Sources of innovation, their combinations and strengths benefits at the NPD project level

Transcription

1 Int. J. Technology Management, Vol. 61, Nos. 3/4, Sources of innovation, their combinations and strengths benefits at the NPD project level Tina Lundø Tranekjer* Department of Marketing and Management, Integrative Innovation Management (I2M), University of Southern Denmark, Niels Bohrs Alle 1, DK-5230 Odense M, Denmark Fax: (+45) *Corresponding author Helle Alsted Søndergaard Innovation Management Group, Department of Business Administration, School and Business and Social Science, Aarhus University, Bartholins Allé 10, DK-8000 Aarhus C, Denmark Fax: (+45) Abstract: External sourcing is increasingly seen as important for obtaining new and valuable knowledge and resources for new product development. However, when it comes to the specifics of choosing between sources and types of relationships, little is known on the NPD project level. This paper strengthens and expands existing research by investigating how the mix of external partner types as well as the relationship strength is related to performance at the project level. The empirical background is a survey conducted among Danish SMEs in Ordinary least square regressions reveal that firms should not only consider the potential benefits of collaboration with external sources but also the downsides, including higher cost and lengthier projects. Firms should look for opportunities in the combination of sources if they are to gain advantages of collaboration, as our analyses show that a mix of market and science sources is related to decreased costs. Additionally, if firms are looking for increased market performance, they should aim at collaborating with suppliers that have a similar knowledge base, whereas if the aim is lower project costs, collaboration with a customer with a similar knowledge base is beneficial. However, the degree of novelty in the new product is lower when companies are very closely embedded with suppliers. Keywords: open innovation; mix of external sources; tie strength; relational embeddedness; knowledge redundancy; NPD project level performance. Reference to this paper should be made as follows: Tranekjer, T.L. and Søndergaard, H.A. (2013) Sources of innovation, their combinations and strengths benefits at the NPD project level, Int. J. Technology Management, Vol. 61, Nos. 3/4, pp Copyright 2013 Inderscience Enterprises Ltd.

2 206 T.L. Tranekjer and H.A. Søndergaard Biographical notes: Tina Lundø Tranekjer is a PhD student in the Integrative Innovation Management unit at the University of Southern Denmark. Her research focuses on NPD and innovation, and in particular how firms take on specific roles as providers and receivers in an open innovation process. Helle Alsted Søndergaard is an Associate Professor in the Innovation Management group at the Department of Business Administration, Aarhus University. Her current research interests are within open and collaborative innovation focusing on the organisational antecedents of openness and the performance related consequences. This paper is a revised and expanded version of a paper entitled Is open innovation a question of quantity or quality? The importance of a few close friends presented at 12th International Continuous Innovation Network (CINet) Conference, Aarhus, Denmark, September Introduction Collaboration in general is believed to enhance organisational learning (Powell et al., 1996) and since new knowledge is usually retrieved from outside the firm, inter-organisational relationships become crucial (March, 1991). This is much the same argument as put forward in the open innovation literature where inbound open innovation is believed to be a major driver for innovation (Gassmann et al., 2010a). Open innovation is defined as the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively [Chesbrough, (2003a), p.1]. In this paper we focus on the inbound flow of knowledge from external sources. To a large extent research in the areas of search strategies and collaborative innovation have focused on identifying the optimal number of external partners (Laursen and Salter, 2006), and the importance of different types of external sources (Faems et al., 2005; Lau et al., 2010; Un et al., 2010). The most notable similarity between these studies is that the analyses are undertaken on the firm level, meaning that search and collaboration are seen as an overall company decision and not a decision based on the requirements of the specific new product development (NPD) project. The overall aim of this article is to emphasise the importance of the project level in understanding the outcome of involving external sources in NPD arguing, in line with other scholars, that the decision to involve external sources is made on a project-by-project basis (Bahemia and Squire, 2010; Bonesso et al., 2011). To our knowledge only a few have studied the effect of involving different types of external sources at the project level in the context of open innovation (Bahemia and Squire, 2010). One exception shows that the involvement of external sources may have a negative effect on project cost and speed (Knudsen and Mortensen, 2011), but a little is known regarding the specific mix of external sources and how this affects project level performance. Furthermore, contributions remain relatively silent on the importance of the strength of the relationship between the firm and the sources in question. It may well be the strength of the relationships and not just their existence that is important [Huizingh, (2011), p.4]. Existing contributions to the question of search depth, meaning how often the source is used, focuses either on the extent of reuse of existing knowledge (Katila and

