URN 09/1059 Evaluation of Grant for Research and Development & Smart Final Report A report prepared by PACEC on behalf of DIUS / LDA PACEC Public and Corporate Economic Consultants www.pacec.co.uk 49-53 Regent Street Cambridge CB2 1AB Tel: 01223 311649 Fax: 01223 362913 504 Linen Hall 162-168 Regent Street London W1R 5TB Tel: 020 7734 6699 Fax: 020 7434 0357 e-mail: [email protected]March 2009 Ref: H:\0807\41LDA\Rep\Final\Final Report v6.doc
Evaluation of Grant for Research and Development & Smart Page i
The Evaluation Team
This report has been prepared by PACEC on behalf of the London Development Agency (with the
other English RDAs) and the Department for Innovation, Universities and Skills (DIUS).
The PACEC project team included:
Rod Spires Project Director
Nic Boyns Project Manager
Matt Rooke
Mark Cox
Stephanie Wright
Paul Ellis
Professor Alan Hughes, Director of the Centre for Business Research at the University of
Cambridge and Director of the Innovation Research Centre provided advice and assistance with
the analysis.
The Steering Group on the project consisted of Simon Griffiths and Gary Hellen (London
Development Agency), Kevin Sharp (DIUS) with Carol Candler (ONE NorthEast).
PACEC Contents
Evaluation of Grant for Research and Development & Smart Page ii
Contents Executive Summary.......................................................................................................................... v
X1 Introduction.......................................................................................................................... v X2 Background to Smart and GRD........................................................................................... v X3 Principal conclusions .......................................................................................................... vi X4 Other key findings on evaluation issues.............................................................................vii X5 Methodology ........................................................................................................................ x
1 Introduction ..............................................................................................................................1 1.2 Background to SMART and GRD........................................................................................2 1.3 The evaluation methodology ...............................................................................................3 1.4 Analysis of the business surveys.........................................................................................8 1.5 Structure of the report..........................................................................................................9
2 Take up and market penetration of the GRD scheme ...........................................................10 2.1 Introduction........................................................................................................................10 2.2 Number of awards and application success rates.............................................................13 2.3 Market penetration.............................................................................................................15
3 Characteristics of Awards Made ............................................................................................19 3.1 Introduction........................................................................................................................19 3.2 Characteristics of the awards ............................................................................................19 3.3 Background of award winners ...........................................................................................22 3.4 Objectives in participating..................................................................................................27 3.5 Alternative and additional funding .....................................................................................29 3.6 Additionality of projects......................................................................................................38 3.7 Comparisons of unsuccessful applicants and award winners...........................................42
4 Intermediate effects and outputs ...........................................................................................52 4.1 Introduction........................................................................................................................52 4.2 Satisfaction of award winners’ objectives in participating .................................................52 4.3 Exploitation of project outputs in the marketplace.............................................................52 4.4 R&D and innovation activities............................................................................................56 4.5 The ability to attract further finance ...................................................................................59 4.6 The effects and outputs of unsupported projects ..............................................................64
5 Effects on the business performance of award winners ........................................................71 5.1 Introduction........................................................................................................................71 5.2 Comparison of award winners’ and unsuccessful applicants’ performance......................71 5.3 Award winners’ views of the effects of the schemes on business performance ...............73 5.4 The Benefits from Types of GRD Expenditure ..................................................................76
6 Wider effects ..........................................................................................................................79 6.1 Introduction........................................................................................................................79 6.2 Effects of supported projects on other businesses............................................................79 6.3 Multiplier and displacement effects ...................................................................................83
7 Economic impacts and cost-effectiveness of the schemes ...................................................86 7.1 Introduction........................................................................................................................86 7.2 Total measurable economic impacts.................................................................................86 7.3 Value for money ................................................................................................................92
8 Firms’ assessments of the schemes......................................................................................94 8.1 Introduction........................................................................................................................94
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8.2 Other support used............................................................................................................94 8.3 Assessments of the scheme..............................................................................................97
9 The Impacts on Stakeholders ..............................................................................................100 9.1 Introduction......................................................................................................................100 9.2 Background......................................................................................................................100 9.3 Stakeholder Policies and Activities..................................................................................100 9.4 Stakeholder Involvement in GRD ....................................................................................102 9.5 The Influence and Role of GRD on stakeholders (SAV) .................................................103 9.6 The Business Benefits and Impacts of GRD...................................................................107 9.7 Summary of key findings .................................................................................................111
10 Conclusions .........................................................................................................................112 10.1 Introduction..................................................................................................................112 10.2 Summary of findings on the evaluation issues............................................................112 10.3 General conclusions....................................................................................................119
Appendix A Research Questions ...........................................................................................122 A2 Research questions.........................................................................................................122 A3 Target outputs..................................................................................................................122 A4 Intermediate effects and outputs .....................................................................................123 A5 Effects on business performance/ behaviour ..................................................................124 A6 Economic Impacts: cost-effectiveness ............................................................................124 A7 Wider effects....................................................................................................................125
Appendix B Methodology .......................................................................................................126 B1 Survey Weighting ............................................................................................................126 B2 Grossing up of Economic Impacts...................................................................................126 B3 Questionnaire for Award Recipients................................................................................129 B4 Questionnaire for Unsuccessful Applicants.....................................................................130 A1 Questionnaire for Stakeholders.......................................................................................130
Appendix C Survey of award recipients by region .................................................................131 C1 Project details & company background ...........................................................................131 C2 Background and objectives to participation.....................................................................138 C3 Additionality of projects....................................................................................................147 C4 Intermediate effects / outputs ..........................................................................................149 C5 Business performance effects & trends...........................................................................157 C6 Wider effects....................................................................................................................157 C7 Other support used..........................................................................................................159 C8 Assessment of the scheme .............................................................................................161
Appendix D Survey of award recipients by sector/scheme....................................................164 D1 Introduction......................................................................................................................164 D2 Project details & company background ...........................................................................164 D3 Background and objectives to participation.....................................................................171 D4 Additionality of projects....................................................................................................180 D5 Intermediate effects / outputs ..........................................................................................182 D6 All firms ............................................................................................................................189 D7 Business performance effects & trends...........................................................................190 D8 Wider effects....................................................................................................................190 D9 Other support used..........................................................................................................192
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D10 Assessment of the scheme .........................................................................................194
PACEC Executive Summary
Evaluation of Grant for Research and Development & Smart Page v
Executive Summary
X1 Introduction
X1.1 In late 2008, the London Development Agency, together with the other RDAs and the
Department for Innovation, Universities and Skills, appointed PACEC to carry out an
evaluation of Grants for R&D (GRD)1. The main requirement of the evaluation was to
assess the achievements and impact of GRD, and its predecessor Smart, on the
national economy. The period would be from the point of the last evaluation in 2001,
and cover the operation of the scheme from 1 April 1998 to 31 March 2008.
X2 Background to Smart and GRD
X2.1 The GRD scheme was introduced by DTI on 1 June 2003 as a replacement for the
former Smart scheme. Between 2001 and the Smart scheme’s closure in March
2003, over 2,500 grants were awarded, worth just under £110M. Since its
introduction in April 2003, GRD has helped almost 1,700 SMEs to research and
develop technologically innovative new products and processes through over £130M
of grant funding. GRD helps small and medium-sized businesses to research and
develop technologically innovative products and processes. The following assistance
is available:
● Micro Projects are simple low cost development projects lasting no longer than 12 months. The output should be a simple prototype of a novel or innovative product or process. A grant of up to £20,000 is available to businesses with fewer than 10 employees.
● Research Projects typically involve planned research or critical investigation lasting between 6 and 18 months. The result of the project could be new scientific or technical knowledge that may be useful in developing a new product or process. A grant of up to £100,000 is available to businesses with fewer than 50 employees.
● Development Projects involve the shaping of industrial research into a pre-production prototype of a technologically innovative product or industrial process. A grant of up to £250,000 is available for businesses with fewer than 250 employees.
● Exceptional Projects involve technology developments which have higher costs. These projects are likely to generate much wider economic benefits and must have strategic importance for a technology or industrial sector. A grant of up to £500,000 is available to businesses with fewer than 250 employees. (Exceptional Project grants are not available in all regions).
X2.2 Responsibility for delivering the scheme transferred from DTI to the RDAs in April
2005. The move to separate policy from delivery was designed to improve the
effectiveness of the scheme by bringing key decisions closer to customers.
X2.3 GRD was set up to help address overall national priorities for:
1 The team included Professor Alan Hughes, Director of the Centre for Business Research at the University of Cambridge
and Director of the Innovation Research Centre, who provided advice and assisted with the analysis
PACEC Executive Summary
Evaluation of Grant for Research and Development & Smart Page vi
● Increased business spend on innovation, including R&D
● An increase in the proportion of firms that innovate
● Increased take-up by UK business of the new technology created by the R&D.
X2.4 Its intermediate objectives are:
● To increase the productivity and profitability of assisted SMEs
● To increase and improve technology use and adaptation, and research and development by individuals and SMEs to improve the overall innovation performance of the SME sector
● To increase the number of successful high growth firms that thrive and achieve their potential and to contribute to an enterprise climate that encourages investment in innovative technology by individuals, firms and financial institutions.
X2.5 The scheme’s longer-term objectives are:
● To overcome the reluctance of SMEs to undertake risky research and development by sharing the costs and the risks associated with these kinds of projects, and to foster a recognition of the importance of maintaining an ongoing programme of research and development.
● To encourage others to invest in potentially risky technological R&D through the knowledge that RDAs have undertaken a thorough appraisal of the financial and technical aspects of a project and is prepared to invest public money.
● To support firms to prove the technical and commercial feasibility of their idea (Research/Feasibility projects) and to develop prototypes (Development projects).
X3 Principal conclusions
X3.1 In relation to the first intermediate objective above to increase productivity and
profitability, the research underpinning the evaluation showed that small but
significant proportions of supported firms reported increases in their productivity and
profitability as a result of their projects. However, these benefits are likely to become
more widespread in time because GRD projects have stimulated important
intermediate effects including increasing R&D spend and improving the capacity and
capability of businesses to manage the process of innovation.
X3.2 In relation to the second objective, the evaluation found strong evidence of increased
and improved technology use and adaptation.
X3.3 In relation to the third objective, the breadth of the evidence indicated that supported
firms are assisted to thrive. The evidence also strongly showed a greater
commitment to R&D and innovation by firms, and suggested that some extra external
financial support was levered-in by the schemes.
X3.4 In relation to the first of the longer term objectives to overcome reluctance to carry out
risky research, it was found that the schemes help to remove a funding gap for R&D /
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Evaluation of Grant for Research and Development & Smart Page vii
innovation projects by SMEs, arising from risk and uncertainty for investors.
Likewise, it was found that firms improve their attitude towards R&D and innovation.
X3.5 In relation to the second longer term objective to encourage others to invest in
businesses, there was some evidence that investors are more likely to put money into
R&D because projects have been thoroughly appraised prior to start-up and this
represents a form of due diligence for them.
X3.6 In relation to the third objective, there was strong evidence that the large majority of
both Research/Feasibility projects and Development/Exceptional projects achieve
their technical and technology objectives and develop prototypes and products.
X3.7 Accordingly, it is concluded overall, that the schemes have been positive and
effective in relation to both their intermediate and their longer-term objectives.
X4 Other key findings on evaluation issues
X4.1 In more detail and reflecting the research brief, some other key evaluation findings
1 The most common reason given by award winners for participating in the schemes was to develop new prototypes, products, and services, but a range of other technology-related objectives were also stated. A very large majority of the award winners surveyed had wholly or largely satisfied their objectives. GRD/Smart, therefore, genuinely encouraged technological innovation in SMEs.
2 GRD/Smart-supported projects involved significant technological innovation. Two-thirds of Smart/GRD projects had resulted in new products reaching the market and for smaller proportions new processes had resulted and R&D services were provided to other organisations. Four in ten award winners said that their project outputs that reached the market place embodied significant technological innovation. A similar proportion said that the level of technological innovation was high and advanced.
3 Distinct market failures and in particular the funding gap were addressed by GRD/Smart. The rationale for the schemes focused on the existence of a funding gap for R&D / innovation projects for SMEs, arising from relatively high levels of risk and uncertainty associated with these activities. The overwhelming majority of award winners were prevented from pursuing their objectives prior to receiving support from the scheme because of a lack of finance / lack of ability to attract finance. Smaller proportions also specifically mentioned risk and uncertainty.
4 The benefits generated by GRD/Smart are largely additional in terms of the intermediate innovation capability impacts and the business performance effects. 70% of projects were wholly additional (and would not have gone ahead at all without GRD support) and a further 26% were partly additional (would have gone ahead, but later and/or on a smaller scale and/or narrower in scope).
5 For almost all stakeholders nationally GRD had a positive role and demonstrated strategic added value (SAV). For two thirds it had levered in their investment and for just over half it had a catalytic role and created support for innovation in the regions.
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6 However, the stakeholders considered that GRD could be improved by involving them more in planning and engaging them to a greater extent, which would help increase the synergies and impact. GRD could be marketed more across the sectors and the application process streamlined. They also thought that the types of innovation schemes could be clarified and for business the number reduced. Stakeholders were keen that GRD should be reintroduced to London.
7 Overall the awards were more focussed on the mechanical engineering, computing, instruments and chemicals sectors. In total these accounted for 48% of the 4125 awards with awards in other manufacturing and service sectors being relatively small.
Intermediate effects and outputs: as a result of GRD
1 Fewer than three-in-ten award winners sought alternative funding for their projects before applying for Smart/GRD funding; and searches were often unsuccessful. However, some businesses did not take it because of the conditions attached and indicated that the GRD funding was adequate. By contrast, although a slightly smaller proportion sought additional funding in conjunction with their awards and to take their products to market, these searches were less likely to fail and included venture capital, equity finance and bank loans. Award winners were, generally, successful in seeking further finance, as a result of GRD, to take their project outputs to market.
2 On the issue of whether the schemes made businesses more likely to attract finance than unsupported businesses, it was found that unsuccessful applicants were twice as likely as award winners to say that a lack of finance prevented them, or would prevent them, from introducing their project outputs into the market place.
3 The Small Firms Loan Guarantee Scheme seems to have played only a small role in financing projects.
4 The evaluation provides some evidence that Smart/GRD winners are more likely to use equity finance compared to unsuccessful applicants and that the award, and progress made on projects, helps them do this.
5 GRD/Smart projects frequently lead to new intellectual property: almost two-thirds of the businesses supported reported that their projects had led to the development of new IP; almost half said that new patents had been applied for; and almost half said that IP (e.g. a patent) had been obtained.
6 One third of businesses had gone on to claim R&D tax credits linked to GRD projects. GRD-supported businesses are more likely to do this compared to Smart-supported businesses.
Effects on business performance/ behaviour
1 On the longer-term impacts of GRD/Smart, with respect to the development of supported SMEs, there was a range of generally positive evidence. It was found, for example, that participating in the schemes made firms more innovative and expenditure on R&D increased. It was also found that virtually all of the businesses experienced improvements in aspects of their capability and capacity. It was also found that two-thirds of businesses reported one or more business performance effects, which were generally thought to be on-going.
2 Smart/GRD stimulated businesses to spend more on R&D and improved skills and attitudes towards R&D.
3 There was strong evidence that supported firms increased their capability to innovate. For example, 78% of supported businesses said that they had
PACEC Executive Summary
Evaluation of Grant for Research and Development & Smart Page ix
become better able to manage innovation / technical risk and 80% said that they had improved their innovation / technical understanding.
4 After supported projects were completed, a lack of finance remained the principal barrier to undertaking future R&D for businesses although GRD had improved the prospects of obtaining finance. However, the lack of finance post-GRD support had diminished compared to the situation pre-support.
5 A little under half of the businesses said that their participation had a positive effect on enabling them to exploit academic / leading edge research. A similar proportion reported that participation led them to collaborate more with the tertiary sector and with research /technology organisations and innovation partners.
Economic Impacts: cost-effectiveness
1 The £239 million in grants during the evaluation period had led to the creation of between 6,000 and 9,000 net additional jobs (without and with multiplier effects respectively) and between £400 million and £600 million net additional Gross Value Added (GVA) (without and with multiplier effects) or over £2.5bn cumulatively. Supported businesses also experienced a range of other business performance effects, and it was found that the schemes were associated with positive spill-over effects (eg through linkages) and multiplier effects.
The cost effectiveness ratios indicate that the net cost per FTE job for GRD was £40k and £27k (without and with multiplier effects). The cost per £1 increase in net GVA was £0.60 and £0.40 (without and with multiplier effects).
On the basis of this evidence, it is concluded that the schemes represent good value for money
2 In terms of both employment generation and GVA effects, Micro projects appear to offer the best value for money overall. Feasibility/research projects offer better value than development/exceptional projects in terms of employment generation, but the reverse is true for the to GVA effects.
3 There was very little deadweight associated with the employment and GVA effects of the schemes. Displacement rates were higher than deadweight rates, but it is difficult to envisage how these might be reduced. Businesses were generally happy with the amount of scheme finance received. Targeting on more innovative companies may improve the impacts but the deadweight ratio may increase if these companies were likely to proceed with their projects anyway. Targeting less innovative companies may raise the deadweight ratios and result in less innovative projects and fewer technology benefits.
4 Where Micro projects are taken up by new starts the investment impacts could be higher for GRD.
5 Businesses increased their R&D expenditure and skills as a result of GRD. As these translated into innovation there was greater collaboration to disseminate the results. The dissemination process was also assisted longer term by the results of R&D and innovation finding their way into new products and services which went to market.
6 RDAs through their Regional Economic Strategies and innovation policies made a significant commitment to innovation and their aims to deliver jobs and Gross Value Added as well as improving the innovation capabilities of businesses. The results of the evaluation showed the Smart and GRD made important and lasting contributions to these aims.
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X4.2 The total economic impacts are potentially somewhat greater because of intermediate
effects on capabilities of businesses and the wider technology diffusion effects.
X5 Methodology
X5.1 An evaluation framework for GRD was developed to guide the research. It was
based on the general approach provided in the BERR / RDA Impact Evaluation
Framework (IEF) guidance and customised for GRD.
X5.2 The research programme to derive the data to populate the framework was shaped
by the aims of the evaluation and by the information available from the RDAs and
DIUS. In addition to quantitative impacts, it took account of the qualitative impacts
which arise from GRD activities and expenditure. This provided a more rounded
analysis of the impacts and benefits and provides evidence of changes in practices
which help underpin the pure economic impacts which arise primarily through the
improved performance of businesses. It comprised an integrated set of tasks agreed
with the Steering Group for the project and resulted in almost 1,000 interviews with
some business beneficiaries and stakeholders.
X5.3 The main components of the research programme were:
● A survey of GRD recipients. This involved a telephone interview with 659 businesses selected to be representative of the wider population of award winners in terms of the type of award received, the size of award businesses, the period of award and the RDA location). Interviews sought to examine, inter alia: their background, reasons for applying, barriers faced and use of other finance; whether their projects would have gone ahead without Smart support; the intermediate and business performance effects of their projects; the extent to which they used other support as well as Smart, the wider effects (e.g. on competitors and suppliers); and their views on the Smart process and how the scheme might be improved.
● Follow-up interviews with Smart / GRD grant recipients. These involved 40 follow-up interviews with a representative sub-sample of award winners, coupled with a review of information relating to some of their projects. The issues covered broadly the same issues as the larger telephone survey of grant recipients, but in greater depth, and to obtain an expert view of the technological value of GRD supported projects.
● A survey of unsuccessful applicants for Smart / GRD grants. This involved a telephone interview with firms selected to be representative of the wider population of 191 non-award winners. This sample of businesses formed a comparison group. The interview was in part to discover what happened to proposed projects that were not supported and, hence, to assist with the examination of the extent to which supported projects would have gone ahead anyway.
● A Stakeholder Survey. This was held with almost one hundred organisations in the RDA areas to examine the views in the impact of GRD on businesses, its influence on the policies, activities, and resources of stakeholders (i.e. Strategic Added Value or SAV) and potential improvements to GRD.
X5.4 There were also discussions with staff at the RDAs responsible for implementing
Smart/GRD. This covered their practices when implementing GRD, the overall
PACEC Executive Summary
Evaluation of Grant for Research and Development & Smart Page xi
innovation aims and policies of the RDAs and the potential relationship between GRD
and other schemes.
PACEC Introduction
Evaluation of Grant for Research and Development & Smart Page 1
1 Introduction
1.1.1 In October 2008 the London Development Agency (LDA) together with the other
RDAs and the Department for Innovation, Universities and Skills (DIUS) appointed
PACEC to carry out an evaluation of Grant for R&D (GRD)2. The main requirement
of the evaluation was to assess the achievements and impact of Smart / GRD3, and
its predecessor Smart, on the national economy. The period would be from the point
of the last evaluation in 20014, and cover the operation of the scheme from 1 April
1998 to 31 March 2008.
1.1.2 A key issue is to examine the effectiveness and efficiency of GRD in terms of the
outputs and outcomes that flow and the benefits to businesses and the economy.
1.1.3 There are also a series of specific research questions. The evaluation should
examine whether the objectives for Grant for R&D/Smart have been met during the
evaluation period. The evaluation should address some specific subject areas and
questions, examples of which are as follows:
● Overall project targets
- To what extent has GRD/Smart genuinely encouraged technological innovation is SMEs?
- To what extent have GRD/Smart-supported projects involved significant technological innovation?
- To what extent are distinct market failures being addressed by GRD?
● Intermediate effects and outputs
- What proportion of GRD/Smart-supported projects have resulted in successful outcomes, eg new products/services?
- To what extent GRD/Smart levered-in private sector finance into supported firms, and what factors influence these investments? Do GRD/Smart make the business more likely to attract finance than unsupported businesses?
● Effects on business performance
- What have been the longer-term impacts of GRD/Smart, with respect to the development of supported SMEs?
- Do supported businesses go on to spend more on R&D than the sector average?
● Wider effects
- Is GRD/Smart good value for money, in terms of economic benefits created, taking into account identifiable spill-over effects through the main diffusion mechanisms?
- Which project types offer the best value for money?
● Economic impacts and cost effectiveness
- What have been the longer-term impacts of GRD/Smart with respect to development and dissemination of significant innovation?
2 The team included Professor Alan Hughes, Director of the Centre for Business Research at the University of Cambridge
and Director of the Innovation Research Centre, who provided advice and assisted with the analysis 3 For brevity and the fact that Smart is now GRD, Smart / GRD are referred to as GRD in the remainder of the report. 4 PACEC. The Evaluation of Smart. August 2001. DTI / SBS.
PACEC Introduction
Evaluation of Grant for Research and Development & Smart Page 2
- What have been the impacts of GRD/Smart with respect to RDA Regional Economic Strategies?
1.1.4 These are given in more detail in Appendix A and considered more fully in the
concluding chapter.
1.1.5 In addition to the national evaluation a review of GRD in London has been carried out
to assess the strategic impact of GRD on London businesses, and to what extent the
grant has delivered activities that no other product is delivering in London. This will
enable comparisons primarily with other LDA and non LDA business support products
and from that enable the LDA to shape future business support in London.
1.1.6 GRD is likely to be revised in April 2009 as part of the Business Support
Simplification Programme (BSSP). Under the terms of the BSSP it is essential that
business support products are regularly reviewed and evaluated to ensure that the
original rationale for their introduction remains, and to assist in the future
development of the product.
1.1.7 The evaluation was also to be compliant with the Impact Evaluation Framework (IEF)
developed by BERR and the RDAs5.
1.2 Background to SMART and GRD
1.2.1 Grant for Research and Development was introduced by DTI on 1 June 2003 as a
replacement for the former Smart scheme. Between April 1999 and the Smart
scheme’s closure on 31 August 2003, over 2,500 grants were awarded worth just
under £110M. Since its introduction, Grant for R&D has helped almost 1,700 SMEs
to research and develop technologically innovative new products and processes
through over £130M of grant funding. GRD helps small and medium-sized
businesses to research and develop technologically innovative products and
processes. The following assistance is available:
● Micro Projects are simple low cost development projects lasting no longer than 12 months. The output should be a simple prototype of a novel or innovative product or process. A grant of up to £20,000 is available to businesses with fewer than 10 employees.
● Research Projects typically involve planned research or critical investigation lasting between 6 and 18 months. The result of the project could be new scientific or technical knowledge that may be useful in developing a new product or process. A grant of up to £100,000 is available to businesses with fewer than 50 employees.
● Development Projects involve the shaping of industrial research into a pre-production prototype of a technologically innovative product or industrial process. A grant of up to £250,000 is available for businesses with fewer than 250 employees.
● Exceptional Projects involve technology developments which have higher costs. These projects are likely to generate much wider economic benefits
5 DTI. Occasional Paper No 2. Evaluating the Impact of RDAs: Developing a Methodology and Evaluation Framework.
2006.
PACEC Introduction
Evaluation of Grant for Research and Development & Smart Page 3
and must have strategic importance for a technology or industrial sector. A grant of up to £500,000 is available to businesses with fewer than 250 employees. (Exceptional Project grants are not available in all regions).
1.2.2 Responsibility for delivering Grant for R&D transferred from DTI to the RDAs in April
2005 following its introduction on 1 June 2003. The move to separate policy from
delivery was designed to improve the effectiveness of the scheme by bringing key
decisions closer to customers. RDAs understand well the opportunities and
challenges faced by businesses in their region and are able to use their close
relationship with regional partners to ensure the support available best meets the
needs of business.
1.2.3 National policy objectives are embedded in a framework agreement that sets out the
respective roles of DIUS and the RDAs and within this RDAs can define relevant
delivery models. Grant for R&D has the following longer term objectives:
● To overcome the reluctance of SMEs to undertake risky research and development by sharing the costs and the risks associated with these kinds of projects, and to foster a recognition of the importance of maintaining an ongoing programme of research and development.
● To support firms to prove the technical and commercial feasibility of their idea (Research/Feasibility projects) and to develop prototypes (Development projects).
● To encourage others to invest in potentially risky technological R&D through the knowledge that RDAs have undertaken a thorough appraisal of the financial and technical aspects of a project and is prepared to invest public money.
1.2.4 GRD was set up to address national priorities for:
● Increased business spend on innovation including R&D
● An increase in the proportion of firms that innovate
● Increased take-up by UK business of the new technology created by the R&D
1.2.5 GRD has some Intermediate objectives:
● To increase the productivity and profitability of assisted SMEs
● To increase and improve the adaptation of technology and its use through research and development by individuals and SMEs and as a result to improve the overall innovation performance of the SME sector
● To increase the number of successful high growth firms that thrive and achieve their potential and to contribute to an enterprise climate that encourages investment in innovative technology by individuals, firms and financial institutions.
1.3 The evaluation methodology
1.3.1 This section outlines the methods used to carry out the evaluation and economic
impact assessment. An evaluation framework for GRD was developed to guide the
PACEC Introduction
Evaluation of Grant for Research and Development & Smart Page 4
research as shown in Table 1.1. It develops the general approach provided in the
1.3.2 To assess the GRD impacts (eg quantified jobs), the evaluation framework shows the
process (or implementation) chain which links policy aims, expenditure (with partners)
which results in the engagement of business and the take-up of GRD. This in turn
impacts on business activities and practices and business performance (eg Gross
Value Added and jobs) and wider spillover effects (including Strategic Added Value)
through the involvement of partners and stakeholders. GRD support culminates in
gross and net economic impacts (eg employment, business creation and Gross Value
Added) and hence the final economic effects at the national and regional levels.
6 DTI. Occasional Paper No 2. Evaluating the Impact of RDAs: Developing a Methodology and Evaluation Framework.
2006.
