AN EMPIRICAL EXAMINATION OF CAUSES AND CONSEQUENCES OF NEW PRODUCT LAUNCH ALLIANCES

DEEPAK JENA University of North Carolina, Chapel Hill

PRASHANT KALE Rice University

JAEYONG SONG Seoul National University

ATUL NERKAR University of North Carolina, Chapel Hill

ABSTRACT

Firms regularly fail to capitalize on their innovation due to lack of necessary complementary assets to commercialize their innovations. A firm’s product launch capability is one such complementary asset that has a significant impact on whether the firm can profit from its innovation. There is considerable heterogeneity among firms in the level of such capabilities. Firms may turn to external alliances during commercialization stage if they perceive that such alliances can help them successful launch a new product in the market. In this paper, we investigate the factors that may influence a pharmaceutical firm’s decision to form a product launch alliance to enhance the commercialization of the firm’s proprietary new drug. We also investigate whether such alliances add value to the innovator firm in the first place. We also identify contingencies that influence the relationship between the product launch alliances and launch success. Using hand collected and proprietary data on pharmaceutical drug launches, we find that certain factors - firm-level, product-level and patent-level - influence the likelihood that a firm will engage in a product launch alliance. We also find that one of these factors may also influence the commercial success of the launched product.

AN EMPIRICAL EXAMINATION OF CAUSES AND CONSEQUENCES OF NEW PRODUCT LAUNCH ALLIANCES

INTRODUCTION

“…innovators with new products and processes which provide value to consumers may sometimes be so ill positioned in the market that they necessarily will fail” (Teece, 1986)

Firms regularly fail to capitalize on their innovation. Prior studies have documented issues that innovating firms face while trying to commercialize their innovation, but no single issue has received more focus in strategy and innovation literature than the issue of specialized complementary assets. Complementary assets are factors such as specialized manufacturing capabilities, product launch capabilities, distribution networks, installation and service capabilities, and complementary technologies that are always needed to bring a product to market (Teece, 1986). Empirical support for the value of specialized complementary assets can be found several industry studies. On the one hand, specialized complementary assets were critical in helping buffer incumbents in the typesetter industry from new entrants, who supposedly entered the market with technically superior products (Tripsas, 1997). On the other hand, incumbents in medical diagnostic industry were more likely to enter a new subfield if they possessed a broad range of specialized complementary assets (Mitchell, 1989). The focus of this paper is on one of such complementary assets, firms’ new product launch capability.

New product launch involve activities that follow the creation of a finished product. These activities may include finalizing distribution partners, identifying customer targets, positioning the product, and finally implementing the launch plan (Beard and Easingwood, 1996). Beard and Easingwood (1996) suggest that product launch stage is “often the most costly

stage of the new product development process” and “even for well-managed new product development processes, there is still an expected failure rate of 30% for new products at launch”. Despite the importance of product launch capabilities, such capabilities are not distributed equally among all firms. There is considerable heterogeneity among firms in the level of such capabilities. Firms may turn to external alliances during commercialization stage if they perceive that such alliances can help them successful launch a new product in the market. In this paper, we investigate the factors that may influence a pharmaceutical firm’s decision to form a product launch alliance to enhance the commercialization of the firm’s proprietary new drug. We also investigate whether such alliances add value to the innovator firm in the first place. We also identify contingencies that influence the relationship between the product launch alliances and launch success.

The paper begins with a literature review of innovation process in the pharmaceutical industry and the industry’s use of alliances in new product development phase. We then provide theory and hypotheses related to the industry’s use of product launch alliances. Subsequently, we describe the data, the empirical methods, and results, and conclude the paper with a discussion of the paper’s contribution.

PHARMA INNOVATION AND ROLE OF ALLIANCES

New proprietary drug introductions are critical to a pharmaceutical firm’s success. Strong appropriability regime enables innovator firms to earn higher profits in the initial years of the drug launch. Profits then quickly dissipate as patent protections no longer become available. Once generic drugs enter the market, prices for drugs fall to as low as ten percent of what they were prior to generic entry (Hemphill and Sampat, 1011). By some estimates, introduction of

generic drugs between 2007 and 2012 reduced the revenue of top pharmaceutical firms by as much as USD 60 billion dollars (Haunschild, Polidoro, and Chandler, 2015, DeRuiter and Holston, 2012). When patents on Pfizer’s blockbuster drug Lipitor expired in 2011, Lipitor’s contribution to Pfizer’s revenue dropped from an average of more than USD 10 billion in the 2000s to about USD 4 billion in 2012. Cheaper generic variants flooding the market resulted in Pfizer’s profits dropping significantly after the patent expiry. Firms like Pfizer are therefore constantly in lookout for new drugs to compensate for this revenue and profit loss. However, new drug development is an extremely low success event. The ex-ante probability of a screened drug molecule ending up in the hands of consumers as a commercial product is as low as 0.01 percent (Rothaermel and Deeds, 2004). The drug development process progresses sequentially from discovery (includes basic research, drug discovery, and preclinical trial), to clinical development (submission of investigational new drug application, clinical trials, submission of a new drug application), and finally to FDA review and approval (Phrma.org, 2015). Once approved by FDA, formulation, scale-up, manufacturing and marketing of product gets underway (Phrma.org, 2015). It takes an average of about 10 years for a new drug to move from initial discovery to the marketplace, costing an average of $2.6 billion in the process (Phrma.org, 2015). Given this long and arduous journey to a new product, pharmaceutical firms often hedge their risk and improve their odds (identify promising drug molecules, reduce the cost of preclinical and clinical trials, increase likelihood of FDA approval, increase chances of a successful product launch) by seeking external alliances with other firms.

