The role of entrepreneurial team experience in the performance of Dutch startups in the cultural and creative industries

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The role of entrepreneurial team experience in the

performance of Dutch startups in the cultural and creative

industries

Master thesis

MSc in Business Administration - Entrepreneurship and Management in the Creative Industries

Name: Elke van Gerven Student number: 10431446 Supervisor: Dr. A.S. Alexiev

University: University of Amsterdam Date: 26-01-2018

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Statement of originality

This document is written by Student Elke van Gerven who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are my original creation and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

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Abstract

This thesis touches upon the relation between the experience of the entrepreneurial team and the performance of Dutch startups in the cultural and creative industries. Next to this the role that industry similarity plays is taken into account. The cultural and creative industry is one of the most innovative sectors and is important for economic and social progress. However, at the moment there are surprisingly few studies on the contribution of cultural and creative businesses and their performance, yet it is assumed that innovation in cultural and creative enterprises is different than in the more traditional sectors. Previous research has reported mixed evidence of a relationship between entrepreneurial experience and business

performance. Based on theory it is proposed that the more previous experience the entrepreneurial team has, the more successful the performance of the startup and that this effect is moderated by industry similarity. A dataset with questionnaires of 106 entrepreneurs of cultural and creative startups has been compiled. A multiple regression analysis has been performed to measure the effects. The results show that there is no complete statistical proof that entrepreneurial team experience influences startup performance. However, there is statistical proof that entrepreneurial team experience influences the founders’ evaluation of the performance of their startup. The interaction between entrepreneurial team experience and industry similarity is not consistent with the expectations. The findings of the current study indicate a need for future research to further investigate what other factors can influence the performance of startups in the cultural and creative industries.

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1 Introduction

New, small businesses with innovative ideas and inventive products and services are setting up at an impressive rate all around us. One of the innovative sectors relevant to economic and social progress is the cultural and creative industry (Schramme, 2015). Creativity today is more than ever an important source of economic growth and employment in our knowledge society. In addition, it is an important source in social development, with culture acting as a major stimulant for social innovation. Interest in issues related to the cultural and creative industries has been on the increase since recently (Schramme, 2015). Creative sectors (such as design, media and entertainment, fashion, gaming and architecture) make cities appealing to tourists, businesses and residents. This industry makes a significant contribution to employment and the growth of new companies in the Netherlands. In addition, the creative sector is a pioneer of innovation in other sectors and provides creative solutions to social challenges. It is one of the fastest growing sectors in the Dutch economy, although there is still a considerable gap between the intentions and the measures applied (Rutten & Koops, 2013). However, creativity and entrepreneurship are not an obvious combination. Creative and innovative business is therefore a challenge for all dynamic companies from a range of widely varying sectors (Nauwelaerts & Frank, 2007).

The Netherlands has many innovative and radical starters, but only a small percentage is ultimately successful (Groenewegen & de Langen, 2012). Given this high failure rate, it is important to identify what can influence the success and failure of these ventures. Many researchers argue that entrepreneurs gain important insight from previous work and entrepreneurial experiences which may adversely affect their new business performance. Literature shows many opposing results regarding the influence of entrepreneurial experience (Cassar, 2014; Toft-Kehler, Wennberg & Kim, 2014; Muñoz-Bullon, Sanchez-Bueno & Vos-Saz, 2015). Contrary to other sectors, little empirical research involving startups in the

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cultural and creative industries is available. The purpose of this study is therefore to gain insight into the effect of entrepreneurial team experience on the performance of a startup in the cultural and creative industries in the Netherlands, and the role of industry similarity in this. The following question will be the focal point in this research:

What is the interaction effect of industry similarity on the relation between entrepreneurial team experience and the performance of a Dutch startup in the cultural and creative industries?

Several studies have already been conducted on the performance of startups (Song, Podoynitsyna, Van Der Bij & Halman, 2008; Van Geenhuizen & Soetanto, 2009). These investigations were aimed primarily at starting companies from different industries. They searched for obstacles that must be overcome for startups to grow and eventually deliver a good performance. There is still little written about success factors in the cultural and creative industries, and particularly the role of entrepreneurial team experience in this has not been adequately discussed. As the cultural and creative industry is a very dynamic environment, the preconditions differ from those in other industries. Conducting a research that investigates the influence of prior experience of the entrepreneurial team on the performance of the

startup can fill the gap where this is under researched. With this gained knowledge, factors other than entrepreneurial team experience can be further investigated, which should provide a better empirical statement of successful and less successful cultural and creative startups in the Netherlands. Starting companies can reproduce these factors and ensure good

performance.

With the initiation of the StartupDelta in 2015, the Dutch government is fully

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the economic crises of recent years, with many regions still struggling with relatively high unemployment, young businesses seem to be a vital aspect (StartupDelta, 2015). Research by the Organisation for Economic Co-operation and Development in 2015 shows that 60% of the job growth in the Netherlands comes from startups (OECD, 2015). The importance of startups for employment and the economy as a whole is therefore considerable. This research can contribute by giving insight into reasons why entrepreneurs in the Netherlands fail to build a successful business. Enterprise is essential in business and failure of new businesses is an important part of this. Companies and starting entrepreneurs can obtain a better

understanding of what influence entrepreneurial team experience can have in the performance of a startup. This may contribute to the reduction of failing startups or to better thought-out choices, and therefore better job growth. This research is also interesting for new nascent entrepreneurs, as it will give them an idea of how to prepare before starting their business. The remainder of this study is organized as following. First, existing entrepreneurial team experience literature will be assessed. Furthermore, the definition of the variables and cultural and creative industries will be clarified. This is followed by an elaboration of the research approach and gathered field data. Findings of the research are presented as results and further evaluated in the discussion section. The limitations and suggestions for future research are subsequently discussed following an outline of the theoretical and practical implications. Finally, the findings are concluded.

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2 Literature review

In this section a deeper understanding through literature on startups and entrepreneurial experience is established. First, it is important to provide a clear definition of startups and the cultural and creative industries, and to discuss the previous research in this sector. Then, the theoretical concept of entrepreneurial team experience will be explained, which is followed by the explanation of industry similarity. Next, the dependent variable of the research will be further expanded on: the performance of the startup. In conclusion, the control variables will be discussed and this section ends with a conceptual model that shows the relationships in this research.

