First, a pun about the X-factor. Want to know where to get it? Modern corporate science has solved your deepest needs!
The chapters of Burns I have read so far have been very thought-provoking. Within the first four chapters, the most valuable statement he makes is on page 25. Here, he notes that the "basic leadership issue" is one of "agency versus structure." Although he doesn't carry this dichotomy very far, it's a useful way to understand the difficulties and ambiguities of the study of leadership.
Organizations, whether they are planned or not, are fundamental to leadership. Thus, the way people relate to one another enters into the question. So far in human experience, we have chosen to talk about such relations in terms of their structure. In the past, hierarchies have been used to understand organizations. Ideas about government tend to be very structurally-focused, under the assumption that a proper structure goes a long way to cause or encourage people to act (lead/follow) properly. In the 20th century, ideas about hypertext have changed how we think about structure and interrelationships.
Hypertext (most usually seen in the form of flowcharts and diagrams) gives us a way to map out complex groups of relationships and the nature of those relationships. However, such maps are only slightly useful, because they describe one of two things: a model of how things should be, or a model of how things are. They are a snapshot in time, and they don't reflect the reality of a situation through time. Heisenberg's uncertainty principle applies as well. By mapping and observing a structure, you may change it. By consciously attempting to reproduce natural structure, you may seal yourself off from the ability to cause that structure to appear.
These are quantifications, of the type that we see described in Navahandi, which shows us a very structural, observational-based mode of understanding. It is a scientific book, which attempts to use observations and tests to predict future occurrences or derive insights.
Burns, on the other hand, does not focus on structure much. Instead, he's interested in agency, the amount that an individual can influence a situation. Instead of thinking in static models, Burns thinks in terms of dynamics. Thus his ideas are harder to pin down. In some cases even, his list of Enlightenment moral ideals changes depending on the situation.
But Burns is interested in what we are interested in, how an individual within a system can influence the system around him. Notice that Burns is himself rather interested in systems. According to Burns, the way to accomplish good is not to actually work within a system to accomplish good. Rather, it is to change the underlying system to make it more likely to produce good. This is transformational leadership.
Thus Burns is not entirely concerned with individual agency or moral responsibility. He is more concerned about the actions of the aggregate and how their expectations/actions/ways of thinking are influenced by a transformational leader.
Oddly, this view doesn't really give us much hope. Or at least, for Burns, there is no ideal leadership. There is always room for change, and there are always enough bad things going on that continual, fundamental revamping is always necessary. He doesn't have much hope for the idea of getting it right the first time.
But I think Burns is right. When we start thinking about the questions of Leadership as a dichotomy of structure and agency, many of the confusions disappear. Sometimes, we're uneasy at talk of structure, and sometimes we're uneasy at talk of agency. By splitting them up and looking at them individually, we can begin to come to an understanding of how each works.
A New Kind of Science
Burns and Navahandi are both very interested in science and what scientific inquiry can teach us. Navahandi has classifications. Burns has the X-factor, which clearly points to his idea of some sort of formulaic element in leadership. There's only one problem. Until recently, no really good way of thinking has been capable of containing all of the factors (the X factor, if it exists, no doubt would the product of an extremely complex equation that itself is the product of an infinity of factors)
Enter chaos theory.
In A New Kind of Science, Steven Wolfram argues for a computerized model that comes closer than anything else in the history of humanity of quantifying reality. It's frighteningly complex, amazingly simple. While Wolfram's computer has not been able to deduce the existence of things such as rice pudding, slumber parties, or MTV, it has been able to model and mimic basic biological interactions and animal social patterns through the use of very simple Artificial Life components. Note, this is much different than Artificial Intelligence. Artificial Intelligence (AI) attempts to figure out thinking, and then program it into the computer. Artificial Life attempts to give software some basic rules for change, put it in an ecological environment with other simple software, and see which pieces of software evolve or go extinct. It observes how the software learns to interact with other pieces of software and watches as a mini-universe develops inside the software.
Instead of mapping out an organization in a slice of time, Artificial Life systems allow you to see development over time. They let you quantify and predict contingency.
Wolfram spent over 10 years in isolation planning his theories, which are themselves an example of transformational leadership. If Wolfram comes off as a success, we may never look at reality the same again.
Do I agree with him? Not really. But as a computer programmer, I always find the lack of precise systemization in sociology and psychology somewhat odd. If you're going to quantify, give me something that's good enough for me to turn into an algorithm. Otherwise, revise and rethink.
But Wolfram has figured out a way to take many of the ideas, assumptions, and goals of many social sciences, and turn them into a working computer model. I don't know if anyone could do any better -- barring the emergence of quantum computing.
Is the X factor really calculable? Probably not, at least not predictably so. It's too bad that Burns gives his ideas the veneer of science. But it's a tempting thought to think that we could derive that factor someday.