Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
How Do Research-Based Start-Ups Overcome The Valley of
Death?
Véronique Bessiere 1, Marie Gomez-Breysse 2, Karim Messeghem 3, Arnaud
Milet 4 and Sylvie Sammut 5
Academic spin-offs (ASOs) have developed considerably in France since the 2000s mainly
because of new legislation that introduced incentives for their creation. However, despite a
strong increase in the number of new ventures, overcoming the valley of death – which often
th
th
corresponds to their 5 to 7 years – continues to be a challenge for most of these companies.
This article studies the growth trajectory of ASOs within the theoretical framework of the
resource-based view (RBV). The results of our quantitative study based on a sample of 206
French ASOs show that four factors determine this trajectory through the critical period:
entrepreneurial orientation (and broader cognitive resources), skills, financial resources, and
support. They also show that commercial, marketing and managerial skills allow start-ups to
overcome the valley of death.
Field of Research: Entrepreneurship
JEL codes: M13, L26, 032
1. Introduction
In the 1970s, Europe began to adopt public policies in favour of the creation of innovative
companies (Mustar et al. 2008). These included academic spin-offs (or university spin-offs –
ASOs/USOs), which are based on the transfer of technologies from a research laboratory to
the commercial market. They are companies created from nothing by one or several
members of a public research body with a view to utilising the technological invention for
economic gain.
In France, legal measures were adopted in late 1999 (“Loi sur l’Innovation et la Recherche »)
to encourage innovation through the transfer of technologies developed by public
researchers, including the creation of academic incubators. Beginning in the 2000s, ASOs
became an increasingly widespread phenomenon in France (Clarysse et al., 2005). Now,
with 13 years of hindsight, we are in a position to observe the path followed by all of the
companies hosted by these incubators and therefore the trajectory of French ASOs. The
study of these spin-offs is worthwhile for at least two reasons: to better understand the
conditions underpinning the success of technology transfers and to evaluate the relevance of
public policies that support their development. However, the diversity of academic incubators,
of their parent organisation, i.e. the public research body, and of regional policies and
programmes (Sternberg, 2014) makes it difficult to conduct research and make general
recommendations (Gisling et al., 2010).
1
Professor, University of Montpellier / IAE – MRM / Labex Entreprendre (corresponding author)
veronique.bessiere@univ-montp2.fr, Tel. 33467144864 – Fax 33467144242, IAE - Université Montpellier 2,
Place Eugène Bataillon, 34095 Montpellier cedex 5 - FRANCE
2
Assistant Professor, COEPTIS – MRM / Labex Entreprendre.
3
Professor, University of Montpellier – MRM / Labex Entreprendre.
4
Research engineer, Labex Entreprendre
5
Assistant Professor, University of Montpellier – MRM / Labex Entreprendre
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
On the whole, such public aid initiatives and the hosting provided by academic incubators
offer support to innovative companies during the creation and start-up phases as well as the
actual technology transfer. But the literature on start-ups has highlighted the difficulty they
experience in surviving beyond the first five years. Overcoming this challenge has been
described as the “Valley of Death” (Branscomb and Auerswald, 2001; Markham, 2002;
Markham et al., 2010). Markham et al. (2010) wrote that this period marks the transition
between the research phase and the development of new products. So how do ASOs
manage to cross this Valley of Death? Is there a particular growth trajectory that allows this
critical phase to be overcome, and what are the determining factors behind it?
This research targets two objectives. First, we set out to define a growth – or success –
trajectory and distinguish it from a more critical trajectory; this distinction is not necessarily
easy to comprehend. We develop a means to measure the trajectory of academic spin-offs
during their first six years. Second, we aim to highlight the determining factors that underpin
the growth trajectory. This allows us to define the fundamental characteristics of those
companies that successfully overcome the Valley of Death.
To achieve this, we adopt the resource-based view, which tends to dominate research on
academic spin-offs (Brush et al., 2001; Heirman and Clarysse, 2004; Mustar et al. 2006;
Wright et al., 2007) and thereby study various determining factors which can be grouped
together under four major categories of resources: technological, human, social and financial.
Our empirical study is based on a homogenous sample of 206 academic spin-offs created in
France between 2005 and 2007 and whose trajectory can be observed until 2013. At the
same time, we adopt a longitudinal and cross-sectional approach to analyse the determining
factors of a trajectory. We show that entrepreneurial orientation, cumulative skills, support
mechanisms and financing are the key factors in the growth trajectory of academic spin-offs.
