Auke Derksen
The role of information technology
in open innovation:
The role of information technology
in open innovation:
A touch point perspective
Auke Derksen
S2194023Msc BA Strategic Innovation Management Master Thesis
ABSTRACT
This study investigates what the organizational touch point (mechanism) is by which IT investment touches upon the relationship between open innovation and innovation performance. A theory testing approach is used to test the hypotheses, in particular the SEM-PLS method is used. The results show that engaging in open innovation leads to a better innovation performance and that this relation is moderated by IT through the touch point firm size. More specifically, it is found that IT has a negative impact on firm size by reducing coordination cost through the labour substitution effect, the coordination structure effect and the outsourcing effect. Firm size in turn negatively moderates the relationship between open innovation and innovation performance. From this I argue that this study contributes to research and practice by unravelling the touch points of IT in relation with open innovation and innovation performance. Also it shows how firms can spend their IT investments more efficiently and
effectively.
Supervisor: J. Q. Dong Co-assessor: F. Noseleit
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1 Introduction
Open innovation is a well-known phenomenon nowadays. In a recent survey 78% of large companies reported to use open innovation (Chesbrough & Brunswicker, 2013) and Van der Vrande et al. (2009) found similar trends amongst small and medium enterprises (SME’s). Traditionally, innovation is viewed as something that is going on inside large technological companies (Lee et al, 2010). But with the globalization, easy availability and mobility of employees with a lot of knowledge, the up-rise of the internet and many other current developments, the traditional model (closed) has come to an end (Huizingh, 2010). This made room for a new type of innovation called open innovation (Chesbrough, 2003a). In contrast to the closed model, open innovation focuses on movement of knowledge and ideas from the company to the environment and the visa versa. Open innovation has received a vast amount of research already. A quick search on the key words ‘open innovation’ on google scholar leads to 3.250.000 results. So, although the literature seems to have set the concept of open innovation as a mature literature field, there are some under-developed parts within the theme open innovation. Information technology is one of them. Technology in general is assumed to support open innovation (Chesbrough, 2003a) but how and why is not extensively researched with the notable exception of Dodgson, Gann and Salter (2006). They argue that technology supports open innovation and that IT in particular plays an important role in facilitating open innovation through enhanced communication. So it seems there is lot to gain from IT in regard to open innovation. The same could be said of firm size. Most research in the area of open innovation seems to focus on large high tech enterprises, mainly through qualitative research (Van der Vrande et al., 2009; Spithoven et al., 2013). This is an interesting observation because SME’s are also very important innovators (Van de Vrande et al, 2009). Just a very few researchers have tried to close this gap in the literature by focussing their research on open innovation in SME’s (Lee et al, 2010; Spithoven et al, 2013; Van de Vrande et al, 2009). Thus it seems that the effects of firm size in general in relation to open innovation are not yet extensively investigated while differences seem to exist. This paper aims to close these gaps in the literature by investigating the role of IT through firm size in relation to open innovation and the effect open innovation has on innovation performance. It is assumed that IT plays a moderating role on the presumed effect with firm size as touch point (mechanism). In order to do so the following research question is formulated:
What is the organizational mechanism through which IT investment moderates the relationship between open innovation and innovation performance?
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in connection with open innovation. Especially the touch point perspective might deliver some interesting insights in the moderating effect of IT and can lead to more research looking for further mechanisms through which IT might influence open innovation and innovation performance.