PACEC Introduction
Evaluation of Grant for Research and Development & Smart Page 5
Table 1.1 Evaluation Framework for SMART / GRD
1: The Objectives and Aims of GRD
To overcome SME resistance to R&D
Increase: R&D spend, innovation
Encourage investment by others
Increase scientific / technical knowledge
Develop products / processes
Increase pre production prototypes
Stimulate important: technologies / sectors
Growth in GVA / economic performance
2: The Inputs, Expenditure and Delivery
Total GRD and Smart expenditure
Increased R&D spend
Expenditure of Partners and leverage (ie risk reduction)
Strategic Added Value through partner involvement
3: Take-up of GRD
Scheme design and delivery
Number of GRD applicants and awards
Age, size, sector of SMEs
Technology categories
Collaborative activity and risk reduction
4: The Intermediate Outputs. Activities and Practices of Businesses
Gross/Net Additional. For example
Addressing market failure issues and barriers eg reduction of risk
Satisfaction / aims of SMEs
Improved understanding of innovation
Improve technology / innovation
Increase R&D activity / spend
Lever in support / finance eg other schemes / partners
Exploit academic research
Better management of innovation
Skills development
Greater networking / collaboration
New products / processes
Successful / new IPR
5: The Impact on the Performance of Businesses
Gross/Net Additional. For example:
Exploitation of products / services
Turnover, employment, business starts
Profitability and productivity
6: Wider Spillover Effects and Strategic Added Value
Transfer of technology / knowledge (eg suppliers)
Transfer of practices (eg suppliers)
Any displacement / leakage / multiplier effects
Strategic Added Value through partner involvement
7: The Final Economic Impact / Outcomes
Gross/Net Additional, for example:
National and regional impact of GRD (Gross Value Added and employment)
Effectiveness / efficiency / economy of programme
Value for money / costs and benefits
1.3.3 To ensure the evaluation is compliant with the IEF the logic chain required to
measure the economic impact is shown below in Table 1.2. The gross (or
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Evaluation of Grant for Research and Development & Smart Page 6
attributable) initial impacts (eg business skills and practices, quantified jobs and
Gross Value Added) are subject to deadweight and what may have occurred
anyway. This results in gross direct outputs. There are then displacement
issues, ie some businesses benefit at the expense of others nationally and in the
regions (or crowding out issues – ie businesses do not start-up and leakage (of
expenditure) effects and indirect multipliers and linkages lead to the net additional
outputs and outcomes. This reflects the IEF guidance which needs to be
customised for GRD.
Table 1.2 Key Steps: Gross to Net Additional Economic Outcomes
Gross attributable impacts (i.e. changes in GVA & employment as a result of the support)
Less
Deadweight – counterfactual (i.e. changes that would have happened anyway)
Equals
Gross additional impact (i.e. effects attributable to the support)
Less
Displacement (i.e. increases in GVA/employment at the expense of competitors)
Equals
Net additional effects (or total measurable annual economic impacts without linkages and multipliers)
Plus
Linkages and multipliers (i.e. effects due to purchases by businesses and their staff )
Equals
Full net additional effects (or total measurable annual economic impacts)
Multiplied by
Average duration (in the case of GVA, how many years the effect lasts)
Equals
Cumulative net effect (i.e. total cumulative measurable economic impact)
Source: PACEC
1.3.4 The impact on Strategic Added Value (SAV) can also be examined through assessing
leverage, influence, synergy with partners and wider support which in turn influences
the overall benefits over and above what they would otherwise have been. The IEF
guidance sets out the leadership / catalyst effects, influence, leverage, synergy and
engagement which needs to be customised for GRD and the London project. The
evaluation framework above also shows SAV, as part of the delivery stage and as a
wider effect.
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The Research Programme
1.3.5 The research programme has been shaped by the aims of the evaluation and by the
information available from the RDAs and DIUS, to undertake it. In addition to
quantitative impacts, it has taken account of the qualitative impacts which arise from
GRD activities and expenditure. This provides a more rounded analysis of the
impacts and benefits and provides evidence of changes in practices which help
underpin the pure economic impacts which arise primarily through the improved
performance of businesses. See the Evaluation Framework. It has comprised an
integrated set of tasks agreed with the Steering Group for the project and resulted in
almost 1,000 interviews with some business beneficiaries and stakeholders.
1 Inception Stage and Research Plan. This involved working with the Steering Group to scope the project fully, agree the focus, and gain insights into the issues. At this stage the relevant policy documents and research reports were identified and contacts within each of the RDAs agreed. PACEC worked closely with the Steering Group to select the sample of businesses and stakeholders for the evaluation to ensure the impacts could be measured with confidence.
2 Interviews with RDAs. This covered how the scheme was operated and insights into the impacts and benefits. The RDAs also suggested a sample of stakeholders to interview in each of the regions (see below).
3 A Desk Study. This covered the key policy documents on Smart and GRD and the evolving policy framework, the management information on the applications for GRD and approvals, and the background information on the RDAs including their Regional Economic Strategies, Innovation Strategies, and policies on innovation and any relevant evaluations carried out on innovation programmes.
4 A survey of GRD and Smart recipients. This involved a telephone interview with 659 businesses selected to be representative of the wider population of award winners in terms of the type of award received, the size of award businesses, the period of award and the RDA location). Interviews sought to examine, inter alia: their background, reasons for applying, barriers faced and use of other finance; whether their projects would have gone ahead without Smart support; the intermediate and business performance effects of their projects; the extent to which they used other support as well as Smart, the wider effects (eg on competitors and suppliers); and their views on the Smart process and how the scheme might be improved.
5 Follow-up interviews with Smart / GRD grant recipients. These involved 40 follow-up interviews with a representative sub-sample of award winners, where there had been positive impacts. The issues covered broadly the same issues as the larger telephone survey of grant recipients, but in greater depth, and to obtain an expert view of the technological value of GRD supported projects. The results are incorporated into the text at appropriate points.
6 A survey of unsuccessful applicants for Smart / GRD grants. This involved a telephone interview with firms selected to be representative of the wider population of 191 non-award winners. This sample of businesses formed a comparison group. The interview was in part to discover what happened to proposed projects that were not supported (and, hence, to assist with the examination of the extent to which supported projects would have gone ahead anyway); and partly to compare the business performance of award winners and unsuccessful applicants (to aid the examination of whether, and to what extent, GRD contributes to competitiveness).
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7 A Stakeholder Survey. This was held with organisations in the RDA areas to examine the views in the impact of GRD on businesses, its influence on the policies, activities, and resources of stakeholders (ie Strategic Added Value or SAV) and potential improvements to GRD.
1.3.6 The survey of award winners and in-depth interviews covered award winners in the
period from 1999/2000 to 2007/08. While award winners in 2007/08 are unlikely to
have experienced the full effect of their awards, they are included in the sample since
they give an insight into how the scheme is currently operating. The survey of
unsuccessful applicants included only those from later years up to 2007/08 because
contact details for unsuccessful applicants in earlier years were not available.
Appendix B to this report includes detailed outlines of the method of analysis used.
1.4 Analysis of the business surveys
1.4.1 The majority of results tabulations in this report are based on univariate analysis of
survey data, showing the proportion of interviewees giving particular responses to
individual survey questions (e.g. % of respondents saying that their overall objective
was to grow rapidly, % saying their objective was to grow moderately, etc.).
1.4.2 In addition, the tables indicate the effective sample size. This is approximately equal
to the number of respondents, but it takes into account the weighting which is used to
ensure that the best estimates of the overall population of award recipients is given.
Further details are given in Appendix B.
1.4.3 As well as showing the results for all respondents combined, the tables disaggregate
the responses according to type of respondent or project. Those tables in the main
body of the report disaggregate the all-respondents responses according to type of
GRD award (Micro, Research, Development), size of firm (micro-, small or medium)
and type of award (ie SMART or GRD). Appendix C contains the main results
tabulations disaggregated according to the RDA regions and Appendix D tabulations
disaggregated according to the sector of firm; and GRD pre and post RDA delivery.
1.4.4 The tables also show where disaggregated results are significantly different from the
results for all respondents using the Chi-squared test at the 95% level (e.g. whether
micro-firms were significantly more likely than firms of all sizes combined to give a
particular response).
1.4.5 Multivariate analysis is used to examine the range of determinants of business
performance and, in particular, the contribution of Smart/GRD. The type of
multivariate analysis used is explained in Chapter 5. However, in brief, its purpose is
to test whether, and to what extent, observed differences in business performance as
between Smart/GRD award winners and unsuccessful applicants are attributable to
Smart/GRD itself; or whether, and to what extent, the differences are attributable to
other variables.
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1.5 Structure of the report
1.5.1 The evaluation framework used was one which is based on a logic chain model in
terms of the impacts and reflects the BERR / RDAs Impact Evaluation Framework7.
This links inputs (i.e. GRD support) to activities (i.e. GRD projects), to intermediate
effects (e.g. changes in business capabilities), to business performance effects (e.g.
growth in Gross Value Added) and, finally, to economic impacts (allowing for wider
positive and effects of projects). The questionnaires and interview agendas were
designed to examine the logic chain and the links in the model.
1.5.2 The structure of the report broadly reflects the logic impact chain and the model.
Hence:
● Chapter 2 focuses mainly on the number and pattern of Smart/GRD awards over the evaluation period.
● Chapter 3 mainly examines the characteristics of award winners, what projects sought to achieve, whether they would have gone ahead unsupported, and the additionality.
● Chapter 4 explores the extent to which projects succeeded, the intermediate effects, and how they changed the capabilities and practices of award winners.
● Chapter 5 analyses the collective business performance effects of projects and assesses the extent to which GRD explains the changes observed.
● Chapter 6 examines the wider positive and negative effects of the scheme.
● Chapter 7 sums the total economic impacts of the scheme and examines the cost effectiveness and value for money it provides.
● Chapter 8 reports a variety of views on the usefulness of different aspects of the scheme, and considers how its operation might be improved.
● Chapter 9 examines the view of stakeholders and partners on GRD and its strategic impact and added value (SAV).
● Lastly, Chapter 10 uses the research findings to address the key evaluation questions .
1.5.3 The Appendices show the key evaluation questions, methodology, and data
disaggregations for regions and sectors and the results of the evaluation of GRD in
London.
7 See attached
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2 Take up and market penetration of the GRD scheme
2.1 Introduction
2.1.1 This chapter examines the delivery of GRD and patterns and trends in the
expenditure and take-up (or participation in GRD) and provides some simple
measures of application success rates and scheme market penetration. Important
distinctions are made between the number of awards and the number of individuals
and firms receiving awards. The latter is somewhat smaller than the former because
it is possible for firms and individuals to receive more than one award.
The Delivery of GRD and Expenditure
The way in which GRD is delivered across the RDAs in fairly similar, reflecting the
national model for Smart/GRD developed by DTI (now DIUS) with some small
variations reflecting regional circumstances and opportunities. The main features are
as follows:
a RDA Aims and Objectives. These reflect the national aims and market failure rationale for GRD and reflect the Regional Economic Strategies adopted by RDAs and, in several regions, the separate innovation strategies where GRD has been specifically referred to as an innovation support scheme.
b All RDAs, at the time of the evaluation, delivered GRD with internal staff, usually within an innovation business support or business finance team.
c GRD was promoted and marketed primarily through attending events (with presentations), leaflets, the RDA websites and via intermediaries including advisers and some specialist individuals / organisations who provided SMEs with advice and could organise GRD applications for funding.
d Prior to receiving applications the GRD team could hold discussions with potential applicants to provide advice on the criteria and what finance was available. At this stage potential applicants could be dissuaded from applying or to defer their applications until there was a better fit with the criteria for funding.
e The criteria for deciding on who gets a grant where businesses are eligible usually include8:
- Alignment to the RES and Innovation Strategy
- Technology (eg novelty and new technology to the industry, innovation which would result in an alternative of significant improvement to products / processes, and technological advance associated with technological challenges
- Commercial considerations, whether there is a market and the route to market
- The business capabilities including skills and innovation as well as viability, track record and the business case for the project
- The need for GRD (in conjunction with applicants’ funding for the project) and how essential it is for the project
- Good design in terms of the approach, methodology and the ultimate product or process
8 DIUS/RDA. GRD Appraisals Process and Criteria
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- The wider aspects which may include economic, social and environmental benefits
RDAs do not formally target industrial sectors with GRD, although indirectly
applications may be drawn from the sectors / clusters they prioritise which is the case
with most RDAs.
Technical advisers can be sought to help appraise applications and would be drawn
from established government or RDA contacts on a confidential basis.
The delivery process also involves a wider group of organisations involved in
innovation as stakeholders or partners. These liaise with GRD applicants, refer them
to the RDA teams, assist with the applications and provide ongoing support referral
and signposting to other schemes. These partners and stakeholders also provide
support to businesses through advice and finance.
The overall policies of RDAs provide the strategic context for GRD. All RDAs have a
policy on innovation in their Regional Economic Strategies and four have separate
innovation strategies with a strong commitment to R&D, innovation, and the
exploitation of products, services and processes. These have generated a range of
support for innovation and GRD can form part of a suite of schemes. Figure 2.1
illustrates the relationship of GRD to other generic types of innovation support
programmes. The generic programmes do not necessarily reflect the names used by
RDAs. There are two axes: the product stages and the company stages of
development.
Figure 2.1 RDA Funding Ladder for Innovation
PRODUCTION
TECHNOLOGYPROTOTYPE
IDEA
TECHNICALFEASIBILITY
SEED START-UP EARLY STAGE
GROWTH SUSTAINED GROWTH
PRODUCT STAGE
COMPANY STAGE
PRODUCT
Seed funding
Proof of Concept
GRD
Loan Funds
Other innovation support
HE collaboration
Equity Funds
Source: PACEC
It should be noted that these programmes are sometimes part of physical initiatives
used to support innovation. For example: iNet centres and hubs, innovation centres,
enterprise centres/hubs and science parks.
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In the evaluation period 1999/2000 to 2007/2008 the total expenditure on Smart/GRD
was £239m with some £118m (50%) for development awards, £88.3m for research
projects (37%), £21.9m (9%) for exceptional projects and £10.5m (4%) for micro
awards. Greatest expenditure was in the North West (£39.1m), followed by the East
(£37.1m) and South East (£34.7m) and. Expenditure was lowest in the North East at
around £15.4m.
Table 2.1 The Value of Smart/GRD Awards
Value of awards (£m)
Micro Research Development Exceptional Total
South East 1.6 12.7 14.7 5.7 34.7
East 0.8 14.2 17.5 4.6 37.1
London 0.8 10.1 9.7 1.5 22.1
South West 1.2 5.1 8.0 1.5 15.8
West Midlands 0.5 7.2 8.5 0.8 17.0
East Midlands 1.5 8.9 15.5 0.8 26.7
Yorkshire and Humberside 1.5 10.9 17.7 1.2 31.4
North West 1.9 13.7 19.3 4.1 39.1
North East 0.8 5.5 7.3 1.7 15.4
England 10.5 88.3 118.4 21.9 239.1
Source: PACEC
The average size of awards in England was £399K (exceptional projects), £104k
(development projects), £39k (research projects) and £14k (for micro projects). The
overall average was £57k. Generally, the larger average awards were given in the
East, the South East, London (which may reflect higher costs for businesses in the
regions) and the North East.
Table 2.2 The Average Size of Awards (£k)
Average size of awards (£k)
Micro Research Development Exceptional Total
South East 14 49 105 435 66
East 15 46 107 380 69
London 15 50 124 309 65
South West 13 41 98 381 53
West Midlands 13 28 96 191 44
East Midlands 16 20 106 270 39
Yorkshire and Humberside 13 38 100 407 54
North West 15 46 99 586 62
North East 15 51 108 436 66
England 14 39 104 399 57
Source: PACEC
The share of expenditure by award for all the RDAs was similar to the national profile
and is shown below. The shares for development projects were higher than
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nationally in the East Midlands and Yorkshire and Humberside (as the main form of
grant). Exceptional awards were higher in the South East and micro awards in the
South West. Research awards were highest in London.
Table 2.3 Awards by Value for the RDAs (%)
Breakdown by type of award(%)
Micro Research Development Exceptional
South East 5 37 43 16
East 2 38 47 12
London 4 46 44 7
South West 7 32 51 10
West Midlands 3 42 50 5
East Midlands 6 33 58 3
Yorkshire and Humberside 5 35 57 4
North West 5 35 49 10
North East 5 36 48 11
England 4 37 50 9
Source: PACEC
2.2 Number of awards and application success rates
2.2.1 Table 2.1 shows the number of Smart/GRD awards made during the period covered
by the survey work (1999-2008). It indicates that the number of Research awards
has fallen by a factor of 2 or 3, to become similar to the number of Development
awards.
Table 2.4 Number of Smart/GRD awards, 1999-2008
Type of Award
Period: Development Exceptional Research Micro All types
Apr99-Mar01 257 7 678 120 1,062
Apr01-Mar03 290 14 960 209 1,473
Apr03-Mar05 255 17 268 179 719
Apr05-Mar07 216 6 247 146 615
Apr07-Mar08 122 11 132 81 346
Smart (Apr99-Mar03) 547 21 1,638 329 2,535
GRD (Apr03-Mar08) 593 34 647 406 1,680
Total 1,140 55 2,285 735 4,215
Source: BERR/RDA database, PACEC analysis
2.2.2 Table 2.5 shows the number of applications for Smart/GRD awards and application
success rates, broken down by region, period and size of firm. It reveals that
application success rates have varied from region to region, but it contains two more
striking features. The first is the number of applications peaked in 2001/03, whereas
the success rate bottomed out in 2003/05. The second key feature is that the
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application success rate has been positively correlated with firm size. This is
interpreted as reflecting a greater familiarity with application processes for
Government support schemes amongst non-micro firms, coupled with their greater
ability to marshal the resources necessary to prepare good quality applications.
Table 2.5 Numbers of applications and success rates, 1999-2008
a) No. of applications
b) No. of awards
c) No. of unsuccessful applications
d) Application success rate, (b) as % of a))
By region: South East 1,068 524 544 49%
Eastern 844 538 306 64%
Greater London 717 341 376 48%
South West 638 300 338 47%
West Midlands 708 386 322 55%
East Midlands 1,071 680 391 63%
Yorks & H'side 1,139 581 558 51%
North West 1,117 632 485 57%
North East 352 233 119 66%
By period: Apr99-Mar01 1,694 1,062 632 63%
Apr01-Mar03 2,453 1,473 980 60%
Apr03-Mar05 1,794 719 1,075 40%
Apr05-Mar07 1,239 615 624 50%
Apr07-Mar08 477 346 131 73%
By type of award: Development 2,146 1,140 1,006 53%
Exceptional 105 55 50 52%
Research 3,967 2,285 1,682 58%
Micro 1,439 735 704 51%
By size of firm: Micro 5,692 3,037 2,655 53%
Small 1,596 964 632 60%
Medium 369 214 155 58%
Total 7,656 4,215 3,441 55%
Source: PACEC
2.2.3 Table 2.6 shows the applications and success rate by sector. Overall the awards
were more focussed on the mechanical engineering, computing, instruments and
chemicals sectors. In total these accounted for 48% of the 4125 awards with awards
in other manufacturing and service sectors being relatively small. Apart from some
sectors where the number of applications was relatively small (eg tourism), the
highest success rates were in health, R&D, instruments, mechanical engineering and
chemicals.
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Table 2.6 Number of applications and success rates, 1999-2008 by sector
2.3.2 The variation in the sector-by-sector 'penetration' rates shown in Table 2.3 is
unsurprising, given that the economy comprises many sectors (e.g. retailing or other
personal services) in which few firms undertake research and development of the
type which would make them eligible to participate in the Smart scheme. Some
surveys (e.g. the government's Community Innovation Survey) indicate the number or
proportion of firms that engage in R&D activity, but they either exclude smaller firms
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or are not comprehensive in terms of their sectoral coverage. The method of
measuring penetration reflected in the Table, though not ideal, is the most
satisfactory available.
2.3.3 Smart/GRD have not been targeted on sectors by the RDAs. It is open to different
sectors with applications approved depending on the nature of ideas and their degree
of innovation. It may indirectly attract businesses from specific sectors where the
RDAs have a sectoral/cluster focus and this is the case with most RDAs although the
number of sectors varies.
2.3.4 Table 2.8 shows the number of award winners and market penetration rates, broken
down by region of the UK. The key feature of the table is that it indicates that market
penetration rates for the scheme have tended not vary greatly from region to region.
The exceptions are that market penetration rate in Greater London is just under a half
of the national rate, whereas the rate in the East Midlands has been just over double
the national rate. This probably reflects the fact that the per capita budget was lower
in London (i.e. expenditure to the number of potential SMEs that could be supported)
rather than the average sizes being significantly greater. Again, however, it is
cautioned that the penetration rates shown are measured in a less-than-ideal way.
Table 2.8 Regional breakdown of award winners and market penetration
Region a) No. of awards winners
1999-2008
b) No. of SME Establishments,
2007 (000s) (England)
c) Penetration rate (a) as
% of b))
South East 469 386 0.12%
Eastern 471 241 0.20%
Greater London 300 384 0.08%
South West 254 219 0.12%
West Midlands 347 203 0.17%
East Midlands 568 166 0.34%
Yorkshire & Humberside 502 179 0.28%
North West 544 251 0.22%
North East 199 72 0.27%
All England 3,654 2,101 0.17%
Source: PACEC
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Panel 2.1 Summary of key findings
● In the evaluation period 1999/2000 to 2007/2008 the total expenditure on Smart/GRD was £239m (Table 2.1)
● The average size of awards in England was £399K (exceptional projects), £104k (development projects), £39k (research projects) and £14k (for micro projects). (Table 2.2)
● The share of expenditure by type of award for all the RDAs was similar to the national profile (Table 2.3)
● Just over 4,200 Smart and GRD awards were made, and there were twice as many Research awards as there were Development awards (Table 2.4).
● The numbers of applications peaked in 2001/03, whereas the success rate bottomed out in 2003/05 (Table 2.5).
● Smart and GRD have involved firms in virtually all parts of the industrial spectrum by sector (Table 2.6), although market penetration rates have been uneven to some extent and varied considerably from sector to sector (Table 2.7). The higher penetration rates have been in electrical goods, R&D and chemicals.
● Overall the awards were more focussed on the mechanical engineering, computing, instruments and chemicals sectors. In total these accounted for 48% of the 4125 awards with awards in other manufacturing and service sectors being relatively small.
● Market penetration rates do not vary greatly from region to region (Table 2.8). However, the rate in London is very much lower than the national average. This is mainly because the ratio of expenditure in London was lower compared to the total population of businesses
● During its lifetime, Smart/GRD has involved the equivalent of only 0.18% of all SMEs in England. This reflects the overall budget for grant and the sizes of the grants given (Table 2.8).
● These findings prevent the evaluation question (para.1.1.3), dealing with the extent to which the target audience indicated by the rationale for GRD/Smart is being reached, from being answered fully because of difficulties in defining the target audience quantitatively. However, in broad terms, they suggest that there is considerable scope to increase the market penetration of the scheme.
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3 Characteristics of Awards Made
3.1 Introduction
3.1.1 This chapter examines the characteristics of awards covered by the survey, including:
amount of grant; the amount of grant relative to project cost; and year and duration of
project. It then examines a range of award winners’ characteristics, including their:
sector; region; size; status; age; growth objectives; and, innovativeness. This sort of
information is useful later in the report in interpreting the effects of Smart/GRD
awards on companies’ performance.
3.1.2 The chapter goes on to examine: award winners’ objectives in participating in the
Smart/GRD schemes; and, whether they sought, were offered or used alternative or
additional finance to enable them to undertake their projects. It then addresses the
key evaluation issue of whether, and to what extent, projects supported were
additional (i.e. would have gone ahead anyway without support). Lastly, it compares
the key characteristics of unsuccessful applicants and their projects with those of the
award winners.
3.1.3 The tables in this chapter show survey results first for all award winners combined,
then broken down by type of award (micro, research and development/exceptional),
company size (1-9 employees, 10-49 employees and 50+ employees) and scheme
(Smart or GRD). All references to companies’ size is to their employment size at the
start of their project, rather than currently.
3.2 Characteristics of the awards
3.2.1 Table 3.1 confirms that, by-and-large, the amount of grant offered in the case of Micro
projects was less than the amount offered in the case of Research grants, which was
less than the amount offered in the case of Development grants. It also shows that
the amount of grant offered was broadly correlated with company size. More
interestingly, it indicates that the large majority of Smart offers (82%) were for less
than £45,000, whereas the majority of GRD offers (58%) were for more than this
amount. This may partly be an inflationary effect in the more recent years of GRD but
could be influenced by the maturity of the scheme and the willingness to commit
higher levels of funds per grant.
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A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6aband)
3.2.2 Table 3.2 shows that, in the very large majority of cases, micro awards and
development awards met less than half the project costs. However, the majority of
feasibility awards met more than half the project costs. It also indicates that, the
smaller the company, the greater the likelihood that the award met more than half the
project costs. Overall, Smart awards were more likely than GRD awards to meet the
majority of project costs; this despite the fact that (as Table 3.1 showed) GRD awards
tended to be larger.
Table 3.2 Amount of grant offered as a percentage of total project cost
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6perc)
3.2.3 Table 3.3 indicates that just under half of the projects supported (48%) were started
between 1999/2000 and 2001/02, and that feasibility projects were a feature of this
period. Approaching one-third of the development projects were started in 2006/07,
or later. The time distribution of projects varied little according to company size.
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A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (fy)
3.2.4 Table 3.4 shows that, overall, roughly one-third of projects took less than a year to
complete and two-thirds took less than eighteen months. Unsurprisingly perhaps,
half of micro projects took less than a year, whereas only one-third of feasibility
projects and only 12% of development projects did so. Similarly, the duration of
projects was fairly closely related to company size. Perhaps a more noteworthy
finding is that the pattern of project length was similar for GRD projects to the pattern
for Smart projects; this despite the fact that Table 3.1 and Table 3.2 together imply
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (dur)
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3.3 Background of award winners
3.3.1 A little over half of the award winners were from four sectors (Table 3.5) including
metals and mechanical engineering, R&D, instruments, and computing, and almost
three-quarters were from just seven sectors. The table suggests that there were only
minor variations in the sectoral distribution according to type of award, size of
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q8)
3.3.2 Just over half the awards applied for were for feasibility/research projects, and just
over a quarter were for development/exceptional projects (Table 3.6). Very small
companies (1-9 employees) were most likely to apply for feasibility awards, and
medium-sized companies (50+ employees) were most likely to apply for development
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awards. Small companies were evenly divided between applying for feasibility and
development awards. Projects in the era of Smart were dominated by applications for
feasibility awards, whereas projects in the GRD era were more evenly divided
between micro, feasibility and development projects.
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q9)
3.3.3 Table 3.7 indicates that award winners were drawn from all regions of England, but it
reveals no remarkable variations in the regional distribution according to type of
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q10)
3.3.4 Table 3.8 shows that three-quarters of all award winners had fewer than ten
employees at the time they started their projects. Companies of this size dominated
both the micro and feasibility awards, but companies from across the size spectrum
were involved in development projects. The employment size distribution of Smart
award winners was similar to that of GRD award winners, although (as implied by
Table 3.1 and Table 3.2) GRD projects tended to be bigger, at least measured in
terms of cost.
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A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q11)
3.3.5 Together, Table 3.9 and Table 3.10 suggest that a small proportion of companies
migrated from the ‘independent business with no subsidiaries’ category to either the
‘not trading’ or ‘independent business with subsidiaries’ category (i.e. a few failed and
a few spawned offshoots). Companies undertaking micro projects, those in the 1-9
employee size band and Smart award winners appear to have been the most likely to
cease trading. Companies in the 10-49 and 50+ employee size bands appear most
likely to have spawned subsidiaries.
