An investigation into the role of ties with service intermediaries : do they affect the balance between exploitative and explorative innovation in tech new ventures?

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An investigation into the role of ties with service

intermediaries: do they affect the balance between

exploitative and explorative innovation in tech new

ventures?

Wietse Ferwerda – Nr. 10899286

Final Thesis MSc BA – Strategy Track

Supervisor: Alex Alexiev

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Statement of originality

This document is written by Student Wietse Ferwerda who declares

to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is

original 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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This study empirically examined the influence ties with service intermediaries have on innovation performance in tech new ventures. Moreover, it sought to examine whether these relationships influence the balance between exploration and exploitation. To do so, 85 surveys were collected from representatives of UK and The Netherlands based tech new ventures. Greater variety of intermediary services employed was found to improve innovation performance in new ventures. Stronger ties positively influence innovation performance, but only with respect to talent search intermediary firms. Results with regard to exploitation performance were inconclusive. A positive trend was observed for talent search and law service intermediaries, and a negative trend for technology service intermediaries, but none were statistically significant. Although explorative innovation performance was found not to suffer from increased use of service intermediaries, neither did it improve.

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II. Introduction

New ventures are considered a driving force in development and growth of high tech sectors within countries (Zahra, 1996); growth in industries ranging from software to biotechnology has been partly attributed to the rise of new ventures. However, new ventures tend to fail more often than older organizations do. Stinchcombe (1965) called this the ‘liability of newness’, the higher risk of death that young organizations face relative to more mature organisations due to a lack of resources and fewer relationships with actors external to the firm. Innovation is oft noted as a way for young, small enterprises to overcome the liability of newness (Pahnke, McDonald, Wang, & Hallen, 2014; Schoonhoven, Eisenhardt, & Lyman, 1990; Zhang & Li, 2010). Consider that if a newly founded organizations are successful in producing and shipping an innovative product, they greatly improve their likelihood of survival (Schoonhoven et al., 1990). For organisations operating within the tech sector this is even more relevant; Bahrami & Evans (2000) note that extremely short product cycles, easily shifted consumer preferences, global competition and the innovative nature of the sector make for great opportunities for the innovative, but also the inevitable demise for the firm that stays behind.

Innovation and new ventures can be seen as having a symbiotic relationship: the founding of new ventures spurs innovation, and new ventures use innovation to thrive and survive in an age of global competition. Schumpeter (1934) described innovation as a process by which existing resources and knowledge are recombined into a new or novel idea. When you consider that young enterprises are often challenged in terms of knowledge, resources, and

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relationships with parties external to the firm that can help them acquire those (Delmar & Shane, 2004), thus inhibiting their innovative power.

In a landmark paper, March (1991) argues that there are two distinct forms of innovation, explorative and exploitative innovation, which can be crudely characterized as respectively refining an existing technology or creating a new one (Levinthal & March, 1993). According to the ‘ambidexterity hypothesis’, by balancing long and short term innovation objectives organizations greaten their performance and boost their chances of a longer lifespan (He & Wong, 2004; O Reilly & Tushman, 2004). Failing to maintain this balance however would be ‘destructive’ to the firm (March, 1991). Recently, an exciting new stream of research has highlighted the role that service intermediaries can play in helping young firms access the right parties, networks, resources, and knowledge to inspire innovation (Larrañeta, Zahra, & González, 2012; Zhang & Li, 2010). Service intermediaries, organizations that act as middlemen and facilitate in the diffusion of resources between two parties (Howells, 2006), might be able to give new ventures a better shot at being successfully innovative and stand better odds of survival (Larrañeta, Zahra, & González, 2012; Zhang & Li, 2010). In earlier research a correlation between product innovation and relationships with service intermediaries has been quite well established. However, as Ozer and Zhang (2014) note, different external relationships will have varying effects on innovation, and some relationships are expected to favour particular kinds of innovation. For instance, companies within clusters and with many buyer/supplier relationships grew stronger in terms of exploitative innovation, but at the cost of explorative innovation (Ozer & Zhang, 2014). According to Boschma (2005) this may be the result of what he calls ‘lock-‐