3 Sources of innovation, their combinations and strengths 207 Ahuja, 2002) or on how intensely the firm draws knowledge from search channels (Laursen and Salter, 2006); these contributions find that firms primarily draw deeply from one source and if they draw deeply from more than three sources, they start experiencing diminishing returns. The challenge taken up here is thus related to analysing NPD at the project level in order to contribute to the literature on open innovation with a more comprehensive and in-depth view of the relationship between external sources and innovative performance. We do this by analysing sources and relationships from two additional perspectives. First, by investigating the combination of external sources and how they are related to project level performance. Second, we investigate the nature of the relationship between the firm and its preferred partner using the concept of tie-strength, a concept widely used in the network literature (Burt, 1992; Granovetter, 1985; Uzzi, 1996) but scarcely investigated in open or collaborative innovation (Rindfleisch and Moorman, 2001). Tie strength is analysed in order to uncover how the closeness of the relationship and the degree of knowledge overlap with the preferred partner is related to project performance. We suggest that project level analysis will provide a truer and more diverse picture of innovation activities in firms and their effect compared to studies on the firm level (Molina-Castillo and Munuera-Alemán, 2009). Since NPD projects can be evaluated on various dimensions, we have included performance measures related to market performance as well as cost, speed, and novelty (Huang et al., 2004). The overall aim is thus to provide a more comprehensive perspective on the relationship between combination of external sources of knowledge as well as the knowledge overlap and closeness to the partner and performance at the project level. The main research questions are: 1 How do combinations of external sources affect project performance in NPD? 2 How does the strength of the relationship with the preferred partner affect project performance in NPD and does the type of partner affect this relationship? The article proceeds by discussing the relevant literature supporting the research gaps as well as the development of hypotheses in Section 2, methods and variables are presented in Section 3, the descriptive results and results based on ordinary least square regression are presented in Section 4, finally Section 5 contains the discussion and a conclusion. 2 Theoretical framework and hypotheses The literature on inter-organisational and open innovation suggests that involvement of external sources is an important way of accessing knowledge and resources that are useful for innovation (Chesbrough, 2003b; Knudsen, 2007). The main advantage mentioned in the literature is access to external knowledge bases and the possibility of learning from a partner through the transfer of knowledge (Inkpen and Tsang, 2005; Powell et al., 1996). But also cost and risk sharing are important motivational factors for engaging in relationships with external partners (Faems et al., 2005). A review of the literature on external sources for innovation at the project level identified two research gaps that we wish to address in this paper:

4 208 T.L. Tranekjer and H.A. Søndergaard 1 combinations of sources instead of single sources 2 the strength of relationships with key sources. Regarding the level of analysis, very little research related to the effect of external sources is done at the project level. The project level has primarily been used in studies on the involvement of one specific partner, e.g. supplier involvement in the supply chain management/industrial marketing literature (see Johnsen, 2009, for a review) and in studies on customer involvement in the literature on market orientation (Bonner and Walker, 2004; Rindfleisch and Moorman, 2003; Salomo et al., 2003) and lead users (Von Hippel, 2005). However, when it comes to the more general effect of external sources on NPD projects results are scarce (for a few exceptions see Hoang and Rothaermel, 2010; Knudsen and Mortensen, 2011). Related to the combination of external sources, sources are usually investigated by the breadth dimension meaning the number of external sources involved (Laursen and Salter, 2006) or for their individual effect (Faems et al., 2005; Lau et al., 2010), even though the literature acknowledges the importance of combining different types of sources (Nieto and Santamaría, 2007). It remains to be answered whether using a mix of sources is better than using more of the same type of source. Regarding the strength of relationships, these are primarily investigated by the depth dimension (Katila and Ahuja, 2002; Laursen and Salter, 2006) measured as the frequency of contact ranging from low to medium and high. The depth of inter-firm relationships seen from a relational perspective are widely discussed in the literature on e.g. strategic alliances, but are largely absent in the open innovation literature (Bahemia and Squire, 2010). We thus wish to investigate external sources and their combinations at the NPD project level as well as the strength/nature of the relationships. 2.1 Sources of knowledge and their combinations in NPD projects While the reasons for involving external sources for improving innovative performance and competitive advantage are fairly universal, the involvement of sources can be conceptualised in various ways (Sofka and Grimpe, 2010). One way of conceptualising sources is by looking at the number of sources or search breadth as in the work of Laursen and Salter (2006), who find that there is a limit to the advantage of involving external partners for innovation purposes. The negative effect of too much openness may be due to the firm experiencing attention allocation problems when increasing the number of external partners (Laursen, 2011) or higher marginal costs caused by complexity (Leiponen and Helfat, 2010). Another way of classifying involvement of external sources is by investigating the various source types. The external sources most often investigated in the empirical literature are: customers, suppliers, competitors and universities. Empirical studies show mainly positive effects from involving customers, suppliers and universities in innovation and no or negative effect from involving competitors, (Faems et al., 2005; Lau et al., 2010; Nieto and Santamaría, 2007; Un et al., 2010) primarily due to the complementarity of the knowledge provided to the focal firm. A third way of classifying external sources is studies investigating combinations of external sources. One study combining sources shows that novelty is achievable through a mix of external sources (Nieto and Santamaría, 2007) although it is not quite clear which source combinations are the most fruitful. To our knowledge the only other attempt to study combinations of sources shows only tentative results regarding a positive effect of

