In this lecture, we'll look at the results of the leverage inventory. We've been collecting these for almost 10 years now with MBA, executive MBA, some executive ed students, and we can make some sense of it now. And it'll help you make sense of your results. To begin with, let's remember that the response scale for these is behavioral and it's about frequency, how often you do each of these things. So the response scale is from one to four, rarely or never to almost always. And again, we have four to six questions behaviors that add up to the score for each of these 12 tactics. So the report looks something like this, where we've listed the tactics along the Y axis from allocentrism and down to Ethos. And then we've reported your result, your frequency along the X axis from rarely to always. The first is your self-assessment. These are your scores according to the four to six items for each of these behaviors. The second piece here is the class average or the benchmark, whatever benchmark we happen to use. And it might have been the MBA, or the executive MBAs, or that the participants in this MOOC but at some benchmark. So the intention here is to kind of this is a self-assessment, so it's kind of like putting a mirror up in front of you and saying, "Hey, this is what you think is going on. And yet, we're going to put it in the language of the theory and the literature and give you a framework for thinking about it so that if you do want to change these, dial something up, dial something back, you can think about it in a more focused way". We sometimes do more than just self-assessments, sometimes we assess people's behaviors with a group of folks they've worked with bosses, subordinates, peers. We call these 360 evaluations or third party evaluations. And we've learned some over time about how the third party evaluations relate to the self-assessments. You have the self-assessment, so we thought we might share with you a little bit about what the third parties look like. The big picture is, they're highly correlated. The correlations across all 12 are on average or something like point five seven. Most of the classes we do, we see between point five and point six. Correlation between the scale averages for self-assessments versus the scale averages for 360. Here are the summaries for a pretty large sample, hundreds of students. And you can see that the self-assessments lie below the third party assessments for almost every tactic. And this is this kind of wrinkle we see with every population, where people report doing fewer of these things, or doing them less frequently than do 360 raters, the third party raters who sometimes provide service to us. So we don't know what truth is. We don't know whether people are doing more of these things and just not aware of it or that people believe or infer that others are doing them more than they actually are. But we do see a little bit of a bias, so you can rest assured that the odds are if you had 360 raters that you would have a little bit higher scores across the board. Some of these deltas are bigger than others. So for example, on exchange there's not much of a bias. On allocentrism, not much of a bias. Note that these are behaviors that are a little bit more observable. On intentionality, for example, the gap is higher. On might, the gap is a little bit higher. So you might use this to add a little context, and adjust, or imagine what the results would be like if we asked your peers and bosses and subordinates to rate you. You might wonder about differences between men and women. Certainly, my students often ask me this question. So here's one sample, one year. We have a 108 people in the sample. These are means across the 12, and what you see is probably there are no differences. So where we do see differences, they're the stereotypical differences. So for example, on might, men are rated as exercising might a little more frequently than women are. On allocentrism, the opposite. Women are rated as exercising allocentrism, this understanding others, trying to understand others a little more hard than men are. This is kind of consistent with the stereotype, right, which underlies the fact that we don't know what truth is. We don't know for a fact what they're actually doing. It could be that the ratings are just being consistent with stereotypes. But the big picture is, maybe to a surprising extent that behaviors are rated similarly across the sexes. Men and women aren't seen as acting that differently at least in the MBA and executive MBA samples that we've primarily collected. Now, we've talked so far in terms of 12 tactics and we built the survey around those 12 tactics. That's where the theory is, but it could be that we can talk about it in a simpler way, we can kind of aggregate those. One way to aggregate them is think about hard power and soft power. So traditionally people have talked about power in these ways. For millennia, they talked about one way of one form of power, hard power, military might, maybe economic might, more recently, mostly because of Joseph Nye, people have started talking about soft power. So Nye has a quote here from the Economist. He was certainly writing about this before then. But in this piece right after 9/11, Nye writes, "Throughout history, coalitions of countries have arisen to balance