feed back into the audit and investment decision processes. They learn by their mistakes. However, at the other end of the spectrum you have organisations that don’ t have a well-formulated and tested business case process and, as a result, have little available to learn from.
My view is that various optimistic biases are something that some companies fall into and others don’ t.
At one end, we have sophisticated customers who routinely make big, risky decisions, who would say they have risk management procedures in place that won’ t allow inside-view thinking to apply. Of course, not all large companies would have the best processes but the market doesn’ t let them get it wrong for long. At the other end, you have less sophisticated companies making big bets, and I think this is where optimism is rampant. These are the corner stores, the restaurants that open and close overnight, smaller companies listed on the stock market who bet the farm.
DL: It is interesting to look at interorganisation decision-making, to figure out what types of problems attract biases. There is an old, but still relevant Harvard Business Review article 3 that looks at people’ s risk preferences within an organisation. The article illustrated that people at the bottom of the organisation would mirror the risk preferences of their superiors. This led to gross inconsistencies in decision-making because small gambles should be practically risk neutral, while larger gambles – those made at the top of the organisation – are more likely to be risk averse.
CM: Again, I do think the extent to which biases play a part depends on the sophistication of the managers and the processes. Right now if you went to any of the top 100 companies in Australia, you’ d probably find most have sufficient procedures and experience for these biases not to apply. I would have thought that it is largely where you have infrequent decisions and the size of the risk is large that you’ ll see the pitfalls of optimism.
DL: How do you square that with some of the statistics for failure rates in North American manufacturing? There have been a number of studies showing failure rates of up to 60 per cent after five years and as much as 80 per cent after eight years.
CM: Did the analysis extend to understand the drivers of failure? DL: No, these are just the raw statistics. CM: My hypothesis would be that
“ WHERE YOU HAVE INFREQUENT DECISIONS AND THE SIZE OF THE RISK IS LARGE... YOU’ LL SEE THE PITFALLS OF OPTIMISM.”
optimism may be a contributor to failure. However, other market factors may have been taking place. For example, the entry of unforeseen low-cost manufacturing from competitors in Asia.
DL: In the cognitive psychology of competition work I do, 4 one of things I like to get across in the classroom is that failure in business is normal. One of the ways I demonstrate this is through a simultaneous entry game. You have, say, 14 people in a room and they know the capacity in the industry is three people and if more than three people enter, on average, they are going to lose money. It’ s not about who plays the game best or who best answers the questions, it’ s about averages. So you go through sequentially and ask,‘ Okay, are you in or out of this market?’. You come to market capacity plus one and this person says,‘ I’ m in’.
CM: What I’ d say to a client who says:‘ I’ m in’ is,‘ there is excess capacity in the market now, so how are you going to differentiate what you have?’. There are generally only two ways to enter a crowded market and do well. One is to have something that is so differentiating to the market’ s value proposition that you are not really competing with the rest of the players in that market. The other is to have such superior economics, for example, either through scale or some sort of advantageous buying power, that allows you to compete profitably at a lower price. If either of those things are missing, don’ t go in. Just the ability to compete using business processes and technology equal to that of your competitors will not differentiate you and allow you to succeed.
DL: Yes, probably one of the easiest ways to explain RGN in a few sentences is to take the example of the restaurant business. Almost everyone that opens a restaurant thinks they are in the 90th percentile of cooks. The problem is that everyone else who tries to enter thinks that, and they don’ t have any way of differentiating themselves.
There is evidence and data that suggests that even when decision-makers have reference data at hand, they don’ t always take it into consideration. Instead, they continue to focus on their own optimistic view of market entry capability.
How would you propose companies go about achieving unbiased capability, both in the sense of organisational politics and in generating and acting on accurate data?
CM: There has to be a robust process of audit, a very strong capability of sharing responsibility, and access to learning. Organisations that have poor knowledge management skills don’ t really know if they are using the right set of data. For example, if you’ re trying to work out how big a market is it is very hard to build a budget if you don’ t have access to good data. If there is no shared responsibility about how you are going to go after a market and build it, and a lack of accountability, you are going to struggle. You need all the right levels of enablers in place. You then have other factors: you need the right people whom you need to pay and reward properly. If you set the wrong sort of behaviour, you’ ll also struggle.
DL: The trade-off is intriguing between the need to have objective and realistic data and objective decisions, and yet to get anything done you need a good measure of optimism that’ s inherently not realistic.
CM: Any good business needs optimistic people who are going to say:‘ We can solve this problem, we can find a way to do things differently, we can put two old ideas together and come up with a brand new idea’. At the same time, you need to harness that innovation in a culture that makes it clear certain hurdles must be met. If you get the creative people and the finance people working together as a team, you get growth. And if you look at companies like Apple, Cisco, Intel and Microsoft, that is exactly what you have. ✪
FOOTNOTES
1 Kahneman D. and Lovallo D.,‘ Timid choices and bold forecasts: a cognitive perspective on risk taking’, Management Science, vol. 39, issue 1, pp. 17 – 31, January 1993.
2 Dr Dan Lovallo is a senior lecturer in general management at the AGSM. Chris Manning is a principal at Booz • Allen & Hamilton.
3 Swalm R. O.,‘ Utility theory: insights into risk taking’, Harvard Business Review, 44, pp. 123 – 136, 1966.
4 Camerer C. and Lovallo D.,‘ Overconfidence and excess entry: an experimental approach’, The American Economic Review, vol. 89, issue 1, pp. 306 – 318, March 1999.
20 | AGSM ISSUE 1 • 2001