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ERC Research Showcase presentations 29.01.2018

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Presentation slides from the ERC Research Showcase. January 29th 2018 at the RSA, London.

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ERC Research Showcase presentations 29.01.2018

  1. 1. @ERC_UK ERC Research Showcase 29 January 2018 RSA
  2. 2. Bo Peng, Kevin Mole and Stephen Roper Skills, management practices and productivity in SMEs Acknowledgement: We are grateful to Prof James Hayton (Warwick Business School) for providing the data on which this analysis is based
  3. 3. Opening remarks • L&M practices and quality have received significant attention recently. In the Industrial Strategy we find the assertion that ‘management skills could account for a quarter of the productivity gap between the UK and US’ (p. 169). • Skills alone cannot however drive productivity. • It is only when skills drive practical actions- strategy or practices - that they can drive performance. Un-utilised or under-utilised skills will yield no performance benefits. • Here, we match survey data on management skills and practices in a large group of SMEs with longitudinal data on productivity and examine the causal links between skills, practices and productivity
  4. 4. Skills, practices & performance • In the identification of firm growth Edith Penrose suggested managers have ‘opportunity sets’ where they identify opportunities to grow, as a managerial constraint • Evidence links managerial skills indirectly to firm performance often through their impact on goal setting, and the communication of vision – both of which might be considered management practices • Standard economics suggests that more efficient businesses will grow (e.g. Lucas) • Extensive evidence links HR practices to performance often as a bundle of high performance work practices
  5. 5. Hypotheses • H1: Higher entrepreneurial skill levels will be associated with the adoption of a more structured managerial practices. • H2: Adoption of more structured managerial practices will lead to higher productivity. Skills Practices Productivity
  6. 6. Data sources • Skills, practices • Study undertaken by James Hayton (ERC/WBS) in 2014 (and published in BEIS Research Paper series • Aim to capture generic skills and practices important across all sectors • IDBR based telephone survey of firms with 5-250 employees conducted in 2014, independent companies only • Focus here only on firms with a solo lead manager rather than a team management structure – around 1700 firms • Dimensions of skills: Leadership, Entrepreneurship, Organisational, Technical • Dimensions of strategy and practices: Centralised, formalised, responsive and HRM • Productivity • Business Structures Database for 2017 • Measured as turnover per employee • Matched with L&M survey data using reference numbers
  7. 7. Skills measures Leadership (0.808) • Organising and motivating people • Delegation • Supervise and lead Entrepreneurial (0.734) • Accurately perceive gaps • Identifying market opportunities • Seizing market opportunities • Identifying demands Organisational (0.758) • Allocating limited resources • Organising and co-ordinating tasks • Managing effective working Technical (0.744) • Technical or functional expertise • Product or service development
  8. 8. Strategy and practices Centralised Strategy (0.561) • Strategy set by the CEO • Vision set by the CEO • Strategy implementation led by CEO Formalised Strategy (0.782) • Formalised planning process • Strategic plan • Mission statement Responsive Strategy (0.781) • Competitor analysis • Collaborative strategy formation • Planning involves all staff Human Resource Mment (𝜶𝜶 =0.670) • Training • Performance appraisal • Recruitment practices • Incentive related payments
  9. 9. Key results (n=1,774) (significant relationships) Productivity 2017 Entrepreneur Skills Leadership Skills Organisational Skills Technical Skills Centralised Strategy HR Practices Responsive Strategy Formalised Strategy Adding an additional HR practice adds around 2 per cent to productivity after 3 years
  10. 10. Implications • Strong support for Hypothesis 1 and in terms of HR Practices Hypothesis 2 • Skills matter as they are strongly associated with practices • Practices matter as they lead to productivity growth. Same outcomes for small (5-49) and medium sized (50-249) firms • In policy terms this suggests complementarity between training to build skills and mentoring/coaching to help firms develop practices. • Both may be necessary to maximise the productivity gains
  11. 11. Next steps • To test the mediation between the skills and the practices i.e. do some skills in year 1 directly link to productivity in year 1+3 • To test the same relationships hold for different types of firms such as family firms • To test whether similar or stronger relationships hold for more experienced managers
  12. 12. And the small print … • The statistical data used here is from the Office of National Statistics (ONS) and is Crown copyright and reproduced with the permission of the controller of HMSO and Queens Printer for Scotland. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. The analysis upon which this paper is based uses research datasets which may not exactly reproduce National Statistics aggregates.
