creating innovation in small and medium-sized enterprises

8/6/2019 Creating Innovation in Small and Medium-sized Enterprises

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Working Paper: May 2011

Creating Innovation in Smalland Medium-sized EnterprisesEvaluating the short-term effectsof the Creative Credits pilot

Hasan Bakhshi, John Edwards, Stephen Roper,Judy Scully and Duncan Shaw


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Executive summary

Creative Credits is a new way ofsupporting innovation in SMEs

Creative Credits is a new business-to-businessvoucher mechanism designed to encouragesmall and medium-sized enterprises (SMEs) toinnovate. SMEs receive credits worth 4,000,which they can use to purchase a variety ofcreative services from creative businesses. Thisworking paper looks at the short-term impactof the Creative Credits pilot which operated inManchester City Region in the North West ofEngland from September 2009 to September2010. Future research will address whether the

innovation capacity of SMEs is improved bythe transfer of knowledge, skills and workingpractices from the creative businesses.

Creative Credits differs from other enterprisesupport schemes in key ways.

First, it is the only dedicated business-to-business innovation vouchers scheme in theUK. Previous innovation voucher schemes havefocused primarily on stimulating knowledgetransfer from universities to businesses.

Second, Creative Credits makes use of an onlineCreative Gallery to market potential creativeservice providers to the SMEs receiving credits.As such, the scheme is substantially cheaper torun than other business support schemes whichrely on costly administration and brokerage.

Third, the evaluation of the Manchester pilot isas innovative as the scheme itself: it is structuredas a randomised control trial, with credits beingrandomly allocated to eligible businesses.Applicants not allocated credits are also tracked

over time, which provides a control group,enabling the extent to which Creative Creditsgenuinely added value to be evaluated rigorously.

Creative Credits is intended to addressbarriers to innovation in SMEs

Creative Credits like other innovation voucherschemes is intended to address barriers toinnovation in SMEs which result in inadequatecollaboration. These may reect behaviouralfailures which are prevalent in smallerbusinesses, including inertia, excess levels ofrisk aversion and a tendency towards myopia.Theory suggests that creative businesses maybe less prone to such behavioural failures, andthere is some empirical evidence suggestingthat rms that make greater use of services

from the creative industries have superiorinnovation performance. Creative Credits aimsto boost innovation in SMEs by directly linkingthem to creative businesses.

Creative Credits has proven popularwith Manchesters businesses

The Manchester pilot suggests that the schemeis popular with both creative businessesand SMEs. A total of 300 eligible creativebusinesses from Manchester City Regionapplied to service credits on the Gallery andover 670 SMEs applied to receive credits(perhaps as much as one in eight of all theeligible population of rms). Being SMEsthemselves, the creative service businessesshared many characteristics with the SMEsthat were (randomly) awarded credits: theywere by and large micro and small businessesservicing largely domestic markets and hadexperienced high rates of growth in recentyears. But creative businesses were heavier

investors in research and development, ICT andother forms of innovation expenditure. Whilethere appeared to be little difference in their


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past innovationperformance (as measured bythe percentage of businesses introducing newproducts or services in recent years, or thepercentage of sales that could be attributed tonew products), creative rms were also morelikely to cooperate on innovation with clientsand with other businesses in their industry.

The majority of projects supported by CreativeCredits involved development of SMEswebsites, followed by marketing and videoproduction activities. This no doubt says muchabout the needs of Manchesters SMEs in2009/10. But it also reects the comparativestrengths of Manchesters digital mediaindustries, as represented by their strongpresence on the Creative Gallery.

For every ten credits awarded, eightwere used to support B2B relationshipsinvolving creative servicers that wouldnot have formed in the absence of thescheme

Almost 55 per cent of creative businessesservicing credits claimed to have serviced anSME that was in a different sector from theirusual clients, and over 41 per cent describedthe SME as being outside their usual businessnetworks. Consistent with this, comparing

the behaviour of rms that were not awardedcredits (the control group) with those that were(the treatment group), suggests that CreativeCredits had very strong project additionality.Specically, for every ten credits awarded, eightwere used to create new B2B relationshipsinvolving creative services that would nothave formed in the absence of the scheme,at least in the four to ve-months stipulatedproject completion period. This is broadly thesame as the level of short-term additionalityidentied in the pilot for the well-known Dutchinnovation vouchers programme.

There is evidence of short-termcommercial benets

The Creative Credits projects proceeded largelyas planned, with only 3 per cent deviating fromtheir original plan. Ninety-three per cent ofprojects achieved either all or some of theirinnovation objectives, with around 25 per centbeing associated with other unanticipated

benets. It is too early to establish the impactthat Creative Credits had on the commercialperformance of the businesses involved, but

back of the envelope calculations suggestthat the scheme may have generated short-term additional sales of 514,000 (an averageof 3,430 per credit). Alongside the short-termsales benets, there are also signs that CreativeCredits might have generated short-termstrategic and behavioural gains for the SMEs.

Just over 80 per cent of businesses awardedcredits claimed that the projects had increasedtheir innovative strengths and over three-quarters said that it had stimulated other ideasfor new innovation projects. Three-quarters ofSMEs agreed that their businesss attitude toinnovation had become more positive throughengaging with the scheme.

The ongoing qualitative interviews pointto some mechanisms through which thesebenets occur (e.g. the transfer of skills andknowledge that relate to the process and

content of creativity), but also indicate wherethere are obstacles (e.g. differences in howcreative and non-creative businesses perceivethe value of creativity).

A complete assessment of the schemeseconomic benets must await thelongitudinal evaluation, the full resultsof which will be published by NESTA

This working paper assesses the impacts of theCreative Credits pilot as they stood at the endof the four to ve-month period over which theCreative Credits projects had to be completedunder the rules of the scheme. Many rmsreceiving credits indicated that they expectedthe benets of their Creative Credits projectsto increase, in some cases substantially, in thefuture. At the same time, the surveys suggestthat in the longer term a number of SMEsawarded credits would have proceeded withtheir creative projects in any case. How thesetwo effects net out in quantitative terms, andwhat this means for the long-term additionalimpact of Creative Credits, is something we willuncover in our future research.


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4

Contents

Creating Innovation in Small and Medium-sized Enterprises

Evaluating the short-term effects of the Creative Credits pilot

Part 1: Introducing Creative Credits 6

1.1 Introduction 6

1.2 The rationale for Creative Credits 6

1.3 Creative Credits implementation 8

1.4 Evaluating the Creative Credits pilot scheme 9

Part 2: Proling the companies participating in Creative Credits 11

2.1 Introduction 12

2.2 Characteristics of Creative Credits applicants 12

2.3 Innovation among applicants and non-applicants 14

2.4 Comparing Awarded and Creative Servicer rms 18

Part 3: Proling the Creative Credits projects 21

3.1 Introduction 21

3.2 The Creative Credits projects 21

3.3 Creative Credits projects the Creative Servicers view 22

Part 4: Short-term additionality 24

4.1 Introduction 24

4.2 Project additionality of Creative Credits 25

4.3 Awarded rms views of the projects 27

4.3.1 Projects and planning 27

4.3.2 Sales impacts 27

4.3.3 Strategic and behavioural benets 29

Part 5: Key qualitative ndings 31

5.1 Introduction 31

5.2 The research process: Moving from interviews to results 31

5.3 Findings 32

5.4 Conclusion 36

Part 6: Key ndings and next steps 37

6.1 Introduction 37

6.2 Key ndings 37


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6.3 Next steps 38

6.4 Evaluation lessons 39

6.5 Delivery and brokerage lessons 39

Appendix 1: Description of the non-applicants survey 40

Appendix 2: Estimating short-term additionality full models 41

Appendix 3: Qualitative ndings 44

Acknowledgements 48

NESTA is the UKs foremost independent expert on how innovation cansolve some of the countrys major economic and social challenges. Its work isenabled by an endowment, funded by the National Lottery, and it operatesat no cost to the government or taxpayer.

NESTA is a world leader in its eld and carries out its work through a blendof experimental programmes, analytical research and investment in early-stage companies. www.nesta.org.uk


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Part 1: Introducing Creative Credits

1.1 Introduction

Creative Credits is a new business-to-business(B2B) voucher mechanism designed toencourage small and medium-sized enterprises(SMEs) to work innovatively with creativecompanies. Businesses receive credits worth4,000, which they must match with at least1,000, to spend with creative rms on avariety of creative services.

This working paper looks at the short-termimpact of the pilot scheme which operatedin the Manchester City Region in the North

West of England from September 2009 toSeptember 2010, and was funded by NESTA,Manchester City Council, the North WestDevelopment Agency, the Economic and SocialResearch Council (ESRC) and the Arts andHumanities Research Council (AHRC). Thelonger-term research project, of which thispaper is the rst published output, addresseswhether the innovation capacity of SMEs canbe improved by the transfer of knowledge,skills and working practices from the creativeindustries. This follows previous research byNESTA suggesting that rms accessing creativeservices are, other things equal, more likelyto innovate.1 This working paper looks inparticular at whether the vouchers stimulatedbusiness-to-business (B2B) relationshipsbetween SMEs and creative businesses thatwould not otherwise have formed.

