Efficiency and evaluation analysis of a network of Technology Transfer brokers

Transcription

1 1 Efficiency and evaluation analysis of a network of Technology Transfer brokers Salvatore Amico Roxas - European Commission DG JRC Giuseppe Piroli - European Commission DG JRC Mario Sorrentino - Second University of Naples Porto, 18 July

2 Current understanding 1/2 2 Role of the broker in a technology transfer process DONOR BROKER RECIPIENT In literature: - determine the opportunities for technology transfer in terms of specific requirements; - identify appropriate sources of technical breakthroughs, scientific information and other technological developments (or findings) that should meet the identified needs; - build bridges between donor and recipient; - follow through initial contacts with other complementary services (e.g. on IPR and innovation finance), skills and inputs to accommodate the transfer of the technology.

3 Current understanding 2/2 3 Problem: Limited contributions have been provided so far in order to shed light on: - how to assess (and compare) brokers performances; - investigate on which factors are likely to affect such performances.

4 Research question 4 Aim of the paper is to model a proper production function for the TT brokers and provide insights on how to compare the performance of TT brokers and investigate on the influence of external factors on their efficiency. Goal of the study: - Comparison of the different brokers based on efficiency ; - Design of an appropriate production function; - Analysis of the influence of external factors on brokers efficiency.

5 Our approach 5 Focus on a European Network of Technology Transfer brokers (the socalled Innovation Relay Centre Network ), which is a network established by the European Commission aiming at supporting innovation and transnational technological co-operation in Europe via the provision of services primarily targeted to technology-oriented SMEs, but also available to large companies, research institutes, universities, technology centres and innovation agencies.

6 TT Networks: Focus su IRC 6 Innovation Relay Centres (IRC) Network 71 IRC Nodes (consortia), 210 organizations involved, over IRC consultants 33 countries: EU, Turkey, Iceland, Israel, Norway, Switzerland, Chile Main target: SMEs and Research Organizations Peculiar business model

7 IRC Business Model 7 Main services provided and activities performed by IRCs IRC TT process

8 Data 8 Survey conducted among the IRC nodes Questionnaire structure - Section 1: Information on the host organization - Section 2: Human resources involved in IRC activities - Section 3: IRC services provided by the host organization - Section 4: IRC performance Questions on the reporting period April 2006-March 2007 addressed typically to the manager of the IRC project in the host organization; 210 questionnaires sent; 63 complete answers (30% of the total), representing 40 different IRC nodes, 24 countries and 51 NUTS 2 regions.

9 Efficiency analysis 9 Literature analysis 1) literature on performance indicators for TT; 2) literature on efficiency in TT intermediaries. Detection of inputs and outputs to be considered in the production function of IRC brokers Selection of the most representative inputs ed outputs Outputs Mean Inputs Mean TTT 8 FTE 3.46 TOTR 22 EVENTO 3 TTTUN 16 EVENTP 7 NEWCLIENT 77 Variables TTT TOTR TTTUN NEWCLIENT EVENTO EVENTP FTE Description Number of TT deals signed Number of technology profiles promoted Number of TT deals under negotiation Number of new clients gained Number of brokerage events organized Number of brokerage events participated to Full Time Equivalent (Number of years/man employed)

10 Efficiency analysis 10 DEA (Data Envelopment Analysis) model: Non-parametric approach Efficiency calculated as radial distance from an efficient frontier Efficiency scores between zero (minimum efficiency) and 1 (maximum efficiency) (1) (2)

11 Efficiency analysis: Efficiency scores 11 ID Unit Score ID Unit Score 1 DE Sachsen-Anhalt 0,35 33 RO Romania 0,40 2 HU Kozep-Magyarorszag 0,30 34 SK Bratislavsky kraj 1,00 3 FR Provence-Alpes-Côte d'azur 1,00 35 IT Calabria 0,23 4 IT Toscana 0,31 36 IT Sicilia 0,18 5 FR Provence-Alpes-Côte d'azur 0,45 37 NL Netherland 0,85 6 UK North East 0,64 38 GR Sterea Ellada 1,00 7 EE Estonia 0,24 39 CZ Jihozápad 0,43 8 BG Bulgaria 0,48 40 MT Malta 0,52 9 LT Lithuania 0,32 41 IT Veneto 0,53 10 PL Poludniowo-Zachodni 1,00 42 FR Provence-Alpes-Côte d'azur 0,64 11 PT Norte 0,45 43 FR Haute-Normandie 1,00 12 Turkey 0,36 44 IT Piemonte 0,38 13 ES Comunidad de Madrid 1,00 45 ES Comunidad de Madrid 0,33 14 IT Lazio 0,34 46 Israel 0,94 15 DE Leipzig 0,47 47 IT Campania 0,39 16 GR Kriti 0,33 48 BE Région de Bruxelles-Capital 0,46 17 IT Sicilia 0,62 49 IT Lazio 0,51 18 IT Emilia Romagna 0,19 50 IT Lombardia 0,38 19 SI Slovenia 0,44 51 FR Rhône-Alpes 0,35 20 UK Yorkshire and Humber 0,81 52 IT Trentino Alto Adige 0,69 21 IE Ireland 0,83 53 ES Canarias 0,64 22 FI Länsi-Suomi 0,50 54 FI Finland 1,00 23 ES Cataluña 0,26 55 BE Vlaams Gewest 0,73 24 RO Sud-Vest (Macroregiunea patru) 0,25 56 ES Cataluña 0,56 25 UK South West 1,00 57 IT Puglia 0,22 26 IT Toscana 0,80 58 Israel 0,48 27 GR Attiki 0,44 59 ES Andalucia 0,23 28 IT Umbria 0,38 60 ES Andalucia 0,48 29 ES Comunidad Valenciana 0,40 61 DE Nordrhein-Westfalen 0,68 30 IT Lazio 0,31 62 IT Lombardia 0,35 31 SE Övre Norrland 1,00 63 IT Campania 0,44 32 FR Aquitaine 1,00

