Andre Agassi, a legend in men’s tennis, lost his first three matches to Boris Becker in 1988-1989. Agassi later won 10 of the remaining 11 contests against Becker in his career. Amazon.com, founded in 1994 by Jeff Bezos as an online bookstore, initially struggled to raise investor capital. Amazon eventually became one of the world’s most-valued companies. A Dutch sugar refinery, Cosun Beet Company, improved its yield management practices by offering sugar beet growers a low-code platform to manage their crop development. This helped the firm achieve sustainable growth.
In all these cases, corporations or people achieved a competitive edge by successfully managing the uncertainties facing them. They rationalized others’ behavior, acted preventively to reduce negative aspects of uncertainty, or invested in the jaws of it. However, many organizations still lack capabilities to manage uncertainties, struggling to deal with them. To address these issues, I investigate uncertainty through the lens of economics and link its findings to organizational strategies.
Uncertainty is a barrier between our knowledge and truth. When it widens, reality becomes less clear given the current state of knowledge. Uncertainty takes three different forms. The first is where decision-makers recognize the uncertain elements and their patterns very well. Yet, some variations may persist under some uncontrollable factors. Such known unknowns are referred to as truth uncertainty by economists. A second type features distorted knowns where uncertainty is deliberately created by decision-makers to induce some stakeholders to behave in a selected way, known in economics as epistemological uncertainty. The third case is unknown unknowns such that truth is not known by anybody, which is referred to as ontological uncertainty. Organizations wisely strive to eliminate truth uncertainty, keep epistemological uncertainty at a low level to improve short-term profitability, and invest in ontological uncertainty to sustain long-term profitability.
Truth Uncertainty: “Known” Unknowns
Organizations create value through certain operational activities they have excelled over the years. While they can control such operations along several dimensions, some variations may still exist. Organizational knowledge might correctly identify the truth about an object or a working system. However, a high level of knowledge may not suffice to eliminate process variations, thus leading to truth uncertainty. Elimination of truth uncertainty helps organizations boost profits and reduce quality problems. If total elimination proves impossible, firms can still effectively manage truth uncertainty by employing traditional predictive and prescriptive analytics methods. For example, FMCG companies, such as Pepsi and Colgate, have used a combination of predictive and prescriptive methods to manage truth uncertainty.
There is no strategic value in carrying truth uncertainty. Firms can build organizational capabilities through digital transformation and advanced analytics to absorb uncertainties internally. Otherwise, they would consider paying others to bear it. For example, manufacturers use analytical models to exploit advance demand information and to predict customer demand accurately, helping them absorb demand uncertainty. If absorbing uncertainties internally is not viable, firms may exchange them with other supply chain parties. For instance, producers of commodity products exposed to large, protracted price volatility may eliminate this uncertainty by selling their products in advance via forward contracts. To convince buyers to accept these contracts, producers forego the upside potential of uncertainty when commodity prices rise. Here, the upside of price volatility comprises the cost of transferring the uncertainty to another. If both prediction and transfer are infeasible, organizations suffer from uncertainties and lose profits. In the end, companies have three options in dealing with truth uncertainty: (1) control it internally, (2) transfer it to other supply chain parties, or (3) suffer.
Epistemological Uncertainty: Distorted Unknowns
One of the biggest rivalries in men’s tennis featured Andre Agassi versus Boris Becker. These two stars faced each other 14 times. After losing the first three matches in 1988 and 1989, Agassi won the next eight in a row. He lost only once more to Becker after 1989. Andre’s dominance on the court was due to Becker’s facial ‘tell’ that Agassi described after retiring. In a conversation with journalists in 2017, Agassi disclosed: “if … he put his tongue in the middle of his lip, he was either serving up the middle or the body. But if he put it to the side, he was going to serve out wide.” When he eventually admitted this to Becker, Boris nearly fell off his chair: “I used to go home all the time and just tell my wife: it is like he reads my mind.” In sports, players create uncertainties to trick their foes, and no one expects, for example, Becker to signal where he intends to serve! Those working to resolve such competitive uncertainties attain more career titles and reputation, just as Andre Agassi won eight grand slams versus only six for Boris Becker.
If decision makers’ knowledge suffers distortion because of information inaccuracy or agency conflict, organizations become exposed to epistemological uncertainty. In the case of a tennis match, each player faces epistemological uncertainty created by his opponent. To manage epistemological uncertainty, each player must first rationalize the behavior of his opponent and then optimize his decision. This is exactly what Agassi did in his matches against Becker.
Organizations have incentives to create epistemological uncertainty. Nevertheless, it is not a sustainable strategy to keep epistemological uncertainty at a high level. Imagine a farmer aiming to sell 10 watermelons in the farmers’ market where he always offers the best price. However, less than half of his watermelons are expected to be ripe. Suppose a restauranteur has learned over weeks that only 40% of the farmer’s watermelons are ripe. Whenever the restauranteur attempts to buy watermelons from the farmer, he first asks which ones are ripe. If the farmer does not disclose any information, then the buyer opts out. If the farmer designates five watermelons of the ten, then the odds of selecting a ripe one improves. Here, the restauranteur would likely buy all five. In practice, organizations are free to determine how much to disclose (or not disclose) of their proprietary information with outside parties. They may create epistemological uncertainty that would, in turn, yield increasing profits in the short term. However, too much epistemological uncertainty might repel customers or cause retaliatory actions. Thus, epistemological uncertainty must be kept at low or moderate levels in practice.
To manage epistemological uncertainty with farmers, for example, crop processors have often used crop management systems. This makes the crop development and agriculture supply chain fully transparent for both processors and farmers, reducing epistemological uncertainty and avoiding retaliatory actions. However, processors do not necessarily have any incentive to share their market price trajectories with growers. Indeed, information of a potential price rise is best undisclosed with farmers in negotiating the optimal price for buying crops.
Ontological Uncertainty: “Unknown” Unknowns
The strategic importance of ontological uncertainty for organizations may be aptly expressed in a Chinese proverb: “There is no fish in clear water.” For example, the Amazon.com project was born into ontological uncertainty. When the online retail giant was founded, Jeff Bezos contacted investors to raise capital. However, investors asked him what the Internet was, and they were very sceptical about the future of Amazon. At that time, the future of online retail was highly exposed to ontological uncertainty. Amazon would not have achieved high growth over the years if the future of e-commerce had been well-predicted in the 1990s. In that case big players, such as Walmart, could have invested in developing a better online platform and prevented Amazon’s evolution from an online bookstore into a retail giant in the 2010s.
Ontological uncertainty makes it problematic for wealthy investors to identify and invest in key technologies that customers will value in the future. Here, smaller firms and start-ups fill this gap and achieve sustainable growth. Thus, organizations wisely invest amid ontological uncertainty to foster entrepreneurship and sustainable growth.
Case Questions
What would be potential outcomes of elimination of uncertainties for each uncertainty type?
How can analytical approaches be designed to deal with each type of uncertainty?
What are the threats of epistemological uncertainty in social media?