Abstract
Modern sciences benefit from computing and digitized experiments to innovate. As the capacity to process data increases, so does the need to accumulate those data effectively. Thus, traditional institutional processes and rewards that would guarantee effective data transactions are challenged. Battery R&D is chosen as a highly competitive field to investigate a lack of understanding data-centric collaborations and the micro-foundations causing them. In a multi-case study, researchers and managers across the R&D chain reveal how they cope with data-sharing tensions that arise from conflicting incentives. Moreover, organizational arrangements that ease transactions are linked to those sharing decisions. The findings suggest novel principles to selectively share this specific form of knowledge. Finally, ideas how to design data-sharing platforms that accommodate industry and academia are suggested.
Problem Definition
Pitched against market opportunities in transportation, personal devices and clean energy production, the sheer technical difficulty of advancing battery performance prompted a contemporary to assert that "the classic combination of government research funding and entrepreneurial gumption won’t take energy storage to the next level anytime soon" (IEEE Spectrum 2016). More precisely, the quest for novel batteries is characterized by a costly trial-and-error process with a vast space of possible solutions (Crabtree 2015). Therefore, a sequential approach of transferring knowledge from governmental funded research and academia to the private sector is ineffective. First, publishing a peer reviewed paper adds around two years until broad accessibility. Moreover, much needed “failures” are less rigorously communicated due to a publication bias in experimental material science (Raccuglia et. al 2016). Thus, experiments are duplicated and dead alleys not abandoned fast enough. Lastly, claims from the lab often don’t translate well to mass production because of narrow conditions required to reproduce them and unforeseen problems when scaling up (MIT Technology Review 2015).
In addition to problems stemming from researchers unaware of other’s up- and downstream results, accessing known prior work is another barrier. For example, Walsh et al. (2007) determined that negotiating for biomedical research materials takes academics more than a month for horizontal relationships (university to university) in 21% of the cases and 35% for vertical relationships (university to industry). Interestingly, patenting did not clearly increase refusals to share materials. Also, in the long-term, academic reuse is only moderately impeded due to formal knowledge protection (Murray & Stern 2007). The inability to access knowledge from peers is a mutual problem of basic and applied research. As an academic turned industrial researcher at Sankti3 tasked to scale-up a beyond lithium-ion technology without sharing ideas outside the building, speaking in public or writing papers suspected, “the problems with the science of solid-state batteries could be solved faster by conferring, even if cautiously, with other experts” (LeVine 2015).
In addition to problems stemming from researchers unaware of other’s up- and downstream results, accessing known prior work is another barrier. For example, Walsh et al. (2007) determined that negotiating for biomedical research materials takes academics more than a month for horizontal relationships (university to university) in 21% of the cases and 35% for vertical relationships (university to industry). Interestingly, patenting did not clearly increase refusals to share materials. Also, in the long-term, academic reuse is only moderately impeded due to formal knowledge protection (Murray & Stern 2007). The inability to access knowledge from peers is a mutual problem of basic and applied research. As an academic turned industrial researcher at Sankti3 tasked to scale-up a beyond lithium-ion technology without sharing ideas outside the building, speaking in public or writing papers suspected, “the problems with the science of solid-state batteries could be solved faster by conferring, even if cautiously, with other experts” (LeVine 2015).
Benefits and problems of public-private partnerships
In summary there exist opportunity costs of not discovering complementary knowledge and transaction costs of negotiating reuse once such an opportunity is known contrasted to a fully cumulative innovation process in battery technology. Public-private partnerships (PPPs) are meant to address those shortcomings, at least within their boundaries. As case in point, experts agree that a consortium led by the Vehicle Technology Office advanced the state of vehicular batteries by about 6 years from 1998 to 2010 (Link et al 2015, p. 54). PPPs perform through well-known mechanisms such as central pooling of IP, umbrella NDAs and managing differences in culture and incentive systems between academia and industry. Consortia like the JCESR further intensify knowledge exchange within protected bounds (DOE Industrial Consortia Initiative Case Study). Novel mechanisms include frequent adaptations to a joint roadmap with the ability quickly react on failed experiments. In essence, PPPs reduce costs of integrating complementarity knowledge from multiple stakeholders in return for the effort to onboard them.
However, the model of a PPP is not without challenges. In the field of drug discovery, forging partnerships is reportedly taking up to several years or flatline all together due to concerns of IP ownership (Edwards 2008). In these cases, upfront costs and the ongoing costs of resolving conflicts outweighs perceived synergies and grant money (cf. Busom & Fernández-Ribasb 2008). Such complications prompted advocates to take stipulations about IP entirely off McGill’s charter for a PPP in Neuroscience and through that, seek a new equilibrium between increased R&D productivity vs. uncertainty in appropriation (Owens 2016). Releasing all findings into public domain is however not mandated, but left at the discretion and cost of the individual researcher. In this scenario, absence of rules that limit disproportional commercial gains, differences of knowledge codified in a patent-paper pair and/or lack of cultivation of scientific norms destabilizes an Open Science equilibrium (Mukherjee & Stern 2009). Therefore, transaction costs accruing between individuals instead of intra-organizational will again turn out suboptimal. Also, technology closer to market needs to be protected or at least appropriable to be attractive for coveted industry partners.
The protocol used by the Structural Genomics Consortium to coordinate research among competing firms while protecting their strategic roadmap provides evidence of high welfare and high productivity consortia (Perkmann & Schildt 2015). Furthermore, Rai et al. (2008) suggested a cost effective two-step process to onboard synergistic industry partners with predefined ways to share future revenue. Based on extant literature and analogue domains, the impetus of this study is to make data and predictive models more fungible across academia and industry.
The rules in question are mainly:
The original draft of the proposal is also available here.
However, the model of a PPP is not without challenges. In the field of drug discovery, forging partnerships is reportedly taking up to several years or flatline all together due to concerns of IP ownership (Edwards 2008). In these cases, upfront costs and the ongoing costs of resolving conflicts outweighs perceived synergies and grant money (cf. Busom & Fernández-Ribasb 2008). Such complications prompted advocates to take stipulations about IP entirely off McGill’s charter for a PPP in Neuroscience and through that, seek a new equilibrium between increased R&D productivity vs. uncertainty in appropriation (Owens 2016). Releasing all findings into public domain is however not mandated, but left at the discretion and cost of the individual researcher. In this scenario, absence of rules that limit disproportional commercial gains, differences of knowledge codified in a patent-paper pair and/or lack of cultivation of scientific norms destabilizes an Open Science equilibrium (Mukherjee & Stern 2009). Therefore, transaction costs accruing between individuals instead of intra-organizational will again turn out suboptimal. Also, technology closer to market needs to be protected or at least appropriable to be attractive for coveted industry partners.
The protocol used by the Structural Genomics Consortium to coordinate research among competing firms while protecting their strategic roadmap provides evidence of high welfare and high productivity consortia (Perkmann & Schildt 2015). Furthermore, Rai et al. (2008) suggested a cost effective two-step process to onboard synergistic industry partners with predefined ways to share future revenue. Based on extant literature and analogue domains, the impetus of this study is to make data and predictive models more fungible across academia and industry.
The rules in question are mainly:
- Publishing rights / embargoes
- Co-authorship guidelines
- Licensing schemes
- Voting protocols in CRADAs
The original draft of the proposal is also available here.
