Dr. Deniz Ones brings decades of expertise in industrial-organizational psychology research to this comprehensive discussion about meta-analysis, literature synthesis, and academic excellence. Known for her groundbreaking work on personality at work and meta-analytic methods, Dr. Ones shares practical strategies for conducting thorough literature reviews and developing meaningful research questions. The conversation covers her current projects ranging from cybernetic trait complexes theory to digital twins research, while emphasizing the foundational principles she learned from her mentor Frank Schmidt. Dr. Ones provides actionable advice for graduate students and early career researchers on reading strategies, identifying quality research, and maintaining scientific rigor in an era of increasing publication pressures.
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Transcript
[Richard Landers] (0:00 – 0:21)
Welcome to the Great IO Get-Together. On tonight’s show, quips and queries about the world of work as IO psychology comes alive. Now please welcome our hosts, Richard and Tim.
Welcome everyone to the Great IO Get-Together number 28. Today on the show, we have Dr. Deniz Ones, Hellervik Professor of Industrial Psychology and Professor at the University of Minnesota. Welcome to the show, Denise.
[Deniz Ones] (0:21 – 0:42)
Thank you for having me on The GIG. I have been a follower of The GIG for a while now. And today I hope to keep my enthusiasm in check since I’m known for being a tad too direct and outspoken for my own good.
[Richard Landers] (0:42 – 0:58)
That’s wonderful. Yeah. Well, you know, so to start your work on personality, especially using that analysis, I think is widely known in IO, every corner of the field.
I’m curious what you’ve been working on lately. Can you tell us about it?
[Deniz Ones] (0:58 – 5:18)
Yeah. When people start their question with, you work on personality, I usually get the next question of, tell us a little bit about your personality, especially if they’re trying to get to know me. So thank you for not asking that.
But in short, it’s in progress. My personality. But all kidding aside, yes, I’m known for personality at work and meta-analyses and second order meta-analyses of the topic, meta-syntheses lately.
Exhibiting behavioral consistency, I’m doing more of the same. Relating to the meta-analysis part, I just finished a chapter on meta-analysis with my perennial collaborator, Vish, and its content is very relevant. Who is embarking on doing any type of review, not just the meta-analysis.
So that will be coming up. I just published a paper in the Proceedings of National Academy of Sciences with my former student, Kevin Stanek, on meta-analysis of personality and cognitive ability relations. But the more important thing about that is we followed it up by a full-blown book.
And in the book, we actually, it’s called Anchors and Sales, and we present a theory that explains the findings. We call it the cybernetic trait complexes theory that is a synthesis of that huge big sweeping meta-analysis with Brenton Wernick and Mike Wilmot. We have a series of papers that look at personality, and I’m going to use the word, perhaps, that is scary for some people, success.
Because we’re above, you know, sort of rising above job performance, rising above job satisfaction, people at work, success at work, but looking at success across everything that any subfield of psychology has ever studied, using meta-analyses and modeling some themes that are persistent across different sub-areas in interpersonal relations, at work, in academic settings, and so on and so forth. My student, Ziyu Ren, and former student, Yilei Wang, and I, we have a series of studies and meta-analyses on personality and sleep and their consequences at work. Mentioning Yilei, he and I have been working using large language models and using large models to emulate specific individuals, people, and using that information so that, perhaps, digital twins can be used in social science research.
So we’ve gone sort of into that path. That was a paper that we presented at SIOP this year. It was the top poster.
Stefan Bildschert and Kevin Stanek and I are continuing on our work using mobile sensors to assess psychological variables. With Sarah Tian, I have a perfectionism meta-analysis, and personality is not all that I study, just to remind people. I also have several papers on counterproductivity that are underway, and we just are finishing up also a paper, again, a sweeping paper that took us about a decade to finish up on environmental sustainability actions and their human resources correlates.
And I know that I’m missing a pile of collaborators and projects, so to them, I’m sorry if I’ve forgotten you, but then those projects are probably the most top secret. And in any event, with this ever-growing list of projects, I’m hoping to discover, perhaps, the secret to immortality. So, I’ll have a further.
[Tara Behrend] (5:19 – 6:07)
It’s interesting. It doesn’t sound like you’ve been very busy lately. You’re kind of relaxing.
That’s nice. I hope you’ll keep us posted when you discover the secret to immortality, although, like we talked about this morning, AlphaFold might have beaten you to it. Denise, one of the things that we try to get across in this book is that knowing what you think is important, and also knowing why you think it is important, and reflecting on your influences that have shaped how you think about research, and what you think about as good research.