12 Efficiency analysis: Efficiency scores No data Graphical representation of the efficiency analysis ( EuroGeographics for the administrative boundaries)

13 Influence of exogenous factors 1/3 13 Ordered probit models : y i = β 1 x i + β 2 z i +ε i y i : efficiency scores Efficiency Score Dependent variable Y n. of DMUs % Low < if DMU has a low efficiency score Medium 0.283<X< if DMU has a medium efficiency score High > if DMU has a high efficiency score z i : exogenous factors Variable Description Type Gdp Regional per-capita Gross Domestic Product Control variable R&D Regional R&D expenditure intensity Control variable R&D_hrst Interaction between R&D and human capital in the region Control variable RRSII Revealed Regional Summary Innovation Index Control variable Employees Number of employees of the host organization Host features Inncapcl Innovation capabilities of IRC clients Clients features Snewclients Share of new clients over total clients Clients features Pull Ratio of technology request profile on total profiles promoted Clients features Smes Share of SMES on the number of total clients Clients features x i : inputs and outputs

14 Influence of exogenous factors 2/3 14 Tested hyphotheses: H1: Units operating in more developed and innovative contexts are more likely to perform better. H2: The host dimension should be positively linked to the likelihood an IRC unit reaches a high level of efficiency. H3: Units which have a higher share of technology requests are likely to be more efficient, therefore pull-oriented units are likely to be more efficient. H4: A high share of new clients over the total number of clients has a negative impact on the unit efficiency. H5: The innovation capabilities of the clients positively affect the unit efficiency. H6: The share of clients portfolio represented by SMEs is positively associated with the IRC unit performance.

15 Influence of exogenous factors 3/3 15 Hypothesis Variables Model A s.e. Model B s.e. Model C s.e. Model D s.e. Fte (0.123)** (0.234)*** (0.245)*** (0.207) Evento (0.106)** (0.172)*** (0.194)*** (0.191)*** Eventp (0.064)*** (0.152)*** (0.164)*** (0.164)*** ttt (0.059)*** (0.096)*** (0.106)*** (0.109)*** tttun (0.013)** (0.024)*** (0.024)*** (0.021)*** newclient (0.003)*** (0.010)*** (0.010)*** (0.009)*** totr (0.018)* (0.024)*** (0.024)*** (0.023)*** H1 Gdp (0.499) (0.485) (0.568) H1 R&D (0.010) H1 R&D_hrst (0.000)** H1 RRSII (2.476)** H2 Employees (0.000)** (0.000)** (0.000)** H3 Pull (2.560)*** (2.874)*** (2.180)*** H4 Snewclients (1.747)*** (1.837)*** (1.741)*** H5 Inncapcl (0.490)** (0.532)** (0.544)** H6 Smes (1.467)** (1.517)** (1.579)** observation Pseudo-R Log-likel Wald test χ LR χ *** 27.06*** 26.55*** BIC AIC LR χ 2 (categ.) 65.32*** 39.50*** 38.26*** 38.76*** Note: standard errors in parenthesis. LR χ 2 (categ.): β low β medium β high *p<0.10 **p< ***p<0.01

16 Managerial and policy implications 16 Main findings: a limited influence played by control variables we used (expressing the level of development and innovativeness of the regions); pull-oriented units ones; are more likely to be efficient than the push-oriented lasting relationship with clients strongly affects the DMUs efficiency; DMUs with a higher share of SMEs in the portfolio tend to outperform the other ones; clients with a higher orientation towards innovation are critical to allow the brokers to achieve higher performance; host-effect in terms of reliability and reputation.

17 Main references 17 Arora F., Fosfuri A., Gambardella A., Markets for technology. The Economics of Innovation and Corporate Strategy, MIT press, 2001 Bessant J., Rush H., Building bridges for innovation: the role of consultants in technology transfer, Research policy, 24, pag , 1995 Bozeman B., Technology transfer and public policy: a review of research and theory, Research policy, N. 29, pp , 2000 Hargadon A. B., Sutton R. I., "Technology Brokering and Innovation in a Product Development Firm", Administrative Science Quarterly, 42: , 1997 Hassink R., "Technology transfer infrastructures: some lessons from experiences in Europe, the US and Japan", European Planning Studies, 5, , 1997 Howells J., Intermediation and the role of intermediaries in innovation, Research Policy, n.35, pag , 2006 Leydesdorff L., Cooke P., Olazaran M., Technology Transfer in European Regions, in Journal of Technology Transfer, 27(1), pag 5-13, 2002 Maddala G.S., Limited-Dependent and Qualitative Variables in Econometrics, Cambridge, England: Cambridge University Press, 1983 Morgan E., Crawford N., Technology broking activities in Europe a survey, International Journal of Technology Management, Vol.12, N.3, 1996 Pries F., Guild P., Commercial exploitation of new technologies arising from university research: start-ups and markets for technology, R&D Management 37, 4, 2007 Winch G., Courtney R., The Organization of Innovation Brokers: An International Review, Technology Analysis & Strategic Management, Vol. 19, No. 6, , 2007