Come along as we continue our five-part series on The Magic Teams with Part 2, Teams in the Wild. MINDWORKS host Daniel Serfaty talks with experts Dr. Eduardo Salas and Dr. Scott Tannenbaum, authors of “Teams That Work: The Seven Drivers of Team Effectiveness,” and Dr. Kara Orvis, about real-world teams, from oil rig crews to Wall Street traders to NASA space crews—and everything in between.
Come along as we continue our five-part series on The Magic Teams with Part 2, Teams in the Wild. MINDWORKS host Daniel Serfaty talks with experts Dr. Eduardo Salas and Dr. Scott Tannenbaum, authors of “Teams That Work: The Seven Drivers of Team Effectiveness,” and Dr. Kara Orvis, about real-world teams, from oil rig crews to Wall Street traders to NASA space crews—and everything in between.
Daniel Serfaty: Welcome to MINDWORKS. This is your host, Daniel Serfaty. This episode is part two of a five part series in which we explore the magic of teams. Last week, we learned about the ABCs of teams. If you haven't listened to that episode yet, you'll definitely want to do that after you listen to this one. This week, we are continuing our journey in exploring teams in the wild so to speak. And my three guests today are grandmasters in this field. They are not only students and scholars, but practitioners of the craft of team performance.
Professor Eduardo Salas is chair of the department of psychology at Rice University. His expertise includes assisting organizations across different industries: aviation, oil and gas, law enforcement, healthcare, in helping them foster teamwork, design team training, create a safety culture, facilitate learning and training effectiveness and manage decision making under stress. Dr. Salas, on account of my lifelong friendship with him, we call him Eduardo today. He's one of the most prolific authors in all of psychology. He has co-authored more than 600 journal articles and book chapters. Yes, you heard that right, that's 600. And co-edited and co-authored 37 books and handbooks. Eduardo is also the recipient of several lifetime achievement awards for his work on teams and team training.
My next guess is Dr. Scott Tannenbaum, president of The Group for Organizational Effectiveness. Under his leadership, that company, GOE, has supported over 500 organizations globally, including 30 Fortune 100 companies. For the past 30 years, Scott has advised and researched all kinds of teams from corporate, medical, military teams to teams that operate in more extreme environment such as smoke jumper teams, deep sea dive teams, energy production teams and even aerospace crews at NASA. His research has been sited more than 18000 times. His latest book, co-authored with Eduardo Salas, is called Teams That Work: The Seven Drivers of Team Effectiveness. The book was published earlier this fall by the Oxford University Press and I strongly recommend it to all of you. It's a wonderful blend of the science and practice of teams.
And finally, my third guess is Dr. Kara Orvis, principal scientist and vice president of research, and my colleague, at Aptima, Inc. Kara has studied teams and team leaders for more than 20 years and she's particularly passionate about the intersection of technologies and teams to facilitate teamwork. Whether it's about using technology to assess teams, technology to support teams, or technology that helps people learn together as a team. So fasten your seatbelt my audience and get ready to learn about teams in the wild.
Eduardo, Scott, and Kara, welcome to MINDWORKS. It's really a treat to have all of you and together today, which is really something. And for our audience, I'm going to ask you, Eduardo, of all the choices you had when you were a graduate student and later when you were a young researcher, why did you choose this particular domain of teams in your endeavor? You could have chosen any other field of psychology. Why teams?
Eduardo Salas: Well, like everything else, the interest started in graduate school. One of my mentors, [inaudible 00:03:43], who was an organizational psychologist, gave a seminar in graduate school around teams, groups, collectives, and units. And I took the course, got interested on the topic and that course was kind of at the end of my tenure there at graduate school. And within a short period of time, I was hired by the Navy. And coincidentally, the reason the Navy hired me was to develop a team performance laboratory. That's how this adventure, this journey started, in grad school. My major professor, my advisor had a course. I loved the course. And my job was about teams. And 40 years later, here we are.
Daniel Serfaty: It's amazing how sometimes a single encounter, when we study a single encounter with that professor that day changes your life and shapes your entire life. It's stunning to me, those small moments.
Eduardo Salas: Furthermore, the connection with Scott even. The first project I got in the Navy, I engaged Scott's advisor, [Terry Dickenson 00:04:46], Scott was still in school. The first thing we wanted to do at that time ... Remember, this is 1984, '85, we wanted to do a meta analysis of team performance.
Daniel Serfaty: What is a meta analysis, Eduardo, for our audience?
Eduardo Salas: It's essentially a quantitative integration of the literature. Essentially, you try to uncover an effect size between two variables, an independent variable and a dependent variable for example. Since I was going to develop this lab, the first thing that occurred to me was, "Let's see what the state of the science is." That's how I engaged Terry and Scott and again, 40 years later here we are.
Daniel Serfaty: And you're still writing books with Scott 40 years later. Well, Scott, since your name was mentioned, it's like in political debates, once your name is mentioned you are entitled to an extra minute. You're a founder of a very successful company, GOE, but you were a professor once upon a time. And I'm going to ask you the same question because I'm fascinated how people choose a particular domain and become mastering that domain and then even coach and teach other people in that domain. Why teams?
Scott Tannenbaum: I think like a lot of people growing up, I experienced good teams and bad teams, whether it was sports or schools or other sorts of things. So like most people, I could see the good, the bad, and the ugly. And when I got to grad school, that was one of the areas that you could possibly study. I think the ah-ha for me in grad school was there was some research that existed and there could be better research. As Ed mentioned, we overlapped in grad school and had the chance I think to have some heated debates about topics related to teams. So I got interested in it. And shortly after graduating, several opportunities emerged both on the research side that Ed was describing, but also with some of my earliest clients. And it confirmed everything I thought. There's good teams and bad teams and they could use the help. And if we can do a little better job of researching, I think our advice can get better.
Daniel Serfaty: This is the one question I'm going to ask all three of you, the same question, because I fascinated to know why people choose what they do. Kara, you have a PhD, industrial organizational psychology, very much like Eduardo and Scott in that particular specialty of psychology. You could have studied, I don't know, leadership, organizational behavior, survey taking, all kinds of things like that. Why teams, and why are you focusing on teams?
Kara Orvis: I like to say I was born a psychologist. It was either a psychologist or an actress, but my dad wanted me to actually support myself so psychologist it was. I can't remember a time that I wasn't interested in the people around me. And like Scott, I played sports as a child. I always enjoyed team sports over individual sports. And then, in undergrad at the urging of a friend in the economics department of all things, he encouraged me to take a class in organizational behavior. Because I thought I was going to be a clinical psychologist, and I discovered this whole world about motivation and people in the workplace and learned that there was a whole area of research dedicated to teams and leaders. And that got me hooked I think similar to you, Ed, one class in grad school. It was my last year, my senior year, and I applied to the school that had the latest deadline, which was George Mason University, which really was lucky for me because I got to study under Steve Zaccaro. And I just love the area of research. I love my self experience even working on teams [inaudible 00:08:21].
Daniel Serfaty: Eduardo, I know you've been in it for so long and everybody who has ever studied teams in the past half century has probably read your papers. But my question is that, why is that so important to understand and study how teams work? Is there something magical about that particular human structure? Is there something unique about it?
Eduardo Salas: It's an interesting social phenomenon. So in general, all of us are prone to coordinate, to communicate, to interface with others. And sometimes we do it because we have to and sometimes we do it because we like it. Sometimes we do it because we are in a context that kind of channels to do those kinds of things. So over the years, I've learned to appreciate collaboration and coordination, communication. I would say about 90% of the professionals out there are team sports, they're teams. We collaborate in healthcare, in aviation, in the military, in science and universities now. And so trying to understand this phenomenon, it's been an interesting journey for me.