Pharmaceutical firms not only ally with other pharmaceutical firms but also with biotechnology firms during the product development process. Alliances are prevalent at every stage of the drug development process. Alliances can be either focus on upstream activities

(exploration) or on downstream (exploitation) activities (Rothaermel, 2001). In the context of pharma drug development, upstream (exploration) alliances cover basic research, drug discovery and preclinical and clinical trial activities (Rothaermel and Deeds, 2004). Exploitation (downstream) alliances focus on navigating through the regulatory approval process, formulation, manufacturing, marketing and distribution activities (Rothaermel and Deeds, 2004). Exploration alliances therefore predict “products in development” whereas exploitation alliances predict “products in the markets” (Rothaermel and Deeds, 2004). While there has been substantial research on exploration/upstream alliances in the pharma industry (see Diestre and Rajagopalan, 2012 for a review), we know very little about why firms enter into downstream alliances, and the economic consequences of entering into such alliances. Of the studies that have looked at downstream alliances, few if at all any have looked at product launch stage, the terminating and often the most critical stage of product introduction stage. By product launch stage, we are referring to the phase that follows FDA approval, formulation and manufacturing. The focus of this paper is to develop and test theories relevant to this critical phase of product development. In doing so, we add to theories that predict “products in development” and theories that predict “products in the market” (Rothaermel and Deeds, 2004) to include theories that predict “success of products in the market” in the context of pharmaceutical alliances (Nerkar and Roberts, 2004). In the next section, we introduce the phenomenon of co-promotion alliances in pharmaceutical industry and then develop specific hypotheses related to downstream co-promotion alliances.

PRODUCT LAUNCH ALLIANCES

Product distribution partnership that focuses on the launch of a proprietary new drug in the market could be of two types: co-promotion alliance and co-marketing arrangements. In a co-

promotion arrangement, the subject of analysis in this paper, two pharmaceutical firms (one partner typically is the originator of the drug and the other usually has the license to commercially exploit the drug), form a partnership to launch a single product under a single brand name using a joint marketing strategy (Carter, 2005). The originator of the drug usually manufactures the product, whereas the other partner usually brings in supplemental marketing capability. The two firms usually share detailing and other promotional efforts, and the profits generated from sales are usually divided according to the level of detailing and promotional effort spent by each partner (Carter, 2005). From our data set, we can infer that about twenty per cent of new proprietary drugs launched in the market between 1984 and 2004 were co-promoted. For the rest of new drug launches, the innovator firm decided to launch the product on its own without entering into a co-promotion alliance. Notable examples of co-promotion arrangements include Warner-Lambert and Pfizer’s partnership for Lipitor and between Glaxo-SmithKline and Janssen for Paxil. On the other hand, co-marketing arrangements allows two or more firms to market a product under two or more distinct trademarks in any particular country or often globally (Carter, 2005). The partners usually obtain the product from the originator firm either in bulk or finished product form and are then free to decide and implement their own marketing (price, promotion, distribution) strategy. Co-marketing arrangements are a form of license and supply agreement with no direct sharing of revenue or profits (Carter, 2005). In this paper, we study co-promotion partnerships because our focus is on alliance, and co-promotion arrangements fit the definition of alliance found in literature, whereas co-marketing arrangements fit the definition of licensing. As we noted above, there is considerable variability in the use of co-promotion alliances. From our data sample we infer that, at product launch, only in about one in five case did an originator firm decide to opt for a co-promotion alliance between

1984 and 2004. In the rest of the cases, the originator firm decided to launch the product on its own. Next we theorize factors that may influence the likelihood of a firm opting for a co-promotion alliance.

RATIONALE FOR CO-PROMOTION ALLIANCES

Commercialization capability

In the last two decades there is a huge upsurge in research that emphasizes the role of firm resources and capabilities in strategy and firm performance (Barney 1991). Consequently, the resource or capability perspective (RBV) has been widely used to explain alliance formation and performance (Dussauge et al, 2000).

First, firms often seek to form alliances to get access to complementary resources or capabilities (Harrigan, 1988; Parkhe, 1993). This is because most firms may not internally possess resources and capabilities in all the relevant parts of the value chain that are critical for the eventual success of the firm – in which case they seek to join hands with other firms that are able to address some of the resource/capability gaps. This implies that complementary resources are resources that are dissimilar or non-overlapping in nature. Teece (1986) in this seminal work on innovation has also stressed the importance of such resources. Firms, especially those that are small, often come with a very innovative product, but they often lack many of the downstream capabilities in manufacturing, distribution and sales to successfully commercialize that innovation and capitalize its full potential. The case in point being EMI who actually invented the CT scanner (Teece, 1986) - EMI had an innovation that was extremely valuable from a customer perspective but it simply did not have the downstream distribution and sales maintenance capabilities required to commercialize the innovation. In such situations, firms are

likely to form alliances with other firms that possess the desired complementary capabilities. In the pharmaceutical industry, alliances between biotech companies (which often create path-breaking product innovations) and large pharmaceutical companies (which have well established capabilities to see the product through the regulatory approval stage, distribute it to retailers and pharmacy benefit companies and market it to doctors) are a classic example of such alliances. In contrast however, firms that possess strong capabilities in all the critical parts of the value chain, including downstream commercialization capabilities are less likely to form alliances with others to exploit the potential of their innovation.

Second, firms may sometime possess the resources/capabilities to commercialize the innovation, but they may still decide to join hands with another firm for that purpose for one of two reasons: one, the quality of their own resources/capabilities is not good enough to fully exploit the potential of the innovation; two, by joining hands with another firm that possesses, the two firms are able to bring down the joint costs of distribution or selling. Warner Lambert, which discovered the world’s biggest block buster drug Lipitor, formed an alliance with Pfizer for exactly these reasons. On the other hand, had Pfizer discovered Lipitor it may not have formed an alliance with Warner because Pfizer itself and thus could have perhaps exploited Lipitor’s potential on its own with forming any alliance with others. Another striking example is Searle’s (pharma division of Novartis) use of co-promotion arrangement for its anti-inflammatory drug Celebrex. Searle partnered with Pfizer to bring the product to market because it didn’t have the necessary sales force in the U.S. to maximize the sales potential (Carter, 2005). Merck’s development of a similar product Vioxx around the same time added to Searle’s urgency to get the product to market with a wider reach (Carter, 2005). Thus, the capabilities-based view suggests that firms enter into alliances with other firms to get access to or share

valuable and unique resources that they might lack internally to the same degree. (Das and Teng, 2000).

Thus,

H1: The lower (greater) the commercialization capability of a firm, the greater (lower) the likelihood of a firm forming a downstream co-promotion alliance for its commercialization.

