On the determinants of the internationalization speed of Academic Spin Offs

Plano de Dissertação

Mestrado em Economia e Gestão Internacional

16 de julho de 2013

Cátia Coimbra

Orientadora: Aurora A. C. Teixeira

Structure of the presentation

Motivation

Research question

Literature Review

Methodology

Results

Conclusions and limitations

Motivation

ASOs Studies

Characteristics

University support mechanisms

Industry/governmental environment

Internationalization studies

Determinants (entrepreneurs, business, network, macro-level factors)

e.g., Dahlstrand, 1997; Pérez and Sánchez,

2003; Clarysse et al., 2007; Müller, 2010;

Musteen et al., 2010; Colombo et al.,2011

e.g., Acedo and Jones, 2007; Ripollés-Meliá et

al., 2007; Zuchella et al., 2009; Acedo and

Casillas, 2012; Hauser et al., 2012

Few studies focus on the internationalization process of ASOs (e.g., Styles and Genua, 2004; Johnson, 2008; Pettersen and Tobiassen, 2012) and do not

address the issue of the internationalization speed of these firms.

Research Question Contributions

RQ: Which are the determinants of ASOs’ internationalization

speed?

Theoretical contribution: to bridge the literature on technology

transference, university entrepreneurship and international

business.

Empirical contribution: to add to the scarce empirical literature

on the drivers of ASOs’ internationalization speed.

Literature review

Determinants of the internationalization speed

Entrepreneurs specific factors

• Industry experience

• Educational background

• Number of founders

Business factors

• Innovation

• Source of creation

• Market strategies

• Performance

• Demographic traits

Contextual factors

• S&T support mechanisms

• Obstacles perceived

• University characteristics

• Regional factors

• Sectors

Methodology... RQ and econometric specification

Quantitative analysis (causality)

Which are the determinants of ASOs’ internationalization speed?

Methodology ... Data gatering procedures

Direct questionnaire

300 Portuguese Academic Spin Offs (ASOS), created in the last

10 years, associated to entities belonging to the University

Technology Enterprise Network (UTEN)

Proxy for the internationalization speed: Number of years

from firm inception to the beginning of the international sales

Methodology ... extant literature

Studies Data gathering

procedure

Type of methodology of

analysis Procedure of analysis Analytical approach

Hauser et al. (2012) Questionnaire Quantitative Hypothesis test Regression model for binary outcome variables.

Zucchella et al. (2009) Questionnaire Quantitative - Descriptive/Correlation analysis and logistic

regression

Musteen et al. (2010) Questionnaire Quantitative Hypothesis test Descriptive/ correlation analysis and Poisson

regression analysis

Coeurderroy and

Murray (2008) Questionnaire Quantitative Hypothesis test Descriptive and correlation analysis

Osarenkhoe (2009) Questionnaire Qualitative - Descriptive analysis

Acedo and Jones

(2007) Questionnaire Quantitative Hypothesis test

Structural Equation Modeling (SEM) approach,

specifically Partial Least Squares (PLS)

Luo et al. (2005) Data information

(website’s companies) Quantitative Hypothesis test

Mean, standard deviations and correlations and

multiple regression analysis

Ramos et al. (2011) Statistical information

from ESEE Quantitative -

Means, standard deviation and correlation /

regression model

Acedo and Casillas

(2012) Questionnaire Quantitative Hypothesis test

Partial Least Squares (PLS) technique and

structural model analysis (SEM)

Ripollés-Meliá et al.

(2007) Questionnaire Quantitative Hypothesis test

Descriptive, correlation analysis, t-student test

and linear regression

Autio et al. (2000) Questionnaire Quantitative Hypothesis test Descriptive, correlation analysis and regression

models

Parshantham and

Young (2009)

(just theoretical not yet

tested) Qualitative Hypothesis test (just theoretical not yet tested)

Chang and Rhee

(2011) Data analysis Quantitative Hypothesis test

Descriptive, correlation analysis and Wooldridge

test

Wagner (2004) Data analysis Quantitative Hypothesis test Descriptive analysis and two-stage regression

analysis

Vermeulen and

Barkema (2002) Data analysis Quantitative Hypothesis test

Descriptive and correlation analysis, regression

analysis (Hausman test)

Methodology ... Hypotheses and proxies for the relevant variables

Determinant group Hypothesis Proxy for the

independent variable

Entrepreneur

specific factors

Experience H1: Entrepreneur experience in the same industry increases the

internationalization speed of ASOs.

Same-industry

experience (dummy-

1:yes; 0:no)

Education

H2a: ASOs whose founders have an economic or managerial

education internationalize faster than the ones whose founders

possess other type of education background.

Some of the founders

has an Economic or

managerial degree

(dummy- 1:yes; 0:no)

H2b: ASOs whose founders have an engineering related

education internationalize faster than the ones whose founders

possess other type of education background

Some of the founders

has an Engineering

degree (dummy-

1:yes; 0:no)

Number of founders H3: ASOs with higher number of founders tend to

internationalize faster their smaller counterparts.

Number of founders

(in ln)

Determinant group Hypothesis

Proxy for the

independent

variable

Business related

factors

Innovation

H4a: ASOs with higher innovative value (patents) tend to internationalize earlier than ASOs that do not possess patents.

Number of accumulated patents

(2012)

H4b: ASOs characterized by higher intensity in Research and Development (R&D) internationalize faster the remaining ASOs

R&D intensity (2011)

Source of creation H5: ASOs created by firms internationalize faster than those created by

academics.