Eduardo Salas: And as you know, team science has a long history, maybe 100 years, it depends how you count it. So it's intriguing. And despite 100 years of science, we're still uncovering new things. We're still discovering new things. And we have new phenomena, new challenges like teams of teams, what Kara just described. And so that's what makes this field intriguing, keeps us young, because there are new things coming out and we don't have a prescription. This would have been a short podcast if we had a prescription, but we don't and it's complex and it's dynamic and murky, it has all kinds of complications. So yeah. It's an interesting phenomenon that needs lots of science and that's what makes it interesting.
Kara Orvis: I liked your use of the word magical in talking about teams because I think they're magical, and I'll tell you why. When I was in graduate school, I remember very distinctly when I first realized that there are concepts that only exist in a team, things that don't exist at the individual level. And an easy one to talk about is cohesion. Cohesion is something that you can't experience on your own. It truly is something that only exists if you have a group of people that are working together or learning together or whatever their activity is. And I remember that being an ah-ha moment for me, that there's this whole world of concepts that didn't exist at the individual level. They only exist at that group level. And I always found that really exciting.
Daniel Serfaty: Yes. We're talking about ah-ha moments, Scott. I always learned from you because you always have very vivid examples basically of your mind, which is oriented towards observation of naturalistic or teams in the wild so to speak. And you superimpose your academic, professorial model on it in order to understand really what's inside. I appreciate that in the book, the Teams That Work book that you just published with Eduardo, you bring several examples [inaudible 00:11:40]. In particular, I appreciate in chapter two that you bring the Red Sox team even though you're, for the benefit of our listeners, from New York. And a New Yorker appreciating the Red Sox is really a treat.
Scott Tannenbaum: My family doesn't feel that way, just for the record. They don't think it's a treat. [crosstalk 00:11:57] a little differently.
Daniel Serfaty: When you've observed, and you work with elite corporate teams, I know about your history in leading GOE, did you have some type of ah-ha moment in which your own models of teams and theory of teams just by observing something in the wild was enriched by what you were observing either in sports or in the corporate environment?
Scott Tannenbaum: Yeah. I think there's probably several of them overtime. But one that comes to mind is when we were doing work with a global banking institution. And in particular, the focus was on investment banking teams. And what's interesting about this, and actually it's work that Ed was involved in as well, is we were brought in because investment analysts at this level, this is a very well paid profession. These are folks that provide advice that is used to decide whether you're going to purchase another company or not, acquire stock, et cetera, so very big business decisions. And they had kind of, I would describe them as hypotheses. The leaders had hypotheses about what really mattered here. And they wanted to know if they were right. So we went in and started watching these teams of investment analysts. We watched them when the stock market opened at the crack of done responding to the bell, we watched them working. We had the chance to use other techniques like survey and a lot of interviews of team members, of team leaders, of people that interacted with the teams.
Scott Tannenbaum: And what was interesting is going into this, the leaders had this hypothesis that what you really need is a star investment analyst like the Red Sox need a star center fielder for example. So that was the logic. And that if you just simply put a supporting cast around them a bit and they didn't get in the way too much, it's all about making the start successful. What happened was that it was this natural experiment that occurred, because over time about half their teams were structured that way and the other half of their teams had really formed more as a true team where they were collaborating together. They had a team leader, but it wasn't all about the team leader. And we had a chance to take a look analytically at what was going on there. And because this is a financial institution, they had tons of actual data, financial data, performance data, et cetera. And what was really interesting in this case is it was the exact opposite of what they expected. The teams that were all about the start underperformed. And the teams that were really operating in a collaborative way where the teamwork matters outperformed them in some ways.
Scott Tannenbaum: So one of the reasons this was an ah-ha moment for me is because first of all, I think it reinforced what I had been believing up until that team, that teamwork does make a difference. It showed that in this case a targeted research study could help, because it helped unpack the truth from kind of the myth that existed there. And it also reinforced to me that leaders don't always know what they're supposed to do when it comes to teams. They're responding with their guts, and sometimes their gut isn't right. And in this case, the data showed really what was needed.
Daniel Serfaty: That's a wonderful example where data matters. What you see on the surface, that's part of the magic of teams too, that they work sometimes under the surface and what you see on the surface is not always diagnostic of really what is actually happening.
Scott Tannenbaum: I would say almost every time in my career that I've seen an individual do something that was really outstanding in an organizational setting, there were people who supported that person who enabled it to happen that sometimes it's invisible for the organization but if you look carefully, it was a team phenomenon even though it looked like an individual phenomenon.
Daniel Serfaty: Yes. Yes. I like your example just about that point again at the end of chapter two. I won't tell the audience, they need to buy the book in order to find out why you were right, what looks like sometimes like superstar performance is actually the result of a lot of complex team interactions.
Eduardo Salas: So let me share one ah-ha moment for me early on in my career. It had to do also with Terry and Scott. So going back to the story that my job with the Navy in the mid '80s was to develop a team performance lab, I attempted two things. One, the state of the science. So that's what we began. We attempted to do this meta analysis. But the second thing I did was I spent six months traveling around naval bases looking at teams, observing teams of all kinds. [inaudible 00:16:11] teams, sub [inaudible 00:16:12] teams, ideation teams. And after that tour, I remember having the discussion with Terry and maybe Scott was there. And I said, "I get the impression that all teams are not created equal. All the teams that I helped serve are somewhat different." And I couldn't put my head around [inaudible 00:16:29].
And in the discussions I had with Terry and Bob Macintosh, who was another professor there, I don't know if this a direct quote or something, but the ah-ha moment was that Terry said it's all about the task interdependency. And that's when for now 40 years or so that I've been doing, that was to me an ah-ha moment. "Of course! Task interdependency drives the kind of teamwork you're going to have, the kind of team performance you need to engage in. The kind of behaviors, the kind of cognition." And so to me, that's something that I have carried in my head for all these years. And I make a point to try to always understand the task interdependency that is embedded in the kind of team that I'm looking at.
Daniel Serfaty: That's very good. So what you're saying in fact is that the work that stimulates the team in and by itself has in it a structure that will provoke certain behaviors in the team.
Eduardo Salas: Correct.
Daniel Serfaty: So if you want to understand a team, you shouldn't look just inside, you also have to look on the outside of the team to truly understand how that team works. Is that right?
Eduardo Salas: Yeah. And so again, things that I've learned, I think as Scott and Kara will agree, task interdependency, and we make a point of this in the book, why it's important is because it basically outlines what kind of competencies you need, what kind of competencies matter depending on where you are in this continuum of interdependency. And I think that's been one of the best, I won't say ah-ha, but one of the best insights we've had collectively, those who study teams. And that has driven a lot of good practice. So what do you do when you have low interdependence versus what do you do when you have high interdependence?
Daniel Serfaty: Yes. I see you nodding, Kara. Do you agree with Eduardo? Because my next question for all of you is, okay, so the audience understands a bunch of variables and a bunch of complexities that are associated with teamwork and human teams, but what is a team? How do we define a team? And is that just a group of people who are working on things that have interdependence? Is there more than that?
Eduardo Salas: Actually, one of the [inaudible 00:18:39] papers that I have, it's about the definition of a team that we published in 1992 I believe it was. It's interesting. This discussion is making me connect all the dots, it's amazing. So again, why do we have to define and provide a definition of teams? So we're doing this meta analysis, we're observing all these teams in Navy. We now know there are differences among them, that all teams are not created equal. And then we said, "If all teams are no created equal, the kind of research that we want to do cannot generalize to everybody, to all those teams. We need to focus." And so the definition, Scott, correct me if I'm wrong, the definition came as a result of trying to put boundaries around the kind of team that we were going to look at and that's what we did. So it's two people, two or more who perhaps share goals, they are interdependent and so on. So that's how that definition came.