Nature of the product

Nature of the product in the pharmaceutical industry often determines the scope of profit that can be earned from it. Different product market segments may differ in their attractiveness. For instance, firms in the 1990s considered emerging markets to be attractive from a profit potential given the future size of those markets. Attractive products or markets are likely to induce more future competitors. Thus, firms to protect this future stream of profits may choose to enter into downstream alliances not only to access complementary resources quicker than future entrants but also to create entry barriers for future competition. A second reason for such alliances would be to co-opt downstream firms and prevent them from backward integrating into attractive markets. Moreover, the bigger the potential pool of profit a firm may enjoy the more likely for the firm to share it with downstream partners to enhance the success probability. In the context of pharmaceutical drugs, an important characteristic of the product that can influence the size of the profit pool is whether the proposed drug treats a chronic medical condition or an acute one. Acute conditions are severe and sudden in onset whereas a chronic condition, by contrast is a long-developing syndrome (medical encyclopedia of national library of medicine, accessed 2015). Drugs that treat chronic conditions provide the greatest profit potential since the patient being treated for such chronic condition needs to be prescribed for the drug for a longer period of

time, sometimes for decades, for example in the case of diabetes. For the reasons we noted above, firms are more likely to co-promote a drug if the drug is of the chronic rather than the acute type.

Thus:

H2: The nature of the product influences the likelihood of a firm forming a downstream co-promotion alliance for its commercialization, such that products that treat chronic conditions are more likely to be co-promoted than products that treat acute conditions.

Time to patent expiry

Strong patent regimes enable innovator firms to capture profits from an innovation (Teece, 1986). In the absence of patent protection, competitors/imitators will ensure that any first mover advantage that the innovator firm has is nullified quickly. Firms therefore become highly vulnerable when these sources of such economic rents disappear or are about to disappear. Patent protections are granted for a limited period of time, usually about twenty years from the date of filing. Firms therefore want to maximize the patented products’ commercialization potential within this time period. In case of pharmaceutical drug development, this window of opportunity is even more limited. Firms earn patent protection on the drug molecule (compound patents) and also on the formulation and medical use (formulation patents, medical use patents) (Voet, 2014). The compound patent claims not only the active drug compound but also its salts, esters and hydrates (Voet, 2014). However, as we described above, finding a promising drug molecule is often a preliminary stage of a long and arduous journey towards commercialization. Post the discovery phase (which may take between two to ten years), the lead drug molecule is developed and taken through pre-clinical testing for up to four years (Rothaermel and Deeds, 2004). The

drug then goes through three rounds of clinical trials that can take up to seven years. Post the clinical trials, the FDA review and approval process can take up to another two years. The time gap between finding a promising a drug molecule (and patenting it) and the final approval can be as extremely long, maybe as long as thirteen years. The effective monopoly protection is estimated, on an average since the 1990s, to last an average of twelve years. Most new drugs launched in the market are protected by one or more patent (Hemphill and Sampat, 2011). Researchers and pharmaceutical companies often use the term, nominal patent term, to quantify the duration of patent coverage (Hemphill and Sampat, 2011). Nominal patent term is nothing but the duration between the day the product was approved by FDA and the day of the last expiring patent on the product. The shorter the duration the more pressing the pharmaceutical firm’s need to launch and distribute the product as quickly and as widely as possible. Firms may need to launch the product simultaneously across several global locations to capture the market as quickly as possible. In such a situation, firms are more likely to turn towards an alliance partner to help fulfil this objective.

H3: The greater (lower) the nominal patent term of the drug’s patent, lower (greater) the likelihood of a firm forming a downstream co-promotion alliance for its commercialization.

Patent portfolio

Besides one or two patents covering the active compound, new drugs may also have patent coverage on chemical variants, formulations, medical use, and other relatively minor aspects (Voet, 2014; Hemphill and Sampat, 2011). Hatch-Waxman act mandates firms to declare the patents associated with the drug at the time of FDA product approval. Such patents are listed in the FDA Orange Book. Multiple patents (or patent portfolio) covering a drug product play an

important role in the decision to seek external product launch alliances primarily for three reasons. First, multiple patents (patent thickets) discourage entry in general because they slow down rivals by “obliging them to search for, evaluate and litigate patents that are unlikely to be found valid and infringed” (Hempill and Sampat, 2011). The Hatch-Waxman act provides a regulatory mechanism for generic manufacturers, through the “paragraph IV” challenges, to challenge one or more patents associated with the brand name drug. The para IV challenge provides the generic drug manufacturers an opportunity to enter the market even before the expiry of the all the patents associated with the brand-name drug (Hempill and Sampat, 2011; Voet, 2014). The generic drug manufacturer needs to assert that one or more brand-name patents are invalid, and hence are not infringed by the generic drug manufacturer in launching a “therapeutic equivalent” copy. If the generic drug manufacturer is able to prove (through a formal appeal process) that the said generic drug is not infringing on any brand-name patents, then the FDA in such a case may grant the generic drug manufacturer the right to market its own copy. The first filer (of para IV challenge) gets a 180 day marketing exclusivity on the generic product, which implies that, no other generic drug manufacturer can market an equivalent drug during the same period. If drugs have multiple patents associated with them rather than a few, it will likely be more difficult for the generic drug manufacturer to provide a “challenge”. In other words, the generic drug manufacturer needs to prove the invalidity of more number of such patents. Such an exercise (of trying to invalidate multiple patents associated with a drug) is not only likely to be more costly and but also likely to have a lower probability of success. Therefore additional patents on a drug generally improve the originator firm’s ability to exclude the generic firms (Hemphill and Sampat, 2011: pg 615). Second, patent portfolios effectively increase the nominal patent term of the drug. For example, suppose that a single patent covers a drug and that

the patent covers the active compound. In this case, once that single patent expires, the drug gets exposed to generic competition. However, consider another drug that has two patents attached – one covering the active compound and the other covering the medical use (Voet, 2014: pg 70). Also assume that the medical use patent has a later expiry date than the compound patent. In this case, the nominal patent term gets extended because a generic drug manufacturer cannot launch a drug that contains the same active compound and the same medical use (or indication) as the branded drug it wants to replace. Note that this argument is different than the previous one (i.e., more patents deter competition in general) because of this aspect of temporal extensions (Hempill and Sampat, 2011). Third, multiple patents covering a drug have a positive effect on product awareness through the process of knowledge diffusion. The more the patents associated with the drug are cited by other patents or by academic papers in medical journals, the greater the awareness created regarding the drug. Therefore we argue that if a drug is covered by multiple patents, then the drug is unlikely to be co-promoted through an alliance because multiple patents deter competition and also because multiple patents increase product awareness.