Table 3.9 Which of the following best describes the status of your business at the time the project started?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
An independent business with no subsidiaries 91 95 91 90 91 91 94 91 92
not trading / not yet a business 5 4 6 2 6 1 0 5 3
An independent business with subsidiaries 3 1 1 6 1 6 5 2 4
a subsidiary of a UK owned business 1 0 0 1 1 1 0 0 1
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A1)
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Table 3.10 Which of the following best describes the status of your business now?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
An independent business with no subsidiaries 83 79 85 81 83 82 79 80 89
not trading / not yet a business 9 17 10 3 12 0 0 12 3
An independent business with subsidiaries 5 4 3 8 3 9 11 4 6
a subsidiary of a UK owned business 2 0 1 4 1 3 0 2 1
a subsidiary of an overseas owned business 1 0 1 2 0 3 9 1 1
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A2)
3.3.6 Unsurprisingly, Table 3.11 indicates that, generally, the older the company, the larger
it was. More significantly, it also indicates that Smart award winners tended to be
older than GRD award winners, although this is presumably partly a function of the
fact that the Smart scheme preceded the GRD scheme.
Table 3.11 (if a business) When did your business start trading?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12cbnd)
3.3.7 Table 3.12 and Table 3.13 describe the growth ambitions of the award winners at the
time their project started and now, respectively. The two tables have a number of
interesting features, individually and in combination.
3.3.8 Table 3.12 indicates that the majority of award winners (61%) wanted to grow
moderately at the time their project started, and similar-sized minorities wanted to
stay the same size or grow rapidly. Micro award winners were less ambitious than
others, as were very small companies (i.e. with 1-9 employees). Conversely,
development award winners were more ambitious than others. The ‘not applicable’
category is explained by award winners not yet trading at the time of project start up.
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3.3.9 Compared to Table 3.12, Table 3.13 (showing growth ambitions now) suggests a
decline in the proportion of award winners in the ‘grow moderately’ category and an
increase in the ‘grow smaller’ and ‘not applicable’ (i.e. not trading) categories. These
changes might be explained by the relatively harsh business environment currently.
But there seem to be other factors at play. The two tables indicate a substantial
migration of micro award winners and very small companies into the ‘grow smaller’
and ‘not applicable’ (not trading) categories. Conversely they indicate a slight
movement of development award winners into the ‘grow rapidly’ category. GRD
award winners also appear to be more likely than Smart award winners to have
become more ambitious to grow rapidly and, conversely less likely to have migrated
to the ‘grow smaller’ and ‘not applicable’ (not trading) categories.
Table 3.12 How would you describe the overall growth objectives of your business at the time the project started
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A1)
Table 3.13 How would you describe the overall growth objectives of your business now
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A2)
3.3.10 Table 3.14 shows that just over two-thirds of the award winners had undertaken R&D
in the twelve months before their Smart/GRD project started so that they had some
R&D / innovation capability; and that there was relatively little variation in this respect
according to type of award, size of company or scheme. Almost four in ten
companies overall - and just over half of those who were to become involved in
development projects, those in the 10-49 employee size range, and GRD award
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winners - had introduced innovative products and services. Almost a quarter overall
had introduced innovative processes, again with relatively little variation according to
type of award, size of company or scheme. The table also suggests that just over a
fifth of the companies (i.e. those saying ‘none of the above’) were not innovating just
before undertaking their Smart/GRD projects.
Table 3.14 Which of the following happened in the 12 months before the project started?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14A)
3.4 Objectives in participating
3.4.1 Table 3.15 reveals that, regardless of size of company and type of award, companies
had a wide range of objectives in participating in the schemes. However, the desire
to develop new products and services dominated (for 66%), whilst the desire to
develop new processes was much less common. Testing the technical and/or
commercial feasibility of ideas was also important, as was the aim to obtain finance
(for 46-48%). Almost four in ten companies expressed the desire to help their
business grow (as one of the main aims) which could include turnover and jobs. This
indicates that priority initially was given to technical and commercial feasibility issues
in advance of growth.
3.4.2 The table also suggests that companies applying for development awards had a
wider range of objectives than those applying for other awards. Similarly, those
applying for GRD awards tended to specify a wider range of objectives than Smart
award applicants. It is possible, however, that GRD award winners simply recalled a
wider range of objectives because their projects were more recent.
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Table 3.15 What were your objectives in participating in the scheme, i.e. what did you want to achieve by taking part?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q15A)
3.4.3 Largely to assist with the identification of any market failures inhibiting innovation and
company growth, companies were asked to identify what prevented them from
pursuing their stated objectives without support from the schemes. Table 3.16 shows
that, overwhelmingly, award winners (ie 86%) specified a lack of appropriate finance
as the main barrier. This could include all sources including external, personal, and
in company sources such as revenue for R&D expenditure. There were other factors,
but none was mentioned by more than ten percent of companies overall.
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Table 3.16 Prior to receiving the GRD grant, what, if anything, prevented you from pursuing the objective(s) you have just mentioned?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Lack of finance / ability to attract finance 86 75 90 86 88 81 80 86 86
The cost of research / feasibility 10 11 8 12 10 11 3 6 18
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q16A)
3.5 Alternative and additional funding
3.5.1 Given the dominance of finance as a factor preventing companies from pursuing their
objectives prior to receiving Smart/GRD support, it is interesting to examine whether
and to what extent companies had done to try to obtain alternative and additional
finance to enable their projects to happen.
Alternative funding
3.5.2 Table 3.17 shows that little more than a quarter of overall, but a slightly larger
proportion of very small companies and GRD award winners, had applied for funding
other than a Smart/GRD award before undertaking their project.
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Table 3.17 Before applying for a grant from the scheme, did you seek alternative funding (i.e. instead of, not as well as a GRD award) to enable you to undertake your project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q17)
3.5.3 3.5.3 indicates that, where alternative funding had been sought, a bank loan was
most frequently the source. However, venture capital, other public funding, money
from family/friends and bank overdrafts were also explored relatively frequently.
There was little variation in this pattern according to type of award, size of company
or scheme. The only Other RDA / public sector scheme mentioned more than once
was the TSB technology programme.
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Table 3.18 (Only if alternative funding sought) What type(s) of alternative funding did you seek?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Bank loan 28 39 29 23 28 25 52 33 21
Venture capital finance equity / share capital 16 16 17 14 18 9 0 13 20
Other RDA / public sector funding 15 15 16 15 17 14 0 19 10
Money from family / friends 13 13 14 10 15 5 0 10 16
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18A)
3.5.4 Table 3.19 reveals that the search for alternative funding was often unsuccessful in
that businesses were not offered it.9 Forty two percent of those who sought
alternative funding received no offer of finance. A comparison of the numbers in this
table and Table 3.18 reveals that few of those who sought a bank loan or overdraft
were offered one, although micro award winners were more successful in this
respect. However, the majority of those who sought venture capital, other public
sector funding or money from family/friends were made an offer. The numbers also
indicate that GRD companies who sought venture capital were more successful than
Smart companies who did the same, which possibly reflects a growth in the
availability of venture capital funds over the policy period and the willingness to make
investment.
9 In the current conditions, the ease of obtaining finance for innovating businesses has become harder for some 25% of
businesses, for overdrafts and commercial loans. SME Finance and Innovation in the Current Economic Crisis, Centre for Business Research, University of Cambridge, 2009.
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Table 3.19 (Only if alternative funding sought) What type(s) of alternative funding were you offered?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
None 42 22 50 38 38 49 77 54 26
Venture capital finance equity / share capital 11 6 13 11 12 10 22 7 17
Other RDA / public sector funding 11 14 12 8 13 4 0 13 8
Money from family / friends 10 11 10 9 12 6 0 8 12
Bank loan 8 28 5 4 9 0 22 6 11
Bank overdraft 4 8 4 2 4 6 2 2 8
Other businesses: equity / share capital 4 2 5 3 5 0 0 4 3
Business angel finance: equity / share capital 4 2 7 2 5 0 0 1 9
Business angel finance: loan 4 3 2 8 5 0 0 3 4
Venture capital finance loan 4 10 2 3 3 10 0 3 5
Bank loan with Small Firms Loan Guarantee 2 4 0 3 1 8 0 1 2
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18B)
3.5.5 Table 3.20 shows that, overall, almost half of the offers made were not accepted. In
combination with Table 3.19, it also indicates that acceptance rates did not vary
greatly according to type of grant, size of company and type of scheme.
3.5.6 Amongst those who did not accept offers of alternative finance, just over a quarter
(27%) wanted to remain independent, and a quarter found the offer terms were
unsatisfactory (which could include the costs and repayment terms). They potentially
could lose an element of control, potentially for example, through a release of equity
and/or an influence on their decisions. This indicates a potential reason for the
funding gap in that it relates not just the amount of funds available but to the
conditions attached as investors seek to minimise risk. The perception that the
providers of finance sought this control could to some extent imply that the providers
were risk averse (to some extent) and they wanted to ensure they obtained the
benefits from their investments. Smaller proportions thought that the funding was too
risky (17%) or that the amount offered was too little (7%). Companies running
development projects were more likely than most to say that they wanted to remain
independent, and those running feasibility projects were more likely than most to say
that the funding was too risky.
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Table 3.20 (Only if alternative funding sought) What type(s) of alternative funding did you accept?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
None of the above 45 28 53 41 41 49 98 56 31
Other RDA / public sector funding 11 14 12 8 14 4 0 14 8
Money from family / friends 10 11 11 9 12 6 0 8 12
Venture capital finance equity / share capital 9 5 10 9 10 10 0 6 13
Other(s) 9 9 5 18 10 3 0 1 21
Bank loan 7 27 4 2 8 0 0 6 9
Business angel finance: equity / share capital 5 2 7 2 5 0 0 1 9
Bank overdraft 4 7 4 2 3 6 2 1 8
Other businesses: equity / share capital 4 3 5 3 5 0 0 4 3
Business angel finance: loan 4 3 2 8 5 0 0 3 4
Venture capital finance loan 4 11 2 3 3 10 0 3 5
Bank loan with Small Firms Loan Guarantee 1 3 0 3 0 8 0 1 2
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18C)
3.5.7 Table 3.21 shows that, amongst the majority of firms that did not seek alternative
funding for their projects, the most common reason (especially amongst those
running development projects) was the ability to manage without other finance. This
response suggests that these firms were confident of obtaining Smart/GRD funding
and had tailored their requirements and costs to fit the grant thresholds and what they
could match. Substantial proportions of firms were unaware of other sources of
finance which indicates that information failure was problematic, or they wanted to
stay independent for a small proportion (12%) of businesses. See para 3.3.6 above.
The cost and risk of finance, and previous difficulties were also issues for some firms.
Most of the firms that responded ‘Other’ to this question went on to explain that a
grant was their first or preferred choice of funding and that, for many, they did not
think they would obtain the alternative finance. Where they obtained it, it helped to
match the GRD award.
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Table 3.21 (Only if alternative funding not sought) Why did you not seek alternative funding to enable you to undertake your project?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Able to manage without other finance 27 34 21 37 26 34 20 25 33
Not aware of any sources of finance 16 12 19 12 15 17 4 16 15
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q20A)
Additional funding
3.5.8 Just as Table 3.17 showed that a small minority of firms (28%) had sought alternative
funding for their projects, Table 3.22 shows that a small minority (24%) sought
additional finance to enable them to undertake their projects. As was the case with
alternative finance, GRD award winners were more likely than Smart award winners
to seek extra funding.
Table 3.22 In conjunction with the grant you received, did you seek additional funding (i.e. as well as a GRD award) to enable you to undertake your project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q21)
3.5.9 Table 3.23 can be compared with Table 3.18. It indicates that other public sector
funding was slightly more common for those seeking additional finance than it was for
those seeking alternative funding. This could be because participation in GRD
allowed them to become more familiar with other sources of public sector funding and
how to access it. Also in this sense it could be seen as more complementary than
other sources in that it related to innovation and/or business growth and did not have
conditions that were not compatible. Conversely, bank loans and venture capital
were less important for seekers of additional finance. Table 3.23 also shows that
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bank loans were especially important for micro award winners seeking additional
finance, while family and friends were especially important to very small firms.
Table 3.23 (If additional funding sought) What type(s) of additional funding did you seek?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Other RDA / public sector funding 20 18 25 13 18 33 31 22 16
Bank loan 17 34 6 23 17 14 17 14 20
Bank overdraft 12 14 9 15 9 17 50 15 8
Money from family / friends 11 11 16 3 13 0 0 10 11
Venture capital finance loan 9 5 8 12 8 4 19 7 11
Venture capital finance equity / share capital 8 3 12 5 9 0 0 8 8
Other businesses: equity / share capital 7 2 7 7 6 14 0 8 5
Other businesses: loan 4 1 3 6 4 5 0 5 3
Business angel finance: equity / share capital 4 3 4 4 5 0 0 3 5
Business angel finance: loan 4 9 3 2 4 0 2 4 4
Bank loan with Small Firms Loan Guarantee 3 2 6 0 3 7 0 5 1
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22A)
3.5.10 Table 3.24 implies that firms seeking additional funding were about twice as likely as
firms seeking alternative funding to receive an offer: 20% of those seeking additional
finance received no offer, but 42% of those seeking alternative funding had the same
outcome (Table 3.19). In conjunction with Table 3.23 it also suggests that requests
for other public finance, a bank overdraft and money from family/friends were almost
always successful, and that request for bank loans and venture capital were
successful more often than not. The implication here is that the GRD award provides
businesses with some credibility. Some 48% of businesses said that GRD funding
made it easier for them to obtain other finance (see Table 4.12). Also there were
likely to be further on with their projects and this progress created more certainty, and
potentially reduced risks for other funders. However, this did not imply that finance
was no longer a problem, more that GRD and had allowed businesses to make
progress and this helped them to access it.
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Table 3.24 In each case, were you made an offer of funding?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Other RDA / public sector funding 17 18 20 10 16 19 37 18 14
Bank overdraft 11 11 8 14 8 13 60 14 6
Money from family / friends 10 11 16 2 13 0 0 9 11
Bank loan 10 12 2 19 10 14 0 9 10
Venture capital finance equity / share capital 6 2 8 6 7 0 0 6 7
Venture capital finance loan 6 3 5 8 6 4 3 4 8
Other businesses: equity / share capital 4 0 3 8 5 0 0 6 3
Business angel finance: equity / share capital 4 3 4 4 5 0 0 3 5
Bank loan with Small Firms Loan Guarantee 3 2 5 0 2 7 0 5 1
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22B)
3.5.11 3.5.11 indicates that almost all offers of additional finance were accepted. The small
number of firms that did not accept offered most often explained that they wanted to
remain independent, found the offer terms unsatisfactory, or were not offered enough
money (Table 3.26).
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Table 3.25 (If an offer of additional finance made) Did you accept it?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Other RDA / public sector funding 16 18 19 11 15 19 33 18 14
Other(s) 15 10 12 23 17 11 0 14 18
Bank overdraft 11 11 8 13 8 13 50 14 6
Money from family / friends 10 9 16 2 13 0 0 10 10
Bank loan 10 12 2 21 10 14 17 10 11
Venture capital finance equity / share capital 6 2 8 6 7 0 0 6 7
Venture capital finance loan 6 3 5 10 6 4 19 4 10
Other businesses: equity / share capital 4 0 3 8 6 0 0 6 3
Business angel finance: equity / share capital 4 3 4 4 5 0 0 3 5
Bank loan with Small Firms Loan Guarantee 3 2 5 0 2 7 0 5 1
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22C)
Table 3.26 If any offers were not accepted in previous Q, why did you not use the alternative funding you were offered?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Not enough money was offered 18 0 53 0 29 0 0 47 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q24A)
3.6 Additionality of projects
3.6.1 To measure the additionality of Smart/GRD projects (i.e. the extent to which they
represent activities that would not have happened without the support of the
schemes), award winners were asked whether their projects would have gone ahead
if they had not received an award; and, if so, whether it would have been affected in
terms of timing, scale or scope.. Table 3.28 shows that 70% of award winners said
their project would not have gone ahead without Smart/GRD funding and only 15%
said that their projects would definitely or probably have gone head if they had not
received an award. The tendency for award winners to say that their projects would
definitely or probably have gone ahead anyway did not vary much according to type
of award, company size or scheme. However, firms undertaking feasibility projects
were more likely than other firms to say that their projects would definitely not or
probably not have gone ahead; and firms with development projects were less likely
than others to say so.
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Table 3.28 Would your project have gone ahead if you had not received an award?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28A)
3.6.2 Some projects (ie 15%) might have gone ahead anyway without awards, but this
does not necessarily mean that they would have gone ahead completely unchanged.
Table 3.29 illustrates this point by indicating what the lack of an award would have
meant for the timing, scale or scope of projects that definitely, probably or possibly
would have gone ahead. It shows that the very large majority of projects that might
have gone ahead would have happened later than they actually did (ie 85% of those
that might have gone ahead anyway), and/or that large minorities of projects would
have been smaller (40% of projects that would have gone ahead anyway) and/or
narrower in scope (38%). These businesses represent relatively small proportions of
all businesses.
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Table 3.29 (If project would definitely, probably or possibly have gone ahead) In what way or ways, if any, would the project have differed?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
The project would have happened:
Sooner 1 0 2 0 1 0 0 0 2
Later 85 87 81 91 86 85 73 81 90
at the same time 14 13 18 9 13 15 27 19 8
Effective Sample Size 152 46 57 58 103 35 9 73 85
The project would have been:
Larger 1 2 2 0 2 0 0 1 2
Smaller 40 38 30 54 44 37 18 34 48
no different 59 60 68 46 54 63 82 65 50
Effective Sample Size 152 46 57 58 103 35 9 73 85
The scope of the project would have been:
Broader 3 2 6 0 3 3 0 2 4
Narrower 38 33 28 53 43 32 18 32 46
no different 59 65 66 47 53 65 82 65 51
Effective Sample Size 150 46 57 58 104 35 9 73 85
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B3)
3.6.3 In combination, Table 3.28 and Table 3.29 imply that a large majority of projects were
wholly additional (i.e. would not have happened in an way, shape or form without an
award), as shown in Table 3.28 (ie 70% would definitely or probably not gone ahead),
and that very few projects were wholly non-additional (i.e. would have gone ahead
anyway and would also have been completely unchanged). The average additionality
figure attributable for employment impact is shown in paragraph 7.2.2. Table 3.30 is
derived from the previous two tables and it summarises the additionality associated
with awards. It shows that, taking all awards together, 96% led to wholly or partly
additional projects. Awards for development projects, those to companies in the 50+
employee size band, and GRD awards were associated with less additionality than
others, but they largely additional.
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Table 3.30 Summary of additionality of projects
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Wholly additional 70 68 75 62 73 60 56 73 63
Partly additional 26 28 20 35 23 34 32 22 34
Wholly non-additional 4 4 5 3 4 6 12 5 3
3.6.4 Those companies that gave responses that suggested their projects were wholly non-
additional or were only partly additional (ie they would have potentially gone ahead in
some fomr) were asked how they would have financed their projects, and their
answer are shown in Table 3.31. Most frequently, these companies said that they
would have financed their projects using a bank loan, but it will be recalled from Table
3.18 and Table 3.19 that most applications for bank loans as an alternative to a
Smart/GRD award, in the first instance, were unsuccessful. Indeed, the search for
alternative finance (to GRD) of any kind tended to be unsuccessful, and this suggests
that the firms who say they may have gone ahead anyway may not have been able to
do so and that the estimates of project additionality in Table 3.30 are not unduly
generous.
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Table 3.31 What types of finance would you have used for your project, if you had not received GRD award and gone ahead with your project?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q29A)
3.7 Comparisons of unsuccessful applicants and award winners
3.7.1 The comparison tables in this section the tables show survey responses for all
unsuccessful applicants combined and all award winners combined, largely because
the sub-sample sizes for unsuccessful applicants (i.e. the break downs according to
type of award applied for, company employment size and scheme) were relatively
small. Some of the tables are also truncated to exclude responses given by only a
few firms.
Background characteristics
3.7.2 Table 3.32 indicates that, compared to award winners’ projects, the unsuccessful
applicants’ projects were, on the whole, more recent. This might be because
unsuccessful applicants’ businesses were more likely to cease trading (see Table
3.37 and Table 3.38). Non-trading businesses are more difficult to track down and
more reluctant to participate in surveys.
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Table 3.32 Financial year in which project intended to start / started
Percentage of all respondents (%)
Unsuccessful applicants Award winners
99/00 4 9
00/01 10 21
01/02 11 18
02/03 11 18
03/04 3 5
04/05 18 7
05/06 21 4
06/07 21 10
07/08 1 6
08/09 0 3
Effective Sample Size 51 459
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (fy)
3.7.3 Like the award winners, the unsuccessful applicants were from a range of sectors,
but Table 3.33 shows that the former were less likely than the latter to be in the
computing sector and more likely to be in R&D, ie 11% of award winners in
computing compared to 19% of unsuccessful applicants and 13% of award winners in
R&D compared to 5% of unsuccessful applicants.
Table 3.33
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Computing 19 11
Metals & mechanical engineering 16 15
Man: instruments (medical & other) 13 12
Business services 9 6
R&D 5 13
Man: electrical machinery 4 8
Man: transport 4 3
Transport, storage, comms 4 1
Health, care 4 5
Chemical manufacture 3 9
Effective Sample Size 91 459
Source: PACEC Survey (Q8)
3.7.4 Table 3.34 indicates that unsuccessful applicants were less likely than award winners
to apply for feasibility/research awards, and more likely to apply for micro awards.
But the two distributions are not dramatically different.
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Table 3.34 Type of award applied for
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Feasibility / research 49 55
Development 28 27
Micro 20 17
Exceptional 2 1
Effective Sample Size 92 462
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q9)
3.7.5 In terms of regional distribution, Table 3.35 indicates that unsuccessful applicants
differed most from award winners in that they were more likely to be from the South
East and less likely to be from the Eastern region. Again, however, the two
distributions are not dramatically different.
Table 3.35 Region
Percentage of all respondents (%)
Unsuccessful applicants Award winners
South East 18 13
Eastern 9 14
Greater London 10 8
South West 11 7
West Midlands 10 10
East Midlands 13 16
Yorkshire / Humberside 14 14
North West 11 13
North East 3 5
Effective Sample Size 92 462
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q10)
3.7.6 Similarly, Table 3.36 indicates that unsuccessful applicants and award winners were
broadly the same in terms of their employment size distributions.
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Table 3.36 Employment size (at start of project) – company doing project
Percentage of all respondents (%)
Unsuccessful applicants Award winners
0 to 4 66 59
5 to 9 10 16
10 to 24 17 14
25 to 49 2 7
50 to 249 5 4
Effective Sample Size 85 441
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q11)
3.7.7 Unsuccessful applicants were more likely than award winners not to be trading at the
time they applied for an award (Table 3.37) and they were considerably less likely to
be trading now (Table 3.38). In other word, the two tables suggest that business
failure rates were greater amongst unsuccessful applicants.
Table 3.37 Which of the following best describes the status of your business at the time of application / when the project started?
Percentage of all respondents (%)
Unsuccessful applicants Award winners
an independent business with no subsidiaries 86 91
not trading / not yet a business 12 5
a subsidiary of a UK owned business 1 1
an independent business with subsidiaries 0 3
a subsidiary of an overseas owned business 0 0
joint venture 0 1
Effective Sample Size 85 441
Source: PACEC Survey (Q12A1)
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Table 3.38 Which of the following best describes the status of your business now?
Percentage of all respondents (%)
Unsuccessful applicants Award winners
an independent business with no subsidiaries 73 83
not trading / not yet a business 24 9
an independent business with subsidiaries 1 5
a subsidiary of a UK owned business 1 2
associated company 1 1
a subsidiary of an overseas owned business 0 1
Effective Sample Size 85 441
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A2)
3.7.8 Table 3.39 indicates that, amongst the respondents that were actually businesses,
the unsuccessful applicants were, on the whole younger: fewer were started up in the
1990s, and more were started up from 2000 onwards.
Table 3.39 (if a business) When did your business start trading?
Percentage of all respondents (%)
Unsuccessful applicants Award winners
before 1980 12 11
1980 to 1989 9 13
1990 to 1999 21 34
2000 to 2003 38 26
2004 or later 20 15
Effective Sample Size 70 427
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12cbnd)
3.7.9 Table 3.40 indicates that, at the time they applied, the unsuccessful applicants were
more ambitious than award winners to grow rapidly. However, Table 3.41 shows that
the proportion of unsuccessful applicants saying that they want to grow rapidly fell
between the time of application and now, whereas the proportion of award winners
wanting to grow rapidly remained constant.
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Table 3.40 How would you describe the overall growth objectives of your business at the time your application / when the project started
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Become smaller 0 0
Stay same size 11 18
Grow moderately 55 61
Grow rapidly 27 17
Not applicable 8 4
Effective Sample Size 84 462
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A1)
Table 3.41 How would you describe the overall growth objectives of your business now
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Become smaller 5 5
Stay same size 13 9
Grow moderately 47 53
Grow rapidly 20 17
Not applicable 15 16
Effective Sample Size 84 462
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A2)
3.7.10 Table 3.42 reveals that, during the year prior to applying, unsuccessful applicants
were less likely than award winners to have engaged in any of the methods of
innovating listed.
Table 3.42 Which of the following happened in the 12 months before your application / the project started?
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Undertake R&D 59 68
Introduce innovative products and services 30 39
Introduce innovative processes 14 24
Provide R&D services / contract research to 3rd parties 9 14
None of the above 27 21
Effective Sample Size 89 443
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14A)
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Objectives in applying
3.7.11 Table 3.43 indicates that unsuccessful applicants ranked their objectives in broadly
the same way as award winners, but it is evident that the latter tended to have a
wider range of objectives.
Table 3.43 What were your objectives in applying / participating in the scheme?
Percentage of all respondents (%)
Unsuccessful applicants Award winners
Develop new prototypes / product(s) / service(s) 59 66
Obtain finance 40 46
Test the commercial feasibility of an idea / some ideas 39 46
Help the business to grow 37 39
Test the technical feasibility of an idea / some ideas 37 46
Start up a business 15 7
Improve existing product(s) / service(s) 14 12
Help the business to remain competitive 13 13
Develop new process(es) 8 18
Improve existing process(es) 8 9
Overcome a technical problem 7 20
Improve the image of the firm 6 13
Other(s) (Please specify below) 6 5
Reduce / share the risk of R&D investment 5 16
Produce new scientific / technical knowledge 5 18
Become the market leader 4 9
Gain access to new technology 2 11
Obtain external technical assistance 0 7
Engage with collaborative partners 0 6
Effective Sample Size 92 463
Respondents could select more than one option; so percentages in any column may sum to more than 100 Table excludes responses given by fewer than 5% of both unsuccessful applicants and award winners. A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q15A)
Fate of unsupported projects
3.7.12 The majority of unsuccessful applications were officially declined (Table 3.44) but a
significant minority were withdrawn. Almost a quarter of respondents could not recall.
The most common reason for an unsuccessful application was the judgement by the
RDA that the product in question was not innovative, ie given by 47% of
applicants(Table 3.45). Table 3.46 suggests that some applicants found the paper
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work and detailed required too difficult to carry their applications through. It was
possibly the case that some potential applicants were turned down at the pre
application stage in discussion with RDAs on additionality grounds because they
were well on with implementing their projects.
Table 3.44 What was the specific outcome of your GRD application (or last application, if there has been more than one)?
Percentage of all respondents (%)
Application unsuccessful 60
Application withdrawn 15
Don't recall 24
Effective Sample Size 91
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (N16A)
Table 3.45 As far as you know, why was the application unsuccessful?
Percentage of all respondents (%)
RDA said the project was not innovative 43
RDA provided very little feedback 12
RDA? did not understand project 12
RDA said the product would not work 6
RDA thought the project would not succeed 6
Project was outside scheme remit 6
RDA ran out of money 5
They only support bigger teams 4
Could not raise enough capital to satisfy RDA 3
RDA said the product was too expensive 2
Company profits too high 2
Project had to have an academic involved 2
Too much money in the bank to get a grant 2
Effective Sample Size 54
Table excludes reasons given by once only. Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (N16B)
Table 3.46 Why was the application withdrawn?
