Are barriers to innovation enhancing or impeding innovative activities among start-ups? Insights from Accenture Innovation Awards 2014

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Are barriers to innovation enhancing or impeding innovative activities among

start-ups? Insights from Accenture Innovation Awards 2014

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A quantitative study

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MSc BA Strategic Innovation Management

Master Thesis

Maria-Manuela Apostol

S2727919

June, 2015

Abstract. The fuel of innovation is seen in the efforts of entrepreneurs to grow dynamic business by addressing

the development of new or improved goods, services or processes. However, the existing literature lacks empirical studies about the impact of obstacles which impede start-ups to innovate. Our analysis makes important steps in analyzing the effects of obstacles on start-ups propensity to innovate. In doing so, our research adopts a theory testing approach, first by looking at the relevant literature regarding barriers to innovation and then, empirically analyzing participating start-ups from Accenture Innovation Awards 2014. Moreover, in explaining those phenomena we are building our arguments on theory of dynamic capabilities and absorptive capacity. The results show that cost factors are enhancing the propensity to innovate. It is seen by entrepreneurs as very important obstacles, and, most of them, quite experienced in their industry, are making use of crowdfunding in order to overcome it. On the other hand, contrary to our expectations, market factors positively influenced the innovation activity of startups. By making use of subsidies, newly established firms can overcome those barriers by increasing the investments in their projects. Ultimately, the connectedness across networks (connection to an incubator) makes those start-ups most likely to win an award, therefore enhancing their innovative activity despite encountering harmful barriers for their growth.

Keywords: Innovation barriers, innovation awards, start-ups, incubator

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Executive Summary

Above all, the largest share of innovation, mostly radical, comes from small and young enterprises. Having this as a drive for our research, we are aiming to investigate the new entrepreneurial ventures and the obstacles which those encounter and may render their innovative activities. Under those circumstances, we are going to answer the question “What is the impact of barriers to innovation on the innovation intensity for start-ups?” by empirically assessing participating start-ups in the annual contest Accenture Innovation Awards (2014), from The Netherlands.

In order to answer the research question, three sub-questions were posed and four hypotheses were created based on the analysis of relevant literature regarding barriers to innovation and, supplementary, the impact of incubators on those young initiatives. Moreover, in defining abovementioned terms, we were making use of the dynamic capabilities theory, centered on entrepreneurship field, in combination with the literature of absorptive capacity. We proceed with our analysis based on past research started by the staff from University of Groningen, which discovered that participation in an innovation award competition leads to three effects: signaling of quality, learning and networking (Van der Eijk et al.2013, Borgman 2013). Particularly, we chose to concentrate mostly on the organizational learning perspective.

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Chapter 1. Introduction

“Entrepreneur as innovator”1

Innovation has become a key factor for economic growth, since competition is more intense between businesses as well as nations. It has the underlying logic that in order to become more innovative there is a need for novel companies to start-up the competitive age. In order to stimulate the process of “creative destruction”, as innovation was defined by Schumpeter (1987), we need to know more about the barriers which those new initiatives encounter. Furthermore, the innovation processes is very complex, as a result of cumulative dynamic interaction and learning mechanisms which involve many actors (Bergquist and Ramsing 1999). Undoubtedly, to be an innovative entrepreneur is not easy, it requires overcoming certain obstacles because innovation can expose the firm to additional risks, both internally (e.g. financial and human resources) and externally (e.g. external environmental factors) (Madrid‐Guijarro, Garcia et al. 2009). By being a small firm it is also expected to face relatively more barriers to innovation than larger firms, due to inadequate internal resources and expertise. The impacts of these barriers are mostly on finance, manufacture and manpower (Hadjimanolis 1999).

Having less successful new companies compared to the United States, Europe is seen as a less efficient system in growing young, radical innovations. An important drawback in its economic overall innovation performance is related with the new firms, which are considered to lead the innovative activities. This arise due to the barriers which new firms face, mostly regarding access to finance (Hall, 2008), which in turn has repercussions on the entire economy. In order to cover this gap, and to tackle the barriers which small firms encounter, the new innovation strategy proposed by OEDC countries for 2015 is to make the innovation as one of their main goal and try to accord more support for those enterprises, mostly by increasing policy funding through subsidies (OEDC, 2010). When looking at The Netherlands, in 2014 was ranked in the 5th position by the European Innovation Index and 6th position by the Global Innovation Index, being first from followers’ countries. Although, the Dutch’s aspirations are to reach the top 5 economies globally (Box 2009), disposing of strong technological capabilities and performance. This goal can be attained with the help of entrepreneurial sector by making use of its creativity, innovative approach and continuously growth. Whereas in the Netherlands is relatively easy to start a business, there are several barriers to growth and overcome the start-up phase. Altogether, OECD reports recommend as a great innovative strategy to improve their environment for experimentation which can definitely contribute in achieving Dutch goal. This can be achieved by lowering barriers to expansion for young firms, since small and new firms have a critical contribution to developing radical innovations and ensure technological change by successfully combine the available resources and capabilities (Freel 2000, Schneider and Veugelers 2010)

However, Plummer and Acs (2014) posit that the key for innovation is competition. Therefore, entrepreneurs and incumbents are rivals in finding ideas and commercialize innovations, whereas the action of the one drives the action of the other one. The innovations created by incumbent firms give raise to entrepreneurship, and in turn, the actions of the new firms drive incumbents’ innovation. Despite the fact that large firms dispose of a range of capabilities and financial resources achieved overtime, these are not willing, most of the time, to have many innovations. Therefore, a successful innovation depends on these initiatives to successfully combine the available

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resources and capabilities (e.g. capacity to access finance, understanding market needs, etc.) (D'Este, Rentocchini et al. 2014). In conclusion, the innovation at the level of a start-up is not only important for the startup itself but also for the innovative actions at the level of incumbents, which contribute the most at general economic prosperity. (Freel 2000).

What is more, in order to stimulate innovative behaviors of entrepreneurs, companies and governmental institutes organize awards competitions. These prizes have been introduced as an important inducement for innovation, highlighting outstanding performance and help to improve the bottom line (Azadegan and Pai 2008, Brunt, Lerner et al. 2012). Those awards represent a tangible (trophies, certificates, medals, etc.) or intangible good (a public acknowledgement of excellence) and may carry a monetary prize given to the recipient. In short, an award represents a certificate of excellence in a certain field, for the person, group or organization who has received it.