2.1. Startups

Not every starting company is a startup. With respect to the further investigation it is essential that the definition of a startup is clearly specified. A startup has as many faces as it has

definitions. This is also evident in the literature where various descriptions have been

discussed over the years. Ries (2011, p. 8) defines a startup as “a human institution designed to create a new product or service under conditions of extreme uncertainty”. Blank (2012, p. 12) uses the following definition: “a temporary organization in search of a scalable,

repeatable, profitable business model”. Nguyen-Duc, Seppänen & Abrahamsson (2015) claim that startups are very important in generating innovative products and services that can affect the global economy. In particular, high tech and science-related startups carry a significant part of radical innovation (Balboni, Bortoluzzi, Tivan, Tracogna & Venier, 2012). For example, Nguyen-Duc et al. (2015) state that companies like Facebook, Spotify, Pinterest, and Dropbox once were a startup. Each of these companies has had a very broad impact on the world, or still does.

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An important element that characterizes a startup is the age. Some studies allow a maximum of two or three years in which a company is considered a startup. (Cassar, 2004; Koski & Pajarinen, 2012). However, the startup phase differs per individual company. For example, startups can differ greatly in the duration of the startup phase, the product or service, but also in turnover. Whereas one startup passes the startup phase after two years, another startup only enters a growth phase after five years. Gadet, Vermeulen, Laarhoven & Ruijs (2016) maintain a maximum age of five years in their research into location

considerations of startups in Amsterdam. Within these five years, startups, as described in their research, can go through four different life phases. These phases of life also affect the spatial needs of the startup. A startup starts in a 'pre-seed' phase, in which cost reduction and development of the product or service and business model are centralized. The second phase is the proof-of-concept. The startup is still small-scale, but cooperation is essential to get the product or service going. The third phase is the 'growth phase' in which startups become more independent, acquire their own identity and slowly start making a profit. The final phase is the scale-up. This phase typifies the transition from startup to adult company where large growth and investments are characteristic factors. For each company there is a difference in the time line of the different phases. For example, very successful startups are already a scale-up within a short time, but a startup can also remain in the 'proof-of-concept' phase for a long time (Gadet, 2016). Like Gadet et al. (2016), this research adheres to a maximum age of 5 years at which a company is considered as a startup.

2.1.1. Startups in the cultural and creative industries

Innovation, design and creativity are receiving more and more attention nowadays. At the moment, however, there are surprisingly few studies on the contribution of creative

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enterprises is different from the more traditional sectors (Bakhshi, McVittie & Simmie, 2008). Many studies in the creative industries focus on measuring the economic value of the creative industries itself in terms of jobs, turnover and added value (Bakhshi et al., 2008). This usually involves the turnover of the entire creative industry and its sub sectors (Rutten, Malet, & van Oort, 2011). In addition, some studies assess the value of the creative industry for other sectors. For example, the mobility of employees - and with that the

interchangeability of activities and therefore producers and services - is being considered between the creative industries and other sectors (Rutten et al., 2011).

With the increase in emphasis on perception and other non-economic values, the role of the cultural and creative sector, which pre-eminently specializes in symbolic production, becomes more relevant (Jacobs, van Andel, Schramme & Demol, 2013). As a result, the cultural and creative industries are taking an increasingly central place in the economy. They are a growing source of inspiration and an attractive party for other sectors to be innovative (Jacobs et al., 2013). The rapidly changing social contexts present cultural and creative entrepreneurs with special challenges today (Schramme, 2015). Increasingly higher demands are set upon them by the various stakeholders - including the government, society,

employees, the public: not only must they have a strong vision, they must also be

enterprising, contribute to a harmonious and diverse society, good people management is a required asset and they must (continue to) deliver artistic top quality (Schramme, 2015). In order to examine the effect of the previous experience of the entrepreneurial team on the performance of a startup in the cultural and creative industries, it is important to provide a clear definition of this sector. Manshanden, Raspe & Rutten (2004) assume "meaning" to be a core component of products and services in the cultural and creative industry. They have symbolic value. This means that these goods and services do not only derive value from functional matters, but also the need for personal development and

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profiling, entertainment and decoration. The product or service has a value that goes beyond the actual usage value. Within this definition, three main components are distinguished: the arts, the media and entertainment industry and the creative business services. In addition, emphasis is placed on the creative core (creation and production). This covers publishers (ranging from print media to music), photography, design (from interior to fashion), film-, radio- and television production, performing arts, press, galleries and museums.

2.2. Entrepreneurial team experience

Entrepreneurial team is defined as the management team of the startup (Timmons and Spinelli, 2004). According to Everaert (2010), the management team has the greatest impact on the success of a (in the case of his research, high-tech) startup company. It is important to find people within the management team with complementary skills. Many researchers argue that entrepreneurs gain important insights from previous work and entrepreneurial

experiences, which can influence their new business performance. However, “greater experience may lead entrepreneurs to perceive greater opportunity costs associated with marginal businesses. This could lead to negative or insignificant relationships with survival” (Cooper, Gimeno-Gascon & Woo, 1994, p. 377). Many opposing results are found in

literature regarding the influence of entrepreneurial team experience. “There appears to be no stereotype for a successful entrepreneur and there are wide variations in their backgrounds, personalities, and experience” (Crawford, 2017, p. 4).

Toft-Kehler et al., (2014) investigated why entrepreneurial experience does not always lead to improved financial performance of new ventures. They found that limited experience lowers performance while enhanced financial performance only occurs at substantial levels of experience. According to Shepherd, Zacharakis & Baron (2003) experienced entrepreneurs can suffer from overgeneralization and overconfidence and

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therefore they are more likely to overfit the world by drawing conclusions based on only small samples of experience. They also might be less likely to engage in counterfactual thinking and therefore fail to obtain important insights into how performance in various situations can be improved in the future (Shepherd et al., 2003). As a consequence,

experienced entrepreneurs may become captured in current modes of thought and therefore they may fail to develop better decision policies that can improve future performance (Shepherd et al., 2003).

However, besides the articles that argue the poor influence that experienced entrepreneurs may have, others argue that experience can have a positive influence on performance. Previous experience provides knowledge of organizational routines and skills obtained from previous activities, which can be transferred to the startup (Delmar & Shane, 2006). It provides knowledge of what roles are necessary in organizations and who should fill those roles, and it links the entrepreneur to a network of employees, suppliers, investors and customers (Delmar & Shane, 2006). It also provides knowledge about how to run a startup that has been learned from previous mistakes (Delmar & Shane, 2006). Besides,

entrepreneurs with business experience are more likely to have better access to finance (Westhead & Wright, 1998) and are better able to grow a business (Colombo & Grilli, 2005).