Therefore,

H4: The greater (lower) the number of patents covering the drug, the lower (higher) the likelihood of a firm forming a downstream co-promotion alliance for its commercialization.

CONSEQUENCE OF CO-PROMOTION ALLIANCES

As we noted in the previous sections, factors such as firm’s existing commercialization capability, nature of the product and time to patent expiry are likely to be significant predictors of a firm’s decision to partner with another firm to co-promote a product in the market. However, an important next step for researchers is to be able to theorize whether these alliances matter at

all. Do co-promotion alliances, on an average, economically beneficial to firms? This is a tricky question to answer because endogeneity of such strategic decisions. Firms choose strategies based on their attributes and industry conditions (Shaver, 1998). In other words, firms choose strategies that these firms believe will lead to the highest expected outcomes among all available choices. Therefore, any explanation and prediction of the economic consequence of alliances needs to account for such endogeneity. Put slightly differently, a researcher arguing for one strategy against the other needs to explain her argument keeping in mind the endogeneity of such choices. In our case, if we argue that co-promotion alliances on an average, in comparison to launching the product without any such alliances, lead to better performance, then our arguments should hold even in presence of factors such as firm’s commercialization capabilities and the nature of the product that determine whether the firms will go in for an alliance at the first place.

We make use the value creation-value capture framework in alliances (Wang and Rajagopalan, 2014) to argue that co-promotion alliances are positively associated with product sales performance. Wang and Rajagopalan (2014), in trying to reconcile contradictory findings in alliance literature on the relationship between alliance capabilities and alliance performance (Zollo, Reuer, and Singh, 2002; Hoang and Rothaemel, 2005), suggest that value creation-capture aspects of alliance capabilities might explain such contradictions. They suggest that repeated interaction between the same partners could lead to joint value-creation ability (by decreasing coordination costs), and that such ability is likely to be associated with higher alliance performance. On the other hand, such repeated interactions may also enable the partner firm to siphon away private value from such alliance (Diestre and Rajagopalan, 2012). In such a case, the capture of value is asymmetric among the partners, and thus alliance capability may be negatively associated with performance. Therefore, we infer that the success of an alliance

depends critically on the interplay of factors that influence value creation and factors that influence the capture of value.

We argue that the co-promotion alliances are usually structured in a way that value creation is enhanced for the innovator firm whereas private value appropriation by the partner firm is minimized. In co-promotion alliances, the originator firm usually manufactures the product and uses its own trademark and trade dress (Carter, 2005). The two partners then share detailing and other promotional efforts. The partner firms therefore use a joint marketing strategy to launch a single product under the same brand name with a single price. The profits are then shared based on the amount of promotional and detailing efforts put in by the partner (Carter, 2005). From a value creation perspective, the objective of this alliance is to combine the marketing and product launch capabilities of the two partners. In most cases, the combination brings together complementary capabilities to the fore. For example, under the co-promotion arrangement between Pharmacia and Janssen for the antidepressant Vestra, Janssen promoted the drugs to psychiatrists whereas Pharmacia promoted the drug to primary care physicians (Carter, 2005). Such an arrangement expanded the breadth of promotional efforts while leveraging the strengths of each partner. From a value capture perspective, co-promotion agreements limit the appropriation of private value by the marketing partner. Since the originator firm owns the patent, manufactures the product, and uses its own trademarks, the scope for the partner to appropriate private benefits significantly reduces. It will be helpful to contrast co-promotion alliance with that of upstream R&D alliances between new biotechnology firms and pharmaceutical firms (Diestre and Rajagopalan, 2012). In upstream R&D alliances, pharmaceutical firms often have both the incentive and the ability to appropriate the new biotechnology firm’s knowledge. Diestre and Rajagopalan (2012) find that factors such as the

pharmaceutical firm’s therapeutic area diversity and the breadth of the new biotechnology firm’s knowledge applicability increase the likelihood that the pharmaceutical firm will appropriate the biotechnology firm’s knowledge and use the knowledge outside the scope of the alliance. In the case of co-promotion alliance, the tacit knowledge specific to R&D remains within the originator firm and is not exposed to the other partner. We argue therefore that, the partner’s ability and incentive to appropriate firm-specific knowledge is not going to be significant factor in such an alliance. Therefore, in co-promotion alliance, we posit that factors that positive influence of value creation factors are likely to dominate the negative influence of partner’s private value appropriation factors we discussed above. Thus,

H5: Firms that engage in co-promotion alliances are likely to be associated with higher first-year sales performance on average than firms that opt not to engage in any co-promotion alliance for launching a new proprietary drug

MODERATING FACTORS

Firm’s commercialization capability

We argued that a firm’s commercialization capability is negatively related to the likelihood of engaging in a co-promotion agreement. However, we also suggested that firms may sometime possess the resources/capabilities to commercialize the innovation, but they may still decide to join hands with another firm for that purpose for one of two reasons: one, the quality of their own resources/capabilities is not good enough to fully exploit the potential of the innovation; two, by joining hands with another firm that possesses the capability, the two firms are able to bring down the joint costs of distribution or selling. Firms that possess superior commercialization capabilities are often in a good position to judge whether their internal

capabilities are good enough to exploit the new drug’s potential. These firms may realize that the new drug’s commercialization may need specific capabilities that they do not possess or possess inadequately. Such a realization may trigger the firm’s search for a partner that possesses such capabilities.

We posit that if firms with superior commercialization capabilities were to engage in co-promotion alliance, such arrangements are likely to enhance the performance effect of such alliances. We have two reasons to offer for this argument. First, firms that have superior commercialization capabilities are also likely to have better partner search capabilities (cite: Cohen and Levinthal, 1990?). These firms will be better in (a) identifying gaps in their arsenal and (b) also identifying partners that possess capabilities that can better fill that gap. These skills are likely to increase the preformation value creation of co-promotion alliance (Wang and Rajagpolan, 2014). Second, such firms are also in a better position to increase postformation value creation of the alliance because of the coming together of complementary knowledge bases of the two firms.