Percentage of all respondents (%)
Onerous paperwork / too much detailed information required 42
Could not raise enough additional capital to fund project 20
Partners withdrew 17
Discovered there was a similar product on the market 16
Effective Sample Size 11
Table excludes reasons given once only Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (N16C)
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3.7.13 Table 3.47 shows that only a small proportion of the unsuccessful applicants’ projects
went ahead anyway. And Table 3.48 reveals that, amongst those that went ahead
anyway, substantial minorities went ahead later than originally planned and / or on a
smaller scale. Together, those two tables lend weight to the estimates of project
additionality amongst award winners, shown in Table 3.30.
Table 3.47 What became of the project covered by the application?
Percentage of all respondents (%)
Went ahead anyway 27
Didn’t happen at all 73
Effective Sample Size 90
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28A)
Table 3.48 If the project went ahead, how were the timing, scale and scope affected?
Percentage of all respondents (%)
Timing
Sooner 4
Later 43
At the same time 52
Effective Sample Size 34
Scale
Larger 4
Smaller 29
No different 67
Effective Sample Size 36
Scope
Broader 9
Narrower 7
No different 84
Effective Sample Size 36
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B3)
3.7.14 The unsuccessful applicants whose projects went ahead anyway explained that the
financing came from a range of sources, but the top three were: ‘own capital’, ‘grants
from any source’ and ‘money from family and friends’.
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Panel 3.1 Summary of key findings
● GRD awards were larger than Smart awards, but they covered a smaller proportion of total project costs.
● Half of the award winners came from four sectors: metals and mechanical engineering, R&D, instruments, and computing; and three-quarters had fewer than 10 employees at the time of project start-up.
● There is evidence that receiving an award boosts companies’ growth ambitions.
● Companies had a range of objectives in participating in the Smart/GRD schemes, but the desire to develop new products and services was shared by two-thirds of them.
● Overwhelmingly, for 86% of businesses, a lack of appropriate finance (including all sources) and acceptable conditions prevented them from pursuing their objectives with Smart/GRD support. This finance included both interim business sources (such as operating capital and turnover) and funds for R&D from external sources.
● Only just over a quarter of companies had sought alternatives to Smart/GRD finance before they became award winners. Those who did seek alternative finance were largely unsuccessful in finding the appropriate finance, primarily because of the conditions of funding.
● Those that did not seek finance thought (because of the conditions attached) they would manage without it, were not aware of the sources or thought they may not get it.
● A similar proportion of companies sought additional finance to help them undertake their projects after they received their Smart/GRD awards, but those seeking additional finance were much more successful than those seeking alternative finance. This may have been because they were further on with their projects and the GRD award helped them obtain it.
● Some 70% of award winners said their project would not have gone ahead without the Smart/GRD funding. Very few projects would have gone ahead without Smart/GRD funds.
● Where projects would have gone ahead the large majority would have happened later.
● In terms of their background characteristics (eg sector, region etc), there were some differences between the unsuccessful applicants and the award winners, but the percentages were small, ie 11% of award winners in computing compared to 19% of unsuccessful applicants and 13% of award winners in R&D compared to 5% of unsuccessful applicants.
● The main differences were that unsuccessful applicants were more likely to be in the computing sector, have applied more recently, were not trading at the time of application, be younger as a businesses and have more rapid growth ambitions.
● Unsuccessful applicants were less active in innovation than award winners, before they applied for support and during the last 12 months.
● Unsuccessful applicants had broadly the same objectives in applying as award winners.
● Only a small proportion (27%) of unsuccessful applicants’ projects went ahead anyway, and those that did were often delayed and / or reduced in scale.
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4 Intermediate effects and outputs
4.1 Introduction
4.1.1 The focus of this chapter is on the intermediate effects (i.e. outputs) of firms’
participation in the Smart/GRD schemes. Firstly, the extent to which award winners’
objectives in participating were satisfied is examined. Attention then shifts to the
extent to which project outputs have been exploited in the marketplace. Other effects
and outputs are then considered. The penultimate section focuses on the issue of
further finance used to enable project outputs to be taken into the marketplace. The
chapter ends by comparing the intermediate effects of award winners’ projects with
the intermediate effects of unsuccessful applicants’ projects that went ahead anyway.
4.2 Satisfaction of award winners’ objectives in participating
4.2.1 It will be recalled from chapter 3 that award winners expressed a range of objectives
in participating in the schemes, although the desire to develop new prototypes,
products or services stood out: this being specified by two thirds of them. Table 4.1
shows that 80% of all award winners said that their participation either wholly or
largely satisfied their objectives, whereas only 6% said that their objectives were
satisfied only to a small extent or not at all. It will also be seen from the table that the
extent to which objectives were satisfied varied very little according to class of
respondent.
Table 4.1 To what extent did your participation in the GRD scheme satisfy the objectives you were talking about earlier?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q30)
4.3 Exploitation of project outputs in the marketplace
4.3.1 Table 4.2 reveals that 67% of projects had already resulted, or would result, in new or
improved products reaching the marketplace. Perhaps unsurprisingly, firms that had
undertaken development/exceptional projects were more likely than others to report a
market effect, whereas firms that had undertaken feasibility/research projects were
less likely to report the same. Reflecting the fact that GRD-supported projects were
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more recent than Smart projects, GRD award winners were more likely than Smart
award winners to be unsure yet of the market impact of their projects. Some 25%
had not taken a product / service to market.
4.3.2 Table 4.2 also shows that smaller, but by no means negligible, proportions of award
winners had introduced new/improved processed and /or had delivered R&D or
contract research services. As with new/improved products, firms that had
undertaken development/exceptional projects were more likely than others to report a
market effect related to processes and services. Similarly, GRD award winners were
more likely than Smart award winners to be unsure yet of the market impact of
processes or services arising from their projects.
Table 4.2 Has, or will, the project result in any new or improved products and services or processes reaching the market, or in offering R&D services / contract research to 3rd parties?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A3)
The follow-up interviews with the award winners helped to identify some of the
specific products, services, and processes developed. Some generic examples are
as follows:
● Satellite antenna
● Smart materials
● Gas turbine systems
● Emissions reduction
● Drill bit technology
● Radar systems
● Boilers & controls
● Restrictive access barrier
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● Information security
● Blood antigens
● Fuel cells
● Air conditioning
● New machining processes components
● Semiconductor devices
● Browser interface development
● Wave energy
● Screening device
● Equine diagnostics
4.3.3 Table 4.3 shows that nearly eight-in-ten of the firms that had taken projects outputs to
the market assessed the level of technological innovation embedded in them as being
significant or high. Firms that had undertaken development/exceptional projects were
more likely than firms that had undertaken other types of projects to rate their
technological innovation in this way.
4.3.4 The table also shows that firms that had been supported by GRD awards were more
likely to than those that had been supported by Smart to rate the level of
technological innovation in their project outputs highly. It might be, however, that this
finding merely reflects the fact that Smart award winners had had the benefit of more
time to observe their projects in the market place; and to make an objective
assessment. The survey also found that more than half of the Smart projects with
outputs that reached the market did so before 2004, whereas only 1% of GRD
projects had outputs that had reached the market by the same date.
Table 4.3 (if project outputs taken to the market place) What was the level of technological innovation in these products / services / processes?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q32)
The businesses who were involved in the follow-up questions were asked what
specific outputs contributed to the benefits. The table below shows that for three
quarters of businesses a specific product was responsible for generating the benefits.
All the businesses with feasibility / research awards shared this view as did all those
who received the exceptional awards. The other specific outputs that produced the
benefits were specific services (16%), a process or system (16%), a component
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(14%) but not defined as a product, materials produced (11%) or an output that
formed an enabling technology (11%), eg software which met the GRD funding
criteria.
Table 4.4 Can you say what output contributed to the benefits of GRD?
Percentage of all respondents (by Type of award)
Total Micro Feasibility / research
Development
Exceptional
Materials 11 8 13 15 0
An emerging technology 8 8 0 15 0
An enabling technology 11 8 13 15 0
A component 14 8 13 15 25
Specific products 76 83 100 46 100
Specific services 16 0 38 23 0
A process / system 16 25 13 15 0
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q16A)
4.3.5 When asked what, if anything, had prevented/will prevent them from taking the
outputs of their projects into the marketplace, nearly a quarter of the respondents
cited a lack of finance. This could indicate, on the basis that businesses were aware
of public support, that it was less available for the exploitation stage and that there
may be some issues with the availability of public finance at this stage. Some 12%
said that they thought their sales prospects were inadequate and that a revenue
stream, potentially to attract funders, was not yet apparent, or that the project had
failed to achieve its technical objectives (Table 4.5). However, the most common
response was ‘none of the above’ (i.e. nothing in particular).
4.3.6 Firms undertaking GRD projects were more likely than those undertaking Smart
projects to cite a lack of finance as a barrier, perhaps reflecting the current financial
market turmoil. GRD projects were also more likely to cite failure to achieve technical
objectives; which is slightly curious given that firms undertaking GRD supported
projects were more likely than their Smart counterparts to mention technical
objectives (Table 3.16), and that they were just as likely to say their objectives had
been satisfied (Table 4.1).
4.3.7 Other noteworthy features of Table 4.5 are that it shows that:
● firms undertaking micro projects and very small firms (1-9 employees) were more likely than others to mention lack of marketing skills as a barrier;
● the tendency to say ‘none of the above’ declined with firm size;
● Smart award winners were more likely than GRD award winners to say ’none of the above’.
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Table 4.5 What, if anything, has prevented, or will prevent, you from introducing the products / services as a result of the project into the market place?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q34A)
4.3.8 It should also be explained that firms were invited to specify what had made it
particularly difficult to introduce project outputs into the market place, but their
responses were very little different from the answers summarised in Table 4.5.
Similarly, those saying ‘other’ were invited to specify what this meant, but very few did
so.
4.4 R&D and innovation activities
Part of the aim of GRD is to help SMEs undertake research considered to be risky,
develop and increase business spend on innovation and R&D, and improve the
adaptation of technology, and its use, through research and development. To
examine some of these issues respondents were read a menu reflecting these
possible effects and outputs of their projects, and were asked to say whether the
effects/outputs had happened as a result of GRD. Their answers are shown in Table
4.6; and this indicates that the majority of respondents, regardless of the types of
businesses, acknowledged key effects.
a Firms improved their attitudes/culture towards R&D and innovation (70%)
b Almost two thirds (62%) increased their R&D expenditure and almost two thirds (62%) invested more in significant technological innovation
c Two thirds (67%) invested more in innovation in general
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d Skills were improved as the technological feasibility of project ideas became clear (91%), the commercial feasibility became clear (84%), and technological problems were overcome (89%)
e The quality of products/services improved (78%)
f Businesses improved their understanding of innovation (80%) and were better able to manage innovation and technical risk (78%)
Collaborative activity and networking on innovation had increased for almost half to
three in five of supported businesses.
the findings have the following general features:
● effects on ‘Improved knowledge / skills’ were acknowledged most frequently, along with R&D benefits, and those in the ‘Networks & supply chain relationships’ least so;
● firms undertaking feasibility/research projects were generally less likely than others to acknowledge effects;
● the responses did not vary greatly according to firm size;
● overall, firms supported by GRD were more likely than those supported by Smart to acknowledge effects.
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Table 4.6 Did the project have (or is it likely to have) any of the following effects on your business?
Percentage (%)
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Improved tech. knowledge / skills
Technological problems were overcome 89 92 87 91 89 91 85 88 93 Technical feasibility of the idea has become clear 91 93 90 93 90 94 94 89 95 Commercial feasibility of the idea has become clear 84 90 80 89 83 88 85 81 90 Better able to manage innovation / technical risk 78 81 74 83 75 85 88 79 76 Improved technical knowledge / skills 96 96 94 98 95 97 100 94 98 Improved products / services
Firm improved the quality of its products / services 76 82 71 82 76 75 87 78 74 Improved the quality of its processes 62 60 56 76 61 64 80 62 61 Reduced production costs 39 31 35 52 37 45 46 39 38 Improved products / services 80 83 76 87 80 78 96 81 78 Investment in R&D and innovation
Firm improved its attitudes / culture towards R&D / innovation 70 77 65 74 68 72 80 69 70 Increased R&D expenditure/activity it undertakes 62 55 58 76 59 69 93 58 70 Invested more in innovation in general 67 69 59 82 61 80 98 65 71 Invested more in significant technological innovation 62 64 55 77 60 68 83 61 66 New intellectual property has been developed 65 71 58 77 67 60 55 56 83 New patents have been applied for 47 52 41 55 50 38 31 37 66 Intellectual property (e.g. a patent) has been obtained 48 51 41 59 50 43 35 42 60 Academic / leading edge research exploited 44 48 38 55 46 39 26 41 50 Firm has improved its innovation / tech understanding 80 87 73 88 79 79 92 76 86 Investment in R&D and innovation 90 96 85 98 90 91 98 87 97 Market position
Firm has opened up new markets 68 72 60 82 68 66 80 64 78 Image of the firm has improved 75 77 69 85 75 72 88 75 75 Market position 81 87 75 90 82 77 94 79 86 Networks & supply chain relationships
Firm is now more inclined to use external business support 58 67 56 58 59 57 45 57 60 Has collaborated/networked more with other firms (eg SMEs) 59 66 59 57 60 59 56 60 58 Has collaborated/networked more with universities and colleges 46 42 45 52 47 46 35 46 48 Has collaborated/networked more with other research/technology organisations 44 47 39 53 44 43 46 41 50 Networks and supply chain relationships 81 88 78 84 81 84 86 80 84 Other effects 7 7 7 6 6 5 11 5 10 None of the above 2 2 3 1 2 1 0 3 1 Effective Sample Size 459 76 235 153 324 94 20 271 207 Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q35A)
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The businesses that participated in the follow up discussion were asked about the in-
house capabilities that were combined with the grant award and the other sources of
finance and how important the in-house capabilities were in terms of contributing to
the benefits. Just over half (53%) said their management ability was important (and
for 8% this was very important), 44% cited energy and hard work (for 11% this was
very important) and 42% their ability to focus on the issues and not be distracted by
side issues or other “interesting” issues related to the project (17% said this was very
important). The other important in-house factors were the financial health of the
company (30%), the entrepreneurial and business skills (30%) and the financial
management skills of senior staff including their ability to project manage and build
relationships with external funders (28%). 14% said this was very important.
Table 4.7 What were the important in-house factors and the main ones that allowed you to achieve the benefits?
Percentage of all respondents (by Type of award)
Very important Some importance Important
Entrepreneurial / business skills 8 22 30
Management ability 11 42 53
Financial management skills 14 14 28
Marketing skills 8 11 19
Sales skills 8 14 22
Financial health / position 11 19 30
Energy / hard work 11 33 44
Ability to focus on the issues 17 25 42
Market environment opportunity 8 17 25
Source: PACEC Survey (Q20A)
4.5 The ability to attract further finance
4.5.1 Table 4.8 shows that just over one-third of all award winners had sought further
finance to enable them to take their project outputs to the marketplace; and, in
conjunction with Table 4.2, it suggests that no more than half of the firms whose
projects had actually led to the marketplace had sought further finance. The table
also shows that, the smaller the firm, the more likely it was to have sought further
finance.
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Table 4.8 Since participating in the scheme, has your firm sought further finance to enable it to introduce any new or improved products, services or systems into the market place that resulted from the project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q36)
4.5.2 Table 4.9 shows the sources of further funding sought, offered and accepted. The
first part of the table shows that, most commonly, those seeking further finance
looked to their RDA or some other public sector source. Smart award winners were
more likely than GRD award winners to look to their RDA or another public source,
but they were less likely to specify ‘other’. However, very few of the GRD award
winners explained what ‘other’ was. The first part of the table also indicates that
venture capital and business angel equity and loan finance were popular.
4.5.3 The second and third parts of Table 4.9 imply that most searches for further finance
were successful and that most offers of finance were accepted. For example, 48% of
firms seeking further finance to take their project outputs to market looked to their
RDA/other public source, 42% actually received offers and 40% accepted offers.
4.5.4 The small proportion of firms that did not accept the further finance offered gave three
main reasons for doing so, ie: the funding was too risky; the terms were
unsatisfactory; and not enough money was offered.
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Table 4.9 (if further finance sought to introduce products / services to the market) What type(s) of further finance have you applied for / been offered / accepted.
Percentage (%)
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Finance sought
Other RDA / public sector funding 48 36 52 47 47 59 46 54 36
Respondents could select more than one option; so percentages in any column may sum to more than 100 Table truncated to exclude responses given by fewer than 5% of award winners. A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37C)
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4.5.5 Generally comparisons with finance sought, offered and accepted over different
stages of the project or development capital rather than operating capital (ie when
alternative and additional finance was sought) shows that businesses improved their
prospects of levering in private sector finance, ie offers made, by funders, and
accepted increased over the duration of the project, especially for Venture Capital
funds (equity / share finance), bank loans and finance from other businesses where
merger or acquisitions may occur.. Generally, the applications made over the project
period increases for their forms of capital increases, apart from bank loans, as
businesses look for equity holding partners, probably linked to advice through board
representation.
Table 4.10 shows that half of the firms that did not seek further finance to take their
projects outputs into the marketplace explained that they were able to manage
without. The medium sized firms (i.e. with 50+ employees) were more likely than
others to say this. Small proportions of firms mentioned difficulties obtaining finance,
its cost or its riskiness.
Table 4.10 (If further finance not sought) Why did you not seek further funding to enable you to undertake your project?
Percentage (%)
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Able to manage without other finance 50 52 46 58 47 59 77 51 48
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q39A)
The follow-up interviews examined the additional sources of funding directly levered
in by GRD. Some 50% of businesses said GRD had enabled them to do this. The
levered in sources of funding which had most effect in terms of helping secure the
benefits attributable to GRD were other RDA/public sector funding (47%), bank
overdrafts (18%) and bank loans (12%) and venture capital / business angel equity /
share capital (18% and 12%) sometimes with loans, often combined with advice,
especially where venture capitalists / business angels may sit on the Boards of
businesses. Other forms of funding were not so effective.
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Table 4.11 Which other sources of funding did GRD have most effect
Total (%)
Other RDA / public sector funding 47
Bank overdraft 18
Venture capital finance equity / share capital 18
Bank loan 12
Business angel finance: equity / share capital 12
Venture capital finance loan 12
Money from family / friends 6
Bank loan with Small Firms Loan Guarantee 6
Hire purchase / lease finance 6
Trade credit (from suppliers / customers) 6
Other businesses: equity / share capital 6
Other businesses: loan 6
Business angel finance: loan 6
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q15B)
With regard to the type of funding used in conjunction with GRD, where it was used,
most businesses said it was important but especially bank overdraft facilities for
operating finance (almost all businesses), venture capital and/or business angel
equity funds which often included external advice, and other public sector funding.
4.5.6 Table 4.12 indicates that half of the firms, but more Smart award winners than GRD
award winners, thought that being an award winner made no difference to their ability
to obtain finance. However, a similar proportion of firms, but GRD award winners
especially, thought that being an award winner made it either much or a little easier.
Part of this is because the award winners have had to go through an application
process to review GRD which acts as a form of due diligence. This can give
investors more confidence to invest in the GRD businesses and the project and
potentially removes some of the risk.
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Table 4.12 What effect do you believe being a GRD award recipient has had on your firm's ability to obtain finance?
Percentage (%)
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Made it much easier 16 13 15 18 17 11 20 13 21
Made it a little easier 32 48 25 36 33 31 21 27 43
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q43)
4.5.7 More than half the award winners, but a larger proportion of Smart award winners
than GRD award winners, had not claimed R&D tax credits (Table 4.13). A
substantial proportion of award winners were not sure, but of the reminder, there was
a roughly even split between those that had claimed credits for their Smart/GRD
project and those that had claimed for other projects.
Table 4.13 Has your business claimed R&D tax credits?
Percentage (%)
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
No 54 61 62 35 62 32 32 58 46
Yes other projects only 18 12 14 29 14 27 39 17 19
Yes including this project 15 10 14 20 13 24 11 12 20
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q44)
4.6 The effects and outputs of unsupported projects
4.6.1 The analysis in this section follows the pattern of previous sections in this chapter, but
focusing on comparisons between unsuccessful applicants and award winners at
aggregate level.
4.6.2 It is clear from Table 4.14 that unsuccessful applicants’ projects that went ahead
anyway were considerably less effective than award winners’ projects in relation to
their objectives. Just under half of unsuccessful applicants described their projects
as wholly or largely effective, but four-fifths of award winners did so.
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4.6.3 Interestingly, Table 4.15 reveals that the proportion of unsuccessful applicants that
achieved project outputs that were taken into the marketplace was not much less
than the proportion of award winners doing the same. The unsuccessful applicants
may have been able to take products and services to market because of relatively low
levels of innovation and/or technical risk. Table 3.46 confirms this to some extent in
that 43% of unsuccessful applicants thought that they were refused GRD because
their project was not innovative enough. Few were turned down at the application
stage on additionality grounds (ie their projects would not be additional). This was
probably more likely to have occurred at the pre application stage in discussions with
the RDA. It will be recalled from Chapter 3 however, that relatively few of the
unsuccessful applicants’ intended projects (i.e. just 27% of the total) actually went
ahead.
Table 4.14 To what extent did your project satisfy the objectives you were talking about earlier?
Percentage (%)
Unsuccessful applicants Award winners
Wholly 19 46
Largely 29 34
Partly 37 15
To small extent 4 4
Not at all 11 2
Effective Sample Size 38 458
Source: PACEC Survey (Q30)
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Table 4.15 Did, or will, the project result in any new or improved products and services or processes reaching the market, or in offering R&D services / contract research to 3rd parties?-Product(s) / Service(s):
Percentage (%)
Unsuccessful applicants Award winners
Products:
Yes 61 67
No 24 25
Not sure yet 15 9
Processes:
Yes 35 37
No 51 55
Not sure yet 14 8
R&D services/Contract research:
Yes 20 22
No 64 70
Not sure yet 17 8
Effective Sample Size 38 458
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A1)
4.6.4 Table 4.16 shows that the relatively few unsuccessful applicants whose project
outputs had reached the marketplace rated the level of technological innovation in
their projects in broadly the same way as the award winners whose project outputs
had reached the marketplace. Table 4.17 indicates that the unsuccessful applicant’s
project outputs had reached the marketplace slightly later overall.
Table 4.16 (if new/improved products, processes or services reached market) What was the level of technological innovation in these products / services / processes?
Percentage (%)
Unsuccessful applicants Award winners
Significant 40 39
High 37 40
Moderate 19 17
Low 4 3
Effective Sample Size 35 420
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q32)
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Table 4.17 Year in which new/improved products, processes or services reached market
Percentage (%)
Unsuccessful applicants Award winners
2000 to 2003 30 36
2004 or later 70 64
Effective Sample Size 23 343
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q33)
4.6.5 Table 4.18 shows that unsuccessful applicants were twice as likely as award winners
to cite lack of finance as a barrier to taking their project outputs into the marketplace;
and this finding lends weight to the widespread perception amongst award winners
that being an award winner made it easier for them to obtain further finance for this
purpose (see Table 4.12).
Table 4.18 What, if anything, has prevented, or will prevent, you from introducing the products / services as a result of the project into the market place?
Note: Table excludes barriers mentioned by fewer than 5% of both unsuccessful applicants and award winners Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q34A)
4.6.6 Table 4.19 suggests that unsuccessful applicants whose project outputs were taken
to the marketplace were, in general, less likely than the award winners to
acknowledge other effects on the business, such as technological problems being
overcome, clarifying the technical, or commercial, feasibility of ideas. However, there
were some greater differences shown, a stronger culture in terms of R&D, an
improved understanding of innovation, a stronger inclination to collaborate and
network (with SMEs and HE), and develop intellectual property.
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Table 4.19 Did the project have (or is it likely to have) any of the following effects on your business?
Percentage (%)
Unsuccessful applicants
Award winners
Improved tech. knowledge / skills
Technological problems were overcome 83 89
Technical feasibility of the idea has become clear 85 91
Commercial feasibility of the idea has become clear 79 84
Better able to manage innovation / technical risk 73 78 Improved products / services
Firm improved the quality of its products / services 77 76
Improved the quality of its processes 59 62
Reduced production costs 40 39 Investment in R&D and innovation
Firm improved its attitudes / culture towards GRD / innovation 45 70
Increased R&D expenditure/activity it undertakes 63 62
Invested more in innovation in general 64 67
Invested more in significant technological innovation 62 62
New intellectual property has been developed 53 65
New patents have been applied for 49 47
Intellectual property (e.g. a patent) has been obtained 49 48
Academic / leading edge research exploited 44 44
Firm has improved its innovation / tech understanding 63 80 Market position
Firm has opened up new markets 68 68
Image of the firm has improved 73 75 Networks & supply chain relationships
Firm is now more inclined to use external business support 41 58
Has collaborated/networked more with other firms (eg SMEs) 45 59
Has collaborated/networked more with universities and colleges 39 46
Has collaborated/networked more with other research/technology organisations 25 44
Other effects 4 7
None of the above 1 2
Effective Sample Size 37 459 Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q35A)
4.6.7 The unsuccessful applicants were also asked whether they had obtained R&D tax
credits for the project being discussed, or for others. However, a large proportion of
them did not know (Table 4.20); and this makes it difficult to compare them to award
winners in this respect.
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Table 4.20 Has your business claimed R&D tax credits?
Percentage (%)
Unsuccessful applicants Award winners
No 44 54
Yes other projects only 19 18
Yes including this project 9 15
Not sure 28 13
Effective Sample Size 36 452
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q44)
4.6.8 The survey shows that unsuccessful applicants whose project outputs were taken to
the marketplace were, in general, less likely than the award winners to acknowledge
other effects on the business, such as technological problems being overcome,
clarifying the technical, or commercial, feasibility of ideas. In particular award winners
showed a stronger culture in terms of R&D, an improved understanding of innovation,
a stronger inclination to collaborate and network (with SMEs and HE), and develop
intellectual property.
4.6.9 Overall the results of the research show the positive impacts of GRD in that award
winners, compared to unsuccessful applicants, were much more likely to have
developed and exploited products/services and processes and improved their
innovation and technology capabilities. This does provide some evidence that the
businesses that would benefit from GRD had been correctly selected for support.
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Panel 4.1 Summary of key findings
● 80% of award winners said that their projects wholly or largely satisfied their objectives, and only 6% said the reverse.
● Two-thirds of projects led to products that had reached, or would reach, the market. Smaller, but still substantial proportions of projects led to processes or services that had done, or would do, the same.
● The large majority of award winners (79%) whose projects had reached the marketplace rated the level of technological innovation embedded in the outputs as significant or high.
● Nearly a quarter of award winners cited lack of finance as a barrier to introducing their project outputs into the marketplace, but a larger proportion indicated that there were no particular barriers.
● Award winners’ projects had a wide range of important intermediate effects, such as:
- Improved attitudes to R&D, increased R&D and innovation expenditure and investment in significant technology
- Improved skills as the technical and commercial feasibility of ideas become clearer
- A better understanding of innovation and the ability to manage risk
● Just over a third of award winners sought further finance to help them take their project outputs into the marketplace. Most of those that sought further finance were offered it and accepted it.
● Over the duration of their projects GRD businesses improved their ability to lever in private sector finance especially for Venture Capital funds (share / equity finance) and bank loans.
● Nearly half of respondents agreed that being an award winner made it easier to obtain finance. Part of this is because the award winners have had to go through an application process to review GRD which acts as a form of due diligence. This can give investors more confidence to invest in the GRD businesses and the project and potentially removes some of the risk.
● The minority of unsuccessful applicants’ projects that went ahead anyway were considerably less effective than award winners projects in relation to their objectives.
● Nonetheless, unsuccessful applicants’ projects that went ahead anyway without GRD were as likely as award winners’ projects to lead to outputs that were taken into the marketplace including products/services and processes. However, almost half were not able to obtain GRD funding as their ideas were not considered to be sufficiently innovative. Hence it was probably quicker to take them to market.
● Unsuccessful applicants whose projects went ahead anyway were twice as likely as award winners to cite a lack of finance as a barrier to taking the outputs of their projects into the marketplace.