  13. 13. Areti Gkypali, Enrico Vanino, Stephen Roper and Nola Hewitt-Dundas Assessing the spillovers from publicly funded R&D projects – some initial results
  14. 14. Starting points... • Spillovers from R&D and innovation activity are of two main sorts (Griliches 1992, 1979): – Rent spillovers arise when quality improvements by a supplier are not fully translated into higher prices for the buyer(s). Productivity gains are then recorded in a different firm or industry than the one that generated the productivity gains in the first place. – Pure knowledge spillovers are benefits of innovative activities of one firm that accrue to another without any market transactions. • Spillovers are important as they define the difference between the private and social value of R&D and innovation and provide the rationale for public intervention (Arrow, 1962). The stronger and more positive spillovers the stronger the rationale for public intervention.
  15. 15. Starting points… • Here, we are going to focus on local knowledge spillovers and their innovation impact as: – ‘local knowledge is … a semi-public good that is spatially bounded … local knowledge exchange is prompt or spontaneous because local firms are assumed to be more willing to share knowledge and exchange ideas with other local actors as a result of shared norms, values, and other formal and informal institutions that hold down misunderstanding and opportunism’ (He and Wong, 2012, p. 542). • So our research question is: Do Research Council funded R&D and innovation projects generate local knowledge spillovers which raise levels of innovation in their locality? • The answer is yes. But its not quite as simple as you might think!
  16. 16. Spillovers – one way firms get knowledge to drive innovation… Innovation Outcomes Interactive Learning Spillovers Non-interactive Learning Knowledge Context Encoding Capacity Innovation Ambition Spatial Sectoral Network Learning mechanisms ACAP Source: Roper and Love (2017) ‘Knowledge context, learning and innovation: an integrating framework’, Industry and Innovation, forthcoming.
  17. 17. Two other things you need to know… Process and output spillovers • Spillovers may result from the process of innovating or the outputs of the innovation process • Process spillovers are pure knowledge spillovers resulting from knowledge diffusion – positive • Output spillovers occur through supply-chain (+ve) or local competition (-ve) effects • Research Council projects may generate both types of spillover so their sign is ambiguous ex ante Absorptive capacity • Firms have very different internal resources in terms of R&D, skills and innovation capacity • Smaller firms are typically thought of as having less absorptive capacity than larger firms • Smaller firms may therefore be less able to benefit from spillovers • Smaller firms may benefit less from process spillovers and be more vulnerable to local competition effects.
  18. 18. Our empirical focus here… from spillovers to innovation Innovation Outcomes Interactive Learning Spillovers Non-interactive Learning Knowledge Context Encoding Capacity Innovation Ambition Spatial Sectoral Network Learning mechanisms ACAP Source: Roper and Love (2017) ‘Knowledge context, learning and innovation: an integrating framework’, Industry and Innovation, forthcoming.
  19. 19. Data and methods Augmented Innovation Production Function (AIPF) 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝑛𝑛𝑛𝑛𝑛𝑛,𝑛𝑛𝑛𝑛𝑛𝑛 = 𝑓𝑓 𝐼𝐼𝐼𝐼𝐼𝐼. 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼, 𝐻𝐻𝐻𝐻, 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶, 𝑅𝑅&𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠, 𝛽𝛽𝑥𝑥𝑖𝑖 UKIS  Waves 6-9 ~12k obs GtR*: Number of grants received at the LEP or NUTS3 level * Vanino E., Roper S. and Becker B., (2017). ‘Assessing the business performance effects of receiving publicly-funded science, research and innovation grants’, ERC Research Paper 61
  20. 20. Key results (1): Size matters! • Small firms: (consistently) negative effect of publicly funded R&D spillovers on their innovation performance (c. -3 %) • Medium firms: (consistently) positive effect of publicly funded R&D spillovers on their innovation performance (c. 1 %) • Large firms: (on the whole) positive effect of publicly funded R&D spillovers on their innovation performance (c. 0.5%)
  21. 21. Key results (2): Collaboration matters! • Positive effects of R&D spillovers for medium and large firms which collaborate with at least one partner for innovation (c. 1-1.5%) • For non-collaborating large firms this effect turns negative (c. -2%)
  22. 22. Key Results (3): Geography matters! • Firms located in London/South East: consistently negative effect of R&D spillovers on innovation performance, especially for small firms (c. -2 to -6 %) • Firms located in the rest of the UK: only medium firms seem to benefit from publicly funded R&D. (c. 2 %)
  23. 23. Next steps… • Modelling has proved challenging here so requires more robustness testing of results – Other spillover indicators – Different geographies • Also want to test growth and productivity effects not just effects on innovation. A different modelling exercise • Industry spillovers may also be interesting alongside those related to geography • Working towards a research paper in March/April
  24. 24. Victor Ekpu and Mike Wright E: mike.wright@imperial.ac.uk Demand for growth capital in peer-to-peer business lending markets
  25. 25. Introduction • Debate about role of traditional finance providers • High growth firms more likely to seek external finance – Unmet demand for growth capital limits firm growth and constrain economic recovery (Rowlands, 2009; BIS, 2012; Fraser et al., 2015 • Role of alternative financiers in filling equity gaps – Peer to peer (P2P) lending • £62 million in 2012 to £1.49 billion in 2015 • Funding Circle lending boosted annual economic output by £2.7 billion, measured via gross value added (CEBR, 2016).
