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Innovation, Disruptive Technologies, and SMEs: Constraints and Policy Alan Hughes Centre for Business Research University of Cambridge Presentation at the Six Countries Programme Conference on SMEs and Disruptive Technologies Vancouver June © Alan Hughes June 2003
Disruptive Technology, Innovation and High-Tech SMEs Disruption based on technological innovation measurable only with hindsight? Evidence base for policy focussing on support for innovative experiments –Nature and Incidence of knowledge based SMEs –Distinctive business characteristics –Innovative behaviour –Constraints on meeting business objectives Problems of Public Policy Support and Evaluation for Technology based Innovation © Alan Hughes June 2003
Focus of Presentation CBR panel surveys of UK SMEs Evaluation of SMART scheme to support SME technological innovation © Alan Hughes June 2003
Sources of Data Cosh, A.D. and Hughes, A. (2000) (eds) British Enterprise in Transition: Growth Innovation and Public Policy in the Small and Medium Sized Enterprise Sector ESRC Centre for Business Research University of Cambridge, Cambridge. PACEC (2001) Evaluation of SMART by Boyns,N. Cox,M. Hughes,A. and Spires,R. DTI Evaluation Report Series No 3 September Cosh,A.D. and Hughes,A.(2003) Enterprise Challenged: Small and Medium Sized Enterprises in the UK ESRC Centre for Business Research University of Cambridge, Cambridge. © Alan Hughes June 2003
CBR Surveys Regular biennial survey of independent SMEs in the UK Manufacturing and business services Size Stratified surveys over 2000 firms Latest results based on 5 th survey in 2002 Surveys incorporate questions on innovation input and output constraints and over 200 company specific variables on structure and performance Allows comparison of high-tech and conventional © Alan Hughes June 2003
Macroeconomic Background Latest survey took place against relatively stable macroeconomic conditions with low rates of inflation and interest rates compared to earlier years. However –stagnating demand especially for manufacturing output, –falling capital markets especially for technology related stocks. –expect, therefore, some increase in the importance of demands constraints in manufacturing, and some intensification of financial constraints for high-technology sectors compared to earlier survey periods. © Alan Hughes June 2003
Size Sector and Age Distribution 2002 sample of 2127 firms –35% <10 employees micro –50% 10<100 employees –15% 100<500 employees –61% drawn from manufacturing and around 39% from business services –around a half formed after 1980, and a quarter after 1990, around 14% date from the pre-war period © Alan Hughes June 2003
SMART Smart is the Small Business Service (SBS) initiative that provides grants to help individuals and small and medium-sized businesses to make better use of technology and to develop technologically innovative products and processes. Technology Reviews Grants of up to £2,500 for individuals and small and medium-sized firms (fewer than 250 employees) towards the costs of expert reviews against best practice. Technology Studies Grants of up to £5,000 for individuals and small and medium-sized firms (fewer than 250 employees) to help identify technological opportunities leading to innovative products and processes. Micro Projects Grants of up to £10,000 to help individuals and micro-firms (fewer than 10 employees) with the development of low cost prototypes of products and processes involving technical advances and/or novelty. Feasibility Studies Grants of up to £45,000 for individuals and small firms (fewer than 50 employees) undertaking feasibility studies into innovative technologies. Development Projects Grants of up to £150,000 for small and medium-sized firms (fewer than 250 employees) undertaking development projects. A small number of exceptional high-cost projects may attract grants of up to £450,000. © Alan Hughes June 2003
SMART CHANGES 2003 Smart research and development (R&D) project grants will be replaced by a new R&D grant product on 1 June The important differences are: –Research projects (previously called Feasibility studies) - 60% of eligible project costs up to a maximum grant of £75,000 –Development projects - 35% of eligible project costs up to a maximum grant of £200,000 –Exceptional development projects - 35% of eligible project costs up to a maximum negotiable grant of £500,000 –Micro projects - 50% of eligible project costs up to a maximum grant of £20,000. © Alan Hughes June 2003
High Tech Industries v. High Tech Firms Many firms in high tech inds have low R&D/Sales (45% have zero R&D) Many firms in conventional inds have high R&D/Sales (10% > 3%R&D/sales) Conventional Sectors c.75% of UK firms therefore more R&D intensive firms in conventional sectors High Tech Firms R&D intensity –Mean R&D/Sales % % –% with R&D/Sales >3% % % © Alan Hughes June 2003