And your graduate school advisor, Frank Schmidt, is generally thought of as one of the fathers of meta-analysis. How do you think his influences shaped your thinking, both back then and still now?
[Deniz Ones] (6:07 – 9:34)
I think I was lucky. I hit the jackpot in the academic lottery with having Frank Schmidt as my mentor. He was my doctoral advisor, mentor, and I love the term.
The Germans have a term for it. They call your advisor Dr. Vater or Dr. Mutter, and he was my doctor father, my academic father, and we lost him in 2021, and I miss him terribly. I learned a lot of stuff from him.
I learned content area, to be sure, things like psychometrics and validity generalization. I took meta-analysis with him. I took personnel selection with him, and of course, while he was alive, I always reminded him that in personnel selection, he was wrong because he told us at the time that I took it with him, he told us that personality didn’t predict anything.
That was in 1989, so that was a long time ago. So he opened my eyes to the importance of a bunch of variables like general mental ability. We shared a passion for individual differences, but I learned a lot from his scientific values and how to think about research, things like life outlook, and what he demonstrated both in his teaching and his research perhaps is summarized by the scientific method, objectivity, the desire for truth, being open-minded, having skepticism, lots of skepticism, being rational, and then confidence in empiricism, let the data speak, and measurement, and these remain to this day my North Star. I also learned from him, and this is probably not shared by everybody in the field, but I learned from him that scientific knowledge is not discovered or established by single scientists, by single studies, single teams of researchers.
Science is a communal exercise. We all contribute. We all give something to it so that the next person can take it to the next level.
I also shared several characteristics with Frank. We both had little patience for foolishness, intellectual dishonesty, cowardice. Neither of us shied away from controversy, and research on controversial topics can be especially valuable to advance science, but also to advance society, and beyond all the scientific and methodological approach and content, I learned courage and persistence from Frank, and one other thing, and this is an important one.
Any field which hopes to remain a science, criticisms are never, ever personal.
[Tara Behrend] (9:35 – 9:47)
I think a lot of people need to hear that message, and it must be really rewarding and satisfying to look at your own students then and see those values sort of being passed down again to another generation.
[Deniz Ones] (9:48 – 10:09)
Absolutely, absolutely. I’m very proud of all the students that I have mentored that have gone through Minnesota, and they’ve had students, so this exemplifies this idea that science and knowledge builds one thing on top of another.
[Tara Behrend] (10:10 – 10:31)
Yeah, it’s a shame that you’re a failure with Richard, but otherwise pretty successful, so that’s great. I’m really inspired when I hear you talk about what science should be and how we should approach research. Is there a favorite paper that you have that sort of captures the ideal approach that just did it really well?
[Deniz Ones] (10:32 – 12:44)
I’m glad you asked the question about a paper. I don’t have a single paper because I do not believe that there is one single ideal approach to research. It can be curiosity-driven, right?
Research can be curiosity-driven. It could be real-world-driven. It could be driven by the need to challenge existing but wrong paradigms, right?
So that means it can be inductive, it can be deductive, it can be abductive, and it can be in the lab, it can be in the field, and I also believe that a comprehensive literature review can help avoid pitfalls and reinventing the wheel. So it’s more than just reading a literature or looking at one single thing. Good research has rigorous methods, good sampling, good study design, good measurement, but then it should also have, and a lot of people do not think about this because I think of meta-analysis as sort of a transition phase of using primary studies such that additional research can be built on a solid foundation.
So you’ve got the little bricks that get put together that give you the foundation and then you keep building on it. So any research should also have built-in meta-analytic considerations. So it should report details for replicability.
It should probe generalizability already, and bad research does not consider alternative explanations, brushes aside unexpected findings, does not consider implications for organizations, people, society at large, and myopic, closed-minded research ticks me off.
[Tara Behrend] (12:44 – 13:10)
I love it. Well, I’m glad that you made the point, too, that asking the research question is also something that can be done either rigorously or non-rigorously, and how you frame what a good problem is to focus on matters. So that’s one of the most important things we wanted to talk to you about today is how do you approach that?
How do you think about what’s the right scope of a problem or a research question, and how do you begin that process?
[Deniz Ones] (13:11 – 16:21)
I will sort of describe my own approach to it, not to say that my approach is ideal or should be replicated by everybody. This is sort of what works for me. It might be different for graduate students.