● Less likely than the award winners to acknowledge improvements on capabilities, effects on the business. In particular award winners showed a stronger culture in terms of R&D, an improved understanding of innovation, a stronger inclination to collaborate and network (with SMEs and HE), and develop intellectual property.
● Award winners, compared to unsuccessful applicants, were much more likely to have developed and exploited products/services and processes and improved their innovation and technology capabilities. This does provide some evidence that the businesses that would benefit from GRD had been correctly selected for support.
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5 Effects on the business performance of award winners
5.1 Introduction
5.1.1 This chapter examines both objective and subjective data on the effects of the grant
for R&D on the performance of grant winners. The examination of objective data
starts with a multivariate analysis of the impact of success in winning a grant on the
change in size of the grant winners between the base year of winning the grant and
the current year. Size is measured in terms of turnover and employment. This
multivariate analysis is designed to test whether the award of a grant makes a
difference to business performance in these terms. In doing this it allows for any
tendency for superior performing firms to be more likely to have won a grant in the
first place. The examination of subjective data which then follows is based on a
comparison of grant winners' views on the contribution that winning a grant made to
their turnover, and employment performance with their views on how their business
might have performed if they had not received a grant.
5.2 Comparison of award winners’ and unsuccessful applicants’ performance
5.2.1 The measurement of the impact of successfully winning a grant may be biased if
those who participate are a group selected on characteristics which, by themselves,
would lead to superior performance even in the absence of participation. This is the
well known problem of selection bias.
5.2.2 To address this issue a multivariate analysis of the impact of participation on turnover
and employment performance was carried out which adjusted for selection bias. In
each case a parsimonious specification based on the Law of Proportionate Effect
(LPE) was adopted, in which the logarithm of current year size is a function of the
logarithm of base year size. In addition to opening year values of the relevant
performance variable, and a dummy variable representing success or failure in
gaining a grant the estimated equations also included as explanatory dummy
variables the growth intentions of the applicant at the time of applying for a grant, the
type of grant applied for, the region of the applicant and the sector of the applicant.
To allow for the impact of time on generating performance effects a variable was also
included which measured the length of time in years between the base and current
years. To correct for selection bias the Heckman selection model was employed.
This first requires the estimation of a probit equation determining the factors which
distinguish the successful from the unsuccessful applicants. This is then used to
construct a variable which, when included alongside the other independent variables
in the performance equations, corrects for selection bias. All estimates were carried
out with significance tests corrected for heteroscedasticity.
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5.2.3 The core results are shown in Table 5.1. For simplicity of presentation only the
coefficients on base year size, the success dummy and growth intentions are shown.
These were nearly always statistically significant in the uncorrected estimations.
There were no systematic effects by region, type of grant, or industrial sector or time
between base and current years. In a multivariate context, if participation has a
positive impact on performance, a positive coefficient on the success variable would
be expected.
Table 5.1 The Impact of the Grant for R&D on Turnover Growth and Employment Growth: Regressions of the logarithm of current size on the logarithm of base year size, success in winning a grant and growth intentions, uncorrected and corrected for selection bias.†
Variable Uncorrected Corrected
Turnover Employment Turnover Employment
Base Year Size 0.72** 0.84** 0.72** 0.87**
Success 0.53 0.19** 0.45 -0.60
Grow Moderately 0.45* 0.11* 0.45 0.05
Grow Rapidly 0.73** 0.21** 0.72* 0.07
N 817 817 817 817
R2 0.57 0.70 0.57 0.70
† All regressions included a constant term, and dummy variables for industrial sector, type of grant, and region. These were rarely statistically significant. The length of time between opening and closing years was also included and was significantly positively related to current size in the case of turnover, but not in the case of employment. * Significant at 10% level ** Significant at 5% level or better Source: PACEC - Surveys of Award Recipients and Unsuccessful Applicants
5.2.4 Table 5.1 shows that the equations produced reasonably good fits in terms of
adjusted R2. The table also reveals a generally neutral impact of winning a grant. If
we focus first on turnover it is apparent that success in winning a grant has no impact
on subsequent turnover on either a corrected or uncorrected basis. In the case of
employment a positive impact on an uncorrected basis becomes insignificant when
the correction for sample selection bias is made.10
5.2.5 It is, of course, possible in a statistical analysis to control for only a limited range of
variables which may affect performance. It is an important complementary step,
therefore, to combine econometric analysis of objective outcomes with the more
subjective views of businesses as part of a comprehensive evaluation. For example,
the impacts of GRD on their business practices and business performance (eg
through the growth in jobs and turnover). The following sections set these out.
5.2.6 Previous chapters have shown that the GRD has had important positive effects on
the ability of businesses to develop products and services and take them to market
and strengthen their innovation and technology skills and capabilities.
10 Note the 2001 evaluations of Smart by PACEC showed a stronger impact using the same modelling approach.
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5.3 Award winners’ views of the effects of the schemes on business performance
5.3.1 The award winners were read a menu of possible effects of the schemes on the
performance of their businesses, and they were asked to indicate which ones they
had experienced. Table 5.2 indicates that, collectively, the award winners
acknowledged a wide range of effects: on average, three effects per firm. However,
one third of the award winners acknowledged no effects at all.
5.3.2 The main effects were increases in turnover and the value of the company (around
40% of businesses) and increases in either the value of assets (34%) and
employment (31%). On employment it should be noted that 40% sought to increase
their employment so that the result was somewhat less.
5.3.3 The table also shows that firms undertaking feasibility/research projects were more
likely than others to experience no business performance effect, although it is
probably in the nature of such projects that a substantial proportion will be fruitless.
Firms undertaking development/exceptional projects were correspondingly less likely
than others to acknowledge no effects.
5.3.4 Larger firms (i.e. 50+ employees) were generally more likely than others to
acknowledge any particular effect; and less likely to say that there had been no
effects. By and large, firms supported by Smart were more likely than those
supported by GRD to acknowledge effects.
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Table 5.2 Which, if any, of the following have been the actual business performance effects of your project to date?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q45A)
5.3.5 The award winners were then asked to indicate – and to quantify, if at all possible –
how their turnover and employment had changed as a result of their involvement in
the schemes and how they would have changed anyway without the support of the
schemes.
5.3.6 The responses relating to turnover are summarised in Table 5.3 showing that 40%
did so. This share matches the proportion who sought to grow at the outset.
Focusing on the third part of the table, the figures indicate that a net 38% of firms
suggested that they experienced an increase in turnover that would not have
happened in the absence of the support. This part of the table also indicates that the
mean increase in turnover attributable to the support was £291,000 per firm.
5.3.7 Consistent with the findings in Table 5.2, the table also indicates that the largest
effects were experienced by firms undertaking development/exceptional projects and
those in the 50+ employee size band. Similarly, firms supported by GRD implied a
considerably larger effect than those supported by Smart.
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Table 5.3 Changes in turnover as a result of the Smart/GRD/ support received and changes which would have happened anyway
Total MicroFeas/ resch
Devel/ excep 1-9 10-49 50+ GRD Smart
i) Changes as a result of the GRD/Smart support (gross effects)
Increase 43 47 37 52 40 47 76 47 34
No change 56 49 63 46 59 52 24 52 65
Decrease 1 4 0 2 2 0 0 1 2
Mean size of change, £’000 348 242 290 523 295 450 639 206 440
ii) Changes which would have happened anyway (deadweight)
Increase 11 12 9 14 9 14 25 12 8
No change 85 79 90 78 85 84 75 82 91
Decrease 4 9 1 8 5 1 0 6 2
Mean size of change, £’000 57 45 40 98 47 78 111 43 67
i) - ii) Changes which would not have happened without GRD/Smart support (gross additional effects)
Increase 39 44 33 47 37 39 74 42 31
No change 60 54 66 51 61 61 26 56 68
Decrease 1 2 1 2 2 0 0 2 1
Mean size of change, £’000 291 198 250 424 248 372 528 163 373
5.3.8 The award winners were also asked whether the turnover effects they reported were
likely to be short term or ongoing; and Table 5.4 shows that, regardless of class of
respondent, the majority though that the effects would endure. Some 29% said 2/3
years and 71% said 4 years or more. Smart supported businesses though the effects
would be more long term and this is potentially because they had experienced them
over a longer period compared to GRD supported businesses.
Table 5.4 Do you think that the effects are short term or on-going?
Type of grant Size of company Scheme
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
Short term(2-3Years) 29 39 31 19 31 28 0 27 33
On going (4 or more years) 71 61 69 81 69 72 100 73 67
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q46f)
5.3.9 Table 5.5 performs the same function as Table 5.3, but it examines the employment
effects of support from the schemes. Some 30% had increased their employment
which is below 40% seeking to grow at the outset. Again focusing on the third part of
the table, it shows that a net 26% of firms indicated employment increases that would
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not have happened without support: a smaller net proportion than indicated an
increase in turnover.
5.3.10 Where the changes were quantified, the firms implied an average increase of in 2.1
FTE jobs per firm. In combination with Table 5.3, this implies that it required an
increase of around £140,000 in turnover to generate one FTE job, an amount which
seems plausible.
5.3.11 The table also indicates that the pattern of employment effects according to class of
firm was similar to the pattern of turnover effects, although the differences between
classes were less marked for employment than for turnover.
Table 5.5 Changes in employment as a result of the GRD/Smart support received and changes which would have happened anyway
Total MicroFeas/ resch
Devel/ excep 1-9 10-49 50+ GRD Smart
i) Changes as a result of the GRD/Smart support (gross effects)
Increase 30 37 22 45 27 37 50 29 34
No change 67 60 77 53 71 62 44 69 65
Decrease 2 3 1 3 2 1 6 3 1
Mean size of change, FTE jobs 2.2 1.5 1.0 5.0 1.8 2.6 7.1 1.8 2.5
ii) Changes which would have happened anyway (deadweight)
Increase 8 10 7 10 7 10 22 9 7
No change 87 82 92 80 87 86 78 85 91
Decrease 5 8 2 10 6 5 0 7 2
Mean size of change, FTE jobs 0.1 0.2 0.1 0.2 0.1 0.0 0.8 0.2 0.1
i) - ii) Changes which would not have happened without GRD/Smart support (gross additional effects)
Increase 28 35 20 41 26 33 31 26 31
No change 70 62 78 57 71 65 69 71 66
Decrease 2 2 3 2 3 1 0 2 3
Mean size of change, FTE jobs 2.1 1.3 0.9 4.8 1.7 2.5 6.3 1.6 2.4
5.4 The Benefits from Types of GRD Expenditure
5.4.1 To assess the benefits of Smart/GRD the firms that participated in the follow-up
interviews were asked what the grant had been spend on and the benefits of different
types of expenditure. Table 5.6 below shows that just over 60% of expenditure was
on staff costs followed by materials (16.2%), equipment (9.5%) and professional fees
(mainly legal and financial fees – 4.9%). Other types of expenditure accounted for
less than 2% of expenditure each. The figures were similar depending on the type of
award but with less spent on staff costs for the micro award winners (where the
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Evaluation of Grant for Research and Development & Smart Page 77
proportion on materials was higher) and more spent on staff costs (80%) by the
exceptional award winners.
5.4.2 Of all the items of expenditure that on staff, equipment and materials were generally
seen as the most important in delivering the benefits reflecting the amount spend on
these items and the benefits which resulted.
Table 5.6 What was the grant spent on? -Mean percentages
Average (mean) of all respondents. (by Type of award)
Total Micro Feasibility / research
Development
Exceptional
Staff costs 60.2 45.3 62.9 66.7 80.0
Materials 16.2 24.2 8.1 17.3 4.7
Equipment 9.5 13.3 5.9 8.2 10.7
Other professional fees 4.9 4.2 13.7 <0.5 4.7
External advice on technology issues 1.6 0.9 5.9 <0.5 <0.5
Components 1.4 4.2 <0.5 <0.5 <0.5
Data 1.4 4.2 <0.5 <0.5 <0.5
External advice on innovation 0.6 0.9 <0.5 <0.5 <0.5
External advice on business management <0.3 0.9 <0.5 <0.5 <0.5
External advice on exploitation of products / services <0.3 0.9 <0.5 <0.5 <0.5
Other 3.6 0.9 3.6 7.0 <0.5
Source: PACEC Survey (Q7A)
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Panel 5.1 Summary of key findings
● To address the issue of selection bias in terms of GRD awards a multivariate econometric analysis was carried out on turnover and employment performance which also adjusted for selection bias. It used data for the successful and unsuccessful applicants. The analysis shows a neutral impact of the award on turnover. On employment the positive impact but it is not significant when corrected for sample bias.
● It is important to gain evidence of impact therefore to combine the econometric analysis with the views and evidence of impact from the businesses. The research was designed to work with businesses to make estimates of impact using company data and to test and compare these. The two approaches allow some of the potential weaknesses of both approaches to be put within a wider context, ie the multivariate analysis which relies on more objective information and the views on businesses which are more subjective albeit supported by evidence and company data.
● The award winners acknowledged a range of different effects of the support they received on their business performance, notably increased turnover and the value of the company (influenced by the increased value of assets and increased employment (31%)), but one-third of them acknowledged no effects at all, which is probably a reflection of the finding that 25% had not taken products/services to market and that it takes time for the effects of innovation to feed through.
● A net 38% of award winners indicated that they experienced an increase in turnover that would not have happened without the support they received; and the majority expected this effect to endure.
● Where quantified, the average increase was £291,000 per firm.
● 28% of firms indicated that they experienced an increase in employment that would not have happened without the support they received.
● Where quantified, the average increase was 2.1 FTE jobs per firm.
PACEC Wider effects
Evaluation of Grant for Research and Development & Smart Page 79
6 Wider effects
6.1 Introduction
6.1.1 Based on their own assessments, this chapter explores the effects - both positive and
negative - of award winners’ projects on businesses other than their own. It starts by
examining the extent to which award winners’ projects lead to the transfer of
technological and business performance effects to other firms with which they are
associated. The transfer effects of award winners’ projects are then compared with
the same effects emanating from the projects of unsuccessful applicants. Lastly, it
considers the strength of any (positive) supply chain and income multiplier effects,
and (negative) displacement effects that award winners’ projects might have had.
6.2 Effects of supported projects on other businesses
6.2.1 The first part of Table 6.1 implies that at least 38% of award winners’ projects had led
to the firms’ customers improving at least one aspect of their technology, products,
business performance etc. (i.e. 100% less 47% saying ‘none of the above’, less 15%
saying ‘don’t know’). Similarly, the second part of the table indicates that 20% of
award winners’ projects had led to the firms’ suppliers improving at least one of the
aspects shown. The third part indicates that the equivalent proportion reporting an
improvement affecting competitors was 17%. And the fourth part indicates that the
equivalent proportion reporting an improvement affecting collaborators was 24%.
6.2.2 The figures in the table suggest that recipients of developments/exceptional awards
were more likely than others to report some kind of positive effect on associated
businesses. They also suggest that medium sized firms (50+ employees) were
generally more likely than those in other size bands to report some kind of positive
effect on associated businesses. Smart award winners were on the whole more likely
than GRD award winners to say the same. These findings are interesting in light of
the finding in Chapter 4 that project effectiveness (in relation to objectives that were
predominantly technical) did not vary greatly according to class of respondent.
However, it will be recalled from the same chapter that winners of development
awards were more likely than others to indicate that their projects had had a market
impact, and that GRD award winners were less certain than Smart award winners
about the market impact of their projects.
PACEC Wider effects
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Table 6.1 Which, if any, of the following have been the external effects of your project to date? -
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50D)
6.2.3 In similar fashion to the analysis shown in the preceding table, Table 6.2 compares
the perceptions of unsuccessful applicants about the transfer effects of their projects
with the perceptions of award winners about their projects. It reveals that, overall,
PACEC Wider effects
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unsuccessful applicants were very much less likely than award winners to claim that
their projects had led associated firms to improve the aspects shown. Only 8% of
unsuccessful applicants, but 38% of award winners, claimed an improvement
benefiting customers. Only 4% of unsuccessful applicants, but 20% of award
winners, claimed an improvement benefiting suppliers. Only 5% of unsuccessful
applicants, but 17% of award winners, claimed an improvement benefiting
competitors. And only 3% of unsuccessful applicants, but 24% of award winners,
claimed an improvement benefiting collaborators.
PACEC Wider effects
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Table 6.2 Which, if any, of the following have been the external effects of your project to date? -
Percentage (%)
Unsuccessful applicants Award winners
The firm’s Customers have improved their:
Products 6 24
Technology 5 27
Processes 2 18
Business performance 2 23
innovation practices 1 17
None of the above 81 47
Don’t Know 11 15
The firm’s Suppliers Have improved their:
Business performance 4 12
Technology 2 11
Innovation practices 2 8
Products 2 10
Processes 2 8
None of the above 85 62
Don’t Know 11 18
The firm’s Competitors Have improved their:
Business performance 4 8
Technology 3 13
Products 3 10
Innovation practices 2 10
Processes 2 8
None of the above 84 63
Don’t Know 11 20
The firm’s Collaborators Have improved their
Technology 4 17
Products 3 10
Business performance 3 13
Innovation practices 2 13
Processes 2 12
None of the above 85 61
Don’t Know 12 15
Effective Sample Size 90 418
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q50D)
PACEC Wider effects
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6.3 Multiplier and displacement effects
6.3.1 Table 6.3 is intended to give an indication of the strength of the first round supply
chain multiplier effect associated with any increases in turnover enjoyed by award
winners (see Chapter 5). The third row hints at the extent to which the multiplier
effect will have been dampened by the leakage of expenditure abroad (i.e. by
imports); and it shows that, on the basis of award winners’ rough estimates, the
leakage was only 17% overall, albeit greater than this amongst firms undertaking
development/exceptional projects and GRD award winner. The first and second rows
indicate the extent to which award winners’ local economies and the national
economy, respectively, will have enjoyed multiplier effects. The first row suggests a
relatively strong regional multiplier effect overall. In combination, the first two rows
suggest strong national multiplier effects.
Table 6.3 By value, from where does your firm purchase the goods and services it uses?
Percentage (%)
Average (mean) of all projects.
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
In the same region / part of the country as yourselves 37 44 41 24 40 29 17 40 30
6.3.3 Table 6.5 is intended to measure the displacement effect associated with the
schemes. It shows the extent to which increases in turnover enjoyed by award
winners are at the expense of competitors in the same region, elsewhere in the UK
and overseas. In broad terms, the first two rows suggest that 30% of turnover
increases amongst award winners are at the expense of other UK firms. The third
row indicates that just over a quarter of the turnover increases amongst award
winners are at the expense of overseas rivals. The fourth row reflects the fact that
many award winners believe that their project outputs are novel and, hence, do not
actually displace competitors’ turnover, as such.
6.3.4 The table indicates that the displacement effect within the UK is relatively strong
amongst medium sized firms (50+ employees). Displacement of overseas
competitors’ sales appears to be relatively strong amongst firms undertaking
development/exceptional projects; and it appears to be positively related to firm size.
The lack of a displacement effect associated with novel outputs seems to be most
associated with firms that undertook feasibility projects, the smallest firms and those
that had Smart awards.
Table 6.5 If your firm were to cease trading tomorrow, what proportion of its sales arising from participation in the scheme would be taken by it competitors in the following areas?
Percentage (%)
Average (mean) of all projects.
Total Micro Feas/ resch
Devel/ excep
1-9 10-49 50+ Smart GRD
In the same region / part of the country as yourselves 6 7 7 5 6 6 4 6 6
Evaluation of Grant for Research and Development & Smart Page 85
Panel 6.1 Summary of key findings
● A large minority of projects had positive effect (e.g. improved technology, business performance etc) on the award winners’ customers. Smaller proportions of projects had similar effects on award winners’ suppliers, competitors and collaborators.
● Unsuccessful applicants were very much less likely than award winners to claim such effects.
● The first round supply chain and income multiplier effects associated with turnover and employment increases flowing from award winners’ project outputs appear to be relatively strong, although the latter are difficult to gauge.
● Correspondingly, displacement effects - the tendency for award winners’ turnover increases to be at the expense of competitors within the UK – appear to be muted.
PACEC Economic impacts and cost-effectiveness of the schemes
Evaluation of Grant for Research and Development & Smart Page 86
7 Economic impacts and cost-effectiveness of the schemes
7.1 Introduction
7.1.1 The purpose of this chapter is to derive some measures of the cost-effectiveness (or
value for money) of the schemes with respect to jobs and GVA. This is done by first
using some of the data presented in chapters 5 and 6 to estimate their aggregate
economic impacts. These impact estimates are then set against the costs of the
schemes to obtain the desired measures. It should be noted that some 40% of
businesses sought to grow, including employment and turnover, and 40% and 30%
respectively said they did.
7.1.2 It is emphasised that the economic impacts focused upon here include only those that
the survey research was designed to measure precisely: that is, the aggregate
impacts on award winners’ Gross Value Added (GVA) and employment. Conclusions
on the totality of the economic impacts of the schemes also need to take into account
the intermediate effects of the schemes (e.g. the capability-related effects outlined in
chapter 4), the full range of business performance effects (i.e. those referred to in
chapter 5) and the wider effects (e.g. the effects on customers, suppliers, etc.
outlined in chapter 6) and the longer term diffusion of technology effects.
7.2 Total measurable economic impacts
7.2.1 The process for estimating the impacts of the schemes is illustrated in Table 7.1. It
should be noted that the process produces two different estimates of net additional
effects, one which excludes the multiplier effects of the sort considered in chapter 6,
and one which includes them11. These figures give a lower and upper bound for the
likely impact of the schemes.
7.2.2 Table 7.2 applies the process illustrated in Table 7.1 to the average per firm
employment effect data from chapter 5 and the national level displacement effect
data from chapter 6. The table indicates that, overall, the schemes had a net
additional employment effect of between 1.4 and 2.1 full time equivalent jobs per firm
(with and without the multiplier effects). The average additional impact is 64%
accounting for deadweight and displacement. It also shows a wide variation in the
effect according to class of respondent, with the full net additional effect ranging from
0.9 FTEs amongst firms undertaking feasibility/research projects to 6.0 FTEs
amongst firms with 50+ employees.
11 The Multiplier used is 1.5 which is taken from English Partnerships. A Standard Approach to Assessing the Additional
Impact of Projects. Sept 2004, in line with PACEC’s National Evaluation of Business Links for DTI, 1998
PACEC Economic impacts and cost-effectiveness of the schemes
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Table 7.1 Estimating the total economic impacts of the schemes
Gross attributable impacts (i.e. changes in GVA & employment as a result of the support – see chapter 5)
Less
Deadweight – counterfactual (i.e. changes that would have happened anyway – see chapter 5)
Equals
Gross additional impact (i.e. effects attributable to the support)
Less
Displacement (i.e. increases in GVA/employment at the expense of competitors – see chapter 6)
Equals
Net additional effects (or total measurable annual economic impacts without linkages and multipliers)
Plus
Linkages and multipliers (i.e. effects due to purchases by businesses and their staff )
Equals
Full net additional effects (or total measurable annual economic impacts)
Multiplied by
Average duration (in the case of GVA, how many years the effect lasts)
Equals
Cumulative net effect (i.e. total cumulative measurable economic impact)
Table 7.2 Employment effects per project (FTE jobs)
Total value of grants, £m 239 11 88 140 142 71 26 133 107
Cost per £1m increase in GVA, £m 0.60 0.47 0.46 0.75 0.98 0.50 0.22 1.01 0.39
Cost per £1m increase in full GVA, £m 0.40 0.31 0.31 0.50 0.66 0.33 0.15 0.68 0.26
Cost per £1m increase in cumulative GVA, £m 0.09 0.08 0.07 0.12 0.16 0.08 0.04 0.16 0.06
Cost Benefit ratios (2008 prices)
Total value of grants, £m 280 12 104 164 166 83 31 147 133
Cost per £1m increase in GVA, £m 0.70 0.54 0.55 0.87 1.15 0.59 0.27 1.13 0.49
Cost per £1m increase in full GVA, £m 0.47 0.36 0.36 0.58 0.77 0.39 0.18 0.75 0.33
Cost per £1m increase in cumulative GVA, £m 0.11 0.09 0.09 0.13 0.18 0.09 0.04 0.18 0.08
Source: PACEC
PACEC Economic impacts and cost-effectiveness of the schemes
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7.3 Value for money
7.3.1 Table 7.6 provides some summary cost-benefit ratios (first column) and cost-
effectiveness ratios (second column) for the schemes, with comparisons from the
2001 evaluation (third column). The key employment cost-benefit ratio, in terms of
cost per unit of impact (top half of the table), is that the cost (in 2008 prices) per full
net FTE job was £32,000 (with multiplier effects). However, in order to compare this
with the 2001 analysis of the Smart evaluation in which multiplier effects were not
considered and a cost-effectiveness approach was adopted, this cost effectiveness
ratio is £40,000 per full time job, which is higher than the 2001 figure of £31,000.
7.3.2 The key GVA cost-benefit ratio, in terms of impact per unit of cost is that the return on
£1 investment is £9.00 in terms of cumulative GVA (with multiplier effects). However,
in order to compare this with the 2001 analysis in which cumulative and multiplier
effects were not considered and a cost-effectiveness approach was adopted, this
ratio is a £1.70 return on £1 investment, which is slightly higher than the 2001 figure
of £0.90.
Table 7.6 Value for money measures (based on net additional employment and GVA effects of Smart and GRD). Cost benefit and cost effectiveness ratios
Cost Benefit
Ratios** Cost Effectiveness Ratios
2008 Evaluation 2008 Evaluation 2001 Evaluation
Cost per unit of impact:
Cost per £1 increase in net GVA £0.70 £0.60 £1.10
Cost per £1 increase in full* net GVA £0.47 £0.40
Cost per £1 increase in cumulative GVA £0.11 £0.09 n/a
Cost per net FTE £47,000 £40,000 £31,000
Cost per full* net FTE £32,000 £27,000
Impact per unit of cost:
Increase in net GVA per £1 cost £1.40 £1.70 £0.90
Increase in full* net GVA per £1 cost £2.10 £2.50
Increase in cumulative GVA per £1 cost £9.00 £10.60
Increase in net FTE per £1m cost 21 25 n/a
Increase in full* net FTE per £1m cost 32 37 32
*Full net impacts include multiplier effects; Net impacts exclude multiplier effects. ** Cost Benefit adjusts costs to evaluation year prices. Cost Effectiveness does not adjust costs Note: The 2001 Evaluation is given in 2008 prices using a inflation multiplier of 1.27 Source: PACEC
PACEC Economic impacts and cost-effectiveness of the schemes
Evaluation of Grant for Research and Development & Smart Page 93
Panel 7.1 Summary of key findings
● During the period under review, Smart and GRD awards led to the creation of between 6,000 FTE jobs (without the multiplier) and 9,000 FTE jobs (with the multiplier effect).
● They also led to an aggregate increase in annual business GVA of between £400m (without the multiplier) and £600m (with the multiplier), or over £2.5bn cumulatively.
● The cost effectiveness ratios indicate that the net cost per FTE job for GRD was £40k and £27k (without and with multiplier effects). The cost per £1 increase in net GVA was £0.60 and £0.40 (without and with multiplier effects).
● Each £1 million of Smart and GRD support (in present value) led to increased annual GVA of between £1.4m and £2.1m, cumulative GVA of £9.0m and to between 21 and 32 FTE jobs. The ranges reflect the multiplier effects.
● These effects were net additional, i.e. they allow for deadweight and displacement, and the higher figures allow for multiplier effects.
● The effects shown are only those which the evaluation was specifically designed to measure. The totality of the economic impacts of the schemes will have been greater through, for example, intermediate and technology diffusion effects.
PACEC Firms’ assessments of the schemes
Evaluation of Grant for Research and Development & Smart Page 94
8 Firms’ assessments of the schemes
8.1 Introduction
8.1.1 This chapter starts by examining whether the award winners used other support in
addition to the support provided via the Smart and GRD schemes, where the support
came from and how useful it was. It then focuses on the award winners’
assessments of different aspects of the two schemes.