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2 Theoretical background
2.1 Open innovation practices and innovation performance
As mentioned before, open innovation is a very popular topic in innovation management (Huizingh, 2010). Al sorts of disciplines have shown interest in the concept of open innovation (von Krogh & Spaeth, 2007) and a lot of research areas have evolved over time within the open innovation field (Gassman et al., 2010). The topic started off with the work of Chesbrough (2003a, 2003b, 2003c) who invented the term open innovation, but as shown by Dahlander and Gann (2010) the roots of open innovation lie in many other established theories and concepts like absorptive capacity (Cohen and Levinthal, 1990), complementary assets (Teece, 1986) and the exploration versus exploitation discussion (March, 1991). Recently Vanhaverbeke and Cloodt (2014) revised this theoretical background of open innovation and tried to link open innovation to existing theories in order to establish a coherent understanding of open innovation and give it more legitimacy. In particular they tried to link open innovation to the strategy literature and to different theories of the firm including transaction cost economics, the resource-based view, the resource dependence theory, the relational view, the real options theory, absorptive capacity and dynamic capabilities. They found that open innovation is built with parts of all of these theories. The main idea of open innovation is opening up the innovation process to other entities (Huizingh, 2010). One of the most used definitions of open innovation is one from Chesbrough et al. (2006): “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and to expand the markets for external use of innovation, respectively”, which will be used in this paper as well. The process of inflows of knowledge is referred to as inbound open innovation and the process of outflows of knowledge is referred to as outbound open innovation. We speak of inbound open innovation when external information is used inside the company, while outbound open innovation is happening when others exploit internally generated information (Huizingh, 2010). The process to unite these two activities and balance them is called coupled open innovation (Gassmann & Enkel, 2004). Based on this I argue that open innovation as a whole comprises two sub-dimensions which both explain a different part of the open innovation concept: Inbound and outbound open innovation (Huizingh, 2010), and should be treated as such. This means that open innovation can and should be treated as a second order formative construct as defined by Petter et al. (2007) and Wetzel et al. (2009). Inbound open innovation, further referred to as outside-in innovation, and inbound innovation, further referred to as inside-out, can be achieved through several practices. Thus, open innovation practices (Van der Vrande et al., 2009; Spithoven et al., 2013) are the means by which a company actually engages in open innovation. There are several practices identified in the literature. In this research the following are selected based on the available data to represent outside-in and inside-out open innovation (table 1).
Table 1 Open innovation practices
Outside-in Inside-out
Open innovation practices
Van der Vrande (2009):
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Not included in table 1 is outward IP licensing. Outward IP licensing seems to be an important part of inside-out open innovation (Lichterthaler, 2008). Nevertheless this will not be included in this research because patents are usually only applied for by larger companies due to the great costs that go along with an application. Considering this, only larger companies are able to gain rents from out-licensing and thus will skew the results. Moreover, these variables are not present in the dataset for the years used. Therefore they are excluded from this research.
Open innovation is a construct that is meant to enhance the development of new products and services just like normal innovation is (Chesbrough, 2003a, 2006; Laursen and Salter, 2006). Open innovation practices enables a company to profit from cost savings as well as time savings by sourcing information from external sources. Also firms can benefit from specialised knowledge and expertise from other firms that the company itself does not have and/or cannot develop internally (Spithoven et al., 2013). Furthermore, firms can benefit from commercialising internally generated knowledge or use it in a collaboration. Therefore I assume a positive relationship between the level of engagement in open innovation practices and innovation performance. Formally I state:
hypothesis 1 A higher level of engagement in open innovation practices results in higher innovation performance.
2.2 IT and firm size
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stimulating communication trust and a shared understanding (Kleis et al., 2012). Dong and Wu (2015) agree with the many situations in which IT seems to leverage innovation practices, like mentioned before, and contributed to this body of knowledge by taking a closer look at how social media technology could assist in open innovation practices. In another study Dong and Yang (2015) investigated how IT could enhance a firms absorptive capacity, which is needed to successfully engage in open innovation (chi et al., 2010). Furthermore, there are many advantages of scale and scope that stem from IT usage, which are facilitated by open computer systems, that affect innovation (Tuomi, 2002). All in all this supports the idea that IT is an important moderator regarding the relationship between open innovation and innovation performance. IT can be seen as an important factor that facilitates knowledge flows, both outside-in and inside-out, by enabling and facilitating external innovation collaboration and supporting the management of innovation knowledge respectively.