I’m not sure that this would have worked for me when I was a graduate student, but it works for me. It has worked for me over my career. I prefer keeping the initial scope intentionally very broad.
I like reading, so I encourage people to read broadly in the areas that they might be thinking about. The one thing that I discourage people from doing is, oh, okay, I had a research idea. Let me specifically try to find the three or four or five or 10 papers that have looked at that, and then off I go.
No, no, no, no, no. Read broadly. Get a lay of the land, and when I say broadly, not just broadly in IO psychology.
I really mean in adjacent fields. There are numerous examples that we can come up with where reading too narrowly misses research. So for example, absenteeism, studying absenteeism.
One can look for absenteeism literature in IO journals, in management journals, fine, but did you know that there is a very large stream of research in sociology in absenteeism? So you’ve got to kind of go and look into that literature and see what they discovered and what they found, what their models look like. Continuing with my absenteeism example, well, with computer scientists entering this domain, they are developing models, and they are sort of doing discovery research, the way that I would put it, is they do discovery research, but there might be some nuggets in those as well.
So do not look at borders of different fields, cross borders, read broadly, keep it flexible until you can no longer afford to be flexible is sort of my approach. And then I do sort of idea map. I start sort of noting down, you know, on one big piece of paper, this used to be sort of like the biggest piece of paper I could find, and I would draw things on it.
And I, you know, all of my doctoral students’ dissertations start this way too, where they actually, you know, start, you know, putting ideas, and then they start circling things. Oh, these kind of belong together. These other ideas belong together.
And you can do variants of it, not just on ideas from that literature, but also, you know, you can do it around measures, you can do it about around samples and occupations and things like that. So, you know, you get insights looking at it that way that you don’t get by just reading the literature.
[Tara Behrend] (16:22 – 16:54)
Right. It sounds like when you’re starting out, you need to be incredibly open minded and just figure out the shape of the terrain, and then you can choose where to build your building. And then you can solve immortality after that.
But I wanted to ask you one more thing about synthesis and how you balance integration and coming up with general claims with also preserving those interesting corners and edges that give you context about particular settings or particular flavors of a problem. And how do you know where that balance is?
[Deniz Ones] (16:55 – 20:38)
It’s one of the things that I really like doing. I like writing syntheses. I have sort of pivoted in the last, I don’t know, like five, six years into writing more syntheses.
If you want to see examples, you know, anybody who’s listening might want to check out Wilmoth and Owens. And we have a series of these articles, one on agreeableness, one on conscientiousness. I mean, sort of we’re going down through the big five, as you can imagine, synthesizing the literature.
But I like basing my syntheses on meta-analytic or replicated findings. And if you think about it, meta-analysis is a summary of replicated findings. That’s how I like to think about meta-analysis, right?
So either you’re going to find a meta-analysis or pay attention to replicated findings. And synthesis is pattern recognition, right? That’s how I like to think about synthesis.
And one can use, there are multivariate approaches that can be used to have quantitative synthesis of meta-analytic findings. You know, one example is meta-analytic criterion profile analysis. My colleagues and I published a psych methods paper about it in 2021.
It was a long time coming paper. I learned about the technique, the meta-analytic version of it didn’t exist, but I learned about that in 2006 or 2007, because during my sabbatical, I took a class and learned the technique. And then I always thought to myself, there should be a version of this that can be used in meta-analysis.
And if we do so, then I know that here are the, you know, three different areas that we have to apply it in IO psych, because new synthesis will come from that. And the technique didn’t exist. We created the technique and published it in 2021.
Now we have the first two papers out of it on the review. So I mean, this is sort of, you know, you follow it through. And if you can use quantitative synthesis of even meta-analytic findings, and you can then use cluster analytic techniques to cluster profiles and things like that.
So that ends up being a very empirically driven synthesis. And I like that. Otherwise, basically, I worry about synthesis, because when one does a literature review, separating the wheat from the chaff is very difficult.
And even if you do, preserving what I said earlier about scientific objectivity can be very, very hard, especially if you have a very large literature that you’re trying to deal with. And going back to your question of, you know, how do you preserve, you get the general trends, and let’s assume that, you know, you synthesized and came up with the simpler principles that account for things, but then there are edges and sort of side points that make the literature interesting. The thing that I do is I ask myself, is this consequential?
As in, will a company, will practice, will people change if they knew this piece of information? And has it been replicated? And will that change practice?