1.1. Statement of the problem

According with Hadjimanolis (1999) small and medium sized enterprises (SMEs) are expected to face more barriers in comparison with larger incumbents, due to their inadequate internal resources and expertise. In order to survive these companies have to obtain technology and resources from external sources which, in the struggle of forming strategic networks, make them more innovative in comparison with larger firms. For this reason, quite a lot literature has been written about the barriers which small and medium firms face in their pursue to innovation. However, there is a little empirical evidence on how these barriers affect innovative performance of a small, particularly a start-up enterprise. Moreover, the financial constraints have received a lot of attention in the literature while the non-financial barriers have received little scrutiny. Nevertheless, newly established firms have been included into SMEs category and analyzed at common despite their important role for innovation and radical technological development.

In other words, there is a need for analyze startups, independently from other similar category of firms, since this is a valuable section whereas innovative activities take place. Accordingly, this paper will make the difference between a start-up and SME by using the following definitions: “Small and medium-sized enterprises are independent firms whose personnel numbers fall below certain limits, from 10-50 employee for small firms and until 250 for medium ones”(EU Commission,2014). A start-up, on the other hand, is a young business, with great potential to develop innovations for commercial applications and designed to grow fast - Table 1 (Schneider and Veugelers 2010). Generally speaking, start-ups are most of the time of small size, and are often classified as small firms (Blank 2013). Although this may be true, start-ups often present limited number of employees, but being a small firm is not an immediate consequence of being a start-up. What makes the difference between small firms and startups is the firm’s age. However, within this research start-ups and small firms will be used as one construct, since the literature does not provide evidence about start-ups particularly, being considered in the category of small and medium enterprises.

1.2. Scope and significance of the research

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one that makes the difference by getting the products on the market (Zhang, Yu et al. 2014). Hence, the key for getting products on the market and stimulating innovative activities is competition (Plummer and Acs (2014). Schumpeter (1934) stated that the entrepreneurial innovation in product and processes is the crucial engine for driving change. This interesting relationship between innovation and entrepreneurs was also stressed by (Shane 2012), sustaining that entrepreneurs find new ways of combining resources rather than optimization within existing frameworks (traditional way), come up with new business ideas about how to recombine those resources to exploit new opportunities. Therefore, for a new firm to engage in innovation it requires the ability of the entrepreneur to “access new information, have the capability to turn this information into knowledge, and have processes, procedures and resources to apply this knowledge to exploit the opportunity or opportunities arising.” (Demirbas, 2010, p4). This ability of entrepreneurs represent in terms of Teece (1997) the “dynamic capabilities”. Furthermore, it was recently researched about an alternative way among start-ups of forming external linkages in order to accumulate information and knowledge for their innovation purpose, namely, incubators. In helping with our analysis and having a more compressive view of entrepreneurial phenomena we are going to use the following definition for business incubators, stated by Bøllingtoft and Ulhøi (2005) as “an umbrella term for various arrangements for premature ventures, the aim of which is to address specific aspects of market failures”. (Table 1)

Although, despite the increased attention accorded to start-ups, little empirical evidence on their innovative performance and encountered barriers have been made. The purpose of this study is to analyze whether the barriers to innovation which a start-up face influence their innovative activity, by empirically analyzing participants from an innovation award competition. The underlying reason for analyzing participant is due to the previous research which demonstrated that three main effects are occurring due to participation in an award competition, namely, signaling of quality, networking and learning (Van der Eijk et al.2013, Borgman 2013). Therefore, for entrepreneurial ventures, to be accepted in a competition is already a signal of quality and most of the time the main purpose of their participation is to be observed by stakeholders. Despite the three effects which competition has on participants, we chose to focus our study on the organizational learning effect.

In order to analyze how start-up’s innovative activity is affected by the obstacles to innovation we are going to merge the theory of dynamic capabilities with perspectives from literature on absorptive capacity. According to the theory of dynamic capabilities, those are enhancing innovation and help firms in creating new processes and products which can be used by participants to modify short term competitive positions (which in turn may contribute to the survival and prosper in the long run). The original concept of dynamic capabilities has been modified in a more entrepreneurial direction, referring at “the capacity to sense and shape opportunities and threats and to seize those opportunities, combined with navigating threats, and reconfigure assets to meet changing customer needs and respond to changing technologies”(Fitjar and Rodríguez-Pose 2011). Although this may be true, for a firm to make use of those new opportunities, they need to develop organizational capabilities, which make them able to synthesized and apply the new knowledge acquired, as absorptive capacity’ view indicates (Bays and Jansen 2009). By combining these two approaches, theory of dynamic capabilities and literature on absorptive capacity are both sustaining that firms can constantly update their knowledge and skills (Cohen and Levinthal 1990, Teece et al. 1997).

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1.3. Research questions

Despite the interest in discovering obstacles which companies face in their pursuit to innovation, quite few empirical studies analyzed the barriers which entrepreneurs face and particularly their effects on firm’s level of innovation. The main aim of this study is to provide empirical evidence on the role of obstacles which start-ups encounter in their innovative activity, by analyzing the start-ups participants in Accenture Innovation Awards 2014. Our main goal is to answer the research question: What is the impact of barriers on the innovation

activity for start-ups?. In order to make it easier to answer the main question, we derived there sub-questions:

1. What is the proper measure of innovation among start-ups? 2. What are the barrier to innovation which start-ups face?

3. What is the impact of connection to an incubator for the innovation activity within start-ups?

First sub-question has a theoretical nature and helps us in defining proper instruments for measuring innovation among start-ups, grounded in the extant literature. Second and third sub-questions, besides guiding us through the literature, provide more important empirical impact.

1.4. Overview of the research

This study is structured as follows: Chapter 2 provides the theoretical background about innovation barriers and incubators and synthesizes the hypotheses as well as the conceptual model of this study; Chapter 3 looks into research methodology; Chapter 4 provides information about the sample and empirical findings; Chapters 5 and 6 provide findings as well as business and theoretical implications; future research and limitations of the study are also presented in Chapter 6.

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Chapter 2. Literature review and theoretical model

In this section the most important research related to innovation barriers will be presented and the entrepreneurial literature written about incubators phenomena. Moreover, the hypotheses deduced from the literature reviewed will be formulated as well as the conceptual model which incorporates those hypotheses. The hypotheses specify the relationships between variables, whereas the conceptual model helps in visual sizing those relationships (Bacharach 1989).