  26. 26. Research Questions • Know little about: – Types of firms approaching peer-to-peer lending – Types of capital sought (growth, working capital). • What firm characteristics determine the likelihood of obtaining growth capital from peer-to-peer lending platforms?
  27. 27. • Demand for financing influenced by – Firm age, firm size, sector (Mac an Bhaird, 2010) • Second finance gap – Supplement short term debt with larger amounts of long term debt or external equity (Wilson, et al., 2018). • Maturity – Track record, Less growth ambition, earnings consolidation • Beyond maturity Avoiding decline; reinvigoration of business model • High growth sectors • Various studies show difficulties for these to raise growth capital (eh Menzies, 2016).
  28. 28. • Data • 39,268 loan-level observations • Funding Circle loan book 2010 to 2017 • Variables – DV: demand for growth capital compared to working capital/asset finance loans (1,0) • IVs: – Age => stage (Robb, 2002; Mac an Bhaird, 2010) – Sector • Controls: risk band, region, loan year, macro
  29. 29. P2P Loans by Firm Growth Stage Firm Stage Description Frequency Percent of Total Young (1)* 1-4 years 5,003 12.74 Early Growth (2)* 5-11 years 3,827 9.75 Late Growth (3)* 12-19 years 9,002 22.92 Mature (4)* 20-29 years 2,483 6.32 Old (5)* 30-49 years 5,282 13.45 Very Old (6)* >=50 years 13,671 34.81 Total All firms 39,268 100.00
  30. 30. Dependent Variable: Growth Capital Model 2 Coefficient MEMS Coefficient Constant .141***(0.01) n/a .138***(0.00) Firm Stage Young (base level) n/a n/a -.370***(0.00) Early growth .236***(0.00) .059***(0.00) -.108***(0.01) Late growth .071**(0.05) .018**(0.05) -.300***(0.00) Mature -.082 (0.11) -.020 (0.11) -.366***(0.00) Old .248***(0.00) .062***(0.00) -.185***(0.00) Very old .318***(0.00) .079***(0.00) n/a Industry Property & construction -.659***(0.00) -.162***(0.00) Manufacturing & engineering -.172***(0.00) -.043***(0.00) Transport & automotive -.189***(0.00) -.047***(0.00) IT & telecoms .187***(0.00) .046***(0.00) .415***(0.00) Retail & wholesale .157***(0.00) .0392***(0.00) .389***(0.00) Knowledge services .159***(0.00) .0397***(0.00) .356***(0.00) Leisure & hospitality .167***(0.00) .042***(0.00) .417***(0.00) Stage-Industry Interactions Late growth*IT & telecoms .261***(0.00) Young*Retail & wholesale .245***(0.00) Old* Retail & wholesale .192**(0.02) Old* Leisure & hospitality .371***(0.00) Early growth* Knowledge services .201**(0.03) Late growth* Knowledge services .205***(0.00) Old* Knowledge services .243***(0.00) Demand for Growth Capital in P2P Business Lending Markets N 39,227 39,227 Log likelihood -26538.994 -26698.302 LR chi-square (df.) 1285.82***(19) 967.21***(23)
  31. 31. 0.00% 5.91%*** 1.77%** -2.00% 6.19%*** 7.94%*** -4.00% -2.00% 0.00% 2.00% 4.00% 6.00% 8.00% 10.00% Young Early Growth Late Growth Mature Old Very Old Demand for Growth Capital by Firm Stage (Base Level = Young) Marginal Effects at Means (MEMS) for Model 2
  32. 32. -16.20%*** -4.30%*** 3.92%*** 4.65%*** -4.74%*** 3.98%*** 4.16%*** -20.00% -15.00% -10.00% -5.00% 0.00% 5.00% 10.00% Property & Construction Manufacturing & Engineering Retail & Wholesale IT & Telecoms Transport & Automotive Knowledge Services Leisure and Hospitality Demand for Growth Capital by Firm Sector Marginal Effects at Means (MEMS) for Model 2
  33. 33. Summary Findings • Demand for growth capital higher in early stages of growth than younger firms less than 5 years old (second “finance gap”) • Older firms above 30 years old, higher likelihood of securing growth capital as age increases • Demand for growth capital higher in certain growth stages in the IT and telecoms industry, retail and wholesale sectors, leisure and hospitality and knowledge services industries.
