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

Abstract

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.

Rival Signals and Project Selection: Insights from the Drug Development Process

“…Additionally, our work empirically validates the theoretical findings from Oraiopoulos and Kavadias (2014) motivated by a microelectronics R&D setting: greater competition leads to a diversification of R&D efforts, yet the possibility to effectively learn may induce firms to follow each other into a technological domain. However, whereas they also propose that learning may cause firms to diversify (due to the fact that future improvements may exhibit only marginally better outcomes), we do not find such an occurrence.…”

“…Zschocke et al (2014) show that market competition drives firms to direct R&D budgets to incremental (i.e., close to past efforts) project improvements. Closer to our work, Oraiopoulos and Kavadias (2014) model the tension between followership due to technological learning, and diversification due to market competition. They show that where a rival directs R&D efforts, and what information these efforts reveal, affect the decision to undertake a project in a previously explored technological domain.…”

“…In anticipation of potential downstream competition, rival choices may lead the firm to diversify its own efforts to other markets (Oraiopoulos and Kavadias 2014). Such "negative" market rivalry signals should be associated with a decreased likelihood to select a project in the same indication as a rival project.…”

Configuration of digital and physical infrastructure platforms: Private and public perspectives

“…The project then further progressed through the differentiator platform with a digital‐dominant agenda to extend capability from manufacturing to a digitalized supply chain context. This is a path‐dependent pattern, well known in complex systems innovation (Oraiopoulos & Kavadias, 2014). Proposition Over time configurations “oscillate” from “physical‐dominant” to “digital‐dominant” modes.…”

Global buyer–supplier networks and innovation: The role of technological distance and technological breadth

“…This finding reiterates that with lower technological distance, related technological knowledge will be more recognized and valued by firms, and it will be less challenging to assimilate with the existing knowledge. Thus, the rate of knowledge transfer will be quicker (Oraiopoulos & Kavadias, 2014), and the transfer will have fewer coordination and integration issues (Moreira et al, 2018; Tzabbar et al, 2013).…”