ASOs created by firms (1) or by academics (0)

[dummy]

Market strategies/ Focalization of

strategies

H6a: ASOs which have a global market strategy tend to internationalize faster than the ones that focus their strategy in the domestic market.

Firm strategic focus in terms of market (dummy- 1:global;

0:domestic)

H6b: ASOs producing for a niche market ten to internationalize faster

Market segment (dummy 0: niche

markets; 1:mainstream

markets)

H7: Larger ASOs internationalize faster than their smaller counterparts.

Number of employees plus

founders in terms of FTE (in ln)

H8: Older (more experienced) ASOs tend to internationalize faster. Number of years

since creation (in ln)

Performance H9: Past economic performance influences internationalization speed of

ASOs

Sales/ number of members in terms of

FTE

Methodology ... Hypotheses and proxies for the relevant variables

Determinant group Hypothesis Proxy for the independent variable

Contextual factors

S&T support mechanism (Resource access; Network and

business advice; Financial/capital advice and support; IPR support)

H10: ASOs that resort to technology transfer support from TTOs internationalize faster than the remaining ASOs.

ASO resort to the support of the TTO (dummy- 1: yes; 0: no)

H11: ASOs that attribute greater importance to the S&T support mechanisms respecting a given set of items internationalize faster

than the remaining ASOs

High relevance attributed to the given item (dummy – 1: if ASO considered highly important; 0:

otherwise) Obstacles perceived (U-I

relations; Institutional, regulatory and government; Financial; Managerial; Infrastructures)

H12: ASOs that perceive the item as a major obstacle for its activity tend to internationalize slower than the remaining ASOs.

High relevance attributed to the given obstacle (dummy – 1: if ASO

considered a highly important obstacle; 0: otherwise)

University characteristics

H13a: ASOs that are associated to Universities with higher pool of scientific knowledge tend to internationalize faster than the

remaining ASOs

Scientific pool of knowledge (WOS publications per researcher) (2000-

2007) (in ln) H13b: ASOs that are associated to Universities with higher pool of

advanced applied/commercialized knowledge tend to have a superior level of internationalization speed than the remaining

ASOs.

International patent pool per 1000 researchers (2010) (in ln)

H13c:.ASOs that are associated to Universities with higher proportion of research excellence tend to internationalize quicker

than the remaining ASOs

Proportion of Research units with classified with ‘Excellent’ or ‘Very

Good’ by the FCT

Regional factors

H14a: ASOs located in higher economic developed regions internationalize faster than those from less developed regions

Index of purchasing power per NUT III regions (in ln)

H14b: ASOs located in predominantly urban regions have a higher internationalization speed than those allocated in intermediate or

rural areas.

Urban (1) versus Rural/intermediary (0) regions

(dummy)

Sector (default: microelectronics/Robotics)

H15: ASOS that operate in the sector (Energy; Bio; Micro; Agri Food; Consultancy) outperform those operating in ICT/Software/

Digital Media

Dummy variable: 1 if the ASO operates in ICT/Software/ Digital

Media; Energy/Environment/ Sustainability; Bio/Pharma or

Medical devices/diagnostics; Agri-Food; Consultancy

Methodology ... Hypotheses and proxies for the relevant variables

In 300 ASOs contacted via email and telephone we obtained 111

answers (37.3% response rate).

Out of the total responses, 78 firms (70%) are exporters and 18

expect to export in a nearby future.

Methodology ... Data gathering procedures

41%

29%

8%

22%

Early exporter

Late exporter

Expect to export

Non exporter

1) Descriptive analysis including 111 answers: Differences in means according to ASOs’ 4 categories of ASOs export status (non parametric Kruskal Wallis Test)

Some factors can be discriminated according to their exporting status: Engineering degree; number of founders; R&D intensity; patents; global market; size; age; past economic performance; research units; ICT, energy and consulting sector.

2) Descriptive analysis including 78 firms: Differences in means according to 2 categories of ASOs export status (laggard versus earlier exporters) (non parametric Kruskal Wallis Test)

Some factors can be discriminated according exporting status: Patents or R&D; age,; past economic performance; university patents per researcher; research units; Lisbon region

3) Estimates of Pearson correlation coefficients Age, past economic performance, importance of resource access and consulting sector are strongly correlated to ASOs’ internationalization speed.

4) OLS estimations and logistic regression: Determinants of ASOs’ speed of internationalization

To avoid multicollinearity two groups of 6 models were tested

Methodology ... Descriptive analysis

Results

Conclusions

Contextual factors appear as the most relevant determinants of ASOs

internationalization speed

– TTO support, importance attributed to S&T support mechanisms (network

and business advice; IPR support; resource access), infrastructure obstacles,

university pool of patents, North region and some sectors.

– These determinants are closely related to the specificities of ASOs, namely

associated to the pervasiveness of high levels of technology transfer.

Business and entrepreneur specific factors do not appear as relevant as

the contextual factors although some variables within the former are key

to explain the internationalization speed of ASOs

– Number of founders, engineering and economics/management degree of

founders, size of the founding team, R&D activities, size, firm age and past

performance.

Conclusions

Limitations and paths for further research

• Factors related to founding team and ties with other firms

could be considered in a larger extent.

• It would be methodologically more adequate to resort to

longitudinal or panel data analysis.

• Testing for distinct proxies of internationalization speed

(e.g., time span between start selling and first exports/

subsidiary).

Thank you for your attention