Scott Tannenbaum: What's interesting is, for me, how that's morphed over time. So my recollection is the same as Ed. We needed to draw kind of a box around what we were going to study, and so formal definition. But what's happened overtime through kind of practical experiences is that not all teams, particularly in organizational settings, are this neat cluster of reporting relationships in boxes with a tight boundary around it and defined roles, et cetera. It's just become mushier and more dynamic. And so to me, a team is still more than one person. And I would say they have at least some interdependency and some shared goals, but it doesn't mean that they're completely shared goals. In fact, I think most teams in organizational settings have this combination of pulls and pushes of sharedness, but also individual needs. We see this in senior leadership teams all the time. They have a shared need for the company, but the head of finance also has their own needs that's different than the chief technology officer. I think it's a little bit mushier. And of course there's now also kind of the concept of teams of teams that pops up.
Daniel Serfaty: So it has become a bit mushier as you said from that neat definition of the 1992 paper because we are discovering through the new setting, remote work, technology, et cetera, that there are other organizational forms that operate like teams, but are not really exactly fitting that definition of teams. That's very interesting. I want to go back to that, especially as we will examine the future of teams, if we can project ourselves in the future of that dynamic evolution of this social structure called a team. Kara.
Kara Orvis: I did have a question since you were here, Ed and Scott. I know in graduate school we talked about whether or not teams had to be together for a confined amount of time. That wasn't in your definition Ed, but I was just wondering about your thoughts on that. It wasn't like a family that sort of went in time forever, but that they had to be together for not a short period of time, but a definite period of time. And once the goal was accomplished, once they would separate a team.
Eduardo Salas: So naively, I think now with the years, we thought that we could study teams that had a past, that had a present, and that had a future. So that's why the definition was in that boundary. But along the way, I don't know many teams that have a past, a present and a future, [inaudible 00:21:59] or a long period of time the same membership and so forth. So that was the idea then. But just thinking out loud here a little bit reflecting on all of this, if I were to write a definition, again it would be very different I think.
Scott Tannenbaum: If we think about medical teams, to Kara's point in terms of how long they last, so we've studied trauma teams. Trauma teams, they haven't worked together so they have no past, Ed, right? They're called in. The helicopter lands. Whoever's there, they go, they work on this together. Then, they hand the patient off to someplace else and that team doesn't exist anymore. Flight crews on airlines are like that. They don't fly together all the time. They meet. It's Topeka. They get on a plane together. But then if we think about let's say a doctor's practice, so you go to have a visit at the office, there are people on those teams that stay together a really long time. If you've been going to your doctor for a long time, there's members of those teams that, they do have a past, they do have a present, and they'll probably be around for a long time. So I think of that time thing as more of a variable that influences what's important to teams and the way they need to operate. And what we need to advise a trauma team is different than we do to advise an ongoing team of a medical practice.
Daniel Serfaty: That's true.
Kara Orvis: Something else I've been thinking about is teams, like you might have a five person team and three of those members often work together on different projects with different goals, so essentially different teams. And you might have two or three or four new people weaving in and out of that threesome. So then when you change membership like that, how much change in membership does there need to be to maybe lose some of the teaminess that exists in those three people since they spent so much time working together? It's really complex once you start thinking about things like that.
Daniel Serfaty: Well, but our job here on this podcast is to make the complex simple. And from the last few remarks by the three of you, it's almost as if we have transportable team skills that we can, by plugging into a team in the morning, another team in the afternoon and maybe a team next week, we can actually use those team skills or those team competencies again and again. It's something that comes with us as human beings, can be trained, maybe innate, I don't know. Is that the case indeed that teamwork, let's call them competencies, are transportable?
Eduardo Salas: I would argue that every team training out there, whether it's TeamSTEPPS or CRM or whatever you want to call it-
Scott Tannenbaum: What's CRM?
Eduardo Salas: Crew resource management.
Scott Tannenbaum: Okay.
Eduardo Salas: Which, is the lingo that is used in aviation and a little in healthcare. But essentially, it's team training. And so all of team training is about imparting team based competencies to individuals that you can take from one team to another. That's what I would argue. But again, there are very few teams that are intact. And so the purpose of team training is exactly what you just said, Daniel, is to give you these competencies that you can take to different teams and use them and apply them in different teams. So yeah, I think they're transportable. [crosstalk 00:25:06]
Daniel Serfaty: That's pretty amazing, because if we think of the way we educate even our children, if they can learn those team skills say in soccer and they can apply it back in the classroom, the very same team skills or similar team skills when they're on a computer science project team for example, that is something that is extremely valuable because it's part of the development of the individual, not just the team.
Scott Tannenbaum: I would tend to answer that question a little differently to Ed. And I would say the answer to your question, Daniel, is yes, there are transportable skills and there's also a big chunk of skills and competencies that go beyond that. So for example, a skill like being able to give and receive feedback effectively, we should be teaching that starting in elementary school all the way up through the school system. When they get to law school, lawyers should be learning this. And it doesn't matter where they go in their careers, they can take that and apply that in every team they're involved in. But there's also for example value in understanding the capabilities of your team members, what psychologists like to call transactive memory systems, simply because we can't use simple terms to describe things.
Daniel Serfaty: You have to publish after all.
Scott Tannenbaum: Absolutely. If it's too simple ... I know the podcast is to simplify, but if it's too simple, we're giving it all away. So in the case of knowing your teammates, that can't happen and be transportable. "Yesterday, I was working with this team. Tomorrow, I'm working with a different team." And I take those feedback skills. But I don't know that Kara is an expert in this and Daniel is an expert in that. And that's an example of a knowledge competency that's unique to a team.
Daniel Serfaty: That's a very good, subtle, but excellent point. Thank you, Scott. We're starting to tackle that question actually, but that's a question I wanted to ask, especially if you have examples from your own corporate life or from the myriad of teams that you observe in the field. And I'm sure our audience is asking, is a team more than the sum of its parts? If I have a team of experts basically, can I obtain performance out of that team that goes beyond the sum of all that expertise? Do you have examples of that, that you can point where actually the glue of that particular team training intervention that you were talking about has actually added to the outcome performance of the team in a meaningful and observable way?
Scott Tannenbaum: As you were asking the question, I started thinking about two examples: one in which the sum was greater than the parts, but also one where the sum was less than the parts. So teams don't magically always make it better, but they can. So an example of one where it was less than its parts was an experience that I had as a customer going in to get a suit at an expensive store in New York. And this was a team that was made up of some of probably the finest tailors globally, and outstanding salespeople who have been doing it for a really long time. And they had to work together to create a good experience. And you would think that that would be a great team. But because they were unable to talk the same language, because they weren't able to communicate, because they not only had shared goals but some competing goals, the experience that I had as a customer was actually less than the sum of the parts. I almost wish that I dealt with each of them individually and sequentially.
In contrast, I'm working with an insurance company now. And this may seem like a mundane example, but we're dealing with leadership teams, a regional president and his team. And what happens is when we look at the talent levels in each of these places, it's not necessarily the region that has the highest average talent on that team that's performing the best. It's the ones where the leader is allowing for sort of compensation even for their own deficiencies. And so the high performing regions, you might not predict them based on average performance, but the way it's being led. That's the case where you see that bump.
Daniel Serfaty: Thank you for that counter example with your tailor. But that's certainly understanding that it doesn't just happen. You have this myth sometimes about the teams. "Oh my god, they click." Well, it takes a lot to click. And Eduardo, I'm sure you studied a lot of mission critical teams in particular. I'm interested to- [crosstalk 00:29:22]
Eduardo Salas: Yeah. So the couple that comes to mind that links a number of things that are powerful in team development like simulation, debriefing is a few years back, this is actually before I came to Rice, we were looking at forward surgical teams, FSTs. These forward surgical teams were about 18-22 individuals who would get deployed to at that time Afghanistan, Iran, whatever. And the rate of deployment was one team every month. And they would go to Ryder Center in Miami. And we would go and observe this. And the training was two weeks. I remember this. And all the teams were the same. The first day, it was 18-22 strangers. They had never seen each other. There was only one physician, one surgeon, and the rest were medics, nurses and so forth.