2.1. Innovation barriers

By synthesizing the existing literature on barriers to innovation we found several categories and classifications of those obstacles. Among the most relevant for this research, Hadjimanolis (1999) classified them into external and internal category. Correspondingly, innovation expose firms at internal and external risks, as in terms of Madrid‐Guijarro, Garcia et al. (2009), from internal perspective, financial and human resources seems the most important as for the external ones the environmental turbulences or lack of government support are the most harmful. Furthermore, five important categories identified by researchers for small business were supposed to have negatively influence on their innovation performance. Those constraints are represented by problems with finance, risk and cost of innovation, achievement of external linkages, low level of managerial and marketing skills, regulation and inefficient innovation policies that are implemented by the government and problems related to the acquisition of skilled employees (Galatsanos, 2014). Likewise, Freel (2000), found that barriers which impede small manufacturing firms to innovate are classified in 4 main categories: finance, management and marketing, skilled labor and external information and linkages. Even though has been found a multitude of classifications of barriers in the extant literature, this research has identified that the most important and common encountered obstacles which may impede innovation within start-ups are related to cost, knowledge and market factors.

2.1.1. Cost factors

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would be impossible to provide. Overall, those studies are all concluded that financial constraints have negative impact on innovative activities.

Point often overlooked when studying financial barriers to innovation, is what most recent studies (D’Este, Iammarino et al. 2012, Blanchard, Huiban et al. 2013) claimed that besides the negative impact which cost factors have on the innovation activity, there are also positive aspects. For instance, Blanchard, Huiban et al. (2013) stated that financial barriers to innovation seem to increase firm’s probability to innovate and, in the same time, firms which are engaged into an innovation project had to face obstacles more frequently than firms which are not engaged into any project. To put it more simply, those obstacles have a positive effect on firm’s likelihood to innovate compared with those which are not. Therefore, there is a direct and strong relationship between being an innovative firm and the importance attached to barriers. As a matter of fact, the innovative approach which a firm decides to take depends, in terms of D’Este, Iammarino et al. (2012), on the types of barriers: revealed or deterred barriers. The former barriers refer to the degree of difficulty the innovation process is perceived by the firm and the consequences of learning after involving in the innovation activity. The later, opposed to the first category, is the deterred effect, which prevents firms from committing to innovation. As a consequence, firms which assess revealed barriers are engaged in innovation and overcoming those barriers produce more successful performance. As an example, for newly established firms in the energy industry, financial constraints were not found significant obstacles in pursuing innovation (Costa-Campi, Duch-Brown et al. 2014). According to D’Este, Iammarino et al. (2012), the deterring effect is strong in the case of cost barriers which means that this is the principal factor which impedes firms of committing to innovation, but in the same time, after pursuing to innovation, despite this obstacle, the learning effect is even higher. Therefore, we argued that start-ups will not assess financial constraints as deterrent for their innovation projects and will become more innovative in order to overcome this barrier. As a result, the following hypothesis has been formulated:

H1. Cost obstacles to innovation will positively influence start-up’s innovative activity.

2.1.2. Knowledge factors

The second most encountered category in the extant literature with respect to the obstacles to innovation consists of knowledge factors. As can be expected, information about firm’s external environment (e.g. market opportunities) are crucial for small firms in order to become more competitive (Madrid‐Guijarro, Garcia et al., 2009). Similarly, Mohnen, Palm et al. (2008) have found that shortage of personnel, shortage of knowledge and market uncertainty seems to slow down a project. In the same way, Freel (2000) stated that the most important barrier to form a partnership is due to the lack of trust and the inability to find a suitable collaborators.

When it comes to the lack of knowledge in terms of management and marketing skills, it is influenced by the lack of information about the environment and new technologies. This is a consequence of the fact that those entrepreneurs seems to be over-optimistic regarding their performance (Larsen and Lewis 2007), constituting an important reason for failure in the early stages of their projects. The same is when talking about marketing skills, Galatsanos (2014) argued that lack of marketing and management skills of the entrepreneurs and the staff, affect the ability to develop sufficient marketing skills to promote and exploit their innovation.

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employees. Accordingly, Madrid‐Guijarro, Garcia et al. (2009) sustained that human resources are significantly related with the innovative activities within an organization. Correspondingly, lack of personnel represents important barriers to innovation for start-ups, since employees in small companies can positively influence the likelihood to innovate.

All the obstacles discussed above for small and new firms arise due to the lack of knowledge which leads to insufficient capabilities to acquire external information (absorptive capacity). In turn, the absorptive capacity is affected by the lack of financial resources which a start-up needs to invest in order to be able to recognize and exploit the new information from the exterior. Consequently, the low level of absorptive capacity is restricting a start-up in identifying suitable external partners for innovation and taking advantages of those collaborations (Cohen and Levinthal 1990). Particularly, this pattern of not forming partnership with external companies have been observed by Nietto et al (2010) which affirmed that compared with larger firms, small firms do not use collaboration with external partners for gaining scientific or technological experience. Furthermore, Mohnen and Röller (2004) affirm that it is imperative to know what the technological innovation from outside the company is, and firms which are not assimilate this external information will face it as a barrier to innovation. Altogether, those studies agree that external knowledge of a firm is crucial in ensuring the competitiveness of the company. Therefore, the lack of knowledge represent impediments in the newly established firms trajectory. Hence, the following hypothesis has been deducted

H2. Knowledge barriers will negatively influence start-up’s innovative activity.

2.1.3. Market factors

The third category of obstacles to innovation seems to be likely to occur due to lack of financial funding which in turn, increases uncertainty about markets. This is one of the leading factors in impeding the realization of an innovative initiative (Mohnen, Palm et al. 2008). Similarly, in the view of Schumpeter and Bottomore (1987), market structure may cause obstacles in the form of competition, firm size and appropriability conditions. As a consequence, the disadvantages of newly created firms, in comparison with incumbents, when trying to penetrate large and less competitive markets, is the lack of complementary assets (Teece 1986).

