EXECUTIVE SUMMARY
This study provides an analysis of supplier protectiveness, learning intent, absorptive capacity and knowledge ambiguity on inter-firm knowledge transfer in NPD projects within the Dutch electric-technical industry. The main focus is to examine whether this theoretical framework is applicable in other environments.
Methods of analysis include a correlation matrix and a regression model to examine which effects show up and to test the significance of the relationships. To test these relationships, hypotheses were proposed.
Results of the data analyzed show that all hypotheses have a significant effect and it this is in line with existing academic literature. Furthermore, the results show strong relationships and show highly significant relationships.
This conclusion of this study is that all variables have significant influence on inter-firm knowledge transfer. Furthermore, the results show similarities with other academic studies and therefore the results are generalizable.
The study provide some practical recommendations. For example, the results show that absorptive capacity in terms of high educated personnel will lead to a higher inter-firm knowledge transfer.
Master Thesis MsC BA
Small Business & Entrepreneurship
University of Groningen
Faculty of Economics and Business
June 2015
What is the influence of supplier protectiveness, learning intent, absorptive capacity and
knowledge ambiguity on inter-firm knowledge transfer in NPD projects from a buyers
perspective?
ABSTRACT
The purpose of this study is to explore which factors determine the degree of inter-firm knowledge transfer within New Product Development (NPD) projects. This study tests a theoretical framework and explore the influence of supplier protectiveness, learning intent, knowledge ambiguity and absorptive capacity on the degree of inter-firm knowledge transfer within NPD projects.
Furthermore, a sample of 187 R&D intensive firms within the Dutch electric technical industry was used to test the theoretical framework. The data was gathered via Centraal Bureau Statistiek (CBS) and was analyzed by using SPSS.
The results indicate that supplier protectiveness will increase inter-firm knowledge transfer. Also knowledge ambiguity will lead to a higher intent of the buying firm to increase their R&D expenditures in order to achieve transferability of knowledge in a inter-firm relationship.
This study illustrates a supplier protectiveness could be a trigger for the buying firm to access the supplier’s knowledge. Furthermore, firms should be aware of the fact that high educated people will lead to a higher degree of inter-firm knowledge transfer within NPD projects.
This study contributes to existing literature because it tests a theoretical framework in a new environment. The results are interesting for future research. It examines the dynamics of knowledge transfer of knowledge transfer in NPD projects.
Keywords: Knowledge transfer, Product Development, NPD, inter-firm knowledge transfer, supplier involvement, buyer-supplier relationship.
TABLE OF CONTENTS
1 INTRODUCTION ... 6
1.1 RELEVANCE ... 6
1.2 SCOPE AND DOMAIN ... 7
1.3 RESEARCH QUESTION ... 7
1.4 OVERVIEW OF THE STUDY... 8
2 LITERATURE REVIEW ... 9
2.1 KNOWLEDGE TRANSFER IN NPD PROJECTS ... 10
2.2 LEARNING INTENT ... 10 2.3 SUPPLIER PROTECTIVENESS ... 10 2.4 KNOWLEDGE AMBIGUITY ... 11 2.5 ABSORPTIVE CAPACITY ... 11 3 THEORETICAL FRAMEWORK ... 13 3.1 HYPOTHESES ... 13 3.2 CONCEPTUAL MODEL ... 16 4 METHODOLOGY ... 17 4.1 STRATEGY ... 17 4.2 SAMPLE SELECTION ... 17 4.3 DATASET ... 18 4.3.1 DATA COLLECTION ... 18 4.4 VARIABLES ... 19 4.5 DATA ANALYSIS ... 20
4.6 RELIABILITY AND VALIDITY ... 20
5 RESULTS AND ANALYSES ... 22
5.1 CORRELATION MATRIX AND DESCRIPTIVE STATISTICS ... 22
5.2 REGRESSION ANALYSIS ... 23
6 DISCUSSION ... 27
7 CONCLUSION ... 30
7.1 INTRODUCTION ... 30
7.2 RESEARCH QUESTION AND MAIN FINDINGS ... 30
7.3 IMPLICATIONS FOR THEORY ... 33
7.4 IMPLICATIONS FOR PRACTICE ... 33
7.5 LIMITATIONS AND FUTURE RESEARCH ... 33
1 INTRODUCTION