They would come to Miami on day one. On the third day, they would do a simulation where five, six patients would come at a time. They had to deal with them. The simulation was mimicking war zone conditions. The lights would go off. It was extremely noisy and those kinds of things. And I remember that on the third day in that simulation, the instructors would come into the simulation and will tell the people trying to do triage, the team members, would say, "You, you, you, you, out." And they would go out of the simulation center not knowing what was going on. And the instructor would say, "All of you are dead." They were shocked. [inaudible 00:30:56] were dead. And they said, "Well, the patients that we brought in, you were doing the ABCs," which is air, breathing-
Kara Orvis: [inaudible 00:31:04]
Scott Tannenbaum: Circulation.
Eduardo Salas: ... yeah, and they would forget that they were in a war zone and the patient had an IED for example. And so it was a very emotional awakening for them on the third day. Then, [inaudible 00:31:16] more simulation. But to make a long story short, by the 15th day, they were just a perfect machine of teams. So in those 15 days through simulation and lots of debriefing, very detailed debriefing, they learned about roles and responsibilities, about having a shared mental model about information exchange protocol, about all of that, transactive memory. Because again, they had to become one single ... And it was just beautiful how in 15 days, through all these techniques, they essentially became a team.
Daniel Serfaty: That's a great example. I like that notion, because in a sense the business of experts and teams is to create a single biological organism out of all these disparate cells that has almost like a life of its own and a characteristic of its own in a sense. You just published that book, Teams That Work, Eduardo and Scott. And I have in my library, I have an entire shelf dedicated to books on teams. Why now? Why did you want to publish this particular book and what do you hope your readers will get out of it as opposed to previous books on teams? Scott, since you are the first author, you answer first.
Scott Tannenbaum: This is a book that translates science into practice. So the impetus for it is both from the science side and the practice side. On the practice side, we've been dealing with teams and people ask us for advice regularly. We give talks, we consult, we're working closely with teams. And it's a continual request of, "Boy, this isn't working exactly the way it should." In fact, there's some data that suggests that less than 25% of people feel like their own is high performing. So there's this pull from the practice side. And at the same time, having been in this field for a long time, we started looking at the research really closely and realizing that this body of research had grown to the point, and we're talking about global research, all over, to the point where we actually felt like it was stable enough that we could make some recommendations. Ed earlier alluded to meta analyses. There are close to 50 meta analyses that have now been published on team related topics.
And the nice thing about a meta analysis is I have a lot more confidence in a meta analysis that averages results across a bunch of studies, that gives me a stable finding, than I would from any one study. So between the fact that we now felt like we had something we could say because the field had matured enough and the pull from the marketplace, we thought now was the right time. And so when people said, "Do you have a book for that?" now we can finally say, "Yes, we have a book for that."
Eduardo Salas: [inaudible 00:33:53] at least from my view, how we got together. For a number of years now, between the two of us we developed what in the book we call the seven drivers. This heuristic, the seven Cs of teamwork. And over the years, we started with different Cs. And finally, we agreed on these seven we ultimately published. But every time in my mind at least I give a talk in healthcare or in the corporate world or oil and gas, you name it, first of all, they will love the Cs because as an organizing body of all this knowledge that Scott has talked about, it makes sense to them. And they will always say, "Where can I get more of this stuff? Can you send me something that provides more detail?" And there was always this feedback that actually eventually both of us got, it was, "This is great, but we need more. This is great, but we want more." And that's how, I don't know, two years ago we said, "Let's do it," and here we are.
Daniel Serfaty: So in a sense, having very practical advice but anchored in solid science fundamentally. So it's not what people have become very suspicious of in the management literature about the five ways to become a great CEO, the 14 ways to become a great leader, et cetera. But here, yes, they are those seven dimensions or seven drivers of team effectiveness, but they are anchored in actual science and research and data, yes?
Scott Tannenbaum: Yeah. And I think a key point that we touched on earlier is that one size doesn't fit all. So we talked about for example the difference in interdependency. We talked earlier about teams that have a short life versus a long life. The research has some different advice for those. So the books that are very simple quite often are oversimplifications. "You should all get along. If we minimize conflict, we'll be successful." And the research doesn't support that. It's more complex. But the book tries to sort of find out based on the type of team you are, what can we tell you you need to do to be able to be successful?
Eduardo Salas: [inaudible 00:35:54] also what's interesting about this book, which is a lot of people say, in a [inaudible 00:36:00] would say, "You're not selling anything." And I say, "Well, what do you mean?" "The seven Cs are just really a heuristic again if we don't have a package of things that, 'If you go here, use the seven Cs.'" And so that was also [inaudible 00:36:13] to people, that we were not saying, "These are the seven things you need to do." We said, "No, this is a way to organize and to think about it and things you need to focus on." And so that was also appealing, to your comment, Daniel, that we were not out there with, "These are the seven things that you need to do and if you don't do them you are going to crash," or something like. And so that was also appealing. It was science based, it was formulated, and then like Scott said, once [inaudible 00:36:39], you can have that, you can change. Based on that science, you can draw on, solve your own unique particular problem.
Daniel Serfaty: I wish you success certainly. The book just came out. But I wish the scientists, the graduate students or the professor will buy it because it's chock-full of examples from the field that can illustrate this or that model of team effectiveness. On the other hand, I think the practitioners, the managers, the leaders in the corporate world need that because that enriches a little bit or gives context to the team management practices that they do. I mean, one management practice that you are applying, Kara, and I know that you co-edited a book a few years ago, is a notion of distributed teams, the notion that teams do not have necessarily to be co-located to perform. And certainly, the last eight months of remote work reminded us in a very visceral way the importance of distributed teams. Can you tell us a little bit about that? What's different? What are the things that are unique both in terms of the challenges or the difficulties but also the opportunities?
Kara Orvis: So I'm going to go back a little bit in time to the late '90s when I was in grad school. And one of my first projects was on a multi-university project. I mean, the internet was new. Like I was just learning how to use Google, it was brand new. And we were working through technology with other researchers at other universities. And it was really hard and we had a lot of failures. And honestly, that was the first time I had been on a team that didn't do well. Later on, we did well and we accomplished lots of great things over the years. But I remember Googling the word virtual teams together and got like two hits on Google. If you google that now, you'll get hundreds of thousands if not millions of hits. But it was so hard and I really wanted to understand that, so that's sort of what I studied in graduate school.
I did some work with a professor out of the school of management at George Mason University on this idea of situation and visibility. What I found in working on my own teams and continue to find is that when you're in your space and you're working with someone in another space, you don't really understand their context. You make huge assumptions about where they're working, what tools they have around them. And when they don't perform in ways that you think they should perform, it's really easy to say, "Oh, that person doesn't care." Sort of make what you'd call a personal attribution about their performance.
And a lot of the distributed teams literature talks about the need to over communicate in absence of being together in shared spaces. And so I always tell my team members, especially now during the pandemic now that everybody is distributed, "Communicate what your situational circumstances are. If you have family time or things that need to get done like you're working with your kids on making sure their school is getting done right, put that in your calendar. Let your team members know so that if you're not responding very quickly, they have a good understanding of the context." So that's just one example of what you might see in virtual teams or distributed teams that you don't see in other types of teams. There's many other examples.
Daniel Serfaty: Connecting that back to that fancy word that Scott used before to describe this notion of developing a model, a mental model of sorts of the other person, capability, expertise, but also in your case you add the notion of situation. And as a result of that knowledge, being a better team. Is that what you're saying?
