Luka T GebelIn this piece, I take an evolutionary perspective on the biotechnology sector to assess changes precipitated by digital technology. Particularly, it is interesting to see whether extant management frameworks are still as relevant to the innovation function in the context of digital innovation. Considering the sectoral systems framework by Malerba (2004), strictu sensu, in times of a technological discontinuity and growing modularity in the innovation function, pharma will struggle to lock in existing customers and, therefore, may risk not meeting existing demand.
Key things I find here:
- Increased modularity in the SSI; meaning more actors, de-novo entrants, diversifying entrants, non-market actors taking agency – some with new or better tools and capabilities to solve for clinical needs.
- A possible platformisation of enabling technologies; similarly to the ‘biotechnology revolution’ digital technology enables more specificity in the design heuristics for drug discovery & development – how the technology is sourced, absorbed, and appropriated should be a focal point.
- Cross-sectoral spillovers; fast and dynamic absorption & recombination of knowledge and trends from other sectors challenges existing organisations for innovation.
Large-scale technological transformation brought by digitalisation raises new questions regarding the process and capabilities by which large firms innovate and how new ventures accelerate. Therefore, this paper aims to explore biotechnology leadership on hand of the sectoral system of innovation (SSI) proposed by Malerba 2004. Current literature has not found consensus on which established frameworks for innovation hold and which require an adaptation toward the cross-industry trend of digitalisation (Lanzolla et al. 2018). This study examines shortcomings of the framework regarding the current shift toward digital technologies and finds that (1) increased modularity in the SSI, (2) platformisation of enabling technologies, and (3) cross-sectoral trends, enable a shift of industry dynamics in therapeutic discovery and may allow for co-evolution of the biotechnology sector. Certainly, institutional involvement determines international leadership of biotechnology in the digital age. Although focus will be placed on biotechnology in healthcare, significance of this study will extend beyond industry boundaries and, moreover, manifest itself in emerging market trends and international competitiveness.
The SSI
When proposing the SSI, Malerba combines long-established frameworks such as the Schumpeter’s marks (1934) and Pavitt’s taxonomy of sectors by sources and appropriability for innovation (1984), while recognising the development of interdependencies among agents alongside the concepts of filieres, development blocks (Dahmen 1989), innovation systems, and evolutionary dynamics – now commonly referred to as innovation ecosystems. As such, the main building blocks of the SSI are knowledge and technologies, actors and networks, and the role of institutions. In addition, non-firm organisations and the concept of demand as both a stimulus and constraint for innovation find their significance in the framework. SSI exceeds the note on actor-interdependencies and emphasises on the dynamic and continuous flow of innovation activities redefining the immobility of sectoral boundaries. Co-evolution of the SSI as a result of technological change is discussed also; however, considers to address platform-based innovation and generative character of innovation only tangentially.
The Sector
Biotechnology, essentially, derives itself from increased modularity and converging sciences in the chemistry-heavy pharmaceutical sector. Over recent decades, pharma has evolved from a closed all-activities ‘silo’ organisation to an open and vertically disintegrated structure (Petrova 2014). Public entities have since taken on the role of actively contributing to fundamental science creation, as well as biotechnology bridging the chemistry/ life-sciences divide, specialising in applied science for the biomolecular design. The resulting ‘trifecta model of innovation’ (see Fig. 1.) has allowed incumbent pharma companies to redefine their position as the main commercialising party through drug-centred partnerships for clinical trials and manufacturing and acquisitions of marketable tacit knowledge. Provided the SSI framework, the effort to divide labour by competency while establishing a network of firms and non-firm organisations is key to why the US leads in biotechnological innovation vis-à-vis the European sector (Montobbio 2004). Lower appropriability, as well as high institutional involvement measures have given rise to an Asian biotechnology revolution (Wong 2011).