  34. 34. Implications • Promote peer-to-peer lending platforms as complementary finance for early growth stages/high growth potential firms and well- established firms entering new markets/products. – Older firms might have high growth ambitions to stay ahead of competition & reduce decline risks • Are they discouraged borrowers? And/Or unwilling to see equity dilution? • IT and telecoms industry, retail and wholesale sectors, leisure and hospitality and professional and business support industries • But what about default?.......
  35. 35. • Default risk: borrower's risk rating, sector, loan term, interest rate, purpose most influential • Loans for growth/asset lower default than working capital 1.43% 4.01% 5.28% 5.64% 4.31% 3.07% 5.56% 7.00% 6.93% 5.93% 0.00% 1.00% 2.00% 3.00% 4.00% 5.00% 6.00% 7.00% 8.00% Asset Finance Growth Capital Working Capital Other Total Default Ratio and NPL Ratio by Loan Purpose Default Ratio NPL Ratio
  36. 36. Mark Hart and Stephen Roper Insights from ERC phase 2 SMEs, growth, innovation and productivity – What have we learnt?
  37. 37. Key finding from ERC Phase 2 (2016-17) Stephen Roper and Mark Hart
  38. 38. SME growth and productivity…
  39. 39. Business Population Dynamics – A Primer • Three things we need to remember as we seek to understand the drivers of small firm growth: – Churn – Age – Size Anyadike-Danes, M and Hart, M (2017) “Firm and job dynamics in the United Kingdom before, during and after the global financial crisis: Getting in under the hood” chapter in OECD Business Dynamics & Productivity, April 2017 Anyadike-Danes, M and Hart, M (2016) “Peeling back the layers: separating the effects of age and size on UK job growth, 2008–2015, ERC WP
  40. 40. Churn • UK business population is in a constant state of flux. • Each year around 250,000 firms are born and just over 200,000 die. • So the population (currently just over 1.8 million) typically grows a little, but underlying that growth are much larger inflows and outflows of firms. Source: Bespoke analysis from ERC UK Business Demography Database (1997-2017) – based on the ONS Business Structure Database (BSD) – compiled from annual abstracts from the IDBR.
  41. 41. Age • The most important factor conditioning firm performance is age. • Of the quarter of a million firms born in a particular year, more than 80% are dead by age 10. • Not only does survival depend critically on age, but growth in jobs does too. • By age 10 a relatively small proportion of the surviving firms have grown, most that have grown have not grown very much, and most of those that do grow at all do so in their first five years. Source: Bespoke analysis from ERC UK Business Demography Database (1997-2017) – based on the ONS Business Structure Database (BSD) – compiled from annual abstracts from the IDBR.
  42. 42. Size • Of the quarter of a million firms born in a particular year around 90% have less than five employees. • ….and around 85% of 10 year survivors still have less than 5 employees. • However, size does have some effect: very small firms do grow a little faster than larger firms, but have slightly worse chances of surviving. Source: Bespoke analysis from ERC UK Business Demography Database (1997-2017) – based on the ONS Business Structure Database (BSD) – compiled from annual abstracts from the IDBR.
  43. 43. Job Creation & Destruction • Analyse how the business stock in the private sector in the UK has changed between 1998 and 2017 – specific focus on the key dynamics of job creation and destruction. • These metrics help us to understand the level of turbulence in jobs and to analyse the type of firms which most contribute to job creation / destruction in the UK. • Using employee data, we examined the average annual job creation and destruction rates between 1998-2017, as well as entry and exit rates, and disaggregated both these by sector, size, age and region. Source: See Hart, M; Anyadike-Danes and Bonner, K (2011) “Job Creation and Destruction in the UK – 1998- 2010” BIS Report October 2011 – available at https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/32244/11-1326-job-creation- and-destruction-uk-1998-2010.pdf Headline analysis updated in November 2017.