This article makes a dual contribution. First, from a methodological point of view, we develop
a new way to analyse the trajectory of a homogenous generation of companies. Second, this
approach allows us to carry out an in-depth analysis of the impact of four key factors on
growth trajectory: entrepreneurial orientation, cumulative skills, support mechanisms and
financing. Our results provide important implications for defining public policies, particularly in
terms of public support and financing.
This article is structured as follows. Section 2 presents the literature and our hypotheses.
Section 3 déscribes our methodology and sample. Section 4 presents our findings, first in
univariate and then in multivariate form. Finally, we discuss our findings and present their
implications for public policies and future research.
2. Literature review and hypotheses
The resource-based approach is central to the literature on academic spin-offs (Brush et al.,
2001; Heirman and Clarysse, 2004; Mustar et al., 2006; Wright et al., 2007), particularly
when it comes to studying their development phases (Van Geenhuizen and Soetanto, 2009).
The growth of companies is examined from their cumulative resources and capabilities
(Barney, 1991, 2001 and 2007).
What are the resources and skills that allow academic spin-offs to follow a trajectory of
growth? Adopting the resource-based view and drawing on the work of Brush et al (2001),
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
Wright et al. (2007) focused on four resource categories: technological, social, human and
financial. These resources appear to be key factors in the growth of academic spin-offs.
Technological resources refer to a company’s potential in terms of R&D and innovation.
Through innovation, a company’s entrepreneurial orientation reflects both its capacity to
develop new products and the ability of the entrepreneur to take risks so as to be innovative
and open-minded about organisational learning, a factor in its performance (Zhao et al.,
2011). This gives us the following hypothesis:
H1: spin-offs with strong entrepreneurial orientation are more likely to achieve
sustainable growth beyond their 5th year.
Human resources refer to the skills acquired and accumulated by the company’s founders,
management team and other personnel (Wright et al., 2007). Ghoshal et al. (1997) draw a
link between skills and the growth of companies. Whether entrepreneurial or managerial in
nature, skills determine the growth potential of young companies. Among the factors that
have the greatest impact on the performance of ASOs, the quality of the entrepreneurial and
managerial team is one of the most important (Visintin, Pittino, 2014). Similarly, it would
appear that having and acquiring entrepreneurial skills has an influence on the growth
trajectory of ASOs.
H2: spin-offs with strong entrepreneurial skills are more likely to achieve sustainable
growth beyond their 5th year.
Social resources refer to a company’s network (Lee et al., 2001; Wright et al., 2007). The
mission of those who provide the support is to help their target companies effectively become
part of a network. The social capital generated through this support is likely to improve
growth in the case of academic spin-offs.
H3: spin-offs that rely heavily on entrepreneurial support are more likely to achieve
sustainable growth beyond their 5th year.
Financial resources refer both to the amount and nature of financial resources (Wright et al.,
2007). ASOs may draw on a wide variety of financing sources: personal funds, borrowing or
subsidies. There is a link between financing and the growth of young companies (Cooper et
al., 1994). The literature on venture capital generally highlights the importance of financing
for the success of the project (Bruderl and Schussler, 1990; Shane and Stuart, 2002).
H4: spin-offs that obtain private or public financing are more likely to achieve
sustainable growth beyond their 5th year.
3. Data and Methodology
3.1 Sample
In order to obtain a group of companies that were as homogenous as possible and were
developing in the same economic context, we focus on the generation of companies created
between 2005 and 2007, with support from academic incubators across France. This
corresponds to a total population of 557 ASOs which, at the time the study was launched in
2013, had been in existence for between 6 and 8 years. Our questionnaire was administered
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
online via emails sent to all of the directors concerned. We obtained an overall response rate
of 37% and a rate of 58% in terms of the number of companies still in business.
3.2 The growth trajectory
The growth trajectory is the central concept behind our approach. It reflects the growth
dynamic of each company, from their creation to their 6th year in existence. As made clear by
Biga-Diambeidou and Gailly (2011) and Weinzimmer et al. (1998) in their review of the
literature, few authors take into account the intermediary years when measuring growth. Like
Davidsson and Wiklund (2000), we argued that longitudinal data should be taken into
account. This appears to be a more pertinent approach as it recognises that the growth of a
company is not continuous from its creation until its nth year in existence.
Like 86% of the articles in the literature (Weinzimmer et al., 1998), we consider company
growth in terms of revenue. Our objective was to obtain groups of companies with
homogenous revenue trajectories. This meant obtaining a discriminant variable for revenue
trajectory using six variables corresponding to the revenue figures from the first six years.