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It is argued in the literature that especially smaller firms gain from engaging in open innovation practices (e.g. Van der Vrande et al., 2009). There have been a fast amount of studies that looked into the innovation processes of SME’s (e.g. Vossen, 1998; Acs & Audretsch, 1990). The results from these studies are that although SME’s have the advantage of flexibility and specificity (Lee et al., 2010), they are hindered by the so called liability of smallness (Narula 2004; Dahlander and Gann 2010). This means that they have fewer financial resources and less opportunities to hire specialised employees which causes them to have insufficient means to innovate by themselves and also have smaller innovation portfolios to spread risk (Van der Vrande et al., 2009; Lee et al., 2010; Spithoven et al., 2013). In order to make up for this liability of smallness, SME’s need to look beyond the boundaries of the firm and use external sources to accelerate their innovations. They heavily draw on their networks in order to get the much needed resources and are thus encouraged to collaborate with other companies (Edwards et al., 2005; Van der Vrande et al., 2009). As Van der Vrande et al. (2009) put it: “In today’s increasingly complex and knowledge-intensive world with shortened product life cycles, such networking behaviour has become probably even more important than before.” This point is further stressed by Lee et al. (2010) as they say that with the increasing complexity of technology it can become too complex for one firm to handle and that relevant knowledge is scattered across a multitude of different firms. This makes it increasingly important, especially for smaller firms, to collaborate and to use their network to become more successful in innovating (Lichtenthaler, 2005). This all leads to the believe that open innovation is highly relevant for smaller companies as using external sources to leverage one’s own innovation is the core principle of open innovation. Thus it seems that the smaller the company, the more they can gain from using open innovation practices to enhance their innovation performance. Therefore I hypothesise:
hypothesis 2 IT will negatively influence firm size which in turn will have a negative moderation effect on the relationship between level of engagement in open innovation practices and innovation performance
Following the previous stated reasoning I argue that instead of a direct moderating effect of IT, the use of IT will have a negative impact on firm size which will subsequently have a negative moderating effect on the relation between engagement in open innovation practices and innovation performance. In this way IT will have a positive indirect effect on the relation between open innovation and innovation performance. A summary of the relationships is depicted below in a conceptual model (figure 1).
Figure 1 Conceptual model
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3 Methodology
3.1 Dataset and sample
The dataset that will be used for the statistical analysis in this research is the Mannheim Innovation Panel (MIP), conducted by the Centre for European Economic Research (ZEW) in Germany. This institute has been gathering data annually since 1993. The dataset includes various industries and is representative for Germany. The survey is conducted on behalf of the Federal Ministry of Education and Research together with the Institute of Applied Social Science and the Institute for Systems and Innovation Research. The MIP is the German contribution to the European Commission’s Community Innovation Surveys (CIS) which is an joint effort of Eurostat and the Innovation and SME program to generate data to improve the empirical basis for innovation theory and policy within the EU (Spithoven et al., 2013). The survey examines the innovative activities within the participating countries. To account for generalizability the questionnaires across countries are kept more or less the same. Therefore the MIP can be considered as valuable dataset (Cassiman & Veugelers). Also because of the good coverage of the dataset it can be specifically used to create open innovation indicators (Laursen and Salter 2006). In the initial dataset 3967 cases were collected. Data from the year 2003 and 2004 will be used. After selecting the needed variables from the complete dataset and correcting for missing values, 698 cases remained for the final sample.
3.2 Dependent variables
This research contains two dependent variables, being ‘level of engagement in open innovation practices’ and ‘innovation performance’. As mentioned before, level of engagement in open innovation practices is a second order formative construct. Lowry and Gaskin define both types of constructs as follows (2014): “a formative construct is a variable measuring an assumed cause of or a component of a latent construct. Under this conception, a latent (unobservable) construct is assumed to be defined by a function of its indicators (which in this case are ‘Inside-out’ and ‘Outside-in’). In contrast, a reflective indicator is an observed variable that is assumed to be an effect of a latent construct (which are the others variables). The underlying construct is assumed to cause the values that manifest in the observed variable.” A complete and detailed list of all variables is provided in appendix 1. To establish that level of engagement in open innovation practices is indeed is a formative construct I followed the decision rules made by Petter et al. (2007). They say that in order to be a formative construct, (1) the direction of causality should be from the items to the construct, (2) the indicators need not be interchangeable, (3) the indicators do not necessarily need to covary with each other and (4) the indicators do not need to have the same antecedents and consequences. This questions all lean towards a formative construct. Therefore I can say that level of engagement in open innovation practices really is a formative construct. Level of engagement in open innovation practices is measured by both ‘Inside-out’ and ‘Outside-in’.
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innovation performance (Leiponen and Helfat, 2010; Spithoven et al., 2013). In this research ‘Proportion of total turnover from new or clearly improved products’ and ‘Share of turnover from market novelties’ are used, assuming that in order to fill in these questions a firm actually needs to introduce a new product in order to report turnover from it.