Creative Credits is innovative in four ways.First, it is the only B2B innovation vouchersscheme in the UK. Previous innovation voucherschemes have focused primarily on linkingSMEs with universities and public research

institutions rather than other businesses.Second, Creative Credits makes use of an onlineCreative Gallery to market potential creative

industry partners to the SMEs receiving credits.This is intended to reduce administration and

brokerage. A third innovative aspect of thepilot is that it is structured as a randomisedcontrolled trial, with credits being allocatedrandomly to eligible rms. Applicants notallocated credits are also tracked, whichprovides a control group, enabling the extentto which the scheme genuinely added valueto be evaluated rigorously. Finally, the pilotadopts a mixed-methods approach to theevaluation, combining the quantitativeapproach above to assessing additionalitywith qualitative perspectives on the schemes

behavioural impacts.

In the remainder of this section we set thescene for the evaluative material in latersections. Part 1.2 outlines the rationale forthe Creative Credits scheme and its t withManchester City Regions economic strategy.Part 1.3 provides a more detailed overview ofthe scheme and section 1.4 outlines the pilotevaluation.

1.2 The rationale for Creative Credits

Creative Credits like other innovation voucherschemes is intended to address systemicfailures linked to a lack of collaboration ininnovation. Such systemic or network failurescan lead to sub-optimal levels of innovationactivity.2

These systemic failures have been linked tobehavioural failures which may be particularlyprevalent among SMEs,3 including inertia,

excessive risk aversion and myopia. Inertiais the tendency to accept the status quo,no matter how strong the case for change

1. Bakhshi H., McVittie E. and

Simmie J. (2008) CreatingInnovation: Do the creativeindustries support innovationin the wider economy?London: NESTA. Availableat: http://www.nesta.org.uk/library/documents/Report%20-%20Creative%20Innovation%20v5.pdf ; alsoPotts J. and Morrison K.(2009) Nudging Innovation:Fifth generation innovation,behavioural constraints, andthe role of creative business considerations for the NESTAinnovation vouchers pilot.London: NESTA. Available at:http://www.nesta.org.uk/nudging-innovation

2. Woolthuis, R.K., Lankhuizen,M. and Gilsing, V. (2005)A system failure frameworkfor innovation policy design.Technovation. 25, pp.609-619; also Carlsson, B. AndJacobsson, S. (1997) In searchof useful public policies: keylessons and issues for policymakers. In Carlsson, B. (Ed.)(1997) Technological systemsand industrial dynamics.Dordrecht: Kluwer AcademicPublishers.

3. Potts J. and Morrison K.(2009) Nudging Innovation:Fifth generation innovation,behavioural constraints, and

the role of creative business considerations for the NESTAinnovation vouchers pilot.London: NESTA.


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might be. Excessive risk aversion refers tohow cognitive biases push owners of SMEsto make choices that predict more certainoutcomes, particularly at the boundariesof their knowledge or experience. This isa phenomenon that potentially aficts alldecision-makers and organisations, but is

arguably made worse by the small often oneperson top teams of SMEs. Myopia is thetendency to opt for short-term gain at theexpense of longer-term, strategic decisions.These behavioural failures may contribute toreluctance on the part of SMEs to undertakeinnovation.

Interventions such as the Creative Creditsscheme, which encourage collaboration withcreative businesses, aim to overcome thesebehavioural failures, and thereby nudgeSMEs into innovating. The particular focus

of Creative Credits linking SMEs with theproviders of creative services is prompted bytheoretical arguments that creative businessesare less prone to such behavioural failures4and empirical research suggesting that rmsthat make greater use of services from thecreative industries have superior innovationperformance.5

The logic model6 for the Creative Creditsscheme is shown in Figure 1. This links thejustication for public intervention in the SME-creative relationship, the schemes objectives,the process for the initiative, its outputs andintended longer-term outcomes.

Figure 1 sets out the pilots objective tostimulate new SME-creative partnershipsin order to improve knowledge transfer andinnovation, and to act as a catalyst for longer-term behavioural change.

Figure 1 also makes clear the assumptionsby which the intervention achieves thedesired programme objectives. Here the keyassumption is that the award of a credit willhelp SMEs become more entrepreneurial,less risk-averse and more open to new ideasthrough new collaborations with creative

partners. For the SMEs, the credit providesa stimulus and incentive to engage with acreative partner. For the creative partner thecredit represents a new business opportunity.

The nal element of theCreative Credits logicmodel relates to the specication of schemeoutputs and their relationship to longer-termoutcomes. In the short term, outputs from the

4. Ibid.

5. Bakhshi H., McVittie E. andSimmie J. (2008) CreatingInnovation: Do the creativeindustries support innovationin the wider economy?London: NESTA; also Muller,K., Rammer, C. and Truby, J.(2009) The role of creativeindustries in industrialinnovation. Innovation:Management, Policy &Practice. 11:2, pp.148-68.

6. Donaldson, S.I. and Gooler,L.E. (2003) Theory-driven

evaluation in action: lessonsfrom a $20 million statewidework and health initiative.Evaluation and ProgramPlanning. 26, pp.355-366.

Figure 1: Logic Model for Creative Credits

Low levels of

small business-

creatives

interaction

Motivation and

search barriers

Situation

Funding for

Creative

Credits

Partner

commitment

Inputs

Vouchers will

stimulate

linkages and

innovation

Assumptions

Contingency

factors which

may influence

outputs into

outcomes

Environment

Awareness

Attitudes

Networks

Opinions

Short

Behaviour

Practices

Innovation

Innovation

Collaboration

Outcomes-Impact

Medium

Bottom line

impacts

Long

Demand-led

allocation

of CreativeCredits with

random award

Manchester

City Region

SMEs andCreative

Companies

How we operate Who we impact

Outputs


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pilot will be measured in terms of increasedlevels of SME-creative interaction (relativeto the control group). Our prior assumption,based on the theoretical and empiricalgrounds discussed earlier, is that in time thiswill be reected in increased innovation andenhanced business performance in SMEs,

though this is the subject of the longer-termresearch. Behavioural outcomes might alsobe anticipated, encouraging sustained co-operation and a willingness to engage in otherSME-creative interaction. The logic modelmakes clear, however, that such outcomes willbe contingent on other factors inuencingrms innovation processes, as well as othermarket and contextual factors.

The evaluation approach we adopt in theCreative Credits pilot aims to test the logicmodel in Figure 1. We focus on the causative

elements of the schemes programme theoryrather than undertaking a more normativeapproach to evaluation of the schemes goalsand objectives. In other words, our objectiveis not just to assess programme outcomesbut also to consider whether these outcomesare being achieved through the mechanismsenvisaged in the underlying programmetheory.7 This requires a theoretical analysisof process and causal mechanisms alongsidethe evaluation of outcomes. In empiricalterms, it underlines the value of a mix of both

quantitative and qualitative methods.8

1.3 Creative Credits implementation

Creative Credits was implemented as a regionalpilot focused on the Manchester City Region(MCR). The Manchester Independent EconomicReview (MIER) had previously investigatedcreativity and innovation in the region andfound that large numbers of creative rms,while well connected to rms outside theregion, were poorly integrated into localsupply chain networks.9 The MIER thereforeargued that there may be large and immediatepayoffs to innovation if creative businessescould be better integrated into its supplychain networks. In the MCR the CreativeCredits pilot operated alongside the NorthWest Development Agencys own establishedinnovation voucher scheme which, moretraditionally, focused on knowledge transferbetween universities and SMEs.

A total of 150 Creative Credits worth 4,000each were available within the pilot scheme,with an intention to distribute these equally

between two waves. The rst wave opened forapplications in September 2009 and the secondin February 2010 (Table 1). The scheme waspromoted and marketed through a number ofchannels:

PR Campaign North West News, Crains,

North West Insider, Manchester EveningNews, Metro AM.

Above the Line campaign Crains, NorthWest Insider Networks.

Networks Business Link advisors, NWDAnetworks, Manchester City Council networks,other business consultants and nancialadvisors to SMEs.

Online posts on LinkedIn and Facebook.

Direct emails, telemarketing.

Launch event.

Website.

In promoting and advertising the scheme, carewas taken to avoid selection biases. For example,the telesales companies who were telemarketingthe scheme were encouraged to use a strictlyrandom method in identifying which SMEs tocall. More than 2,000 rms enquired about the

scheme over the two waves.

Online applications from SMEs were checkedfor eligibility by a NESTA project manager.Creative Creditswas open to SMEs in almostany sector of the economy.10 The eligibilitycriteria for the scheme (see also Appendix 1)had a number of further dimensions:

Geographical coverage SMEs and creativerms had to have their main ofce locatedin either the City of Manchester, the City ofSalford, Stockport, Tameside and Trafford(Greater Manchester South), Bolton, Bury,Oldham, Rochdale and Wigan (GreaterManchester North), Congleton, Maccleseld,Vale Royal or Warrington.

