Sourcing: Upstream or Downstream? Exploring Knowledge-based Antecedents of Academic Entrepreneurship and Technology Tran

1. Sourcing Upstream or Downstream? Exploring Knowledge-based Antecedents of Academic Entrepreneurship and Technology Transfer Presented by: Irene Ramos-Vielba1 Co-authors: Pablo D’Este1; Mabel Sánchez-Barrioluengo2;Oscar Llopis3; Alfredo Yegros4 1INGENIO (CSIC-UPV), Spain 2Unit of Human Capital and Employment, JRC, EC, Italy 3ESC Rennes School of Business, France 4CWTS – Leiden University, The Netherlands

2. MOTIVATION • What are the main sources of ideas for scientists’ inventions that display high commercial prospects? • Focus on the two dominant modes of commercialisation: OR • Are knowledge-based antecedents of inventions commercialised through firm creation and technology transfer: similar or different?

3. BACKGROUND: KNOWLEDGE SOURCES 2 key specific individual sources of information & knowledge from regular research activities: • Upstream knowledge sources: Outstanding contributions to science • Those scientists are responsible for a disproportionally large share of discoveries with a high commercial value (Zucker & Derby, 1996, 1998; Lowe & Gonzalez-Brambila, 2007) • Downstream Knowledge sources: Interaction with beneficiaries of research • Those scientist have a greaterawareness & understanding of specific needs & problems faced by non-academic actors (Shane, 2000; Perkman & Walsh, 2009)

4. HYPOTHESES

5. HYPOTHESES 1 • Upstream knowledge sources: Hypothesis 1a:We expect that upstream sources of knowledge are positively associated with academic entrepreneurship and technology transfer. Scientists who have a track record of high impact scientific contributions are more likely to engage in firm creation and technology licensing. Based on: • High impact scientific discoveries – pool of technologies – business opportunities to firms • Technological development – involvement of inventors – unique understanding of scientific knowledge Hypothesis 1b:We expect that upstream sources of knowledge are more strongly associated with academic entrepreneurship than with technology transfer. Scientists who have a track record of high impact scientific contributions are more likely to engage in firm creation than technology licensing. Based on: • Embryonic nature of scientific inventions – more difficult to rely on markets for technology • ‘People transfer’ vs. ‘technology transfer’ – not enough for licensing

6. HYPOTHESES 2 • Downstream knowledge sources: Hypothesis 2a:We expect that downstream sources of knowledge will have a positive effect on academic entrepreneurship and technology transfer. Scientists who have established direct interaction with potential beneficiaries of research are more likely to engage in firm creation and technology licensing. Based on: • Proximity to technology users – unmet technological needs – business opportunities identification • Contact with beneficiaries – understanding of the market place – business opportunities exploitation Hypothesis 2b: We expect that downstream sources of knowledge are more strongly associated with technology transfer than with entrepreneurship. Scientists who have established direct interaction with potential beneficiaries of research are more likely to engage in technology licensing than firm creation. Based on: • Building trust and social capital – reliable partners – transactional agreements • Direct interactions with users – higher technological resolution – identification of a suitable market

7. HYPOTHESES 3 • Interplay between upstream and downstream sources Hypothesis 3a:We expect that upstream and downstream sources of knowledge have a reinforcing effect on academic entrepreneurship. Scientists with a track record of high-impact scientific contributions and direct interactions with beneficiaries of research are more likely to engage in firm creation Basedon: • Central in academic-based networks + reinforced capacity through contacts with beneficiaries Hypothesis 3b: We expect that upstream and downstream sources of knowledge have a substitution effect on technology transfer. The detrimental effect of weak scientific performance on the likelihood of engaging in technology licensing will be compensated by scientists’ greater levels of direct interaction with research beneficiaries Basedon: • Identification of commercial potential – particularly of ordinary (non-outstanding) research findings

8. HYPOTHESES H1a: + Upstream knowledge sources H1b: Academic entrepreneurship H3a: + H2a: + Downstream knowledge sources H1a:+ Upstream knowledge sources Technology transfer H3b: - H2a: + H2b: Downstream knowledge sources