5 Sources of innovation, their combinations and strengths 209 combining suppliers and competitors, and a negative effect of combining universities with customers or competitors respectively (Knudsen, 2007). Common to all the studies mentioned and regardless of how sources are classified, they analyse the role of sources at firm level where innovative performance is measured as e.g. product innovation and turnover from new products, while there are few studies at the project level (Bahemia and Squire, 2010). We argue that investigating the involvement of external sources at the project level is just as important, since each NPD project may require different types of input from different types of external sources, thus not necessarily benefitting from involving more of the same sources. The involvement of sources is therefore not a one-off event but a decision to be made project-by-project (Bahemia and Squire, 2010; Bonesso et al., 2011). Existing studies focusing on different types of sources/combination of sources at the project level were reviewed (see Appendix 1 for a list of relevant articles). These studies focus on either the importance of technology sourcing strategies for NPD projects (Kessler et al., 2000), or the effect of knowledge novelty and diversity on NPD projects (Bonesso et al., 2011), or the role of alliance experience (Hoang and Rothaermel, 2010) with a focus on the science link (Cassiman et al., 2010). Only Knudsen and Mortensen (2011) investigate the effect of increasing openness in NPD projects. The empirical open innovation literature analysing involvement and combinations of external sources at the project level is thus very limited. This is also pointed out by Bahemia and Squire (2010) who propose a conceptual framework counting three dimensions of inbound openness breadth, depth and ambidexterity. The first aim of this article is thus to investigate how the combination of sources is related to NPD project level performance. As the different types of external sources provide knowledge of a different nature (e.g. technological knowledge or information on markets and needs), sources are divided into market and science sources (Sofka and Grimpe, 2010; Vega-Jurado et al., 2009). In this way we break away from the existing studies done at the project level, that only distinguish between applying external sources or not (Bonesso et al., 2011; Knudsen and Mortensen, 2011). We expect market sources to improve product innovation by providing knowledge about needs and markets (Miotti and Sachwald, 2003; Nieto and Santamaría, 2007), and science sources to influence innovation by directing the firm towards new technologies as well as new markets (Belderbos et al., 2004; Nieto and Santamaría, 2007). Besides the fact that different sources provide different types of knowledge, different types of sources may also influence different performance measurements in different ways. NPD project costs are expected to increase and speed is expected to decrease when involving both market and science sources, explained by attention allocation problems (Laursen, 2011). Although these results are based on a firm level study, it is fair to argue that these problems also will occur at the project level. Furthermore, it is shown in project level studies that involving external sources (compared to not involving them) can increase costs and decrease speed (Knudsen and Mortensen, 2011). Earlier studies on NPD projects have shown that costs may increase as a result of more coordination efforts and speed is reduced due to the added effort of integrating external knowledge (Kessler et al., 2000). Market performance (e.g., sales or profit) and degree of novelty is expected to increase with a combination of market and science sources based on the argument that different sources provide different types of knowledge and that the knowledge provided can complement the firm s own knowledge base and direct the firm towards new markets

6 210 T.L. Tranekjer and H.A. Søndergaard and lead to more novel innovations (Nieto and Santamaría, 2007). This leads us to expect that: H1 The mix of market and science sources is positively related to market performance and novelty, and negatively to cost and speed in NPD at the project level. 2.2 Relationships and tie strength The mere existence of an external source cannot necessarily be expected to make a difference as regards innovation output and performance while the strength of the relationship may (Huizingh, 2011). Important questions related to the relationship are: Has the firm collaborated with the partner before, is the relationship characterised by trust and can the firm easily understand and assimilate the knowledge available through the partner? This can be evaluated through the concept of tie strength which builds on the notion that ties between firms can either be strong or weak (Granovetter, 1985, 1973). Inherently, it is unclear whether strong or weak ties are most advantageous for innovation. Some scholars argue for the strength of weak ties since weak ties can foster and speed up innovation by giving a company access to otherwise difficult-to-reach knowledge (Noordhoff et al., 2011; Powell and Grodahl, 2005; Rogers, 2004; Uzzi, 1996). Concepts such as boundary spanning and structural holes suggest that firms can benefit from establishing contact to actors in disparate network clusters (Burt, 1992; Zaheer and Soda, 2009) and firms that span multiple markets and technology domains thus get access to various types of knowledge which they can combine in innovative ways (Hargadon and Sutton, 1997). Furthermore, one of the main reasons for inviting an external actor into any innovation project is to get access to complementary assets and new knowledge (Teece, 1986) while as Bonner and Walker (2004) claim a customer relationship that is too embedded will contribute only to incremental innovation. Other scholars provide arguments for the strength of strong ties saying that strong ties encourage reciprocity and joint problem solving (Ahuja, 2000; Rindfleisch and Moorman, 2001) and stimulate inter-organisational knowledge transfer (Dyer and Nobeoka, 2000). Ahuja s 2000 study shows that firms with ties that offer both resource sharing and information spill-over experience more innovation output than ties that offer only the latter, which calls for a mix of strong and weak ties. However, firms with a large number of strong ties get less benefit from weak ties indicating that strong ties have a greater impact on innovation. In their study on tie strength Rindfleisch and Moorman (2001) show that in an inter-organisational context strong ties (here with channel members) are more likely to serve as a bridging function than weak ties (with competitors) due to the higher level of closeness (embeddedness) and higher level of knowledge overlap (redundancy). Relational embeddedness, an indicator of the motivational dimension of tie strength, is defined as the degree of reciprocity and closeness among new product alliance participants while knowledge redundancy, an indicator of the structural aspect of tie strength, is defined as the degree of similarity in the new product-related information, capabilities, and skills among new product alliance participants [Rindfleisch and Moorman, (2001), p.3]. A common knowledge base (high level of redundancy or overlap) is thus necessary for a company to understand and acknowledge partner contributions (Enkel, 2010), and easy knowledge access is argued to be more important than getting access to a broad knowledge base (Un et al., 2010).