If it does, I have to somehow mention it, even though it might be an online supplement.
[Richard Landers] (20:38 – 21:45)
I think that’s an excellent turn into the sort of next section of questions, which is really about kind of the practical strategies one might take when trying to understand literatures. You know, a lot of the readers of the book are really coming from a either early career or grad student kind of perspective, where they’re just exposed to this, you know, massive research literature that they don’t really have any, you know, foundations for, they don’t know where the touch points are to start. And they often struggle with trying to make these kind of big picture synthesis evaluations when they don’t have anything to base that on.
So as a starting point, do you have recommendations or approaches that you take in doing things like balancing or prioritizing, let’s say, older versus newer works, or the specific authorship of individual papers? What kind of signals maybe would you recommend to a novice or a young researcher? What should they be looking at?
[Deniz Ones] (21:45 – 27:44)
Read the classics. You cannot ignore classics. And I make a distinction.
And people tend to think, you know, when we say classics, oh, those are the old studies. That’s what people tend to think. That’s not true.
The way that I think about them is that let’s make a distinction between studies from the distant past versus classics, okay? The distinction is important. Both serve different purposes, and they have different roles.
But classics are milestones. They usually represent an inflection point in the development of an area, in the development of a literature, and their impact persists over time. And they become foundational to an entire set of additional studies.
So it’s not, they don’t just sort of sit and, you know, this study got done, got cited a few times. No, no, no. They become foundational for an entire stream of research.
So their insights, their findings are impossible to ignore because they remain relevant and useful as the area of research has advanced. So that’s what a classic is. And those must be read.
You cannot not read them if you’re going to understand a given literature. Now, how about the older stuff? Is there value in that also?
There might be value in it, but in prioritizing to read, that might not be the place to start. Read the foundational classics first, then look at some of the more contemporary things. But with contemporary, I should highlight that one ought to be very vigilant.
Why do I say that? Because quality in more recent studies is much more variable than older studies. Why do I say it?
Because in the old days, research, everything was done by hand. Prior to 1985, everything was done pretty much by hand. Yes, there were computers, whatever, but even when I was a graduate student in late 1980s, I was computing reliability by hand.
I was computing factor analytic solutions by hand, and so on and so forth. So you have to be super careful about your measures, your samples, because doing a study meant a lot of sweat and tears and effort. And usually you were cranking, like I said, the results by hand.
Newer studies are easier to crank out. Give me 24 hours and I’ll give you a study. Get a panel sample, throw a couple of measures together, and click a few buttons, and you have your results, and perform a write-up.
Off you go and submit it someplace. It’s not that difficult to at least create. Publications are a different story, but to create a paper, a beginning-to-end product, is much easier.
There are also problems lately of lower quality papers being published because of the pressure to publish. Questionable research practices, predatory journals, you know, I can go on and on about each of these things. And the unfortunate truth about some of our leading journals is that they have a preference for novelty over endurance.
So, you know, things that may or may not replicate, this is novel, this has to go out. So all of these things result in a fragmented variable quality literature that makes life a lot more difficult for a novice. So coming into this, how are you going to be able to say, oh, this study is really good, this study is not all of that good?
So there are things that you can potentially do, you know, but then you need some familiarity with the literature for that even. And sometimes knowing the classics in the literature can help. Some people use authors as sort of a signal of quality.
You know, if an author has published a lot of papers in a sub-area, and their work has received lots of citations, that might be an indicator. But I try to remain open-minded because there might, you know, I do not write off any paper. I always approach each paper with an open mind.
And sometimes institutional affiliations of authors might be of interest. I do pay attention to what journal papers are appearing in. I don’t cite things that are in predatory journals.
I don’t bother with them. Sorry. So even though you may start looking at each paper as a blank slate, you can prioritize authors that have been producing for a long time in that field, and start reading their works, or reading a lot of papers that have been, that are garnering citations.
That’s sort of the type of thing that I would advise students to do. But don’t overlook the classics, ever.
[Richard Landers] (27:45 – 27:57)
So I’m curious more about this, paying attention to citations. So classic being different than old. Would you also say that highly cited is different from classic?
[Deniz Ones] (27:57 – 29:06)
Absolutely. There are papers that get cited because they’re portmanteau. You can hang whatever you want to it, right?
And they get a lot of citations. I mean, there are three or four different ways that you can become a citation classic when you publish a paper. Develop a measure, and you’ll become a citation classic.
