So with that introduction under our belts, we're gonna move now into the four main segments of this module. We are gonna talk about describe collaboration networks, when we talk about collaboration patents we are gonna focus specifically on thinking about in terms of networks. And then later we will move into mapping, on how to map collaboration networks, how to evaluate them and how to intervene. So moving into segment two, how do we describe our collaboration networks. So what I'm gonna do in this segment is to introduce you to a number of building blocks that can basically provide some vocabulary, some terminology for analyzing collaboration networks. But let's make sure that we understand exactly where we're coming from because I went and introduced you to the idea of networks pretty fast. So what are organizational networks? Just to make sure we really get this. So let me show you first a picture that is gonna look like many pictures that you're probably familiar with. So this is a formal structure of an organization. It's basically an organizational chart. It's a real organizational chart, it comes from the exploration and production division of a large petro-chemical company. And this is the senior executives and they're reporting structure within this organization. So you can see who reports to whom just like the organizations that many of you will be working for. You'll probably recognize you have a reporting structure, an organization chart in most organizations, right? But when we talk about an organizational network we mean something a little bit different. For this particular organization for exploration and production division. We can draw an organizational network that tells us who actually interacts with who. Who actually collaborates with whom in this organization not whose supposed to collaborate with him or who formally reports to him, but who actually interacts with him and the network that you can see when you draw this kind of a chart is really quite different from the one you see in the formal organizational chart. So if you take Cole who's at the center of the informal network, you see if you look at the formal structure the organizational chart, Cole is way down there in the side reporting structure, right? But in the organizational network he's very, very central that means many people are coming to him and he's probably communicating and interacting with many people to get his work done. So we see quite a different structure and it sort of draws attention to some people that we might not notice as much if we're only looking at the formal organizational chart. So we have the formal structure but when we're talking about organizational networks we're talking about the informal structure of the organization, who actually works with who? It's worth noting that there are many different kinds of organizational networks so we're gonna be focusing throughout this module on collaboration networks. Which might be who shares information with whom, how information flow around the network, who shares knowledge with whom. But we could draw networks that are simply based on who talks to whom. Communication, who's friendly with whom, were friendship ties. Who gives advice to whom, who trusts whom. So there are many different, and their could be many others. There are many different basis for drawing network maps, but we're really focusing on collaboration networks here. So the kinds of ties that we're interested in, collaborations between employees inside organizations. So back to the example of a collaboration network that I showed you in the previous segment. So again, if you remember there are 15 people in this new product development team, they're in three different parts of the organization, but they're working together to get things done, and often seeking information from each other in order to get their work done. So, I wanna take a minute to ask you to think about this question. Just looking at this network, who do you think you might want to be? And why? So if you look at the network, who do you think you might want to be in that network and what would be your reasons? Just take a second to think about that. So, a quick reaction, if you're looking at this network for the first time, might well be, well, I think I might wanna be Paul or maybe I wanna be Helen in this network, right? And why would we say Paul or Helen? Probably, because they're very central, they look pretty important in this network. They're in the middle of all the action, they've got lots of people coming to them and maybe they're going to quite a few people themselves. And so they're in the middle of where it's happening, right? And so that seems like maybe a good network position to be in. But can you think of any reasons why you wouldn't wanna be Paul or Helen? Why is not a good idea sometimes to be in the middle of a network? So one possible reason that you might be thinking If you actually have a lot of people coming to you for information or advice might be, well it's actually takes a lot of time. When a lot of people are knocking on your door and asking you all the time for things that they need, it takes away from your structured work time. So that might be one reason, right, so maybe you're feeling a little bit overloaded if you're central in the network. Now somebody you might not have wanted to be. If you think about who would you least like to be in this network, right. Who would you least want to be? If you look at the map, probably pretty quickly you might say well I wouldn't want to be Kevin. Kevin's pretty isolated out there. He's all by himself. Nobody's talking to him. He's not talking to anybody. It doesn't seem like it's much fun to be Kevin and that's probably true. Kevin seems pretty cutoff. Now why might Kevin like being cut off? Why might this actually be, in some ways, an advantageous position? Well it's not good to be out of the network flows, but everybody's leaving Kevin alone. Maybe Kevin just gets on with his job, really enjoys what he's doing, has plenty of time to get things done. So these are just some very initial intuitions. But what they start pointing us towards is this idea that where you're positioned in a collaboration network really matters. So the structure of the network and your position in that network is gonna matter a lot for you to get your work done and for how overloaded you feel, how satisfied you feel, whether you enjoy being in the workplace, lots of different things. So when we look at a network map like this, there's a lot going on and we can get a little bit of an intuition about it. But what I wanna do is to build in some real basic building blocks here to help us look at this kind of a network with more analytic rigor. And think about, okay, so what are the specific things that we can learn when we look at even the simple kind of network like this? And this is a pretty simple network, it's only 15 people. So let's move towards thinking about how can we describe these collaboration patterns? And I'm gonna introduce you to five building blocks. Network size, strength, range, density, and centrality. Okay, and we're gonna work through each of these one by one. And I'm gonna just use a very simple form of a network to help you understand the basic idea behind each of these because they all have different ways to describe even the kind of network that we just looked at, as well as more complex networks inside organizations. So let's look at network size to building blocks first, those are building blocks. Network size. Okay, so building block one network size is very straight forward, right? We've got two people two employees in our organization, say Sarah and Ted, and what we're doing here. Is we're mapping the number of people that they collaborate with, or that they go to and exchange information with, right? And we can see that Sarah goes to five other people or has collaborative ties with five people. Ted, on the other hand, has collaborative ties with eight people, right? So Ted has a larger network than Sarah. Very basic, generally is it a good idea to have a larger network? Yeah, it's probably a good idea when you're networks relatively small, the more people that you're connected to, the more information