Size range SMEs and creative rms hadto have fewer than 250 employees andturnover of less than 46 million at the timeof application.

Legal status both SMEs and creativerms had to be either limited liability

companies, limited liability partnerships,general partnerships (added in Wave Two) orindustrial or provident societies.

7. Chen, H.-T. (1990) Theory-

Driven Evaluations. London:Sage.

8. Jackson, A. (2001) Anevaluation of evaluation:problems with performancemeasurement in smallbusiness loan and grantschemes. Progress inPlanning. 55, pp.1-64; alsoWhite, H. (2008) Of probitsand participation: The use ofmixed methods in quantitativeimpact evaluation. IDSBulletin. 39, pp.98-109.

9. Manchester IndependentEconomic Review (2009)Innovation, Trade andConnectivity. Manchester:

MIER. Available at: http://www.manchester-review.org.uk/projects/view/?id=719

10. For state aid related reasons,rms in primary industries(Agriculture, Forestry orFisheries) were excludedfrom the scheme. Businessesin the creative industrieswere also barred fromapplying.


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VAT-registered SME applicant rms had tobe registered for VAT.

A total of 672 SMEs made eligible applicationsfor theCreative Creditsscheme: 312 in the rstwave and 501 in the second; 141 applying inboth waves.

A lottery was held to allocate Creative Creditsto rms with 75 rms in each wave beingnotied that they had been awarded a credit.In total, 22 per cent of the businesses thatapplied were awarded a credit. Awards weremade to rms in the rst wave in October 2009(Table 1).

SMEs awarded credits were encouraged toidentify a creative partner and develop acollaborative project proposal. To help with thisprocess a web-based marketplace a CreativeGallery of approved creative rms (whowere responsible themselves for uploadinginformation and details of sector and workexperience) was designed and made availableto all eligible SMEs.11 The businesses wereencouraged to select their preferred supplierfrom the Gallery by themselves. SMEs werepermitted to make independent contact withcreative suppliers on the Gallery; creativebusinesses on the Gallery were also permittedto make direct contact with SMEs, as normalbusiness practice.12 The Gallery was set up toexplore the potential for a minimal brokeragemodel and reduce the burden of administrativecosts associated with the pilot. In the event,the 150 Creative Credits were spent on amuch smaller number of creative businesses, 79in total, as might be expected in a competitivemarketplace. In fact, one creative businessserviced no fewer than 13 credits in total.

First wave Awarded rms were required tosubmit their nal project proposal for approval

by late November 2009 together with the nameof their servicer i.e. the creative businessthey had chosen to service their credit.

(Other creative companies on the Gallery whowere not selected are referred to as non-servicers.)13 Project eligibility criteria werechanged slightly between Waves 1 and 2 totighten the denitions of innovation projects.SME-creative partnerships were then given fourfurther months (ve in the case of Wave 2) tocomplete their project and the SME could claimthe credit once they had been invoiced by theircreative partner.

1.4 Evaluating the Creative Credits pilotscheme

The Creative Credits pilot has been structuredas a randomised control trial (RCT) with thecredits being allocated randomly and thesuccessful SMEs representing the treatmentgroup. This random allocation was used toavoid any systematic bias in the characteristicsof rms winning credits and to help providea more robust indication of the extent ofadditionality of the credits.14

A major research project into Creative Credits(including an evaluation of the business-to-business voucher mechanism) began inSeptember 2009 and will continue until March2012, 18 months after the completion of thelast Creative Credits projects. The evaluationconsiders both the short-term effects of theCreative Credits pilot and its potential longer-term outcomes. The evaluation uses a mixedmethods approach combining both quantitativeand qualitative approaches and is based on a

longitudinal data strategy (Figure 2).

Table 1: Principal dates for the Creative Credits scheme

Wave One Wave Two

Opened for Applications 9th Sept 2009 1st Feb 2010

Deadline for Applications 9th Oct 2009 1st March 2010

Creative Credits randomly awarded 17th Oct 2009 3rd March 2010

Project Proposal Deadline 28th Nov 2009 16th April 2010

Project Completion Deadline 31st March 2010 15th Sept 2010

Payment Claim Deadline 16th April 2010 15th Sept 2010

11. To be approved, creative

rms had to have been agoing concern for at leastone year with supportingaccount documentation,and also have professionalindemnity insurance.

12. Analysis of the Wave Onechoices revealed that therewas a statistical correlationbetween position onthe Creative Gallery andsuccess in being chosen asa Servicer; those positionedearlier being more likelyto be selected. In WaveTwo, this possible bias wasaddressed by requiring theAwarded SME to performa keyword search on theGallery to generate differentorderings according to theSMEs requirements.

13. In the event that anAwarded SME proposed touse their credit for a projectthat did not satisfy theeligibility criteria, the SMEwas asked to go back andrework their proposal. If theSME could not identify aneligible project within thedeadlines, the credit wasallocated, again on a randombasis, to a business on thereserve list.

14. Technically, the randomisedcontrol trial is unblindedin that both the treatmentand control groups knowwhether or not they havereceived a credit.


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This working papers primary focus is on theshort-term additionality of the scheme, but thewider evaluation has been guided by a series ofresearch questions reecting the logic modeloutlined earlier (Figure 1):

1. How does the effectiveness of this methodof business-to-business knowledge transfercompare with more standard innovationvoucher models which stimulate knowledgetransfer from universities to businesses?

2. Does the voucher model encourage newbusiness connections between creative andnon-creative businesses that would nototherwise form?

3. To what extent have the credits led toadditional innovation by non-creativebusinesses; that is, innovation that wouldnot otherwise have happened?

4. Has the pilot met its aims, objectives andoutcomes? If so, what are the characteristicsthat have made the model successful?

5. How does the pilot compare to otherinnovation voucher pilots and has it beensuccessful in developing a distinctive methodfor business-to-business engagement?

6. How has the pilot developed and what arethe key features of the iterative process?

7. How has the Creative Gallery developed?Is it successful as a format for business-to-business interaction?

8. What types of brokerage activitiesoccurred? Which ones were the most

effective?

9. How much time was spent on brokerage?What was the quality of the brokerageelements?

10. Does the model require an elementof brokerage above or below whatwas originally foreseen? What are theimplications of this going forward?

11. Is the voucher pilot a scalable model thatcan be transferred to other organisations? Ifso, who would these be?

The initial baseline survey, at the time thecredits were allocated, and the second survey(around the time of completion of the project)have been completed both for Wave One andWave Two rms, allowing us to ascertain thepilots short-term additionality. In subsequentsections we report an analysis of these twosurveys and the short-term impacts of thecredits. Subsequent surveys and papers willfocus on the longer-term impacts of the

scheme.

0

Figure 2: Timeline for Creative Credits projects and their evaluation

Projectapproval

Partnership

agreed

1 month

Projectundertaken

5 or 6 months

Sign-off

Project timeline

Qualitative analysis

Impact period 16 months

Impact period 26 months

Survey 1

Awarded and

non-Awarded

firms

Survey 1

Creative

Servicers

Survey 2

Awarded,

non-Awarded and

Creative firms

Survey 4

Awarded,

non-Awarded and

Creative firms

Survey 3

Awarded,

non-Awarded and

Creative firms


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1

Part 2: Proling the companies participating in Creative

Credits

Key ndings

Creative Credits applicants

A total of 672 SMEs made eligible applications for the Creative Creditsscheme: 312 inthe rst wave and 501 in the second; 141 applying in both waves. Applicants varied bysector but the strongest concentration was in business services, including consulting andprofessional services. The turnover of the applicant group also varied relatively widely,although over half of all applicants had an annual turnover of less than 500k.

Around one in eight of the eligible population of rms applied for credits, with rms with10-50 employees signicantly over-represented.

The size distribution of Creative Credits applicants was broadly similar to that for theNWDA innovation voucher scheme, although a larger proportion of rms with ten or moreemployees applied for the Creative Credits scheme.

A total of 328 applications were made by creative businesses to appear on the CreativeGallery, of which 28 were either ineligible or duplicate applications. By far the mostcommon services offered (79 per cent) were Design or web design with 63 per cent ofbusinesses listing this as their primary offering. As with the SMEs, the majority of theCreative Service providers were small, with 41 per cent reporting annual turnover of lessthan 500k.

Creative Credits applicants were signicantly more likely to have a high proportionof graduate employees (more than 40 per cent of the workforce) than the broaderpopulation of eligible rms.

Creative Credits applicants were signicantly more likely to have engaged in priorinnovation than rms in the broader eligible population.

Creative Credits applicants were also more active users of a wide range of externalbusiness support organisations than the broader population of eligible rms.