Kara Orvis: Yeah. That's exactly what I'm saying because altogether, one space, we understand that space. But what we found in that research that Catherine and I did together was that people take their own experience and put it onto someone else that they're working with. They're not apt to imagine that their situation is different. And I think it's easier when you have things like a Zoom technology where you can see someone's office space. Right now, you guys are looking at me. I'm sitting quietly. But do you know that my son and daughter are in two rooms next to me and are e-learning? And I'm waiting very patiently hoping everybody's quiet and the dogs don't bark. And I did share that with you, but we just don't anticipate very well what people's circumstances are. And therefore, it's important for people to share that in ways they wouldn't in a face to face environment.
Daniel Serfaty: That's good that you're opening that door. I intended to ask that question a little later but I'm going to ask it now also to both Scott as a CEO, but also Eduardo as a professor and a department head, is experience of the past eight months with the pandemic. I assume many of your students Eduardo are virtual or at least learning in a virtual environment and same for you Scott in terms of both customers and employees, did that prompt you to rethink the notion of teams or to expand it or to modify it in any way?
Scott Tannenbaum: Yeah. So my experience during the pandemic is different in my dealing with clients than it is dealing with my team members. We've operated virtually within my company for a really long time. We have shared space to be able to connect physically and obviously we're not doing that. But the majority of our interactions on the team has been remote for a long time. So we've had to go through that learning curve, the same learning curve that Kara described: how you work together, how you maintain the bubble, how do you know how people are doing? And I think we've developed our mechanisms for sustaining kind of situation awareness.
What's been interesting to me is now dealing with various customers, some of whom in the past would have insisted that we all have to be in the room together. And one of the interesting insights, actually two that come to mind, one is leaders who are the start of the pandemic basically said, "There's no way that I could run my team this way. If I can't see what's going on, who knows what they're doing? And the second that this thing is over, I am pulling everyone back into the office," who are now overtime saying, "Hey, you know, there are some interesting advantages to running a team this way and maybe we should have a hybrid model going forward."
The second observation, and this relates a little bit I think to Kara's comments about knowing each other's situation, is that this has forced a humanizing element in some cases in the way we work. So it used to be that at the meetings, if we did a video camera meeting, a web cam meeting, everyone's dressed up nicely and you show up, it's like you're on camera. Make sure we have makeup ready, the whole deal. Now, it's like we've reached a point where people can be themselves. It's creating a little bit more psychological safety. The kid that runs in in the background, the dog that barks, the lawnmower that's going on outside, it's like we're humans. We have life. And I think it's becoming more acceptable in many of my clients, the way they view themselves and the way they view us. And I'm hoping that that is one of the few positive things that we can take forward about this.
Daniel Serfaty: What a wonderful insight. Thank you for sharing that, Scott. I do believe indeed before I ask Professor Salas because I want really to see the perspective of the teacher and the department head, you have several professors that are on your team, but this notion that I agree with you, that has created I believe this distribution of work and distribution of operation, paradoxically has created a level of intimacy, of empathy, of perspective taking that was not there before. These are in a sense skills that were kind of more dormant before that suddenly have become very important. I don't know if that's your experience, Eduardo, too.
Eduardo Salas: Absolutely. So my department, we have 16 professors, five [inaudible 00:44:36], five, six [inaudible 00:44:38] neuroscientists. You've got to understand the context here at Rice. So March of 2020, Rice University had never had an online course. April first, Rice University had 1900 online courses. Everything has shifted. So I've been chair five years. The kind of questions that I ask today, the kind of emails that I send, the kind of gestures that I do are all about intimacy, about humanizing things. So I'll tell you exactly one thing I did today. So last Friday we had a faculty meeting and we had it on Zoom. And like most universities now are struggling with budgets and so we're going to get a cut in the next academic year. So I'm telling this to my faculty, "We're going to have this cut, all these things are going to happen." And the meeting started at 3:00 on Friday and it's about 4:30 and I'm saying, "Okay. Happy hour, let's go [inaudible 00:45:38]." I mean, it was complete science. These are 17 professors, nobody said a word. I was trying to make a joke, make something light, nothing. There was zero.
They're all burnt out, they're all stressed. And so I said, "I have to do something." Long story short, I said, "I'm going to send them just a box with cookies and little things to eat." Actually, my niece who lives in Houston is a pastry chef. So I told her, "I need 30 boxes of little things." So she did that. I bought cards that said thank you, and I wrote to each one of my staff. There are like 25. 17 faculty, 16 faculty, we have 18, 19 non-tenure track professors who just teach. And all day yesterday I wrote little notes for them. They went out today and I've been getting the emails of thank you, of emotion. Never in my life I would have thought about doing something like this.
Daniel Serfaty: Oh, we know that.
Eduardo Salas: Exactly!
Scott Tannenbaum: I'm surprised your handwritten note was readable having worked with you extensively now for years. [crosstalk 00:46:40]
Kara Orvis: Ed, I think that's really sweet. Don't listen to these guys.
Eduardo Salas: Yeah, they've known me for a long time. But it's kind of like the interactions I've had, especially like you, Kara, with the female professors, it's about the kids who are next day in the meeting and the kids are there. And so it's a different interaction and it's a little more intimate. And the other thing I've learned about all of this since March is the need for connection. And so these cookies I thought was going to be that connection. From my emails I've been getting this morning, it's doing that. So that's the world we live in now.
Daniel Serfaty: I agree. That's why I think the IO psychology, industrial organization psychology community should, and I'm sure they are already, but should study this time, because I think we see a transformation of the very nature of work relationships. And I think both teamwork, which is the topic of the day, as well as leadership and what that entails is being transformed in front of our eyes. We are transforming it as leaders too. But I think at some point we need to zoom out literally and look a little bit about, maybe the definition of what a team is has changed, has been enriched, not diminished, but enriched by this experience.
So to switch back a little bit from great teams to poor teams, people don't like to talk about failures in this field, but we all witness sometimes teams that fail somehow. Not systematically, but had one or two failures. And I know many of you have studied military teams or space teams or aviation teams or healthcare teams for that matter in which sometimes a team mistake caused pretty catastrophic failure. Can you describe that a little bit if you want have one example you want to share with our audience about, how does that happen? What particular failure of teamwork specifically, of people not practicing one of the seven Cs perhaps, communication and collaboration and coordination, cause eventually a disaster?
Scott Tannenbaum: I shared earlier a very mundane situation, my buying a suit. And the consequences of that were simply that I didn't buy a suit. But Daniel, as you point out, sometimes the failures have much greater consequence. You may recall several years ago the Costa Concordia sank of the coast of Italy. This was a cruise ship. They got a little too close to shore, they capsized, and people lost their lives around this. We actually went out in the cruise industry after that, spent time in different cruise lines to try and understand a bit about team phenomena, leadership phenomena, et cetera.
There were a bunch of factors that contributed to this, but there's no question that there was a teamwork breakdown that occurred. And you could look at the crew members there and say there was ample experience for them, that they should have known better. How could a ship that size get that close to shore? And what happens is that it was a breakdown is psychological safety that was part of this. Someone should have been able to say to the captain, "Captain, heads up, we're a little close." And if that didn't work, "Captain, we're too damn close!" and should not have accepted the proximity to shore on that. And to me, that's a classic foundational breakdown in team effectiveness that relates to one of the underlying psychological factors, which is psychological safety.
Daniel Serfaty: And so as an expert consultant, what would be your advice then to fix that particular type of failure that eventually, through a chain of events, led to a catastrophe, to a disaster?
Scott Tannenbaum: Yeah. So as you can imagine, there's a lot of moving parts in this. So part of it is preventing the accident. And part of what we also discovered were things that need to be done to prepare the teams in the event of an accident. So let me say a bit about the latter first and I'll come back to the former. But you have now bartenders, musicians, cleaning staff that are asked all of a sudden to help support an evacuation onboard a ship. And so we're asking people who don't necessarily have these skills to begin with. One of the things that we discovered was really important is scenario based training and practice to be able to prepare folks on this. Similarly, where the captain and his crew reside, there's an interaction that goes on there as well. And team based training is one of the interventions where leaders are being taught not just how to navigate, there's this natural tendency to move towards the technical skills, but also to teach leadership and team skills.
