Does your company succeed by learning faster or learning more?
Researchers in the strategy field who study sustainability of advantages – the ability to make more money – tend to focus on speed and efficiency. The company that gets the product to market first wins.
So why do some firms willingly take on multifaceted, complex projects? Companies that set speed and efficiency as their goals eventually get to the same level. Why, then, do some companies perform better than the rest, year after year after year?
Strategy and entrepreneurship professor Scott Rockart gained insight into the question while working at McKinsey & Co. as a consultant early in his career. Marvin Bower, credited as the founder of management consulting and a longtime McKinsey member, espoused choosing clients because of what could be learned from them, not solely based on the money that could be made.
“That idea is particularly true in professional-service and knowledge-work settings,” Rockart said. “Rather than assume everyone will get to the same capability endpoint eventually and focus on how fast they get there, the choices these firms make will determine their endpoint. They might not get there faster, but they’ll go further.”
The strategy field examines why some firms make more money than others. Hypercompetition says that all advantages are temporary. Firms must try to go from one temporary advantage to the next. But that doesn’t explain examples of a couple of firms, particularly among investment banks, that dominate the industry for a long time. Rockart posits that those firms know something their competitors don’t, and they might have learned those advantages by taking on complex projects that pushed them to solve problems they wouldn’t have been exposed to had they taken on only projects that required simple solutions.
To test his theory, Rockart looked at differences in firms’ improvement trajectories, drawing on data in investment banking, which are primarily public. These trajectories are paths over which a firm’s capabilities develop with activity. They can be steep or gradual (the rate), and they can approach plateaus that are high or low (the potential of improvement). Other researchers have not distinguished between rate and potential or have focused on understanding differences in rate but not potential. Rockart developed an empirical model that allows the rate and potential to be estimated jointly.
The computer tablet industry faces this dilemma: Should a firm make a product quickly and efficiently that gets to market before its competitors, or should it develop a product that incorporates the latest technology and might be more useful and long-lasting once it comes on the market? The latter choice likely would involve multiple changes in the manufacturing process, which would be a financial setback each time. But the payoff would be a more desirable product that integrates the developing IT infrastructure in a way that no other firm’s does.
“The upside is that you’ll be creating things that other folks will not have thought about,” Rockart said. “If you automate a lot of the steps, you’ll be exposed to fewer new ideas.”
In helping firms decide whether to aim for speed or complexity, Rockart distinguishes between operations and strategy. The operations field focuses on finding the best way to get to the endpoint, and successful companies find ways to maximize efficiency and get to the endpoint first. In strategy, companies look at what sorts of options are open to them and what the ramifications are of choosing various options.
If one choice always proved better than the other, all successful firms would make the same choices. But that’s not the case. Rockart said firms must consider what works best in their industry: getting a product out first or producing something that will be better able to adapt to changes in customer needs. Then, companies need to consider their own individual strengths. Some entrepreneurial firms may benefit from first-mover advantage if they have strong R&D and marketing divisions. Other firms that have huge resources could move effectively after any product uncertainty has been resolved; they are better off being the second-mover.
His research involves mathematical modeling (formal theory) coupled with empirical study. In his working paper “Capability Development and the Potential of Improvement Trajectories,” co-authored with Nilanjana Dutt, a Duke University doctoral candidate, he observed that researchers tend to assume the starting point and the endpoint in graphs of improvement trajectory rates are the same for each company, and that the interesting point of study can be found in what happens in between the starting point and endpoint. But that method does not distinguish between the rate and the potential of improvement trajectories.
The math behind the theory involves estimating some variables or Rockart and Dutt saw problems with that method when it came to connecting the mathematical model with the empirical data.
“Assuming the same endpoint allows you to work out one of those equations,” Rockart said. “But we’re saying that, by doing so, you’re throwing out too much to get to that empirical equation. You’ve lost some of what’s interesting about that theory in order to make it easy to estimate how things affect learning rates.”
One result surprised Rockart: The data don’t support that taking on large, complex projects slows down a firm’s improvement rate. Rockart won’t speculate why in the paper, but it is a point that caught his attention. And one that lends support to the overall message of his study: Working on larger projects can lead to both temporary and persistent capability advantages.
- Firms can make choices that cause them to learn faster or to learn more.
- Advantages based on learning faster are temporary; rivals will eventually catch up. Advantages from learning more may be sustained over time.
- Decisions involve tradeoffs: Do you gain a competitive edge through speed or through a better product?