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2.1 Introduction

In this section we prole the group of SMEsand creative companies that participatedin Creative Credits. In Part 2.2 we brieysummarise the characteristics of applicantSMEs. Part 2.3 then compares applicants tothe eligible population of rms in terms ofexporting and prior innovation behaviour. Thissection draws on information on the eligiblepopulation of rms from Companies House and

a specically conducted survey of non-applicantcompanies. In Part 2.4 we then comparethe baseline characteristics of the Awardedand Servicer groups as a prelude to the laterdiscussion on additionality. Part 3 later in theworking paper provides a more detailed proleof the Creative Credits projects themselves.

2.2 Characteristics of Creative Creditsapplicants

In this section we provide an overview of theapplications received by the Creative Creditsscheme based on administrative data. A totalof 672 SMEs made eligible applications for thescheme: 312 in the rst wave and 501 in thesecond; 141 applying in both waves. Seventy-ve credits were available in Wave One, meaningthat around one in four applicants was allocateda credit. In fact, 69 credits were serviced in WaveOne, three rms deferred using their credit untilWave Two, and four were offered but declined acredit. One of these was reallocated and serviced

during Wave One and three were reallocated atthe same time as the Wave Two Credits.

The higher numbers of applications in thesecond wave meant that the chance ofreceiving a credit was roughly only one inseven. In the end, two rms were offered butdeclined a credit; their credits were reallocatedto rms randomly selected from a reserve list.

Amongst applicants, the sectors with greatestrepresentation were services businesses, inparticular: Consultancy, Professional Services,General Business Services, and Retail. No

applications were received from the Aerospaceor Medical sectors. Applications were receivedfrom all boroughs included in the programme.15Figure 3 illustrates the geographical breakdownof the applicant and awarded groups. Theturnover of the applicant group variedrelatively widely (Figure 4), although more thanhalf of all applicants had an annual turnover ofless than 500k. Within this group, a fth of allapplicants reported that their turnover was lessthan 100k per year.

Comparing the group of Creative Creditsapplicants to the eligible population ofcompanies in the MCR provides an indicationof the penetration of the scheme. Using datafrom Companies House, we estimate that therewere around 5,050 eligible rms in the MCR ofwhich 672, or around one in eight applied forthe Creative Creditsscheme (Appendix 1).

This size prole was broadly similar to those inthe NWDA innovation voucher scheme (Figure5) with micro rms (dened as businesseswith fewer than ten employees) dominating

applications to both schemes. CreativeCredits had a signicantly larger proportion

2

A comparison of the Awarded and Creative Servicers groups suggests:

Creative SMEs servicing Creative Credits have on average grown more rapidly thanAwarded SMEs over the previous three years.

Creative Servicers are more likely to have engaged in R&D investments than Awarded

SMEs.

Creative Servicers are much more likely to have invested in other forms of innovation,such as computer software, hardware, design and innovation-related training.

Despite their more innovative behaviour, however, there is no evidence that CreativeServicers are more successful than the Awarded SMEs in generating new sales from theirinnovations.

15. As the checking toolused was not set up forCheshire East and West,the old borough names ofCongleton, Maccleseldand Vale Royal wereused and were worded asFormer borough of oncommunications.


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3

Figure 3: Percentages of applicants and Awarded rms by borough

Figure 4: Percentages of applicants and Awarded rms by business size (turnover)

30

25

20

15

10

5

0

Percentage of

applicants and

Awarded firms

Bolton

Bury

CityofManchester

CityofSalford

Congleton

Macclesfield

Oldham

Rochdale

Stockport

Tameside

Trafford

ValeRoyal

Warrington

Wigan

Borough

Awarded firms percentage Applicants percentage

60

50

40

30

20

10

0

Percentage of

applicants and

Awarded firms

0

499k

500k

999k

1m

1.4

9m

1.5mt

o1.9

9m

2mt

o2.4

9m

2.5m

2.9

9m

3m

3.4

9m

3.5m

3.9

9m

4m

4.4

9m

4.5m

4.9

9m

5m+

Noanswer

Turnover

Awarded firms percentage Applicants percentage


14/49

of applications from SMEs with ten or moreemployees, however.

What of the creative service providers involvedin the scheme? A total of 328 applications weremade to appear on the Gallery, of which 28were either ineligible or duplicate applications.Over time a further nine rms were removedfrom the Gallery, either at their own requestor due to insolvency. By far the most commonservices offered (79 per cent) were Design orweb design with 63 per cent of businesseslisting this as their primary offering (Figure 6).Over half of businesses offered Advertisingor PR; 20 per cent of businesses listed thisas their primary service. Other services wereoffered by signicantly fewer rms. As with theSMEs, the largest proportion of creative serviceproviders (41 per cent) were from the city ofManchester with a stronger concentration ofcreative service providers in the city than SMEs(Figure 7). As with the SMEs, the majority ofthe creative service providers were relativelysmall, with 41 per cent reporting annual

turnover of less than 500k.

2.3 Innovation among applicants andnon-applicants

More detailed data allow us to compare thecharacteristics ofCreative Credits applicantsand non-applicants. This comes from twosources: for applicants, information is availablefrom the baseline survey of Awarded and non-Awarded rms conducted as rms started theirCreative Credits projects; for non-applicants, aspecial survey was undertaken using a samplingframe broadly similar in size and sector tothe applicant group (see Appendix 1). Twopoints need to be borne in mind, however, inconsidering the comparisons in Tables 2 to 5.First, the benchmark non-applicant surveyachieved a relatively low response rate (c. 13per cent) so the resulting sample size is small;based on the population and achieved responsethis suggests a sampling error of around 6per cent. This means that estimates from thenon-applicant survey have to be treated withconsiderable caution as they are subject tosome inaccuracy. Second, the non-applicant

survey was conducted on the same timeline asthe Wave Two data to which it is compared.

4

Figure 5: Comparison of Creative Credit applicants size prole and that in the NWDAinnovation voucher scheme

Fewer than ten

employees

10-50 employees

More than 50

employees

0 10 20 30 40

Percentage

50 60 908070

NWDA innovation vouchers Creative Credits

81.0

62.5

17.0

31.8

2.0

5.7


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5

Figure 6: Primary service offered by creative businesses listed on the Gallery

70

60

50

40

30

20

10

0

Percentageof creative

businesses

listing asprimary

service

Designor

WebDesign

ThePerforming

Arts

Advertising

orPR

Computer

Games

Film

and

Video

Publishing

Artand

Antiques

Designer

Fashion

Television

andRadio

Crafts

Architecture

Music

Software

Service Offered

Figure 7: Creative businesses from each borough

45

40

35

30

20

25

15

10

5

0

Percentage

of creativebusinesses

in borough

Tam

eside

Trafford

W

igan

Stoc

kport

Bury

Roc

hdale

Warrington

Maccle

sfield

Vale

Royal

B

olton

Oldham

Cong

leton

BoroughCityofManchester

CityofSalford


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A comparison of the Creative Credits applicantsand non-applicants suggests:

Creative Creditsapplicants were less likely tobe exporters than the broader population ofeligible rms (Table 2).

Creative Credits applicants were signicantlymore likely to have a high proportion ofgraduate employees (more than 40 percent of the workforce) than the broaderpopulation of eligible rms (Table 3). Thispartly reects the disproportionate numberof applicant rms in the (graduate-intensive)business services sector.

Creative Credits applicants were much moreactive users of a wide range of externalbusiness support organisations than thebroader population of eligible rms (Table

4). In part, despite the broad-based natureof the schemes marketing, it might be thatthese linkages encouraged rms to apply forCreative Credits.

Creative Credits applicants were signicantlymore likely to have engaged in prior innovation

than rms in the broader eligible population(Table 5). In particular, around three-quartersof Creative Credits applicants reportedhaving introduced product innovations in theprevious three years compared with only 42.9per cent of non-applicants.

Forty-two per cent of Creative Creditsapplicants who had undertaken priorinnovation had previous experience ofworking with creative service providers,compared with only 20 per cent of non-applicants (Table 5).

6

Table 2: Export share of sales ofCreative Creditsapplicants and non-applicants

Table 3: Proportion of workforce with a degree: Creative Creditsapplicants and non-applicants

Creative Credits applicants Non-applicants

n % n %

Do not export overseas 289 65.2 33 54.1

Up to 5% 90 20.3 9 14.8

Between 6-15% 23 5.2 8 13.1

Between 16-25% 16 3.6 3 4.9

Between 26-50% 11 2.5 1 1.6

Between 51-75% 8 1.8 4 6.6

Between 76-100% 6 1.4 3 4.9

All rms 443 100.0 61 100.0


n % n %

Less than 15% 185 42.8 27 43.5

15% to 24% 42 9.7 13 21.0

25% to 39% 40 9.3 11 17.7

40% or more 165 38.2 11 17.7All rms 432 100.0 62 100.0

Note: Applicants data from baseline survey, includes both Awarded and non-Awarded groups. Non-applicants data fromsurvey of non-applicants conducted at the time of the award of Wave Two Creative Credits (Appendix 1). Pearson Chi-Square is 17.066 (p=0.009).