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in’, negatively effecting openness and flexibility and inhibiting the learning capability of an organization. In a review of ventures operating within clusters, Pouder and John (1996) found that with stronger ties within a cluster, ventures tend to look only at close rivals, narrowing the scope of innovation. Being too close to other ventures is further found to constrain innovation in terms of product newness, making innovations more alike with the competition (Bonner & Walker, 2004). So on the one hand innovation is aided by having ties with other firms by providing ideas, knowledge and resources, while having stronger ties with these firms is also associated with lock-‐in and similar thinking, suppressing innovation (Söderqvist & Kamala Chetty, 2013). Within the context of service intermediaries, the interplay between relationships and the balance between exploration and exploitation has not been researched, while knowledge of this interplay might prove interesting for entrepreneurs, intermediaries, researchers and societies alike. Tipping the scales in favour of exploitation and foregoing the balance might stifle the chances at longevity and competitive advantage. Do new tech ventures compromise or enhance their potential for innovation and as a result improve, or jeopardize, their performance and chances of survival by forging relationships with these service firms? Considering the consequences of tipping the balance, insights into antecedents of both exploitation and exploration could prove very useful. Research into ties with service intermediaries holds the promise of lifting the veil of some of the antecedents. Therefore, the question this paper aims to provide an answer to is:

What is the influence of relationships with service intermediaries on both exploitative and explorative innovation in tech new ventures?

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To provide answer to this question in a structured fashion, this paper is ordened in a way that is compliant with business research practices. This means that in the literature review, a short summary is presented of past research relevant to the concepts under study. From the literature, hypotheses are developed, and a theoretical framework is constructed. In the subsequent section the research method is described. This includes descriptions of the measures used, the way the data is gathered, information about the sample. Limitations of the chosen research methods are briefly discussed. It is followed by the results section, where the results are presented in such a way that may be understood how the researcher came to his conclusions. In the subsequent chapter, discussion, an attempt is made to interpret the results within a broader context. The last chapter, the conclusion, provides a brief overview of the research. Moreover it discusses what insight the findings provide in answering the main research question.

III. Literature review

In his seminal work, Schumpeter (1934) described how entrepreneurs transformed economies by means of being more innovative than their competition. Clayton Christensen (1997) observed that firms that dominate a type of technology for a time, usually do not dominate the successive technology. In order to do so, companies employ exploration competencies, which means harvesting ideas and expertise from numerous sources (Christensen, 1997). This means that companies value insights, resources, know-‐how and knowledge from others and other companies (Wolpert, 2002), preferably knowledge from different industries and sectors (Katila, 2002). By establishing relationships with

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other parties, new ventures are able to recombine resources in ways superior to more mature organizations (Nelson & Winter, 1982). In order to make this happen, new ventures need to tap into networks, capabilities, resources and financing that they have not themselves (Katila, Rosenberger, & Eisenhardt, 2008). The introduction of an intermediary to help young firms overcome their somewhat disadvantaged position and help them fulfill their goal in surviving and out-‐innovating other firms by helping them get access to the parties that have the resources makes intuitive sense. But does relying on intermediaries to spur innovation favour exploitative innovation over explorative, as research seems to suggest (Boschma, 2005; Ozer & Zhang, 2014)? Before going any further however, lets first define the concepts under study.

Service intermediaries

Research into service intermediaries has been somewhat dispersed in terms of definition and concepts used to describe the phenomenon. Howells (2006) gives a nice overview of what terminology has been used in earlier research, and what definitions were given for the concept of service intermediaries. Core to the emergence of research into service intermediaries is the sentiment that innovation search is becoming more of an open process, whereby firms look outside the boundaries of their own firm in order to make innovation happen, inspiring collaboration between different actors (Coombs, Harvey, & Tether, 2003; Howells, 1999). Intermediaries exist not only to link different parties, but help search and transform ideas, recombining existent knowledge to fit the needs of the organization (Hossain, 2012). Service intermediaries can help companies attain valuable extra-‐industry knowledge

