STI policies Theore.cal underpinnings and measurement issues

Transcription

1 STI policies Theore.cal underpinnings and measurement issues A"la Havas Ins+tute of Economics, CERS, HAS Doctoral Summer School 28 June 2017, Saka Manor, Estonia

2 Outline Mo+va+on Models of innova+on, innova+on in economics paradigms STI policy ra+onales derived from theories in EC and OECD policy documents Indicators: neutral measurement tools or heralds of policy concepts? Discussion and conclusions

3 Mo.va.on Contrary to various previous claims, countries do not converge, various palerns can be observed: catching-up, stagna+on, falling behind and fluctua+ons (mixed performance) The age old ques+ons s+ll remains: Why some countries are ge"ng persistently richer? How miracles can be performed? What prevents others to follow suit or at least narrow the gap?

4 TW, JP, KR, KP: GDP per capita, USA = 100 Source: own compila+on based on Maddison project data

5 8 LA countries: GDP per capita, USA = 100 Source: own compila+on based on Maddison project data

6 AT, DE, N-IT, IE: GDP per capita, USA = 100 Source: own compila+on based on Maddison project data

7 SE, FI, IL: GDP per capita, USA = 100 Source: own calcula+on based on Maddison project data

8 Central Europe: GDP per capita, USA = 100 Source: own compila+on based on Maddison project data

9 Bal.c countries: GDP per capita, USA = 100 Source: own compila+on based on Maddison project data

10 Mo.va.on (2) GDP is far from being a perfect measure, but could be a star+ng point for relevant in-depth analyses Many different explana+ons and factors: natural endowments (Norway vs. ) history, external powers, wars structural changes, realloca+on of produc+on factors (sta+c, or alloca+ve efficiency) improved use of produc+on factors (dynamic efficiency) o what are the relevant produc+on factors: capital, labour, human capital, social capital? expansion of exis+ng produc+on factors (a larger number of skilled people), crea+on of new ones, e.g. radical innova+ons ( crea+ve (?) efficiency ) Various policy approaches

11 Mo.va.on: types of policy advice Best prac+ce, one size fits all recipes, silver bullets or panacea from interna+onal organisa+ons, gurus get the macroeconomic fundamentals right (Washington consensus) invest in high-tech (the Holy Trinity of ICT, bio, nano), start-ups, science parks, incubators, accelerators, introduce IPR (Bayh-Dole Act, tech-transfer offices, ) promote clusters Systemic view: actors and the linkages, co-opera+on among them ins+tu+ons: the rules of the game (guiding behaviour, direc+ng flows), norms, ways of thinking, trust, the role of physical, legal and knowledge infrastructures, framework condi+ons the policy governance sub-system

12 Mo.va.on: economics paradigms Rival theories on firm behaviour managerial implica+ons Rival theories, policy ra+onales, policy prac+ce policy implica+ons, efficacy of public spending BUT: no one-to-one rela*onship between a given economics paradigm (or any other theory) and actual policy measures Other factors influencing policy prac+ce might include: poli+cal, electoral considera+ons of the incumbent government constraints posed by available resources (funds, ideas, policy design and implementa+on capabili+es, etc.) influence by other countries prac+ces, opposi+on par+es, lobbyists, pressure groups, NGOs; ac+vi+es of chari+es, founda+ons consulta+ons with stakeholders

13 A caveat Framework condi+ons Innova+on and economic ac+vi+es Strategies of various actors (business, academic; domes+c, foreign; networks, clusters, ) Past performance Aspira+ons/ drive/ norms/ values of entrepreneurs, shareholders, managers Economic performance Policies (domains; levels of governance) Societal and environmental issues NGOs Mindsets, methods, processes

14 Main theses The systemic view offers a more relevant framework to analyse innova+on processes and performance than the science-push (SP) model of innova+on The market failure (MF) argument lends scien+fic support to the SP of model innova+on Significant opportunity costs of STI policies based on SP and MF Yet, the SP approach is s+ll highly influen+al in the EC observa+ons, proposi+ons in policy documents indicators to monitor/ assess performance

15 Main theses (2) Possible reasons: SP and MF: simple, straighiorward reasoning can be quan+fied, looks rigorous compelling focal point for orchestrated poli+cal ac+on Triadic compe++on Systemic view: too complex, [can be] perceived as vague hardly any formal models (history-friendly models, simula+on) no simple, one size fits all recipes systemic failures: a demanding task to iden+fy them dialogues with stakeholders are +me-consuming and costly Sociological factors, too Policy implica+ons non-sti policies have strong(er) impacts on innova+on performance a daun+ng challenge to orchestrate them