Compared to financial barriers for which the learning effect is very pronounced, in the case of market barriers the learning effect is very low. Therefore, if firms find market barriers as a real impediment to pursue innovation, the likelihood of not going further is higher. In the same study (D'Este, Rentocchini et al., 2014, p 487), the authors give the explanation for why the market barriers impede firms to proceed with innovation: “presence of markets dominated by established incumbents where it is not feasible for new, smaller firms to engage in innovation based competition“. Similarly, Hausman (2005) explains the market barriers in industries dominated by oligopolies giving example of Coke and McDonalds, in front of which small business might be not well prepared to capture a substantial share of this market. Even though, this market obstacle seems to be related with financial barriers, this handicap can be translated in terms of lack of support provided by the state when entering into the market; therefore small companies are dealing with market entrance barriers imposed by the incumbents. Concluding, financial differences affect the innovation among the new firms in terms of resources needed to cope with market dominators. In light of these, the following hypothesis has been formulated:

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2.2. Incubators

Generally speaking, innovation is a social process and connectedness is an important factor which has positive effect on the innovativeness of firms from the network, by providing valuable information necessary to fuel innovation and adoption (Hausman 2005). In terms of Al-Mubaraki and Schröl (2011) the scope of an incubator is to support start-ups and entrepreneurial businesses by providing a number of services and resources to its clients. Connection to an incubator offers to start-ups several advantages by making them able to enter into the market more quickly and with a faster development of new products and services. These effects arise when incubators guide those entrepreneurial ventures on their way to innovation success. Among others advantages offered, the most important ones is the networking opportunities (Galatsanos, 2014).

Furthermore, channel networks affect the adoption process by encouraging and facilitating the lack of partnership which can negatively affect the possibility of innovation activities or the degree of successful innovations (Robertson et al. 1996). What is more, incubators are more often linked with universities from which start-ups can acquire expert services and administrative support (Bøllingtoft and Ulhøi 2005, Al-Mubaraki and Schröl 2011). Similarly, Mas-Verdú, Ribeiro-Soriano et al. (2015) argued that incubators alone cannot ensure success of a start-up, there are only catalyst for entrepreneurship, stimulating innovation and regional development. Those incubators provide entrepreneurs with basic infrastructure, financial resources and different types of services and information necessary in creating new companies. Therefore, being connected to an incubator will positively influence the level of innovativeness in a start-up by lowering the number and impact of the obstacles on those new enterprises. Henceforth, the following hypothesis was formed:

H4. Connection to an incubator will positively moderate the relationship between barriers to innovation and start-up’s innovative activity, assuming that start-ups which were connected will more easily overcome the obstacles to innovation.

2.3. .Conceptual model

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Chapter 3. Methodology

In this chapter will be described the specific methodology used to conduct this research by looking at the strategy, sample selection and description, data collection and analysis. Moreover, since this study was making use of secondary data, confirmatory factor analysis will be presented as well. Nevertheless, the quality criteria assessed for this study will be explained within this chapter.

3.1. Research strategy

For this study the empirical cycle stated by van Aken, Berends et al. (2012) will be followed, adopting a theory testing approach. Correspondingly, this research will be concentrated on the last part of the empirical cycle, namely the theory testing part, following the deduction of the hypothesis, testing and evaluation of the results steps.

In the beginning, the most important condition for differentiating among various research strategies is to look at the questions which guide the study (Yin 2013). Therefore, looking back to our research question, “What is the impact of barriers on the innovation activity for start-ups?”, this inquests for an exploratory approach in order to quantify the barriers phenomena, as we mentioned above. This exploratory study, ask for a survey strategy which will be able to respond to the “what” from our research question. Turning to the quantitative research methodology, numerical data is needed in order to measure the causal relationship between obstacles and the level of innovation within start-ups (Bulmberg, Cooper et al. 2011). Therefore, secondary data will be used, data which has been collected via a pre-made survey, comprising all the participants of Accenture Innovation Awards 2014. The study relies only on what happened before the participation in the contest, following an ex-post facto design of collecting data. The data will be analyzed and processed using the statistical program SPSS 22 and since the dependent variable is measured using a categorical respectively continuous metrics, logistic and multiple ordinary least square (OLS) regressions will be used. Those procedures will assess the research question in order to provide empirical evidence whether the barriers which a start-up faces contribute or impede its innovative activity.

3.2. Sample selection and description

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3.3. Data collection

As Bacharach (1989) indicates, variables used in an empirical study must be defined in terms of measurements. Therefore, each variable will be described in the following section, whereas the table from Appendix 2 provides supplementary information about each of them.

3.3.1. Dependent variables: Innovation Award Performance, Capital Consumed.

A key point for this research is the most disputably measurements of innovation activity. Most of the papers are trying to consider all the events or factors which influence innovation (Hall, Lotti et al. 2009, Sood and Tellis 2009, Rao 2010, Hervas-Oliver, Garrigos et al. 2011) because only looking at just one measure, a lot of information will be missed from the analysis. For example, the most frequently variables used in measuring innovation is R&D spending and patent applications, which is not above criticism. In the OECD reports it is acknowledged that spending on innovation is more than spending on R&D because in order to develop new products or processes, besides R&D, firms invest as well in other tangible and intangible assets (Publishing 2010). However, for small business only R&D measures could underestimate innovative activities, since firm’s size has been found to be negatively correlated to R&D intensity (Hall, Lotti et al. 2009). Although, since this sample is formed from start-ups, a new method should be applied because they usually do not dispose of necessary resources to apply for patents. This is in line with what Zhang, Yu et al. (2014) concluded, that patent application is not a proper measure for the innovation intensity.

Consequently, we are going to quantify innovation by making use of two measurements grounded in the literature, namely, Innovation Award Performance and Capital Consumed. The first measurement for innovation has the underlying logic proposed by Bays and Jansen (2009), that only competing for a prize can improve the skills of entrants since those awards are important inducements of innovation. The second measurement of innovation chosen, has its root in that companies which are investing more in their projects are likely to be more innovative (Costa-Campi, Duch-Brown et al. 2014). Two aspects were captured within our constructs: the “newness to the market”, by using the first measurement, respectively “newness to the firm” by using the second measurement (Schneider and Veugelers, 2010).