Our measurement is based on a dynamic approach to the trajectory that led the company to
its highest revenue figure over the period concerned. We feel that this point – maximum
revenue – is essential in determining whether or not a trajectory is a growth trajectory. We
apply to each company a base of 100: this point represents its own maximum revenue
achieved during the period. This allows us to conduct both an inter-company comparison and
a longitudinal comparison of the trajectory. We then group together companies with similar
trajectories by using ascending hierarchical classification. This gives us three groups of
companies (Fig. 1).
Figure 1: The 3 growth trajectories – cluster analysis
Of the 206 companies from this generation who responded to the survey, 159 were able to
produce data that enabled an analysis of their trajectory over the first six years. 91
companies experienced growth, 56 experienced a slowdown and 12 experienced a decline.
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
The companies experiencing a decline faced difficulties long before the critical period studied
as part of our analysis. In order to focus our analysis on the sustainability of growth, we
therefore compared the companies experiencing growth with those experiencing a slowdown.
3.3 Explanatory factors of the trajectory
To test the four hypotheses we examined the link between the growth trajectory of each
company and the data collected from their representatives.
H1 - Entrepreneurial orientation: Here we used a scale tested by Covin and Slevin (1989)
to consider entrepreneurial orientation in terms of 9 items, the answers used a Likert scale (1
to 5). The overall score is the average from all 9 items.
H2 - Skill acquisition: According to Radosevich (1995), the most common model for
academic spin-offs is that of the “inventor-entrepreneur”, whereby the company is created by
those behind the technology or knowledge being transferred. However, entrepreneurship is a
whole other business which requires knowledge and skills already in place and/or to be
acquired so that the three phases of the creation process can be completed: the idea, the
development of a project and the launch of the business (Charles-Pauvers, Schieb-Bienfait,
Urbain, 2004). The aim is to identify the place of these skills within the growth trajectory of
spin-offs and identify how these structures acquire the skills they need to develop.
H3 - Support: Two main questions are addressed.
- Support structures: By asking directors “Have you received support from the
following structures?”, we were interested in the different types of structures that
supported the company over the last three years, such as incubators or business
angels. In each case the director indicates whether or not they have received support
from the structure identified (9 yes/no responses).
Benefits of support (“When your company was first set up, did you experience the
following benefits from the support you received?”): We identified three major services
provided through support (Abduh et al., 2007): access to related services
(infrastructure, renown), training/coaching (marketing/commercial, finance, intellectual
property rights, legal and management) and access to a network (research and
company networks).
H4 - Sources of financing: Directors were asked to indicate the sources of financing they
did and did not obtain when the company was first set up and over the last three years. The
list of possible sources was taken from the work of Saemundsson and Lindholm Dahlstrand
(2005) and adapted to France. This produced a range of binary variables: 1=obtained; 0=not
obtained. A factor entitled “duration of public financing excluding research tax credits” was
also analysed.
4. Findings: factors determining the growth trajectory
As explained above, the dependent variable here is whether the company is on a growth
trajectory or a slowdown trajectory. For the sake of brevity, detailed tables are not presented
here. We only comment on the main results i.
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
We used non-parametric tests to analyse the link between growth trajectories and the various
factors. Fisher’s exact test was used to test the hypotheses of independence between the
growth trajectory and qualitative factors. We use the odds ratio to measure the power of the
link between the growth trajectory and each factor. The hypotheses on continuous variables
were tested using Kruskal-Wallis tests.
4.1 Entrepreneurial orientation
We found that companies experiencing growth have more pronounced entrepreneurial
orientation than the others, who favour innovation, proactivity or risk-taking. On average,
these companies achieved a score of 3.5/5 (compared to 3.04/5 in the case of companies
experiencing a slowdown). 50% of these companies achieved a score of at least 3.5/5
(compared to 3.1/54 companies experiencing a slowdown). Cronbach’s alpha (0.81) allows
us to calculate an average score for the 9 items relating to entrepreneurial orientation. This
score proves to be inextricably linked to growth trajectory. These results confirm H1.
4.2 Skill acquisition
Companies experiencing growth have generally acquired more skills than others. This is
particularly true in the case of research, management and commercial and marketing skills.
The acquisition of skills in these three areas significantly increases the chances that the
company will follow a growth trajectory (multiplied by 1.52, 1.84 and 3.15). The percentage of
companies experiencing growth that have acquired experience in research and management
and commercial and marketing skills is more than 10 points higher than that of companies
experiencing a slowdown. 81% of companies experiencing growth acquire skills in research
(compared to 74% of companies experiencing a slowdown). In terms of management and
commercial and marketing skills, these figures are 82% (70%) and 91% (76%) respectively.
These results confirm H2.