16 There is no single model of the innova+on process: enterprises can differ very significantly in their approaches to innova+on. (Smith, 2002) MODELS OF INNOVATION

17 Models of innova.on Linear models science-push: basic research is the main source of innova+on Basic science Design and engineering Manufacturing Marke+ng Sales market-pull: demand is the main source of innova+on Market need Development Manufacturing Sales

18 Models of innova.on (2) Systemic (or: networked) models chain-linked model mul+-channel interac+ve learning model

19

20

21 From a theore+cal perspec+ve, there must be doubts about whether any general theory of innova+on is possible. (van de Ven et al., 1999) ECONOMICS OF INNOVATION AND POLICY RATIONALES

22 Classical economics Technological, organisa+onal, ins+tu+onal and market changes including their co-evolu+on - were central research themes for classical economists [without using the term innova+on] Adam Smith (1776) David Ricardo (1817) John Stuart Mill (1848) Karl Marx (various)

23 Neo-classical economics Alloca+ve efficiency is in the centre of analysis: a sta+c approach [a short-term issue] Technological, organisa+onal, ins+tu+onal, and market changes are exogenous variables The main new objec+ve (in the 19 th century): to develop sophis+cated models of general equilibrium and by doing so to turn economics into a hard science, exemplified by Newtonian physics Walras (1874/1954, p. 71), for example, perceived the pure theory of economics or the theory of exchange and value in exchange as a physico-mathema+cal science like mechanics or hydrodynamics. (cited in Clark and Juma, 1988: 206)

24 Classical vs. neo-classical economics Two func+ons of decentralised markets: alloca+on of resources transmission of impulses to change Classical economist had inclined to focus on the laler Fundamental dynamic proper+es such as the rela+onship between expansion of markets, division of labour, and produc+vity growth in Smith, or the increasing organic composi+on of capital in Marx, are examples of a class of proposi+ons argued on the grounds of the irreversible transforma*ons originated by processes of what we could call dynamic compe++on. Moreover, their neglect of explicit microfounda+ons was jus+fied on the grounds of what we may term a holis+c or macroins+tu+onal assump+on about behaviour: it seemed obvious to them that, for example, given an opportunity, capitalists were ready to seize it, or that their ins+tu+onal func+on was to invest and accumulate the surplus. (Dosi and Orsenigo, 1988, p. 14)

25 Mainstream vs. evolu.onary economics Risk vs. uncertainty (op+misa+on) Ahistorical models vs. history counts path-dependent, cumula+ve processes learning by doing, using and interac+ng Informa+on vs. knowledge (codified, tacit) & skills learning capabili+es many types and sources of knowledge collabora+on Representa+ve agents vs. heterogeneity learning, path-dependence diversity heterogeneity has become an issue in mainstream econ, too Linear vs. networked (interac+ve) model of innova+on V Bush, 1945: science-push model (Say s Law: supply creates its own demand)

26 Policy implica.ons Uncertainty no op+mum Waste is inherent in the innova+on process duplica+on : (i) learning in a wider circle (ii) diversity maintained error : where not to search Variety, uncertainty adap+ve policy (policy learning) Diffusion and exploita+on of knowledge is not automa+c adequate policy tools are needed to foster Co-ordina+on of various policies affec+ng innova+on processes and performance

27 Contras.ng policy ra.onales Market failures in genera+ng new informa*on unpredictability of knowledge outputs from inputs inappropriability of full economic benefits of private investment in knowledge crea+on indivisibility in knowledge produc+on (Nelson, 1959; Arrow, 1962) Policy advice (jus+fica+on for interven+on) boost private R&D expenditures o subsidies o protec+on of intellectual property rights fund public R&D ac+vi+es

28 Contras.ng policy ra.onales (2) System failures in genera+ng, diffusing and exploi+ng knowledge types and sources of knowledge o R&D-based knowledge o prac+cal knowledge engineering ac+vi+es: design, scaling up, tes+ng, tooling-up, trouble-shoo+ng; ideas from suppliers and users; inventors ideas; prac+cal experiments, o knowledge embodied in materials, equipment, sowware, modes of learning o formal (R&D, both intra- and extramural) o informal: learning by doing, using and interac+ng proper+es of innova+on systems types, quality and frequency of interac+ons learning, capabili+es to exploit knowledge