  44. 44. Definitions • Job creation can be broadly defined as the positive gross change in employment, summed over all businesses that expand or start up between two points in time. • Likewise job destruction is the negative gross change in employment summed over all businesses that contract or close between two points in time. Source: See Hart, M; Anyadike-Danes and Bonner, K (2011) “Job Creation and Destruction in the UK – 1998- 2010” BIS Report October 2011 – available at https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/32244/11-1326-job-creation- and-destruction-uk-1998-2010.pdf Headline analysis updated in November 2017.
  45. 45. Job Creation & Destruction Update - 2017 Source: ONS Business Structure Database
  46. 46. Job Flows – 1 in 5 jobs changing ‘locations’ in 12 months Source: Brief commentary on method and interpretation in ERC Blog December 2017 – available at https://www.enterpriseresearch.ac.uk/turbulent-times-business-usual-4-5-million-jobs-created- destroyed-12-months/
  47. 47. HIGH-GROWTH FIRMS?
  48. 48. Background • During the last decade High-Growth Firms (HGFs) – sometimes referred to as ’Scale-Ups’ – have increasingly become an established feature of the UK business policy landscape. • Indeed, HGFs are mentioned in the government’s recently published policy document ”Building our Industrial Strategy”, and are now considered sufficiently important that the Minister for Small Business has taken on the role of ”Scale-Up Champion”. • Whilst we know something of the characteristics of these firms – about their age, size, sector and location – we know relatively little about the dynamics of the HGF population as it evolves over time. • For the most part attention is focused simply on the annual count which is not an entirely appropriate measure of HGF activity.
  49. 49. Key Findings • The average age at which a firm becomes categorised as a High-Growth Firm (HGF) – that is, records its first High-Growth Episode (HGE) – is about six years (see chart on next slide) • Tracking HGFs over their lifetime, we show that almost two- thirds of HGEs recorded during a 3-year period, and conventionally referred to as HGFs, are actually repeat episodes being recorded by HGFs ’born or first classified some years previously (see chart on next slide). • Although there is some variation across cohorts the picture looks pretty consistent: by age 10, 40% of all 10+ firms have experienced at least one HGE.
  50. 50. High Growth Episodes – Age of Firm and Repeats? • On average around two- thirds of the HGEs recorded by the UK’s HGFs are repeat episodes. • The widest swings occur in and around the GR period, with the largest shares being recorded in 2008/11 and 2011/14. • Looking at the average age of HGFs – or rather the average age at which a firm records its first HGE. • The most striking feature of the age series is its remarkable constancy – it hardly varies at all – average age is never greater than 7 years, and rarely lower than 6 years.
  51. 51. Start-up Cohort 1998 & HGEs
  52. 52. Summary of analysis of Cohort 1998: Anyadike-Danes & Hart (2015) “Fecundity, Fertility, Survival & Growth: HGFs in the UK and their contribution to job creation – a demographic perspective” ERC Working Paper (September 2015) DOI: 10.13140/RG.2.1.3360.8089
  53. 53. Final Thought • Having started the ball rolling a decade ago with our work for NESTA (Vital 6%) we are now clearly of the view that “There’s no such thing as a High-Growth Firm only firms that have high-growth episodes” • That should be the focus on policy moving forward and we await the development of the business support offers in 2018!
