This paper evaluates prospective technology areas, development strategies, and various innovation resources in China's pharmaceutical sector through the use of a hierarchical decision model. The results indicate that although domestic SMEs are the major preferred innovation alternative, it is followed closely by foreign MNCs. The sensitivity analysis indicates that the effectiveness of policy decisions are influenced by certain high technology areas. Recombinant therapeutic proteins, recombinant vaccines, and monoclonal antibody technologies are identified as the major areas that will influence the priority of innovation resources. The research crafts a research framework to formulate innovation strategies in dealing with the uncertainties of technology development and policy decisions in the biopharmaceutical industry.
We combine agency theory with the law and finance approach to analyze how the legal protection of investors and the corporate ownership structure affect corporate investment in research and development (R&D). We use information from 956 firms from the five most R&D-intensive industries in 19 developed countries. Our results show that better protection of investors’ rights by the institutional environment has a positive influence on corporate R&D. We also find that corporate ownership concentration works as a substitute for legal protection. This finding means that R&D investment of the firms in the countries with poor legal protection increases as ownership becomes more concentrated. Our results also show that the identity of shareholders has a relevant effect: Whereas banks and nonfinancial institutions as shareholders result in lower R&D, institutional investors as shareholders increase corporate investment in R&D.
Regions are increasingly being viewed as eco‐systemic agglomerations of organizational and institutional entities or stakeholders with socio‐technical, socio‐economic, and socio‐political conflicting as well as converging (co‐opetitive) goals, priorities, expectations, and behaviors that they pursue via entrepreneurial development, exploration, exploitation, and deployment actions, reactions and interactions. In this context, our paper aims to explore and profile the nature and dynamics of the Quadruple/Quintuple Helix Innovation System Model or Framework (government, university, industry, civil society, environment) as an enabler and enactor of regional co‐opetitive entrepreneurial ecosystems which we conceptualize as fractal, multi‐level, multi‐modal, multi‐nodal, and multi‐lateral configurations of dynamic tangible and intangible assets within the resource‐based view and the new theory of the growth of the firm. Co‐opetitive fractal innovation and entrepreneurship ecosystems are defined and discussed, and examples of regional innovation policies and programs are presented. Furthermore, the concept of multi‐level innovation systems is analyzed, taking into account the existence of knowledge clusters and innovation networks, while alternative aggregations of multi‐level innovation systems are proposed based on their spatial (geographical) and non‐spatial (research‐based) functional properties.
Standard financial and economic theories suggest that the stock value of R&D intensive High Technology Small Firms (HTSF) undergo a geometric random walk. Such a model neglects to account for observed periods where firms experience large fluctuations due to uncertainty related to its R&D activities, external competitive or regulatory environments. Empirical evidence also shows that the behavior of these firms is difficult to describe – let alone predict – using this Gaussian process. With ambidexterity as a theoretical basis, we show that the value of HTSF can be statistically described as the result of a combination of two distinct random walks: an exploitative steady state component characterized by Neo-Marshallian equilibrium and low volatility; and a more dynamic component with high volatility reflecting bursts of large and rapid changes associated with Schumpeterian outcomes of explorative processes. A mixture of two normal distributions provides an overall function that is more reflective of the empirical evidence and provides a quantitative measure for the theory that firms engage in concurrent exploration/exploitation activities. A linear relationship between the two components of the mixture distribution that describe the stock value of these firms also emerges. By understanding this dual nature and its impact on stock value, firms can better manage resources and prepare for the increase in variability that are associated with exploration activities. A more accurate financial description of HTSF that reduces or that anticipates uncertainty may lead to financial tools and option pricing methods that put a premium on the value of HTSF markets, incentivizing investors to invest more in such firms.
Research and development service firms (RDSFs) are a particular type of technology‐based knowledge‐intensive business services (KIBS). RDSFs provide clients with R&D services on a contract basis, and operate as knowledge intermediaries linking research and market. They are innovative in their own right, as well as supporting innovation efforts by their clients; they rely on their own innovation efforts to be competitive and to develop new value propositions for their clients. The present paper explores the innovation process in RDSFs, drawing on semi‐structured interviews with founders and senior managers of 32 companies in the United Kingdom. Our findings suggest that RDSFs vary considerably in terms of their primary innovation drivers (i.e. whether they are mainly driven by market demands or by technological opportunities) and the outcomes they pursue (i.e. whether their outputs are mainly services to clients or a mixture of services and products and/or intellectual property). Four major orientations of RDSFs were identified: (i) technology‐based innovation exploiters; (ii) science‐focused innovation explorers; (iii) client‐driven innovation integrators; and (iv) open innovation translators. This variety among firms normally belonging to the same, small subsector of KIBS, suggests the need for caution in generalising about behaviour in terms of such statistical groupings.