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(Katila, 2002) while keeping sensitive information relatively safe from competitors (Wolpert, 2002) (although this view is somewhat contested, see; Katila et al., 2008; Pahnke et al., 2014). Yet in this master thesis, a clear definition of the concept has not been given. For this function, instead of developing a new one, Howells’ (2006) working definition of the concept will be employed, as it provides a relatively clear overview of the concept. According to Howells (2006), service intermediaries can be defined as follows:

An organization or body that acts as an agent or broker in any aspect of the innovation process between two or more parties. Such intermediary activities include: helping to provide information about potential collaborators; brokering a transaction between two or more parties; acting as a mediator, or go-‐between, bodies or organizations that are already collaborating; and helping to find advice, funding and support for the innovation outcomes of such collaborations. (p. 6)

Tech new ventures

In accordance with Zhang & Li (2010) in this study, tech new ventures are defined as companies that were founded less than 8 years ago and are active in the technology sector. The technology sector is a broad field and consists of companies that are active in fields ranging from infrastructure, health care, e-‐ business to software and app-‐creation; Hecker (1999) gives an overview of the industries that can be considered to be high-‐tech. The Dutch government considers this high-‐tech industry so important that it has been named a ‘top sector’, and made it central to its economic policies as to preserve its national competitive advantage (van der Wiel & van der Kroon, 2014).

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Innovation; explorative and exploitative

The ambidextrous firm, that is the firm that masters both explorative innovation and exploitative innovation, outperforms firms that have a relative imbalance between the two concepts (He & Wong, 2004). The concepts of explorative and exploitative innovation are closely related to radical and incremental innovation (Andriopoulos & Lewis, 2009), and combining the two within an organization yields longevity and increased performance by balancing short and long-‐term objectives and more adequate risk-‐taking. According to March (1991), the explorative organization is vulnerable in the sense that its actions are characterized by uncertainty and are remote in time, and are often at odds with actions that provide short term benefits. Actions that are associated with explorative innovation are search, variation, risk taking and discovery (Levinthal & March, 1993; March, 1991), and these actions lead to new designs, different methods of distribution or new markets (Abernathy & Clark, 1985). Exploitation on the other hand is associated with adequately meeting the needs of existing clients and markets, and incremental innovation (Benner & Tushman, 2003). It builds on existing processes, organizational knowledge, and skills (Levinthal & March, 1993), and the firm that focuses too much on the short term is at risk of missing a big change. Consider Kodak, once a dominant behemoth in the photo-‐industry, now reduced to a marginal player for missing the leap to digital photography and foregoing an ambidextrous balance for exploitation (O Reilly & Tushman, 2004).

The link with innovation and performance has been made abundantly clear in prior research. For many organizations, old and young alike, in order to remain competitive they need to continuously innovate (Derfus, Maggitti, Grimm,

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& Smith, 2008) and develop (or acquire) resources that are difficult to imitate, have a certain rarity, are valuable and are difficult to imitate (Barney, 1991). Product innovation then is critical for companies to survive in an adaptive markets, technology and competition (Dougherty & Hardy, 1996). In fact, developing, producing and shipping innovative products is important for revenue building and boosts the survival rate of (new) ventures (Schoonhoven et al., 1990) and paramount in overcoming the liability of newness, the tendency of younger organizations to have higher rates of mortality than older organizations do (Freeman, Carroll, & Hannan, 1983; Stinchcombe, 1965). However, in the context of new ventures and service intermediaries, the distinction between both explorative and exploitative innovation has not clearly been made, despite the implications that ambidexterity or unbalance might have. It is in exactly this relationship that this paper hopes to bring novelty of ideas and insights.