Note: Applicants data from baseline survey, includes both Awarded and non-Awarded groups. Non-applicants data fromsurvey of non-applicants conducted at the time of the award of Wave Two Creative Credits (Appendix 1). Pearson Chi-Square is 16.310 (p=0.001).


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Table 4: Use of business support organisations: Creative Credits applicants and non-applicants


n % n %

Private consultancy (433/63)* 220 50.8 24 38.1

Business Link (443/63)*** 296 66.8 22 34.9

North West Development Agency (417/63)*** 176 42.2 15 23.8

Innovation vouchers (410/63)*** 57 13.9 3 4.8

Other public business support (409/63)** 95 23.2 7 11.1

Note: Applicants data from baseline survey, includes both Awarded and non-Awarded groups. Non-applicants data fromsurvey of non-applicants conducted at the time of the award of Wave Two Creative Credits (Appendix 1). Respondentnumbers in brackets (applicants responses/non-applicants responses). Total respondent numbers vary due primarily to thenumber of rms responding Dont know. Independent sample t tests for difference in mean values are indicated by asterisk:* denotes signicance at the 10 per cent level; ** at 5 per cent and *** at the 1 per cent level.

7

Table 5: Innovation activity: Creative Creditsapplicants and non-applicants


n % n %

A. Types of innovation

Any new or signicantly improved goods 338 76.5 27 42.9

or services (442/63)***

New to the market, i.e. introduced before 174 54.7 16 25.4competitors (318/63)***

B. Innovation cooperation (% innovating rms)

Other businesses within your enterprise 79 22.8 7 20.0group (347/35)

Suppliers of equipment, materials, services or 176 50.6 17 48.6software (348/35)

Suppliers of design or other creative services 142 42.0 7 20.0(338/35)**

Clients or customers (352/35) 245 69.6 22 62.9

Competitors or other businesses in your 78 22.5 2 5.7industry (347/35)***

Consultants, commercial labs, or private 79 23.0 2 5.7R&D institutes (344/35)***

Universities or other higher education 72 20.9 5 14.3institutions (344/35)

Note: Applicants data from baseline survey, includes both Awarded and non-Awarded groups. Non-applicants data fromsurvey of non-applicants conducted at the time of the award of Wave Two Creative Credits (Appendix 1). Respondentnumbers in brackets (applicants responses/non-applicants responses). Respondent numbers vary due primarily to thenumber of rms responding Dont know and in part B of the table due to the focus on innovating rms only. Independentsample t tests for difference in mean values are indicated by asterisk: * denotes signicance at the 10 per cent level; ** at 5per cent and *** at the 1 per cent level.


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8

In general terms, this suggests that CreativeCredits applicants were more focused oninnovation than non-applicants, more likely tohave worked with external partners (includingcreative servicers) and with higher internal skilllevels than non-applicants.

2.4 Comparing Awarded and CreativeServicer rms

In this section we provide an overview ofthe baseline characteristics of the groups ofAwarded and Creative Servicers engaged inCreative Credits projects. Comparisons relateto the basic characteristics of the enterprises ineach group, and their innovation activities.

The Awarded and Servicer rms shared broadlysimilar characteristics (Table 6). CreativeServicers had median sales of 200,000in the year prior to the pilot. Median salesfor the Awarded SMEs were slightly higherat 290,000. In each case, however, meanturnover was signicantly higher due to the

inclusion in each group of some larger rms.The Creative Servicers had grown relativelyrapidly over the previous three years, marginallyfaster than the Awarded SMEs. In terms ofownership, the vast majority (over 90 per cent)of Creative Servicers and Awarded rms wereindependent single-site organisations (Table6, part B). Firms in both groups predominantlysold only in the UK market, with three-quartersof rms having no export sales (Table 6, part C).

Table 6: Characteristics of Creative Credits Awarded and Creative Servicers

Servicer Awarded

N=78 N=149

A. Firm Size and Age

Median turnover (000) 200.0 290.0

Turnover growth (% per year) 35.5 25.2

Business age (years) 6.0 8.8

B. Ownership status (% rms)

A subsidiary or associated company 3.8 3.4

An independent single-site organisation 93.5 81.9

Other 1.2 3.4

The headquarters of a multi-site organisation 1.2 11.4

Total 100.0 100.0

C. Export Prole

Do not export 74.3 74.0

Up to 5% of sales exported 20.3 8.9

5-15% of sales exported 2.7 4.9





Total 100.0 100.0


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9

Table 7: Creative Servicers and non-creative industry Awarded SMEs Investment ininnovation

Awarded Servicer

N=142 N=78

Research and development (% businesses)

Internal 66.2 77.0

External 20.7 23.9

R&D tax credit 6.3 11.1

Other investment in innovation (% businesses)

Advanced machinery 19.4 10.4

Computer hardware 45.1 58.1

Computer software 50.0 67.6

External knowledge 22.5 31.9

Design expenditure 47.5 57.7

Training for innovation 40.1 56.0

Note: Survey numbers are less than the total number of awarded and servicers due to survey non-responses.

Table 7 outlines Creative Servicers and Awardedbusinesses engagement with research anddevelopment (R&D) and their investment inother innovation activities in the three yearsprior to their participation in the scheme.Overall, 77 per cent of Servicers reportedhaving undertaken some in-house R&D, and

23.9 per cent also had contracted externalR&D. Just over 11 per cent said they hadbeneted from tax relief under the R&D taxcredit scheme.

This prole of R&D activity is broadly similarto that of Awarded SMEs but with somenoteworthy differences. In particular, CreativeServicers were, overall, more likely to beengaged in R&D than Awarded SMEs. Thatthe Servicers had greater prior experience ofR&D than the Awarded group may suggest anelement of organisational capability that they

could transfer to SMEs through the credits.

Businesses were also asked whether they hadinvested in different types of intangible asset(a measure of investment in innovation see NESTA (2009)16) in the previous threeyears. The most common form of intangibleinvestment by Creative Servicers was that incomputer software (67.6 per cent) followed

closely by computer hardware, design andtraining for innovation. Creative Servicers weremuch more likely to have invested in thesecategories of innovation than the AwardedSMEs.17 One area where this pattern is reversedis investment in advanced machinery, whereonly 10.4 per cent of Creative Servicers had

invested in advanced machinery, compared to19.4 per cent of Awarded SMEs.

In terms of innovative outputs, 82.4 per cent ofCreative Servicers reported having introduceda new or improved product or service overthe three years prior to the pilot, with a thirdindicating that this was a new to marketinnovation (Table 8). SMEs in the Awardedgroup were equally likely to have introduceda new or improved product or service over theprevious three years but, interestingly, weresignicantly more likely to have introduced

new to market innovations. The suggestion isthat these creative rms were more focused onincremental product and service change thanon radical innovations.

Another standard indicator of innovationoutputs is innovative sales, and here we focuson two indicators: the proportion of salesderived from new to market products and

16. NESTA (2009) TheInnovation Index: Measuringthe UKs investment ininnovation and its effects.London: NESTA.

17. This is consistent withScheffel and Thomass(2011) nding thatindustries employingproportionately greatercreative workers tend to

spend more on intangibleassets.


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0

Table 8: Creative Servicers and Awarded SMEs innovation performance

Awarded Servicer

N=142 N=78

A. Innovating share of rms (% rms)

Innovation in goods/services 79.6 82.4

New to market innovation 55.6 32.6

New to business innovation 64.8 76.9

B. Innovative sales (%)New to the market innovative sales (%) 23.8 20.5

New to the business innovative sales (%) 39.3 37.5

Note: Survey numbers are less than the total number of awarded and servicers due to survey non-responses.

the proportion of sales derived from new tobusiness products (Table 8). Both measuresare expressed as a share of sales at the timeof the baseline survey (at the outset of theCreative Credits projects), and can be regardedas measures of the market success of businessinnovation. On average just under 24 per cent

of Awarded rms sales derived from new tothe market products compared to 20.5 percent for Creative Servicers. This suggeststhat the Awarded SMEs were, if anything,more successful than the Creative Servicers ingenerating new sales from their innovations.

For those businesses that reported innovatingduring the three years prior to the project, the

baseline survey also asked businesses whetherthey had co-operated with other organisationson their innovation activities (Table 9). Themost common cooperation partners amongboth groups were clients or customers, butwith signicantly more Creative Servicers (79.3per cent) claiming to cooperate with clients or

customers than Awarded SMEs (70.5 per cent).A greater proportion of Creative Servicers(29.8 per cent) than Awarded SMEs (24.1 percent) had also cooperated with competitors orother businesses in their industry. In contrast, asmaller proportion of Creative Servicers claimedto have cooperated on innovation activitieswith universities and other research bodies.

