Despite ample evidence this approach was wrong, until relatively recently the vast majority of economic and financial theory was based on the assumption that people always made rational decisions in these areas.
The academic fields of behavioral finance and behavioral economics have changed this by including social, psychological and emotional factors in the analysis of economic and financial issues.
A very good overview of behavioral issues that trip people up is 10 Behavioral Pitfalls: A Primer from Morningstar. This is part of a broader - and very interesting - special section on behavioral finance.
While targeted at investors, behavioral issues also cause problems for business decision makers.
Of the 10, I think the following 5 are most relevant to entrepreneurs and small business owners:
1. Loss Aversion key quote:
Investors have been shown to be more likely to sell winning stocks in an effort to "take some profits," while at the same time not wanting to accept defeat in the case of the losers ... It also doesn't help that we tend to feel the pain of a loss more strongly than we do the pleasure of a gain. It's this unwillingness to accept the pain early that might cause us to "ride losers too long" in the vain hope that they'll turn around and won't make us face the consequences of our decisions.
In our work we often see examples of small businesses refusing to cut their losses, even when it's clear there is little or no hope of a turn around.
2. Sunk Costs key quote:
Another factor driving loss aversion is the sunk cost fallacy. This theory states that we are unable to ignore the "sunk costs" of a decision, even when those costs are unlikely to be recovered.
Bad business decisions are often made because of an unwillingness to accept that money already spent is gone.
3. Anchoring key quote:
Ask New Yorkers to estimate the population of Chicago, and they'll anchor on the number they know--the population of the Big Apple--and adjust down, but not enough. Ask people in Milwaukee to guess the number of people in Chicago and they'll anchor on the number they know and go up, but not enough. When estimating the unknown, we cleave to what we know.
Anchoring is one reason why innovation is hard. Because we're anchored in our current way of thinking, it's hard to see alternatives.
4. Confirmation Bias key quote:
Too often we extrapolate our own beliefs without realizing it and engage in confirmation bias, or treating information that supports what we already believe, or want to believe, more favorably.
Regular readers know that confirmation bias is my second favorite bias, just after survivor bias. We see it all the time, including in our own work (it's a common research problem) and how we run our own business.
For great, nightly examples of confirmation bias, watch Fox News or MSNBC. Both cable networks are masters at twisting data and information to support their prior beliefs.
5. Framing Effect key quote:
The framing effect addresses how a reference point, oftentimes a meaningless benchmark, can affect our decision.
Let's assume, for example, that we decide to buy that television after all. But just before paying $500 for it, we realize it's $100 cheaper at a store down the street. In this case, we are quite likely to make that trip down the street and buy the less expensive television. If, however, we're buying a new set of living room furniture and the price tag is $5,000, we are unlikely to go down the street to the store selling it for $4,900. Why? Aren't we still saving $100?
Unfortunately, we tend to view the discount in relative, rather than absolute terms. When we were buying the television, we were saving 20% by going to the second shop, but when we were buying the living room furniture, we were saving only 2%. So it looks like $100 isn't always worth $100 depending on the situation.
The framing effect often comes into play when actual performance is compared to a prior plan - even if the plan assumptions were bad or the performance is different for reasons outside of the plan.
We've seen lots of examples of good products being killed because they didn't hit a plan number - and weak products getting continued investments because they did hit a plan number - even if everyone knows the plan was bad.