Why are Knowledge-Based /High-Tech Firms Important? © Alan Hughes June 2003
High Tech Producers and High Tech Users Focus here is on high tech producers Impact on productivity growth at macro level depends upon both output of high- tech producers and high tech users –US productivity acceleration post 1995 mainly accounted for by wholesaling, retailing, financial services © Alan Hughes June 2003
Golden Oldies v New Kids on the Block Great current emphasis on spin-outs from universities and start- up… New kids on the block –Seed bed role –But tiny proportion of all knowledge based start-ups –Very small proportion grow substantially (e.g. in 2002 only 125 of US university licensed firms yielded > $1.million) Emphasis also needed on existing firms…Golden Oldies –Much more important for productivity growth –Key difference UK/EU v, USA not start-up but rapid growth after start-up –Sustained innovation to disrupt leader –Examine constraints on innovation and growth © Alan Hughes June 2003
Competitive Position All firms have low numbers of perceived competitors (4-6) High Tech perceive greater proportion of these overseas –Mfg 31% –Services 15% High Tech esp. services more reliant on largest customers (40%-50% sales from top 5 customers) © Alan Hughes June 2003
Why Collaborate? For all firms expanding product range expertise comes top For High Tech relatively more important –To share R&D –Develop specialist products –Access overseas markets For High Tech relatively less important –Keep current customers © Alan Hughes June 2003
Competitive Advantage High Tech Firms emphasise absolutely and relatively –Product Quality and Specialisation –Creativity and Flair High Tech Firms place low emphasis absolutely and relatively –Price –Speed of service © Alan Hughes June 2003
Innovation Activity High tech firms are more innovative –Product –Process –New to firm –New to industry © Alan Hughes June 2003
Significant Constraints High Tech mfg –Demand –Finance for expansion –Marketing skills –Overseas market access (relatively) High Tech Services –Overdraft finance –Marketing skills Acquisition of Technology not a common constraint © Alan Hughes June 2003
Key Characteristics and Evidence Based Policy Issues High-tech firms not high-tech sectors Product Development Focus with few Competitors Overseas Orientation –Exports and Collaborative agreements –Collaboration High Collaboration –Customers and Suppliers k ey collaborators Constraints –Finance still an issue –Marketing skills (link to focus on new prod. devpt) © Alan Hughes June 2003
SMART WINNERS (1) At time of award –93% independent single site businesses –50% less than 10 staff, 30% 10<50 staff –86% were formed as new start ups –66% less than 10 years old –87% already had R&D expenditure and staff Median £70K Median 2 full time staff © Alan Hughes June 2003
SMART WINNERS (2) Success Rate – applications, awards –Success rate for applications Lower for smaller firms Rising as scheme matures Sector concentration –IT, computing –Scientific instruments –Electrical engineering © Alan Hughes June 2003
Evaluating SMART like programmes Counterfactuals, Selection Bias and Information failure –Randomization –Matched Control groups..multiple characteristics –Selection modelling –Instrumental variables –Subjective Counterfactuals –Scheme Based Information Needle in a haystack Skewness of Outcomes Additionality © Alan Hughes June 2003
Smart Evaluation Methodology Comparison of Successful v. Unsuccessful applicants Survey data plus program information Econometric analysis with correction for selection restricted to data gaps for unsuccessful firms pre 1995 Subjective counterfactuals Case Studies Focus on award winners to post effects © Alan Hughes June 2003
Award Winners v Losers Winners were on average faster growing post award –Robust to sample selection bias –But rarely statistically significant © Alan Hughes June 2003
Subjective Estimates of SMART Impact Turnover –C.57% no change, 41% some increase Exports –C 70% no change, 28% some increase Employment –C 64% no change, 32% some increase Profitability –C 46% no change, 53% some increase © Alan Hughes June 2003
Skewness of Outcomes Typical result is no change But some firms outstandingly successful –Top 5% growers 50% all SMART associated sales –Top 20% growers 80% all SMART associated sales Pareto Distribution © Alan Hughes June 2003
Conclusions on SMART Successful scheme with positive outcomes dominated by small proportion of high fliers –Skewness to be expected –Focus on average effects misleading Scheme has evolved in response to monitoring of use and outcomes Evaluation requires that information requirements on baseline data and outcomes should be built into scheme Further work should focus on characteristics of super growers and management constraints/strategy © Alan Hughes June 2003
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