Table 9: Creative Servicers and Awarded SMEs cooperation on innovation(percentage of product or service innovators)

Awarded Servicer

N=112 N=58

Other businesses within your enterprise group 20.2 28.5

Suppliers of equipment, materials, services or software 50.9 46.0

Clients or customers 70.5 79.3

Competitors or other businesses in your industry 24.1 29.8

Consultants, commercial labs, or private R&D institutes 23.6 17.2

Universities or other higher education institutions 21.6 14.0

Government or public research institutes 12.7 7.1


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1

Part 3: Proling the Creative Credits projects

3.1 Introduction

In this section we prole the projectssupported by Creative Credits. We rst presentdata on the nature of the projects, and thenon the partnership aspects of the projects asperceived by the Creative Servicers.

3.2 The Creative Credits projects

The main characteristics of the Creative Creditsprojects are summarised in Table 10, based on

the main thrust of the project undertaken. Byfar the most common theme was the upgradingand development of rms websites, with

marketing and video production signicantlyless common.

The following website development proposalsare fairly typical:

Bring site from Web 1.0 to Web 2.0.

Accessibility for all users. Greater enhance

the capability for SEO on the site. CMS

system to manage content and images,

giving a better level of control than

previously. To create an outstanding website

by delivering a cutting edge design to

encourage the viewer to remain on the site

and purchase services as well as view thesite as a valuable source of information,

encouraging repeat visits to the site and

Key points

Website development dominated the scheme, representing around two-thirds of allprojects. Production of marketing materials and video production the next mostcommon activities were much less frequent. This pattern is consistent with thedistribution of primary activities of creative businesses listed on the Creative Gallery.

Well over half of Creative Servicers had serviced an SME that was in a different sectorfrom their usual clients, consistent with Creative Credits having succeeded in creating newbusiness-to-business relationships for creative businesses.

Only one-fth of Creative Servicers said that theirCreative Credits partner was locatedcloser than their usual clients, suggesting the scheme had only a limited impact in

embedding Manchesters creative businesses more deeply into local supply chains.

There is some evidence that the scheme could lead to longer-term partnerships betweencreative businesses and non-creative SMEs, something we will explore rigorously as webuild up a longitudinal data set for the study.


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grow the strong reputation the company

already enjoys.

To rebrand the company, to design new

fonts and design for the name . To

design a new logo that can be used on the

website and other items. To redesign the

website completely incorporating the new

redesigned logo.

As Table 10 suggests, other SMEs used theCreative Credits scheme to develop new videocontent and marketing materials. For example:

It is proposed to create a suite of exible

video tools to have a brand dialogue with

potential opinion leaders, the wider media

industry and potential customers. Reinforce

the positive outcomes of the service.

Highlight [X] as leaders in this industry. The

resulting video content would be used at

trade shows, in proposals to clients and on

the company website.

Produce informative and educational video

targeting professionals and laymen

What, How, Why format using state of the

art mixed media techniques. Create fresh,dynamic content demystifying the science

and technologies.

Four or ve short funny videos set for

viral release to raise awareness of [Y]. The

videos will form a series that will be seededaccording to the target market and sent

out at regular intervals as part of a creative

marketing campaign. There is scope for

the series to be continued, potentially

encouraging viewers/customers to submit

their own videos or ideas with the best ones

being made.

The dominant emphasis in the projects onwebsite development, video production andmarketing is consistent with the types ofservices that creative businesses listed on theCreative Gallery (Figure 7).

3.3 Creative Credits projects theCreative Servicers view

In the baseline survey, Creative Servicers wereasked how the projects had come about andhow these projects had related to their otherbusiness (Table 11). The key points were:

In the majority of cases (66 per cent), theinitial contact that led to the Creative Creditsproject was made by the Awarded SME. In

2

Table 10: Breakdown of all Creative Creditsprojects (Waves 1 and 2)

Number of Projects Percentage of projects

including this as primary or

secondary goal

Web 81 60%

Marketing 14 11%

Video 13 10%

Brand Development 10 9%

Logo 8 8%

Publication 8 15%

PR Campaign 6 5%

Market Research 2 1%

New Media (iPhone App) 1 1%

Product Design 1 1%

Total 144

Note: In a small number of projects (6) there was a range of objectives making it difcult to identify clear primary orsecondary goals.


23/49

around a third of cases the initial approachwas made by the creative business.

Almost 55 per cent of Creative Servicersclaimed to have serviced an SME that wasin a different sector from their usual clients,and over 41 per cent described the SME asbeing outside their usual business networks.

Almost all (96.5 per cent) of the AwardedSMEs that creative businesses serviced werewithin 20 miles, as might be expected given

the size of the Manchester City Region(which is around 45 miles at its widest point).Only 19.6 per cent of Creative Servicers

said that their partner was located anycloser than their usual clients, suggestingthat the scheme had only limited success inembedding Manchesters creative businessesmore deeply into local supply chains.

The great majority (over 70 per cent) ofCreative Servicers described their CreativeCredits projects as trials with the possibilityof some follow-on if successful. This is anearly sign that the scheme has the potentialto create longer-term B2B relationships

between creative SMEs and SMEs in othersectors.

3

Table 11: Proling the Creative Credits Partnerships*

Servicers N=59

A. Who made rst contact?

The client made rst contact 66.1

We made rst contact 33.9

All businesses 100.0

B. Distance from Creative Credits partner (road miles)

Less than 2 14.0

2-4 15.8

5-7 15.8

8-10 14.0

11-20 36.8

Greater than 20 3.5

Total 100.0

C. Comparing Creative Credits partner to usual clients

Creative Credits partner is larger 7.4

Creative Credits partner is in a different sector 54.5

Creative Credits partner is closer geographically 19.6

Creative Credits partner is outside normal business networks 41.8

D. Creative Credits project is:

A standalone project with little future potential 26.5

A trial project with possible follow-on if successful 71.2

The rst of a series of projects with substantial potential 42.4

*In cases where Creative Servicers were servicing multiple credits, the Servicer was asked to pick one project for thepurposes of the survey.


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4

Part 4: Short-term additionality

4.1 Introduction

In this section we focus on the additionalityof theCreative Credits scheme. Twocomplementary approaches are adopted. First,in Part 4.2, we use rms revealed preferencesand adopt a multivariate approach to modellingthe additionality of Creative Credits, controllingboth for potential selection bias and thedifferential characteristics of Awarded andnon-Awarded rms. The focus here is on theimmediate additional impact of the CreativeCredits scheme and the main question is:

RQ4.1: Having applied for a credit, by howmuch if at all did the award of a credit

actually increase the probability that the

project went ahead within the schemes

ve-month timeline?

In other words, by observing how rms actuallybehaved, we can test for the effect of theaward of a credit on rms over and abovethe effect on rms of having applied for thescheme. Additionality would require that rmswhich received a credit had a higher probabilityof undertaking the project. Our additionalityquestion is short-term and relates to theimpact of the scheme rather than its longer-term outcomes for innovation or other aspectsof rm behaviour. These will be examined in

subsequent papers based on data collectionlater in the project.

Key results

The lottery-based allocation of Creative Credits meant that there was very little systematicvariation in the characteristics of Awarded and non-Awarded groups.

The econometric analysis suggests short-term additionality of 78 per cent. In other words,for every ten credits awarded, roughly eight were used to create new B2B relationshipsinvolving creative services that would not have formed in the absence of the scheme. Thisis broadly the same as the level of additionality identied in the pilot programme for thewell-known Dutch innovation vouchers programme.

The Creative Credits projects proceeded largely as planned, with only 3 per cent deviatingfrom their original plan. Ninety-three per cent of projects achieved either all or some

of their innovation objectives, with around a quarter being associated with otherunanticipated benets.

Sales impacts varied considerably between projects but to date (at the end of the projectperiod) the scheme had generated additional sales of 514,000, an average of 3,430 perintervention.


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In Part 4.3, we take a second approach toexamining the schemes additionality bylooking at Awarded SMEs own assessment ofthe impact of their Creative Creditsprojects. Aswell as RQ4.1 we consider four specic researchquestions:

RQ4.2: Did the Creative Credits projectsproceed according to plan, and what were

the reasons for any delays?

RQ4.3: What impact did the Creative Credits

projects have on the bottom line of credit

recipients? To what extent would these

impacts have been achieved without the

credit?

RQ4.4: What strategic impacts did the

Creative Credits projects have?

RQ4.5: What was the extent of behaviouraladditionality from the Creative Credits

projects?

This part of the analysis is based on the self-assessment data provided by 132 (88 percent) of the 150 rms that received a creditand covers both the rst and second waves ofthe pilot. It is important to acknowledge thatthis information was collected from SMEs at,or around, the end of theirCreative Creditsproject and therefore reects the immediate

impacts of the scheme.18

The focus is thus moreon organisational and short-term behaviouralimpacts than on longer-term impacts on thebottom line. That said, some Awarded rmsreported relatively large sales benets evenover this short period; others said that it wastoo early to consider the potential benets.The implication is that the prole of benetsoutlined below may signicantly underestimatethe cumulative impacts of the creative projectson Awarded SMEs.