7 Sources of innovation, their combinations and strengths 211 Although weak ties and the new knowledge they provide are clearly established as imperative for innovation, when dealing with a specific project we expect the firm to identify one partner that is more preferred than others and form stronger ties in order to facilitate the transfer of knowledge. This leads us to expect that, at the project level, strong ties are most important for performance. H2 Strong ties with the preferred external partner is positively related to project level performance. H2a H2b A higher level of closeness with the preferred external partner is positively related to project level performance. A higher level of knowledge overlap with the preferred external partner is positively related to project level performance. 2.3 The moderating role of the preferred partner Empirical studies show that the most common sources of innovation for manufacturing firms are customers and suppliers (Laursen and Salter, 1996; Leiponen and Helfat, 2010). These sources provide knowledge and ideas to the receiving firms innovation activities and recent studies have shown that this also has beneficial effects on the providers own innovative activity (Tranekjer and Knudsen, 2012). However, collaborating with customers and suppliers may offer very different advantages or even disadvantages for the innovating firm. Thus, apart from investigating the general relationship between closeness and complementary knowledge on the one side and performance on the other, we are also interested in investigating whether the choice of partner influences the effect of tie strength. The question is whether the effects of tie strength differ when the partner is either a customer or a supplier. Collaborating with customers (downstream in the supply chain) can give access to knowledge on market needs, resulting in optimisation of efforts and thus higher probability of hitting the market with the right products (Fredberg and Piller, 2011). On the other hand, manufacturers may become too dependent on the needs, interests and experience of customers (Fredberg and Piller, 2011), perhaps even loosing valuable know-how (Gassmann et al., 2010b) or risking customer opportunism (Noordhoff et al., 2011). Regardless of whether the aim of customer collaboration is staying close in order to acquire the sticky needs knowledge that is so important for innovation (Von Hippel, 2005) or not staying too close and avoiding the current customers trap (Bower and Christensen, 1995), we expect the choice of a customer as the preferred or most important partner to be important. Existing studies suggest that novelty determines the importance of tie strength with customers meaning that incremental projects require stronger ties than radical ones (Bonner and Walker, 2004). Despite the contradiction in the literature, we expect that if the preferred partner is a customer, the positive effect of strong ties on performance is maintained. Collaborating with suppliers (upstream in the supply chain) may imply that suppliers adjust their products to manufacturers needs and share knowledge about new technologies for parts and components (Roy and Sivakumar, 2010). Roy and Sivakumar (2010) also suggest that knowledge from a supplier is easier to integrate directly as it is

8 212 T.L. Tranekjer and H.A. Søndergaard more of a technical nature and therefore directly related to design and development as well as easier to access because the supplier and the firm are in the same (or similar) industry (Un et al., 2010). Moving from an arm s length relationship to a close relationship with a supplier also ensures trust and commitment between the supplier and the firm (Walter, 2003). Furthermore, a common knowledge base with the supplier means that the supplier is able to understand the firm s needs (Hoegl and Wagner, 2005). In general collaborating with suppliers is shown to reduce cost and speed as well as to improve quality (Johnsen, 2009; Ragatz et al., 2002) and firms benefit from involving suppliers because of common goals and complementary capabilities (Un et al., 2010). Furthermore, in their conceptual paper Roy and Sivakumar (2010) suggest that embedded supplier relationships are more likely to result in incremental innovation than embedded customer relationships, especially if customers are highly innovative and their needs are at the forefront of the market. They also suggest that knowledge redundant supplier relationships are more likely to result in both incremental and radical innovation than if these are with customers, indicating that knowledge overlap is central for supplier relationships when transferring technological knowledge. Therefore we expect the following: H3 The positive relationship between strong ties and project level performance is moderated by the preferred type of partner (customer or supplier). H3a H3b The positive relationship between a higher level of closeness with the preferred external partner and project level performance is moderated by the type of partner (customer or supplier). The positive relationship between a higher level of knowledge overlap with the preferred external partner and project level performance is moderated by the type partner (customer or supplier). The conceptual model with the three hypotheses is shown in Figure 1. Figure 1 The conceptual model (see online version for colours)