To date, while the developmental expenditure soars (Scannell et al. 2012), less molecular entities than ever before gain market approval (Jack 2011). Causality for Erooms law can be found in stricter approval procedures, deficiency in ‘low hanging fruits’, and a persisting lack of firm’s competence to capitalise on other actor’s established competencies (Petrova 2014). The development of a new medicine, from target identification through approval for commercialisation, takes over 12 years and averages a cost of $2.6b (Mohs & Greig 2017). Throughout this process, ~80% of the costs incurred are in reference to attrition rate (Morgan et al. 2011); the amount of failed molecules as clinical candidates.
The Innovation Function in Medicine
Innovation opportunity in biotechnology heavily relies on scientific breakthroughs converging into the sector and, hence, on external accessibility of knowledge. As such, publicly-funded and university-based research for knowledge creation has manifested itself as primary source for knowledge creation (Khulji 2006). Among academia, the production of science is increasingly collaborative (Adams et al. 2005, Wuchty et al. 2007), and supported by new open-sourced modes for innovation such as open data science and algorithm competitions (Balasco 2019) and even crowdsourced drug discovery in cases such as orphan drugs (Krämer 2019). Increasing interdisciplinarity in the field of medical science requires interaction between the established fields to bridge heterogeneity and create knowledge accessible for industry.
Moreover, industry has recognised that involving academic science-leaders at the early stage of drug discovery allows for earlier clinical failure, decreasing overall attrition rate (Pritchard et al. 2003). The SSI affirms the importance of non-firm organisations in innovation, however, finds that within-firms experience and geographic expertise (within the supply chain), also referred to as ‘advanced integration capabilities’, are crucial to transform knowledge into an actionable artefact for innovation (Malerba 2004). University research has since has taken on a major role, guiding the healthcare industry away from random screening for drug discovery and toward molecular biology; exploring the biological basis of disease models for ‘rational drug design’. The resulting ‘triple helix’ (Leydesdorff 2012) of university-industry–government relation increases specificity and accuracy in drug innovation. This allows firms to capitalize on their R&D investments more effectively (D’Este and Patel 2007, Prinz et al. 2011); respectively encouraging organisational strategy toward open innovation modes. As such, from 2000 to 2011, half of all new American approved drugs were introduced through temporary organisations (Myshko 2014).
Indeed, rapid technological change precipitated the co-evolution of biotechnology from pharmaceuticals. The introduction of molecular biology and genetics enhanced our understanding of the biological basis of disease models for small molecule discovery for pharmaceutical drug-agents. Genetic engineering pervasively shifted manufacturing practices toward synthesizing specified proteins with existing therapeutic properties (McKelvey 2001). Genomics technologies emerged as a convergence from the two, providing the enabling technology for more specific biological characterisation and functional validation of unknown drug targets (Hopkins et al. 2007). Since 2003, the price of sequencing a person’s genome has dropped from $2.7 billion to $699. The commoditisation of genomic sequencing provides a platform for new innovative technologies such as recombinant DNA, gene therapy, and genetic modification. These tools offer substantial promise for personalised medicine directed toward the treatment of chronic diseases and congenial disorders. However, regulatory and economic hurdles are uncertain ex ante and will considerably influence the cycle time of innovation, as well as international leadership dynamics in biotechnology.
Cost-containment policies have constrained healthcare delivery especially in Europe and, vis-à-vis, make the US an attractive market for the introduction of new treatments. Indeed, strict price controls in southern Europe, as well as reference-pricing and diffusion of offering policies direct European biotech firms’ attention toward the US market. The introduction of biosimilars in Europe has seen little effect lowering the price level of the reference drug (Morton et al. 2018). Singapore’s state-led biotechnology sector has gbled its R&D spending over the past decade (Wong 2011) and established institutes specialising on applied biomedical science (Anna 2009). Further, both Singapore and Taiwan have emulated the American concept of policy-led UI collaboration and entrepreneurial university engagement (Lee & Win, 2019, Andrea et al. 2017). South Korea also illustrates the rapid surge of biotechnology in Asia, firstly defined in reverse engineering transforming into endogenous innovation capabilities (Lopez 2009). Indeed, lower appropriability measures and less ethical governance surrounding science innovation, gave way for the first gene-edited babies, designed to be naturally immune to the human immunodeficiency virus (Li et al. 2019).
