Innovation, Disruptive Technologies, and SMEs: Constraints and Policy Alan Hughes Centre for Business Research University of Cambridge Presentation at.

Innovation, Disruptive Technologies, and SMEs:

Constraints and PolicyAlan Hughes

Centre for Business Research University of Cambridge

Presentation at the Six Countries Programme Conference on SMEs and Disruptive

TechnologiesVancouver June 5-6 2003

© 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


Focus of Presentation

• CBR panel surveys of UK SMEs

• Evaluation of SMART scheme to support SME technological innovation


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 1994-1999. ESRC Centre for Business Research University of Cambridge, Cambridge.

• PACEC (2001) Evaluation of SMART 2001. 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 1999-2003 ESRC Centre for Business Research University of Cambridge, Cambridge.


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 5th 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’


Economic Indicators and the CBR survey periods

-4

-2

0

2

4

6

8

10

12

14

16

1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

%

GDP grow th(constantprices) %paShort-terminterest rate %

RPI inflation %pa

Unemploymentrate %


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.


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


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 ReviewsGrants 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 StudiesGrants 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 ProjectsGrants 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 StudiesGrants of up to £45,000 for individuals and small firms (fewer than 50 employees) undertaking feasibility studies into innovative technologies.

• Development ProjectsGrants 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.


SMART CHANGES 2003

• Smart research and development (R&D) project grants will be replaced by a new R&D grant product on 1 June 2003.

• 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.


What is a small Knowledge-Based/High-Tech Small firm?

Variety of definitions proposed but consistent elements are:

Dependence upon application of scientific or technological skills, or knowledge, in production of new goods or services

Emphasis upon technology component of activities as a source of competitive advantage

Independent owner/controlled status with employment less than some varying specified cut off point (e.g. 200 or 500 workers)

In practice in most studies hi tech firms are identified as “firms in high-tech industries” and high-tech industries are then identified as:

Industries with high or above average R & D/sales ratios (e.g 20% above mean for all industries)

Industries with above average shares of technologists, scientists and higher professionals in their labour force (e.g 20% above mean for all industries)

But this is misleading high-tech firms can occur in any sector


High-Tech Firms Defined

CBR SME dataset allows classification of firms irrespective of industry in to High-Tech and conventional categories by using firm level data

Classification is possible in terms of various factors eg:

- R & D/Sales Ratio

- Proportion of Technologists Scientists and Higher Professionals

- Self perception of sources of competitive advantages

For simplicity High-Tech defined here as firm with R&D/Sales 2% -


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 1997-2002

– Mean R&D/Sales 1997 12.6% 2002 16.5%

– % with R&D/Sales >3% 1997 57.4% 2002 56.1%


The Distribution of Firms by R&D to Sales Ratio in High-tech and Conventional Sectors 2002 – Firm-based definition of high-tech.

Number of

Firms Mean

R&D/Sales Ratio

R&D/Sales Ratio %

% 0 >0<1 1<2 2<3 >3

High-tech Manufacturing 183 14.7 2.7 0.5 1.6 20.2 74.9 Conventional Manufacturing 918 0.2 71.7 17.0 11.3 0.0 0.0

All Manufacturing 1101 2.6 60.2 14.3 9.7 3.4 12.4 High-tech Services 238 18.0 39.5 3.4 3.8 11.8 41.6 Conventional Services 435 0.1 83.7 12.4 3.9 0.0 0.0

All Services 673 6.4 68.1 9.2 3.9 4.2 14.7

All High-tech 421 16.5 23.5 2.1 2.9 15.4 56.1 All Conventional 1353 0.2 75.5 15.5 8.9 0.0 0.0

All Firms 1774 4.1 63.2 12.3 7.5 3.7 13.3

The differences across sectors in the proportion of firms with no R&D, in the median R&D/Sales ratios and in the distribution of firms across size categories of R&D/Sales, are all significant at the 5% level or better.


Why are Knowledge-Based /High-Tech Firms Important?

“Smaller technology firms are entrepreneurially driven and form an important seedbed out of which innovation experiments flow to be tested in the wider economic context. Smaller firms with competitive advantages can grow very rapidly and diffuse their products into the economic structure, so raising the level of average practice economic performance in their sector. It is because of this connection between diffusion and average efficiency that barriers to growth for the individual firm become barriers to raising the national level of economic performance”

ACOST (1990)


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


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 21000 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


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)


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


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


Innovation Activity

• High tech firms are more innovative– Product– Process– New to firm – New to industry


Innovative activity in high-tech and conventional firms 1999-2002 Percentage of Firms Introducing:-

No of Firms

Product

Innovation

Process

Innovation

Product or Process

Innovation+

Novel

Product Innovation

Novel

Process Innovation

% of firms Planning to

Introduce New Products or

Processes in Next 3 years

High-tech Manufacturing 204 84.3 60.8 91.2 58.3 28.9 94.6 Conventional Manufacturing 1072 47.3 44.3 58.2 20.4 14.3 58.8

High-tech Services 272 71.3 55.7 76.8 44.1 28.2 78.9 Conventional Services 542 45.0 36.0 51.8 22.0 14.0 53.4

All Firms 2130 53.5 45.3 62.2 27.7 17.5 63.6

+ Including innovations in supply, storage or distribution systems. All measure of innovative performance across firms are significantly different at the 5% level or better.