3.3 Independent variables
There are four independent variables that will be used in this research: ‘IT’, ‘firm size’, ‘Inside-out’ and ‘Outside-in’ all being reflective in nature (see Appendix 1). As mentioned before in the theory section outside-in and inside-out will be measured based on a few aspects that represent such behaviour. The firm size latent variable is constructed with number of employees and turnover, following Shalit and Sankar (1977). The IT variable is made of the variables invis and invs. For IT the measure IT spending divided by sales is the desired one, for it gives ratio of IT spending’s compared to the total sales made. This variable is constructed by multiplying the two indicators (appendix 1).
3.4 Control variables
Following previous literature I have included several control variables (appendix 1) which are consistent with previous literature (Spithoven et al., 2013; Parida et al., 2012). Firstly I have included R&D intensity. Companies with a strong R&D basis tend to be more open and also more capable of integrating external knowledge due to a higher absortive capacity (Cohen & Levinthal, 1990). Therefore, companies who have a higher R&D intensity are found to be more open (Cassiman & veugelers, 2006). Also younger firms tend to more innovative (Rosenbusch et al., 2011) thus firm age is included. Furthermore there is controlled for degree of internationalization, a merger with another firm within the last three years and for the branch a company is in, since data from different branches is collected.
3.5 Research design
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1 “Propose a model that is consistent with all currently available data to test the theory. 2 Perform data screening (see previous subsection)
3 Examine psychometric properties of all variables in the model.
4 Examine the magnitude of the relationship and effects between the variables being considered in the proposed model.
5 Examine the magnitude of the standard errors of the estimates in the proposed model and construct confidence intervals for the population parameters of interest. 6 Asses and report power of the study.”
SmartPLS 2.0 software is used to carry out the analysis and to estimate the parameters of the measurement and structural model. This is a SEM-PLS based program that works with a graphical user interphase. To calculate the structural model and the outer loadings the PLS algorithm will be used, and to obtain the standard errors of the estimates the bootstrapping tool as implemented in the software will be used. For bootstrapping 500 replications and cases similar to the final sample size are used, following Lowry and Gaskin (2014).
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Table 2 Overview of psychometric properties
AVE Composite Reliability R Square
Firm size 0.99 1.00 0.00
Innovative performance 0.79 0.88 0.35
Inside-out 0.44 0.84 0.00
Outside-in 0.48 0.78 0.00
Table 3 Intercorrelations of latent reflective variables
Firm performance Firm size Inside-out Outside-in
Firm performance 0.89*
Firm size -0.02 0.99*
Inside-out 0.44 0.12 0.66*
Outside-in 0.49 0.05 0.54 0.69* * Square root of AVE on the diagonal
Table 4 Overview of factor loadings and corresponding T-value and P-value
Latent variable Indicator Indicator loading T-value P-value
Outside-in Qkupd 0.783 43.315 P = 0.01 Qlipd 0.586 11.845 P = 0.01 qunpd 0.679 20.227 P = 0.01 qwipd 0.702 27.061 P = 0.01 Inside-out Koop 0.773 36.347 P = 0.01 Wein1 0.882 43.442 P = 0.01 Wein2 0.700 20.865 P = 0.01 Wein4 0.521 11.310 P = 0.01 Weinic3 0.659 17.856 P = 0.01 Weinic4 0.614 13.723 P = 0.01 Weinic5 0.493 7.614 P = 0.01
Firm size Bges 0.996 140.272 P = 0.01
Um 0.995 127.480 P = 0.01
Innovation performance Mneup 0.849 42.916 P = 0.01
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Having established the factors underlying the measurement model, the structural model can now be explained. The first and second order constructs are constructed following the steps below according to the outlines set by Wetzels et al. (2009):
1 The first order latent variables (open innovation, innovation performance, firm size, controls) are constructed by linking the indicators to the constructs in a reflective manner starting the measurement model. The blue circles are latent variables while the yellow squares represent the indicators (appendix 2).
2 The second order latent variables (outside-in, inside-out) can now be constructed by relating them to their accompanying indicators.
3 The structural model can now be constructed by linking all the latent variable to account for the anticipated paths. The two second order latent variables are formatively connected to open innovation. This is indicated by the inward pointed arrows. To make the model runnable with SmartPLS 2.0 firm size has to be connected to the independent variable. This is only done for this purpose.
4 Now a moderating effect can easily be created in SmartPLS by indicating the moderating variable and the predictor variable, firm size and open innovation respectively.