2.2.1. Effectuation theory

The effectuation theory works to understand how experience changes the way people think (Read & Sarasvathy, 2005). Apart from the type of company, technology, location or history of the successful entrepreneur himself, the research of Sarasvathy (2001) showed that

experienced entrepreneurs all learn the same kind of lessons. Sarasvathy (2001) found a way to bring these lessons together in a logical coherent method, as a scientific theory.

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the right resources, you start from the existing resources and network contacts and look for a feasible goal. This line of thought is typical for experienced entrepreneurs: get to work as quickly as possible and achieve results, with the resources you have (Sarasvathy, 2001). Empirical findings of studies by Dew, Read, Sarasvathy & Wiltbank (2009); Dew, Read, Sarasvathy & Wiltbank (2011), and Harms & Schiele (2012) show that experienced entrepreneurs do indeed tend to rely on effectuation rather than entrepreneurs with less experience. Effectuation is the inverse of causation (Read & Sarasvathy, 2005). With causation, a goal is set beforehand, an extensive plan is made to achieve this, and then all means are gathered to achieve that goal according to plan (Read & Sarasvathy, 2005). “Effectuation theory implies that the successful entrepreneur, rather than displaying a single personality type or background, has acquired a unique ability for pattern recognition which, like that of a great chess player, gives them to the ability to envision opportunities”

(Crawford, 2017, p. 10). This suggests that experienced entrepreneurs tend to work according to effectuation rather than less experienced entrepreneurs, which will help them in making a success of their business.

2.2.2. Human Capital theory

Many authors in the entrepreneurial experience literature discuss human capital (Gimeno, Folta, Cooper & Woo, 1997; Davidsson & Honig, 2003; Colombo & Grilli, 2005). Human capital is usually referred to in terms of the entrepreneur’s success level. Human capital includes the competencies, knowledge and experience of an entrepreneur. The theory of human capital refers to certain knowledge that gives individuals increased cognitive skills they can handle more productively and efficiently with activities (Davidsson & Honig, 2003). In-depth knowledge can play a crucial role in intellectual performance. It reinforces the integration of new knowledge and helps to integrate and adapt to new situations (Weick,

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1996). In line with the human capital theory, experienced entrepreneurs are therefore expected to gain advantage over their competitors because of their increased human capital. As demonstrated in previous research, “ventures founded by more experienced entrepreneurs begin their lives further up the learning curve because the human capital that their founders provide is more valuable to the performance of the new ventures than the human capital of inexperienced founders” (Delmar & Shane, 2006, p. 225).

Human capital can be divided into two types: general human capital and specific human capital. General human capital constitutes the knowledge and skills that can be used in general, regardless of the sector in which one operates (Dimov & Shepherd, 2005). Specific human capital is the knowledge and skills that are valuable in a particular sector but are not useful in other industries (Blair, 1999). Industry similarity is part of specific human capital, which will be discussed in the next paragraph.

Based on the theory on entrepreneurial experience, the following prediction is made:

H1: The more previous experience the entrepreneurial team has, the more successful the startup’s performance will be.

2.3. Industry similarity

“Industry similarity is the extent to which an entrepreneur's previous ventures are similar to their current venture in terms of the industry in which they operate” (Toft-Kehler et al., 2014, p. 458). With reference to specific human capital, an entrepreneurial team with more industry experience has a better understanding of how to satisfy customers in that industry because such information is usually only accessible through industry participation (Delmar & Shane, 2006). Many of the skills and much of the information required to effectively exploit an opportunity can only be learned through employment in the industry, and social ties to

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suppliers and distributors are created over time through activity in an industry (Delmar & Shane, 2006). Specifically, “individuals with great industry-specific and

entrepreneur-specific human capital are in an ideal position to seize neglected business opportunities and to take effective strategic decisions that are crucial for the success of the new firm” (Colombo & Grilli, 2005, p. 800).

Entrepreneurs starting businesses closely related to the activities they used to perform in the past are more likely to have developed networks of relationships (Cooper et al, 1994). Higher levels of industry experience allow entrepreneurs to develop market-specific

knowledge and key relationships with suppliers and customers and their business will need to efficiently compete on the market (Barney, Busenitz, Fiet, & Moesel, 1996). These networks and relationships would give them credibility and the ability to improve their sales

development, gain credit and achieve other forms of cooperation (Cooper et al, 1994). An entrepreneurial team with industry experience should be able to draw on a larger number of pre-existing ties than entrepreneurial teams who lack such experience. As a result, they should enjoy a higher trust level and credibility in their industry. Furthermore, if an existing tie would be disrupted, entrepreneurial teams with industry experience will be more likely to have pre-existing replacement ties in their networks (Toft-Kehler et al., 2014).

Muñoz-Bullon et al., (2015) investigated what factors enable nascent entrepreneurs to successfully create profitable new firms, and found a greater effect of team resource

heterogeneity when the team has more experience in the industry in which the new business will compete. Criaco et al. (2016) showed in their research that having previous experience in the same industry is beneficial for startup survival when the technological knowledge within an industry is high in intensity and scope, while previous experience across different

industries is beneficial when this knowledge is low in intensity and scope. Dencker & Gruber (2014) found that high-risk opportunities favor founders with managerial experience, whereas

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low-risk opportunities favor founders with industry experience. Research has also shown that industry experience increases venture growth (Cooper et al., 1994) and the likelihood of an initial public offering (Shane & Stuart, 2002). However, other work has failed to find any significant relationship between industry similarity and business performance (Kalleberg & Leicht, 1991; Chandler, 1996). As a many of these studies are deprecated, the results of current research can be very useful. Based on the theory discussed, it can be concluded that entrepreneurial teams with more industry experience are likely to have an advantage over other entrepreneurial teams in developing their new businesses. That is why the following hypothesis has been made:

H2: Industry similarity is positively moderating the relation between experience of the entrepreneurial team and the performance of the startup. Specifically, where entrepreneurs

have experience in the cultural and creative industries, this will strengthen the relationship between the entrepreneurial team experience and the performance of the startup.

2.4. Startup performance

The dependent variable in this study is startup performance. In order to test the effect of the independent variables, the notion of startup performance must be properly defined. Brush & Vanderwerf (1992) discuss performance along two dimensions: survival and success. They describe survival as the opposite of failure. A business fails when it ceases to exist as an economic entity. Failure can occur when a business is unable to satisfy its financial

obligations or because it is unable to meet the objectives of the entrepreneurs. Success occurs when the business creates value for its customers in a sustainable and economically efficient manner (Brush & Vanderwerf, 1992). Although it may take several years for a startup to earn a profit, its ability to create lasting, hard-to-imitate value, suggests that if it survives those

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initial years, superior levels of profitability and growth compared to its competitors should occur (Brush & Vanderwerf, 1992).