Innovation awards

Involving in a competition stimulate firm’s innovativeness, concentrating their R&D efforts and create an effective innovation program to induce innovation above what would have occurred without it (Kay 2011). In other words, we used Innovation Award Performance, by looking at the status of the participants in the competition: accepted to participate in the final or not. Therefore, two values were attributed to this variable, namely, 1 for being accepted in the final and 0 for not being accepted. Due to the limited numbers of finalists, only 30 registration (6% of the analyzed sample) have the value 1 and the rest (94%) the value 0. All concepts approved in the final are screen on the same criteria by the jury formed by market experts and experts from the industry in which start-ups activate, enforcing the power of this variable. Following criteria are taken into consideration when assessing the scores for finalists: overall impression, impact, growth potential, concept innovativeness, quality of pitch and ability to answer questions of the entrepreneur.

Capital consumed

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our study, this is measured by looking at the total investment in the concept presented in the competition. Compared to the first dependent variable, this one is measured on a continuous scale, by asking the respondents about the total investment in the concept, measured in euro. Moreover, due to the skewed distribution of data, natural logarithm transformation was applied to it in order to bring it to a normal distribution format.

3.3.2. Independent variables: Cost, Knowledge and Market Factors

There are three independent variables which can have an influence on the dependent variables and are deducted from 8 constructs representing the most encounter obstacles to innovation in the literature (Mohnen et al. 2008, Madrid-Guijarro et al. 2009, D'Este et al. 2014). For collecting data about the impediments encountered by the subjects in their innovative activity, respondents were asked to choose from a range of 4 values to which extent they have seen those obstacles as deterring for their project. Using a 4 points Likert scale, the degree of obstacles were measured in terms of 1=“very preventing”, 2=”preventing”, 3=”somewhat preventing” and 4=”not preventing”. By using confirmatory factor analysis the constructs were assessed to be significant for this analysis. The factor analysis is presented in “Data analysis” section from this chapter.

3.3.3. Control variables: Firm Size, Type of Innovation, Type of Funding, Previous Participation, Gender,

Experience

Based on existing literature we used the subsequent variables to control for factors which are known to influence the level of innovation within start-ups (Madrid-Guijarro et al., 2009, Costa-Campi, Duch-Brown et al., 2014). First, the size of the firm is measured in terms of the numbers of employees. This continuous variable, has been normalized by applying natural logarithmic (ln) transformation, since there were visible differences in the distribution of the data. Second, we controlled for Type of Innovation and Previously Participation which are binary variables . The Type of Innovation was measured by attributing the value 1, if the start-up participated with a product innovation and with 0 otherwise (service or business model). The Previous Participation was assessed as well as the Type of Innovation, using binary coding, by attributing the value 1, if the start-up was not at the first participation in the competition, and with 0 otherwise. Dummy variables were created for each of the five types of funding which a start-up could have made use of. Therefore, five more variables have been introduced in the analysis, namely, Subsidies, Crowdfunding help, Bank loan, Venture Capital and Equity. Last but not the least, looking at the entrepreneur’s characteristics stated in the survey, we decided to control for Gender, whether if the company is run by a man or a woman, and if the entrepreneur has more than 3 year experience in the industry in which the company activates.

3.3.4. Moderator: Connected via Incubator

As a new variable discussed in the entrepreneurial literature, connection to an incubator reflects the interaction between firms and incubators (Mas-Verdú, Ribeiro-Soriano et al. 2015). The use of business incubators is a dichotomous condition which establishes if the start-ups in this sample were making use of an incubator or not prior to their enrolment in the award competition. This variable was coded with 1 if the start-up admitted being connected and with 0 otherwise.

3.4. Quality criteria of research

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criteria of a research has also been taken into consideration. This means that the level of industry needs to be assessed, then the quality of the sample and finally the timeliness of the data as well.

When looking at the quality of the data collected, this is based on the quality of the data source, namely Accenture, which was mentioned above. Then, in order to check for the quality of the sample selected from the survey, the response rate of the population has to be analyzed along with the quality of the source of data (validity criteria). As we have been confronted with 89 missing data for the independent variables, the final sample contains 81% of valid cases which will be further introduced in the analysis. All concepts included in the research are screened on the same criteria and with the help of Accenture specialists, the accuracy and precision of the questionnaire has been assured. In order to ensure the reliability of the research, the constructs used were tested using confirmatory factor analysis including the relevant barriers questioned by Accenture. The results of the analysis can be seen in the next sub-chapter, contributing in increasing the reliability of this study.

Finally, the generalization of the study is also assessed. First, from the perspective of the timeliness of data, the newness of the sample is very relevant, presenting data collected in 2014 for the 8th edition of the annual competition Accenture Innovation Awards from The Netherlands. Second, looking at the level of industry, the sample contains start-ups activating in 11 industries, namely, (E) –Retail , Consumer Products and Services, Travel and Transportation, Health, Public Service, High-Tech, Communications, Media and Entertainment, Financial Services, Energy and Chemicals, and Sustainability. Therefore the results are not restricted by one industry comprising start-ups from different categories.

3.5. Data analysis

3.5.1. Confirmatory factor analysis

On the conditions expose in the methodology part regarding the nature of data, confirmatory factor analysis was performed in order to check if the constructs above mentioned are indeed measure the three obstacles of innovation proposed. Following the general procedure for factor analysis (Field, 2012), initial checks, main analysis and post analysis steps were taken. Firstly, the sample size criterion was assessed (sample size greater than 300) and indices of correlation between variables greater than .3 (R Pearson >.3) and not higher than .9 in order to avoid multicollinearity (Appendix 1). Secondly, for factor extraction, Kaiser’s criterion and the graphical representation of a scree plot was used in deciding the numbers of factors. Factor loadings above 0,5 were used for factor grouping. The measure of sampling adequacy – Kaiser-Meyer-Olkin ( K-M-O =.715.) – is above the minimum criterion of .5, showing that the degree of common variance among the initial constructs is considered to be relevant for pursuing with factor analysis. In the same manner, the Bartlett’s test of Sphericity (χ2=725,851, p<.001) support the use of factor analysis in this study.

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Table 2: Component loadings for Barriers to innovation (N=375)

Note: factor loadings over .5 appear in bold

3.5.2. Empirical approach

Before starting to test the hypotheses, the variance inflation factor (VIF) will be used in order to quantify the severity of multicollinearity between variables. All the VIF values of the multicollinearity tests were below 3, which means that there is no significant correlation between analyzed variables (Field 2013). Furthermore, the distribution of the continuous variables was assessed in order to control for normal distributed data. Variables which presented skewed distribution were normalized using logarithmic transformation, namely, Capital Consumed and Firm Size (Pallant 2013). Moreover, the influence of outliers was identified by comparing the mean and the %5 trimmed mean of the distribution (mean without outliers). The difference between those two means was insignificant, as a result, outliers were not influencing the analysis and were not removed from the sample.