Hypotheses and conceptual model

This study is an attempt to further remove the veil that is the link between service intermediaries, new ventures, and explorative versus exploitative innovation. While earlier research has found evidence of a relationship between service intermediaries and new venture innovation performance (Larrañeta et al., 2012; Zhang & Li, 2010), the distinction between exploitation and exploration innovation has not earlier been made in this research context. Besides, both studies cite a need for a testing of the different variables amongst different contexts and locations (Zhang and Li, 2010; Larrañeta et al., 2012). Some studies even go as far as proposing that exposing a company to outsiders, they expose their technological or innovative core to the competitors, possibly inhibiting the

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ability of these organisations to outcompete others on the basis of product innovation (Katila et al., 2008; Pahnke et al., 2014; Wolpert, 2002). In order to put the proposed relationship between service intermediaries to the test, the first hypothesis is formulated in accordance with the results of Zhang and Li (2010):

H.1 Having ties with service intermediaries is positively related to innovation performance in new ventures.

Secondly, proposed is that increasing variety of service intermediaries enables for higher levels of product innovation. As product innovation is associated with accessing large quantities of knowledge and resources external to the firm, as Wolpert (2002) and Katila et al. (2008) suggested, accessing a greater variety of service intermediaries would create greater opportunities to successfully innovate. So, it follows that:

H.2 Higher variety in ties with service intermediaries is positively related to higher levels of successful product innovation in new ventures.

As access to resources and finance grows, and new ventures are better able to tap into networks and knowledge outside of the firm, it is expected to see innovation performance grow. Service intermediaries can help organisations recombine existing knowledge and transform and realize existing ideas (Hossain, 2012), a process described by Levinthal and March (1993) as incremental innovation. As Bonner and Walker (2004) argued, being closer to other firms will

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lead to innovations that are more akin to the competition. Ozer and Zhang (2014) observed similar effects in a study of firm networks. Innovation then seems to be more likely to occur but more narrow in scope as ties to other firms are getting stronger. In line with this reasoning it is hypothesized that stronger ties with service intermediaries increases exploitative product innovation. Thus, the third is formulated as follows:

H.3 Stronger ties with service intermediaries is positively related to higher exploitative product innovation in new ventures.

The fourth hypothesis is related to the third hypothesis, in the sense that together they show how the balance between exploitation and exploration is affected by the use of service intermediaries. Boschma (2005) observed that whenever firms develop stronger ties with other firms, this has the potential to constrain flexibility and learning within an organization, narrowing the focus of innovation. In a similar vein, Bonner and Walker (2004) observed a decline in product novelty as ties with outsiders grew. This might be related to the observation that when companies look more at their direct and close rivals, they are more likely to miss out on trends and developments outside of their network (Pouder & John, 1996). Even more poignant are the results of a recent study by Ozer and Zhang (2014), who found exploration innovation performance suffered from cluster membership. Extrapolating from these earlier studies, it is proposed is that having stronger ties with service intermediaries inhibits explorative product innovation.

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H.4 Stronger ties with service intermediaries lead to lower explorative product innovation.

To give some clarity, the hypotheses have been summarized into a conceptual model, outlining the supposed relations between the variables under study. The model can be found in figure 1: conceptual model. Do note that the control variables have been added to this model. These will further be explained in the method section of this paper.

Figure 1: Conceptual model

IV. Methodology

All data has been collected within a timespan of 3 weeks in May and June of 2015, using a survey distributed amongst 1151 companies in the UK and the

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Netherlands. Companies and company executives were approached via email and invited to participate in an online survey regarding characteristics and performance of their respective businesses. The corresponding email can be found in appendix 1: cover Letter. The online survey took no more than 10 minutes to complete. To increase the chances of finding enough companies within the limited time-‐constraints typical to a graduate student, research efforts of two students were combined. Two surveys were merged and combined, with similar but different topics and the same target audience. The effect that this had on the length and complexity of the survey has been carefully monitored as not to discourage participants from completing the questionnaire. As the survey is composed of internationally employed English measures but was intended for use in the Netherlands, the questionnaire has been translated to Dutch with utmost care. To ensure validity, the questionnaire has been back-‐translated to English by a bilingual speaker, native in both Dutch and English. The retranslated version was then compared to the original, where extra measures were implemented to ensure interpretation of both the English and the Dutch versions of the questionnaire where the same. Both versions of the survey can be inspected, as they are attached as appendeces. The English version of the survey can be found in appendix 2. The Dutch version of the survey is attached as appendix 3. Furthermore, the questionnaire has been pre-‐tested by presenting it to three managers of different high-‐tech new ventures, to eliminate any misunderstandings or ambiguities that might arise.