There are many different possibilities in startup literature to measure the performance of a startup (Witt, 2004; Stam & Schutjens, 2005). Because startup performance is a

multidimensional and complex construct, it is recommended to use multiple indicators to measure it (Zahra, Neubaum & El-Hagrassey, 2002). This is because profitability only usually does not correctly portray the performance of a startup. For example, a cultural or creative startup can grow very quickly and gain a lot of market share. This is crucial for long-term survival (Lau & Bruton, 2010), but in the short long-term a startup may not be profitable. Also, for some cultural and creative startups, profit might not even be of great relevance. Because the ‘rules’ in the cultural and creative industry from those in other industries, it is important to bring together good indicators for the performance of the startups (Braaksma et al., 2005). Financial performance indicators alone are therefore not enough to measure the performance of a startup in the cultural and creative industries. That is why both objective and subjective performance indicators will be used.

2.4.1. Objective and subjective performance indicators

It is important for most startups to raise sufficient capital to finance their production process and to grow rapidly in order to gain market share (Lau & Bruton, 2010). With this financial capital the startups can optimize the production process and bring the product or service to the market (Tseng, Chiu & Chen, 2009). Raising sufficient capital for the startup is an essential indicator of the performance of startups (Dahlqvist, Davidsson, & Wiklund, 2000). Startups in the cultural and creative industry often need a few years to produce and scale up the product or service. This means that they cannot make a profit right at the beginning of their existence. Because startups often spend a lot of time developing the

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product or service, the development will be a good performance indicator as well to make a comparison between different startups (Tseng et al., 2009).

Once startups have gained the required capital and have developed the product or service, it is time to bring it to the market. Startups must be able to sell these products or services. That is why the growth rate of the sold products or services from a startup is also a good performance indicator (Lau & Bruton, 2010). If the sale of the product or service goes as scheduled, market share can be gained and profit can ultimately be made (Lau & Bruton, 2010).

The retrieval of capital, the development and sale of the products or services will be the objective performance indicators. The objectives set in the business plan will indicate whether the targets with regard to the objective performance indicators have been met (Miller, Washburn & Glick, 2013). The objective performance indicators will not always provide insights into the entrepreneurs’ perceptions of and satisfaction with their startups’ performance (Zahra et al., 2002). That is why subjective performance indicators will be used as well. This will be discussed in more detail in the method section.

2.5. Control variables

In order to measure the impact of entrepreneurial team experience on startup performance, it is important to check for three variables. The control variables in this study are startup age, number of employees and number of founders.

The age of a startup can affect performance (Lee, 2001). Since older startups have had a longer time to develop their products and services, it is important to check this out with regard to the performance analysis (Lazonick & Tulum, 2011). Also, the number of employees that a startup has may affect the performance of a startup (Lazonick & Tulum, 2011). Startups with more employees can develop their product or service faster and have

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more manpower to sell the product or service (Lee, 2001; Tseng, 2009). Furthermore, empirical research shows that the entrepreneurial team size at launch is strongly correlated with new venture success (Colombo & Grilli, 2005; DeTienne, McKelvie & Chandler, 2015). Besides, Crawford (2017) showed in his research that the importance of the initial team size (number of founders) was an important driver of growth and long-term business profitability.

2.6. Conceptual model

Based on the literature review presented in this chapter, a conceptual model has been

developed (figure 1). The model shows the relationship between the independent, dependent and moderating variable.

Figure 1. Conceptual model

Experience of the

entrepreneurial team Startup performance

Industry similarity H1 H2 Startup age Number of employees Number of founders

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3 Research Design

This chapter provides an outline of the methodology used in this research. First, the research question and working propositions will be discussed, followed by an explanation of the procedure. After that, characteristics of the collected sample will be outlined. Next, the measures of the different variables will be explained. Lastly the approach for the analysis is further enhanced. Please refer to the appendix for the complete questionnaire.

3.1 Research question and procedure

The purpose of this study is to attempt to come to an understanding of how entrepreneurial team experience can influence the performance of a startup in the cultural and creative industries in the Netherlands. The following research question is the focal point: What is the

interaction effect of industry similarity on the relation between entrepreneurial team experience and the performance of a Dutch startup in the cultural and creative industries?

To answer the research question, a quantitative research method was applied. To collect and analyze data, the study comprised a questionnaire among startups in the

Netherlands that established not more than five years ago. A cross-sectional survey was used because the research was related to a comparative study in which the relationship between the experience of the entrepreneurial team and the performance of the startup was identified. This appeared to be the most appropriate method because it enables the use of statistics to test the hypotheses and to generalize findings. A closed-ended survey was used to ensure high reliability. Multiple answers per question increased the validity of the survey (Fowler, 2009). The startups were approached through an email asking them to complete an online survey. Some of the startups were contacted by telephone. They were also approached personally, with paper versions of the survey being distributed.

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3.2. Sample

The population of interest for this study is Dutch startups in the cultural and creative industries. The research was conducted using a non-probability convenience sampling technique. The respondents were selected by contacting the startups by email, telephone and in real life. The data was collected through the use of an online questionnaire and a paper version of the survey. In order to maximize the statistical impact of the research, the study endeavored to reach as many respondents as possible. Different communication tools were used to find appropriate startups. The website dutchstartupmap.com was a convenient tool to find a great deal of contact details of startups in creative design, fashion, gaming, music and print media. Social media (mainly Instagram) has been used to find startups in fashion and jewelry. The Facebook pages ‘Young creators group’ and ‘Amsterdam startups’ were also very useful in finding young entrepreneurs. Finally, meetup.com was a convenient tool to find the location and dates of meetups, where startups get together and where paper versions of the survey were allowed to be distributed. A total of 115 startups participated and 108 of those finished the survey completely. The aim was to have an equal distribution of startups in publishers, photography, design, film-, radio- and television production, performing arts, press, galleries and museums.

3.3. Measures

3.3.1. Entrepreneurial team experience

Entrepreneurial team experience was measured on a validated 5-point Likert scale. The entrepreneurs were asked for their level of experience in the following areas: management, sales/marketing, finance, research and development and manufacturing. This measure indicates the overall experience of a venture's entrepreneurial team (McGee et al., 1995). A reliability analysis was performed to check if the scale could be used as a reliable scale. The

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scale had a Cronbach’s alpha of 0.76 and could therefore be considered as a reliable scale.