Thus,

H6: Firm’s commercialization capability positively moderates the positive relationship between engaging in a co-promotion alliance and first year sales performance

Time to patent expiry

We argued earlier that greater the nominal patent term of the new drug, the lesser the likelihood that the innovator firm will engage in a co-promotion agreement. Said in a different way, this also means that the closer the patent(s) is to expiry, the higher the likelihood that the firm will engage in a co-promotion alliance. We argued that this motivation derives from the

need to commercialize as soon and as widely as possible, in order to maximize the sales within the limited patent protection period the firm enjoys. However, we argue that firms that engage in a co-promotion alliance under circumstances where patent(s) on the product is expiring sooner than later are not likely to gain much benefit from such alliance. Our arguments are based on the following reasons. First, given the limited time at hand, firms may be in a hurry to form an alliance as soon as possible. These firms may not have enough time to do a thorough due diligence during the partner selection process. Therefore, firms may end up joining hands with partners that have lower commercialization capabilities than may be required to maximize success in the initial years of sales. Second, if the nominal patent term is relatively low, not many partners may be willing to invest time and effort (in terms of detailing and promotional dollars) to promote such a drug. Some partners may perceive that the drug, given the limited window of opportunity, may not give them the adequate return on their investment to warrant an alliance. Therefore the firm wanting to engage a partnership may have to choose from among a limited set of partners that are willing to accept a lower return on investment. Given the above two reasons, engaging in a co-promotion alliance may lead to sub-optimal performance.

Thus,

H7: Nominal patent term of the drug positively moderates the positive relationship between engaging in a co-promotion alliance and first-year sales performance

Patent portfolio

We noted earlier that a new drug may be protected by multiple patents, one or few covering the active compound, while other patents may cover chemical variants, formulations, medical use, and other relatively minor aspects (Voet, 2014; Hemphill and Sampat, 2011). In

general, we expect products with multiple patents to be more useful to patients who get prescribed such drugs. For example, medical use patents cover both approved and unapproved (off-label) medical use or indications of the drug for treatment of specific diseases (Voet, 2014: page 70). Drugs with more number of such approved indications are likely to have greater scope or applicability, and thus are likely to provide greater benefits to patients. Therefore, we posit that the main effect of large patent portfolios on the product’s initial sales performance is likely to be positive. However in this section, and given our focus on alliances, we are more interested in investigating the moderating effect of the patent portfolio on the relationship between product launch alliances and product sales performance. We argue for a positive moderating relationship for the following reasons. We earlier posited that patent portfolios in general deter competition and also that multiple patents increase the nominal patent life of the drug. Conditional on the innovator firm having multiple patents protecting its drug and also on the innovator firm’s willingness to engage in a co-promotion alliance, we suggest that the originator firm will have a higher likelihood of finding a partner with superior product launch capabilities. High quality partners may be attracted by potentially high rate of returns to investments due to the greater scope of the product. Such partners may also be attracted to the partnership because of their perception that the product is not only just “well-protected” but also protected for a much longer duration. Finally, drawing on our earlier argument that drugs with multiple patents tend to be well diffused within the medical field, such drugs are likely to be more visible and acceptable to medical practitioners. It follows that partners marketing such drugs are likely to spend lower promotional costs than drugs that are less diffused in medical literature. Therefore, the dual incentive of higher potential sales and lower potential costs is likely to attract partners with superior product launch capabilities.

Therefore,

H8: The number of patents covering a drug positively moderates the positive relationship between engaging in a co-promotion alliance and first-year sales performance

METHODS

Data and sample

The data for our study comes from a proprietary database owned by IMS Health, a leading provider of information for the healthcare and pharmaceutical industry. Our dataset contains detailed therapeutic level data for a sample of new brand-name drugs launched in the pharmaceutical industry between 1984 and 2004. Our dataset includes information on 414 new proprietary drugs that were launched during this twenty years period. New brand-name drugs can be differentiated from generic drug launches, which are typically a “copies of branded pharmaceuticals, made after the original patent has expired” (Morton, 1999: 421). For each new drug, our dataset contains information about product launch date, manufacturer, whether or not the manufacturer engaged in a co-promotion alliance, drug’s therapeutic classification, detailed product promotion and sales data and other product related variables. In addition to product and alliance related information, we also needed patent information for all the drugs in our sample. More specifically, we needed a list of patents that existed at the time of the approval of the product. It is important to make the distinction between patents that were listed at the time of FDA approval and patents that were subsequently added after the approval, also known as follow-on patents (Ouellette, 2010). We exclude follow-on patents since we are concerned with only those patents that influence a co-promotion alliance decision. The patent data comes from the FDA Orange Book (or more formally known as the Orange Book: Approved Drug Products

with Therapeutic Equivalence Evaluations). The Orange book lists patent information (patent number, patent expiration date, other regulatory exclusivity dates) for all the drugs approved by the FDA. FDA has been updating the Orange Book every year since 1980. The current version (35th edition, 2015 Orange Book) duplicates the previous editions of the Orange Book and lists additional drugs that were approved during 2014. However, a major issue with collecting patent information from the latest Orange Book is that the most current Orange Book only lists unexpired patents. Additionally, the most current version could also include follow-on patents, but there is no way to differentiate between the two types of patent. To overcome this challenge, we accessed all archival versions of Orange Book. Since our drugs in our sample were launched between 1984 and 2004, we accessed archival annual versions of the Orange Book dating back to 1984. Orange Book versions became electronically available starting with the 2000 version (Hemphill and Sampat, 2009; 2011). For the drugs that were approved prior to 2000, we scanned the print copies of the Orange Book available in the library. For the drugs that were approved between 2000 and 2004, we were able to access electronic versions of the Orange Book. This procedure helped us reliably identify patents that were listed at the time of product approval, an important criterion for our context.

Measures

Dependent variable

We are interested in two broad set of questions: what factors influence a pharmaceutical firm’s decision to engage in a co-promotion alliance, and what are the performance consequences of engaging in such type of alliance. Accordingly, we have developed two dependent variables: first, ally is a binary variable that the value of one if the focal firm launched

the product through a co-promotion alliance, and zero, if the focal firm decided to launch the product on its own. Second, initial sale captures the total sales (in USD million dollars) in the first full year of the product’s launch in the US market. We believe that initial sales level is an appropriate indicator of new product success in the pharmaceutical industry, where the primary objective is to maximize the revenues associated with the fixed costs of product development and launch (Gatignon et al, 1990).

Commercialization capability

We operationalize a firm’s commercialization capability as the total number of new products that the originating manufacturer had launched during the three-year period prior to launch.

Nature of the product

The nature of the product is operationalized as a dummy variable chronic, which takes the value of one if the new drug targets a chronic medical condition and zero if otherwise.