29 Contras.ng policy ra.onales (3) Policy advice (jus+fica+on for interven+on) tackle system failures that hamper genera+on, diffusion and u+lisa+on of any type of knowledge required for successful innova+on o promote learning (individuals, organisa+ons) o facilitate co-opera+on, networking to generate and disseminate knowledge Besides the above, correc+ve ra+onales the role of the state in crea+ng new markets (Mazzucato, 2016) challenge-driven innova+on policies innova+on policy for transforma+ve change (Schot and Steinmuller, 2016)

30 EU STI policy prac.ce The systems approach has become widespread in academic and policy-making circles, esp. at the EC and the OECD (Sharif, 2006; Dodgson et al., 2011) Yet, a persistent devo+on to high-tech can be observed Fundamental R&D, mostly undertaken and funded by governments, provides the founda+on for future innova+on. Science is vital to innova+on, especially to generate step changes such as the discovery of the transistor or vaccines. (OECD, 2010: 3-4) EC, 2013: investment in knowledge, knowledge intensity, knowledge upgrade ; knowledge = R&D images on the covers of EU and OECD publica+ons monitoring tools, too ranking on scoreboards, league tables a/en0on of poli+cians, policy-makers (opinion leaders)

31 Eurostat covers

32 OECD covers

33 EU STI policy prac.ce (2) A recent survey for the European Research and Innova+on Area CommiLee (ERAC): decision-makers in most EU member states s+ll follow the science-push model of innova+on (Edquist, 2014a, 2014b)

34 INDICATORS: NEUTRAL MEASUREMENT TOOLS OR HERALDS OF POLICY CONCEPTS?

35 Selec.on of indicators Systema+c efforts to measure RTDI since the 1960s Widely used guidelines: Frasca+ (R&D), TBP, Oslo (innova+on), Patents, and Canberra (HR) Manuals Yet, it is not straighiorward to find the most appropriate way to assess R&D and innova+on performance R&D: a complex, mul+faceted process it cannot be sufficiently characterised by 2-3 indicators That applies to innova+on a for*ori The choice of indicators: an important decision; reflects the explicit or implicit views of those experts and policy-makers who have chosen them. Indicators are subjec+ve in that respect, but perceived as objec+ve (expressed in numbers)

36 Composite indicators Poli+cal significance: compress informa+on into a single figure eye-catching scoreboards A major difficulty: choosing an appropriate weight to be assigned to each component ( ) even using accepted approaches like BoD [Benefit of the Doubt] or factor analysis may result in dras+cally changing rankings (Grupp and Schubert, 2010, p. 74) Mul+dimensional representa+ons, e.g. spider-charts reflect the mul+dimensional character of innova+on processes and performance Analysts and policy-makers can iden+fy strengths and weaknesses, and hence set more precise targets for policy ac+ons

37 Types of knowledge Codified vs. tacit Scien+fic/ theore+cal vs. prac+cal knowledge Learning by doing, using, and interac+ng technological, organisa+onal, mgmt, finance, marke+ng, personnel, etc. rou+nes e.g. unofficially modified programmes for CNC machines All types are crucial for a successful innova+on process socio-economic development (profits, growth)

38 Modes of innova.on The DISKO survey has iden+fied two modes of innova+on (Jensen et al., 2007) S&T mode: exploits (in-house) R&D results DUI mode: relies on learning by doing, using and interac+ng (prac+cal knowledge) Both DUI and S&T modes of innova+on are important for Danish firms Combining DUI and S&T modes improves innova+on performance Sources of informa+on for innova+on: significantly higher importance of business sources (Community Innova+on Survey)

39 Highly important business sources of informa.on for product and process innova.on, EU members, Source: Eurostat, CIS 2012 Notes: Data for Cyprus, Luxembourg and Malta are not included in this figure. CIS2014, covering the period of , does not provide data on the sources of informa+on for innova+on ac+vi+es of enterprises.

40 Highly important scien.fic sources of informa.on for product and process innova.on, EU members, Source: Eurostat, CIS 2012 Notes: Data for Cyprus, Luxembourg and Malta are not included in this figure. CIS2014, covering the period of , does not provide data on the sources of informa+on for innova+on ac+vi+es of enterprises.

