2. MARCO TEÓRICO
2.4. Fundamentación contextual
Knowledge and innovation are inextricably bound (e.g. Asheim and Coenen, 2005a, 2005b; Binz et al., 2014; Frenz and Ietto-Gillies, 2009; Muller and Zenker, 2001). In a knowledge economy model, organisational interactions form feedback loops to aid the flows of innovation-related information and materials among the development, diffusion and deployment of knowledge (Carlsson et al., 2002; Rickne, 2001). In particular, knowledge deployment is specified as (a) knowledge
implementation for innovation activities and (b) knowledge commercialisation for transforming products or services into market value (Dvir and Pasher, 2004; Maier,
innovation process can be conceptualised within four boundaries: (1) knowledge development, (2) knowledge diffusion, (3) knowledge implementation and (4) knowledge commercialisation.
Regional competitiveness is decomposed into capacities, capabilities and performances that are embedded in the knowledge-based innovation process.
Capacitiesreflect available system proprietary resources to determine initial ability for innovation. R&D expenditure and R&D personnel are essential physical
resources that trigger the operation of knowledge-based innovation processes (Wang and Huang, 2007).Capabilitiesare a system’s ability to expedite resource utilisation for performance generation (Almond and Powell, 1967; Amit and Schoemaker, 1993; Bergek et al., 2008; Hekkert et al., 2007, 2011; Johnson, 1998). On the one hand, because an innovation system represents aggregate knowledge production processes (Asheim and Isaksen, 1997), R&D activities represent the system’s ability to build a knowledge base. On the other hand, product and process innovations are
prerequisites for the commercialisation of knowledge (Adner and Levinthal, 2001; Chen and Guan, 2012; Porter, 1980; Smolny, 1998; Zhang and Li, 2010).
Performancesare considered within the context of knowledge development and commercialisation. Patents are widely used proxies that account for technological knowledge produced through R&D in knowledge development (Acs et al., 2002; Chen and Guan, 2012; Fritsch, 2002; Griliches, 1990; Henderson and Cockburn, 1996; Nelson, 2009; Rosell and Agrawal, 2009; Weck and Blomqvist, 2008).
Consistent with Schumpeter’s (1934) ‘Economic Development’, the market value of new or improved products and/or services is a significant indicator. Sales and profits represent the financial performance of industry’s technological innovations (Foster et
al., 2008; Yam et al., 2011). Sales reflect the market expandability of products and services, whereas profits reflect the residual funds available for R&D investment and business stability.
Competitive capacities, capabilities and performances may enable high fluidity of knowledge flows that contribute to smooth operations of the knowledge- based innovation system (OECD, 1997). Figure 25 illustrates the following
knowledge-based innovation process: (a) financial and human resources enable (b) R&D activities to create (c) new knowledge in university labs, industrial firms and public research institutes (Cruz-Cázares et al., 2013). This knowledge is transferred to the industry sector (Caldera and Debande, 2010). In addition to the knowledge produced by industrial R&D, the transferred knowledge is used to conduct (b’) product and process innovations in the industry sector (Abernathy and Utterback, 1978). The industrial innovations launch new or improved products/services leading to (c’) financial benefit (e.g. sales and profits) in the marketplace. The earnings build (a) financial capacity, which then enables R&D organisations to employ (a)
researchers to implement (b) additional R&D projects.
The feedback structures and changes in the flows of knowledge stocks form time-based innovation mechanisms, which stimulate the dynamics of the innovation system (Autio, 1998; Diez, 2001; Nelson and Winter, 1982). Regional
competitiveness reflects the systemic nature of the evolutionary innovation process (Meyer-Stamer, 2008). As a result, the dynamics of knowledge flows embedded in the feedback structures create dynamic regional competitiveness over time, as illustrated in Figure 26.
Figure 26. Dynamics of regional competitiveness in the knowledge-based innovation process
Meanwhile, policy influences regional competitiveness (Asheim and Coenen,
2005a, 2005b) and creates dynamic ─ short-term, long-term, or both ─ effects
(OECD, 2009) that spread across multiple parts of the system over time (Jervis, 1997). Compared with no intervention, the dynamic feature of policy effects allows the consideration of four evolutionary directions of regional competitiveness (see
Figure 27). Policy A (top-right quadrant) improves regional competitiveness in both the short and long term. Policy B (top-left quadrant) is not ideal from a long-term perspective; however, it can be defined as appropriate within the given timeframe from t to t’ because it promotes improvement despite a slow development phase. Policies C and D are seen as ineffective interventions. On the one hand, policy C (bottom-right quadrant) is not appropriate within the timeframe because it results in decreased improvement although it has long-term potential beyond the timeframe, which is attributed to an increase in time-based growth. On the other hand, policy D (bottom-left quadrant) is the least appropriate intervention because it leads to a decrease in both average change and time-based growth.13
13In this study,average changeis estimated by calculating the average differences between
incremental simulation results (e.g. between 1% adjustment and 2% adjustment, between 2% adjustment and 3% adjustment, etc.) in terms of regional competitiveness indices across years (i.e. from 2003 to 2011 in this study). In addition,time-based growthis estimated by calculating the change of differences between polar years (i.e. between 2003 and 2011 in this study) in incremental simulation results in terms of regional competitiveness indices.
Figure 27. Types of policies
Considering the effect of the described policy interventions, this study provides explanations for the way in which innovation policy determines the dynamics of Busan’s regional competitiveness using a system dynamics method.