商业书籍的阅读方法论

    商业建议类书籍的书评人总有汗牛充栋的资料供其研究。每每一本书递送至家门口时，它看上去总像是最后一本。所有的战略处方皆有全面的研究支持，每位作者似乎都具备令人敬畏的资质。出于两个原因，我们很难确定谁正在为相关讨论增添价值。其一，没有人有那么多时间读完所有这些著作；其二，作者往往有很强的动机从令人生厌的数据中归纳出确凿的结论。

    “为了支持一个假设，作者总是自觉或下意识地给数据强加一个其实并不存在的理论模型，”迈克尔•雷诺说。“毕竟，如果你盯着一块磅饼足够长时间，猫王的轮廓就一定会出现（流行音乐巨星猫王以喜欢磅饼著称——译注）。”雷诺曾与蒙塔•艾哈迈德合作撰写了《三个规则：卓越的公司如何思考》(The Three Rules: How Exceptional Companies Think)一书。

    《三个规则》同样遵循了这类书籍的常规范式：援引令人印象深刻的研究数字（2.5万家公司45年的发展历程），然后使用几页的篇幅阐述其研究方法。于是，我询问了雷诺一个问题：他自己是如何阅读商业书籍的？有没有一种方式让我们谦卑且迅速地评价研究结论，但同时又不放弃质疑精神呢？

    雷诺的第一个处方是：务必记住，有说服力的故事需要讲故事的人忽略杂音。对于公司传记类书籍来说，这一点尤为中肯，因为这种体裁需要讲述者省略在事后看来无关宏旨的人物和事件。

    他的第二个告诫与如何评价作者的因果关系论断有关。大多数聪明人都知道，不要把相互关系错误地理解为因果关系，但我们一直都在犯这个错误。或者，我们常常以其他人没有证明因果关系为由，不予理会他们的论断（仅仅因为某一个事件发生在另一个事件之后并不意味着前者导致了后者的发生）。的确如此，雷诺说，但“没有人能够拿出因果关系的证据。”因果关系确实存在——要是不存在的话，人们恐怕就没有那么大的激励一大早离开家去工作了——但在一个复杂的系统中，我们很难证明这一点。需要说明的是，任何有人类存在的系统皆是复杂的系统。

    此外，雷诺还建议我们小心提防菲尔•罗森茨维格所称的“晕轮效应”(the halo effect)。换句话说，一定不要让一家公司在某个领域的标志性成就所反射的荣耀影响你评价它在其他领域的表现。

    接下来要注意数据和你自身的局限性。为什么要充分考虑自身的局限性呢？看到具有统计意义的数据时，我们的直觉或许是非常可怕的。当我们捧起一本阐述某些公司的书籍时，我们倾向于假定这些正在被作者详细分析的公司其实已经取得了值得关注的成就。

    但“这是一个值得质疑的假设，”雷诺说。“如果两家公司的盈利能力差异微乎其微，比如说，某个五年期间内的资产收益率相差0.1%，那么你是否会悉心研究这两家公司，以理解导致业绩差异的行为差异呢？当然不会。因为这个时间段太短，而这个差异又几乎可以忽略不计。”

    所以，我们一定要留意样本的选择，以及分析的时间框架。“在一个很短的时期内，运气成分很可能大于技能因素。”

    雷诺的最后一个建议涉及商业成功案例研究频频遭到指摘的一面。一家受到某本流行商业书籍称赞的公司——比如吉姆•柯林斯在其2001年的著作《从优秀到卓越》(Good to Great)一书中表扬过的电器城公司( Circuit City)——最终令人大失所望。批评家们随后一拥而上，纷纷指责作者搞砸了研究（“嘿嘿，等一下，你不是说这家公司非常了不起吗，怎么才过了三年，它就破产了呢？你其实并不知道你自己在说什么。”）这种评价不仅有失公允，而且相当短视。雷诺说：“在他们看来，首先必须好好研究一家永远都表现优异的公司，然后才可以从中提炼出某种结论。这种观点完全是无稽之谈。”   

    People in the business of reviewing business advice get it by the truckload. After a while, every book delivered to your door looks just like the last one. All strategy prescriptions are backed by comprehensive research and every author is impressively credentialed. It is hard to determine who is adding value to the conversation for two reasons: One, no one has time to read all these books; two, there's tremendous incentive for an author to spin hard conclusions out of mucky data.

    "It's very tempting, consciously or subconsciously, to impose a pattern on data that isn't really there in order to support a hypothesis," says Michael Raynor, co-author with Mumtaz Ahmed of The Three Rules: How Exceptional Companies Think. "After all, if you stare at the poundcake long enough, Elvis's profile will surely appear."

    The Three Rules conforms to type by citing impressive study numbers -- 25,000 companies over 45 years -- then allocates several pages to unpacking their study methodology. So I asked Raynor how he reads business books. Is there a way to assess research claims quickly, respectfully, but skeptically?

    Raynor's first prescription is to remember that persuasive storytelling requires that the storyteller leave out the weeds. This is especially relevant to corporate biographies, since the form requires the narrator to omit people and events that turn out to be irrelevant only in hindsight.

    His second note of precaution is about what to do when presented with causal claims. Most smart people know not to mistake correlation for causality, but we do it all the time. Or we dismiss someone else's claims by saying that they haven't proved causality (just because one event happened after another doesn't mean the first happening caused the second). True enough, says Raynor, but "nobody has evidence of causality." Causation exists -- there would be less incentive to leave the house in the morning if it didn't -- but it's difficult to prove in complex systems (and any system that includes humans is a complex system).

    Raynor also advises watching out for what Phil Rosenzweig dubbed "the halo effect." In other words, make sure you aren't letting the reflected glory of a company's signature achievement in one arena color your view of their performance in other areas.

    Next, be aware of the data's limitations and your own. Why dwell on your own limitations? Our intuition as to what's statistically significant can be terrible. When we pick up a book that profiles certain companies, we tend to assume that the companies being profiled have, in fact, delivered noteworthy performance.

    But "that's an assumption that's worth questioning," says Raynor. "If two companies differ in profitability by 0.1% in return on assets over a five-year period, would you study those two companies to understand behavioral differences that drive performance differences? Of course not. Because it's too small a difference over too short a period of time."

    So watch for sample selection and time frame. "In a short season, luck can overcome skill."

    Raynor's last note concerns an all too common criticism of business success studies. Say a company praised in a popular business book -- for example, Circuit City in Jim Collins's 2001Good to Great -- ultimately disappoints. Critics then pile on to say that the author botched the analysis. ("Hey wait a minute, you said that company was great and then three years later they're in bankruptcy. You don't know what you're talking about.") That's unfair – and shortsighted. "This whole notion that you have to study a company that is perpetually excellent before you can learn something [from them] is nonsense," Raynor says.