Patent portfolio

Patent portfolio is operationalized as the number of unique patents covering the drug product at the time of FDA approval.

Nominal patent term

We follow Hemphill and Sampat (2011) in defining the nominal patent term of a drug as the duration (in years) between the day the product was approved by FDA and the day of the last expiring patent on the product. We adopted the following procedure in calculating the nominal patent period for each drug. For drugs that were covered by one or more patents at the time of

approval, we identified the last expiring patent from the portfolio. The nominal patent term is the difference in years between date of product approval and date of expiry of the above patent. For drugs that were not covered by any patent at the time of approval but were protected by other type of regulatory exclusivity, for example new drug formulation, pediatric exclusivity, Orphan drug exclusivity etc., (Voet, 2014; page 98), we identified the latest occurring exclusivity date. Regulatory exclusivities, such as the ones listed above, provide additional protection when a drug is covered by patents, but provide exclusive protection when a drug is not covered by any active patent. This can happen if the new drug launched is an improvement (example dosage forms, delivery systems and conditions of use) over an earlier launched drug (Voet, 2014; page 158-159). The nominal patent term, in this case was calculated by the lag between date of product approval and the last expiring exclusivity date. There were about thirty drug products which didn’t have any patent or regulatory exclusivity information listed in the Orange Book. We coded the nominal patent term as zero for such cases.

Controls

We control for recent alliance experience through a dummy variable allexp that takes a value of 1 if the firm has had a co-promotion alliance in the last one year. We control for competitive intensity in the particular therapeutic area by noting the order of entry of the particular product in a therapeutic category. Only innovator brand products are included to derive the order of market entry. The competition variable counts the number of branded products in the same therapeutic area (USC5 level) as the focal product already in market at the time the focal product was launched. We control for pre-launch diffusion of the lead molecule by counting the number of times the lead molecule has been cited in academic journals before launch of the product. We also control for pre-launch novelty by including a dummy variable that takes the

value of one if the compound is an established molecule and zero if it is not an established molecule. We also control for the number of approved indications at the time the product was launched. Indications are symptoms or diseases for which the FDA has approved the product for treatment. We control for whether the drug is a line extension product (dummy variable), i.e., whether the new drug is a new form, strength, or delivery mode for an established molecule. We also control for the drug’s therapeutic area at USC5 level. For the second-stage performance regression model (explained in the next section), we control for two additional variables. We control for marketing effort and costs by including two variables: journal dollars is the estimated cost of product advertising in medical journals in the first year of launch. Contacts are product-level interactions where a pharmaceutical company representative and a physician or hospital-based pharmacist has a discussion regarding the uses and benefits of the product. A contact can also be a service visit, a drug fair attended by a director of pharmacy, or a delivery of a product sample.

Methods

The first set of analysis is focused on finding factors that significantly predict the likelihood that the originator firm will launch the newly approved drug through a co-promotion arrangement. The dependent variable therefore is a binary variable ally that takes the value of one if the firm executes a co-promotion alliance and zero otherwise. To take into account the binary nature of our dependent variable, we run a probit regression model with heteroskedastic robust standard errors. First, we run the model with only control variables as the predictors, and then sequentially add our main explanatory variables - commercialization capability, nature of the product, nominal patent term, and patent portfolio – into the subsequent models. The “full model” includes all the three explanatory variables and the controls in a single model.

Our second set of analysis uses sales performance as the dependent variable. Our main explanatory variable is the endogenous binary variable ally. The ally variable represents a choice that the firm makes regarding the decision to engage in a co-promotion or not. As we argued before, firms self-select these decisions based on their expectation of future performance. Given the endogeneity of the main explanatory variable, an OLS regression that doesn’t take into account such endogeneity (due to self-selection) is likely to produce biased coefficient estimates. We account for such self-selection in our model by running an endogenous treatment effects model (Guo and Fraser, 2014; Maddala, 1983) using stata program etregress (previously treatreg) with two-step estimation. The endogenous treatment effects model is a variant of the classical Heckman selection model (Heckman, 1979). The difference between the two models is the type of endogeneity addressed (cite). The classical Heckman addresses sample-selection bias issues whereas the endogenous treatment effects model addresses self-selection bias issues. The treatment effects model consists of a selection equation (whether or not to ally) and an outcome (sales performance) equation. The treatment effects model differs from sample-selection models in two aspects: first, the treatment dummy variable (ally in our case) enters the outcome (second stage) equation directly, and second, the outcome variable (sales performance in our case) is observed not only for firms that choose to ally but also for firms that launch their products without an alliance (Guo and Fraser, 2014). Stata’s etregress canned package (previously called treatreg) runs these two models at one go, thus ensuring that standard errors are adjusted.

The first stage (selection equation) is a probit analysis with the ally variable as the dependent variable. This is equivalent to the “full model” that we explained in the previous paragraph. The second stage (performance equation) is technically an OLS regression that also accounts for self-selection. In addition to other predictor variables, the second stage equation

includes an additional variable, the inverse mills ratio as a predictor. The inverse mills ratio, also called the non-selection hazard, is estimated from the first stage regression. The function of this variable is to control for any bias that results from self-selection.

Results

Table 1 provides the summary statistics. Twenty one per cent of drugs in our sample were launched through a co-promotion alliance. These drugs were extension of an established molecule in only about seventeen per cent of the cases. Also an established molecule was used as an ingredient in the product in about thirty one per cent of the case. These two data points suggest that most of the products had a high novelty factor. Sixty three percent of the products treated a chronic condition. An average firm had commercialized about six products before the launch of the focal product. Table 2 provides the pairwise correlation analysis of the variables under study.

Table 1 and Table 2 in about here

Table 3 summarizes result of the probit analysis. Model 1 runs the analysis with control variables. Model 2 adds the commercialization capability variable into the model. The coefficient and the marginal effects of the capability variable is significant and in expected direction (ME=-0.01, p<0.05). Therefore, we find support for H1. The coefficient and marginal effect of chronic is model 3 is also significant and in hypothesized direction (ME=0.147, p<0.05), thus lending support to H2. We also find support for H3 in model 4 since the nominal patent term variable is significant and in expected direction (ME=-0.008, p<0.05). Patent portfolio variable is negative and significant (ME=-0.06, p<0.001) in model 5, thus lending support to H4. Model 5 includes all the three variables into the regression equation. All the predictor variables retain their

statistical and economic significance except for the nominal patent term variable which switches sign and become insignificant.