  54. 54. PRODUCTIVITY
  55. 55. A Simple Story of Productivity! – 2008-15 Turnover Growth Job Growth Zero Zero ‘Green Zone’ + + + - - - Only one ‘space’ where growth in T/O; Jobs and productivity are all +ve – the ‘green zone’ But sparsely populated with firms – approx. 10% …and more than half of them where there is very little growth – the blue triangle Rule of thumb – 74% of firms which grow turnover grow productivity; 21% of firms which grow jobs grow productivity
  56. 56. Productivity and OECD High-Growth Firms? • Only 20% of 10+ employee firms in the ‘green zone’ are HGFs (T/O definition) • Only 5% of 10+ employee firms in the ‘green zone’ are HGFs (Jobs definition) • So from a productivity perspective HGFs are not an important group of firms
  57. 57. ‘Average’ productivity: a cautionary tale • The discussion has traditionally focused on ratios of outputs to inputs computed at the economy-wide level, an average productivity measure which we refer to here as the ‘aggregate’ measure • Researchers now have access to firm-level performance data and can compute an average productivity measure directly from firm-level productivity levels which we refer to here as the ‘mean’ measure
  58. 58. 58 Mean Vs Aggregate Productivity – summary statistics 2008 2015 2015/08 units ratio firms number 250323 turnover £bn 1393.85 1929.82 1.385 jobs 000 9656.1 10137.4 1.05 turnover per firm £m 5.57 7.71 1.384 jobs per firm number 38.57 40.5 1.05 average turnover per job: 'aggregate' £'000 144.35 190.37 1.319 average turnover per job: 'mean' £'000 160.2 170.8 1.066
  59. 59. 0 0.05 0.1 0.15 0.2 0.25 0.3 1 1.6 2.7 4.5 7.4 12.2 20.1 33.1 54.6 90 148.4 244.7 403.4 665.1 1096.6 1808 2981 4914.8 8103.1 13359.7 22026.5 36315.5 ratio turnover per job (tperj) £K Figure 2: productivity by firm size, selected sizes, relative frequency ratio, 2008 (log scale) 1.6 jobs 2.7 jobs 90 jobs 148.4 jobs
  60. 60. What have we learned? • Measurement matters: different measures of “average productivity” and its growth are not created equal • Size matters too! The relationship between productivity and firm size is quite subtle: a large proportion of all sizes are concentrated in the middle of the productivity distribution. There are slightly more smaller firms below the middle, and slightly more larger firms above the middle • There are “long tails” of both under-performing and over-performing firms particularly at the small end of the firm size distribution
  61. 61. And turning to innovation and innovation support …
  62. 62. Innovation and productivity (adapted from the OECD) Growth at the global frontier Growth at the national frontier Growth of non-frontier firms Diffusion of NTF innovation Diffusion of NTF innovation NTM innovation Aggregate Productivity Growth
  63. 63. The geography of NTM and NTF innovation in England
  64. 64. Does partnering matter for NTM and NTF innovation? • Does partnering matter for NTM and NTF innovation? • Micro-firms in NI (N=1000) – the extreme case of limited absorptive capacity? • OTOH very small firms may have the most to gain from partnering • Key result: Partnering matters in both cases but NTM networks can be broader Source: Roper, S., & Hewitt-Dundas, N. (2017). Investigating a neglected part of Schumpeter’s creative army: what drives new-to- the-market innovation in micro-enterprises? Small Business Economics, 49(3), 559-577.
  65. 65. Can working with a university influence NTM innovation and its success? • Collaborating with universities will reflect the type of knowledge the firm is seeking as well as their own internal knowledge profile • Provide frontier-edge knowledge for NTM innovation • Reduced risk of moral hazard • Offset by two-worlds paradox (Hall, 2003; Bruneel et al. 2010) differences in institutional logics and priorities may lead to tensions around project timelines, rewards, commercialisation and administrative procedures • Can learning from prior collaboration help firms to overcome this paradox? • Analysis of the UK Innovation Survey with different finding for larger and smaller firms Prob of NTM innov Sales of NTM innov Small +22 % +1.3% Medium +21 % +15.8% Large + 21 % +12.3% Source: Nola Hewitt-Dundas, Stephen Roper & Areti Gkypali (2016) Can learning help to overcome the ‘two-worlds’ paradox in university-business collaboration? Effects of university collaboration by firm size Key findings 1. University collaboration is good for NTM innovation but…. 2. There is an issue in the commercialisation of NTM innovation in smaller firms
  66. 66. Adopting TQM or ISO 9000: The implications for innovation? • What happens to innovation when a firm adopts a new quality improvement method? • Does this effect differ between ’hard’ (rule based) QIMs and ‘soft’ (organisational) QIMs? • Considered this for sample of Irish manufacturing firms (n=1358) • Evidence of larger disruption effects from (hard) ISO 9000 than (soft) Quality Circles. TQM is somewhere in between. Long term effects are beneficial. • Lesson: Quality improvement and innovation should be considered together. Anticipate a short-term hit too. Time Innovation Output A B I1 I2 I3 Source: Bourke, J and Roper, S (2017) Innovation, quality management and learning: short-term and longer-term effects, Research Policy, forthcoming but available on-line.
