours. You don't want to be known as the company that only looks at that. You want to be the company that cares about its people that looks at the employee as a whole, which is where you start getting into all these different biases.
Shawna Reibling 30:20
Hiring practices vary all over the board and one of the new ones that I've heard has been, you don't go in for an interview, you go to share your life story. And it's called the life story interview when you go to apply to work for certain companies. And how does that if, what kind of hiring practices that and is it effective?
Peter Fisher 30:40
I haven't heard of that. You want to have some human element to the sense of you want to be able to get along with this person. But it doesn't matter, he can get along with them. If they can't do the job, they're not going to be useful to your organization. I think I would describe that as an unstructured interview, which is on the low end of effective predictors of job performance. The problem with those types of interviews, like that's fairly common, not necessarily that exact process, but that idea of unstructured interview where you don't want to sit down and ask a series of scripted questions and just write them down and not have anything else go on, you just ask those questions and base your decisions on their answers to these questions. People don't like doing that they want to be involved in the decision making process in terms of making value judgments and things like that.
But the problem is, you don't know what the right answer is. If your response to what are you looking for out of this question is I'll know it when I see it, it's by definition a bad interview question. Because there's no way to compare the answers to them, between people, and that's what the selection is you're trying to compare applicants to decide who's going to be the best for the job. And there's no reliability to it in terms of perhaps you may see value in someone's response to that question, and someone else would see no value in it. Or perhaps today you see value in it and tomorrow, you think after looking into it a little bit, you would get the exact same answer and you may see zero value in it, because for whatever reason your experiences have changed. Or if you hear the same answer a year from now, your experiences within that year may change your perceptions of the value of their answer. And that's not because their answer is different. If it's the exact same answer, and the job is the exact same, then, in theory, it should have the same value to you. But yeah, if you if you don't know what the right answer is for a question, or at least you don't have an example of a good answer, or an example of a bad answer, then it's not a good interview question.
And if your interview question is, tell me your life story, how do you have an example of a good life story versus a bad life story? Or how do you know that this person's life story is good in terms of their future ability to perform at your company? And there are many types of questions like that, for example, the question, and perhaps in some cases, for some people, this is a good question, if they've gone through the process of determining what a good versus bad answer is, I'm not saying every single cases is a bad question but, the question why do you want to work at this company?
If you haven't gone through the process of determining what a good answer what a bad answer is, how to score these types of answers, if you're just going in saying "I'll know it when I see it", then it's not a question that's worth asking. And it's a question that's going to introduce error into your decision and that error could be in the form of systematic bias of one form or another and it's going to open you up to legal challenges.
Shawna Reibling 33:48
What about tasks testing as part of an interview process? So candidates are all asked to complete an assignment that is made up of some of the tasks that are involved in the job?
Peter Fisher 33:59
Yeah, that is a totally valid approach. That is something that I teach in my recruitment selection class, as something that is very effective and determining someone's going to be good on the job or not. And in many situations, not really possible to do that, for example, you might not want to send a job applicant who's trying to be a firefighter into a burning building, and just see what happens. It is definitely a good predictor of job performance, if the specific job you're looking at can work with that, if you can apply that to your job.
Shawna Reibling 34:34
What advice do you have for HR professionals at growing companies who are establishing hiring practices?
Peter Fisher 34:41
So for smaller companies, I think, on average, companies start to hire a dedicated HR person around 50 employees. So at that point, it's important to have a process set up. So for smaller companies who are probably staffed by the entrepreneur and a couple specialists in whatever field it is, I think if you're in a situation where you can associate yourself with a startup incubator, a fast emerging company, who have some experience in terms of hiring and have the ability to connect you with someone who has the experience, that's important. Don't just try to emulate someone else because, not only is your context completely different, there's no reason to believe that they're doing it right in the first place, they may have been lucky. I talked about earlier, you can be successful, just based on luck, it happens. Not everyone can be bad but, it's entirely possible to be successful, just based on luck and not based on doing things the right way.
Determining what you need from I guess, let's take an example, you have a small company started by an engineer, and you're looking for a salesperson, you need to figure out what knowledge skills ability and other attributes, that person needs to be successful on the job and that's going to take some work in terms of exploring everything about your own company, finding someone who meets the needs of your company.