9. DATA & VARIABLES

10. DATA SOURCES • SURVEY DATA • A large scale survey on researchers at CSIC, Spain, all fields of science • Sample frame: the whole population of tenured scientists at CSIC: 3165 scientists • Scientists were invited to participate in an on-line survey, April - May 2011 • Response rate: 41%; 1295 valid responses // 1220 final valid responses • Respondents are representative of the target population: age, gender, academic rank (with minor differences regarding scientific field) • SECONDARY SOURCES OF INFORMATION • Administrative sources of information from CSIC: • Socio-demographic data (i.e. gender, age, academic rank, scientific field & institute of affiliation) • Information on R&D contracts, consulting and licenses • Publications from Web of Science (WoS) • No. of publications published by each scientist • No. of citations received by each paper • Directory of academic inventors & spin-offs (founded prior to 2008)(PhD Thesis, 2008)

11. MEASURES: DEPENDENT VARIABLES We build our measures from the responses to the survey 1. We capture entrepreneurship as: FIRM CREATION (Spin-offs) Whether scientists have engaged at least once, over the period 2009-2011, in: • the establishment of a company • DV1: Engagement in spin-off formation as a dichotomous variable: “1”: Engagement “0”: No engagement • 2. We capture technology transfer as: LICENSING AGREEMENTS (Licenses) • Whether scientists have engaged at least once, over the period 2009-2011, in: • technology licensing agreements (based on patents or other industrial / intellectual PRs) • DV2: Engagement in licences agreements as a dichotomous variable: • “1”: Engagement • “0”: No engagement

12. MEASURES: DEPENDENT VARIABLE Proportion of scientists involved in spin-offs and licensing

13. MEASURES: DEPENDENT VARIABLE Proportion of scientists involved in spin-offs and licensing

14. MEASURES: INDEPENDENT VARIABLES (I) 1. Upstream knowledge sources: Outstanding contributions to science No. of articles published in WoSoverthe 5-year period (2004-2008) which are among the top 10% most cited in their scient. fields Considering a citation window of 3 years (year of public. + 2 years) 51% of scientists in our sample have zero papers among the 10% most cited

15. 2. Downstream Knowledge sources: Interaction with the beneficiaries of research Volume of income generated through ‘R&D contracts’ and ‘consulting’ activities over the 5-year period (2004-2008), in which a scientist has been PI. MEASURES: INDEPENDENT VARIABLES (II) 45% of scientists in our sample have zero income from R&D contracts or consulting

16. MEASURES: CONTROL VARIABLES • HABITUAL COMMERCIALISER • A continuous variable that measures the volume of income generated by scientists, as PIs, from licensing of IPRs, covering a period of 10 years prior to the survey. It is meant to control for those scientists who are frequently engaged in technology transfer (licensing) activities • PAST ENTREPRENEURIAL EXPERIENCE • A dichotomous variable that indicates whether the scientist was involved in firm creation previous to 2008. • SOCIO-DEMOGRAPHIC • Age, gender (male=1), academic status (professor=1) • APPLIED FOCUS • A categorical variable measuring whether the respondent’s research objectives are strongly driven by the practical use and application of research findings • MOTIVATIONAL FEATURES: • ‘Controlled’ and ‘Autonomous’ motivationto conducting scientific research • PUBLICATIONS: Number of articles published(ln) • ORGANISATIONAL SUPPORT: Sum of the services used by a scientist • SCIENTIFIC DISCIPLINES:(8 dummies:“Biology & Biomedicine” is the reference category)

17. RESULTS

18. RESULTS: BI-VARIATE PROBIT MODEL Dependent variables: Spin-offs and Licensing(each variable take values 0 - 1) (Obs. 1220) * p < 0.1, ** p < 0.05, *** p < 0.01 (two-tailed) (Not all controls have been included in this table) • Past experience (habitual commercialisers / past entrepreneurial) always an antecedent • Applied orientation of research significantly associated with both commercialisation activities

19. RESULTS: BI-VARIATE PROBIT MODEL Dependent variables: Spin-offs and Licensing(each variable take values 0 - 1) (Obs. 1220) * p < 0.1, ** p < 0.05, *** p < 0.01 (two-tailed) (Not all controls have been included in this table) • Upstream knowledge sources: a positive impact on spin-offs / weak & negative on licensing

