Secondary markets in the Information Technology (IT) industry, where used or refurbished equipment is traded, have been growing steadily. For Original Equipment Manufacturers (OEMs) in this industry, the importance of secondary markets has grown in parallel, not only as a source of revenue, but also because of their impact on these firms' competitive advantage and market strategy. Recent articles in the press have severely criticized some OEMs who are perceived to be actively trying to eliminate the secondary market for their products. Others have policies that enhance their secondary markets. The goal of this paper is to understand how an OEM's incentives and optimal strategies vis-à-vis the secondary market are shaped contingent on her relative competitive advantage, product characteristics and consumer preferences. The critical tradeoff that we examine is whether the indirect benefit from maintaining an active secondary market (the resale value effect) can outweigh the potentially negative effect of the sales of used products at the expense of new product sales (the cannibalization effect). To that end, we develop a durable good model where the OEM can directly affect the resale value of her product through a relicensing fee charged to the buyer of the refurbished equipment. We analyze the OEM's strategy in both the monopoly and the duopoly cases, characterize the optimal relicensing fee set by the OEM, and draw conclusions on the conditions that favor stimulating or deterring the secondary market.
Forthcoming in Management ScienceR&D projects face significant organizational challenges, especially when the different units who run these projects compete among each other for resources. In such cases, information sharing among the different units is critical, but it cannot be taken for granted. Instead, individual units need to be incentivized to not only exert effort in evaluating their projects, but also to truthfully reveal their findings. The former requires an emphasis on individual performance, while the latter relies on the existence of a common goal across the organization. Motivated by this commonly observed tension, we address the following question: how should a firm balance individual and shared incentives, so that vital information is both acquired, and equally importantly, disseminated to the entire organization? Our model captures two key characteristics of R&D experimentation: information is imperfect and it is also costly. Our analysis yields several important implications for the design of such incentive schemes and the management of R&D portfolios.
W e formalize R&D as a search process for technology improvements across different technological domains. Technology improvements from a specific domain draw upon a common knowledge base, and as such they share technological content. Moreover, different domains may rely on similar scientific principles, and therefore, knowledge about the technology improvements by one domain might be transferable to another. We analyze how such a technological relatedness shapes the direction of R&D search when knowledge generated from past search efforts disseminates to rival firms. We show that firms optimally diversify their search efforts, even toward domains that are riskier and less promising on expectation. This is amplified for higher competition intensity, i.e., higher cross-product substitutability. Our work also suggests that different sources of learning about the domains may have opposite effects on the direction of search. Higher ability to infer the potential of an explored domain prompts the clustering of searches, whereas the ability to learn across domains prompts diversification. Finally, we discuss the technological landscape properties that prompt firms to engage in a sequential R&D search, instead of a parallel competitive search.
This article develops a framework for driving innovation under highly ambiguous conditions. An analysis of the most novel medicines of the past 20 years shows that a very large group of small companies created more breakthroughs, at considerably less overall cost, than a much smaller group of very large companies. This article’s findings present the first large-scale empirical validation of the theoretical literature predicting the superiority of decentralized parallel searches in ambiguous environments. Accordingly, companies that attempt to “de-risk” the innovation portfolio by narrowing their search efforts to minimize failures run the risk of filtering out the next breakthrough.
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