3.3.2. Startup performance

It is statistically difficult to compare the performance of the startups with each other because they all have different objectives. For example, there is a difference in how many products or services a startup expects to sell in the first years. A startup can have a target of selling 100 products while another startup’s target is to sell 10,000 products. This makes it difficult to measure the performance of startups. This research intends to solve this problem by measuring performance against the set objectives in the business plan (Miller et al., 2013). This allows the performance of different startups to be compared with each other.

In order to measure the performance objectively, it has been examined whether the targets with regard to the performance indicators have been met. On a 5-point Likert scale the entrepreneurs were asked how far behind or ahead of schedule they were for the following statements: ‘your business has gained sufficient capital / turnover according to the objectives

set in the business plan’, the development of the product / service of your business runs (ran) according to the timetable established in the business plan’ and ‘the sale of products/services are in line with the established growth rate in the business plan’. As each startup has a

business plan with forecasts of the three items, the content validity of the measuring

instrument is high. The reliability is also high because the entrepreneurs can see whether they are ahead or behind the business plan (Miller et al., 2013). The scale had a Cronbach’s alpha of 0.72 and could therefore be used as a reliable scale. Another option to objectively measure startup performance would have been to ask the entrepreneurs for the market share growth and return on equity of their businesses (Zahra et al., 2002). However, as entrepreneurs might be uncomfortable exposing these numbers, or it might cause them too much effort to

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calculate them, these measures were not applied. Besides, closed-ended questions ensure high reliability and a better response rate (Fowler, 2009).

In order to obtain even better reliability and validity of the variable startup

performance, this research also made use of subjective performance indicators (Zahra et al., 2002). The questions ‘How important is this goal for your company? (unimportant - very important) and ‘How satisfied are you with the company’s achievement of this goal? (dissatisfied - very satisfied)’ were asked on a 5-point Likert scale for the following indicators: return on investment, return on equity, net profit margin, return on assets, sales growth, market share growth and growth in the number of employees. A reliability analysis was performed and the scale of both these questions together had an Cronbach’s Alpha of 0.80.

3.3.3. Industry similarity

Industry similarity was measured as the total number of years of experience in the industry across all founding team members (Delmar & Shane, 2006). On a 4-point scale participants were asked how much experience they and their co-founders had in the industry of their current business: less than one year, 1-2 years, 3-4 years, more than 4 years.

3.3.4. Control variables

The control variables used in this study are startup age, number of employees and number of founders. A startup is likely to have achieved more goals in the business plan if it has existed for a long time and if it has more employees at its disposal (Miller et al., 2013). The number of founders reflects the size of the entrepreneurial team (Deligianni et al., 2017). The first question in the survey was in what year the participant’s business was founded. Later this year, this question was transformed to age (1-5 years) in order to use it as a variable

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in SPSS. The second question was how many employees the participant’s business had. The third question was if the participant had any co-founders, and if so, how many. Testing these control variables allows for the fact that the age of a startup, number of employees and founders can affect the relationship between the independent variable and the dependent variable of this study. Also, the relationship between the startup age, number of employees and number of founders was tested directly on the performance.

3.4. Analysis

The analysis of the research was conducted in SPSS. Before the data was analyzed in SPSS, data was transformed (Field, 2009). Also, the different items of the dependent and independent variables were merged to perform an analysis in SPSS. In order to analyze whether the independent variables (entrepreneurial team experience and industry similarity) have an effect on the dependent variable (startup performance), this study used a multivariate regression analysis (Field, 2009). A multivariate regression analysis is a way to test the correlation between independent and dependent variables (Field, 2009).

Before the multivariate regression analysis was performed in SPSS, a factor analysis was performed for the independent variables ‘entrepreneurial team experience’ and ‘industry similarity’ to transform the items into factors (Field, 2009). This factor analysis is performed to establish which items of these variables correlate with each other and belong together (Field, 2009). This is to verify that the theory of the method also applies to this research. After the factor analysis was performed, the (transformed) variables were used for the multivariate regression analysis. In order to check if the performance items belonged together, a reliability analysis was performed (Field, 2009).

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4 Results

This chapter describes the performed SPSS analyzes. The results give an indication of the relationships between the variables. First, the descriptive statistics of the respondents are described. Second, the data preparation is discussed. Third, the correlations are presented. Lastly, the results of the regression analysis are discussed.

4.1. Descriptive statistics

Out of the 115 startup founders who started the survey, 108 finished it completely, which equals a completion rate of 93.9%. Of these 108 startup founders, two owned a business that established more than five years ago. Therefore, only 106 of the respondents were adopted in the analysis. Of these 106 startup founders, two did not answer the question ‘How many employees does your business have?’, and therefore these were marked as missing values. The average age of the startups was 2.06 years, most of them established in 2015 (31.2%). The startups had on average 5.48 employees, 69.2% of them had between zero and five employees, 30.8% more than five. 56.7% of the respondents started the business on their own and 43.3% had one or more co-founders.

The startup performance variable was divided into three variables: performance evaluation, performance importance and objective performance, this will be further explained later in this section. On a 5-point scale, performance evaluation had a mean of 3.20. Most of the respondents evaluated their performance between 3 and 4, which means they were neither satisfied nor dissatisfied or somewhat satisfied with their business’ achievement of goals. Performance importance had a mean of 3.24. 67% of the respondents believed their business’ goals to be moderately or very important. Objective performance had a mean of 2.90. Most of the respondents were behind, on, or ahead of their business’ schedule. The variable

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moderate amount or a lot of experience. On a 4-point scale, industry similarity had a mean of 2.88. Most of the respondents had between 3-4 years, or more than 4 years of experience in the industry of their current business. All demographics can be found in table 1.

Table 1. Descriptives N = 106 Variables Frequency in % 1. Startup age 2017 2016 2015 2014 2013 2012 8 29 32 25 9 3 7,5 27,4 31,2 23,6 8,5 2,8 2. Number of employees* 0 – 5 6 – 10 11 – 15 16 – 20 21 – 25 35 – 40 72 19 6 1 3 3 69,2 17,9 5,8 1 2,9 2,9 3. Number of founders 1 2 3 4 4 + 59 30 14 1 2 56,7 28,3 13,2 1,0 1,9

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4. Performance Evaluation 5. Performance Importance 6. Objective Performance 7. Entrepreneurial team experience 8. Industry Similarity 1 - 2 2 - 3 3 - 4 4 - 5 1 - 2 2 - 3 3 - 4 4 - 5 1 - 2 2 - 3 3 - 4 4 - 5 1 - 2 2 - 3 3 - 4 4 - 5 1 - 2 2 - 3 3 - 4 9 32 52 13 10 16 71 9 16 47 39 4 24 51 26 7 37 23 46 8,5 30,2 49,1 12,3 9,4 15,1 67,0 8,5 15,1 44,3 36,8 3,8 22,6 48,1 24,5 6,6 34,9 21,7 43,4 *based on N = 104 4.2. Data preparation

As presented in the method section, the Cronbach’s Alpha of all scales were higher than 0.70. The scale means of all items that were used to measure the variables were calculated. A factor analysis was used to examine the underlying structure of a group of items (Field,

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2009). In this case the factor analysis was used to find out whether the items that have been distinguished in the theory, could also be distinguished in reality (Field, 2009).