9 Sources of innovation, their combinations and strengths Data and variables 3.1 Data collection The data for this study was collected in a cross-sectional survey of Danish firms in The questionnaire was pre-tested with five innovation management researchers from universities and with two innovation/r&d managers from manufacturing SMEs with knowledge of and interest in the NPD area. The population consists of Danish manufacturing firms (NACE 10-37) and firms conducting manufacturing related research and development (NACE 72) with less than 500 employees (SMEs). A total of 3,086 firms were contacted by telephone to ensure willingness to receive the questionnaire. In case of acceptance, the name and address of either the R&D or innovation manager was obtained. In a few cases, only the general company address was obtained. The telephone interviews resulted in 1,241 usable company addresses to which the questionnaire was sent in August The response rates calculated on the original population is 11.5% and calculated on the number of acceptances to receive the questionnaire 28.6%. The chi-square tests for non-response, showed no significant difference across industry, size and geographical regions. The analyses focuses on the most important NPD project, defined as the project that, in the period 2007 to 2009, incurred the largest resources and where the outcome was a new or improved product introduction. The reason for defining the most important project this way is based on the expectation that considerations about external sources are essential for projects with significant resource allocation and furthermore we expect it to be relatively easier for the respondent to remember projects with significant resource allocation. A certain volume of resource allocation was not specified as this may be relative to the size of the firm. Respondents were asked to identify the most important partner for this project from the list of possible external sources and were subsequently asked to specify the nature of the relationship with the partner (closeness and knowledge overlap). While other studies use company or general attributes as determinants for performance due to of lack of project level data (Bonesso et al., 2011), we collected both dependent and independent variables at the project level. 3.2 Variables The regression used for the analytical part is an ordinary least square, and the models are built with different dependent variables. The dependent variables are performance measures at the project level: project speed, project cost, project market performance and degree of product novelty. See Appendix 2 for the full wording of the items. All the performance measures concern the most important project in the three-year period The motivation for applying more than one performance measurement is that different types of collaboration may serve different purposes, where the two main goals of innovative effort are cost reduction and market expansion [Belderbos et al., (2004), p.1479]. Both cost and speed are measured as an average of three items (Appendix 2) adapted and modified from Rindfleish and Moorman (2001). Both Cronbach s alpha are above 0.70 which is the lowest limit of acceptability (Hair et al., 1998), so the internal consistency is high. Market performance is measured as an

10 214 T.L. Tranekjer and H.A. Søndergaard average of three items adapted from Maydeu-Olivares and Lado (2003). Cronbach s alpha is at (see Appendix 2). Degree of novelty is measured as an average of two items adapted from Cooper and Kleinschmidts (1987) scale on product advantage. As we wanted a measure of innovation performance we identified the items from this scale that are highly focussed on novelty. Cronbach s alpha is at (see Appendix 2). The independent variables used in the first part of the analysis (related to the first research question and for testing hypotheses 1) were market and science sources. In line with Sofka and Grimpe (2010) and Faems et al. (2005), the list of possible external sources are divided into two groups: market sources (value chain partners) and science source, based on the argument that the characteristics of the knowledge provided from the two partner types differ. In the second part of the analysis (related to the second research question and testing hypotheses 2 and 3) we add knowledge redundancy, relational embeddedness, and two dummies reflecting whether the most important partner is a supplier or a customer. The two groups of sources: market and science are extracted from the same overall question: Who actively participated in the product development project? (see Appendix 2). The market source variable is measured by aggregating responses to the five market sources: supplier, customer, competitor, consultants, and other partners in the value chain. The science source is measured by aggregating responses to the four science sources. On average firms use 1.66 market sources in their most important NPD project (mode = 2). The mean for science sources is 0.70, which means that firms on average use less than 1 science source in their most important NPD project (mode = 0) (see Table 4). Subsequently, respondents were asked to indicate the preferred or most important partner for this specific project. In order to test the effects of suppliers and customers being the preferred partner on the relationships between tie strength and project performance two dummies were developed. Dummy supplier is a dummy indicating that the most important partner is a supplier and dummy customer is a dummy indicating that the most important partner is a customer (see Appendix 2). The two measures of tie strength knowledge redundancy and relational embeddedness are based on Rindfleisch and Moorman s (2001) article (see Appendix 2). As these are established scales, factor analyses were conducted on both constructs. Knowledge redundancy, based on four items, produced a one-factor solution explaining 53.25% of the variance (Cronbach s alpha = 0.701). The simple average of the four items is calculated for the variable. The mean is 2.68, which means that firms on average have a fairly low degree of redundancy (overlap in the knowledge base) with the most important partner in NPD projects (Table 1). The factor analysis for the five items in the construct of relational embeddedness showed that one of the items had a low impact (0.474) and it was therefore excluded. The one-factor solution with the four items explains 63.9% of the variance (Cronbach s alpha = 0.807). To construct the variable, the simple average of the four items is calculated. The mean is 5.32, which means that firms on average have fairly embedded (close) relationships with the most important partner in their most important NPD projects (Table 1). The internal consistency is high for both knowledge redundancy and relational embeddedness as Cronbach s alpha are above 0.70 (Hair et al., 1998).