you're gonna be getting and so on. If you're network starts getting really large though maybe you don't want more and more people so. There's nothing to say that anything, there's a particular network size that's good or bad at this point. We're just saying, how do we describe networks? Well, we can describe them in terms of the number of people you're connected to, in terms of its size. Now, here's the second building block. This is network strength, okay. So if you look at this network, here we have Sarah and Ted, and they both have five network ties, five connections to people that they're collaborating with. But the thicker lines mean that the ties are stronger, right? And so what you see is even though they both have five connections, Sara's ties, four out of the five of them are really pretty strong ties. Where as Ted's ties, only one of them is a strong tie, the others are relatively weak ties and that can matter. Strong ties can be really advantageous. You can really build trust with those people. You can exchange information with them a lot. You can believe what they say. But it also takes a lot of time to maintain strong relationships. So trade-offs again a network strength. So we've got network size, and network strength, are two important building blocks here. Again, we have stronger ties, we have weaker ties. The third building block is network range. So network range is a measure of how many different groups you're connected here. So here Sarah and Ted both have five collaborators, or five people that they can turn to, exchange information with. But Sarah, say Sarah works in marketing in the marketing department, all of Sarah's five connections are within the marketing department. Whereas Ted is connected to someone in the marketing department, but he's also connected to somebody, say the red dot is R and D, and the blue dot is Human Resources, the green dot is finance, and the yellow dot is operations, right? So, Ted has a much wider network range, right? He has a high range network. Whereas Sarah's is a low range network. And when you have higher network range, you're connected to many more different sources of information. So your ability to get information from different sources, from a wider range of sources, is much greater. So that's the third building block. The fourth building block is network density. So this is getting a little bit more complicated here. So network density, you start with, just for simplicity's sake, Sarah and Ted both have five people that they exchange information with. But now what I'm gonna do is give both Sarah and Ted, each of the people that they're connected to, I'm gonna give each of those people five people that they exchange information with, and you can see some quite different patterns emerge. So I'm gonna give each of Sarah's connections five connections of their own. What I'm giving them, is the same five people as exist already in Sarah's network. So each of those people is connected to each other and that's what we call a very dense network. On the other hand, I'm gonna give Ted's five people, five people that they're connected to. Actually I'm gonna give them four, but five people they are connected to but they are all connected to really different people, right. So each of them has five different people that they are connected to and so you can see really different patents in this network as a result of the way that their second order connections work like their friends of their friends or the connections of their connection. So, Sarah has a high density network, where everybody knows each other basically and Ted has a low density network, where really Ted is the presenter, and the people around the edges really don't know each other at all. So, again there can be benefits to a high density versus a low density network, right. So if you're in a high density network, where everybody knows each other, that has some advantages. There's a lot of trust in that kind of information, because if somebody tells you information, you can easily confirm with your other friends whether that information's right or not. But in a high-density network, you only have those five people to give you information. If you are in a low density network, Ted has a huge advantage in a sense of he has access not only to the five people that he has connections with but those five people are getting information from an additional in this case four people each. And so they're getting a lot of extra information that doesn't exist in the high density network. So in a low density network, you have access to a lot more information, but you don't have a lot of that trust and verification because somebody can tell you something, and you have no way to verify whether that source is reliable you have nobody else who's connected to them. So that's a high density versus a low density network. And then the last building block, which is very related, is the idea of network centrality. So, Sarah and all her contacts are all equally central in their networks. They're all connected to the same number of other people and they're all connected to the same set of other people. So they're all equally central, but you can quickly see that Ted's network. There's a lot of variation and who's most central. Ted has most central there. He really basically has access to all the information and that's sort how to system. The people that he is connected directly to are moderately central but the people that they're connected to are relatively peripheral. They only have basically access to, one of Ted's friends. So centrality, we had those intuitive ideas about centrality right from the beginning when we looked at our new product development team. Here, we can see that centrality is actually something we can measure and track. And that is has different implications, depending on the configuration and structure of your network. So back to our example of this collaboration network. You see some, these five building blocks give us a different way to think about, or a more rigorous way to analyze the network features for each individual on this network, right? So, we can ask, for each of these individuals, what's the size of their network, right? It's pretty clear that Paul has a pretty large network, and Kevin has a pretty small network, right. And we got that just by looking at this map in the first place. We can look at network strengths, and whether those ties are strong, and perhaps two-directional, and whether they're pretty weak. We can look at network range. So if you think about that, who has the highest range in this network. So remember range is about how many different part of the organization for example, different groups you're connected to. So here, it's pretty clear that Paul has the highest network range, right? Because Paul is connected, he's in finance, and he's connected to people who are both in manufacturing and in marketing, and nobody else is connected to all three groups in that way, so he has the highest range. If you think about density, who has the densest network? So one of those groups where everybody knows everybody. So Helen's network looks pretty dense, right? Whereas Paul's network is pretty space, right? Paul doesn't have a lot of people who know each other. Paul's one of those very central people In a networks that's pretty sparse. Helen is a pretty central person in a network that's pretty dense. So they're both high in centrality. Helen's network is denser than Paul's network just by eyeballing this. These are numbers that we can calculate and are formulae for actually calculating how dense a network is and how central people are in their networks. So when we think about this question of who do you wanna be in this network and why? We can actually apply these building blocks, analytic building blocks to start understanding and preparing across individuals, and to understand their network positions. So those are the basic building blocks for understanding and describing collaboration networks. As we're gonna move forward, we're gonna think about how to map those networks. How do we generate a network map that helps us to see who is doing what and who is collaborating with whom inside an organization that we wanna study?