Some of this starts even with cultural norms and small tolerance of things that erode psychological safety that you might not notice when you tolerate that overtime. Part of the advice is you can't do that because then, when there's this one rare moment where you really need it, people are uncomfortable speaking up.
Daniel Serfaty: Maybe a related question to expand on one of the remedies that you just mentioned is this notion, "Okay. Maybe we can't change the culture, maybe we can't train particular individuals. But maybe we can train a team." Eduardo, I know that early in your career you focused very much on optimal ways to train teams, especially mission critical teams in the United States Navy. Give our audience a single way to focus. What's the best way to train a team? And I know it's a loaded question. There are books written about that. But after filtering out the noise, what is the first thing that you will focus on as you look for a way to optimize team performance through training.
Eduardo Salas: I think there are two powerful countermeasures or two powerful interventions I guess. One is simulation. I'm a big believer in simulation because, what do you do in a simulation? You practice under hopefully the conditions that you're going to perform under. But simulation by itself is not enough. You need feedback, debriefing. And I think there's a meta analysis on this. Scott did one meta analysis on debriefing, that teams that debrief outperform those that don't by 20-25%. I mean that's a whopping effect. To me, the best team training I've seen is simulation based team training where you get these two components, simulation and debriefing. But [inaudible 00:53:05] emphasize the debriefing component, the military, as you know, it's full of simulators. In peacetime, all they do is train. And where do they train? In simulations, simulators.
But you know, Daniel, back when we met a long time ago, [inaudible 00:53:18] operator stations were not that great because they didn't facilitate debriefing. They did not facilitate meaning feedback. When I go to organizations and say, "How do I improve teams?" I say, "You have to use simulation." Now, let me make a kind of an editorial thing for the audience. To simplify things, training, any kind of training including team training, it does four things. You get information, demonstration, practice, and feedback. Those are the four components of any good training. Most of the team training out there, most of it is information, demonstration based meaning PowerPoint. You see a bunch of videos of good and bad performance and maybe you role play a little bit.
And we know from the science that the learning occurs when you have practice and feedback. So again, there's TeamSTEPPS with this medical team training program out there used in 70% of hospitals and three million people have been trained using TeamSTEPPS. It's an acronym for something that I can't remember. It has to do with patient safety and performance. But anyway, you have to use simulation for these things to work. And in healthcare, we're seeing a lot of simulation now and debriefing and stuff like that. So that's basically the advice I give. You have to be able to allow for people to practice under the conditions that they are going to perform under.
Daniel Serfaty: And obviously, what simulation does, it allows folks to explore the boundaries of the possible, something that would be very dangerous or costly perhaps to explore just by practicing in real environment. I'm thinking for example of pilot training when you can do some maneuvers that are extremely dangerous, but you can do them in a simulator and get the feedback on, how do you perform at the limit of performance?
Eduardo Salas: I want to give you an example of how powerful to me it was. So during the first Gulf War in 1990, '91, one of the tasks that we were given ... this was another ah-ha moment for me about simulation, we had a task to interview pilots coming back from sorties. So they were going from the air carriers, dropping bombs, coming back. When we interviewed the pilots, the majority said, "It looked like we'd been there before." And we were saying, "What do you mean it looked like you'd been there before. You've never been there. This is the first time." And it was the mission rehearsal, the simulations that they were trained for the sorties that gave me the idea that when they went there it was so familiar when it wasn't really, the first time they were going there. So that to me gave me one of these, "Wow!" Again, this is [inaudible 00:55:56] development, this idea of the power of simulation, accelerates expertise, gives you all the stuff that we're talking about. Sorry, Dan.
Daniel Serfaty: No, no, no, no problem. I think that illustrates very well, that's a very good example to the point that you have. Scott, any addition?
Scott Tannenbaum: Yeah. There's no question that the type of simulation that Ed's describing, they're very powerful. So the idea of having this technology, it gives the impression that you're flying over Afghanistan, a great learning opportunity. Mannequins simulating a patient. They have high physical fidelity but they're also very expensive. So the concern I have sometimes when we talk about simulation is that all my corporate colleagues, they stop listening because they think simulation is only these kind of multimillion dollar simulators. So I just want to put in a pitch to say, in addition to those, there's low fidelity simulations that can work really well. And one of the most basic ones is just to do sort of a cognitive walkthrough. For example, we were working with oil rig crews. A platform in the Gulf of Mexico is part of the team, and part of the team is in Houston. And all we had was a simple video connection between them. And we said, "Okay. We're going to start talking about a situation that's evolving and you talk out loud about what you would do, who you would contact, and what you would do. Tell us what you're thinking, what you're doing, et cetera."
And we just evolved the scenario. And we said, "Now, this has happened next. Who do you contact?" And some of the ah-has were things like, "Wow, so it's okay for us to call the chemical engineer onshore at midnight?" "Well, yes. If this problem occurs, it's absolutely okay." So there's an example of very low tech, there was no simulator, that served sort of a similar purpose.
Daniel Serfaty: Some people are talking about the difference between physical fidelity and perhaps cognitive fidelity. And that's perhaps what you are illustrating here, that sometimes imaginations are powerful enough to project basically situations in artificial environments into real life because they are cognitively similar.
Eduardo Salas: Games, [crosstalk 00:57:55].
Daniel Serfaty: Yes.
Eduardo Salas: I'll give an example. So the astronaut's here in Houston. So if we go to Mars, it's going to take about 10 months to get there. So the idea is that during those 10 months, there's going to be a lot of training going there. So one of the projects I worked was to develop a teamwork skill training for the astronauts, the four astronauts that would go on the way to Mars. And so the company I was working with decided, "Okay. We're going to do a game." To my surprise, I thought that the astronauts were going to reject this because it was an emergency healthcare related type of game that had nothing to do with space exploration. But they loved it. It was a game that had four people, that they have to engage and solve a problem and then rescue some people and so on. But that also made me think that gaming, which is a form of simulation, it's also a very useful, practical, effective technique for this. And again, what the astronauts liked, it was more of the cognitive fidelity issues than the physical thing.
Daniel Serfaty: I wish actually that some of the things that you're describing for astronaut training and for Navy training or cruise ship training could be applied even in a mild environment, in a corporate environment. We don't have enough of that. I can tell you as the head of an enterprise there are some games you can play that come out of the business school tradition, but there's very little true simulation. And I wish indeed that there's an opportunity for someone to disrupt that industry by introducing games for managers and CEOs and executives the same way aviation uses simulation to train. I think there is a shift of culture. Talking about CEOs and managers, Kara, before we switch to a more futuristic view of teams, if you have some advice to give to all those team leaders out there that are listening to this podcast about how to improve your skills as a team leader, not just as a project leader, but as the leader of a team.
Kara Orvis: A long time ago I helped a wonderful woman named [inaudible 01:00:09], who you know very well, on her dissertation. And she was studying team leadership and she was looking at this concept of sense making, which is not just telling people what to do like, "Hey, you on the team do this and you on the team do that." But explaining to the team why they should be doing those things separately or together. I always try to myself, as well as encourage other leaders to, as they're describing things to their team, help provide that sense making, the reasoning why they should be doing what they're doing and why they should be doing it together the way that they should. You guys were just talking about simulations for team training, but oftentimes it's the leaders who have to train the team members on how to work together.
And providing that sense making, that reason why behind not only what they should do, but why they should be doing it, I think is so valuable. The other advice I would give team leaders is don't be afraid to tackle team issues. You want your team members to feel good about the team experience. You want them to be motivated to work towards the team goals. You want them to understand how to work with each other. So when you see something going wrong in the teams, really try to understand why they're not performing the way that they should and talk to them, or remove those barriers, or increase those skills, or increase that knowledge. And so those are the two pieces of I guess advice I would give to team leaders.