11 Sources of innovation, their combinations and strengths 215 Table 1 Descriptive statistics and correlation table N Min Max Mean Std. dev Market performance Cost * 3 Speed ***.465*** 4 Novelty ** Market sources *** Science sources ***.222*** *** 7 Knowledge redundancy ** Embeddedness ** *** ** 9 Dummy supplier * Dummy customer *** *** 11 Size ** * ** ** 12 Low tech **.131* * Medium tech * *** 14 High tech ** * * *.187***.429*** 15 Turbulence **.015** * ** * Notes: ***Correlation is significant at the.01 level **Correlation is significant at the.05 level *Correlation is significant at the.1 level

12 216 T.L. Tranekjer and H.A. Søndergaard We use control variables for industry, size and market turbulence. Industry is measured on the OECD scale with dummies for high-, medium-, and low-tech firms. 1 Size is measured in number of employees. The variable is skewed and therefore the logarithm (log) is used to correct for the skewness. Market turbulence is measured based on the scale from Jaworski and Kohli (1993), a factor analysis confirmed a one-factor solution (Cronbach s Alpha = 0.760) (see Appendix 2). To construct the variable, the simple average of the five items was calculated. 4 Results The results section will be split into two parts first presenting results for research question 1, which is concerned with combinations of sources, and then for research question 2, which is concerned with the role of tie strength. For both sections the presentation will start with descriptive statistics followed by results from the regression analyses. Table 1 presents the correlation matrix. Market and science sources correlate with cost, and science sources correlate negatively with speed. Preliminary indications from the correlation table thus indicate that the sources vary in their influence on the individual performance measurements. Knowledge redundancy and relational embeddedness correlate positively with market performance and relational embeddedness correlates positively with novelty indicating that tie strength also has an impact on NPD project performance. To uncover whether the assumption for an ordinary least square is fulfilled, we checked for multicollinearity, normal distribution and homogeneity of variance. The correlations (Table 1) give no indication of multicollinearity. Nevertheless, the variance inflation factor (VIF) was calculated. The VIF for all models are between and mean VIF for all models are below 1.69 which indicates that there is no problem with multicollinearity (Wooldridge, 2009). Still, the recommended use of standardised variables in the regression for testing the interaction effects to increase the power of the models and to reduce any potential multicollinearity is followed (Frazier et al., 2004). The Shapiro-Wilk W test and the skewness/kurtosis test for normality are used; the results are not significant, therefore it is not possible to reject that the sample is normally distributed. The Breusch-Pagan/Cook-Weisberg test for heteroskedasticity is used and the results are not significant indicating that there is no problem with heteroskedasticity. The assumptions for an ordinary least square are fulfilled. The robust function in Stata (version 11) is used for the regressions. Table 2 Distribution of firms by industry Industry Frequency Percent High tech firms Medium tech firms Low tech firms Total

13 Sources of innovation, their combinations and strengths 217 Table 3 Distribution of firms by number of employees Number of employees Frequency Percent 0 9 employees employees employees employees Total General information on the firms participating in the survey is presented in Tables 2, 3 and 4. Most firms come from medium technology sectors most of them are fairly small with between 10 and 49 employees and a majority of them have introduced new (new or significantly improved) products to the market within the past three years. Table 4 Distribution of product innovative firms Introduced new products ( ) Frequency Percent Yes No How do combinations of external sources affect project performance in NPD? (RQ 1) The descriptive statistics related to the most important NPD project reveal that for 98.3% of the firms the product is still on the market. In Table 5, all frequencies for the sources drawn on for innovation are presented. More than 60% of firms involved suppliers or customers. It is not surprising that collaboration with competitors is quite rare (3.6%) (Chen et al., 2011) due to the risk of knowledge leakage/spill-over. For the aggregated variables market and science sources the chi-tests showed no significant differences with respect to industry and size. Table 5 Types of partners used in the most important NPD project Who was actively involved in the most important NPD project? Market sources Frequencies Percentage Suppliers % Customers % Competitors 7 3.6% Consultants % Other partner in the value chain % Science sources Private laboratories % Other private research institutions % Universities % Other public research institutions 5 2.6%