In order to verify that there were really two dimensions for the variable startup

performance (subjective performance indicators and objective performance indicators), a

Principal Axis Factoring analysis with Varimax rotation was performed. Two factors were clearly present based on an eigenvalue higher than three. However, they were not

distinguished based on the objective and subjective performance indicators. The subjective performance indicators measured first of all the importance of return on investment, return on equity, net profit margin, return on assets, sales growth, market share growth and growth in the number of employees for the founders’ startup. Secondly, the subjective performance indicators measured the satisfaction degree of the founders with these goals. It appeared that the performance importance items loaded on different factors than the performance

satisfaction items. Specifically, the performance importance items loaded on one factor, and the performance satisfaction items loaded on the other factor together with the objective performance items. For this reason, the two different variables performance importance and

performance evaluation were constructed. A reliability analysis was performed for both these

variables, and performance importance had a Cronbach’s Alpha of 0.78 and performance evaluation had a Cronbach’s Alpha of 0.88. In summary, the three separate constructed variables performance importance, performance evaluation and objective performance represent the overall startup performance.

4.2.1. Correlations

In order to give an overview of the variables used in this study, a correlation matrix was developed with the variables’ means, standard deviations and reliability values (table 2). The correlation matrix shows that the dependent variable performance evaluation has a significant

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high positive correlation with objective performance (r = .808, p < 0.01). This high correlation makes sense, because it could suggest that the startup founders who evaluated their performance positively, are on or ahead of the schedule of their business plan. The variable performance evaluation also has a significant positive correlation with industry similarity (r = .195, p < 0.05) and entrepreneurial team experience (r = .208, p < 0.05). Besides that, performance evaluation is significantly positively correlated with all of the control variables, startup age (r = .227, p <0.05), number of employees (r = .231, p < 0.05) and number of founders (r = .168, p < 0.10).

The other dependent variable performance importance is significantly positively correlated with entrepreneurial team experience (r = .360, p < 0.01). This could suggest that startup founders who are more experienced, find the profit and growth goals of their business more important, and the other way around. Besides this, performance importance shows a significant positive correlation with number of employees (r = .190, p < 0.10) and number of founders (r = .169, p < 0.10).

The last dependent variable, objective performance, is significantly positively correlated with startup age (r = .208, p < 0.05).

The independent variable industry similarity is significantly positively correlated with entrepreneurial team experience (r = .416, p < 0.01). This makes sense because it could suggest that the more overall experience the startup founders have, the more experience they have in the industry of their current business, and the other way around. Industry similarity also has a significant positive correlation with the number of employees (r = .171, p < 0.10). The last independent variable experience is positively correlated with number of employees (r = .339, p < 0.01). This could suggest that the more experience the startup founders have, the more employees does their businesses have.

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The control variable age is positively correlated with number of employees (r = .416, p < 0.01) and number of founders (r = .218, p < 0.05). This makes sense because it suggests that the older the startup, the more employees it has. Last of all, the control variable number of employees is positively correlated with number of founders (r = .352, p < 0.01). This could suggest that the more employees the business has, the more founders it has, and vice versa.

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Table 2. Means (M), Standard Deviations (SD), and Pearson correlations for all Variables Variables M SD 1 2 3 4 5 6 7 1. Performance Evaluation 3.20 .724 (.875) 2. Performance Importance 3.24 .657 .149 (.775) 3. Objective Performance 2.90 .684 .808*** -.003 (.718) 4. Industry similarity 2.87 1.10 .195** .075 .043 (-) 5. Entrepreneurial team experience 2.69 .776 .208** .360*** .066 .416*** (.757) 6. Startup Age 2.06 1.20 .227** .052 .208** .095 .145 (-) 7. Number of Employees 5.48 7.45 .231** .190* .153 .171* .339*** .416*** (-) 8. Number of Founders .433 .498 .168* .169* .085 -.036 .148 .218** .352***

Note. N = 104. Founders was coded as 0 = one founder and 1 = more than one founders. Age was measured in

years. Industry similarity was measured on a 4-points scale and all other scales on a 5-points scale.

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4.3. Regression

A hierarchical regression analysis was performed. All variables were tested for the

assumption of homoscedasticity and normality of residuals (see Appendix 1). Table 3 shows the results of the regression analyses. In the first step, a model with objective performance as the dependent variable was used. Industry similarity, number of employees, founders

(dummy variable) and startup age were used as the independent variables. A moderating effect for industry similarity on the relationship between entrepreneurial team experience and objective performance was added. The model was not statistically significant. However, there was one predictor variable marginally significant, namely startup age (β = 0.71; p < 0.10). The model shows that the relation with entrepreneurial team experience is positive but non-significant, thus hypothesis 1 cannot be fully supported. It als shows that the interaction effect is negative, but also non-significant, so hypothesis 2 can also not be fully supported.

In the second step, a model with performance importance as the dependent variable and experience, industry similarity, number of employees, founders (dummy variable) and startup age as the independent variables was used. The presence of a moderating effect of industry similarity on the relation between experience and performance importance was checked again. This model was statistically significant (F = 2.68, p < 0.05) and explained 16.8% of variance in performance importance. However, no significant effects were found within the model and so there is still no proof that the hypotheses can be (partially) accepted. In the third step, the dependent variable performance importance was replaced for performance evaluation. The model was statistically significant (F = 2.68, p < 0.05) and explained 14% of the variance in performance evaluation. This means that the results of the regression analysis can be examined. The model shows that there was a marginally

significant positive effect for experience on performance evaluation (β = .57, p < .10), which means that the more experienced the entrepreneurial team is, the better they evaluate their

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performance. Because of this, hypothesis 1 can be partically accepted. There was also a significant positive effect for startup age on performance evaluation (β = .71, p < .05). This means that the older the startup, the more positive the founders evaluate their performance. The interaction effect for industry similarity and entrepreneurial team experience was negative but non-significant, which means that hypothesis 2 is not supported.