A first sample of companies was found using the CapitalIQ database, a paid database of companies supplied by Standard and Poors, licensed to the University of Amsterdam. Companies were selected based on the application of

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search criteria in accordance with the high-‐tech criteria as proposed by Hecker (1999), location (the Netherlands or the UK), and company age (founded no more than 8 years before the research was conducted). Furthermore, 8 incubators and their companies, active in the high tech sector were approached to participate, as were startups that were registered in the TechBritain startup database.

To ensure legitimate and consistent results, and to exclude reregistered or dependent ventures, the founding date as well as independence was cross-‐ referenced to the response of the ventures. Furthermore, to ensure compliance with the high-‐tech criteria as proposed by Atuahene-‐Gima and Li (2002), the founding team was screened for technical or scientific backgrounds. Consistency with research by Zhang and Li (2010) was provided by dividing the questionnaire into separate parts, with as an added advantage that this helps in countering common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Common method bias occurs when a respondent answers questions consistently regardless of their content, reducing the value of these results. Moreover, it was hoped that by cutting the questionnaire into several parts respondents were less likely to drop out immediately after opening the survey and witnessing the extent of the questionnaire.

In total, 88 respondents filled out the survey. Of those, 85 respondents met the criteria of a new venture (n=85). As the survey was sent to 1151 companies, the observed response rate was about 7.4%, lower than anticipated, especially considering several reminders were sent. Of the responses, 16 were from the UK and 69 were from the Netherlands. None of the respondents indicated to be involved in less than half of the strategic decisions, with a mean

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involvement of 4.73 on a 5-‐point Likert scale. The average age of the companies involved was 3.61 years, and included in the results are no companies over 8 years of age. The biggest company involved reported employing 57 employees, on average the companies employed 7.12 FTE. Both the founding team and employee compositions were skewed towards technicians/scientists. On average the respondents had about 9 years of experience in the industry they were active in. Finally, on average there were 2.26 people involved as founders of the companies. See table 1 for more information on the sample and the distribution of data.

Table 1: Descriptive statistics

Measures

Non of the measures used in this research have been self-‐constructed. Instead, only prior developed measures have been used that have been tested and proven in terms of their validity and reliability. This also provides the opportunity to compare this research and its outcomes with earlier research and understand its implications in a broader context.

Independent measures

Descriptive Statistics

Min. Max. Mean Std. Dev. Skewness Kurtosis

Years of experience .5 34.0 9.02 7.80 1.07 .67

Participation in strategic decision making 3 5 4.73 .54 -‐1.91 2.83

Company age .0 8.0 3.61 1.91 .34 -‐.71

Founding team size 1 8 2.26 1.21 1.84 5.52

Founding team education 1 5 3.65 1.62 -‐.67 -‐1.22

Firm size 0 57 7.12 7.97 3.69 19.08

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The independent measure ‘ties with service intermediaries’ that serves as a predictor variable in this research has been developed by Zhang and Li (2010) and consists of four questions, where respondents were asked to rate the extent to which they have had close relationships to law, financial, talent search or technology service intermediaries respectively. Response was given on a 5-‐point Likert scale, from ‘no contact at all’ to ‘to a large extent’. The second hypothesis proposes a different independent measure, variety. This variable was created by combining the other four predictor variables into a single indicator of variety. As respondents could indicate wether or not they had made use of a distinct type of intermediary service at all, or to a certain extent. Using dummy variables, only responses that indicated having made use of a certain type of intermediary service were counted, and tallied into the new construct, variety. For the questions, see table 2: independent variables, measures and questions. As the questions represent different constructs, no internal reliability analysis was performed.