4.3 Support
Our results show that a company’s position on a growth trajectory is closely linked to the
support it has received, and in particular to both the number and type of support structures.
Companies supported by a greater number of structures are among those on a growth
trajectory. They are supported on average by 2.6 structures (2.01 in the case of companies
experiencing a slowdown). 50% of them are supported by at least 3 structures (compared to
2 for companies experiencing a slowdown). Their relative chances of being on a growth
trajectory are multiplied by 2.19 where support is provided by at least 3 structures. Our
analysis also shows that companies experiencing growth are those who enjoy the most
benefits from the support they received in international development over the last three years
(odds ratio: 2.96 **). The percentage of companies experiencing growth that benefited in this
way over the last three years is 2.2 times greater than that of companies experiencing a
slowdown. These results confirm H3.
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
4.4 Financing
Several sources of financing either at start-up and/or over the last three years are significant
in terms of being positioned on a growth trajectory. As well as credit from public institutions
and other forms of public aid, financing provided by business angels also appears to be
significant both at the time of business creation and over the last three years. Receiving
financing from this type of structure when the business is first set up multiplies one’s chances
of being on a growth trajectory by almost 3. Based on Cronbach’s alpha (0.71), we were able
to regroup these variables as a single factor. This aggregated variable, referred to as the
“Financing factor”, is produced using a multiple correspondence analysis (MCA). This factor
is significantly correlated to a trajectory of growth. These results confirm H4.
4.5 Control variables
As is customary in the study of company performance, sector and size were introduced as
control variables. We also introduced the initial link with research (namely whether or not the
ASO is primarily driven by a researcher). The tests carried out show that no control variable
has a significant link to a company’s position on a given trajectory.
4.6 Multivariate analysis: logistic regressions
The multivariate analysis allows us to introduce the four types of resources that determine
trajectory together and thus assess the predictive quality of the overall model. It also allows
us to test the potential effect of the three control variables on the overall model and to
examine whether the control variables have any interaction effect on the four resource types.
Methodology
We carried out an analysis of different multivariate models with the constraint that the four
resource types had to be integrated simultaneously without multicollinearity. The independent
and control variables were integrated stepwise based on the Akaike information criterion
(AIC). The overall quality of the model obtained is evaluated in terms of the percentage of
correct forecasts.
Results
We tested a series of models distinguished by the variants introduced when defining the
independent variables in order to ensure non-collinearity. The four types of resources are
significant in all of these models and the percentage of correct forecasts is high (around
70%). Furthermore, no control variable is significant when integrated stepwise. These results
therefore confirm the positive effects of the four resource types on the growth trajectory and
validate their joint effect. The introduction of interactions does not modify the results. The lack
of interaction effects allows us to validate the relationship between the trajectory and its four
determining factors, regardless of size, sector or ASO type.
5. Conclusion and implications
The empirical study carried out on a sample of 206 French academic spin-offs that were
incubated between 2005 and 2007 validates the four hypotheses tested. These results were
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Proceedings of Annual Shanghai Business, Economics and Finance Conference
3 - 4 November 2014, Shanghai University of International Business and Economics, Shanghai, China
ISBN: 978-1-922069-63-4
obtained based on different types of skills, entrepreneurial orientation, support and financing
taken from measurement scales that were previously validated in the literature. The particular
methodological contribution made by this study comes from the trajectory variable and its
statistical ability to be categorized. Companies that experience initial success are divided into
two major categories, as seen in Figure 1: those which continue to experience growth and
those which experience a slowdown. A detailed analysis of the relationship between
trajectory and size or sector revealed no links or interactions. We can therefore validate the
robustness of the variable to capture the trajectory as a truly intrinsic development rather
than as a phenomenon generated by the effects of sector or size (which might for example
provide easier access to financing or explain the presence of more extensive internal skills).
The results show that the spin-offs most successful in emerging from the Valley of Death are
those which accumulate resources and develop capabilities. They confirm the importance of
four main resource types: technological, financial, human and social (Mustar et al., 2006).
This research carries managerial implications for universities and policy makers. Although
regional context plays an important role (Sternberg, 2014), it is clear that support programs
designed to operate in the long term are of more benefit in overcoming the Valley of Death.
This should encourage policy makers not to concentrate solely on the start-up phase but
instead to favour long-term support. Universities and academic incubators should note that
support must emphasise the importance of belonging to a network so as to have access to
advice and also to facilitate the accumulation of skills. Reliance on surrogate entrepreneurs
may also be a strategy worth considering (Lundqvist, 2014).
End Notes:
i
Full tables are available on request to the corresponding author.
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