4.2 Project additionality of CreativeCredits

In this section we exploit the randomisedcontrol trial nature of Creative Credits toconsider its project additionality, i.e. ResearchQuestion 4.1:

RQ4.1: Having applied for a credit, by how

much if at all did the award of a credit

actually increase the probability that the

project went ahead within the schemes

ve-month timeline?

Our analysis closely follows the approach usedin the evaluation of the Dutch pilot innovationvoucher project with a view to generatingcomparable results.19

The key point here is that rms in the non-

Awarded and Awarded groups were both ableto access creative suppliers from the Galleryand complete their innovation projects whetheror not they had a credit to contribute towardsit. Of the 314 Applied rms that respondedto the survey, 35 rms (11.1 per cent) wentahead anyway with their projects within theCreative Credits four to ve-month projecttimeline (Table 12). Among the Awarded groupof 150 rms; 134 (89.3 per cent) commissionedprojects (Table 12).

This group of 464 rms 150 Awarded and314 non-Awarded rms form the basis forour estimates of short-term additionality. Thedependent variable in our economic analysisthen takes value 1 if a rm commissioned aproject within the four to ve-month CreativeCredits timescale and 0 otherwise (Table 12).This reects rms revealed preference and isprobably a better guide to additionality thanany more subjective assessment of the likelyuse or non-use of the credit.

5

Table 12: Firms commissioning projects during the Creative Credits period

Number of projects

commissioned

Total number of rms (464) 169

Voucher winners awarded by lottery (Awarded group, 150 rms) 134

Non-awarded group in lottery (non-Awarded group, 314 rms) 35

18. At time of these surveys,

107 (81 per cent) of the 132completing the survey hadcompleted their CreativeCredits project, 15 (11per cent) reported havinglargely nished and theremaining ten (8 per cent)had started work on theproject but not yet nished.

19. See Cornet, M., Vrooman,B. and van der Steeg, M.(2006) Do innovationvouchers help SMEs to crossthe bridge towards science?In: CBP Discussion Paper.No 58.


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6

20. Cornet, M., Vrooman, B. andvan der Steeg, M. (2006)Do innovation vouchers helpSMEs to cross the bridgetowards science? In: CBPDiscussion Paper. No 58.

21. Ibid.

Estimating this type of additionality involvesusing a model with a treatment term reectingthe impact of the credit on the probability thata rm commissions a project. In its simplestform this can be written as follows:

GA = + 1

CC + (1)

Where: GA is a 0/1 variable taking value 1if the rm goes ahead with its innovationproject within the schemes four to ve-monthtimescale, and CC is a treatment term (dummyvariable) which takes value 1 if the rm didreceive a credit. A positive and signicantcoefcient on the credit treatment term wouldsuggest additionality. Table 13 presents theresults of estimating (1) using OLS. Without acredit, a rm has a probability of 11.1 per cent

of commissioning an innovation project. This is78.1 per cent more likely when a rm receives acredit, suggesting strong project additionality,and closely in line with the Dutch innovationvoucher scheme.20

One drawback of equation (1) is that itimplicitly assumes that the credits wererandomly distributed across the group of rmsthat applied for the scheme and that there is nosystematic bias in the allocation mechanism. Ofcourse, the lottery allocation of credits should

have accomplished this random distribution. Ifthe characteristics of the Awarded and non-Awarded groups varied, however, this mightstill cause some bias in the estimation of thecoefcient on the treatment term. To test this,the usual approach is therefore to examinewhether there is any potential bias and toestimate a second equation to control for theprobability of selection bias.21 In Appendix 2,we therefore estimate a form of Probit modelallowing for potential sample selection and

then present results for this if any evidence ofsample selection is found.

One other element of potential bias that wealso explore in our analysis is our paymentof cash incentives to maintain response ratesduring the longitudinal analysis. In theory, such

incentives may encourage disproportionateresponses from rms with atypicalcharacteristics, causing bias in the results. Wetherefore allow for this in the treatment modelspresented in Appendix 2.

We rst explore whether there is any systematicrelationship between the characteristics ofrms and the receipt of a credit; this is doneusing simple Probit models (Table 18). Twofactors do prove signicant across the threedifferent models reported: micro or new rms(i.e. those with fewer than ten employees (or

none) in the previous year) were less likelyto have received a credit than larger rms,and younger rms were also less likely tohave received a credit. There was also someevidence that Awarded rms were more likelyto have no business plan. These effects couldin principle bias the estimates of additionalityin the second stage of the modelling procedureand are therefore potentially importantcontrols. Note however that these resultsare the reection of the limited size of theCreative Credits project rather than having any

economic interpretation.

The second stage of modelling theadditionality of the credits is the estimation ofthe treatment model, i.e. equation 1 relatingto the probability that rms commissioned aproject. Detailed results for this stage of theanalysis are included in Appendix 2, whichincludes models which allow for potential biasdue to selection biases and the potential effectof incentives. We nd that the payment of

Table 13: Simple OLS treatment model regression

Number of observances 464

Adjusted R-squared 0.5766

Variable Coefcient s.e. t-stat

Creative Credit 0.781 0.031 25.13

Constant 0.111 0.017 6.30


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7

incentives to rms is statistically insignicantin these models, suggesting that this iscreating no discernable bias in the additionalityestimates (Table 18). These results support thevalidity of the additionality estimates derivedfrom the OLS results presented in Table 13.

4.3 Awarded rms views of the projects

In this section we consider Awarded rmsviews of the conduct and success of theirprojects. These are clearly subjectiveassessments stated preferences but ingeneral terms provide a positive view ofthe impact of Creative Credits on attitudes,behaviour and sales. Later sections examine theshort-term behavioural effects of the projects.First, we consider whether projects went to

plan.

4.3.1 Projects and planning

Research Question 4.2 asked:

RQ4.2: Did the Creative Credits projects

proceed according to plan, and what were

the reasons for any delays?

Awarded rms reported that just over halfof all Creative Credits projects (56 per cent)achieved all of their innovation objectives

with a further 37 per cent achieving someof their innovation objectives (the remainingrespondents were uncertain whether or nottheir innovation objectives had been met).Projects largely proceeded as planned withonly four projects (3 per cent) deviatingsignicantly from the original project plan. Ofthe 29 companies that did report delays to theCreative Credits projects: six attributed theseto competing pressures on their own rm; 14attributed these to delays on the side of theCreative Servicer; and nine attributed them toexternal factors.

The Creative Credits projects had unanticipatedinnovation benets for 35 Awarded SMEs (26per cent). These unanticipated gains variedwidely, but included the upgrading of skills andsystems:

I think in general, the way we all approach

customers has altered and improved, the

media training that we received was really

benecial.

This product had much wider appeal withour clients than we had anticipated

We secured a new client in the process of

meeting the potential creatives.

In some cases the project also clariedobjectives and attitudes:

It helped to clarify the USP of the business,

and how we can use this to much greatereffect in gaining a competitive advantage

over our rivals. Our USP is innovation and

thought leadership, as opposed to delivery

of leadership and management training.

The website that was designed as part

of the Creative Credits project we recently

undertook is key to the implementation

of our business growth and sustainability

strategy.

Changing the mindset of the chief

executive that there is a serious difference

between in-house amateur design andgiving a brief to a professional graphic

design agency. The difference in quality was

clear to see.

Working with the approved consultant

opened up a number of areas for thought,

discussion and ultimately action in line with

the objectives of this work.

And in one case it helped the rm to gainfurther funding:

The NESTA support with the new venture

was acknowledged as a signicant reason

why our bank decided to help fund the

project with a small business loan.

Overall then, 93 per cent of the projectsachieved either all or some of their originalobjectives with the vast majority adheringrelatively closely to the original plan andtimeline. In around a quarter of cases, therewere signicant unanticipated benets, addingto the potential longer-term impacts of theprojects.

4.3.2 Sales impacts

In this section we consider the additionalimpact of theCreative Credits projects on thesales of the Awarded group of rms as outlinedin Research Question 4.3.

RQ4.3: What impact did the Creative Credits

projects have on the bottom line of credit

recipients? To what extent would these

impacts have been achieved without the

credit?


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A substantial proportion of rms expected

signicant future benets though it will havebeen too early for them to have experiencedthem (Table 14). Interestingly, most rms (50.8per cent) anticipated that the full benets ofthe project would only be evident over threeyears. This accords broadly with other studieswhere the persistence of the benets of R&Dand innovation support have been put ataround three years.22 It is also consistent withthe theoretical argument discussed earlierthat engagement with creative businesseshelps SMEs make longer-term (less myopic)

decisions.23

So, the short-term benetsdiscussed in this section are likely to understatethe longer-term benets of the scheme.