So for HR professionals, in theory, these are people who have taken HR type courses, not necessarily the entrepreneur of a company who has perhaps an engineering background, or whatever it is, someone who knows a bit about HR, developing these practices, I think the number one thing that they're going to have to do is conduct job analysis. And if you are in HR, then you know what I'm talking about. If not, that basically comes down to figuring out everything it is about the job that you're hiring for, that's the knowledge skills, abilities, and other attributes that are required to successfully perform that job and learning everything you need to know about that job, everything can know about that job before you start exploring potential candidates and trying to figure out who going to fit that job. But you need to understand the job you're looking at before you can start picking out people to fill that position.
And it might sound like hollow advice if you've got a background in HR but, so many people are doing these types of job analyses, they're missing out on the fundamentals, and that they're hiring based on values what they believe might fit that job.
Shawna Reibling 37:23
How should employers use other tools like interviews, resumes, and reference checks in the hiring process?
Peter Fisher 37:31
It really comes down to standardizing things. I mentioned earlier, the unstandardized interview is like a serious issue. You need to have the exact same questions exact same criteria for every single candidate, and you need to come up with what is a good answer an example a good answer, an example of a bad answer, and in between in order to be able to rank candidates effectively.
If you don't have those options, they don't have that available before you go into it and you're just deciding what you think might be a good answer, what you think might be a bad answer on the spot in the interview, then your decisions at the time will be impacted by your individual biases, stereotypes, things that you're not expecting to impact your decision will affect your decision. And when you go back to ranking rank people based on their responses, if you don't have examples of good or bad answers, if you have different types of criteria or questions for all the different applicants, you're not going to be able to effectively rank them and your decision is going to be based on intuitions and gut feelings, things that are not good predictors of long term job performance.
Shawna Reibling 38:43
Why do you think that HR practitioners may not use the most recent science in how to make the best hire?
Peter Fisher 38:49
So I think that has to do with this belief in HR practitioners have that they are the key critical part of the process, that it's their decision making power, that gives them their position in the company. They want to believe that they're the key part, that they're the ones who are good judges of people and that's why they're there.
And they also want other people in the organisation to respect for them and their decision making. There are structures within organizations where individuals hold power based on their ability to make decisions.
So for example, the CEO who decides that this is the market, we're going to go after, the marketing manager who decides this is the marketing campaign we're going to use, the HR manager who decides this is the employee we're going to hire, that is where they draw some of their power within the organization from and if you offload that to a test that anybody could administer, then your HR manager loses some of that power. They're not the ones actually making the decision, it's an automated mechanical process.
It totally makes sense that people want to hold on to whatever power whatever influence they have in the organization. But that negatively impacts the organization, because it's not the best decision that's necessarily being made. To argue that an intelligence test is a better predictor of job performance than your own intuition could be perceived as a threat to your ego.
For example, if you base some of your self worth on your ability to predict who's going to be a good job performer, and then you find out that this test, whatever it is, it's simple intelligence test or more complicated, is a better predictor of job performance, then you're losing a little bit of your self worth. So there is some individual ego incentive to try to be the one to make the final decision and avoid these more mechanical tests.
Shawna Reibling 40:54
It seems like your work, you know, unpacks many ways that our modern workplace is put together and how, how worth is assigned within a company.
Peter Fisher 41:02
Yeah, for sure. And I just want to be clear, I'm not the first person to say that intelligence tests or integrity tests are good predictors, this is stuff that's been around for decades. Almost everything I'm saying is not new information, it's all well known. And there are people out there who may listen to this, who are experts in this field who may be rolling their eyes at stuff I'm saying because they might feel that it's obvious, but based on the research that I've been doing, it's not obvious to a lot of people.
Shawna Reibling 41:29
How has your research benefited the fields and your participants engaged in the study?
Peter Fisher 41:35
I think the stuff that we've been doing so far has been more diagnostic. So just figuring out what is happening, what's going on the stuff we're working on in the immediate future, just to move forward with it and see how we can help these companies to address the issues that we've identified.
Shawna Reibling 41:56
Thank you very much for speaking with me today.
Shawna Reibling 42:07
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