8.2 Other support used
8.2.1 Table 8.1 shows that almost half of the award winners used support over and above
that provided by the schemes. It also shows that there was little variation in the use
of other support according to class of respondent, except that firms undertaking
feasibility/research projects were a little less likely than others to do so.
Table 8.1 Did you access any other support or advice in relation to your project in addition to the support from the scheme?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53A)
8.2.2 Table 8.2 indicates that the other support came mainly from business
advisers/consultants and from the tertiary education sector, although GRD award
winners were somewhat less likely than Smart award winners to use the latter. Few
award winners – but rather more GRD award winners than Smart award winners -
had used venture capitalists / business angels for support. However, it was shown in
Chapter 3 that relatively few award winners had used risk finance in addition to their
awards.
PACEC Firms’ assessments of the schemes
Evaluation of Grant for Research and Development & Smart Page 95
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53B)
8.2.3 Table 8.3 describes how the award winners that had used other support rated it. It
shows that the very large majority described the support as ‘Useful’ or ‘Very useful’.
The highest proportion of award winners describing the support as ‘Very useful’
related to support from independent advisers/consultants and business joint venture
partners. The types of support that attracted the lowest proportion of ‘Very useful’
ratings were the tertiary sector and larger research/ technology companies, but these
two types were also those that attracted the lowest proportion of ‘Not useful’ ratings.
8.2.4 On the whole, award winners undertaking development/exceptional projects were the
most likely, and those undertaking micro projects the least likely, to describe support
providers as ‘Very useful’. It is also notable that a significant proportion of award
winners undertaking micro projects described tertiary sector support providers as ‘Not
useful’.
8.2.5 The table reveals no very clear pattern according to company size, except that the
smallest firms were generally more likely than others to describe the support as ‘Not
useful’. It also shows that GRD award winners were generally more likely than Smart
award winners to describe their support as ‘Very useful’.
PACEC Firms’ assessments of the schemes
Evaluation of Grant for Research and Development & Smart Page 96
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53C6)
8.2.6 Award winners were also asked if they would continue to use other support in the
future; and, if so, from where. Table 8.4 shows the responses and it reveals a very
similar pattern of support to that shown in 8.2. This is not surprising, however, given
that other support used to date was generally rated highly.
PACEC Firms’ assessments of the schemes
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Venture capital / Business Angel advisers 5 1 5 6 5 4 0 0 12
Business joint venture partners 5 2 5 6 5 5 0 4 5
Other 14 16 16 10 14 10 25 14 15
Effective Sample Size 146 37 67 49 100 33 8 77 78
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53D)
8.3 Assessments of the scheme
8.3.1 Table 8.5 summarises how the award winners rated aspects of the scheme; and it
indicates that every aspect was rated relatively highly overall by all classes of
respondent. The highest overall rating of the schemes was in terms of ‘benefits to the
business’ and the lowest was in terms of ‘application procedures’.
Table 8.5 How would you assess the following aspects of the scheme? Mean Score (1=Very poor, 2=poor, 3=fair, 4=good 5=very good).
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A8)
PACEC Firms’ assessments of the schemes
Evaluation of Grant for Research and Development & Smart Page 99
Panel 8.1 Summary of key findings
● Almost half the award winners had used support over and above that provided by the schemes, mainly from business advisers/consultants and from the tertiary education sector.
● Most users of this other support described it as very useful or useful.
● Every aspect of the schemes was rated relatively highly by award winners, but firms undertaking micro projects were a little more likely than others to express dissatisfaction.
PACEC The Impacts on Stakeholders
Evaluation of Grant for Research and Development & Smart Page 100
9 The Impacts on Stakeholders
9.1 Introduction
9.1.1 This chapter reports on the interviews with almost one hundred stakeholders across
the regions. The contacts were provided by each of the RDAs on the basis of those
engaged in innovation in both the public and private sectors. It considers the nature
and scope of the Strategic Added Value (SAV) and how it has contributed to the
objectives of GRD. Capturing this catalytic and influencing role is essential to
understanding the contribution of SAV to the generation of outputs and outcomes through the design and delivery of projects and programmes and in its own right
through the influence on partner and stakeholder behaviour and performance.
9.1.2 We used an analytical framework for assessing SAV which explored strategic
leadership and catalysts in terms of how stakeholders articulate the regions
requirements for support for innovation and the level of importance attached to it. We
also investigated the strategic influence stakeholders have in the context of engaging
in regional innovation agendas and in relevant activities and levels of
cooperation/collaboration. Leverage is also looked at and the synergies which may
or may have not developed as a direct response to GRD. This chapter also looks at
the business benefits and impacts as perceived by stakeholders and the constraints
or otherwise of GRD and where improvements may be needed.
9.1.3 In order to achieve the tasks outlined above we undertook a series of interviews with
a range of stakeholders each bringing their unique perspectives about the role and
influence of GRD. We discussed a range of topics and primarily focused on
stakeholder innovation policies and activities, involvement in GRD, influence of GRD
on stakeholders, the business benefits / impacts of GRD and improvements to GRD
and innovation policy
9.2 Background
9.2.1 For the initial stage of the evaluation we spoke with some 75 stakeholders with the
fieldwork still in progress. Around a quarter of these were independent business
advisers/SME consultants and an equal proportion were stakeholders from the HE
sector. One in ten were venture capital companies or business angels and the
remaining respondents were from local authorities (5%) and large
research/technology companies (5%). The remainder of stakeholders we spoke to
were ‘other’ types of organisation, for example business associations or high tech
initiatives with a knowledge of GRD.
9.3 Stakeholder Policies and Activities
9.3.1 Innovation was seen as being very important by 95% of stakeholders, no one thought
that innovation was not very important to their region. A total of 83% of stakeholders
PACEC The Impacts on Stakeholders
Evaluation of Grant for Research and Development & Smart Page 101
who we spoke with said that they did have either a strategy, policy, aim to support
innovation in their respective region or sub-region. When we looked at this aspect in
more detail over half, 54%, said that they had a formal strategy and/or policy
document with 42% maintaining that there strategies or policies were of an informal
nature. Finally, 17% said that they had governing body/board or management
decision which comprised their support for innovation.
9.3.2 In terms of the types of activities which the stakeholders undertake to support
innovation in their regions, overall they are quite active in terms of attending
networking events and workshops and seminars with 83% saying that they had
attended the former and 79% attending the latter. Furthermore 77% had developed
partnerships/collaborations. Likewise stakeholders were also active in terms of liaison
with other stakeholders with 68% saying that they liaised with others on activities,
programmes and initiatives, a further 63% had consulted on policies and 62% had sat
on panels or working groups.
Table 9.1 Activities which the stakeholders undertake to support innovation in their regions
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (q9a)
9.3.3 In terms of the direct support which the stakeholders provided to businesses there
was a wide array of the types of support mentioned. Around two thirds (68%) acted
as brokers or referred businesses to others, similarly 64% assisted with
collaborations and partnerships and a further 62% provided advice and mentoring
and 60% ran workshops or seminars. Over half of the stakeholders provided business
diagnostics (55%) and 53% ran networking events. Smaller proportions of
stakeholders gave grants to businesses (21%) and 23% provided equity or share
capital. Finally, 14% offered loans directly to businesses to assist them with
innovation.
PACEC The Impacts on Stakeholders
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Table 9.2 Direct support for innovation provided for businesses
Percentage of all respondents
Total
Referral / brokerage 68
Assist with collaborations / partnerships 64
Business advice / mentoring 62
Run workshops / seminars 60
Business diagnostics 55
Run networking events 53
Grants 21
Equity / share capital 23
Loans 14
None 8
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q10A)
9.3.4 In a spatial context three quarters (74%) of stakeholders focussed mainly on regional
businesses and a further 37% said that their core focus was upon local/sub-regional
business. A further 22% worked with businesses nationally. In terms of the types of
businesses which the stakeholders focussed upon 58% of them concentrated their
efforts on all types of businesses, 33% micro businesses, 41% start ups and the
same for spin outs. Only 16% of the stakeholders worked with larger businesses.
Just under a third focussed upon working with both R&D (30%) and high tech
business (32%) and a further 27% worked with leading edge technology businesses.
Finally, just over a third, 37%, worked with businesses who were in specific industrial
sectors.
9.4 Stakeholder Involvement in GRD
9.4.1 The second part of the survey with stakeholders specifically looked at the level and
extent of involvement in GRD and the impact, influence and role which GRD may
have had stakeholders. Almost all the respondents said that they were aware of GRD
offered by their respective RDA. In terms of the extent to which stakeholders
understood the GRDs aims and level of support almost two thirds (63%) understood
GRD and its aims to a large extent with a further 28% expressing that they
understood GRD to some extent and 8% a little.
9.4.2 A total of 76% of stakeholders said that they had been involved in GRD, and as
Figure 9.1 shows this involvement mainly in the context of referral of businesses to
GRD with 86% stating this. A further 63% said that they had assisted with GRD
applications and 58% of stakeholders said that they participated in GRD networks.
Fifty four percent said that they were mainly advisors to businesses with GRD and
the same proportion attended GRD events. Finally, only 14% said that they were a
panel adviser on the scheme on applications.
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Figure 9.1 Types of GRD involvement which stakeholders engaged in
86
6358
54 54
14
0
10
20
30
40
50
60
70
80
90
100
Ref
erra
l of
busi
ness
es to
GR
D
Ass
iste
d w
ithG
RD
appl
icat
ion(
s)
Par
ticip
ated
inne
twor
ks
Adv
iser
tobu
sine
sses
with
GR
D
Atte
nded
GR
D e
vent
s
Pan
el a
dvis
eron
the
sche
me
/ on
appl
icat
ions
Source: PACEC Survey (Q12D)
9.5 The Influence and Role of GRD on stakeholders (SAV)
9.5.1 Stakeholders were asked a range of questions about the extent to whether GRD had
played any role in influencing their operations and activities. In terms of the extent to
which the stakeholders shared the overall operations and delivery of GRD, 30% said
‘wholly’, 29% ‘largely’, 35% to some extent and 6% ‘not at all’. Furthermore 57% of
stakeholders ‘wholly’, 24% ‘largely’ 17% to some extent and 3% did not at all share
the overall objectives of GRD.
9.5.2 Stakeholders were probed further about whether their overall policy direction and or
strategy in terms of commitment to innovation had changed in response to GRD and
21% stated that it had strengthened and 79% said there had been no change at all. A
similar picture emerged in that 25% said that their overall direction had strengthened
in terms of adaptation of policies and how they now fit with those of GRD.
Stakeholders nationally also expressed that their levels of liaison and or consultation
on policies had increased (11%) and their liaison / consultation on
activities/programmes/initiatives had likewise increased though to a larger extent
(20%). Finally, 11% said that they have increased their attendance and workshops
and 3% said that this has reduced.
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Table 9.3 Overall policy direction / strategy changes in response to GRD-Activities strengthened or increased
Percentage of all respondents
Total
Commitment to innovation 21
Adaption of policies with GRD 25
Liaison / consultation on policies 11
Liaison / consultation on activities 20
Attending workshops / seminars 11
Source: PACEC Survey (Q15A)
9.5.3 We asked stakeholders questions which explored whether they had experienced any
changes in terms of their activities and levels of business support, i.e. whether they
had increased, reduced or hadn’t seen any changes in response to GRD. Overall
most stakeholders hadn’t seen any significant changes both in terms of reductions
and there being no change in activity at all across the range of activities which they
were involved in. Table X illustrates below that the main increases in activities and
business support were in the following areas; Business advice / mentoring (24%)
Referral / brokerage (24%) and Assistance with partnerships / collaboration (25%)
Table 9.4 Changes in business support for innovation as a result of GRD- Increased
Percentage of all respondents
Total
Business diagnostics 13
Business advice / mentoring 24
Workshops / seminars 19
Networking events 17
Grants 10
Loans 7
Equity / share capital 14
Assistance with partnerships / collaboration 25
Referral / brokerage 24
Source: PACEC Survey (Q16A)
9.5.4 We went on to ask the stakeholders about whether their focus had changed in terms
of the types of business they now supported as a result of GRD. Overall their was
very little impact with some small adjustments around the 12% mark for spin-outs
and leading edge businesses and 14% of stakeholders reporting that their focus had
increased with high tech businesses.
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Table 9.5 Focus on types of businesses in response to GRD- Increased
Percentage of all respondents (by Region)
Total
All businesses 9
Start-ups 9
Spin-outs 12
Micros 10
Medium businesses 7
Larger businesses 4
High tech businesses 14
Leading edge businesses 10
Source: PACEC Survey (Q17A)
9.5.5 There were small increases in terms of resource allocation in response to GRD.
Thirteen percent of stakeholders nationally said that they increased staff skills for
innovation and the same proportion had increased management of innovation, 8 %
had increased their workforce and 7% had increased the amount of finance.
Table 9.6 Changes in resources in response to GRD Increased
Percentage of all respondents
Total
Finance allocated 7
Number of staff 8
Staff skills for innovation 13
Management of innovation 13
Source: PACEC Survey (Q18A)
9.5.6 In order to establish the impact GRD may have had on preventing stakeholders from
introducing any new services the findings were unequivocal with 96% saying that
GRD had not prevented this and 3% were not sure. Likewise all the stakeholders said
that they had not scaled down or wound up any other similar services because of
GRD.
9.5.7 To capture further information on the impacts and SAV more fully and reflecting the
Impact Evaluation Framework (IEF) guidance12, stakeholders were asked to provide
further information on GRDs overall impacts in terms of stakeholders policies,
activities and resources. Overall 89% of stakeholders felt that GRD had been a
positive influence. Furthermore 26% of stakeholders felt that GRD had exerted a
strategic influence and only 15% reported that it had demonstrated strategic
leadership on innovation. 56% thought GRD had created confidence in regional
innovation and a further 67% thought it had levered in investment. for innovation
Finally, 49% thought that GRD had increased information and knowledge exchange
12 DTI. Occasional Paper No 2. Evaluating the Impact of RDAs: Developing a Methodology and Evaluation
Framework. 2006.
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Table 9.7 What role GRD played in terms of organisation’s policies, activities and resources
Percentage of all respondents
Total
Had a positive influence overall 89
Had a catalytic role / stimulus 56
Demonstrated strategic leadership on innovation 15
Exerted a strategic influence 26
Created confidence in regional innovation 56
Levered in our investment.. for innovation 67
Increased our commitment 39
Increased our engagement 44
Increased our collaboration 48
Stimulated a scaling up of innovation strategy 46
Enhanced the quality of our innovation activities 46
Strengthened our role as a partner in innovation 48
Increased synergy with our policies / activities 51
Increased information / knowledge exchange 51
Source: PACEC Survey (q21a)
9.5.8 Stakeholders were asked about what they would have done in the absence of GRD in
terms of changing their innovation. Across all three areas around a half of all
stakeholders said that they would not have changed, a further 32% said that they
would have changed their resources partially, 28% activities partially and 36%
policies partially. Only very small proportions said they would have changed their
resources wholly (1%) activities wholly (3%) and policies wholly (1%). Finally 20% of
stakeholders reported that they would have changed their activities ‘largely’ in the
absence of GRD. Where changes would have occurred anyway stakeholders
reported that in terms of timing these would have happened later (72%) would have
been smaller in scale (82%) and narrower in scope (70%)
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Figure 9.2 Changes to activities, policies and resources anyway to support innovation in the absence of GRD
1
3
1
11
20
11
32
28
36
55
49
51
0 10 20 30 40 50 60
Resources
Activities
Policies
%
Not at all
Partially
Largely
Wholly
Source: PACEC Survey (Q22A3)
9.6 The Business Benefits and Impacts of GRD
9.6.1 As part of the evaluation we asked stakeholders about their views on the benefits and
impacts for business. Overall the findings were positive in that large proportions of
stakeholders felt that GRD had indeed presented businesses with positive impacts
and benefits. In total 94% of stakeholders thought that GRD had helped to develop
new products and processes. A further 91% also felt that GRD had assisted in
improving technological knowledge and/or skills and had also increased activity and
or investment in R&D and innovation. Ninety one percent felt that it had improved
products and/or processes of businesses.
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Table 9.8 Perceived benefits of GRD are for the businesses
Percentage of all respondents
Total
Improved technological knowledge / skills 91
Improved products / processes of businesses 91
Developed new products 94
Developed new processes 94
Increased activity / investment in R&D and innovation 91
The firm has invested more in technological innovation 84
New intellectual property has been developed 85
Academic / leading edge research exploited 79
Improved market position 85
The image of the firm has improved 71
The firm has collaborated ... more with other firms 62
The firm has collaborated ... more with unis/colleges 63
The firm has collaborated ... with other research..orgs 68
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q23A)
9.6.2 We asked stakeholders about what the business performance benefits may have
been for businesses. Overall the key benefits as a result of GRD were the increased
value of companies as a result of GRD (67%), increased turnover in businesses
(61%), and increased employment (54%). They also reported increased value of its
assets (54%), Increased productivity (46%) and Increased profit margin on sales
(44%).
Table 9.9 Perceived actual business performance effects for businesses’ projects to date
Percentage of all respondents
Total
Increased the value of the company 67
Increased overall turnover / sales 61
Increased employment 54
Increased the value of its assets 54
Increased productivity 46
Increased profit margin on sales 44
None of the above 20
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q24A)
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9.6.3 In terms of stakeholder perceptions on the level of impacts on businesses GRD may
have had, overall 72% felt that it had either a very strong or strong impact on micro
business and similarly 73% felt it had either very strong or strong impact on research
orientated businesses. Similar proportions of stakeholders felt that GRD had a limited
impact, 9% micros and 7% for research.
Table 9.10 Strengths of GRD impacts for businesses
Percentage of all respondents
Micro Research / development
Very strong impact 32 29
strong impact 40 46
some impact 18 18
Limited impact 9 7
Source: PACEC Survey (Q25 A1 & A2)
9.6.4 Around a half (55%) of stakeholders felt that GRD had some unique aspects to it and
85% suggested that there could be some improvements or changes to GRD for
businesses and in assisting with applications. The main improvements which were
mentioned were application procedures (61%), marketing / promotion of GRD (59%),
and flexibility (52%). Around a third (33%) thought that time taken for payments to be
made could be improved and a quarter (25%) felt that the amount of the grant could
be enhanced.
9.6.5 At a more strategic level half of stakeholders had adapted their policies in response to
GRD and just over a quarter said it exerted a strategic influence. Greater
involvement of stakeholders in the planning and delivery of GRD, encouragement for
stakeholders to bring forward applications and to play a role as advisers to business
could stimulate greater stakeholder commitment. This would potentially change the
allocation of their resources toward GRD and help make it a more joined-up
programme.
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Table 9.11 Potential GRD improvements
Percentage of all respondents
Total
Application procedures 61
Marketing / promotion of GRD 59
The flexibility of the scheme 52
Time taken for payments to be made 33
Support from the GRD team 30
What the grant can be spent on 28
Amount of grant 25
Support from other advisers 25
Involvement of partners 23
Other 28
Number of respondents 61
Respondents could select more than one option; so percentages in any column may sum to more than 100 Source: PACEC Survey (Q27B)
9.6.6 In terms of whether both businesses and stakeholders are reasonably clear about the
different types of support schemes for innovation, 69% of stakeholders reported that
they felt businesses were not and 43% of stakeholders felt that they themselves were
not. There was also concern about there being too many innovation schemes with
41% of stakeholders raising this. However, 46% thought the amount is about right
with a further 16% reporting there being too few. Stakeholders were finally asked if
they thought that there are any gaps in the support for innovation in the context of the
current economic situation and in total 69% thought that this was the case and that
support needed to be refined and customised.
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9.7 Summary of key findings
Panel 9.1 Summary of key findings
● Stakeholders awareness of GRD was significant and overall they largely understood its aims and objectives
● Stakeholder involvement was mainly around referring businesses to GRD and assisting them in the application process.
● Only a small proportion of stakeholders reported that their overall policy direction and or strategy had been strengthened in response to GRD.
● Overall most stakeholders hadn’t seen any significant changes in terms of reductions across the range of activities which they were involved in.
● The main increases in activities were in Assistance with partnerships / collaboration Business advice / mentoring Referral / brokerage
● There were marginal increases in terms of resource allocation in response to GRD.
● GRD had no impact on preventing stakeholders from introducing any new services nor the scaling down or winding up any other similar services.
● The vast majority of stakeholders said that GRD had a positive influence, levered in their investment, and created greater confidence in regional innovation support policies. However only small proportions thought it had demonstrated strategic leadership on innovation and a quarter considered that it had exerted a strategic influence
● Small proportions of stakeholders said that their commitment to innovation had strengthened
● Around half of the stakeholders said they wouldn’t have changed their policies, activities and resources in the absence of GRD and that the changes which would have taken place anyway would have occurred and mainly later on a smaller scale and narrower in scope.
● Nine out of ten stakeholders said GRD had helped businesses develop new products and processes and improve their technological knowledge and skills. These were seen as positive business impacts.
● In terms of improvements to GRD stakeholders were keen to have more strategic involvement in it through consultation and greater engagement with RDAs.
● In terms of delivery stakeholders’ suggested streamlining and speeding up the application procedures for businesses together with greater marketing and/or promotion of GRD to businesses.
● To encourage stakeholders to allocate their resources to GRD and help it become more joined-up, stakeholders could be stimulated to be more involved in the planning and delivery.
● Stakeholders also considered that they were reasonably clear about the different types of support schemes for innovation, though on the other hand they thought that businesses were not and what was available could be made clearer and how schemes related to one another, eg a funding ladder.
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10 Conclusions
10.1 Introduction
10.1.1 This final chapter first uses evidence presented in previous chapters to answer the
questions specified in the terms of reference for the evaluation in a straightforward
fashion. It then draws more general conclusions about the extent to which
GRD/Smart have achieved their intermediate and longer-term objectives.
1. To what extent has GRD/Smart genuinely encouraged technological innovation in SMEs?
10.2.1 Table 3.15 showed that award winners’ most common reason for participating in the
schemes was to develop new prototypes, products, and services. A range of other
technology-related objectives were also stated. Table 4.1 then showed that a very
large majority of the award winners surveyed had wholly or largely satisfied their
objectives and their technology / product development aims. It was also shown that
most projects would not have gone ahead without support from the schemes. It is
concluded, therefore, that GRD/Smart genuinely encouraged technological innovation
in SMEs to a considerable extent.
2. To what extent have GRD/Smart-supported projects involved significant technological innovation?
10.2.2 To a considerable extent. Table 4.2 showed that two-thirds of Smart/GRD projects
had resulted in new products reaching the market and that smaller proportions had
resulted in new processes and R&D services being provided. Four in ten award
winners said that their project outputs that reached the market place embodied
significant technological innovation (Table 4.3). A similar proportion said that the
level of technological innovation was high.
3. To what extent are distinct market failures being addressed by GRD/Smart?
The rationale for Smart/GRD is that there are market failures and imperfections
related to funding, and a funding shortage or gap, which mean that firms often find it
difficult to obtain funds to invest in the development of technologically innovative
products and/or processes, which will improve their competitiveness13. The market
failure is particularly apparent for SMEs where potential investors are unaware of how
to assess the potential value of a new technology and prospects for exploitation.
Characteristics of market failure indicate that:
13 BERR/DIUS. The Business Case for the R&D for SMEs product. Smart/GRD 2003-2007.
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a Because of the small size of the firm and the risk surrounding the potential returns (high risk, small reward), funders do not feel able to manage the risks associated with R&D projects. The result of this market failure can be described as an R&D ‘funding gap’ for SMEs
b The return from SME R&D projects tends to be longer term and typically takes several years. Traditional sources of finance for SMEs often prefer faster returns
c SMEs often know their capability to successfully run an R&D project but funders have no information or evidence of this. The costs of “due diligence”, in part to assess this, can be high compared to the return
d A SME may need a high share of its resources for R&D and returns may be uncertain. Market failures mean it is difficult for them or funders to insure against risk
10.2.3 Again, GRD addressed these issues to a considerable extent. Table 3.16 showed
that the overwhelming majority of award winners (ie 86%) were prevented from
pursuing their objectives prior to receiving support from the schemes because of a
lack of finance / lack of ability to attract finance ie the funding gap. This finance
included both internal business sources (such as operating capital and turnover) and
funds for R&D from external sources. One in ten businesses also specifically
mentioned that there was uncertainty over the cost of research, the feasibility of ideas
and the financial returns. Just over a quarter sought alternative finance to GRD, and
almost half of these were not successful in that they were not offered it on favourable
or acceptable terms. One of the main alternative sources offered was other public
funding. Usually other public funding sources for innovation or other forms of support
for businesses (that seek to address market failures) do not offer the same levels of
funding as GRD or cannot be used with the same flexibility for innovation. Larger
sums are available where they support capital works (such as premises). The
majority of businesses did not seek alternative funding because they decided they
could manage without it (arising partly from the conditions attached), were not aware
of sources or did not think they would obtain it. These findings also illustrate market
failure or the funding gap.
Almost half the businesses said that being an award winner made it easier for them to
obtain additional finance. Part of this is because the award winners have had to go
through an application process to receive GRD which acts as a form of due diligence.
This can give investors more confidence to invest in the GRD businesses and the
project and potentially removes some of the risk. This represents a positive
externality in that funders are more inclined to invest in innovative businesses and the
supply of finance for them potentially increases.
4. To what extent are the benefits generated by GRD/Smart genuinely additional?
10.2.4 To a very considerable extent. Table 3.30 indicated that 70% of projects were wholly
additional (would not have gone ahead at all without support) and a further 26% were
partly additional (would have gone ahead, but later and/or on a smaller scale and/or
narrower in scope). It follows from this that the benefits flowing from the schemes
were, for the most part, genuinely additional.
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5. How did the GRD/Smart projects contribute to the RDAs' strategic added value, and how is this measured?
10.2.5 For almost all stakeholders nationally GRD had a positive overall influence and
demonstrated Strategic Added Value (SAV). For two thirds it had levered in their
investment and for just over half (56%) it had a catalytic role and created confidence
in support for innovation in the regions. For half of stakeholders, GRD had increased
the synergy with their other innovation policies and increased the information
exchange, collaboration, and their partnership role (Table 9.7). Nationally, 25% of
stakeholders had adapted their innovation policies reflecting GRD, 21% their
commitment to innovation and 20% their liaison and consultation activities. The
resources for innovation, the number of staff, staff skills, and the management of
innovation had increased (and been strengthened) for a small proportion of
stakeholders (ie 7-13%).
10.2.6 However, the stakeholders considered that GRD could be improved by involving them
more in planning and engaging them to a greater extent, which would help increase
the synergies and impact. GRD could be marketed more across the sectors and the
application process streamlined. They also thought that the types of innovation
schemes could be clarified and for business the number reduced. Stakeholders were
keen that GRD should be reintroduced to London.
10.2.7 Overall the awards were more focussed on the mechanical engineering, computing,
instruments and chemicals sectors. In total these accounted for 48% of the 4125
awards with awards in other manufacturing and service sectors being relatively small.
Intermediate effects and outputs
1. What proportion of GRD/Smart-supported projects have resulted in successful outcomes?
10.2.8 It has already been noted that the vast majority of businesses (ie 98% in Table 4.1)
said they had satisfied their objectives and that for 67%, their projects had resulted in
new products or services reaching the market. Table 4.6 also showed that supported
projects had had a wide range of other positive intermediate business effects on
award winners as follows :
- Improved attitudes to R&D, increased R&D and innovation expenditure and investment in significant technology
- Improved skills as the technical and commercial feasibility of ideas become clearer
- A better understanding of innovation and the ability to manage risk
10.2.9 Some 60% of unsupported businesses also developed products and services which
were taken to the market. However, these were generally seen as less innovative
compared to the GRD supported businesses and it was easier and quicker to develop
them.
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2. To what extent has GRD/Smart levered-in private sector finance into supported firms, and what factors influence these investments? Do GRD/Smart make the business more likely to attract finance than unsupported businesses?