  67. 67. Assessing the effectiveness of UK innovation support • UK policy delivered through InnovateUK and the other research councils (particularly EPSRC) focuses on supporting NTM innovation • Our analysis matches projects (GTR) with data on business performance (BSD) over 2006-16 period • This covers grants provided to over 10,000 firms and we compare the performance of these firms to a closely matched control group • Firms participating in UK Research Council projects (including Innovate UK) grew their turnover and employment 22.5-28.0 per cent faster in the six years after the grant, than similar firms which did not receive support. • The net effect is a 6.2 per cent productivity boost after 6 years. Source: Vanino, E Roper, S and Becker, B (2017) ‘Assessing the business performance effects of engagement with publicly funded science’, ERC Research Paper 61 0 20 40 60 All firms Manuf - HT Manuf - LT Services - KI Services - NKI Micro Small Medium Large Top Quartile 2nd Quartile 3rd Quartile 4th Quartile Turnover growth effects
  68. 68. Additionality by productivity quartile 0 20 40 4th 3rd 2nd 1st 0 50 100 4th 3rd 2nd 1st -20 0 20 40 4th 3rd 2nd 1st Employment growth effects Turnover growth effects Productivity growth effects
  69. 69. Additionality by productivity quartile 0 20 40 4th 3rd 2nd 1st 0 50 100 4th 3rd 2nd 1st -20 0 20 40 4th 3rd 2nd 1st Employment growth effects Turnover growth effects Productivity growth effects 60.35 11.555.722.5 0 100 1st2nd3rd4th Allocation of support for business R&D
  70. 70. The key lessons … For policy • L1: Marked differences exist between innovation performance in different parts of the UK. We need to understand this better. • L2: We have some effective instruments for NTM innovation but we need to refine our targeting to maximise productivity impacts • L3: Connectivity generally and University-Business collaboration are good for innovation. We may need to do more to broker and help commercialise outcomes for smaller firms . For managers • L1: Partnering matters for innovation, particularly for smaller, independent companies. • L2: Working with universities and in research grants has positive innovation and growth benefits • L3: Innovation may be strongly influenced by other managerial actions such as quality improvement. Be careful to keep your eye on the ball !
  71. 71. @ERC_UK ERC State of the Art (SOTA) Reviews
  72. 72. Overview • A publication innovation for the ERC • Short (3-4 pages) reviews of the state of evidence/knowledge on specific, topical issues/questions • Clearly summarise a range of sources of relevant and robust research and policy literature – noting key evidence points and resources for further information • Commissioned from UK and international experts • Provide an opportunity to widen ERC expert networks and contacts • Propose to produce 20 SOTA reviews in Year 1, and 10 in both subsequent years
  73. 73. Questions • What would be your suggestions for priority themes/questions for the SOTA reviews? • Examples… – What policies are most effective in encouraging greater female entrepreneurship? – What does the evidence tell us about the relationship between ambition and innovation in SMEs? – What is the relationship between exporting and productivity in SMEs? …Over to you!
  74. 74. Mark Hart and Stephen Roper Planned ERC Activity 2018-20
  75. 75. Key ERC activities • Knowledge creation – Core research programme agreed with ERC Funders Group – for 2018- 21 this has an increasing emphasis innovation and productivity – Commissioned projects - more specific (and sometimes short-term) one off projects for Funders and other organisations • Knowledge integration and synthesis – SOTA reviews – Data development and matching – Advisory roles and responsive activities (e.g. HMRC) – Consultancy (e.g. OECD, HS2) • Engagement, influence, impact • Capability building – internal and external
  76. 76. Core research projects 2018-19 Feb-May Themes June to January 2019 February 2019-September 2019 State of the Art (SOTA) Briefings on key aspects of SME growth and developm ent Finance and Investment Investing for the future? Investigating the determinants and barriers of investment in smaller firms Investment, non-borrowing and place. How does SMEs’ willingness to invest and borrow vary with place? How has this changed through time? Leadership and management practices Leadership and management practices and the take-up of innovation. How do internal and boundary spanning management practices influence adoption in different sectors and localities? L&M capabilities – levering external assets for growth –How do internal and boundary- spanning management practices enable firms to most effectively take advantage of such external resources? Innovation and growth Innovation and productivity in SMEs – which types of innovation and which combinations of innovation drive SME productivity? Knowledge to money – What are the links between IP protection, innovation and growth? Diffusion and productivity Understanding local productivity disparities – What explains productivity differences between local areas? Learning from the best (i.e. most productive) SMEs – What are the most productive SMEs doing right? How can we effectively diffuse these practices?