Who Drives Innovation?
Cockburn and Henderson (2001) show that collaborations and increasing modularity have led to more effective drug innovation, as well as Grassman and Reepmeyer (2005) assessing an increase in disease specialisation for niche markets and genome-specifically targeted medicine. Chan et al. (2007) provide analytical support for the adjustment cost incurred by risk-seeking market entrants, often fails to outweigh the transaction cost (collaborating for complementary assets) for commercialization. The trend of rising R&D expenditure of incrementally improving pre-existing drugs (creative accumulation) over breakthrough innovation drugs (disruptive innovation) may suggest a trend toward a stagnating industry, finding itself in a productivity crisis (Hopkins et al. 2007). Like so, the SSI framework suggests that high appropriability and high cumulativeness at the firm level lead to a Schumpeter mark II pattern (Malerba 2004). Cockburn (2007), however, advocates that incremental innovations aid in aligning pre-existing treatments toward future disruptive technological changes and, hence, that the return on investments is not yet evident. As established by Teece (1986), owners of complementary assets are more likely to assume profit from innovation than innovators themselves. Therefore, high inelasticity of demand for clinical solutions (Comanor 1986), extending cycle times, and high entry barriers in the established ‘trifecta’ enables pharma to absorb changes in technology faster, assume for technological discontinuities, and, hitherto, has enabled pharma to evade ‘creative destruction’ (Hopkins et al. 2007, Malerba 2004).
Digital technologies pervasively redefine all facets of the drug discovery process (Brown et al. 2018), pressuring incumbent firms to be dynamic to ensure accessibility of external knowledge. Emerging new agents, such as data-analytics specialists and technology vendors in the healthcare industry redefine organisational strategy and provide an innovation platform for existing firms. Variety creation, as form of dynamics of the SSI, can also be observed in the creation of hybrid UI departments and accelerator programmes. Indeed, combinatorial innovation and increasing modularity in the therapeutic delivery pipeline may allow for innovation generativity (Baldwin & Clark 2000). Similarly, platform-based technology ecosystems are emerging as powerful new ways of organising interdependent innovation activities among incumbent agents, as well as new entrants (Gawer 2010, Yoo et al. 2012, Jacobides et al. 2018).
The commoditisation of genomic sequencing provides a platform for new innovative technologies such as recombinant DNA, gene therapy, and genetic modification. The new, highly knowledge-intensive technology requires new types of competencies, based in external knowledge harder to integrate by incumbent firms. Regulatory and ethical governance already define boundaries to scientific pursuits in genome editing and recombinant DNA in the US and Europe. National governance of such technologies may result in less developed countries to emerge as scientific inventors and redefine international leadership in biotechnology.
Moreover, the trend toward personalised medicine places universities and academic research-centres at the source for data generation for drug discovery, as well as stage II clinical trials – redefining the trifecta model of innovation. Regarding hospitals, however, the IT transformation thus far shows little significance in making delivery more effective or less expensive (Sahni 2017). Adopting to the electronic health record shows little correlation to quality of care (Lin et al. 2018). Similarly, Foroughi & Stern (forthcoming) argue that digital technology shows minor significance redefining the incumbency advantage in the medical device industry. – Nonetheless, considering the SSI framework strictu sensu, provided a technological discontinuity and growing importance of the university body in drug development with the patient, pharma will no longer be able to lock in existing customers and, therefore, risk not meeting the existing demand.
The SSI provides some applicable insight on technological change regarding industry dynamics and sectoral co-evolution; however, the framework has not been updated to new themes defined by digitalisation in biotechnology. An adaptation toward cultural heterogeneity, specifically the SSI’s applicability to low appropriability countries, could potentially explain the internationalisation of firms’ and non-firm organisation’s R&D. An adaptation of the framework toward ecosystem dynamics, affordance and generativity of innovation, and platform-based innovation may enable new insight on international leadership and entrepreneurship in knowledge-intensive sectors.
I just wonder how would big firm and companies react in terms of market and control towards a vast growing entrepreneurship?
Nice post!