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Chapter 4. Results

This chapter presents the results from the statistical analysis, including preliminary analysis of the initial constructs which measure the barriers to innovation, descriptive statistics of the variables and statistical analysis. Based on the analysis section, the hypothesis proposed will be further accepted or rejected.

4.1. Preliminary analysis

Figure 3 and Table 3 provide an overview of the perception of barriers by the entrepreneurs from the sample. Table 3 present the means of the two groups which help us in assessing the differences between the finalists and non-finalists from the competition.

Firstly, an overview over the barriers to innovation encountered by respondents is presented in Figure 3. Data in is ordered by proportions, recording “did not encounter”. As ca be seen the majority of start-ups did not recognize to encounter most of the barriers, merely the ones associated with knowledge and market obstacles. What is more, start-ups did not recognize to face barriers regarding lack of information about news technologies, which is due to the nature of those start-ups, which mostly adopt new technology to solve existing problems. On the other hand, it can be seen that financial barriers (internal and external) were indeed greater obstacles for more than 50% of start-ups, recognizing as ”preventing” and “very preventing”. Not only financial resources were found to be real obstacles to innovation, but also the cost of innovation as well. As can be noted, around 70% of the start-ups recognized the financial and cost barriers to be damaging for their business. When looking at the differences between the two groups (Table 3), finalists have a stronger perception of financial barriers to innovation than the other group. Although, regarding knowledge factors there are no important differences in their perception. Important to notice is the perception of finalists regarding market barriers, which is lower than non-finalists.

Figure 3: Perception of barriers to innovation

0 5 10 15 20 25 30 InternalFinancialResources ExternalFinancialResources InnovationCosts QualifiedStaff FindingCooperationPartners LackOfMarketInformation MarketDominations LackOfInformation

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Table 3: Perception of barriers to innovation by finalists and non-finalists

4.2. Descriptive statistics

Table 4 presents the descriptive statistics and correlation matrix for our constructs. As can be observed, the average start-ups are composed of 6-7 employees and invest around 392 000 euro in their innovations and seems that the product and non-product innovations are equally distributed among them. Moreover, the majority of entrepreneurs were man, with more than three years of experience in the industry. What is more, most of the entrepreneurs were not making use of external financing, but the most encountered financing form is represented by subsidies, whereas the least used is crowdfunding. Last but not the least, few start-ups were connected to an incubator, we can see from the mean (mean=.27) that most of them were not making use of a network prior their participation in the competition.

Turning to the correlation coefficients, should be mentioned that we made use of two methods for assessing the correlation coefficients: Pearson correlation (r) for continuous variables and Pearson Chi-Square correlation (R) between dichotomous variables (Field, 2012). Correspondingly, Table 4 reports correlation coefficients for all variables used in this study. Between the two dependent variables is a significant correlation (r=.184, p<.05), in accordance with the literature, since we are measuring the same concept. With attention to the barriers to innovation, all three factors are positively correlated one with another having a significant level of acceptance (p<.05). What is more, being connected to an incubator have positive effect on the likelihood to be accepted in the final (R=8.56, p<.05). With attention to the Cost Factors, this variable is positively correlated to Connection via Incubator (r=.212, p<.05), in line with has been hypothesized about the moderation effect. Although, Knowledge Factors are only significantly correlated with the other two barriers. Moreover, Market Factors are positively correlated with the Capital Consumed, which seems to be different from what we hypothesized that market obstacles will impede the innovative performance of a start-up.

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Table 4: Descriptive Statistics and Correlation Matrix

1

in the regression analysis Capital Consumed and Company Size have been transformed using natural logarithms (ln) 2

correlation between dichotomous variables, was measured using Pearson Chi-Square method **Correlation is significant at the 0.05 level

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4.3. Testing the hypotheses

4.3.1. Dependent variable: Innovation Award Performance

Table 5 illustrates the results from logistic regression performed for the first measurement of the dependent variable. Initially, the control variables were introduced in the regression analysis (Model 1). Afterwards, the independent variables were tested along the control ones (Model 2), then, the moderator variable was introduced along the control and independent variables (Model 3) and finally, the moderation effects were tested in Model 4. Firstly, the overall significance of the Model 1 was assessed, having the level of Chi-Square χ2=17.650 significant (p<.10). Turning our attention to the significance of control variables, Firm Size (b=.492, p<.05), Crowdfunding (b=.995, p<.05) and Experience (b=2,220, p<.05) were found to be significant and contributed in explaining between 4 and 10.1 percentages of the variance in the dependent variable. Secondly, the independent variables were tested in Model 2. The overall model was significant (χ2=23.528, p<.05) increasing in comparisons with Model 1, as well as the variance explained (between 6.3 and 14.8 percentage). In what concern the significance of independent variables, we can observe that besides Cost Factor (.374, p<.10) the rest of obstacles were found to be insignificant.

Thirdly, the moderator variable was introduced along the control and independent ones, observing an improvement in the significance of the model (χ2 =27.734. p<.05) and the variance explained from the dependent variable increasing until 17.4 percentage. Therefore, Connecting via Incubator was found significant (b=.923, p<.05), increasing the variance explained form the dependent variable. Finally, when testing the moderation effects proposed in Model 4, it can be observed that all the interaction effects are insignificant. The level of significance of the model has increased (χ2 = 31.935, p<.05) and Cost Factors (b=.560, p<.10) are again significant, increasing the variance explained to 19.9 percentage. Although, Connection via Incubator is not significant anymore (b=.865, p>.10) compared with model 3.

