Preemptive Search and R&amp;D Clustering

Cardon, James H.; Sasaki, Dan

doi:10.2307/2555891

Cited by 26 publications

(26 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, waiting does not payoff and both firms undertake search in the first period. Our result is consistent with Cardon and Sasaki () who find that firms become more likely to invest in the same project as the correlation across different projects increases. In their model, however, the result is driven by the fact that higher correlation increases the likelihood of successful preemption, while in our setting higher correlation reflects less value from learning for the follower.…”

Section: Equilibrium Randd Search Strategiessupporting

confidence: 92%

The Path‐Dependent Nature of R&D Search: Implications for (and from) Competition

Oraiopoulos

Kavadias

2014

Production and Operations Management

View full text Add to dashboard Cite

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.

show abstract

Section: Equilibrium Randd Search Strategiessupporting

confidence: 92%

The Path‐Dependent Nature of R&D Search: Implications for (and from) Competition

Oraiopoulos

Kavadias

2014

Production and Operations Management

View full text Add to dashboard Cite

show abstract

“…In terms of the preemptive nature of R&D, our paper is closest to Cardon and Sasaki (). They show that firms have incentives to cluster on the same project even if potential technologies are ex ante equally promising and there are no informational spillovers.…”

Section: Introductionmentioning

confidence: 75%

Selection Biases in Complementary R&D Projects

Choi

Gerlach

2014

Economics Manag Strategy

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in

show abstract

“…Many of these studies incorporate strategic relationships among innovators and/or venture capitalists (see, for example, Dasgupta and Maskin (1987) and, more recently, Cardon andSasaki (1998), Gerlach et al (2005), and Bergemann and Hege (2005)). Because the focus of these studies is not the success or failure of particular R&D investments, R&D processes are appropriately simplified, typically to one-shot processes.…”

Section: Introductionmentioning

confidence: 99%

R&D in clean technology: A project choice model with learning

Oikawa

Managi

2015

Journal of Economic Behavior & Organization

View full text Add to dashboard Cite

A c c e p t e d M a n u s c r i p t Highlights of "R&D in Clean Technology: A Project Choice Model with Learning"• A model of many-step R&D on environmental technology with researcher's learning about the potential of its R&D project.• The optimal R&D subsidy with consideration of learning is higher than in the no-learning case.• The R&D subsidy regime is superior to the Pigouvian tax regime unless suppliers have sufficient incentives to continue cost-reduction efforts after the new technology successfully replaces the old one.• When there are multiple R&D projects, a uniform subsidy is more socially desirable than a selective subsidy. AbstractIn this study, we investigate the qualitative and quantitative effects of an R&D subsidy for a clean technology and a Pigouvian tax on a dirty technology on environmental R&D when it is uncertain how long the research takes to complete. The model is formulated as an optimal stopping problem, in which the number of successes required to complete the R&D project is finite and learning about the probability of success is incorporated. We show that the optimal R&D subsidy with the consideration of learning is higher than that without it. We also find that an R&D subsidy performs better than a Pigouvian tax unless suppliers have sufficient incentives to continue costreduction efforts after the new technology successfully replaces the old one. Moreover, by using a two-project model, we show that a uniform subsidy is better than a selective subsidy. JEL classification codes: D83, O33, Q55, Q58 $ We are grateful to Hideki Konishi, Takeshi Ogawa, and the seminar and conference participants at GRIPS, Hitotsubashi University, Tohoku University, the JEA meeting, and the SEEPS meeting for their helpful comments. We also thank an anonymous reviewer for his or her insightful comments and suggestions.

show abstract

Preemptive Search and R&D Clustering

Cited by 26 publications

References 1 publication

The Path‐Dependent Nature of R&D Search: Implications for (and from) Competition

The Path‐Dependent Nature of R&D Search: Implications for (and from) Competition

Selection Biases in Complementary R&D Projects

R&D in clean technology: A project choice model with learning

Contact Info

Product

Resources

About