dominant powers. And the search for traditional shifts in the balance of power a new state challengers is underway. While potential coalitions to check American power could be created, it's unlikely they'd become firm alliances unless the U.S. handles its hard coercive power in an overbearing, unilateral manner that undermines its soft or attractive power. The important ability to get others to want what you want." So he was writing right on the heels of 9/11 basically cautioning the US about how they used hard power because it might impact their soft power. He had been talking for decades at this point about the impact of soft power, the importance of soft power. The argument basically being that one of the reasons the US had been so influential in the world in the 20th century was not just because of economic and military might, but because of their soft power. Other countries, other people wanted to be like the US. So others have talked about it broadly this way, the organizational scholar Jeff Pfeffer who has written a lot on power talks about some individual traits that are conducive to power. And he organizes them in two buckets, he organizes them as the capacity to garner support and allies. In this bucket, he puts sensitivity to others, flexibility, and the ability to submerge your ego. And a second bucket is the ability to survive in a competitive arena. In this bucket, he puts energy and physical stamina, focus, the ability to tolerate conflict. This looks a lot like hard power in the second bucket and soft power in the first bucket. So this is clearly the way people have been thinking about power, and we might be able to divide our 12 tactics into that. But we can also ask the data, what is the right way to simplify? Can we reduce from 12 down to two? Or maybe we can reduce all the way down to one or maybe we shouldn't go that far, maybe we should only reduce down to three. You can answer that question using a technical factor analysis. This is a way to reduce the dimensionality of data. We can distill 12, or if we had 20 or 30, we can distill those down to maybe two attributes, or three attributes. This method will tell us how far down you can go before you start losing, before you start throwing away information. It will also help us identify key patterns in the data. So we're going to use factor analysis to see can we simplify your report for you essentially. You have to remember 12 or can you kind of distill it down to something less. This is what we find when we use a factor analysis on on these reports. It tells us that there are three factors in the data, and this has been robust across a number of samples at this point. A factor is basically a weighted average, so it's going to take each of these 12 tactics and so give each tactic a certain weight. A tactic might have a lot of weight for some factors, and and not much weight on another factor. These are the weights for the three factors in our data. So factor A, big weights on ethos and might, low weights, actually negative weights on allocentrism and everything else is kind of in the middle. Factor B, the middle factor, big weights on team, networks, coalitions, pathos, exchange, and allocentrism. A little bit on SA and agency, but mostly it's those six kind of relationship tactics in the middle. And on the third factor, factor C, we see most of the weight comes from these bottom four, intentionality, logos, situation awareness, and agency. So what does that look like? One of the things about factor analysis that doesn't tell you what these things are, it doesn't give you names for these things, it just tells you what the weighted averages are. So if you look at the first, it looks a lot like hard power. So we call it a hard power. If you look at the second, these are all these relationship tactics. It looks a lot like soft power, so we call it soft. We were expecting those too. The interesting bit is this third factor, it is these three meta tools intentionality, situation awareness, and agency with the addition of logos. We didn't know that logos would necessarily travel with these other three, but it does. And we end up calling this smart power, and we end up with these three factors. We can distill your 12 down to three broader strategies, hard power, soft power, and smart power. We borrow the term again from Joseph Nye. So this is more recent work from him in a book a few years ago he published called The Future of Power. He has a chapter on smart power, and it was the first time we were exposed to the terminology. It turns out the Obama administration had been talking about that for years. In fact, he quotes Secretary of State, Hillary Clinton in her confirmation hearings as saying, "We must use what has been called smart power. The full range of tools at our disposal, diplomatic, economic, military, political, legal, and cultural picking the right tool or combination of tools for each situation." So this fits very well with our notion of meta tools and it maps almost perfectly onto the three that we have in that bin, the meta tool bin. And then additionally, our data say, "Well, you should also have logos in there." This logical reasoning. So that is the first cut on our data and a description of the data. And in the next section, we'll want to ask, "Okay, fine. But how does that relate influence?"
