In “Good to Great, or Just Good,” authors Bruce Niendorf and Kristine Beck evaluate Jim Collins’ book, Good to Great. Although Good to Great was touted as being a great business book, Niendork and Beck point out that the book is based on false assumptions due to two fatal research errors: data mining and confusing association and causation amongst the five commonalities of success Collins finds in the chosen 11 firms.
Rather than deriving the theory from the data, as Collins had intended, the authors assert that Collins’ use of data mining provided five random patterns among the 11 firms, but did not actually test that data against other firms to see if these are in fact determinants of success or just coincidence.
In addition, although Collins attributes the five characteristics as being the cause of success at the 11 firms, he does not provide adequate research to do so. According to Niendorf and Beck, it is unclear whether the success of the firms allowed these 5 characteristics or vice versa. Thus, one cannot assume that this was the cause of success or rather a symptom of success.
It is interesting that even with these flaws, the book became a success amongst high profile managers. Perhaps this goes back to my point in my previous blog regarding evidence-based management where managers/organizations try to find one-size fits all solutions to success. Rather than reading a pop-culture book about management that provides solutions with no real evidence and take it at face value, managers would benefit more from seeking the knowledge ad then evaluating it to see the logic about whether these solutions really worked for the 11 firms, and more importantly, if it would work for their firm. Managers also need to question whether there are companies who have these characteristics who may not be successful. It is important to continuously work toward gaining knowledge, but if we do not question where the information came from, how it worked, why it worked, etc. then we are setting ourselves up for failure.