14 218 T.L. Tranekjer and H.A. Søndergaard Table 6 Regression model the mix of sources Market perf. Cost Speed Product novelty Market perf Cost Speed Product novelty Goals of the NPD project Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Constant 3.755*** 3.083*** 4.169*** 3.112*** 3.724*** 3.143*** 4.180*** 3.091*** Controls Low-tech.055 Omitted Omitted Omitted.043 Omitted Omitted Omitted Medium-tech * *.171 High-tech Omitted.663**.739**.895*** Omitted.725**.717*.881** Market turbulence * * Size ** ** ** ** Independent variables Market sources ** ** Science sources *** **.265***.004 Interaction Market*Science * N R² Significance Notes: ***p <.01, **p <.05, *p <.1 Scale from lower cost to higher cost

15 Sources of innovation, their combinations and strengths 219 Firms primarily choose partners based on previous experience (65%). Thus, a firm s experience with a partner is an important element in understanding the selection of partners for NPD projects. For testing hypotheses 1, models are built with the two independent variables, controls and the interaction between market and science sources. The interaction is calculated as a multiplication of the single variables. An ordinary least square regression is applied. Table 6 presents the models and results. Models 1 to 4 present the results of the two types of sources and their direct relationship with the four different NPD project performance measures. In the market performance model (Model 1) none of the sources are significant, nor is the model. The cost model (Model 2) shows that market sources are significantly related to cost, indicating that the use of more market sources increases project costs. The speed model (Model 3) shows that science sources are significantly related to speed, indicating that the use of more science sources means slower NPD projects. In the novelty model (Model 4) none of the sources are significant. These results support and expand the findings from Knudsen and Mortensen (2011) who found negative effects on speed and cost from the use of external sources in general, however not specifying the external source type. Models 5 to 8 show the results from combining market and science sources. Model 5 (market performance), Model 7 (speed) and Model 8 (novelty) show no significant result from the interaction between market and science sources. In Model 6 the interaction between market and science sources reveals a significant relationship with cost. If firms use market sources together with science sources, project costs are lower. In Model 6 there is still, as in Model 2 a direct effect from market sources, showing a direct relationship between market sources and higher costs (partial moderation). We did four more regressions (not presented in any table) with the market and science variables to test the robustness of the result. Four different combinations of levels of market and science sources were introduced as interaction terms: low number of market sources and science sources, high number of market sources and science sources, low number of science sources and market sources, and finally high number of science sources and market sources. All four models were significant supporting the robustness of the results. Only the cost model (Model 6) shows significant results from the mix of market and science sources, although with the opposite result of what we expected. This means that companies who mix market and science sources can report lower project cost. We thus find no support for H1. However, the changes from introducing the interaction effects in Model 6 contribute to the overall models significantly (partial F <.05). Regarding the control variables, results show that larger firms experience both higher cost (Models 2 and 6) and higher novelty (Models 4 and 8) in their NPD projects than smaller firms. Furthermore, high-tech firms also tend to have higher cost (Models 2 and 6), lower speed (Models 3 and 7), and more novelty (Models 4 and 8) in their NPD projects.

16 220 T.L. Tranekjer and H.A. Søndergaard 4.2 How does the strength of the relationship with the preferred partner affect project performance in NPD and does the type of partner affect this relationship? (RQ 2) Table 7 shows that for 42.1% of the firms the most important partner is a customer, for 36.0% it is a supplier and for 10.7% a consultant. Very seldom the most important partner is a science source. In general SMEs find it difficult to work with universities and research institutions partly due to the difference in time horizon for development projects (long term versus short term) (Fontana et al., 2006). A total of 65.0% of the firms indicate that they have had previous collaborations with the most important partner. Table 7 Most important partner in the most important NPD project (preferred partner) Who is the most important partner? Frequencies Percentage Suppliers % Customers % Competitors 1 0.6% Consultants % Other partner in the value chain 9 5.1% Private laboratories 3 1.7% Other private research institutions 3 1.7% Universities 2 1.1% Other public research institutions 2 1.1% Other 4 2.2% The same models are used for testing hypotheses 2 and 3 as for hypothesis 1, adding the additional independent variables of knowledge redundancy and relational embeddedness and the two dummies for the most important partner. The interaction effects are calculated in a similar fashion as for hypotheses 1. The results are shown in Tables 8 and 9. Models 9 12 present the results of the direct effects from the independent variables on performance. In the market performance model (Model 9), embeddedness is significant at the p <.1 level indicating that NPD projects done by firms with embedded (= close) relationships with their most important partner tend to experience higher levels of market performance, supporting H2a. At the same time knowledge redundancy is significantly related to market performance indicating that firms whose knowledge base overlap with their most important partner s have higher levels of market performance, supporting H2b 2. In the cost model (Model 10), the speed model (Model 11) and the novelty model (Model 12) neither knowledge redundancy nor embeddedness are significant.