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Table 5: Logistic regression

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4.3.2. Dependent variable: Capital Consumed

In testing the second dependent variables, multiple ordinary least square (OLS) regression was performed and four models were formulated (Table 6). Firstly, the control variables were introduced in Model 1. Therefore, when to control, the variables account for 8,3 percentage from the variance in the dependent variable, and, surprisingly, Subsidies (b=.683, p<.10) were positively related to the Capital Consumed. Secondly, the independent variables were introduced along the control ones and an improvement can be seen in the variance of the dependent variables, the model accounting for 12.4 percentage of its variance. Moreover, Market Factors (b=.421, p<.05) were found to positively influence the dependent variable. Also, Previous Participation (b=.993, p<.10) in the contest was found significant along the Subsidies (b=.814, p<.05). Thirdly, the moderator variable was introduced along the control and independent ones in Model 3, observing no improvement in the variance of the dependent variable(R2=.124), and a slightly decrease in the fittest of the model (F=3.081). Interestingly, Connection via Incubator (b=.078, p>.10) is not significant anymore for this dependent variable. However, when adding the moderation effects in Model 4, it can be observed an improvement in the total variance explained (12.7 percentage) but none of the moderation factors were significant. Similar with the logistic analysis, firms size was significant in all four models tested (b1=.986, b2=1.024, b3=.993, b4=.968, p<.001).

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Table 6: Multiple linear regression

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4.4. Overview

By analyzing the results above we can now assess the level of significance from the hypotheses proposed, in terms of the two measurements of the independent variable (Table 7). When looking at Hypothesis 1 regarding the importance of cost obstacles (Table 5 and 6), this was only accepted in terms of the first dependent variable, Innovation Award Performance (Model 2 and Model 4). The third hypothesis, regarding market obstacles, it can only be assessed for the second dependent variable, Capital Consumed (Table 6). Although, opposite from what it was hypothesized, Market Factors positively enhance the propensity to invest in the concept, therefore Hypothesis 3 will be rejected. In what concern Knowledge Factors, in both analysis those are not significant. Under those circumstances Hypothesis 2 cannot be assessed. In the same way, the interaction effects are also found insignificant in both analysis, therefore the Hypothesis 4 cannot be assessed either. Interesting to notice, Connection via Incubator, Crowdfunding and Experience were found to be significant for the first dependent variable, whereas for the second analysis, Subsidies and Previous Participation were positively influenced the Capital Consumed. All those variables and their significance will be analyzed in the discussion chapter.

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Chapter 5. Discussion

In this chapter, the results of the statistical analysis will be discussed and the findings will be connected with the relevant literature. Our study contributes to the research of dynamic capabilities and literature on absorptive capacity, as well as increasing the research in the entrepreneurial field. Whereas most of the empirical literature have concentrated on testing the negative effect of barriers to innovation which larger or medium companies face, the objective of this study was to explore the differing effects of barriers on innovation activity among start-ups. One important key point for this research was posit by Blanchard, Huiban et al. (2013), as besides the negative effect of barriers to innovation, they also provide firms with positive parts which are underlined in the learning effects. Starting with this in mind, we decided to test whether the barriers identified in Chapter 2 have positive or negative effects on start-ups. Under those circumstances, the dependent variable – Start-up’s Innovative Activity - was measured in two ways: by assessing the acceptance in an innovation competition and level of capital invested in the innovative concept. The independent variables were represented by three obstacles to innovation, namely, Cost, Knowledge and Market Factors (Mohnen et al. 2008, Madrid‐Guijarro et al. 2009, D'Este et al. 2014) and a supplementary new variable introduced in the literature (Mas-Verdú, Ribeiro-Soriano et al. 2015) as a moderator - Connection via an Incubator. Those relationships have been analyzed using logistic regression as well as multiple OLS regression, whereas one hypothesis was sustained in terms of first measurement and one was rejected in terms of the second measurement of innovation. For the rest of hypotheses was not found enough support to be assessed (Table 7).

When looking at the first hypothesis, we stated that “Cost obstacles to innovation will positively influence start-up’s innovative activity”. The result is interesting in the way that is mostly opposite to what have been discovered so far. Different from the general belief (Hadjimanolis 1999, Freel 2000, Larsen and Lewis 2007), we determined that in the case of cost barriers, besides assessing it as preventive for their business, this barrier does not negatively affect the level of innovativeness for start-ups but enhancing it. As a matter of fact, the results show that most of the finalists made use of crowdfunding in order to overcome this obstacle. As is stated in Appendix 3, what was also important is the experience of the entrepreneur in the industry. As a result, the learning effect is important in overcoming the cost barrier and enhance startup’s innovativeness, even though, in term of D’Este, Iammarino et al. (2012) it is very harmful, representing a reason for withdrawal from a competition and fail without learning. Furthermore, when analyzing the companies from the perspective of a competition, it is important to acknowledge as Zhang, Yu et al. (2014) stated, that those competitions are taking into account the small difference between innovation and invention, looking besides the newness of the idea and take into consideration also the commercialization of it. Therefore, by being accepted in the final of the completion, most of the time the concept has already provided revenues to the company.

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operational costs (Galatsanos, 2014). An example of how the operational costs were reduced is presented by Larsen and Lewis (2007), when the manager of a SMEs trying to avoid losing control over his company and avoiding the venture capital funding used personal financial resources, by relocating the company to his home, in order to overcome cost obstacles. Added to these ways, it is clear that start-ups face intensive financial obstacles to innovation, but in the same manner there are more alternative ways in overcoming those barriers, mostly depending on their adaptability to the environment and flexibility of their capabilities in finding new and innovative ways of overcoming cost obstacles.

The second hypothesis, for which was stated that Knowledge Factors will negatively influence the propensity to innovate was not found to be significant. In the same time, this hypothesis cannot be rejected or accepted. Even though seems that knowledge barriers provide a considerable learning effect, for the analyzed sample this was not statistically demonstrated.

Drawing our attention to the third hypothesis, we posit that “Markets dominated by large incumbents will negatively influence the propensity to innovate of a start-up”. Contrary with was stated in the theoretical part, results seem to be completely on the opposite side, therefore Hypotheses 3 was rejected, measured by the Capital Consumed. For the sample analyzed, market obstacles had positive and significant effect in the propensity of innovating. Therefore, the companies which faced markets dominated by incumbents also faced financial obstacles. By looking back at the discussion above regarding financial constraints, those actually helped start-ups in innovating even more, as a result, the financial constraints were overcomed. The explanation for this result can be found in the dynamic capabilities literature. According to it, those capabilities make start-ups able to create and extent their resource base in order to modify its competitive position in the award contest. Furthermore, absorptive capacity has as well an explanation here, whereas high level of absorptive capacity will be a part of firm’s decision in allocating resources for the innovative activity (Cohen and Levinthal 1990). As a result, when facing market barriers to enter, most of start-ups will concentrate their investments only in one direction (here, the participating concept) in order to overcome this barrier. What is striking for this analysis, the types of funds used by the company in order to overcome those barriers are related to subsidies. Although there are few studies which evaluate the subsidies programs for young firms (Schneider and Veugelers, 2010), the explanations will be that market failures are rooted in financial barriers, therefore the government intervention. However, when looking at the sample analyzed, in the Netherlands the sustaining policies have been applied, since those new initiatives who encountered market barriers made use of subsidies. For example, in the Dutch Community Survey (CIS, 2002-2003) two third from the respondents found financial barriers and higher costs as not hampered (Mohnen, Palm et al. 2008). Therefore, many firms benefit from different public support programs to encourage investment in their innovation activities (Publishing 2010). Although, it is clear from the same study that larger firms receive more support than small ones, which should be a signal of alarm for governmental institutions to narrow their innovation investments more into the entrepreneurial sector.