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Table 3. OLS Regression Analysis with Three Dependent Variables

Performance

Evaluation Performance Importance Performance Objective

Estimates B SE β p B SE β p B SE β p 1 Industry similarity .28 .25 .43 .26 -.33 .21 -.58 .13 .03 .25 .05 .91 2 Entrepreneurial team experience .54 .32 .57* .09 -.18 .28 -.22 .52 .27 .32 .30 .84 3 Startup age .43 .21 .71** .04 -.22 .18 -.42 .22 .41 .21 .71* .05 4 Number of employees .01 .01 .13 .29 .00 .01 .04 .73 .01 .01 .10 .41 5 Number of founders .18 .17 .12 .28 .09 .14 .07 .54 .03 .16 .02 .85 6 Industry similarity * Experience -.07 .10 -.42 .47 .11 .08 .74 .18 -.00 .10 -.02 .98 R2 .140 .168 .075 Adjusted R2 .075 .105 .005 F-value 2.17** 2.68** 1.08

Note. N = 104. Founders was coded as 0 = one founder and 1 = more than one founders. Age was measured in

years. Industry similarity was measured on a 4-points scale and all other scales on a 5-points scale.

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5 Discussion

In this section the results of the regression analyses presented in the previous section are discussed and possible explanations are presented. First, the results of the hypotheses are discussed. Secondly, limitations of this study and directions for future research are provided. Lastly, theoretical and practical implications are presented.

5.1. Hypotheses

Hypothesis 1 - The more previous experience the entrepreneurial team has, the more

successful the startups’ performance will be. This hypothesis can be partially accepted.

However, there is no complete significant proof that startups with experienced founders perform better. First of all, the regression model with objective performance as dependent variable and entrepreneurial team experience as independent variable, was not significant. The relationship between these two variables was also not significant (β = .27, p = .84). This means that there is no statistical proof that the previous experience that entrepreneurs have, influences the performance of their startup with reference to the objectives set in their business plan. A possible reason for this can be that experienced entrepreneurs are more likely to suffer from overconfidence and counterfactual thinking by drawing conclusions based on small samples of experience and overgeneralization (Shepherd et al., 2003). For these reasons they might fail in developing better decision policies that can improve performance (Shepherd et al., 2003). Another possible explanation might be that the

entrepreneurs have had more old than new experiences (Spanjer & Witteloostuijn, 2017). Old experience deteriorates as time passes. It becomes less accurate and detailed. An entrepreneur may believe that correct inferences are drawn, but due to the lack of accurate and detailed knowledge, this may not be true (Spanjer & Witteloostuijn, 2017). For these reasons, experience does not have to have a positive influence on objective performance. However,

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nor has a negative influence been found, so these reasons should be considered as possible suggestions for the absence of a positive effect.

Secondly, the regression model with subjective importance as dependent variable, was significant. However, the predictor variable entrepreneurial team experience was not

significant (β = -.22, p = .52). Therefore it can not be said that there is statistical evidence that entrepreneurial team experience influences the extent to which the entrepreneurs believe the profit and growth goals of their startup to be important. A possible explanation for this might be that for some cultural and creative startups, artistic goals are more important to them than profit and growth goals (Peltoniemi, 2015).

The third regression model with performance evaluation as a dependent variable was statistically significant, and also the predictor variable entrepreneurial team experience was significant (β = .57, p < .10). This means that there is a statistical significant relation between entrepreneurial team experience and performance evaluation. If entrepreneurial team

experience increases with one, performance increases with .54. In other words, the more experience the startup founders have, the more satisfied they are with the performance of their startup. This corresponds with what has been said in the literature. Previous experience provides knowledge and skills that have been learned from previous activities, and

knowledge about previous mistakes which can be transferred to the startup (Delmar & Shane, 2006). Besides, it links the entrepreneur to a network of employees, suppliers, investors and customers (Delmar & Shane, 2006). This enables them to grow a business. This finding is also in line with the theory of human capital, which refers to certain knowledge that gives individuals increased cognitive skills leading to a more productive and efficient activity (Davidsson & Honig, 2003). Literature around the theory of effectuation also suggests that experienced entrepreneurs are more likely to rely on effectuation and therefore have more feasible goals created from existing resources and networks (Dew et al., 2009; Dew et al.,

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2011; Harms & Schiele, 2012). This may also explain why experienced entrepreneurs are more satisfied with their performance. However, it is interesting that a significant relation between entrepreneurial team experience and performance evaluation was found, but no significant relation between entrepreneurial team experience and objective performance. If the entrepreneurs statistically evaluate their startup’s performance better when they are more experienced, one expects that the objectives set in the business plan will be on or ahead of schedule. An explanation for this could be that the objectives set in the business plan might sometimes be too optimistic.

A critical question with respect to entrepreneurial team experience is whether depth or breadth of the experience is more valuable (Toft-Kehler et al., 2014). Important advantages may result from certain combinations of previous experiences. Having smaller amounts of different types of previous experience could lead to a more extensive expertise than having a larger amount of one type of experience (Toft-Kehler et al., 2014). This also takes the quality, variety and complementarity of experiences in account (Song et al., 2008).

Hypothesis 2 - Industry similarity is positively moderating the relation between experience of

the entrepreneurial team and the performance of the startup. Hypothesis 2 can be fully

rejected. For none of the three regression models any statistical proof was found that industry similarity moderates the relation between entrepreneurial team experience and startup

performance. There was no direct effect of industry similarity on startup performance found either. There are several possible explanations for this. The founders’ technological

knowledge within their industry could have been low in intensity and scope. According to Criaco et al., (2016), previous experience across different industries is in this case more beneficial. Also, more startups with high-risk opportunities than low-risk opportunities might have been involved in this research. According to Dencker & Gruber (2014) high-risk

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opportunities favor founders with managerial experience, whereas low-risk opportunities favor founders with industry experience. Another possible explanation is that the measured experience was limited to the number of years that the founders spent in a particular area, without measuring the quality, variety and complementarity of experiences (Song et al., 2008). If this had been included, it could have led to different outcomes.