Research in new product development (NPD) has shown that NPD goes beyond the boundaries of individual firms. Early and extensive involvement of suppliers in the design and development stages of NPD has been associated with improvement across a range of dimensions, including product quality, project costs and development cycle time (Carr et al, 2008). In recent years, the cooperation between suppliers and buying firms in NPD has evolved dramatically from a relationship, in which the supplier is an outside participant to one in which the supplier has become an involved partner (Appleyard, 2003; Cannon and Homburg, 2001; Colombo et al., 2011; Joshi, 2009; Paasi et al., 2010). Currently, suppliers are proactively joining with their buyers and participating in NPD projects and processes (Squire et al., 2009; van Echtelt et al., 2008; Wijnstra et al., 2010). The involvement of the supplier in NPD projects with the buying firm leads to lower development costs, improved quality and reduced cycle times (Simonin, 1999; Lau et al., 2010). Furthermore, scholars acknowledge that knowledge is a source of competitive advantage and highlight the importance of acquiring knowledge from suppliers in NPD projects in today’s volatile, complex and uncertain environment (Dyer and Hatch, 2004; Kang and Kang, 2009; Sherman et al., 2005).
NPD often relies upon close and frequent interactions with firms external to the firm and firms are prudent with the transfer of ‘sticky knowledge’ (von Hippel, 1994). This close interactions between firms indicate that suppliers are proactively joining with their customers and participating in NPD (van Echtelt et al, 2008).NPD is defined as an iterative process of gathering, creating and evaluating information for developing new, quality and defect-free products (Shankar et al., 2013).
A large body of empirical research suggest that one of the most frequent and important sources of new knowledge are a firm’s suppliers (Hult et al, 2006, Song et al, 2008). It is also argued that transferring knowledge from supplier to buyer and vice versa has become central to firm success (Lane et al. 2001).
From a knowledge based view (KBV), knowledge is a strategically important resource that provides firms with a sustainable competitive advantage due to its valuable, rare, non-substitutable, socially complex and heterogeneous nature (Grant, 1996). Also it is stated that firms which can successfully transfer and absorb knowledge across boundaries accumulate a range of performance benefits. These benefits includes a reduce of failure rates and an increased productivity (Cohen et al, 1990).
1.1 RELEVANCE
Lawson and Potter (2012). It was argued that it is was interesting to examine whether the theoretical framework and their results were representative. According to the relevance of this study, this study will contribute to existing literature. As said before, it will examine whether an existing theoretical framework holds in another environment. The results will indicate whether certain assumptions can be accepted or rejected.
1.2 SCOPE AND DOMAIN
According to the scope and domain, this study will examine the influence of inter-firm knowledge transfer in NPD projects from a buyers perspective in the Netherlands among R&D intensive firms in the electric-technical industry with only 10 employees or more. R&D intensive firms are firms which have frequent new product developments. Firm’s characteristics are an own R&D department and constantly ongoing R&D activities.
The four variables which are stated in the research question will be tested in only NPD projects. So within academic literature, much is said about inter-firm knowledge transfer. However, an important aspect of this study is that it is focused in only NPD projects. These NPD projects implies that two partners (supplier-buyer) develop new products or services. According to this, conclusions will be made only on these four variables which have an influence on inter-firm knowledge transfer. The aim of this research is to test each variable and what the influences are. If significant side-effect show up during this study, then these side effects will be taken into account. This study is restricted to influences, not for suggestions to improve inter-firm knowledge transfer.
1.3 RESEARCH QUESTION
The research question examines the influence of inter-firm knowledge transfer between firms from a buyers perspective. The constructs which are examined to answer the research question are inter-firm knowledge transfer (reliance supplier-buyer), learning intent, protectiveness, knowledge ambiguity and absorptive capacity. These constructs will be discussed in the literature review. The research question will test the variables and will generalize the variables whether this also account for firms in a buyer-supplier relationship. The variables will examine the main effects or influences in an inter-firm relationship in NPD projects.