Table 2: Independent variables, measures and questions

Independent variables

Please indicate the extent to which you have had close relationship to the following:

Mean Std. Deviation

N Accounting and financial service firms -‐ e.g. auditors and tax advisers, financial

advisers, accountancy companies, banks, insurance companies, investment companies, etc.

3.16 1.163 85 Law service firms -‐ firms that offer legal guidance, intellectual property

services -‐ e.g. lawyers, law offices 2.68 1.147 85

Firms that offer new talent recruitment; e.g. headhunters, executive search

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Technology service firms -‐ firms that offer Software development, Integration and maintenance, Hardware, Networking, management and maintenance, Information security, IT management consultants, Mobile services, Web applications

3.01 1.332 85

Dependent measures

All dependent measures were also lifted from prior research. As none of the measure contained any counter indicative items, no reversing of answers had to be performed. Firstly, the measure of innovation performance was first featured in Brown and Eisenhardt (1995) and subsequently used by Zhang and Li (2010), and consists of 5 questions that can be found in table 3 . Answers were given on a 5-‐point Likert-‐scale. The scale was compounded to reflect a single measure of innovation performance, and has a Cronbach’s alpha of .774, well above the threshold acceptable for constructs of this nature (Field, 2013).

The measures for exploratory and exploitative innovation were lifted from Jansen, Van Den Bosch and Volberda (2006) and have never been used before in the context of service intermediaries. Both measures consist of 7 questions pertaining to the exploratory and exploitative nature of the innovation of the firms under study, measured on a 5-‐point Likert-‐scale. As has been done with innovation performance, the scales have been compounded to reflect a single measure for exploratory and one for exploitative innovation. For the measure of exploratory innovation a Cronbach’s alpha of .719 was found. The measure of exploitative innovation displayed a slightly higher internal consistency with a Cronbach’s alpha of .747. Both are well within the range of acceptable. The questions can be found in table 3 .

Lastly, a measure of environmental uncertainty was used as a control variable. This measure was adapted from Miller (1987), and was also used by

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Zhang and Li (2010). As has been done with the others, this measure has been compounded to a single variable displaying the amount of environmental uncertainty perceived by the different companies. Testing the measure on internal consistency yields a somewhat dissapointing Cronbach’s alpha of .490. This is however not totally unexpected as it consists only of three questions, and according to Field (2013) more questions usually equals a higher alpha. As to keep the research comparable to the results of Zhang and Li (2010), the measure will still be used as a control variable. The questions of which the measure consists can again be found in table 3: dependent and control measures.

Table 3: dependent and control measures, questions

Dependent and control measures

Mean Std.

Deviation

N α if Item Deleted

Exploratory innovation, α =.719

Our unit accepts demands that go beyond existing products and services. 4.13 1.232 85 .714

We invent new products and services. 4.49 .796 85 .699

We experiment with new products and services in our local market. 4.20 .936 85 .676 We commercialize products and services that are completely new to our unit. 3.98 1.102 85 .663

We frequently utilize new opportunities in new markets. 4.08 1.093 85 .646

Our unit regularly uses new distribution channels. 3.56 1.063 85 .709

We regularly search for and approach new clients in new markets. 4.04 1.052 85 .697

Exploitative innovation, α =.747

We frequently refine the provision (the current offering) of existing products

and services. 4.15 .852 85 .720

We regularly implement small adaptations to existing products and services. 4.31 .845 85 .731 We introduce improved, but existing products and services for our local market. 3.41 1.266 85 .719 We improve our provision’s efficiency of products and services. 4.06 .836 85 .704

We increase economies of scale in existing markets. 3.55 1.220 85 .693

Our unit expands services for existing clients. 3.78 1.127 85 .689

Lowering costs of internal processes is an important objective. 3.40 1.320 85 .754

Innovation performance, α =.774

We are frequently introducing new products. 3.45 1.075 85 .710

We are being first in new product introductions in the market. 3.60 1.197 85 .760