5 The model is now ready to be estimated by using the PLS algorithm and bootstrapping functions. I obtain estimates of the indicator loadings as well as the structural parameters. With bootstrapping I obtain the standard errors which will result in t-values which can be used to assess the significance. Any t-value above 1.648 is significant at the 0.10% level. Any t-value above 1.964 is significant at the 0.05% level and any t-value above 2.583 is significant at the 0.01% level.
6 The psychometric properties can now be assessed (see above).
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4 Data analysis
In order to find answers to the hypotheses the structural model is tested as explained in the previous chapter. The PLS algorithm and bootstrapping results can be found appendix 2. As shown in figure 2 there is a pronounced relationship between open innovation and innovation performance, positive and significant (β = 0.387, T-value = 7.661, P < 0.01 ), thus providing strong support for hypothesis 1. Furthermore, the estimation results show that IT has a very minor negative and significant relationship with firm size (β = -.031, T-value = 2.399, P < 0,05) and the moderating effect of firm size on the relationship between open innovation and innovation performance is also negative and significant (β = -0.117, T-value = 2.680, P < 0.01). Both findings lead to strong support for hypothesis 2. The variance explained by the model in terms of R2 for the dependent variable innovation
performance is 0.350 which is considered large (Cohen, 1988) (appendix 2). A global fit measure (GoF) is proposed by Tenenhaus et al. (2005) as a baseline to validate the model globally. This measure is calculated by taking the square root of the minimum average AVE (0,5) multiplied by the R2 (0.35). Calculating this for the model makes the GoF equal
to 0.42 which is considered as large (Gof > 0.36), indicating a good model fit (wetzels et al., 2009). None of the added control variables yielded a significant effect accept for R&D intensity (β = 0.246, T-value = 4.722, P < 0.01), explaining part of the R2 as expected.
*P < 0,05, **P < 0.01
Figure 2 Analysis results stating path coefficients
4.1 Robustness test
In order to test for reverse causality a robustness test is done in addition to the normal causal relationship test. Robustness tests examine how core coefficients behave when certain changes are applied to the model. When coefficients are found to be fragile it could mean that a certain misspecification error could exist. If coefficients do not change much this can be taken as proof of robustness (Lu & White, 2014). Robust findings can be taken as evidence that the found coefficients can indeed reliably be seen as the true causal relationships of the associated regressors (Lu & White, 2014). In previous literature there has been some discussion on the relationship between IT and firm size. Im et al. (2013) for instance considered also a reverse effect between IT and Firm size. In order to
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check for reverse causality I re-run the model with a one year time lag of firm size. From the results of the robustness test I can conclude that reverse causality does not seem to be a problem here (β = -.031, T-value = 2.499, P < 0,05) (appendix 4) as the coefficient is identical to the original model. A quick look at the other coefficients also supports further prove of robustness as they only have minor differences with the original model (table 5). Table 5 Original model and robustness test coefficients
Original model coefficient Robustness test coefficient
Open innovation -> innovation performance β = 0.387 β = 0.384
IT -> firm size β = -0.031 β = -0.031
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5 Discussion
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5.1 Theoretical implications
From a theoretical perspective this research adds to the growing body of literature on open innovation and to IT management literature. Especially it adds to our understanding of how IT can be used in combination with open innovation to get to a better innovation performance. Moreover this study has made a start with unravelling the mechanisms through which IT moderates this relationship and explains how IT acts as a moderator. By understanding how and why IT influences this relationship this study helps IT and open innovation research progress from a more general association between open innovation and IT to a more detailed and specific explanation of the paths and relations. These insights can provide more knowledge on how to gain more efficiency with a given level of IT investments and how to spend it effectively. By uncovering firm size as touch point this study also adds to the accounting information systems research (AIS). It advances this field by showing that differences in allocating the IT budget can result in different outcomes (Kobelsky et al., 2008). Furthermore, the establishment of one mechanism could yield more research questions regarding other mechanisms through which IT might exercise influence with the potential of further knowledge creation in this area and others. It can also introduce more tools to steer open innovation with a fixed IT budget. Thus I argue that this study indeed opened up an interesting research area but it is only a very first step. Many more research is needed to discover other possible touch points of IT and their relations to open innovation to gain a full understanding of all the mechanisms that might have an influence. When many touch points are discovered a complete and powerful toolbox can be created for managers, which will be further explained in the next subsection.
