When is theory powerful?
Whether they like it or not, decision makers use theory all the time, even if it is only their own private beliefs about why things happen and the likely impact of their decisions. Theory does not need to be complex – it is simply an explanation for what causes what, and how – without which there can be little confidence in the likely effect of any strategy we develop or decisions we might take.
Theory is powerful when it is general (works in a wide variety of situations) useful (tells us something we can affect) and true. We should be skeptical about supposed “rules” of successful strategy that might seem to make sense, but are not in fact reliable, such as “the first firm to enter a new market will always beat firms who follow later.” This would be a general rule, and useful, but is unfortunately not true. Confirming the soundness of management theory is far from easy. Large scale controlled experiments are generally not possible or desirable and management typically resists becoming a lab rat! Nevertheless, there is a strong case for at least some experimentation — as is commonly done for new product launches – and there is an increasing use of “business intelligence” and analytics to support decision making. Business school research often seeks to confirm theories about what causes what by collecting large quantities of data and looking for statistical correlation between possible causes and effects. Unfortunately, the uncertainty and complexity of real-world causality is often so severe, and difficult to trace, that even a strong correlation offers little more than mild support for any theory. As Professor Clayton Christensen of Harvard Business School has remarked, we can often say little more than the business equivalent of “most flying things have feathers and flap their wings.” Attempts to design flying machines based on that statistically significant observation were not notably successful!We can start to deal with this problem by identifying parts of the explanation for performance where concrete causal connections canbe stated confidently. To do this we need to work back through the problem and identify the key resources that affect the system. Whether the outcome of concern is financial or non-financial, or a combination of both, the process is the same. Whatever the focus, the key issue remains identifying how performance is changing through time. Taking the example of airline Ryanair — profit results from the revenue the company receives from the fares that passengers pay, and from other items, minus its operating costs. These are split into some major categories — staff costs, the costs of operating aircraft, airport operations and routes — plus marketing and other costs. The causal relationships here are clear and unambiguous:
- profits = total revenue minus operating costs
- total revenue = fare revenue plus ancillary revenue
- operating costs = aircraft costs plus route costs plus airport costs plus staff costs plus marketing costs plus other costs
If that causal explanation for profits is accurate for y/e March 2006, and the business has been conducting the same activity in the past, it was also accurate for every previous year. It is therefore possible to join up time charts of those items in the same way, as shown in the figure above. Each chart in this figure portrays the historic values for the item named. Although this diagram may be an unfamiliar view of a company’s income statement, it is just showing in a graphical, causal layout the same data we normally see in spreadsheet form.
What these diagrams mean…
Since the approach relies heavily on diagrams such as the one above (this is Figure 2.3 from the book), it is important to be clear about their features. The box at the lower right of the diagram gives a detailed legend for the time charts in the figure. Every item includes a specific, quantified scale on the vertical axis, and a specific time scale on the horizontal axis. The current value “today” — usually the latest time for which data is known — is highlighted as the green value, just above a vertical dashed line for the time at which it applies. The time path of historical data is shown as the solid green line. When adding forecasts, these are denoted by a dashed green line.
It is also important to be clear about what is meant by the connecting arrows in these charts. Word-and-arrow diagrams are common throughout books on management and strategy and usually imply some kind of causal relationship between the factors that are linked by arrows. Often, such implied relationships encompass a whole chain of causality, with all the ambiguity and complexity discussed above. In SMD, every such link will have the more localized and precise meaning that ‘A’ can be calculated or estimated from the values of ‘B’ and ‘C’ at each point in time. The figure above follows this rule — it displays the relationships in the company’s income statements in a graphical, time-based form. These relationships hardly merit the term “theory”, being simply the conventions by which we determine a company’s profits, but they are nevertheless rigorous, reliable and well understood.
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Until next time…
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A small, but critical change in perspective…
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A common response to the principles described in chapter 2 is ‘but that’s obvious!’– well yes it is, but if you don’t ask the right questions accurately, you are not likely to find the right answer. I pointed out to a consulting firm some time ago that their revenues come from the projects they do, and the fees for those projects, and that their profitability depends on pricing the staff time on each project correctly. 
They had previously been exhorting their management to ‘improve staff utilisation’, but they had not appreciated that their people have no decision lever connected directly to this ratio [aside from simply firing people]. So a simple start-point was to put together a model of what a profitable project actually looked like – how many man-days by which types of staff, costing how much, and priced at what level. Their targets for growing revenue and profits therefore came down to how many clients had to be won, delivering how many projects, requiring how many staff.
Obvious, of course – but it was not laid out in existing plans.
If you are interested in the topic – there’s a great article on the importance of theory from Clay Christensen at Harvard and Michael Raynor of Deloitte – “Why Hard-Nosed Executive Should Care About Management Theory”, Harvard Business Review, Volume 81, No.9, (September 2003), 66-75.
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