Develop a method, you’ll become a citation classic. Do a sort of really large-scale meta-analysis. There’s a good chance that it’ll become a citation classic.
I mean, so there are some things that you can do that’ll ensure a paper is a citation classic. Beyond that, though, is the paper being cited because of the insights, or the conclusions, or the syntheses that it has derived, versus if it’s just a portmanteau of, oh yeah, anytime somebody wants to cite something about personality, here is the portmanteau paper. That’s the one thing.
Cite that, and then you’re done. Look at why a paper is being cited. Sometimes people are cited because everybody hates them.
[Tara Behrend] (29:07 – 29:23)
I always find it scary when I look back at why a paper is being cited, and it turns out the citing paper said exactly the opposite of what the person’s claiming, or it’s completely unrelated. You must come across that a lot as you’re doing syntheses and going back and checking. What do you make of all that?
[Deniz Ones] (29:24 – 29:46)
People don’t read. People basically use what other people are citing. Oh, it was cited here, and therefore, I should cite it.
Yeah, it was cited in that context. Sounds good. Let me plump it in.
And it’s laziness. That’s what I chalk it up to, laziness. Got to read.
Everything that you cite, you’d better have read carefully.
[Tara Behrend] (29:47 – 30:00)
I suppose it’s a good opportunity for a novice, though, if they discover an error like that, or if everybody has been citing a paper wrongly, that there’s an opportunity for them to correct the record and make a contribution. That’s another reason to read, is that maybe you get a paper out of it.
[Deniz Ones] (30:00 – 30:01)
Absolutely.
[Richard Landers] (30:03 – 30:29)
How would you actually recommend approaching individual research papers in this kind of way? I think for many folks, your first class, first year grad students in particular, it’s a little overwhelming. So what would you recommend as a strategy to balance all of this in pursuit of this broader understanding of the literature, in pursuit of synthesis of meaningful ideas?
What would you suggest they do?
[Deniz Ones] (30:29 – 36:07)
The one thing that they do need to do is whatever studies that they have identified, and there is some value in having good mentors that can help you pick articles that you should prioritize reading. Because if you have 100 papers and you only have time to read 50 of them, which 50 should be read? A good mentor will help you and say, here are the reasons why you can gain a lot by reading these papers over those papers.
But for novices, what I want to talk about is how to read a paper deeply, deep reading a paper. There’s a difference between schema paper, reading a paper, and deep reading a paper. And deep reading a paper is a skill that graduate students should invest in developing during their graduate student days.
So when you approach a paper, the very first thing that people tend to do is read the abstract. I’m going to tell them, skip the abstract entirely. The one thing that you do need to do is take a look at the introduction, skim it first, and try to get an idea of what is the big question that they’re trying to answer.
Big question, not any specific question. Big question. What is this paper about?
What is this entire field trying to solve? That helps you focus your attention to the right things. And that actually also can help you identify agenda-motivated research, because that type of research erodes scientific objectivity.
Once you have read the introduction, you should be able to summarize in four or five sentences what the big question is, in your own words, without any aids. Here are the four or five sentences that this research is trying to address. Then look at the specific questions or hypotheses that the authors are trying to get a handle on.
Those you should make sure to understand. Then you’ve got to move on to methods, because that’s where you’re going to get at how the authors are going to answer the questions, the specific questions that they have posed. This is going to come like a big ask, but if you don’t know the methods, you’ve got to understand the methods.
That’s why it is so important for our graduate students to have really good methodological and statistical training early on in their graduate student days, because that is the only way that they will be able to know or understand what the methods of a paper are. The methods is where a study lives or dies. If you know the methods well, you can identify deal-breakers, pay attention to sample size, things like reliability, potential mechanisms that can reduce variability in focal measures and collapse all of your findings down the stream.
Those are the types of things you should be able to identify when you read the methods. If you don’t know the method, take the day or two that it’ll take to get at least familiar enough to understand the basics of it. That’s a big ask, but an important one.
Then look at the results. First tables. Don’t read what the authors are saying.
First tables. Now that you have a handle on the methods, see if you can understand and interpret what’s being reported. What answers do the results provide for the specific questions that were posed that you identified early on?
Then read what the authors said. Second. And you can then say whether you agree or disagree.
Are there any alternatives? Maybe you came to different conclusions, but maybe you get persuaded, oh, I hadn’t thought about that way of looking at the results tables. Maybe you learned something by looking at, oh, yeah, okay, if you look at it that way, that’s a reasonable conclusion too.