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We are developing new products with superior quality. 4.04 1.029 85 .756

We are using new products to penetrate markets. 3.67 1.199 85 .721

Environmental uncertainty, α =.490

It has been difficult to forecast how technologies will change in this industry. 3.14 1.093 85 .419

Competitors? actions have been highly unpredictable. 2.72 1.031 85 .245

Product market conditions have been changing very fast. Nog 3.54 1.129 85 .498

Besides environmental uncertainty, control variables for company age (in years after the founding of a company till the date the survey was filled in), size (measured in number of FTE units involved) and employee background (to what extent the workforce is composed of people with technical and/or scientific background) were used. Furthermore the size of the founder team was used as a control variable, and the founding team backgrounds were considered in the same way as was done for employees. This is to comply largely with previous research (Atuahene-‐Gima & Li, 2002; Zhang & Li, 2010), meanwhile balancing the need to keep the model simple and not using too many control variables, possibly influencing the predictive power of the model (Field, 2013). Zhang and Li (2010) use two more control variables, namely venture independence and foreign invested venture (whether the venture had a foreign origin). These variables were also considered for this research and incorporated in the survey. Only 6 companies reported being a dependent venture, and only 4 companies reported being a foreign invested venture. As those represented only a very small minority in the sample, they were excluded from the analysis.

It must be noted that the instrument in question relies solely on self-‐ report, and therefore represents the perceptions and interpretations of the subjects filling out the questionnaire. Great care then must be taken in interpreting and applying the results into rigorous scientific analysis. Self-‐report

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measures seem adequate as there is hardly public information available of the concepts under study, and considering the quantitative nature of the study. Yet it does put limitations on the study as to how and to what extent the findings can be generalized and interpreted. It must be noted that to combat the possibility of respondents filling out the survey with socially desirable responses, the anonymous treatment of the results was carefully explained and stressed.

V. Results

Sample characteristics

Firstly, a correlation analysis between the different variables. The results are displayed on the next page in table 4. Besides the correlations between the different control, predictor and dependent variables, the standard deviations and the means for the variables are included in the table.

Only between the different types of intermediaries and between different types of innovation can significant results be spotted. The extent to which companies report use of law service firms positively correlates significantly with the extent to which they use financial service firms, as is the case between technology service firms and talent search firms. Furthermore, all types of innovation (innovation performance, exploitative innovation and explorative innovation) correlate significantly with each other. The correlations are relatively low, the highest correlation observed between law and financial service firms is still below a correlation of .8, therefore giving no indication of problems of multicolinearity. As a correlation matrix with correlations below .9 or .8 provides only meager support for lack of multicolinearity (Field, 2013), individual tests for multicolinearity will be performed on a per hypothesis basis.

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All variables under analysis were forced questions in the survey, meaning that a survey could not be filled in unless an answer to all the relevant questions was provided. This means that for all analyses and constructs the number of respondents is as large as the entire sample. Thus, for all measures and constructs the number of respondents is equal to 85 (n=85).

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H 1: Ties and innovation performance

The first hypotheses stated that new ventures that report stronger ties with service intermediaries show higher innovation performance. The expected observation then is that for all service intermediaries, a positive correlation will be found between the extent to which they are involved with a company and the amount of innovation performance reported by said company. A hierarchical regression analysis was performed to asses said relationship. The regression analysis was controlled for the influence of employee education, size of the company measured in FTE, size of the founding team, founding team education, age of the company measured in years and the level of environmental uncertainty experienced by the respondents. First the variables were tested for multicolinearity. None of the variables displayed a tolerance for multicollinearity below .6, indicating multicolinearity is not a problem. In a two step fashion, first the control variables were fed into the model, and in the second step the predictor variables were added. As can be seen in table 5, the proposed relationship between service intermediaries and innovation performance holds true, but only for talent search firms, as this is the only significant relationship at the p<.1 level (b=.149, p=.091). Financial service intermediaries show an insignificant positive relationship (b=.030, p=.776), as do technology service intermediaries (b=.020, p=.799). Law service intermediaries display a highly insignificant negative relationship (b=-‐.022, p=.826). The combined model has an R2 _{of
.098,
which
means
it
explains
about
9.8
%
of
the
observed
variance
in} innovation performance. This is an improvement of .045, or 4.5 %, over step 1, which has an R2_{of
.054,
predicting
only
5.4%
of
observed
variance
in
innovation} performance. None of the control variables for which has been tested are shown