5.1.2. Control variables

For the control variable startup age some significant effects were found. First of all, the regression model with objective performance as an independent variable was not significant, but within this model there was a significant effect for startup age (β = 0.71; p < 0.10). In other words, the older the startup, the better its performance related to the objectives set in the business plan. This makes sense, because older startups have had a longer time to develop their products and services (Lazonick & Tulum, 2011). The longer the startup exists, the more goals established in the business plan can be achieved. Besides, a significant effect for startup age on performance evaluation (β = .71, p < .05) was furthermore found. In other words, the older the startup, the better the founders evaluated their performance. There was no significant effect for startup age on the performance importance. This means that there is no statistical proof that the older the startup, the more important certain goals are for the startup’s founders.

For the control variables number of employees and number of founders there were no significant effects found. This is in contrast to what was expected. Startups with more

employees can develop their product or service faster and have more manpower to sell the product or service (Lee, 2001; Tseng, 2009). Also, previous research showed that the size of the entrepreneurial team influences the success of a new business (Colombo & Grilli, 2005; DeTienne, McKelvie & Chandler, 2015). Therefore it is surprising that in this research no

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significant effects for these variables were found. An explanation for this could be that most of the startups that participated in this research started the business on their own (56.7%), and the startups had on average only 5.48 employees.

5.2. Limitations and future research

This study has several limitations that at the same time provide opportunities for further research. First of all, the sample was small. This small population reduces the chance that significant results could be found. This could explain why no significant results were found for the second hypothesis and partially for the first hypothesis. For results that are prone to generalization and are representative of the entire startup population, future studies should involve a greater sample. This would also increase the external validity of the research (Saunders, Lewis & Thornhill, 2016).

The second important limitation of this study is the management perception when answering the questions. Because the entrepreneurs have completed the survey themselves, there is a chance of a bias because, for example, they present their performance as being better than it actually is, or they claim they have more experience than they actually do. Further to this, individuals were asked if they had any co-founders, and if so, how much experience they had. Here too is a chance of bias because it might be difficult to answer this question in behalf of your co-founder. Future research could prevent this problem by having all founders complete the survey.

Thirdly, this research has only been conducted on startups that are still in existence. In order to investigate the difference in performance, there should ideally also be a number of startups who failed. This would have resulted in more realistic statements. Startups can also differ in the ambition they have, with regard to what is expressed in their business plan. This will result in a difference in performance among the participating startups. Future research

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could prevent this situation by asking for their market share and return on investment. However, as entrepreneurs might be uncomfortable exposing these numbers, it could deter them from participating in the survey. A bias may furthermore arise between the companies that participated in the survey and the companies that did not. Startups with a good

performance are possibly more tended to participate in such research than startups with a poor performance.

Another limitation this study was faced with is that individuals were able to skip questions when answering the survey. Nevertheless, the number of missing values proved very small. Moreover, the survey did not ask the question in what sector of the cultural and creative industries the participants were active. Asking this question could have ensured the equal distribution of startups in publishers, photography, design, film, radio and television production, performing arts, press, galleries and museums.

Lastly, as many previous studies in the discussed literature, this study relied on a cross-sectional sample. However, a longitudinal design could offer greater precision in assessing how and why previous entrepreneurial experience influences the performance of businesses (Toft-Kehler et al., 2014). A longitudinal design might be better suited to measure how expertise evolves over time, as learning is a continual and dynamic process (Toft-Kehler et al., 2014).

5.3. Theoretical and Practical implications

Despite the above mentioned limitations, this study enhances our understanding of the role of entrepreneurial team experience and industry similarity in the performance of startups in the cultural and creative industries. It is one of the first to investigate this specific industry. There are surprisingly few studies on the contribution of cultural and creative companies and their performance, yet it is assumed that innovation in these businesses is different from that in the

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more traditional sectors (Bakhshi et al., 2008). With the increase in the emphasis on

perception, symbolism and other non-economic values, the role of the cultural and creative sector becomes gradually more important (Jacobs et al., 2013). As a result, the cultural and creative industries are taking an increasingly central place in the economy. They are a

growing source of inspiration and an increasingly attractive party for other sectors to innovate (Jacobs et al., 2013). Cultural and creative entrepreneurs are up against special challenges today: not only must they have a strong vision, they must also be enterprising, contribute to a harmonious and diverse society, they have to be good people managers and deliver artistic top quality (Schramme, 2015). This research offers support to cultural and creative

entrepreneurs and gives them insight into factors that may affect the performance of their startup.

So far, most studies have focused on research into the success factors of new ventures, but only little has been written about the specific influence of entrepreneurial team

experience. It is important to know if entrepreneurs with more experience have better performing businesses than entrepreneurs with little experience. It gives companies and starting entrepreneurs a better idea of what may affect their business’ performance, and therefore more well-considered choices can be made. Startups that do not achieve their expected growth and profit goals fail or are suspended by their entrepreneurs in order to prevent future losses (Cassar, 2014). Based on the hypothesis, there is no complete significant proof to state that entrepreneurial team experience influences startup performance. However, the results indicate that prior entrepreneurial team experience can be useful for a successful performance, as there was a significant effect for performance evaluation. This is in line with what is stated in previous research. Entrepreneurs can benefit from this advantage by first deliberately participating in professional activities before they intend to start a new business.

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With the knowledge gained in this research, factors other than entrepreneurial team experience can be further investigated, which should provide a better emperical statement of successful and less successful cultural and creative startups in the Netherlands. Starting companies can reproduce these factors and ensure good performance. If startups deliver a better performance, this also has economic benefits for the Netherlands. If the success factors are determined, making more startups successful, this is positive for employment in the creative industry and, moreover, the success of these companies can lead to a spillover effect to other industries and sectors.

In several researches it is recognized that besides experience, factors relating to the skills, motives and characteristics of the entrepreneurs influence startup performance (Hmieleski & Baron, 2009; Toft-Kehler etl al., 2014; Muñoz-Bullon et al., 2015). It is therefore important to look more carefully at the impact of other factors than previous

experience. Examining these factors will give entrepreneurs a more complete picture of what can affect the performance of their startup. Current findings may encourage continuous efforts to include an approach that aims to gain insight into the complex interaction of

individual, organizational and environmental variables in startup performance. Such research is crucial and ultimately forms the survival of startups (Hmieleski & Baron, 2009).

This study found no significant proof that industry similarity moderates the relation between entrepreneurial team experience and startup performance. These findings are inconsistent with what is stated in previous studies, which may be because of this study’s focus on the cultural and creative industries, or because of the small sample. This can encourage new nascent entrepreneurs to start their businesses, as their lack in industry experience might not be too much of a problematic aspect. However, because of the

inconsistency with what is stated in previous research, the absence of a significant result in this study suggests that this needs to be further investigated, before statements like this can be

The role of entrepreneurial team experience in the performance of Dutch startups in the cultural and creative industries