Daniel Serfaty: I'm going to put you on the spot right now. Are you practicing as a leader yourself, as a team leader, those skills?
Kara Orvis: Yes.
Daniel Serfaty: Are you actually implementing them on a daily basis?
Kara Orvis: Absolutely. I try every day to do that, to explain the reason why. And speaking of low fidelity training, one thing that Aptima had created for a project for military teams who were out in the field deployed was to actually talk about past experiences the leader had had with their team or another team, present that scenario to the current team, and ask them what they would do. Listen to their response, really hear what they would do. Have them explain why they would do that, and that would give the commander, the leader an opportunity to correct them or say, "Yes. That's a great response and here's why I like your path that you chose on what to do in this situation." But yeah, I do try to do those things every day.
Daniel Serfaty: I thank the three of you for all the very wise but very practical advice that you are giving here both in terms of how to train, how to develop leaders, what are some of the key components you have to watch. I think our audience is going to ask for more. But I'd like in our remaining time to explore a little bit some future topics, especially given that you are so anchored in so many years of experience looking at human teams. We already explored the effect that this pandemic is having on understanding this notion of technology enabled, but distributed teams and kind of the new doors, the new opportunities it is opening. As you know very well, in the more futuristic but it's already happening in some professional domains, we're introducing new forms of intelligences, and I'm using the term in plural on purpose, into the human teams. We're introducing robots that work with humans. We're introducing artificial intelligence bots or artificial intelligence beings literally that observe how humans are doing things and learn from it and change as a result and adapt.
And I wonder as we evolve toward that future of multi-species teams literally, what's going to happen to team science? Should we apply blindly what we know about teams that work and say, "Well, it works with humans. There is no reason it shouldn't work with artificial intelligence and human teams." Or is there a possibility for developing a whole new science, a whole new insight perhaps? Kara, you want to start?
Kara Orvis: I've been thinking about this recently. First, I think we have to understand these nonhuman teammates and aspects of them that may or may not be different than human team members. But earlier, Ed and Scott were talking about this idea of generic skills that an individual brings to a team. And I believe, and I just wrote a paper with a colleague of mine, Sam Dubrow, where we took a look at some of those generic teamwork skills and we considered what these machine teammates were like and what made them special.
Daniel Serfaty: What's a generic teamwork skill for example?
Kara Orvis: A generic teamwork skill or trait, we were looking at traits too, like communication, ability to communicate with others or tolerance for ambiguity was one of the traits we looked at. We took a lot at some of those generic teamwork skills and we made a case in our paper that some of those skills probably do transfer over to human-machine teams. They're just as important in a human-machine team as they are in a human-human team. But some other skills may become more important in a human-machine team. And then some other skills might not be as important in a human-machine team. And so I believe that we can take things from the team's literature and it will apply to those kinds of teams. Do I think everything will apply? Probably not. But that's an example of, if we're going to design humans to work in human machine teams, what are those skills and traits that we're going to want to train and select for that are going to allow them to deal well with those nonhuman team members?
Daniel Serfaty: And I want to hear from Eduardo and from Scott on that, but I think it's very important that your community take the lead on that because left to their own devices, artificial intelligence developers work very fast and don't wait several years to have the right P values, will actually design an artificial intelligence system without taking into account the treasure trove of insight that our community, your community can give them. You're nodding, Scott. Do you agree? Tell me more about those future teams.
Scott Tannenbaum: Yeah. So if we think about them, the teams you described, [inaudible 01:06:33] think about them as let's say hybrid teams, right? It's a mix of human and other intelligences. Let's first start with the assumption that we're talking about in this case intelligences that are somewhat visible to the other team members. They don't have to be physically visible, but they're robotic or virtual. They're not so deeply embedded that we don't even know they're happening. So in those cases, you almost naturally as a human think about them in some ways as team member. So it makes me think about analogous phenomena in hybrid teams versus all human teams. And I can point out some of them, but it also tells me there's some research that's needed.
So what do we know with human teams? Trust matters. And we know that in judging whether we trust another human, there is a judgment made about ability. Like, do I think you can do what you said you're going to do? And character like, do I think you're going to do the right thing for me, that you care about me, et cetera? So what's the equivalent phenomena. Do those apply directly or differently when we start talking about a teammate who is not human. We know role clarity for example matters a lot in teams. So, Daniel, are you responsible for this? Am I responsible for this? What's the equivalent when we've got a hybrid here? Is it programmed in? Does the AI just make a decision to fire, to clean, to do? Who owns the decision? Is that clear and transparent? We know backup matters.
Daniel Serfaty: What's backup?
Scott Tannenbaum: A backup is, I am monitoring, I see that you need some assistance so I offer help. I fill in for you either partially or fully in some ways. In human teams, that's kind of a characteristic of high performing teams that have interdependency. So how and when do you human and AI backup each other? What are the implications for team composition? Can I compose a team where I know AI is able to step in and do some other things even if it's not their primary task? And can I as a human serve as backup for AI? You think about sometimes like, "Oh, the intelligence can run on its own. But are there times where I should be monitoring and seeing this is now evolving into a space that the AI was not programmed for and I need to back up?" So I share some of those as examples that we should use what we know about team science and we should probably study those phenomena in these hybrid teams.
Daniel Serfaty: Yes. Eduardo, if you can take on that topic, and also maybe expand on that notion of training as a team, on the training part how do you develop those teams? Are they totally new kinds of competencies that need to happen, or they are just variants of what we know?
Eduardo Salas: Let me make I think maybe a bold statement on this. I don't think we need to be afraid of human automation, human AI teams. I think that the way to tackle this is to stick to the basics like we always have. So instead of studying teams, we need to study the nature of teamwork. And so I don't care whether you have automation or a robot as your teammate. I want to understand what is the nature of your interaction. If we take what we know in team science into a team task analysis, you look at coordination, demand analysis, if you focus on understanding that, then I think you will get the kind of competencies, the kind of need that they have. And so I think it's that. We stick to the basics. And for years, at least the 40 years since we started all this [inaudible 01:09:57] movement and [inaudible 01:09:57], it has served us well. So that's what I will focus on. So to answer your question about training, training may or may not look any different.
But I'll give an example, that it kind of, it made me think about this. So Scott and I were asked by a manufacturer to look at a new kind of team that they were forming, which was a robot, a human, and an algorithm, automation. They used to work as a three person team, humans. And now they have changed so they have all kinds of problems. At the end, to me, what I got out of that was it's the nature of the teamwork that matters, not who is next to you who is a machine or a robot. And that's what we need to do I think. And so in the work that I've been ... once in a while I get asked to consult on human-robot or robot to robot teams. At the end, we talk about the same stuff. Backup behavior, informational exchange. We talk about the same stuff.
Daniel Serfaty: Thank you. I think going back to first principles will be very important here, but also be open minded enough to understand that because we don't have enough words in the English language, we still call that intelligence, artificial intelligence, we still call that teams maybe because we don't have a word for that new form of social structure. And there is a lot of controversy in the human machine community about whether or not that AI is a tool of the human or it's a teammate. And we're going to have a series, as part of this series of podcasts, debates specifically about that, tool or teammate. And the issue here is also about ... maybe one of the differences I want to offer to you is about that transparency or that trust. At that level, AI behaves sometimes in a very unpredictable way. And not because it's capricious, it's because it absorbs an ungodly amount of data. And from time to time there is an emerging behavior that emerges and happens because of some deep structure that the AI has learned.
And the AI learns not only deep structure about the task and the task interdependence, but also about the human teammate. And therefore, that kind of unpredictability is really interesting because it forces us to have a level of explainability and transparency perhaps for each other that occurs very naturally with humans because that's our DNA perhaps, but doesn't occur naturally between humans and intelligent machines.