41 S&T vs. DUI mode of innova.on in the EIS EIS are only relevant for R&D-based innova+ons (S&T mode) 7 could be relevant for both S&T and DUI mode EIS 2004 no harmonised data that could be used to construct measures of learning by doing and using [DUI]. We would contend, though, that these limita+ons of the data reflect the same bias at a deeper level. The on-going development of harmonised S&T indicators over the post-war period has resulted from poli+cal ini+a+ves at the EU and interna+onal levels. The lack of DUI measures reflects poli*cal priori*es and decision-making rather than any inevitable state of affairs. (Jensen et al., 2007: 685; emphasis added AH)

42 The 2002 EIS indicators New S&E graduates (ISCED 5a and above) per 1000 popula+on aged Relevance for R&D- based innova.on X Relevance for non-r&d- based innova.on Popula+on with ter+ary educa+on (% of years age class) b b Par+cipa+on in life-long learning (% of years age class) b b Employment in medium-high and high-tech manufacturing (% of total workforce) Employment in high-tech services (% of total workforce) Public R&D expenditures (GERD BERD) (% of GDP) Business expenditures on R&D (BERD) (% of GDP) EPO high-tech patent applica+ons (per million popula+on) USPTO high-tech patent applica+ons (per million popula+on) X X X X X X SMEs innova+ng in-house (% of manufacturing SMEs) b b SMEs involved in innova+on co-opera+on (% of manufacturing SMEs) b b Innova+on expenditures (% of all turnover in manufacturing) b b High technology venture capital investment (% of GDP) X

43 The 2002 EIS indicators (2) PCT patents applica+ons per billion GDP (in PPS ) Capital raised on parallel markets plus by new firms on main markets (% of GDP) i Relevance for R&Dbased innova.on X X Relevance for non- R&D- based innova.on Sales of new to market products (% of all turnover in manufacturing) b b Home internet access (% of all households)?? ICT expenditures (% of GDP) b b Share of manufacturing value-added in high-tech X

44 The Innova.on Union Scoreboard The EIS was renamed as Innova+on Union Scoreboard in 2010 IUS : 25 indicators 10 are only relevant for R&D-based innova+ons (S&T mode) 4 mainly capture R&D-based innova+ons 7 could be relevant for both S&T and DUI mode 4 reflect DUI mode

45 The 2016 Innova.on Union Scoreboard indicators Relevance for R&D- based innova.on Relevance for non-r&d- based innova.on New doctorate graduates (ISCED 6) per 1000 popula+on aged X Percentage popula+on aged having completed ter+ary educa+on b b Percentage youth aged having alained at least upper secondary level educa+on Interna+onal scien+fic co-publica+ons per million popula+on Scien+fic publica+ons among the top 10% most cited publica+ons worldwide as % of total scien+fic publica+ons of the country Non-EU doctorate students as a % of all doctorate students b X X X b R&D expenditure in the public sector as % of GDP Venture capital investment as % of GDP R&D expenditure in the business sector as % of GDP X x X Non-R&D innova+on expenditures as % of turnover X SMEs innova+ng in-house as % of SMEs b b Innova+ve SMEs collabora+ng with others as % of SMEs b b Public-private co-publica+ons per million popula+on X

46 The 2016 Innova.on Union Scoreboard indicators (2) PCT patents applica+ons per billion GDP (in PPS ) PCT patent applica+ons in societal challenges per billion GDP (in PPS ) (environment-related technologies; health) Community trademarks per billion GDP (in PPS ) Community designs per billion GDP (in PPS ) Relevance for R&Dbased innova.on X X Relevance for non- R&D- based innova.on X X SMEs introducing product or process innova+ons as % of SMEs b b SMEs introducing marke+ng or organisa+onal innova+ons as % of SMEs Employment in fast-growing enterprises in innova+ve sectors (% of total employment) Employment in knowledge-intensive ac+vi+es (manufacturing and services) as % of total employment Exports of medium and high-technology products as a share of total product exports Knowledge-intensive services exports as % total service exports b x x x X b Sales of new to market and new to firm innova+ons as % of turnover b b License and patent revenues from abroad as % of GDP X

47 The evolu.on of the EIS and IUS indicators, Indicators reflec.ng only R&D-based innova+ons mainly R&D-based innova+ons both types only non-r&dbased innova+ons mainly non-r&dbased innova+ons Number of indicators