Table 3 in about here

Table 4 provides us with the result of the two-step endogenous treatment effects model. Model 1 in table 4 is the output of the selection equation (the first stage probit) that determines whether a firm is likely to co-promote a product or launch it on its own. This model is approximately equivalent to “full model” 4 in table 3. The subsequent models summarize the results of the “performance equation”. The coefficient of “ally” dummy in model 2 is insignificant thus not lending support to H5. Thus we fail to reject the null that new drugs that are launched through a co-promotion alliance are likely to, on an average, perform no differently in terms of initial sales than drugs that are launched without such alliances. Our main effect of co-promotion alliance on initial sales is therefore not supported. The coefficient of the interaction term (ally*launch capability) is positive and but not significant at five per cent level, thus not lending support to H6. We also do not find support for H7, where the coefficient of interaction term (ally*nominal patent term) is positive but insignificant. We however get support for H8 because the coefficient is positive and significant (b=32.98, p<0.001). Thus we find evidence that patent portfolios positively moderate the relationship between co-promotion alliance and initial sales performance.

DISCUSSION AND CONCLUSION

In this paper, we argue for and find some evidence that downstream product launch alliances can help technological firms take the critical leap from having a great product to having a commercially successful one. Several internal firm and product specific factors influence a

pharmaceutical firm’s decision to engage in downstream co-promotion alliances when launching a novel proprietary drug in the market. A firm’s existing commercialization capability is one such firm specific factor. We find that a firm’s existing commercialization capability, measured as the number of product launches that firm has done in the past three years, negatively influences the firm’s decision to ally. We also find evidence that the nature of the new drug (chronic vs. acute) influences the originator firm’s decision to ally with a marketing partner. We find that drugs that treat chronic conditions, because of their higher long term profit potential, are more likely to be marketed through a co-promotion alliance. Firms are likely to preserve this profit pool by co-operating rather than competing with some additional firms. We also find that the nominal patent term of the drug, defined as the as the duration (in years) between the day the product was approved by FDA and the day of the last expiring patent on the product, negatively influences the decision to ally. This rationale is driven by the firm’s urgency to commercialize the product as soon as and as widely as possible before the marketing exclusivity period ends. We also find that the larger the patent portfolio protecting the drug, the less likely was the innovator firm to partner with another firm to launch the product. However, we also find evidence that larger patent portfolios help in increasing sales if a firm were to go for co-promotion alliance. We however do not find evidence to support our argument for the main effect of co-promotion alliance on first year sales performance.

Contribution

We contribute to the literature in three ways. First, we add to the nascent literature on downstream alliances. While upstream R&D alliances have been well studied in the field of strategy and innovation, literature on downstream commercialization alliances in high tech industries have been relatedly sparse. Prior literature studying downstream pharmaceutical

alliances have usually focused on predicting “products in market”. We go a step further and explain “success of those products in market”. This link between making innovative products and generating success is important to make, given the high risks involved in commercialization of products. Second, our paper helps address answer an important question i.e. do alliances matter? While previous alliance studies have studied critical determinants of alliance success, in our knowledge, very few (if at all any) have tried answering the question: do alliances matter? Our dataset and methodology allows us to be able to investigate an answer that question. We also contribute to literature by exploring conditions when some alliances matter more than others. Third, we provide a more objective measure of alliance success. Our measure of product-level initial sales performance allows us to be closer to the phenomenon (success of a newly product). Previously researchers, hamstrung by the paucity of data on objective measure of downstream alliance performance, had used measures such as the firm’s overall accounting performance or other measures such as abnormal stock returns.

Future research

Future research can look at a dyadic or a network level view of co-promotion alliances. At a dyadic level, researchers can investigate issues such as partner’s knowledge complementarity, prior experience working together and so on. For example, does prior alliance arrangement in upstream activities predict whether the partners will engage in downstream alliances? Or does success in one type of alliance spillover to other types? At an overall network structure level, we can investigate how the existing structure of alliances between firms in the industry influences a focal firm’s decision to engage in a co-promotion alliance. A network level study may look at multiplexity of ties binding different firms in the industry (upstream R&D alliances, downstream alliance for clinical trials and getting regulatory approval, and of course

downstream co-promotion ties). The presence or absence of certain types of ties could enable or hinder alliance formation. At an ecological level, we could also investigate whether co-promotion ties benefit the overall industry. Silverman and Baum (2002) argue that downstream alliances are beneficial for the industry as a whole because such alliances increase the industry carrying capacity (Hannan and Freeman) by increasing the level of resources available to industry participants. Downstream alliances also reduce competitive intensity because these alliances do not pose high foreclosure risks to rivals (i.e. the presence of ties between two partners in a downstream alliance doesn’t foreclose opportunities for other firms in the industry to partner with these two firms). Risk of foreclosure is high in case of upstream alliance (Silverman and Baum, 2002).

Table 1: Descriptive Statistics Variable Description N Mean Std. Dev. Min Max Ally Co-promotion alliance (Y/N) 414 0.21 0.41 0.00 1.00 Initial Sales First year sales (USD million) 414 77.11 132.52 0.01 1417.9 Commercialization capability # of products launched (last 3 years) 414 5.53 6.10 0.00 34.00 Chronic Chronic (Y/N) 414 0.63 0.48 0.00 1.00 Nominal patent term Lag between approval date and date of last expiring patent 414 10.65 5.55 0.00 21.02 Patent Portfolio # of patents covering the drug 414 1.84 1.95 0.00 17.00 Pre-launch Novelty Established molecule (Y/N) 414 0.31 0.46 0.00 1.00 Product Extension Product extension (Y/N) 414 0.17 0.38 0.00 1.00 Pre-launch Citation # of pre-launch citations (in 000) 414 0.93 2.62 0.00 28.23 Launch Indications # of approved indications at launch 414 1.62 1.40 1.00 14.00 Competition # of prior launches in therapeutic category 414 8.95 10.83 0.00 68.00 Alliance Experience Whether allied in last one year (Y/N) 414 0.11 0.32 0.00 1.00 Promotion - Journals Promotion in Journal (USD million) 398 3.66 4.41 0.00 30.97 Promotion - Contacts # of contacts established (in 000) 408 227.34 255.77 0.01 1623.2