17 Sources of innovation, their combinations and strengths 221 Table 8 Regression model tie-strength Market performance Product novelty NPD project goals Cost Speed Model 9 Model 10 Model 11 Model 12 Constant 4.093*** 3.717*** 4.980*** 3.471*** Controls Low-tech Omitted.876*** Omitted 1.026** Medium tech * *** High-tech.159 Omitted 1.099** Omitted Market turbulence *** Size * *** Independent variables Market sources ** Science sources *.347***.006 Knowledge redundancy.383** Embeddedness.232* Dummy supplier Dummy customer N R² Sig Notes: ***p <.01, **p <.05, *p <.1 scale from lower cost to higher cost For hypothesis 3 we investigate whether the fact that the preferred partner is a customer or a supplier has an effect on the relationship between tie strength and performance. In line with earlier studies 78% of the firms in our survey have either suppliers or customers as their most important partner (see Table 7), supporting the relevance of only these two partner types as moderators. Models present the results from the moderating effect of a supplier as the most important partner on the relationship between knowledge overlap and performance. We may support H3b as both the market (13) and the cost (14) models show significant positive moderating effects of suppliers, although there were no significant results for the speed model (15) and the novelty model (16). Results thus indicate that firms with a knowledge overlap with the supplier experience higher levels of market performance, but also significantly increased costs. The changes from introducing the interaction effects in Models 13 and 14 contribute significantly to the overall models (partial F <.05).

18 222 T.L. Tranekjer and H.A. Søndergaard Table 9 Regression model tie-strength Market perf. Cost Speed Novelty Market perf. Cost Speed Novelty NPD project goals Model 13 Model 14 Model 15 Model 16 Model 17 Model 18 Model 19 Model 20 Constant 3.992*** 3.700*** 4.963*** 3.470*** 4.094*** 3.728*** 4.988*** 3.617*** Controls Low-tech Omitted.932*** Omitted 1.038** Omitted.878*** Omitted 1.041** Medium-tech * *** * ** High-tech.062 Omitted 1.084** Omitted.159 Omitted 1.097** Omitted Market turbulence *** ** Size ** *** * *** Independent variables Market sources ** ** Science sources *** *.347***.018 Knowledge redundancy ** ** Embeddedness * *** Dummy supplier Dummy customer Interaction Redundancy*Dummy supplier.807***.471* Embeddedness*Dummy supplier * Redundancy*Dummy customer Embeddedness*Dummy customer N R² Sig Notes: ***p <.01, **p <.05, *p <.1 Scale from lower cost to higher cost

19 Sources of innovation, their combinations and strengths 223 Table 9 Regression model tie-strength (continued) Market perf. Cost Speed Novelty Market perf. Cost Speed Novelty NPD project goals Model 21 Model 22 Model 23 Model 24 Model 25 Model 26 Model 27 Model 28 Constant 3.903*** 3.648*** 4.859*** 3.456*** 4.094*** 3.672*** 4.970*** 3.544*** Controls Low-tech Omitted.926** Omitted 1.038** Omitted.837** Omitted 1.073** Medium-tech * *** * *** High-tech.066 Omitted 1.043** Omitted.159 Omitted 1.134** Omitted Market turbulence *** ** Size ** *** * *** Independent variables Market sources ** ** Science sources *** *.343***.002 Knowledge redundancy.717*** **.176* Embeddedness Dummy supplier Dummy customer Interaction Redundancy*Dummy supplier Embeddedness*Dummy supplier Redundancy*Dummy customer.666** 339(tent.) Embeddedness*Dummy customer N R² Sig Notes: ***p <.01, **p <.05, *p <.1 Scale from lower cost to higher cost

20 224 T.L. Tranekjer and H.A. Søndergaard Models present the result from the moderating effect of a supplier as the most important partner on the relationship between embeddedness and performance. The results in the novelty model (20) indicate that firms with a highly embedded relationship with suppliers experience low levels of product novelty, thus supporting H3a. The significant direct effect from relational embeddedness indicates a partial moderation. The changes from introducing the interaction effect in Model 20 contribute significantly to the overall models (partial F <.05). In the market performance model (17), cost model (18), and the speed model (19) the moderating effect from supplier on the relationship between embedded and performance were not significant. Models present the results from the moderating effect of a customer as the most important partner on the relationship between knowledge overlap and performance. These results also support H3b as the market (21) and the cost (22) models show negative moderating effects of customers, although again there are no significant results for the speed model (23) and the novelty model (24). Results show that if firms have a high level of knowledge overlap with the customer, they experience a decrease in market performance from their NPD project, but also decreasing cost (cost only tentatively). For the market performance model (21) the direct effect from knowledge redundancy is still present indicating a partial moderation. The changes from introducing the interaction effects in Model 21 and 22 contribute significantly to the overall models (partial F<.05 and F <.1). Model present the result from the moderating effect of a customer as the most important partner on the relationship between embeddedness and performance, and no significant results were found, thus, not supporting H3a. All results are presented in Figure 2. Figure 2 Overview of the results (see online version for colours)