5.2 Managerial implications
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5.3 Limitations and directions for future research
Like most studies this research too has a few implications. First of all the dataset used for the calculations was conducted in Germany. This might have implications for the generalizability of the findings to other countries with other cultures. Also deleting missing values could have biased the dataset in a unforeseen manner. It is possible that certain type of companies did not fill in a particular question and therefore this type was excluded from this research. Furthermore, looking at the literature, there might be more open innovation practices which are interesting to include in further research (e.g. Spithoven et al, 2013; Lichtenthaler, 2008; Van de Vrande et al., 2009). Because of the use of an already existing dataset and time constraints some practices are excluded, such as out licensing agreements (Lichtenthaler, 2008). Another possible limitation is the use of a one year time lag. As explained in Im at al., (2013) IT needs some time for its effects to spread out. The coefficient found for IT influencing firm size could therefore be bigger when a longer time period is used (Im et al., 2013). The next limitation is the possible reverse causality between the two variables IT and firm size. As argued in Im et al (2013) larger firms are internally more diverse, they usually have more resources to spend, and have more connections to handle. Therefore they can and should use IT to coordinate and facilitate all these factors, which would imply reversed causality by positive influence of firm size on IT investments. The robustness test provided no evidence to assume reverse causality. Looking at the few other studies that looked into this relationship there seems to be very mixed results. Delone (1981) for instance found a positive relationship between firm size and IT investment whereas Harris and Katz (1991) found a negative relationship. Moreover, Turner (1982) and Gremillion (1984) found the relationship to be non-existent. Because of the mixed results in previous literature as well as the results from the robustness test I argue that reversed causality does not cause any problems in this study until further research more clearly points towards a common finding and finds generally accepted empirical results for such a relationship.
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6 Conclusion
This research started with the question: What is the organizational mechanism through which IT moderates the relationship between open innovation and innovation performance? This study found that firm size is the mechanism through which IT exercises it’s moderating effect. Furthermore, the results confirm that engaging more in open innovation practices leads to a better innovation performance. Moreover, the results showed that IT has a positive moderating effect on the relationship between level of engagement in open innovation practices and a firms innovation performance. More specifically the relationship is found to be a mediated moderation effect, with firm size as touch point (Tams et al., 2014). With this findings I can conclude three things:
1 Investing more in IT can yield more benefits from engaging in open innovation. 2 By identifying a touch point, firms can spend their IT budget more efficiently and
more effectively towards their goals
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Acknowledgements
I would like to thank a few people who supported me in writing this master thesis. First of all I would like to thank my supervisor Mr. Dong for guiding me in the process and providing me with helpful insights and tips when I needed them. Also I would like to thank my co-assessor Mr. Noseleit for taking the time to comment on my draft version and helping me to improve the quality of my thesis. Furthermore I would like to thank my uncle Mr. ten Brinke who was so kind to discuss the findings of this thesis with me and helped me to look at the findings from another perspective and come to new insights. Lastly I would like to thank my father-in-law Mr. Wolf for designing the layout of this master thesis.