20. RESULTS: BI-VARIATE PROBIT MODEL Dependent variables: Spin-offs and Licensing(each variable take values 0 - 1) (Obs. 1220) * p < 0.1, ** p < 0.05, *** p < 0.01 (two-tailed) (Not all controls have been included in this table) • Downstream knowledge sources: strongly positive & significantly associated with both

21. RESULTS: BI-VARIATE PROBIT MODEL Dependent variables: Spin-offs and Licensing(each variable take values 0 - 1) (Obs. 1220) * p < 0.1, ** p < 0.05, *** p < 0.01 (two-tailed) (Not all controls have been included in this table) • Both together: • Upstream knowledge sources: only positively associated with firm creation • Downstream knowledge sources: positively associated with both firm creation & licensing

22. RESULTS: BI-VARIATE PROBIT MODEL: Interplay Dependent variables: Spin-offs and Licensing(each variable take values 0 - 1) (Obs. 1220) * p < 0.1, ** p < 0.05, *** p < 0.01 (two-tailed) (Not all controls have been included in this table) Interaction term: positive but not significant for firm creation / negative & significant for licensing Spin-off: neither reinforcing nor detrimental effect when scientists draw on both sources jointly Licensing: substitution effect between both sources

23. DISCUSSION AND EMERGING CONCLUSIONS

24. Discussion and emerging conclusions • Information asymmetries and differences in prior knowledge influence the individual capacity to identify and exploit business opportunities • The probability that scientists engage in firm creation and technology transfer is strongly associated with knowledge sources derived from their research activities(i.e. upstream / downstreamknowledge) • The relationship between knowledge sources and commercialisation is substantially different for firm creationvs. licensing: • For firm creation: we identify alternative knowledge-based configurations as antecedents • For licensing: we identify a dominant role of downstream knowledge sources as knowledge-base antecedent

25. Discussion and emerging conclusions • Alternative knowledge-base antecedents to academic entrepreneurship • Since both upstream and downstream knowledge sources have a significant and positive association with firm creation • The lack of an interplay (either positive or negative) • Supports the claim for three alternative knowledge-base antecedents of academic entrepreneurs: • Star-scientists: science-push logic • Engaged-scientists: demand-pull logic • Bridging-scientists: hybrid logic

26. Discussion and emerging conclusions • Importance of downstream knowledge sources with regards to technology transfer • Licensing is strongly and positively associated with interaction with users • Role of downstream sources is further emphasised by the negative interplay with upstream sources • Suggests that downstream sources play a significant role to identify market opportunities, and particularly critically to identify market opportunities from ordinary (non-outstanding) research findings

27. Thanks INGENIO [CSIC-UPV] Ciudad Politécnica de la Innovación | Edif 8E 4º Camino de Vera s/n 46022 Valencia tel +34 963 877 048 fax +34 963 877 991

28. Heterogeneity of scientists according to upstream and downstream performance ‘Star Scientists’ ‘Bridging Scientists’ upstream • concurrent Spin-offs: 4% Licenses: 22% Spin-offs: 2.4% Licenses: 13.6% ‘Engaged Scientists’ downstream Spin-offs: 1.1% Licenses: 10.4% Spin-offs: 6.3% Licenses: 24%

29. EMERGING CONCLUSIONS • Alternative knowledge-base antecedents of academic entrepreneurship and technology transfer. - Upstream knowledge sources: high impact scientific contributions - Downstream knowledge sources: interactions with beneficiaries of research • Scientists’ engagement in commercialization strongly influenced: • Not a reinforcing nor a substitution effect on spin-offs: Concurrent levels of upstream & downstream do not lead to a higher (or lower) probability of entrepreneurship • Substitioneffectonlicenses: Interactionwithusershigherbenefitonnon-outstandingresearchersfor KT activities • Three alternative pathways for entrepreneurship:‘Star’, ‘Engaged’ and ‘Bridging’ scientists • Dominant pathway for technology transfer: “demand-pull” or “market driven”

30. FIGURE: INTERPLAY UPSTREAM-DOWNSTREAM Interaction effect between upstream and downstream for technology transfer (licensing) The effects of low vs. high values of downstream sources on the probability of licensing are larger for low levels of upstream knowledge sources.