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to have a statistically significant influence on innovation performance. The first hypothesis can be accepted but only with regard to talent search service intermediaries. Other service intermediaries seem to hardly inspire any form of innovation performance amongst the studied new ventures.

Table 5 . Hierarchical regression analysis of service intermediaries and controls on innovation performance

90.0% Confidence Interval for B

Step 1 _B _{Std.
Error} _Sig. _{Lower
Bound
Upper
Bound}

(Constant) 2.658 .525 .000 1.784 3.532

Number of founders .105 .076 .171 -‐.022 .232

Founding team education .004 .071 .953 -‐.114 .123

Firm size (in FTE) -‐.007 .012 .557 -‐.027 .013

Employee education .073 .081 .365 -‐.061 .208

Age of the company .010 .048 .839 -‐.071 .090

Environmental uncertainty .136 .121 .262 -‐.064 .337

Step 2

(Constant) 2.402 .605 .000 1.395 3.409

Number of founders .112 .081 .173 -‐.024 .247

Founding team education .027 .073 .708 -‐.094 .148

Firm size (in FTE) -‐.012 .013 .330 -‐.033 .009

Employee education .053 .085 .534 -‐.089 .195

Age of the company .008 .049 .878 -‐.075 .090

Environmental uncertainty .106 .123 .390 -‐.098 .310

Financial service firms .030 .103 .776 -‐.143 .202

Law service firms -‐.022 .102 .826 -‐.191 .147

Talent search firms .149 .087 .091 .004 .295

Technology service firms .020 .078 .799 -‐.110 .150

Note: R2 for step 1= .053; R2 for step 2= .098

To give some insight into the slope and extent of the effect, a plot of the significant predictor is displayed in figure 2 . The effect of talent search firms on innovation performance can be seen to have a midly positive slope.

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Figure 2: Plot of the effect of reported ties with talent search intermediaries on innovation performance.

H2: Variety and innovation performance

The second hypothesis stated that with higher variety of service intermediary usage come higher levels innovation performance in new ventures. To test this effect, first a new variable (variety) had to be created. The extent to which new ventures had close relationships with the four different service intermediaries that were defined was rated by the ventures on a 5-‐point likert scale. A 1 on the 5-‐point likert scale corresponds with no contact at all, any higher number is counted as a contact. The extent of the relationship then is not considered in this hypothesis, but the number of different intermediary types is taken into account. The range of possible service intermediaries is between 0

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and 4, where 0 informs us that no service intermediaries were used, and 4 shows that all predefined different types of intermediary services were used. There were no issues found with regard to multicolinearity. A new hierarchical regression analysis was performed. In the analysis, the control variables employee education, size of the company measured in FTE, the size of the founding team, founding team education, age of the company measured in years and the level of environmental uncertainty experienced by the respondents were considered. The newly created measure of variety was used as the predictor variable. See table 6 for an overview of the results of the regression analysis. It shows that employing different types of intermediary services has a significant positive effect on overall innovation performance (b=.184, p=.042) at the p<.05 level. Do note that number of founders is also positively correlated with innovation at a p<.1 level (b=126, p=.098). The model shows an R2_{of
.103,
an} improvement of .050 on the base model with only the control variables. This means that variety in intermediary services used explains about 10.3% of the variance observed in innovation performance. The second hypothesis then can be accepted, as variety has been shown to significantly improve innovation performance of new ventures. As new ventures employ more types of intermediary services they report higher levels of innovation performance.

An investigation into the role of ties with service intermediaries : do they affect the balance between exploitative and explorative innovation in tech new ventures?