Eduardo Salas: That's a great point, Daniel, because what I do worry about about all this stuff, really it's not a team issue per se, or maybe it is, I don't know, but what I think about is ethical issues. For example-
Daniel Serfaty: Tell me more about that. What are you worried about?
Eduardo Salas: Well, I'm worried about that these things will have a dark side as they're interacting where there's no boundaries. What I'm afraid of sometimes is it's confronted with ethical issues. So healthcare is going in that direction a little bit with robotics and all this kind of stuff. And they're beginning to look at the ethics of this. Because can the AI, can the automation, can the robot detect who they have in front of them, what kind of person they have, what kind of history they have, I mean all this kind of stuff. So you're right, they have a [inaudible 01:13:11]. What they're more worried about, the trouble if you will with more AI is about ethics and who monitors.
Daniel Serfaty: Is our field, Kara, Scott, equipped to deal with the ethical considerations that comes with this introduction of new learning, intelligent machines in our work? What does the IO psychology community have to offer in that area, or should we leave that up to the philosophers? I'm asking the tough questions.
Scott Tannenbaum: So we can't leave it to the philosophers, although they have a role in this. We can't leave it to the technologists because they have a role in this. In some ways, psychologists, IO psychologists somewhere can sort of bridge there. Historically, we have worked in man-machine interface. Historically, we have asked questions about ethical and appropriate behavior at work. We do interface with technology. So we're not the right people to program it. We're not the right people to ask the big questions. But maybe sort of where the rubber meets the road, we're the right folks to be able to facilitate and ask the right questions. Earlier, when you were talking about, Daniel, what implied to me kind of this emergent learning that occurs that's inherent in some forms of AI, that that's where some of the risk points occur because they're quantum leaps or they're divergent. And they could be much better or they could be much worse in some ways. It made me think of a parallel in some of the research that we've been doing on informal learning in humans.
So informal learning is in contrast to training where there's a preset objective, a preset curriculum, a preset group of experiences to learn X. Informal learning occurs very naturalistically. Humans do this all the time. The vast majority of learning in organizations is informal learning. So as we try to prepare people to be better, faster informal learners, one of the risk factors is they're going to try things that they probably shouldn't try and they get in trouble. So we've been coaching organizations to think about red, yellow, green charts. You're going to take this person, they're relatively novice, they're starting to learn, we're going to put them in a situation. What are those things that, if they get a chance to do it, just run, green, don't ask? What are those things that are yellow like, "Do it, but only if there's some backup there?" And what are those things like, "We don't want you touching this thing in the nuclear power plant facility and testing it," red? Is there an equivalent to that in the case of emergent intelligence?
Daniel Serfaty: There is an equivalent. But what would worry me as both a technologist and a practitioner of teams is not the red, the yellow, or the green, it's the color that I haven't designed the system for.
Scott Tannenbaum: That's good.
Daniel Serfaty: That's really the known unknown so to speak that we have to worry about. But I would like to spend another several hours with you guys and maybe I will in another podcast. But I would like right now for each of you to share with our audience kind of a bit of a forecast of, how do work teams, if you look at work teams, pick a domain, or in general look say in 10, 15 years, are they the same teams? Are we going to see an evolution of the very concept of teams, evolution either coming from the field or evolution coming because there are going to be some new scientific breakthroughs or development that's going to enable us? So help us imagine, help our audience imagine the next 10 years, 15 if you prefer. So who wants to take that on first?
Eduardo Salas: The future is about teams of teams. It's about multi team systems, teams of teams where your teammates are automated, maybe a robot, an algorithm, something like that. And so I think that's where we're headed in terms of what the science needs to do. But I think we still need a trauma team, four or five individuals taking care of trauma. We still need a pilot flying an airplane I think. At least I wouldn't go into an airplane that is fully automated. And so we're going to need to stick to the basics as well. But I think what I see on the horizon is teams of teams, people with conflicting priorities, many bosses, is the, Kara, day to day activity that she's been doing where we need more work. So if I were to say my next book on teams was called, it would probably be around Multi Team Systems That Work, that would be the title 10 years from now. At least that's what I'm thinking.
Daniel Serfaty: Thank you, Eduardo. That's a pretty exciting future and Kara is already, as you said, already in the middle of the future. So Kara, you may want to respond to that before I pass the microphone to Scott.
Kara Orvis: I guess the question is two parts for me. One is are the tasks for which we have teams going to change in any meaningful way? And I think it's not going to change that drastically in 10 years. I think we'll still need very similar types of teams to do similar types of tasks. I do believe we're going to have access to more technology as team members. So I do believe this concept of human-machine teaming is going to become more important. And with the pandemic, and I have worked virtually for 10 years now distributively, I think people are going to be more open to technology enabled teams, like technology that helps people work well together. I just don't see huge changes in the foreseeable future. If I read an article from 50 years ago, a lot of those concepts still are meaningful today as they were then. So I don't see a lot of change, a lot of meaningful change. I could be wrong, but that is what I'll say.
Daniel Serfaty: Scott, what's your take on this one?
Scott Tannenbaum: I don't think that maybe I'm sharp enough to see a discontinuous, nonlinear direction in this. And maybe I can answer this question kind of with a corporate lens, like looking at work teams in corporate settings. So what do we know? We know the trendline has been greater use of teams. We know this from reports that are interviewing CEOs. They're thinking more and more about teams as building blocks. Collaboration requirements are up 50% in organizations. It's almost impossible to work as an individual contributor lone wolf anymore. So if I follow that trendline, I would expect to see more, which sometimes scares me because although I'm a team person I think you have to form teams for the right reasons and sometimes they're not being formed for the right reasons. So that's a caution. Spans of control seem to be getting larger, so we have more people reporting into leaders. That model seems to be more prevalent. So when you have teams, the leader has more people that they've got to be able to work with and keep an eye on, which means they're less able to see everything.
Scott Tannenbaum: So to me, the trend is more shared leadership. Not that we're formerly appointing a second or a third formal leader, but teams that operate in a way that team members are expected to step up and demonstrate some leadership behaviors. I think we'll see more fuzzier boundaries. Already, I talked about kind of a fuzziness, but even more of that. Which, is part of that team of teams, but even mushier maybe. I agree with Kara. More than likely there will be a greater use of technology, perhaps some nonhuman team members maybe more in terms of decision aides in the corporate environments than kind of team members. And I also expect to see teams that are more rapidly adjusting. So huddling, debriefing, membership changing more dynamically in these corporate settings with a mix of probably live and virtual work that we learned from the pandemic. But at the end of the day, the drivers remain the same. We've just got to figure out how the drivers apply to that new environment.
Daniel Serfaty: Thank you for your answer, the three of you, because I think that on the one hand, you feel confident that the basis, the fundamentals are probably not going to change. What's going to change is perhaps the circumstances, a much more complex, connected world that will enable these teams of teams and multi team systems to work and basically multiple memberships in multiple teams maybe simultaneously. So maybe our next generation is going to be more connected and because they're are going to be more connected, they're going to belong to more teams than we do. And because of that, they will develop perhaps a larger portfolio so to speak of team competencies. That's an exciting future. Thank you again, Dr. Eduardo Salas, Dr. Scott Tannenbaum, and Dr. Kara Orvis for being my guest today. You really made it both extremely insightful but also entertaining.
Daniel Serfaty: And to my audience, make sure you check out Eduardo Salas and Scott Tannenbaum's new book, Teams That Work: The Seven Drivers of Team Effectiveness, published by the Oxford University Press. It's a must read for anyone who leads any type of team. Thank you for listening. This is Daniel Serfaty. Please join me again next week for the MINDWORKS podcast and tweet us at MINDWORKS Podcast or email us at [email protected] MINDWORKS is a production of Aptima, Inc. My executive producer is Ms. Debra McNeely and my audio editor is Mr. Connor Simmons. To learn more or to find links mentioned during this episode, please visit aptima.com/mindworks. Thank you.