48 The relevance of EIS/ IUS indicators The IUS indicators could be useful in se"ngs where the dominant mode of innova+on is the S&T mode In prac+ce, however, both the S&T and DUI modes of innova+on are fairly important The SII could be low for an innova+on system with a low level of innova+on ac+vi+es altogether, or a low level of R&D-based innova+on ac+vi+es (while other types of innova+ons are abundant) The economic weight of LMT sectors; the importance of the DUI mode of innova+on A beler reflec+on of innova+on processes and performance by the IUS is needed to underpin effec+ve and sound STI policies

49 Another EC league table R&D intensity (2011) Excellence in S&T (2010) Knowledgeintensity of economy (2010) Ireland Sweden United Kingdom Belgium France Netherlands Denmark Finland Hungary Hungary is above Germany, Austria, and the EU27 average?!? Source: EC (2013a): 5

50 Knowledge-intensity of economy Knowledge = scien+fic knowledge; misleading name High-tech export FDI Share of high-tech goods in industrial exports, Ireland 46.6% 48.9% 52.2% Hungary 29.9% 30.6% 35.5% Netherlands 27.4% 25.2% 29.1% United Kingdom 26.1% 25.1% : France 22.5% 23.0% : Finland 20.0% 19.7% 17.1% Slovak Republic 16.9% 19.4% : Source: own calcula+on based on OECD.Stat data, extracted on 9 Sept 2013

51 Knowledge-intensity of economy (2) IR, HU: High-tech export by foreign-owned firms products developed outside IR and HU assembly of high-tech goods semi-skilled, low-paid workers Ac+vi+es, products, firms, sectors Foot-loose vs. anchored FDI reloca+on, e.g. electronics industry in Scotland, Wales in the 1980s and early 1990s and then in CEE in the 1990s, 2000s

52 Framework of the Global Innova.on Index 2016

53 The relevance of GII indicators A remarkable effort a broad coverage of countries 82 indicators in the 2016 edi+on Yet, severe weaknesses concerning business innova+on ac+vi+es a non-negligible mismatch between the headline no+ons (pillars and their sub-pillars) and the actual components (indices or indicators) selected R&D and innova+on are conflated A strong bias towards R&D-based (S&T mode) innova+ons, and thus the DUI mode is eclipsed

54 DISCUSSION AND CONCLUSIONS

55 A persistent devo.on to high-tech and its pikalls The systems view of innova+on has not become a dominant, systema+cally applied paradigm in policy circles, in spite of the rich set of policy-relevant research insights and some op+mis+c claims concerning the take-up of the systemic approach to innova+on Austrian paradox On the one hand, macroeconomic indicators on produc+vity, growth, employment and foreign direct investment indicate that overall performance is stable and highly compe++ve. On the other hand, an interna+onal comparison of industrial structures reveals a severe gap in the most technologically advanced branches of manufacturing, sugges+ng that Austria is having problems establishing a foothold in the dynamic markets of the future. (Peneder, 1999: 239)

56 A persistent devo.on to high-tech and its pikalls (2) STI policies based on the science-push model neglect the wide variety of types, forms and sources of knowledge the importance of distributed knowledge bases o collabora+on among actors o the significance of ins+tu+ons governing collabora+on to generate, diffuse and exploit all types of knowledge required for innova+on the LMT sectors Massive opportunity costs lost improvements in produc+vity unborn new products and services unopened new markets undelivered new jobs Spending public money guided by a biased (incomplete) policy ra+onale is ques+onable Ergas (1986), (1987): mission- vs. diffusion-oriented policies

57 Possible reasons for the observed persistence of the high-tech myth Simple, straighiorward reasoning Unprecedented achievements of major R&D efforts during World War II Major scien+fic results reported con+nuously since then As regards community crea*on it may be argued that a simple one-dimensional indicator ( ) can be iden+fied as a focal point for orchestrated poli+cal ac+on: we can all unite on transforming Europe to a high-tech knowledge-based economy. (Laestadius et al., 2005)

58 Possible reasons for the observed persistence of the high-tech myth (2) Triadic compe++on misplaced, misleading EU US comparison major structural differences Sociological factors STI policy-makers finance ministry staff influen+al scien+sts as advisors (formal and informal channels) quest for evidence-based policies formal modelling, quan+ta+ve analyses