  77. 77. Commissioned projects Business resilience in disadvantaged groups (£750k, JP Morgan Foundation, 24 months, Nov 2017) • Analysis of personal and business resilience among business founders from disadvantaged communities • ERC leading 5 country study with research partners in Spain, Italy, France and Germany • Key outputs: Research outputs and business development tool kits Micro-business Britain (c. £500k, BEIS, Nov-March 2018) • Survey of c. 11,000 micro-businesses in UK, Ireland and the US with a focus on tech diffusion, innovation and ambition • Partners in the US (Georgia Tech) and Ireland (Cork) (Pro-bono) • Key outputs: Research dataset covering key IS strategy issues Diffusion and Productivity in Foundries and Metal Forming Companies (£400k, IS Funding, 36 months ) • Which innovations have driven productivity growth? How can we encourage their wider diffusion? • ERC working with two industry associations for the two sectors • Key outputs: Research outputs and practice models for developing diffusion Other projects: Design economy 2017 – Design Council, £60k, Jan to April 2018 Impact assessment of Account Management – Scottish Government, £23k, Nov to April 2018 SMEs in NI – set of projects for Department for the Economy (NI), £75k, March 2018-19 Growth Hub data matching and analysis- BEIS
  78. 78. Data development and integration • Key aspect of ERC work has been addressing UK data deficit – UK and in partnership with OECD (Dynemp; Multiprod) • Key data sources and matching: – Business Structure Data UK Innovation Survey – Employer Skills Survey Longitudinal SB Survey – SME Finance Monitor Insolvency service data – Gateway to Research CRM data • Main new addition in 2018 will be IPO data and hoping to build IP histories for firms to set alongside performance and innovation data • ERC also support the User Groups for the Longitudinal Small Business Survey and the UK Innovation Survey
  79. 79. Vicki Belt ERC Impact and Engagement Plans 2018-2021
  80. 80. ERC Strategic Goals To be THE UK’s ‘go to’ centre of research expertise on SME growth, innovation and productivity • Providers of independent, trusted data and insight, based on rigorous analysis • Delivering relevant research that focuses on the issues that policy makers and businesses face • Giving useful advice, and practical, actionable recommendations
  81. 81. Context • UK is facing big challenges - Brexit; Industry 4.0; productivity gap • Rapid pace of change for policy makers and businesses • Increased demand for timely insights and advice from trusted sources. But… “Major hurdles remain in connecting policy makers with the wider research community”. Policy makers are looking for: ‘honest brokers’, ‘expert advice’ ‘impartiality’, ‘a guide to current thinking’. (Lawrence, et al, 2016) • Clear need for information that is accessible, properly packaged and communicated
  82. 82. Firm foundations • ERC has many key strengths: – Good networks – policy, academic and business – Responsiveness and flexibility – Openness and willingness to share expertise – Range of research outputs – Highly regarded stakeholder-focused events – Solid infrastructure
  83. 83. Phase 3: Building on success Maximising impact • More structured stakeholder contact management • More defined project focus (…with flexibility built in) • Focus on producing accessible outputs • More research synthesis Widening awareness • Nation-wide engagement • Increased media activity • Increased social media presence
  84. 84. Mechanisms/activities Stakeholder engagement • Initial stakeholder mapping, and development of a new strategy • New steering committee, and refreshed advisory panel • Project advisory groups, milestones and communications plans • Annual conference, seminars and workshops Outputs • Policy briefings, infographics and blogs • SOTA Reviews • Annual State of Small Business Britain report Dissemination • Ring-fenced budget for media/PR activity, use of Comms Agency and wider connections Evaluation • New KPIs • Ongoing monitoring and feedback
  85. 85. Engaged scholarship Research idea Research design Ongoing research Analysis Outputs and dissemination Post-research phase Engage Stakeholder identification, mapping and approach Mutually define aims, engagement plans, impact pathways Communicate and review progress, address issues Share emerging findings, discuss and mutually interpret meaning Discuss and agree how to best present findings, integrate stakeholders in dissemination activity Maintain stakeholder relationships, review and monitor impact capture learning Impact
  86. 86. @ERC_UK Feedback on themes for ERC State of the Art (SOTA) Reviews
  87. 87. Thank you! Questions and comments? More information at http://enterpriseresearch.ac.uk/ Contact us: Steve Roper stephen.roper@wbs.ac.uk Mark Hart mark.hart@aston.ac.uk Vicki Belt vicki.belt@wbs.ac.uk This work contains statistical data from ONS which is Crown Copyright. The use of these data does not imply the endorsement of the data owner or the UK Data Service at the UK Data Archive in relation to the interpretation or analysis of the data. This work uses research datasets which may not exactly reproduce National Statistics aggregates.

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