M&SOM Review

Companies often engage in alliances for the development and introduction of new products. These alliances play a particularly important role in the highly innovative, and lucrative, biopharmaceutical industry. The global sale of biopharmaceutical products has surpassed $1 trillion annually. Much of this value comes from on-patent new product introductions. Over half of all new product approvals are awarded to alliances.

When an innovator (licensor) who owns the intellectual property rights to an innovation and a partner (licensee) come together to form an alliance, they need to ensure that the alliance is structured such that the needs of both parties are met and that the skills of the two parties are optimally deployed. We see two commonly found archetypes of alliance structures in biopharmaceutical alliances: (1) a sequential alliance where an initial set of tasks carried out by an innovator is followed by further development or marketing by a partner such that only one party is expending resources at any point in time, and (2) a collaborative alliance structure where once a license agreement is signed, both parties chip in concurrently on more equal terms.

Both archetypes can be successful. Crestor, targeting high cholesterol, came to market through a sequential alliance. It was initially developed by Shionogi of Japan, and then licensed to AstraZeneca who further developed and marketed the product. Plavix, for the treatment of heart disease, was brought to market by a collaborative alliance. While Sanofi-Aventis initially owned the IP, it was co-developed by Sanofi-Aventis and Bristol-Myers Squibb. The annual global sales for the two drugs were at $7 and $9 billion at their peak. Clearly, both alliance structures have the potential to attain great success.

But are there factors that influence the choice between these two archetypes? And how should two parties design the innovation process for an alliance? Just as importantly, what are the performance implications of such a choice? To answer these questions, we drew on contract theory, a division of economics that deals with frictions that arise when two parties enter a contractual relationship. We identified asymmetric/private information, the holdup problem, and risk-aversion as factors that should have a bearing on the choice of alliance structure. These translate to a high asymmetric information, a high degree of nascency, and a high probability of success favoring collaborative alliances over sequential alliances as portrayed in Figure 1 and described in more detail below. Using a database of over 2,100 biopharmaceutical alliances, we tested whether the predictions of contract theory hold.

Spider Graphs for three alliances

Figure 1. Spider Graphs for Three Alliances

Asymmetric (Private) Information

When one party has private information, or is better informed than the other, it can use this information to its advantage. This can hinder their relationship. A solution to the problem is for the holder of private information to carry out an action that credibly conveys, or signals, the information it has to the other party. In the biopharmaceutical context, private information may be the consequence of differing fields of expertise of the parties. This can lead to a relative disadvantage for the partner compared to the innovator in evaluating the quality of the innovator’s research. An innovator who is confident in the quality of its product can send a credible signal by undertaking a collaborative alliance which demands more resources from the innovator. Therefore, when asymmetric information is high, or the two firms have differing areas of expertise, we predict a collaborative alliance.


The Holdup Problem

When one party makes upstream relationship-specific investments while the other makes downstream decisions, the former may worry about not being reimbursed for its investment, or being held-up. This leads to underinvestment on that party’s part and below par performance. As a remedy, contract theory suggests having the party with a comparative advantage make a relationship specific investment and allocating control rights to them. Investments in biopharmaceutical alliances, such as clinical trials or training for marketing of a drug, are relationship specific and have little value outside the alliance. As we move from earlier to later stages of development, comparative advantage shifts from the innovator who knows the product candidate best to the partner who has prior clinical trial and marketing experience. For late stage alliances when only the steps where the partner has a comparative advantage remain, we expect a sequential alliance structure that passes both investments and control rights to the partner. Conversely, for early stage alliances, when many steps where either party has comparative advantages remain, we expect a collaborative alliance, where both parties make investments and have control rights. Therefore, for alliances with product candidates with a higher degree of nascency, we predict a collaborative alliance.


Risk Aversion

Not all parties in a strategic interaction have the same risk preferences. Contract theory suggests that the less risk averse party ease the risk concerns of the more risk averse party by limiting their exposure to risk. The degree of concern, in turn, depends on the level of risk. Larger firms with larger product portfolios are in a better position to absorb risk. The average partner in our sample is seven times larger than the average innovator. Therefore, the average innovator may be more risk averse when compared to the average partner. Using historical failure rates for different disease indications we calculated failure/success probabilities for each alliance in our dataset. For higher risk (lower success probability) alliances, we predict that the innovator’s exposure to risk is limited through a sequential alliance, which involves potential downsides mainly for the partner. Conversely, for alliances with a higher success probability, we predict a collaborative alliance where the innovator is also exposed to downside risk.

Three of these alliances are portrayed in the spider graphs of Figure 1. The larger the area of the triangle for each alliance the more likely the alliance is predicted to be collaborative. Indeed, the first alliance from left was sequential and the other two were collaborative. Three out of four alliances in our database conform with the predictions of our model, thus providing strong support for contract theory predictions.

Of course, a major question remains: What if an alliance does not conform to the predictions of contract theory? We find that alliances that do not address contract theory-based concerns by appropriately designing the new product development process as sequential or collaborative are 30% more likely to end in a termination than those that do. Given the large sums committed to alliances, an average of $307 million per alliance from 2008 to 2012, this is a substantial added risk of not getting a return on a very large investment. Therefore, a key role of operational design choices for the new product development process should be to address concerns of asymmetric information, holdup, and risk aversion.

Last, if you would like to see whether our model predicts a collaborative or sequential alliance structure for your alliance, please go to www.niyazitaneri.com/online-tools or click on the image below.


Taneri N, De Meyer A (2017) Contract theory: Impact on biopharmaceutical alliance structure and performance. Manufacturing Service Operations Management 19(3):453–471.

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