W h y s o m e c o m p a n I e s m a k e t h e
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| FREQUENTLY QUESTIONS Q: Did you originally identify more than eleven good-to-great possibilities and, if so, what good-to-great examples did not make it into the The eleven good-to-great companies were the only examples from our initial universe of Fortune 500 companies that met all the criteria for entrance into the study they do not represent a sample. (See Appendix l.A for the selection process we used) The fact that we studied the total set of companies that met our criteria should increase our confidence in the findings. We do not need to worry that a second set of companies in the Fortune 500 went from good to great-not by our criteria, anyway-by other methods. Q Why did only eleven companies make the cut There are three principal reasons. First, we used a very tough standard (three times the market over fifteen years) as our metric of great results. Second, the fifteen-year sustainability requirement is difficult to meet. Many companies show a sharp rise for five or ten years with a hit product or charismatic leader, but few companies manage to achieve fifteen years. Third, we were looking fora very specific pattern sustained great results preceded by a sustained period of average results (or worse. Great companies are easy to find, but good-to-great companies are much more rare. When you add all these factors together, it is not surprising that we identified only eleven examples. I would like to stress, however, that the "only eleven" finding should not be discouraging. We had to set a cutoff and we chose a very tough one. If we had set a slightly lower hurdle-say, 2.5 times the market or ten years of ability-then many more companies would have qualified. After completing the research, I am convinced that many organizations can make the journey from good to great if they apply the lessons in this book. The problem is not the statistical odds the problem is that people are squandering their time and resources on the wrong things. Q What about statistical significance, given that only eleven companies made the final cut as good-to-great examples and the total size is twenty-eight companies (with comparisons We engaged two leading professors to help us resolve this question, one statistician and one applied mathematician. The statistician, Jeffrey Tat the 212 Collins University of Colorado, looked at our dilemma and concluded that we do not have a statistics problem, pointing out that the concept of "statistical significance" applies only when sampling of data is involved. "Look, you didn't sample companies" he said. "You did a very purposeful selection and found the eleven companies from the Fortune 500 that met your criteria. When you put these eleven against the seventeen comparison companies, the probabilities that the concepts in your framework appear by random chance are essentially zero" When we asked University of Colorado applied mathematics professor William P. Briggs to examine our research method, he framed the question thus What is the probability of finding by chance a group of eleven companies, all of whose members display the primary traits you discovered while the direct comparisons do not possess those traits He concluded that the probability is less than 1 in 17 million. There is virtually no chance that we simply found eleven random events that just happened to show the good-to-great pattern we were looking for. We can conclude with confidence that the traits we found are strongly associated with transformations from good to great. Q Why did you limit your research to publicly traded corporations Publicly traded corporations have two advantages for research a widely agreed upon definition of results (so we can rigorously select a study set) and a plethora of easily accessible data. Privately held corporations have limited information available, which would be particularly problematic with comparison companies. The beauty of publicly traded companies is that we don't need their cooperation to obtain data. Whether they like it or not, vast amounts of information about them area matter of public record. Q Why did you limit your research to US. corporations We concluded that rigor in selection outweighed the benefits of an international study set. The absence of apples-to-apples stock return data from non-U.S. exchanges would undermine the consistency of our selection process. The comparative research process eliminates contextual "noise" similar companies, industries, sizes, ages, and so forth) and gives us much greater confidence in the fundamental nature of our findings than having a geographically diverse study set. Nonetheless, I suspect that our findings will prove useful across geographies. A number of the companies in our study are global enterprises and the same concepts applied wherever they did business. Also, I believe that much of what we found-Level 5 leadership and the flywheel, for instance-will be harder to swallow for Americans for from other cultures. |