Table 2: Correlation

Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Ally 1 1.00 Initial Sales 2 0.02 1.00 Commercialization capability 3 -0.07 0.21 1.00 Chronic 4 0.07 -0.01 0.08 1.00 Nominal patent term 5 -0.14 0.19 0.09 0.09 1.00 Patent Portfolio 6 -0.18 0.21 0.13 0.04 0.52 1.00 Pre-launch Novelty 7 -0.07 -0.13 -0.10 0.08 -0.12 0.02 1.00 Product Extension 8 -0.11 -0.06 0.00 0.10 -0.03 0.10 0.56 1.00 Pre-launch Citation 9 0.01 -0.06 -0.08 0.09 -0.03 -0.02 0.26 0.19 1.00 Launch Indications 10 0.10 0.03 0.06 -0.22 -0.15 -0.03 0.03 0.01 0.06 1.00 Competition 11 -0.02 -0.10 -0.07 -0.05 -0.16 -0.07 0.14 0.13 -0.06 0.05 1.00 Alliance Experience 12 -0.02 -0.05 0.28 -0.01 0.01 0.00 -0.04 0.04 0.00 0.03 -0.06 1.00 Promotion - Journals 13 0.20 0.38 0.22 0.09 0.08 0.02 -0.14 -0.11 -0.05 0.13 -0.09 0.04 1.00 Promotion - Contacts 14 0.20 0.63 0.18 -0.05 0.06 0.05 -0.12 -0.06 -0.08 0.24 0.01 0.02 0.61 1.00

Table 3: Probit analysis of likelihood of entering a co-promotion alliance

(1) (2) (3) Coef SE ME Coef SE ME Coef SE ME

Competition -0.004 0.009 -0.001 -0.005 0.009 -0.001 -0.002 0.009 -0.001 Launch Indications 0.016 0.057 0.004 0.031 0.057 0.007 0.021 0.057 0.005 Pre-launch Citation -0.007 0.042 -0.002 -0.015 0.042 -0.003 -0.004 0.042 -0.001 Pre-launch Novelty 0.071 0.228 0.017 0.056 0.232 0.013 0.049 0.228 0.012 Product Extension -0.692* 0.303 -0.168 -0.623* 0.302 -0.148 -0.697* 0.298 -0.167 Alliance Experience -0.158 0.250 -0.038 0.071 0.266 0.017 -0.176 0.248 -0.042 Commercialization capability (H1) -0.041* 0.016 -0.010 Chronic (H2) 0.616* 0.252 0.147 Intercept -0.688 0.736 -0.699 0.754 -0.915 0.737 N 386 386 386 Robust standard errors in second column, marginal effects in third + p<0.1 * p<0.05 ** p<0.01 *** p<0.001

(4) (5) (6) Coef SE ME Coef SE ME Coef SE ME

Competition -0.006 0.009 -0.001 -0.008 0.009 -0.002 -0.007 0.009 -0.002 Launch Indications 0.014 0.057 0.003 0.033 0.059 0.008 0.058 0.059 0.013 Pre-launch Citation 0.005 0.040 0.001 -0.017 0.045 -0.004 -0.026 0.047 -0.006 Pre-launch Novelty -0.007 0.229 -0.002 0.015 0.240 0.003 -0.006 0.258 -0.001 Product Extension -0.676* 0.298 -0.162 -0.650* 0.312 -0.150 -0.598+ 0.308 -0.133 Alliance Experience -0.154 0.251 -0.037 -0.154 0.249 -0.036 0.011 0.255 0.002 Commercialization capability -0.037* 0.016 -0.008 Chronic 0.734** 0.264 0.163 Nominal patent term (H3) -0.033* 0.017 -0.008 0.013 0.021 0.003 Patent Portfolio (H4) -0.259*** 0.066 -0.060 -0.281*** 0.085 -0.062 Intercept -0.414 0.730 -0.261 0.683 -0.617 0.708 N 386 386 386 Robust standard errors in second column, marginal effects in third + p<0.1 * p<0.05 ** p<0.01 *** p<0.001

Table 4: Two-step endogenous treatment effects model of initial sales (in USD million)

(1) (2) (3) (4) (5) Ist Stage Probit

2nd Stage OLS Nominal patent term 0.009 0.053 0.051 -0.343 -0.343 (0.022) (1.069) (1.067) (1.186) (1.081) Patent Portfolio -0.252** 6.454* 6.643* 6.741* 5.997* (0.087) (2.973) (2.973) (2.995) (2.991) Competition -0.008 -1.091* -1.115* -1.053* -0.946+ (0.010) (0.532) (0.531) (0.534) (0.537) Commercialization capability -0.037* 1.629+ 1.157 1.647+ 1.958* (0.017) (0.951) (1.031) (0.951) (0.959) Launch Indications 0.097 -11.610* -11.317* -11.478* -12.615** (0.082) (4.627) (4.627) (4.628) (4.656) Chronic 0.668* -21.636 -21.622 -21.657 -28.876+ (0.285) (14.525) (14.504) (14.517) (14.745) Pre-launch Citation -0.003 1.071 1.110 1.051 1.461 (0.048) (2.304) (2.301) (2.303) (2.320) Pre-launch Novelty -0.104 5.434 5.812 5.403 8.800 (0.272) (13.538) (13.522) (13.531) (13.647) Product Extension -0.548 1.455 0.695 2.108 7.302 (0.343) (16.328) (16.317) (16.342) (16.502) Alliance Experience -0.013 -23.531 -24.404 -22.683 -21.074 (0.323) (15.613) (15.608) (15.643) (15.723) Promotion - Journals 1.279 1.054 1.327 1.250 (1.404) (1.415) (1.405) (1.376) Promotion - Contacts 0.378*** 0.378*** 0.376*** 0.382*** (0.028) (0.028) (0.028) (0.028) Ally (0 or 1) (H5) -0.748 -8.143 -13.622 0.292 (48.429) (48.760) (51.207) (48.345) Ally X Commercialization capability (H6)

2.221 (1.887) Ally X Nominal patent term (H7) 1.595 (2.072) Ally X Patent Portfolio (H8) 32.988*** (9.771) Intercept -5.209 -59.240 -57.403 -59.322 -66.555 (435.030) (54.227) (54.170) (54.198) (54.594) lambda -2.272 -4.079 -3.695 -28.174 (28.632) (28.627) (28.673) (29.534) N 396 396 396 396 396

Standard errors in parentheses

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