59 The relevance of the systemic view of innova.on Provides a more appropriate framework to analyse innova+on processes and performance a rich picture on the modes of learning and innova+on STI policies should promote learning in its widest possible sense: competence building at individual, organisa+onal and interorganisa+onal levels in all economic sectors in all possible ways, considering all types of knowledge, emana+ng from various sources, and taking different forms

60 The relevance of the systemic view of innova.on But it is complex, and can be perceived as vague no one size fits all, easy to digest and implement policy prescrip+ons (as opposed to the market failure argument) cannot be easily formalised history-friendly modelling, simula+on; yet, not as appealing as e.g. the endogenous growth models Demanding in terms of analysis and policy design what type of system failure in which sub-system

61 Evolu.onary, system and policy failures: Difficul.es for policy-makers Evolu+onary failures genera+on of technological opportuni+es learning by firms (accumula+on of capabili+es) lock-in in inferior technology (competence trap), trade-offs o explora+on vs. exploita+on (current vs. future profits) o variety genera+on vs. selec+on tough selec+on low variety lock-in weak selec+on ineffec+ve firms, waste of resources, limited dynamics/ growth o +ght IPR vs. explora+on of new approaches/ diverse competence base

62 Evolu.onary, system and policy failures: Difficul.es for policy-makers (2) System failures (problems) missing or weak elements ( nodes, actors) missing, weak, inappropriate connec+ons among the actors transi+on (system dynamics) Policy failures weak learning (e.g. from previous prac+ce, interac+ons, good prac+ces) inflexibility in implementa+on lack of understanding of sectoral characteris+cs poor (no) vision-building ineffec+ve co-ordina+on (Malerba, 2009; cf. Bach and MaL, 2005; Smith, 2000) Unit of analysis/ policy interven+on micro meso macro

63 Lost key and the lamp post? Measurement: easy to measure Analysis: readily available data needs to be measured needs to be analysed Indicators used by by the OECD and the EC (Eurostat): poli+cal decisions Jensen et al. 2007; similar claims by Godin Measurement, analysis, policy-making virtuous or vicious circles

64 Further policy implica.ons Several policies affect innova+on processes and performance, perhaps even more strongly than STI policies, and hence policy goals and tools need to be orchestrated across several policy domains Analysts and policy-makers need to avoid the trap of paying too much alen+on to simplifying ranking exercises based on composite indicators, and devote their efforts to conduct thorough compara+ve analyses instead New indicators are needed, which beler reflect the evolu+onary processes of learning and innova+on The choice of an economics paradigm to guide policy evalua+on is likely to be decisive

65 Thank you!

66 DEFINITIONS (NOTIONS)

67 Types of innova.ons New or significantly improved goods (products, services) and processes [technological innova+ons] organisa+onal methods (business prac+ces, workplace organisa+on, firms external rela+ons) [organisa*onal innova+ons] marke+ng methods (product design, packaging, product placement, product promo+on, pricing) [marke*ng innova+ons] OECD: Oslo Manual (concerned with the business sector only) Market innova+ons: entering new markets (inputs, outputs) Financial innova+ons (not men+oned by the Oslo Manual; could be classified as a new service, or business prac+ce; 2008 global financial crisis) Public service innova+ons No+ons without a widely accepted, sound defini+on (with many uses, defini+ons; debates among authors) Frugal innova+on serving people with lille means

68 Types of innova.ons (2) Social innova+ons new solu+ons that simultaneously meet social needs and create new social rela+onships or collabora+ons solu+ons for exclusion, depriva+on, aliena+on, lack of wellbeing; leading to significant human progress and development Responsible research and innova+on a transparent, interac*ve process by which societal actors and innovators become mutually responsive to each other with a view to the (ethical) acceptability, sustainability and societal desirability of the innova+on process and its marketable products in order to allow a proper embedding of scien*fic and technological advances in our society Inclusive innova+ons process: including disadvantaged groups in produc+on outcome: mee+ng previously unmet demand or need systems of produc+on and delivery: integra+on of different market and non-market mechanisms inclusion in the innova+on system: including marginalised knowledge systems and prac+ces in the innova+on process Sustainable innova+ons, innova+on for sustainable development