10.2.10 Table 3.17 showed that fewer than three-in-ten award winners sought alternative
funding for their projects before applying for Smart/GRD funding; and Table 3.19
showed that searches were often unsuccessful (42% failed). By contrast, although
Table 3.22 showed that a slightly smaller proportion (24%) sought additional funding
in conjunction with their awards, Table 3.24 showed that these searches were less
likely to fail (ie 20% failed). Table 4.9 also showed that award winners were,
generally, successful in seeking further finance to take their project outputs to market
(ie 15% failed), although the source of finance here was most often RDAs, rather than
the private sector. It appears that the schemes helped to lever-in private sector
finance to some extent and that successful applications made by GRD businesses in
terms of offers made and accepted, increased over the duration of the project,
especially for Venture Capital funds (equity / share finance) and bank loans.
On the issue of whether the schemes made businesses more likely to attract finance
than unsupported businesses, Table 4.18 showed that unsuccessful applicants were
twice as likely as award winners to say that a lack of finance prevented them, or
would prevent them, from introducing their project outputs into the market place. (ie
50% to 24% respectively).
Some 48% of award winners thought that the GRD award in itself had made it easier
to obtain other finance.
3. What is the role of the government’s Small Firms Loan Guarantee Scheme (SFLGS) in financing projects (including those that went ahead without a grant as well as those that did)?
10.2.11 The SFLGS seems to have played only a small role in financing projects. Tables
3.24 and 3.25 showed that only 3% of the minority of award winners that sought
additional funding for their projects were offered and accepted bank loans backed by
the SFLGS.
Chapter 3 also showed that only a little over a quarter of the unsuccessful applicants’
projects subsequently went ahead; and that the SFLGS did not really figure as a
source of finance for these unsupported projects. In fact, only 1% of all unsuccessful
applicants’ projects (including those that went ahead and those that did not) attracted
a loan backed by the SFLGS.
4. Are GRD/Smart recipients more receptive than other SMEs to equity finance?
10.2.12 The evaluation surveys provides some evidence of this. Chapters 3 and 4 indicated
that award winners were more likely than unsuccessful applicants to apply for and
receive most forms of finance including equity finance.
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5. To what extent do GRD/Smart projects lead to new intellectual property?
10.2.13 Table 4.6 indicated that projects frequently lead to new intellectual property: almost
two-thirds of the businesses supported reported that their projects had led to the
development of new IP; almost half said that new patents had been applied for; and
almost half said that IP (e.g. a patent) had been obtained.
6. Do businesses go on to claim R&D Tax Credits?
10.2.14 One third of businesses had gone on to claim R&D tax credits linked to Smart/GRD
projects or other projects at some stage in their development (Table 4.13).
7. Is there evidence of any significant change in these effects since April 2005?
10.2.15 Businesses supported by GRD were more likely than those supported by Smart to
claim R&D tax credits. This may have been caused by changes in the administration
of R&D tax credits, including greater publicity during the GRD period.
Effects on business performance/ behaviour
1. What have been the longer-term impacts of GRD/Smart, with respect to the development of supported SMEs?
10.2.16 There was a range of evidence on this point, and it was reasonably positive overall.
Tables 3.12 and 3.13 suggested that participating in the schemes had the effect of
making firms more ambitious to grow, but that the effect might not endure. Tables
3.14 and 3.15 indicated that participation made firms slightly more innovative. Table
4.6 indicated that virtually all of the businesses experienced improvements in aspects
of their capability and capacity, notably improved attitudes to R&D, increased R&D
and innovation expenditure, improved skills for innovation (to test the commercial and
technical feasibility of ideas) and a better understanding of innovation and risk
management. And Table 5.2 showed that two-thirds of businesses reported one or
more business performance effects, which were generally thought, by them, to be on-
going, ie increased turnover (43%), increased value of the company (40%) and the
increased value of company assets (34%).
2. Do supported businesses go on to spend more on R&D than the sector average?
10.2.17 The evaluation survey did not answer this question directly, but the results show that
Smart/GRD businesses increased their expenditure on R&D. Generally GRD
supported businesses improved their attitudes and the culture of the company
towards R&D and their understanding of innovation more than unsupported
businesses (Table 4.19).
3. Do beneficiaries increase their capability to innovate?
10.2.18 Table 4.6 strongly suggested that this is the case. For example, 78% of supported
businesses reported that they had become better able to manage innovation /
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Evaluation of Grant for Research and Development & Smart Page 117
technical risk, 80% reported that they had improved their innovation / technical
understanding and 70% said that attitudes and the company culture towards
innovation had been strengthened. Businesses were also more inclined to use
external support and collaborate and network more with innovation partners.
4. What are the barriers to supported businesses undertaking future R&D projects? Are they different to those evident before the grant?
10.2.19 A lack of finance remained the principal barrier to undertaking future R&D, but its
dominance post-support had diminished compared to the situation pre-support.
Correspondingly, firms were much more likely post-support to say that ‘none of the
above’ (i.e. nothing in particular) prevented them from undertaking R&D.
5. Does the grant help SMEs to exploit academic research/leading edge scientific thinking? How can we achieve better outcomes?
10.2.20 Table 4.6 showed that a little under half of the businesses reported that their
participation had had the effect of enabling them to exploit academic / leading edge
research. A little under half reported that participation led them to collaborate more
with the tertiary sector and with research /technology organisations. Tables 8.2-8.4
showed that a large minority of businesses had received support from tertiary sector
advisers and that they were positive about the support they received. It is difficult,
therefore, to suggest ways in which better outcomes in this respect might be
achieved.
Economic Impacts: cost-effectiveness
1. Is GRD/Smart good value for money, in terms of economic benefits created, taking into account identifiable spill-over effects through the main diffusion mechanisms?
10.2.21 Tables 7.3 and 7.5 indicated that the £239 million (or £280 million in 2008 prices) in
grants during the evaluation period had led to the creation of between 6,000 and
9.000 net additional jobs (without and with multiplier effects respectively) and
between £400 million and £600 million net additional GVA (without and with multiplier
effects), or a cumulative figure of £2.5bn.
10.2.22 The cost effectiveness ratios indicate that the net cost per FTE job for GRD was £40k
and £27k (without and with multiplier effects). The cost per £1 increase in net GVA
was £0.60 and £0.40 (without and with multiplier effects).
10.2.23 Table 5.2 also showed that supported businesses experienced a range of other
business performance effects, while Chapter 6 indicated that the schemes were
associated with positive spill-over and multiplier effects. On the basis of this
evidence, it is concluded that the schemes do represent good value for money.
10.2.24 The total economic impacts are potentially somewhat greater because of intermediate
effects on capabilities of businesses and the wider technology diffusion effects.
PACEC Conclusions
Evaluation of Grant for Research and Development & Smart Page 118
2. Which project types offer the best vfm?
10.2.25 In terms of both employment generation and GVA effects, Micro projects appear to
offer the best value for money (Tables 7.3 and 7.5) in terms of employment, and
Feasibility/research offering the worst. However, in terms of GVA effects,
Development/exceptional projects appear to be worse than both Feasibility/research
and micro projects.
3. What scope is there for improvement in the additional benefit/cost outcomes?
10.2.26 Tables 7.3 and 7.5 also showed that there was very little deadweight associated with
the employment and GVA effects of the schemes. Displacement rates were higher,
but it is difficult to imagine how these might be reduced. Restricting the support to
entirely new technologies might reduce the amount of displacement, but entirely new
technologies are generally not suitable for commercialisation. In any case, the sort of
displacement associated with schemes like Smart and GRD might be regarded as a
form of Schumpeterian ‘creative destruction’.
10.2.27 Table 8.6 showed that more than two-thirds of the firms surveyed described the
amount of grant as Good or Very good; and this suggests that there might be scope
for reducing the costs of the support by reducing the grants. However, it is not clear
how elastic participation is with respect to the level of grant; and it is not clear,
therefore, what benefits might be sacrificed in order to achieve cost savings.
4. Are investments in new/pre-start ups more or less likely to yield returns than investments in more established businesses?
10.2.28 As noted in paragraph 10.2.25, micro projects appear to offer better value for money
than other types of projects. To the extent that micro projects are more likely than
others to be undertaken by new/pre-start-up businesses, if follows that scheme
investments in these types of businesses yield more than investments in more
established businesses.
5. What is the impact on job creation?
10.2.29 As noted in paragraph 10.2.21, the schemes had generated between 6,000 and
9.000 net additional jobs (without and with multiplier effects) during the evaluation
period.
Wider effects
1. What have been the longer-term impacts of GRD/Smart with respect to development and dissemination of significant innovation?
10.2.30 Some two thirds of businesses had also increased their R&D expenditure and activity
and improved their R&D skills and culture, invested more in significant innovation,
and generated new intellectual property. Increase collaborator and networking has
assisted with the dissemination of this innovation. It was also shown that at least two-
PACEC Conclusions
Evaluation of Grant for Research and Development & Smart Page 119
thirds of Smart/GRD projects had resulted in new products, processes or services
reaching the market; and that the large majority of firms described the technological
innovation in these outputs as significant or high (paragraph 10.2.2). Some three
quarters of GRD supported businesses who increased their turnover (ie 40%) said
that the effects would last for four years or more and not for a shorter period.
However, the extent to which these effects endure cannot be fully measured as the
context and economic circumstances may change.
2. What have been the impacts of GRD/Smart with respect to RDA Regional Economic Strategies?
10.2.31 The review of RDA policies including in Regional Economic Strategies showed that all
had a policy on innovation and four had separate innovation strategies. Within these
there was a strong commitment to stimulating R&D and product / process innovation
and development. The majority of strategies also support and encourage
collaborative activity in pursuit of, and to disseminate, the practices and results of
innovation. The ultimate aims were for innovation support to contribute to regional
economic development through additional jobs and GVA. All RDAs provided financial
support for innovation and six gave it priority with GRD specifically mentioned as a
support programme by five.
10.2.32 The evidence shows that in terms of the RDA policies GRD had stimulated the ability
of businesses to manage innovation and technical risk and strengthen the innovation
culture in businesses. Expenditure on R&D and investment in innovation had also
increased along with collaborative activity. In terms of economic impacts some 40%
of businesses had increased their turnover and a third increased their employment.
Overall GRD had stimulated between 6,000 and 9,000 net additional (without and
with multiplier effects respectively) jobs and between £400 million and £600 million
annual Gross Value Added (without and with multipliers), or over £2.5bn cumulatively,
which were distributed across all regions. There were also important spillover effects
for suppliers, (through the purchases of goods and services), customers (through
improved products), competitors (through the dissemination of demonstration effects
of goods/services) and collaborations to help increase regional capability and
capacity. Overall GRD has contributed significantly to the high level economic aims
of the RDAs.
10.3 General conclusions
10.3.1 To recap, the intermediate objectives of Smart/GRD were to:
● increase the productivity and profitability of assisted SMEs
● increase and improve technology use and adaptation, and research and development by individuals and SMEs to improve the overall innovation performance of the SME sector
● increase the number of successful high growth firms that thrive and achieve their potential and to contribute to an enterprise climate that encourages investment in innovative technology by individuals, firms and financial institutions.
PACEC Conclusions
Evaluation of Grant for Research and Development & Smart Page 120
In relation to the first of these objectives to increase productivity and profitability, it
was shown that there had been some improvements in productivity and profitability.
Because of the nature of innovation it can take some time for productivity and
profitability effects to feed through. Table 5.2 shows that 23% of businesses had
improved their productivity and 22% their profitability. This was underpinned by the
evidence that 67% introduced new products/services and there was an improvement
in the ability to manage innovation and risk (78%). However, these benefits might
become more widespread in time because GRD projects – whose intermediate
effects were often stronger than those of Smart projects (see Table 4.5) - have had
relatively less time to bear fruit, in terms of business performance effects. For
example, for Smart businesses, technological problems were overcome for 88%, the
technical feasibility of ideas was clearer for 89% and the commercial feasibility was
clearer for 81% (ie the intermediate effects), while 49% would increase sales, 32%
employment and 25% improved their productivity. The intermediate impacts for the
factors above on the GRD businesses were 93%, 95% and 90% respectively,
indicating that the business performance effects of GRD may in time be greater than
Smart, subject to other factors such as the economic context.
10.3.2 In terms of the adaptation, and use, of technology, some 91% and 84% of businesses
respectively they were able to clarify the technical risk and commercial feasibility of
ideas. Some 67% introduced new products and services. These factors helped
improve the performance of businesses (subject to displacement which the evidence
showed was a small proportion of gross effects) and there were productivity and
profitability gains. Some of the benefits of the technology, in the view of SMEs, were
transferred to suppliers (cited by 11% of assisted GRD businesses), customers
(27%), competitors (13%), and collaborators (17%).
10.3.3 With respect to increasing the number of high growth businesses the evidence shows
that some 70% of GRD supported businesses were seeking to grow (ie 17% rapidly
and 53% moderately), an increase since they were initially funded through GRD. The
programme in the period 1999/00 to 2007/08 had provided grant support for over
3,600 businesses overall. In addition 43% of businesses had experienced / or would
experience growth in turnover, 31% in employment and 22% in their profits. Some
80% had improved their technological capabilities and understanding of innovation
and 67% had developed products and services as a result of the GRD funding.
10.3.4 Accordingly, it is concluded overall, that the schemes have been relatively effective in
relation to their intermediate objectives.
10.3.5 The longer-tem objectives were to:
● overcome the reluctance of SMEs to undertake risky research and development by sharing the costs and the risks associated with these kinds of projects, and to foster a recognition of the importance of maintaining an ongoing programme of research and development.
● encourage others to invest in potentially risky technological R&D through the knowledge that DTI has undertaken a thorough appraisal of the financial and technical aspects of a project and is prepared to invest public money.
PACEC Conclusions
Evaluation of Grant for Research and Development & Smart Page 121
● support firms to prove the technical and commercial feasibility of their idea (Research/Feasibility projects) and to develop prototypes (Development projects).
10.3.6 In relation to the first of these objectives, it has already been concluded above (see
paragraph 10.2.3) that the schemes help to remove a funding gap for R&D /
innovation projects by SMEs arising from risk and uncertainty. Likewise, it was
shown above (Table 4.5) that firms improve their attitude towards R&D and
innovation.
10.3.7 In relation to the second of these objectives, there was some evidence that investors
are more likely to put money into R&D because projects have been thoroughly
appraised (see Table 4.9).
10.3.8 In relation to the third objective, there was strong evidence (see, for example, Table
4.1) that the large majority of both Research/Feasibility projects and
10.3.9 Accordingly, it is concluded overall, that the schemes have been relatively effective in
relation to their longer-term objectives.
PACEC Research Questions
Evaluation of Grant for Research and Development & Smart Page 122
Appendix A Research Questions
A2 Research questions
A2.1 The evaluation should examine whether the objectives for Grant for R&D/Smart have
been met during the evaluation period. As a minimum, the evaluation should address
the following subject areas.
A3 Target outputs
A3.1 Questions:
● To what extent has Grant for R&D/Smart genuinely encouraged technological innovation in SMEs?
● To what extent have Grant for R&D/Smart supported projects involved significant technological innovation? Is there evidence of significant regional variation in this respect?
● To what extent are distinct market failures being addressed by Grant for R&D/Smart?
The rational for Smart/GRD is that there are market failures and imperfections related
to funding which mean that firms often find it difficult to invest in the development of
technologically innovative products and/or processes, which will improve their
competitiveness. The market failure is particularly apparent for SMEs where potential
investors are unaware of how to assess the potential value of a new technology.
Characteristics of market failure indicate that:
● Because of the small size of the firm and the risk surrounding the potential returns (high risk, small reward), funders do not feel able to manage the risks associated with R&D projects. The result of this market failure can be described as an R&D ‘funding gap’ for SMEs
● The return from SME R&D projects tends to be longer term and typically takes several years. Traditional sources of finance for SMEs often prefer faster returns
● SMEs often know their capability to successfully run an R&D project but funders have no knowledge or evidence of this. The costs of “due diligence” can be high compared to the return
● A SME may need a high share of its resources for R&D and returns may be uncertain. Market failures mean it is difficult to insure against risk
● To what extent are the benefits generated by Grant for R&D/Smart genuinely additional? Is there evidence of significant regional variation in this respect? To what extent, if any, have lenders/investors become more willing to risk funding these types of project since the 2001 evaluation?
● How did the Grant for R&D/Smart projects contribute to the RDAs' strategic added value, and how is this measured?
A3.2 Analysis:
● Firms’ characteristics (number of successful and unsuccessful applicants interviewed, age of firms, sector, affiliation, origins, growth objectives, R&D workers and R&D expenditure)
PACEC Research Questions
Evaluation of Grant for Research and Development & Smart Page 123
● Applicants’ objectives in applying for Grant for R&D/Smart, barriers to objectives, alternative and additional funding, additionality of projects.
● Comparison of unsuccessful applicants and grant recipients in terms of background, objectives, R&D workers and expenditure. Ideally, this comparison should cover both during and after the project. The successful contractor will need to correct for R&D level of grant to look at additionality aspects.
● Analysis of strategic added value at Region level for each RDA, following the guidance at pages 19-21 and Figures 2.3 and 2.4 of the Impact Evaluation Framework .
A4 Intermediate effects and outputs
A4.1 Questions:
● To what extent has Grant for R&D/Smart genuinely encouraged technological innovation in SMEs?
● What proportion of Grant for R&D/Smart supported projects have resulted in successful outcomes, and over what time profile have the identified outcomes reached fruition?
● To what extent - both before, during and after completion of supported projects - has Grant for R&D/Smart levered-in private sector finance into supported firms, and what factors influence these investments? Do Grant for R&D/Smart make the business more likely to attract finance than unsupported businesses? Are banks more likely to lend alongside Grant for R&D/Smart or are they more willing to lend money alongside RDAs to help meet the applicants share of the project costs?
● What is the role of the government’s Small Firms Loan Guarantee Scheme in financing projects (including those that went ahead without a grant as well as those that did)?
● Are Grant for R&D/Smart recipients more receptive than other SMEs to equity finance?
● Does Grant for R&D/Smart increase a business’ attractiveness as a target for takeover or merger? Is a trend developing?
● To what extent do Grant for R&D/Smart projects lead to new intellectual property?
● Do businesses go on to claim R&D Tax Credits?
● Is there evidence of any significant change in these effects since April 2005?
A4.2 Analysis :
● Satisfaction of firms’ objectives
● Proportion of projects that resulted in new or improved products and services or processes. Analyse by age and size of business.
● Value of sales (and their distribution). Analyse by age and size of business.
● Barriers to introduction of results into market place
● Other effects (skills, quality, R&D investments, market position, reduced costs, networks and supply chain relationships).
● Search for further finance.
PACEC Research Questions
Evaluation of Grant for Research and Development & Smart Page 124
● Effects and outputs of unsupported projects. What happens to businesses that were refused a grant: i) because the project had too little risk, ii) because the project aims were unachievable, and iii) because there was no perceived market for the results? Did the project go ahead anyway? How was it financed? Was it successful?
A5 Effects on business performance/ behaviour
A5.1 Questions:
● What have been the longer-term impacts of Grant for R&D/Smart, with respect to the development of supported SMEs?
● Is there evidence of significant regional variation in this respect?
● Do supported businesses go on to spend more on R&D than the sector average?
● Do beneficiaries increase their capability to innovate?
● What are the barriers to supported businesses undertaking future R&D projects? Are they different to those evident before the grant?
● Does the grant help SMEs to exploit academic research/leading edge scientific thinking? How can we achieve better outcomes?
A5.2 Analysis:
● Analysis of objective data
● Univariate and multivariate analysis of changes in value-added, export and employment, comparing successful and unsuccessful applicants. The analysis should take into accounts differences in firms’ characteristics between the two groups of applicants which might lead to successful firms’ superior performance even in the absence of participation (selection bias).
● Analysis of subjective data
● Award winners’ views of effects of awards on business performance (value-added, export and employment).
● Use of external consultancy. Participation in other schemes. Engagement with knowledge base. Is there a trend towards greater interaction and sustainable linkages?
A6 Economic Impacts: cost-effectiveness
A6.1 Questions:
● Is Grant for R&D/Smart good value for money, in terms of economic benefits created, taking into account identifiable spill-over effects through the main diffusion mechanisms?
● Which project types (Micro, Development and Research.) offer the best vfm? For the purpose of this piece of analysis, Development and Exceptional Development projects should be classed as one project type. How long does it take for returns to come through?
● What scope is there for improvement in the additional benefit/cost outcomes?
● Are investments in new/pre-start ups more or less likely to yield returns than investments in more established businesses?
● What is the impact on job creation?
PACEC Research Questions
Evaluation of Grant for Research and Development & Smart Page 125
A6.2 Analysis:
● Total economic impact
● Impact on award winners’ turnover, value-added, export and employment (grossed up from sample to total scheme) minus deadweight and displacement.
● Cost of the scheme
● Value for money, Cost per unit of impact, impact per unit of cost by region.
● Comparative impact by project type (Micros v Research v Development (incl Exceptional Development)
● Number of jobs created/Cost per job.
● Conduct a robust quantitative economic impact evaluation of the project /programme that assesses performance including: achievement of gross outputs and expenditure against its approved targets; adjustment from gross to net outputs after accounting for deadweight effects, displacement, substitution and multiplier effects, as appropriate; assessment of the contribution of outputs to attributable outcomes including any unintended outcomes/effects; assessment of additionality and an assessment of wider impacts on each region’s economy or more broadly if significant.
● Assessment of Strategic Added Value - broadly the added value realised through coordinating and influencing activities that result in project /programme outcomes being delivered by others, in addition to outcomes directly delivered through funded activities.
● Value for money assessment: using ‘3Es analysis’ (i.e. economy, efficiency, effectiveness) to produce ratios of costs to inputs (i.e. economy) and ratios of RDA/LDA, total public (including RDA/LDA), private sector and total costs to outputs (i.e. efficiency /cost effectiveness); additionally using economic cost-benefit analysis (HMT Green Book compliant) to provide monetary values for social as well as financial returns on investment that quantifies total costs and benefits over time to produce a cost benefit ratio - whilst making clear the assumptions used to formulate these measures;.
● An assessment of GVA
A7 Wider effects
A7.1 Questions:
● What have been the longer-term impacts of Grant for R&D/Smart with respect to development and dissemination of significant innovation?
● Is there evidence of significant regional variation in this respect?
● What have been the impacts of Grant for R&D/Smart with respect to RDA Regional Economic Strategies?
A7.2 Analysis :
● Spillovers
● Impact of project on firm’s suppliers, customers and competitors in terms of technology, practices, produces, processes, business performance.
● Displacement rates
PACEC Methodology
Evaluation of Grant for Research and Development & Smart Page 126
Appendix B Methodology
This methodology covers survey weighting, the grossing up of economic impacts and
the topics included in the questionnaires for award winners and unsuccessful GRD
applicants.
B1 Survey Weighting
B1.1 For the univariate and bivariate analyses of award winners conducted throughout the
report, the survey results have been weighted so as to be representative of the whole
population of award winners by the following characteristics:
● Scheme (GRD or Smart)
● Result (award or failure)
● Government Office Region
● Type of award (micro, feasibility, development, exceptional)
B1.2 As a result of this weighting procedure, the results quoted in univariate and bivariate
cross-tabulations throughout the report are our best estimates of characteristics of the
population of award winners as a whole.
B1.3 The Effective Sample Size (ESS) is given at the foot of each table. This is the
number of respondents of an un-weighted survey which would give rise to the same
margin of error. The margins of error are given in the following table.
Table A7.1 Margins of Errors for different Effective Sample Sizes
Effective Sample Size Margin of Error Effective Sample Size Margin of Error
15 25% 100 10%
20 22% 125 9%
30 18% 150 8%
40 15% 200 7%
50 14% 300 6%
60 13% 400 5%
70 12% 500 4%
80 11% 800 3%
Source: PACEC
B2 Grossing up of Economic Impacts
Overview
B2.1 The procedure for estimating the total economic impacts of the scheme is outlined
below in Table A7.2.
PACEC Methodology
Evaluation of Grant for Research and Development & Smart Page 127
Table A7.2 Estimating the total economic impacts of the schemes
Gross attributable impacts (i.e. changes in GVA & employment as a result of the support – see chapter 5)
Less
Deadweight – counterfactual (i.e. changes that would have happened anyway – see chapter 5)
Equals
Gross additional impact (i.e. effects attributable to the support)
Less
Displacement (i.e. increases in GVA/employment at the expense of competitors – see chapter 6)
Equals
Net additional effects (or total measurable annual economic impacts without linkages and multipliers)
Plus
Linkages and multipliers (i.e. effects due to purchases by businesses and their staff )
Equals
Full net additional effects (or total measurable annual economic impacts)
Multiplied by
Average duration (in the case of GVA, how many years the effect lasts)
Equals
Cumulative net effect (i.e. total cumulative measurable economic impact)
Source: PACEC
B2.2 All the effects were measured for both Full Time Equivalent (FTE) employment and
Gross Value Added (GVA).
● FTE was estimated as the sum of full time employment, and half of part time employment given by companies in the survey.
● In order to estimate Gross Value Added (GVA), 38% of turnover was used, following an analysis of the 2006 Annual Business Inquiry
B2.3 The gross effects were estimated by directly asking the companies how their
turnover and employment had changed as a result of their involvement in the
scheme.
● In terms of correct attribution, interviewees were clearly asked for the impact as a result of the GRD support it received.
● In cases where interviewees were not willing to quantify the effects, the Yes/No information obtained earlier in the questionnaire was used to decide whether to infer the mean of non zero effects or to infer a zero effect.
● The gross effect includes the effects both of any increased production and of any increased R&D.
PACEC Methodology
Evaluation of Grant for Research and Development & Smart Page 128
B2.4 Deadweight was estimated by asking how much of that change would have occurred
anyway. The difference between these two quantities is the gross additional effect.
● The respondent’s estimate of deadweight was in the light of their assessment as to whether the project would have gone ahead if they had not received a SMART award (in Q25) and in the light of their application for alternative funding which was instead of, rather than as well as, SMART award (Q17). In cases where these were not consistent, the respondent was prompted to revise their estimate of additionality.
B2.5 To measure displacement, companies were asked to what extent their sales would
be taken up by regional and national competitors in the hypothetical event of their
ceasing to trade immediately. This allows us to estimate the proportion of any benefit
to the company which would occur at the expense of its competitors. By subtracting
this from the gross additional effect, we arrive at an estimate of the net additional
effect, or the total measurable economic impact.
B2.6 Because some studies do not consider linkages and multipliers, a separate full net
additional effect is given which includes an estimate of the effect of purchases by
businesses and their staff. The Multiplier used is 1.5 which is taken from English
Partnerships’ “A Standard Approach to Assessing the Additional Impact of Projects.
Sept 2004” and is in line with PACEC’s National Evaluation of Business Links for DTI,
1998.
B2.7 In the case of GVA, the average duration is estimated by asking companies whether
the effect was short (2.5 years) or medium (5 years) term. These effects, discounted
at 3.5% to reflect present value estimates, are then multiplied by the full net additional
effect to obtain an estimate for the cumulative net effect.
Estimating missing values
B2.8 For all members of the population of award recipients for whom an answer was not
given for a particular question (either because they did not take part in the survey or
because they did not respond to that particular question) answers for that question
were inferred from those who did respond. The were based on the
● Result: whether a grant was awarded or not
● Type: Whether the grant was a Micro, Research, Development or Exceptional
● Region: One of the 9 Government Office Regions of England
● Scheme: whether the grant was GRD or SMART
B2.9 In around 90% of cases there were at least four responses14 from other companies
matching the four key characteristics (result, type, region and scheme), and the mean
of these responses was used. In the remaining cases there were at least four
responses from other companies matching three of there characteristics (result, type
and region) and the mean of these responses was used.