This implies that when comparing the longerterm net benets of the Creative Credits

scheme with the short-term benets, two

opposing considerations must be netted out:rst, long-run additionality will be less thanshort-term additionality, insofar as some rmsawarded Creative Credits will in the longer termundertake their creative projects even in theabsence of a credit; and second, the long-termeconomic benets of the projects on Awardedrms will likely be greater than in the shortterm.

One key measure of interest is the impact ofthe credit on the sales of the SMEs concerned.

Table 15 summarises the information providedby the Wave One and Wave Two Awarded rmsdetailing the impact of the credit on theirsales. These gures represent the additionalsales generated by each rm that would nothave been generated without the credit. In this

8

Table 14: Timing ofCreative Credits project benets

Table 15: Distribution of sales impacts of Creative Credits at the end of the funded period

Number of Percentage of

responses responses

All benets already received 4 3.0

All benets anticipated within two years 50 37.9

All benets anticipated within three years 67 50.8

More than three years to get all benets 8 6.1

No benets from credit 2 1.5

Dont know 1 0.8

Total 132 100.0


responses responses

Reduced sales by between 10,000 and 20,000 1 0.8

Had no impact on sales 49 37.1

Increased sales but by less than 5,000 14 10.6

Increased sales by 5,000 to 10,000 14 10.6

Increased sales by between 10,000 and 20,000 3 2.3

Increased sales by more than 20,000 5 3.8

Dont know 46 34.8

Total 132 100.0

22. BIS (2009) RDA Evaluation:Practical Guidance onImplementing the ImpactEvaluation Framework.London: Department forBusiness, Innovation andSkills.

23. Potts, J. and Morrison,K. (2009) NudgingInnovation. London: NESTA.


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sense these turnover impacts are additional.It is possible using these gures to derive arough estimate of the aggregate impact of thescheme on rms sales to date. We do this by:

1. Assuming the distribution of responsesin terms of sales impact is typical of allrms that received credits, so scale up

the number of responses in each categoryproportionately.

2. Taking the mid-point of each sales categoryrange as representative of the sales benetof rms in each group.

3. Multiplying the grossed up number ofrms in each group by the average salesimpact and then aggregating across all salesgroups.

Adopting this approach suggests a total salesimpact of 514,000 at the end of the CreativeCredits funded period, an average of 3,430per intervention.

Two observations are important here. First,as indicated earlier, the majority of Awardedrms suggested that it was as yet too early toevaluate the impact of the scheme, with thefull benet only accruing over a period of twoto three years. Secondly, the distribution ofgains is in fact highly asymmetric with a smallnumber of rms gaining very substantially from

their credit and many more deriving smallerbenets.

4.3.3 Strategic and behavioural benets

Alongside the short-term sales benets ofthe Creative Credits projects there is also thepotential that the projects might generateshort-term strategic and behavioural benetsthat might be expected to lead to futureincreases in prot. This is reected in ResearchQuestions 4.4 and 4.5:

RQ4.4: What strategic impacts did the

Creative Credits projects have?

RQ4.5: What was the extent of behavioural

additionality from the Creative Credits

projects?

Again, it is important to remember that thesebenets are those reported by Awardedrms at the immediate end of the CreativeCreditsproject. The recipients were askedabout a number of strategic benets, whichare outlined in Table 16. The most commonperceived benets were a strengtheningin the innovative capacity of the SME,knowledge transfer and that working with acreative partner had stimulated ideas for otherinnovation projects. A less commonly reportedbenet was access to specialist equipment orfacilities.

The high proportion of Awarded rmshighlighting strategic benets from thescheme, such as increased knowledge transfer

and ideas for new innovation projects, ispromising. It is also strongly consistent with thebroad objectives of the scheme. These strategic

9

Table 16: Strategic benets of Creative Credits projects


responses responses

Increased the innovative strengths of the business 106 80.3

Beneted from knowledge transfer from the creative partner 104 78.8

Stimulated other ideas for new innovation projects 103 78.0

Enabled to access specialist skills or talents 95 72.0

Increased willingness to innovate 95 72.0

Gained the inside track on new developments or market opportunities 67 50.8

Enabled to access specialist equipment or facilities 46 34.8

Total possible number of responses 132 100.0

Note: Firms were able to highlight as many strategic benets as necessary.


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0

Table 17: Attitudinal changes resulting from the Creative Credits scheme

Do you agree with the following statements: Number of Percentage of

rms agreeing responses

with statement

N=132

We are more likely to engage in innovation in the future because of our 100 75.8participation in the Creative Credits scheme

The businesss attitude to innovation has become more positive 100 75.8

Senior management are more receptive and committed to innovation 93 70.5

The Creative Creditsscheme made us aware of our innovative potential 90 68.2

We were already committed to innovation so theCreative Credits scheme 74 56.1has not changed our views

benets should provide the basis for signicant

positive outcomes in the longer term.

Consistent with the strategic benets ofthe Creative Creditsprojects noted earlier, arelatively high proportion of Awarded rmsreported that the project had been importantin changing their attitudes (Table 17). Almost76 per cent of Awarded SMEs said they weremore likely to engage in innovation because oftheir participation in the scheme.


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1

Part 5: Key qualitative ndings

5.1 Introduction

This section discusses the emerging ndingsfrom the qualitative research that relateinnovation and knowledge transfer to SMEsworking relationships with other businesses.Further ndings that relate business processesto rms working relationships can be found inAppendix 3.

5.2 The research process: Moving frominterviews to results

In total, 42 semi-structured interviews wereconducted across the two waves which covered

25 of the Creative Credits pairs. In Wave One,nine of the Creative Suppliers serviced 13 ofthe SMEs and, in Wave Two, nine Creative

Servicers serviced 12 of the SMEs (one CreativeServicer did not want to be interviewed).Grounded Theory24 provides us with thetechniques to develop new theory throughinductive enquiry. In-depth interviews allowedus to explore perceptions and meanings, andto probe rms in more detail than is possiblein quantitative surveys.25 Questions focusedon: company background, strategy, thenature of the Creative Credits project, theexperience of working with a creative company,communication, innovation/creativity, learningand Intellectual Property. All respondentsagreed to be taped and their interviews weretranscribed. This allowed us to analyse the datain its totality several times after the interview.26

In our data analysis we combine a manualapproach and a software-supported approachusing NVivo 7.0. Specically, we initially

Key ndings

Impact and the innovation landscape: SMEs interpret innovation in different ways. Thisvariation in what innovation means to them inuences the nature of their workingrelationships with other businesses.

Creativity transfer: The creative transfer of skills and knowledge that relate to the processand content of creativity is a contested domain between the Awarded SMEs and CreativeSMEs but not addressed in the literature on knowledge transfer.

Value of creativity and tacit knowledge: Awarded SMEs and Creative SMEs differ in howthey perceive the value of creativity.

Intellectual Property (IP): There is a lack of knowledge about IP in the SME communitygenerally, and respondents opinions on IP are couched in both negative and positiveviews of it.

24. Strauss, A. and Corbin, J.(1998) Basics of QualitativeResearch: Techniques andProcedures for DevelopingGrounded Theory. California:Sage Publications Inc.

25. Miller, R. and Brewer, J.(Eds) (2003) The A-Z ofSocial Research. London:Sage Publications Ltd.

26. Gillham, B. (2000) CaseStudy Research Methods.London: Continuum.


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conducted some content analysis by codingfor predened topics.27 This approach enablescodes to emerge from the data, which helpsto prevent researchers from missing issuesof importance through having a predenedstructure.

However, a concern with NVivo is that the wayit structures analysis could lead to imposingxed hierarchical conceptualisations onthe data, which may not be appropriate forstructuring the analysis.28 Whilst the NVivo7.0 software was used because of its datamanagement efciency, the researchersimmersed themselves in the transcriptsand compared coding to check if anythinghad been missed through this hierarchicalstructuring process. This helps to addressBrymans29 critique of Grounded Theory thatcoding involves taking small fragments of text

from the data which may lead to the loss ofcontextual information.

The rst stage, open coding, involved theresearcher going through each transcriptto identify concepts and properties. Here,anything that appeared relevant (e.g. conceptssuch as innovation, creativity, strategy,processes, communication, creative projectexperiences/issues, intellectual property,etc.) was coded into common categories.For example, anything relating to scheduling

problems was coded under the heading timeissues and manually as time lag in delivery.

In the second stage, axial coding, the higher-level categories were built from groups ofseveral categories from the open coding. Thisenabled relationships between the categoriesto emerge. For example, the time issues code(from above) was grouped under a high-levelcategory called project problems. And thecode identied as time lag in delivery wasrelated to breakdown in trust.

In the nal stage, selective coding, categorieswere rened until clear relationshipsbetween them were identied, leading to thedevelopment of a theory about the data. Thisstage merged similar/overlapping categoriestogether and removed duplication. This led tothe production of a rened tree structure ofcategories, which enabled important themesto be uncovered. For example, the relationshipcategory of valuing creativity was selected ascreativity transfer.

Two types of triangulation, by data andinvestigator,30 support the e

creating innovation in small and medium-sized enterprises

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