Otherwise, are authors overselling their findings? Are there any unnoted weaknesses, things that the authors are glossing over? And then you go and read the discussion.
Do you agree with what the authors are saying, what their weaknesses were? Do you agree with whether or not the next steps in research should be the ones that the authors stated or not? These are all important things.
At that point, go back and read the abstract. And one last step, and you’re not done at that point either, one last step, read what other researchers have said about that article.
[Tara Behrend] (36:09 – 36:36)
So I think I know how you’re going to answer the next question, but I’ll ask it anyway, which is, you know, a lot of people think that AI can do it all. Do you think that AI summary systems can be useful for literature review? So Adobe will summarize a PDF for you.
Gemini will, you know, summarize a document. Are these tools useful for literature review, or are they just cheap shortcuts?
[Deniz Ones] (36:39 – 37:53)
They’re as useful as an extended abstract with potential for hallucination. How about that for an answer? And you know what I think of just abstracts.
So they are a very, very poor substitute for reading. I have experimented with a bunch of these systems, so I know what they can and cannot do. And they miss critical features all the time.
They have nothing to say about adequacy of the measures. They have nothing to say about the adequacy of the sample. There is no way that those systems will pick up an omitted variable.
Oh, you know, that variable should have been included. And without including it, the whole research enterprise of the study sort of falls apart. It will never do an analysis like that.
And those are all invisible to the summary tool. Now, never say never, I will see what AI systems, whether AI systems can think critically once they acquire artificial general intelligence. We can have that conversation when that happens.
[Tara Behrend] (37:54 – 38:17)
I like the optimism that we might get there. But I mean, I totally agree with you that it can’t really read beyond the text, and it doesn’t have the context that would be needed to do any of the things that you said are important when reading paper. So I hope every person replicates that experience and puts a paper in there to see what the summary is that comes out.
You won’t be impressed.
[Richard Landers] (38:18 – 38:32)
Closely related, are you worried about the increasing trend in people using AI to write peer review reports? They seem to be more positive, that’s what I saw. They’re more complimentary to the authors.
[Deniz Ones] (38:34 – 39:30)
I don’t think I have received one of those yet. So I’m not sure what that will look like once I receive it. You cannot ask a peer review from AI.
But the place where large language models may be useful is once a reviewer writes their entire review, making sure that it is not too harsh, as in like, moderate my language so that you author, that was a dumb thing to do. There are reviewers that’ll tell you exactly in those terms, you know, that was a dumb thing to do. Why did you do that?
Well, that can be said in a more polite way, in a way that might be more developmental. So maybe there is a place for AI in that area.
[Tara Behrend] (39:31 – 39:54)
I really like that suggestion. I think that’s very reasonable. Well, let’s wrap up with asking you one last question, which is, you know, you’ve worked in this field for a while, you’ve observed the way that people approach literature reviews and generating research questions.
And if you could wave a magic wand and change one thing about how the field does these, what would you change?
[Deniz Ones] (39:55 – 41:45)
For research reviews, I wish I could change the blind spot and the even arrogance that some IO psychologists have that rely on only Western, industrialized, English speaking countries, and the research that come from those, and only including those in their review. There is no reason in this day and age to only rely on articles in English. You can drop them into chat GPT and other language models and outcomes a reasonable version in whatever your preferred language is.
So that narrow view that limits our understanding and that skews our knowledge base to Western, industrialized, Anglo, that can go away. So I wish that would go away in doing reviews. For the idea generation piece, this might come as a surprise, but I think that sometimes authors are too feathered to the published literature.
We need much more practice driven questions. Organizations and society at large have many challenges and issues that need our help. And having our head only in our archives is a sure way to obsolescence, because if IO psychologists do not address these questions arising from practice, other fields will gladly take our place.
[Richard Landers] (41:46 – 41:50)
That was great. So thanks so much for coming. I think our viewers learned a lot.
[Deniz Ones] (41:51 – 42:03)
Thank you for having me on. I know that I rambled a bit, but I hope that this was useful to your listeners. And I’m thankful for your hospitality.
It was so great to have you here.
[Richard Landers] (42:03 – 42:17)
That’s it for another gig. To stay in touch, subscribe on YouTube, check out our website at thegig.online, join our LinkedIn group, sign up for our email notification list, and join our Discord. Thanks for joining us and see you next time for another great IO get-together.