14 Reducing the minimum responses below 4 resulted in large changes in the overall estimates, whereas increasing the
minimum responses above 4 had little effect on overall estimates
PACEC Methodology
Evaluation of Grant for Research and Development & Smart Page 129
Summary statistics
B2.10 Once numerical results had been inferred for all known award recipients, summary
statistics are produced in two forms:
● Average employment / GVA effects per project
● Total employment / GVA effects
B2.11 Value for money estimates are then calculated for the following five benefits:
● Net additional FTE and Net additional GVA
● Full net additional FTE and Full net additional GVA
● Cumulative GVA
B2.12 Value for money estimates look at costs in two ways:
● The value of the awards (Cost Effectiveness Analysis)
● The value of the awards in 2008 prices (Cost Benefit Analysis) using ONS inflation deflators.
B3 Questionnaire for Award Recipients
B3.1 The questionnaire for award recipients covered the following areas:
● Project description, cost, start and finish dates
● Award: Type (eg micro and research etc), and value
● Company sector, region, size, growth objectives (pre award and at the time of the evaluation)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6aband)
Table C1.2 Grant offered as a percentage of total project cost
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6perc)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 132
Table C1.3 Financial year in which Project started
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (fy)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (dur)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 133
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q8)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 134
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q9)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q10)
Table C1.8 Employment size (at start of project) – company doing project
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q11)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 135
Table C1.9 Which of the following best describes the status of your business at the time the project started?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
an independent business with no subsidiaries 91 93 90 86 98 88 91 93 94 83
not trading / not yet a business 4 5 4 5 0 9 6 3 4 3
an independent business with subsidiaries 3 2 4 6 0 1 2 5 0 7
a subsidiary of a UK owned business 1 0 1 0 0 0 1 0 0 5
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A2)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 136
Table C1.11 (if a business) When did your business start trading? (Enter year)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12cbnd)
Table C1.12 How would you describe the overall growth objectives of your business at the time the project started
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A1)
Table C1.13 How would you describe the overall growth objectives of your business now
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A2)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 137
Table C1.14 Which of the following happened in the 12 months before the project started?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14A)
Table C1.15 Which of the following happened in the past 12 months
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14B)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 138
C2 Background and objectives to participation
Table C2.1 What were your objectives in participating in the scheme, i.e. what did you want to achieve by taking part?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q15A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 139
Table C2.2 Prior to receiving the GRD grant, what, if anything, prevented you from pursuing the objective(s) you have just mentioned?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Lack of finance / ability to attract finance 86 84 81 90 77 86 88 88 93 85
The cost of research / feasibility 10 24 4 8 19 3 8 7 9 8
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q16A)
Table C2.3 Before applying for a grant from the scheme, did you seek alternative funding (i.e. instead of, not as well as a GRD award) to enable you to undertake your project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q17)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 140
Table C2.4 (Only if Yes ticked in Q17) What type(s) of alternative funding did you seek?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Bank loan 28 26 26 25 47 72 19 10 33 3
Venture capital finance equity / share capital 16 27 25 20 30 0 6 21 6 3
Other(s) 16 9 8 23 5 5 11 12 28 39
Other RDA / public sector funding 15 6 16 20 18 31 15 1 18 17
Money from family / friends 13 13 24 14 6 0 17 17 5 8
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 141
Table C2.5 (Only if Yes ticked in Q17) What type(s) of alternative funding were you made an offer of funding?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Venture capital finance equity / share capital 11 20 25 7 8 0 6 16 4 3
Other RDA / public sector funding 11 7 16 13 0 16 4 1 19 17
Other(s) 11 8 8 15 5 5 15 5 9 36
Money from family / friends 10 7 24 11 6 0 10 12 5 0
Bank loan 8 13 4 13 19 0 6 5 8 0
Bank overdraft 4 14 4 0 12 1 2 0 0 3
Other businesses: equity / share capital 4 6 0 5 0 0 0 12 2 17
Business angel finance: equity / share capital 4 13 9 0 6 0 2 0 0 8
Business angel finance: loan 4 5 8 2 0 0 0 17 0 0
Venture capital finance loan 4 0 6 5 0 0 0 12 6 0
Bank loan with Small Firms Loan Guarantee 2 0 5 0 0 5 0 5 0 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18B)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 142
Table C2.6 (Only if Yes ticked in Q17) What type(s) of alternative funding did you accept?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 11 7 16 11 0 16 4 1 19 18
Money from family / friends 10 7 24 11 6 0 10 12 5 0
Other(s) 10 8 8 15 6 5 16 0 4 32
Venture capital finance equity / share capital 9 15 20 7 0 0 6 15 4 0
Bank loan 7 7 4 11 22 0 4 5 8 0
Business angel finance: equity / share capital 5 13 9 0 6 0 2 0 0 8
Bank overdraft 4 14 4 0 14 1 0 0 0 3
Other businesses: equity / share capital 4 6 0 5 0 0 0 12 2 18
Business angel finance: loan 4 5 8 2 0 0 0 17 0 0
Venture capital finance loan 4 0 6 5 0 0 0 12 6 0
Bank loan with Small Firms Loan Guarantee 1 0 3 0 0 5 0 5 0 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18C)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 143
Table C2.7 (Only if No ticked in Q17) Why did you not seek alternative funding to enable you to undertake your project?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Able to manage without other finance 27 26 25 8 27 6 37 35 37 48
Not aware of any sources of finance 16 0 30 33 27 12 12 16 10 24
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q20A)
Table C2.8 In conjunction with the grant you received, did you seek additional funding (i.e. as well as a GRD award) to enable you to undertake your project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q21)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 144
Table C2.9 (If Yes to Q21) What type(s) of additional funding did you seek?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 19 16 25 5 13 20 11 30 39 23
Bank loan 17 15 12 11 14 17 28 20 20 4
Other(s) 15 14 19 19 11 16 8 6 25 32
Bank overdraft 12 12 0 0 28 16 24 5 7 0
Money from family / friends 10 14 2 0 14 0 7 17 19 28
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 145
Table C2.10 In each case, were you made an offer of funding?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 16 11 19 5 14 20 0 30 39 23
Other(s) 14 10 19 18 12 16 11 6 25 23
Money from family / friends 10 15 2 0 14 0 7 17 19 19
Bank overdraft 10 11 0 0 25 16 24 5 0 0
Bank loan 10 11 12 0 5 9 4 20 20 4
Venture capital finance equity / share capital 6 5 25 12 4 9 0 0 0 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22B)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 146
Table C2.11 If so, did you accept it?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 16 11 19 5 9 21 0 30 39 23
Other(s) 16 10 19 25 18 16 11 6 25 23
Money from family / friends 10 14 2 0 11 0 7 17 19 19
Bank overdraft 10 11 0 0 27 16 24 5 0 0
Bank loan 10 14 12 0 5 9 4 20 20 4
Venture capital finance equity / share capital 6 4 25 12 4 9 0 0 0 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22C)
Table C2.12 If any offers were not accepted in previous Q, why did you not use the alternative funding you were offered?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Not enough money was offered 15 0 0 0 100 100 n/a n/a n/a n/a
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q24A)
C3 Additionality of projects
Table C3.1 Would your project have gone ahead if you had not received a GRD award?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 148
Table C3.2 If definitely, probably or possibly, in what way or ways, if any, would the project have differed, if it had gone ahead?-Timing
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B1)
Table C3.3 How the Scale of the project would have differed
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28C)
Table C3.4 How the Scope of the project would have differed
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B3)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 149
Table C3.5 What types of finance would you have used for your project, if you had not received GRD award?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q29A)
C4 Intermediate effects / outputs
Table C4.1 To what extent did your participation in the GRD scheme satisfy the objectives you were talking about earlier?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q30)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 150
Table C4.2 Did, or will, the project result in any new or improved products and services reaching the market
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A1)
Table C4.3 Did/will the project result in new or improved processes
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A2)
Table C4.4 Did/will the project result in new R&D services/contract research
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A3)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 151
Table C4.5 (if Yes to 31) What was the level of technological innovation in these products / services / processes?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q32)
Table C4.6 What, if anything, has prevented, or will prevent, you from introducing the products / services as a result of the project into the market place?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q34A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 152
invested more in innovation in general 67 79 63 71 70 73 64 63 63 49
invested more in significant technological innovation 62 79 68 67 66 62 47 59 64 46
New intellectual property has been developed 65 73 74 80 69 60 55 54 58 78
New patents have been applied for 47 58 51 53 48 44 43 38 42 53
Intellectual property (e.g. a patent) has been obtained 48 59 56 64 51 50 45 34 38 36
Academic / leading edge research exploited 44 52 44 65 49 32 32 39 48 46
firm has improved its innovation / tech understanding 80 84 89 84 80 61 84 67 78 90
firm has opened up new markets 68 72 55 71 67 77 76 68 66 57
The image of the firm has improved 75 86 73 71 78 77 82 62 72 67
firm is now more inclined to use external business support 58 49 54 76 62 62 65 51 58 51
firm ... more with other firms (eg SMEs) 59 59 72 61 49 62 55 49 62 69
firm ...more with universities and colleges 46 52 39 60 36 40 49 37 52 56
firm ... with other research / technology organisations 44 53 44 57 33 42 44 34 44 53
Other effects 7 4 3 5 4 5 16 4 8 7
None of the above 2 5 0 2 3 0 0 2 7 0
Effective Sample Size 465 42 55 66 46 50 66 76 63 33 Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q35A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 153
Table C4.8 Since participating in the scheme, has your firm sought further finance to enable it to introduce any new or improved products, services or systems into the market place that resulted from the project?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q36)
Table C4.9 (if yes in Q36) What type(s) of further finance have you sought?-Applied for
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 48 60 36 31 55 55 49 51 48 36
Other(s) 19 19 14 20 13 27 18 18 25 5
Venture capital finance equity / share capital 11 2 13 28 17 5 5 1 11 32
Bank loan 8 8 4 12 3 8 10 4 9 9
Business angel finance: equity / share capital 8 13 16 5 4 0 15 8 3 5
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 154
Table C4.10 Offer made:
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 42 60 24 21 33 49 42 55 44 41
Other(s) 17 18 12 18 14 26 18 11 22 6
Venture capital finance equity / share capital 7 2 12 14 6 3 5 0 11 22
Bank loan 6 7 4 10 4 8 5 4 3 6
Business angel finance: equity / share capital 6 13 10 4 4 0 5 9 0 6
Venture capital finance loan 6 4 6 9 4 6 5 6 0 16
Money from family / friends 5 9 10 5 4 0 0 4 8 4
Other businesses: equity / share capital 5 0 16 7 0 0 2 13 0 23
Bank overdraft 3 4 0 0 4 11 5 4 0 0
Business angel finance: loan 2 2 7 1 0 6 0 0 2 4
Bank loan with Small Firms Loan Guarantee 1 0 4 2 0 0 0 0 0 6
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37B)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 155
Table C4.11 Offer accepted:
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Other RDA / public sector funding 39 60 24 15 24 49 42 46 44 41
Other(s) 16 18 12 17 14 26 18 19 12 6
Venture capital finance equity / share capital 6 2 12 11 6 3 5 0 5 20
Money from family / friends 5 9 10 5 4 0 0 4 5 4
Bank loan 5 5 4 6 4 8 5 4 0 6
Other businesses: equity / share capital 5 0 16 7 0 0 2 13 0 23
Business angel finance: equity / share capital 5 13 7 2 4 0 5 9 0 6
Venture capital finance loan 5 4 6 6 4 6 5 6 0 16
Bank overdraft 3 4 0 0 4 11 5 4 0 0
Bank loan with Small Firms Loan Guarantee 1 0 4 2 0 0 0 0 0 6
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37C)
Table C4.12 (Only if No ticked in Q36) Why did you not seek further funding to enable you to undertake your project?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Able to manage without other finance 50 39 59 42 51 40 74 53 34 37
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q39A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 156
Table C4.13 What effect do you believe being a GRD award recipient has had on your firm's ability to obtain finance?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
Made it much easier 16 21 8 26 29 18 9 9 19 14
Made it a little easier 32 34 29 35 35 25 39 32 24 35
Made no difference 50 45 56 38 36 56 51 58 51 51
Made it a little more difficult 1 0 7 0 0 0 0 0 0 0
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q43)
Table C4.14 Has your business claimed R&D tax credits?
Percentage of all respondents (by Region)
Total S East
East Eng
Lon don
S West
W Mids
E Mids
York/ Humb
N West
N East
No 54 47 58 41 52 65 55 59 54 53
Yes other projects only 18 20 14 14 19 16 21 24 12 20
Yes including this project 15 19 18 24 7 6 13 8 23 16
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q44)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 157
C5 Business performance effects & trends
Table C5.1 Which, if any, of the following have been the actual business performance effects of your project to date?
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q45A)
C6 Wider effects
Table C6.1 Which, if any, of the following have been the external effects of your project to date? -The firm’s Customers Have improved their:
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 158
Table C6.2 -The firm’s Suppliers Have improved their:
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50B)
Table C6.3 -The firm’s Competitors Have improved their:
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50C)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 159
Table C6.4 -The firm’s Collaborators Have improved their:
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50D)
C7 Other support used
Table C7.1 Did you access any other support or advice in relation to your project in addition to the support from the scheme and is it likely to continue, e.g. from HEIs, consultants or other Government schemes?
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53A)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 160
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53B)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53D)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 161
C8 Assessment of the scheme
Table C8.1 How would you assess the following aspects of the scheme? Mean Score (1=Very poor, 2=poor, 3=fair, 4=good 5=very good).
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A3)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A4)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A5)
PACEC Survey of award recipients by region
Evaluation of Grant for Research and Development & Smart Page 163
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A6)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A7)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A8)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 164
Appendix D Survey of award recipients by sector/scheme
D1 Introduction
D1.1 The sectoral breakdown used is shown in the following table
Table D1.1 Sectors
Short name Long Name Standard Industrial Code
(SIC03)
Man Elec Manufacture of Electrical Items 30-33
Man Metal Manufacture of other Metallic Items 27-29,34,35
Man Oth Manufacture of non metallic items and other production e.g. Chemicals, Food, Textiles, Construction
1-26,36-45
Serv R&D Service: Research and Development 73
Serv Soft Service: Computer software 72
Serv Oth Other service: e.g. Business services and Health 50-71,74-95
Source: PACEC
D2 Project details & company background
Table D2.1 Amount of grant offered (£k)
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6aband)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 165
Table D2.2 Grant offered as a percentage of total project cost
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (q6perc)
Table D2.3 Financial year in which Project started
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (fy)
Table D2.4 Project duration (Months)
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (dur)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 166
Table D2.5 Company Sector
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q8)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 167
Table D2.6 Type of award applied for
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q9)
Table D2.7 Region
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q10)
Table D2.8 Employment size (at start of project) – company doing project
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q11)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 168
Table D2.9 Which of the following best describes the status of your business at the time the project started?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
an independent business with no subsidiaries 91 94 85 92 89 99 89 91 93 89
not trading / not yet a business 4 2 8 5 8 0 3 5 2 5
an independent business with subsidiaries 3 4 3 1 1 0 6 2 4 4
a subsidiary of a UK owned business 1 0 2 1 0 0 0 0 1 1
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A1)
Table D2.10 Which of the following best describes the status of your business now?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
an independent business with no subsidiaries 83 89 77 87 82 84 73 80 88 88
not trading / not yet a business 9 1 14 9 11 7 14 12 3 2
an independent business with subsidiaries 5 5 4 4 2 4 8 4 5 8
a subsidiary of a UK owned business 2 1 3 0 3 1 2 2 1 1
a subsidiary of an overseas owned business 1 1 1 0 1 2 3 1 1 1
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12A2)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 169
Table D2.11 (if a business) When did your business start trading? (Enter year)
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q12cbnd)
Table D2.12 How would you describe the overall growth objectives of your business at the time the project started
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A1)
Table D2.13 How would you describe the overall growth objectives of your business now
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q13A2)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 170
Table D2.14 Which of the following happened in the 12 months before the project started?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14A)
Table D2.15 Which of the following happened in the past 12 months
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q14B)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 171
D3 Background and objectives to participation
Table D3.1 What were your objectives in participating in the scheme, i.e. what did you want to achieve by taking part?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q15A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 172
Table D3.2 Prior to receiving the GRD grant, what, if anything, prevented you from pursuing the objective(s) you have just mentioned?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Lack of finance / ability to attract finance 86 89 86 84 89 91 79 86 87 85
The cost of research / feasibility 10 12 12 6 8 13 7 6 18 17
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q16A)
Table D3.3 Before applying for a grant from the scheme, did you seek alternative funding (i.e. instead of, not as well as a GRD award) to enable you to undertake your project?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q17)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 173
Table D3.4 (Only if Yes ticked in Q17) What type(s) of alternative funding did you seek?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Bank loan 28 29 19 39 25 17 35 33 23 19
Venture capital finance equity / share capital 16 8 3 17 23 34 23 13 16 25
Other(s) 16 17 25 7 20 11 16 11 27 20
Other RDA / public sector funding 15 10 15 22 19 15 13 19 10 10
Money from family / friends 13 13 14 5 18 12 17 10 17 14
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 174
Table D3.5 (Only if Yes ticked in Q17) What type(s) of alternative funding were you made an offer of funding?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Venture capital finance equity / share capital 11 5 0 15 15 27 15 7 14 19
Other RDA / public sector funding 11 4 12 13 19 13 10 13 9 8
Other(s) 11 10 17 2 19 6 14 3 25 20
Money from family / friends 10 8 12 5 15 12 11 8 11 14
Bank loan 8 9 5 3 5 9 14 6 11 10
Bank overdraft 4 9 2 0 5 3 0 2 9 6
Other businesses: equity / share capital 4 2 10 5 0 3 0 4 4 2
Business angel finance: equity / share capital 4 1 0 4 8 8 11 1 4 14
Business angel finance: loan 4 9 0 1 4 0 8 3 1 7
Venture capital finance loan 4 6 3 0 0 10 8 3 3 7
Bank loan with Small Firms Loan Guarantee 2 4 0 0 0 2 3 1 0 5
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18B)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 175
Table D3.6 (Only if Yes ticked in Q17) What type(s) of alternative funding did you accept?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 11 4 12 13 20 11 10 14 9 7
Money from family / friends 10 8 12 5 16 12 11 8 11 14
Other(s) 10 6 15 2 18 6 14 1 22 20
Venture capital finance equity / share capital 9 2 0 12 10 27 15 6 11 15
Bank loan 7 8 5 0 5 9 14 6 8 9
Business angel finance: equity / share capital 5 1 0 4 9 8 11 1 4 14
Bank overdraft 4 10 0 0 5 3 0 1 9 6
Other businesses: equity / share capital 4 2 10 5 0 3 0 4 4 2
Business angel finance: loan 4 9 0 1 4 0 8 3 1 7
Venture capital finance loan 4 6 3 0 0 10 8 3 3 7
Bank loan with Small Firms Loan Guarantee 1 4 0 0 0 0 3 1 0 4
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q18C)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 176
Table D3.7 (Only if No ticked in Q17) Why did you not seek alternative funding to enable you to undertake your project?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Able to manage without other finance 27 30 25 30 19 20 32 25 35 30
Not aware of any sources of finance 16 12 22 13 22 13 17 16 15 14
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q20A)
Table D3.8 In conjunction with the grant you received, did you seek additional funding (i.e. as well as a GRD award) to enable you to undertake your project?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q21)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 177
Table D3.9 (If Yes to Q21) What type(s) of additional funding did you seek?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 19 20 23 29 15 23 0 22 22 9
Bank loan 17 7 15 31 14 10 27 14 12 28
Other(s) 15 14 11 2 24 12 41 13 22 14
Bank overdraft 12 23 12 2 10 10 5 15 6 9
Money from family / friends 10 6 9 14 15 17 0 10 14 7
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 178
Table D3.10 In each case, were you made an offer of funding?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 16 15 19 21 15 23 0 18 21 6
Other(s) 14 14 7 5 21 9 41 13 21 11
Money from family / friends 10 6 10 15 12 17 0 9 15 8
Bank overdraft 10 22 9 0 10 10 5 14 5 8
Bank loan 10 6 15 4 9 10 18 9 8 12
Venture capital finance equity / share capital 6 6 12 6 4 6 0 6 9 4
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22B)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 179
Table D3.11 if so, did you accept it?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 16 16 19 20 12 23 0 18 20 6
Other(s) 16 14 7 7 21 14 41 14 23 11
Money from family / friends 10 5 10 14 12 17 0 10 12 8
Bank overdraft 10 22 9 0 10 10 5 14 5 8
Bank loan 10 6 15 8 9 10 18 10 11 12
Venture capital finance equity / share capital 6 6 12 6 4 6 0 6 9 4
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q22C)
Table D3.12 If any offers were not accepted in previous Q, why did you not use the alternative funding you were offered?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Not enough money was offered 15 0 0 0 35 0 100 37 0 0
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q23A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 180
Table D3.13 (Only if No ticked in Q21) Why did you not seek alternative funding to enable you to undertake your project?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Able to manage without other finance 70 71 66 72 64 67 77 75 59 59
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q24A)
D4 Additionality of projects
Table D4.1 Would your project have gone ahead if you had not received a GRD award?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 181
Table D4.2 If definitely, probably or possibly, in what way or ways, if any, would the project have differed, if it had gone ahead?-Timing
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B1)
Table D4.3 -Scale
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28C)
Table D4.4 -Scope
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q28B3)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 182
Table D4.5 What types of finance would you have used for your project, if you had not received GRD award?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other(s) 31 41 31 12 40 22 32 37 18 34
Bank loan 18 14 30 17 15 15 17 18 20 17
Money from family / friends 9 10 3 9 14 13 4 7 11 9
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q29A)
D5 Intermediate effects / outputs
Table D5.1 To what extent did your participation in the GRD scheme satisfy the objectives you were talking about earlier?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q30)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 183
Table D5.2 Did, or will, the project result in any new or improved products and services or processes reaching the market, or in offering R&D services / contract research to 3rd parties?-Product(s) / Service(s):
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A1)
Table D5.3 -Process(es):
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A2)
Table D5.4 -R&D services/contract research:
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q31A3)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 184
Table D5.5 (if Yes to 31) What was the level of technological innovation in these products / services / processes?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q32)
Table D5.6 What, if anything, has prevented, or will prevent, you from introducing the products / services as a result of the project into the market place?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q34A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 185
Table D5.7 Actual or Likely
Percentage of all respondents (by sector and scheme)
invested more in innovation in general 67 75 62 64 61 66 67 65 79 60
invested more in significant technological innovation 62 72 59 54 58 63 64 61 72 58
New intellectual property has been developed 65 64 59 64 82 69 61 56 85 80
New patents have been applied for 47 48 43 46 70 39 40 37 66 65
Intellectual property (e.g. a patent) has been obtained 48 45 45 46 62 39 55 42 59 61
Academic / leading edge research exploited 44 49 34 28 62 48 53 41 52 48
firm has improved its innovation / tech understanding 80 82 72 78 86 78 82 77 85 87
firm has opened up new markets 68 78 67 69 56 76 59 64 82 73
The image of the firm has improved 75 83 66 64 73 79 82 75 82 66
firm is now more inclined to use external business support 58 71 50 53 61 60 52 57 62 56
firm ... more with other firms (eg SMEs) 59 67 47 56 70 63 56 60 60 56
firm ...more with universities and colleges 46 49 45 33 55 47 55 46 57 35
firm ... with other research / technology organisations 44 52 38 35 57 45 41 42 54 43
Other effects 7 7 3 9 8 7 6 5 8 12
None of the above 2 0 3 4 0 4 4 3 1 2
Effective Sample Size 465 122 98 85 56 63 53 271 135 78 Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q35A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 186
Table D5.8 Since participating in the scheme, has your firm sought further finance to enable it to introduce any new or improved products, services or systems into the market place that resulted from the project?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q36)
Table D5.9 (if yes in Q36) What type(s) of further finance have you sought?-Applied for
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 48 61 38 51 33 51 39 54 45 22
Other(s) 19 13 27 18 17 9 30 14 27 28
Venture capital finance equity / share capital 11 7 8 7 25 12 10 10 11 14
Bank loan 8 3 7 13 5 8 15 6 8 13
Business angel finance: equity / share capital 8 10 6 4 15 2 10 9 1 16
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 187
Table D5.10 Offer made:
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 42 56 33 49 22 41 31 46 39 22
Other(s) 17 11 21 16 19 10 31 13 23 28
Venture capital finance equity / share capital 7 2 6 7 18 8 10 6 7 15
Bank loan 6 3 7 9 5 4 7 5 5 10
Business angel finance: equity / share capital 6 10 6 1 12 0 0 6 1 12
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37B)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 188
Table D5.11 Offer accepted:
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Other RDA / public sector funding 39 53 33 49 12 41 31 44 39 16
Other(s) 16 9 17 16 23 10 31 11 22 33
Venture capital finance equity / share capital 6 2 6 6 17 8 5 5 6 13
Money from family / friends 5 5 9 5 0 3 2 4 9 2
Bank loan 5 3 5 9 2 2 7 4 4 7
Other businesses: equity / share capital 5 2 12 1 14 6 0 6 3 6
Business angel finance: equity / share capital 5 8 6 1 12 0 0 5 1 10
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q37C)
Table D5.12 (Only if No ticked in Q36) Why did you not seek further funding to enable you to undertake your project?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Able to manage without other finance 50 59 51 55 32 53 41 52 48 48
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q39A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 189
D6 All firms
Table D6.1 What effect do you believe being a GRD award recipient has had on your firm's ability to obtain finance?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
Made it much easier 16 16 9 12 22 28 15 13 26 15
Made it a little easier 32 36 29 33 25 24 39 27 42 43
Made no difference 50 48 56 55 52 45 44 59 31 38
Made it a little more difficult 1 0 4 1 2 0 0 1 0 4
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q43)
Table D6.2 Has your business claimed R&D tax credits?
Percentage of all respondents (by sector and scheme)
Total Man Elec
Man Metal
Man Oth
Serv R&D
Serv Soft
Serv Oth
Smart GRD pre
GRD post
No 54 49 62 51 56 52 57 58 44 50
Yes other projects only 18 19 13 21 17 20 18 17 20 19
Yes including this project 15 18 15 13 14 12 15 13 23 15
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q44)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 190
D7 Business performance effects & trends
Table D7.1 Which, if any, of the following have been the actual business performance effects of your project to date?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q45A)
D8 Wider effects
Table D8.1 Which, if any, of the following have been the external effects of your project to date? -The firm’s Customers Have improved their:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 191
Table D8.2 -The firm’s Suppliers Have improved their:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50B)
Table D8.3 -The firm’s Competitors Have improved their:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50C)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 192
Table D8.4 -The firm’s Collaborators Have improved their:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q50D)
D9 Other support used
Table D9.1 Did you access any other support or advice in relation to your project in addition to the support from the scheme and is it likely to continue, e.g. from HEIs, consultants or other Government schemes?
Percentage of all respondents (by sector and scheme)
A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53A)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 193
Table D9.2 If yes, what support was used?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53B)
Table D9.3 Is it Likely to continue?
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q53D)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 194
D10 Assessment of the scheme
Table D10.1 How would you assess the following aspects of the scheme? Mean Score (1=Very poor, 2=poor, 3=fair, 4=good 5=very good).
Statistics of all respondents. (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A1)
Table D10.3 -Support from the GRD team:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A3)
Table D10.5 -Amount of grant:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A4)
Table D10.6 -What the grant can be spent on:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A5)
PACEC Survey of award recipients by sector/scheme
Evaluation of Grant for Research and Development & Smart Page 196
Table D10.7 -The flexibility of the scheme:
Percentage of all respondents (by sector and scheme)
Respondents could select more than one option; so percentages in any column may sum to more than 100 A number is shown in bold where, taking into account the margin of error due to sampling, we are 95% certain that it is different from the number in the left hand total column (using a Chi-Squared statistical test) Source: PACEC Survey (Q54A7)
Table D10.9 -Benefits to your business:
Percentage of all respondents (by sector and scheme)