69 Degree of novelty Used in surveys; new to the firm (CIS) market (CIS) world Used in qualita+ve analyses (unit of analysis) goods, processes o incremental o radical technology systems a set of technologically and economically interconnected goods and processes, affec+ng several companies or an en+re sector in the same +me, occasionally leading to the emergence of new industries (e.g. canals, gas and electric light systems, plas+c goods, electric household devices) techno-economic paradigms examples: the (first) industrial revolu+on; steam and railways; steel, electricity, and heavy engineering; oil, automobile, and mass produc+on; info-communica+ons

70 Systems of innova.on Na+onal and regional actors (boundaries of the system) func+ons of the system (Edquist) interac+ons (various flows) among the actors the ins+tu+ons ( rules of the game ) guiding/ influencing their behaviour and interac+ons dynamics (evolu+on) of a system sub-systems (by func+ons, actors, regions ) Ecoystems Freeman (1987), Lundvall (1992), Nelson (1993), Edquist (1997), Metcalfe, Georghiou, Malerba (2002), Cook, Bergek, Jacobsson, Carlsson, Lindmark and Rickne (2008), Hekkert et al. (2007),

71 Sectoral system of innova.on and produc.on (SSIP) A mul+dimensional, integrated and dynamic view of sectors A set of new and established products A set of agents carrying out market and non-market interac+ons for the crea+on, produc+on and sale of products Specific knowledge base, technologies, inputs and demand Agents: individuals and organisa+ons; with their specific learning processes, competences, organisa+onal structure, beliefs, objec+ves and behavioural palerns Interac+ons: communica+on, exchange, co-opera+on, compe++on and command; shaped by ins+tu+ons Processes of change and transforma+on through the co-evolu+on of various elements

72 The relevance of SSIP A useful analy+cal tool mul+dimensional: set of agents; market & non-market transac+ons dynamic: knowledge base, products, interac+ons, selec+on mechanisms, boundaries of sectors integrated (comprehensive) view of sectors: more reliable theore+cal and policy conclusions Best prac+ce vs. good prac+ces naïve vs. intelligent benchmarking Con+nuity and change (dynamics) people and their accumulated knowledge

73 Shortcomings of SSIP Missing dimensions of the original defini+on: the impacts of external actors, factors, forces o na+onal and regional systems of innova+on o mul+na+onals, interna+onal orgs, other global forces dis+nc+on between types of dynamics o evolu+on o transi+on the role of actors and ins+tu+ons ( rules of the game ) in reproducing, stabilising, adjus+ng and changing systems (Geels, 2004; socio-technical systems)

74 Types of dynamics Firms (organisa.ons) Sectoral systems Na.onal systems Evolu.on, learning normal adjustment refinement of steam engines varie+es of capitalism Transi.on priva+sa+on steam electricity planned market economy

75 STI policies Science policy is concerned with the development of science and the training of scien+sts Technology policy has as its aims the support, enhancement and development of technology (e.g. military, environmental protec+on) Innova*on policy takes into account the complexi+es of the innova+on process and focuses more on interac+ons within the system Dodgson and Bessant, 1996 Poten+al conflicts/ tensions between S, T, and I policies evalua+on of universi+es/ publicly financed R&D units/ individual researchers by publica+ons and cita+ons vs. business-academia collabora+on (involvement in innova+on co-opera+ons) different norms/ cultures at businesses and HEIs, PROs commercial secrecy vs. publish or perish project management prac+ces (deadlines, milestones, budget, termina+on, )

76 APPENDIX: EXAMPLES FROM CLASSICAL ECONOMISTS

77 Technological, org l, inst l and market changes in classical economics Adam Smith (1776) Division of labour: organisa+onal innova+on (using modern terminology) Also: learning, inven+on and introduc+on of machinery, the emergence of sectors, philosophers or men of specula+on Co-evolu+on of transport technologies, markets, and division of labour, leading to economic development (Book I, ch. III) examples stretch from the case of ancient Egypt to his contemporary Holland in +me and cover Africa, Asia and Europe in space

78 Technological, org l, inst l and market changes in classical economics (2) David Ricardo (1817) Technological and market innova+ons (using modern terminology) Sudden Changes in the Channels of Trade, the influence of machinery on the interests of the different classes of society on output, trade, profit, and employment (ch. 19 and 31) John Stuart Mill (1848) Technological (product and process), organisa+onal and financial innova+ons (using modern terminology) Inven+on vs. innova+on (introduc+on) Labour: skills and knowledge, general diffusion of intelligence