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


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 key collaborators

• Constraints– Finance still an issue– Marketing skills (link to focus on new prod. devpt)


CAPITAL MARKET FAILURE AND THE RATIONALE FOR SMART

SMEs invest too little in innovative technology for new products and / or services

SMEs have difficulty in obtaining debt or equity finance for research and innovation in packages of less than around £250,000 o Cost of due diligence, including technological

appraisal, makes deals below this size unprofitable for lenders and investors

Scheme has disbursed over £250 million to over 5000 firms since 1988


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


SMART WINNERS (2)1988-1998

• Success Rate– 14700+ applications, 4000+ awards– Success rate for applications

• Lower for smaller firms• Rising as scheme matures

• Sector concentration– IT, computing– Scientific instruments– Electrical engineering


Numbers of applications and success rates, 1988-1998

a) No. of applications

b) No. of

awards

c) No. of unsuccessful applications

d) Application success rate, (b)

as % of a)) By period: 88-92

6,563

1,291

5,272

20

93-95 5,119 1,384 3,735 27 96-98 3,088 1,359 1,729 44 By size of firm: Micro

11,049

2,175

8,874

20 Small 3,021 1,405 1,616 47 Medium 700 454 246 65 Total 14,770 4,034 10,736 27


Sectoral breakdown of award winners and market penetration Selected SIC Divisions

Description

a) No. of awards winners

'88-'98

b) No. of UK SME Establishments, 1998

(000s)

c) Penetration rate (a) as % of b))

23-24 Fuels / Chemicals 243 6 4.34

29 Machinery and equipment nec

327 18 1.82

30 Office machinery and computers

161 2 7.12

31 Electrical machinery/apparatus

158 7 2.26

32 Radio, tv/comms equipment

248 4 6.70

33 Medical, precision instruments, etc

486 7 6.67

72 Computing and related activities

245 119 0.21

73 Research and development

162 3 4.86

All SIC Divisions

3,144 2,307 0.14


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


Smart Evaluation Methodology

• Comparison of Successful v. Unsuccessful applicants

• Survey data plus program information • Econometric analysis with correction for

selection restricted to 1995-8 data gaps for unsuccessful firms pre 1995

• Subjective counterfactuals• Case Studies • Focus on award winners to 1998..post effects


Award Winners v Losers 1995-8

• Winners were on average faster growing post award– Robust to sample selection bias– But rarely statistically significant


Annual rates of change in award winners' and unsuccessful applicants' turnover, exports and

employment.

Mean values of % change in:

Turnover Exports Employment

N % N % N %

Award Winners 147 22 86 28 165 17

Unsuccessful Applicants 152 13 78 21 160 14


Table 5.2 Changes in turnover, employment, exports and productivity, by year of application.

Median values of % change in:

Turnover Employment Exports Productivity

Year: N % N % N % N % 1995 Award Winners 34 100.0 38 33.3 22 122.2 34 27.3

Unsuccessful Applics. 20 61.8 23 37.5 23 33.3 17 0.0 1996 Award Winner 29 52.9* 33 42.9* 14 137.6 29 25.0

Unsuccessful Applics. 13 10.0 17 0.0 5 100.0 12 17.5 1997 Award Winners 37 0.0 41 0.0 19 0.0 37 0.0

Unsuccessful Applics 16 27.7 15 25.0 6 85.3 15 0.0 1998 Award Winners 58 22.0 64 0.0* 35 42.9 57 0.0

Unsuccessful Applics 14 29.2 17 50.0 9 33.3 14 0.0

Source: PACEC - Surveys of Award Recipients and Unsuccessful Applicants Note * Significant difference at the 10% level between Award Winners and Unsuccessful Firms (Mann-Whitney Test)


Table 5 The Impact of Awards on Performance: Estimated coefficients on "success" Dummy variable in Regression Analysis of Performance

Effects (corrected for selection bias) Turnover Employment Productivity Exports

Year N coeff. N coeff. N coeff. N coeff.

1995 53 0.252 51 0.180 51 0.903 28 1.706

(0.08) (0.18) (1.01) (0.49)

1996 41 1.331 40 1.122 40 0.674 17 1.352

(1.62) (1.72)* (1.86)** (1.74)

1997 53 -0.182 56 -0.208 52 0.151 24 -0.274

(-0.71) (-1.20) (0.62) (-0.62)

1998 71 -0.401 72 0.175 70 0.840 44 3.361

(-0.17) (0.27) (0.63) (2.09)

Note: * Significantly different from zero at 10% level ** Significantly different from zero at 5% level 't' statistics in brackets


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


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


Total value of sales resulting from projects

(cumulative % sales against cumulative % companies)

Cumulative % of companies

Cu

mu

lati

ve %

of

tota

l sa

les

0

10

20

30

40

50

60

70

80

90

100

0 10 20 30 40 50 60 70 80 90 100

:


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


Innovation, Disruptive Technologies, and SMEs: Constraints and Policy Alan Hughes Centre for Business Research University of Cambridge Presentation at.

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