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connected to an incubator prior the participation in the award competition were assessed by the jury as being more innovative, being previously provided with basic infrastructure, financial resources and different types of services and information necessary for creating their business. Cohen and Levinthal (1990) argued that the capability to absorb and apply external relevant information depends on the absorptive capability of the firm. Therefore, the relational capability, derived from the level of absorptive capacity (Fitjar and Rodríguez-Pose 2011) is essential in order to enhance firm’s innovative performance, in this study seen as connection to a network.

The significance of learning effect is presented as well (Appendix 3), observing Previous Participation to be significant for the Capital Consumed. Correspondingly, prior related knowledge permit assimilation and exploitation of new knowledge. Relating to our sample, those prior knowledge have contributed in increasing firm’s absorptive capacity in what concern the experience with the innovation contest. Contrary, we cannot say the same about the gender. Although, seems that 81% of entrepreneurs were male, this variable was insignificant when measuring the level of innovation within start-ups. However, the learning effect of the previous participation has increase as well the amount of capital allocated for innovative activity.

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Chapter 6. Conclusions

Since the hypotheses have been empirically assessed from theoretical perspectives, this final chapter will provide answers to the research questions. The theoretical implication as well as business ones will be discussed. Additionally, the limitation of the present research will be presented within this chapter.

This study is based on the previous efforts of the staff from University of Groningen (RUG) in discovering and analyzing barriers to innovation by studying the differences between winners and non-winners. It has been argued that there are 3 main effects of participating to an innovation award competition, namely signaling of quality, learning and networking (Van der Eijk et al., 2013; Borgmann ,2013; de Roo, 2014). Within this study, we are focusing on the learning effect for start-ups in overcoming the obstacles and increasing their innovation activity. Although, there were other relevant theories on which we can base our approach, for example Resource Based View Theory is the closest one, but is perceived as a static framework, whereas not all firms have the same capabilities to access resources and focuses on how individual firms generate returns based on their own resources and capabilities. Although, we need to analyze from the perspective of effectively orchestration of the few resources which a small firm dispose both internally and externally, recombine them and increase their revenues(Teece 2007). Therefore, despite the alternatives, we decided that dynamic capabilities and absorptive capacity approaches are promising both in terms of future research potential but mostly in gaining a competitive advantage in the rapid changing environment.

6.1. Answering the research question

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6.2. Theoretical and business implication

Our analysis contributes to the extant literature on entrepreneurship by analyzing the relationship between obstacles and innovative performance of start-ups. Nevertheless, those results contribute to research of dynamic capabilities and literature on absorptive capacity. Furthermore, the nature of sample analyzed makes possible the contribution to a larger four years research of barriers to innovation encountered by participants in awards competition. Also, the influence of being connected to an incubator in overcoming certain barriers to innovation has received scarce scrutiny, this term being newly introduce in the literature. In this study we demonstrated that connection to an incubator has positively impacted the innovativeness in a start-up and knowledge exchange, contributing as well to the relational view of dynamic capabilities.

From the business perspective, three parties can benefits from this research. Firstly, entrepreneurs can get insights in becoming more innovative by getting connected to an incubator or social networks. By doing this, the possibility to be accepted in an innovation competition will increase, thereupon their chances of profiting from learning, networking and signaling of quality capabilities. But mostly, an incubator will offer access to a wide range of resources and guide it to innovation success. Secondly, Accenture can benefits as well from this study. It will be able to use this information in advertising larger firms in partnering up with the analyzed start-ups and providing statistical reasons for investing in it. And finally, as it was stated in the introduction of the research, the Dutch Government can benefit as well, whereas this research was taken into account the actual concerns encountered in the OECD’s reports (Box 2009), and provides some insights how The Netherlands can improve its desired position as a top leader in innovation. By looking at the barriers which are encountered in the entrepreneurial field, they can provide more help for those entrepreneurs in overcoming the initial phase of a start-up, by offering more incentives in terms of subsidies, awards or loans.

6.3. Limitations

Several limitation of this study need to be assessed. In the first place, the sample size represents an impediment in this empirical analysis. Accenture Innovation Awards covers only a small part of the start-ups from the Netherlands; therefore the results cannot be generalized to all the startups from the country. Moreover, the dependent variable – Innovation Awards Performance - was not a very good representation, since only 6% of the registrations received the value 1 and the rest the value 0. This was also a limitation in choosing the finalists and not the winners of the competition. Another weak point for this analysis, was the lack of data provided which impede in using more measurements of innovation. The companies were reluctant in providing data about their turnover, the total investment in the concept or the percentage of the revenues from the new concept in the belief that those data will negatively influence their chances to acquire a prize in the competition. What is more, we could not take into account the nature of innovation due to the lack of information gathered from the survey. Hence, our study did not distinguish between breakthrough innovations and incremental ones.

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business research. And finally, the barriers identified in the questionnaire did not provide any information about policy regulation barriers.

6.4. Future research

This study also provides several directions for future research. Firstly, it will be more accurate to investigate those barriers on a larger sample by making use of data collected through several years. Further studies can collect data about other barriers which startups specifically may encounter, different from the ones collected within the AIA survey (Appendix 4). Several studies have introduces in their analysis, besides the barriers tested within this research, other obstacles to innovation (e.g. short-term focus, uncertain demand, etc.) which future research can take it into account by synthesize those factors and empirically tested.

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Are barriers to innovation enhancing or impeding innovative activities among start-ups? Insights from Accenture Innovation Awards 2014