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1 Detailed variable list
Independent variables Name Meaning Outside-in innovation (OI)
Van der Vrande (2009): - External networking Spithoven et al. (2013): - Search strategy Parida et al. (2012): - Technology scouting - Technology sourcing Qkupd Qlipd Qunpd Qwipd
Information from clients/demand side is essential for product innovation in the last 3 year
Innovations from suppliers has been essential for product innovation in the last 3 year
Information from competitors/firms in the same sector has been essential to product innovation in the last 3 year Information from the academic/scientific community sphere has been essential to product innovations realised in the last 3 years
Inside-out innovation (IO) Spithoven et al. (2013): - Cooperation Lichtenthaler (2008): - Strategic alliances Koop Wein1 Wein2 Wein4 Weinic3 Weinic4 Weinic5
Innovation-related cooperation with other firms or public research establishments
Form of collaboration: collaborative research Form of collaboration: Contract research
Form of collaboration: Licensees/purchase of technologies from scientific institutions
Scientific collaboration partner: University for applied science Scientific collaboration partner: HGF-Centre
Scientific collaboration partner: Max-Planck-society
Moderating variables Name Meaning
Information technologies (IT) =
Invs*Invis
Invs Invis
Total gross investment/turnover IT investment/total gross investment
Firm size (FZ) Um
Bges
Turnover in millions of DM multiplied by a firms specific random number
Number of employees
Dependent variables Name Meaning
Innovation performance (IP) Umneu
Mneup
Proportion of total turnover from new or clearly improved products
Share of turnover from market novelties Open innovation Outside-in
innovation Inside-out innovation
See independent variables
See independent variables
Control variables Name Meaning
R&D intensity (RD) Fues Total R&D expenditure as a share of turnover Firm age (FA) Gruend Firm founded within the last 3 years Degree of internalization (DI) Exno No exports
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Explanation replacing variables
– All yes/no questions are substituted so that nein (no) = 0 and ja (yes) = 1
– The weinic1-12 variable is replaced by numbers assigned to each different answer such that: • Blank = 0 • Ausland = 1 • Berufsak = 2 • Bundesfo = 3 • Fachhoch = 4 • Fraunhof = 5 • HGF-Zent = 6 • Industri = 7 • Landesfo = 8 • Leibniz- = 9 • Max-Plan = 10 • Sonstige = 11 • Technisc = 12 • Universi = 13 • Verband, = 14
– The variable gk3n will be substituted such that:
• <50Besch = 1 • 50-249Besch = 2 • >=250Besch = 3
– Dependent variables are substituted by taking the median value so that: • 0<x<5 = 2 • 10<=x<15 = 12 • 15<=x<20 = 17 • 20<=x<30 = 24.5 • 30<=x<50 = 39.5 • 5<=x<10 = 7 • 50<=x<75 = 62 • 75<=x<=100 = 87.5 • X = 0 = 0
– The IT variable is made of the variables invis and invs. For IT the measure IT spending divided by sales is the desired one, for it gives ratio of IT spending’s compared to the total sales made. To make this variable we need both variables mentioned above. Invis is the IT spending divided by total investment. Invis is the total investment divided by sales. To get to spending divided by sales we have to multiply these to variables to rule out the total investment part. This leaves us with the IT spending divided by sales.
appendix
2 Assessing psychometric properties
Figure 1 PLS algorithm results: Indicator factor loadings and β-values
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3 Variance inflation factor test
results output
GET DATA /TYPE=XLSX
/FILE=’X:\My Desktop\Latent variable scores VIF.xlsx’ /SHEET=name ‘Blad1’
/CELLRANGE=full /READNAMES=on
/ASSUMEDSTRWIDTH=32767. EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT. REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Openinnovation
/METHOD=ENTER Insideout Outsidein.
Regression
NotesOutput Created 09-JUN-2015 11:27:54
Comments
Input Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data File 698
Missing Value Handling Definition of Missing User-defined missing values are treated as missing.
Cases Used Statistics are based on cases with no missing values for any variable used.
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL /CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Openinnovation /METHOD=ENTER Insideout Outsidein.
Resources Processor Time 00:00:00,05
Elapsed Time 00:00:00,01
Memory Required 3248 bytes Additional Memory Required for
Residual Plots
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Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 Outside-in, Inside-outb . Enter
a Dependent Variable: Open innovation b All requested variables entered
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 1,000a 1,000 1,000 ,0209184
a Predictors: (Constant), Outside-in, Inside-out
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 697,703 2 348,852 797227,620 ,000b
Residual ,304 695 ,000
Total 698,007 697
a Dependent Variable: Open innovation b Predictors: (Constant), Outside-in, Inside-out
Coefficientsa Model Unstandardized Coefficients Standardized Coefficients Collinearity Statistics
B Std. Error Beta t Sig. Tolerance VIF
1 (Constant) -2,604E-5 ,001 -,033 ,974
Inside-out ,725 ,001 ,725 768,321 ,000 ,704 1,421
Outside-in ,399 ,001 ,399 422,545 ,000 ,704 1,421
a Dependent Variable: Open innovation
Collinearity Diagnosticsa
Model Variance Proportions
Dimension Eigenvalue Condition Index (Constant) Inside-out Outside-in
1 1 1,544 1,000 ,00 ,23 ,23
2 1,000 1,243 1,00 ,00 ,00
3 ,456 1,841 ,00 ,77 ,77
appendix
4 Robustness test results
Figure 1 PLS algorithm results: Indicator factor loadings and β-values