The answer the research question will give a deeper insight what are the main influences of the variables in the Netherlands and examines which variables are significant in a buyer-supplier relationship. After this study, theory is confirmed or variables are added in order to clarify inter-firm knowledge transfer in NPD projects. Side effects could show up and these side effects will be taken into account in this study.
What is the influence of supplier protectiveness, learning intent, absorptive capacity and knowledge ambiguity on inter-firm knowledge transfer in NPD projects from a buyers perspective?
To answer the research question, the following sub questions need to be answered:
What are the common definitions of the variables in current academic literature? What is the current view towards inter-firm knowledge transfer in NPD projects?
These questions has to be answered before the research questions can be answered. The definitions and current view are described within the literature section.
1.4 OVERVIEW OF THE STUDY
2 LITERATURE REVIEW
A growing body of literature examines how firms learn and develop new products through collaboration with their key suppliers (Athaide et al., 2009; Dowlatshahi, 1998;Knudsen, 2007; Lawson et al.,2009; Petersen et al., 2005). The current view of knowledge transfer is that there is a growing recognition that many resources, and especially strategic resources and new sources of knowledge lie beyond traditional boundaries of a firm (Das and Teng, 2000). As a result, buying firms can draw on their supplier’s knowledge to improve product development activities. With collaborative relationships, it could help to overcome difficulties of transferring knowledge across inter-firm boundaries (Lawson et al., 2009). However, ineffective relationship management with potential buyers during NPD can be an important contributor to new product failure in technology-based industrial markets (Ahtaide et al, 2009). Academic literature suggest that there is evidence that firms which are able to transfer knowledge effectively are more likely to survive and generate a competitive advantage, compared to those firms that are less adept at knowledge transfer (Argote et al., 2000).
Nowadays, involving suppliers within NPD is becoming more and more important. Involving suppliers in NPD projects is a knowledge intensive process and the process of sharing knowledge between the supplier and buyer is critical to the success of the new product, the project as a whole and for the company himself (Lawson et al., 2009; Petersen et al., 2003). On the other hand, Inkpen and Tsang (2005) stated that while inter-firm knowledge transfer is important to the success of NPD, transferring this knowledge is difficult to achieve. Another reason is that when firms have a strong intention to learn new knowledge from the supplier, it may reduce the enthusiasm of the supplier to provide such knowledge (Easterby-Smith et al, 2008).Also it is expected that buying firms committed to direct involvement in supplier NPD provide more personal , face-to-face contact and interaction with their suppliers. This enables the buying firm to be successful in transferring tacit knowledge and accrue performance improvements as a result of their investments. These investments will dampen the knowledge ambiguity which is a crucial factor within inter-firm knowledge transfer. Buying firms that engage in direct involvement of supplier NPD to transfer tacit knowledge may include activities such as training of supplier’s or buyer’s personnel and a dedicated R&D team of the supplier (Krause et al., 2007).
2.1 KNOWLEDGE TRANSFER IN NPD PROJECTS
Knowledge transfer is seen as the extent to which NPD knowledge flows from the supplier to the buying firm. It refers to the movement of a body of the supplier’s knowledge, skills, ideas and experience to the buying firm (He et al., 2011; Knudsen, 2007).
2.2 LEARNING INTENT
Within existing literature, research on inter-firm knowledge transfer has identified several different determinants: Motivation of knowledge transfer, characteristics and absorptive capacity (Easterby-Smith et al., 2008; Grant, 1996; Szulanski, 1996). The motivation of knowledge transfer is often referred to the desire to learn from their suppliers knowledge and is a major motive for the buying firm (Handfield et al., 1999).This is also referred as learning intent and within academic literature it is stated that the learning intent of a firm is a key determinant of inter-firm learning. It is argued that the stronger the intention to learn, the higher the chance that knowledge will be transferred and the buyer firm will learn from the supplier’s technology (Inkpen, 2000; Tsang, 2002). A buyer’s learning intent is defined as the desire and will of the buying firm to learn new knowledge from its supplier (Lawson and Potter, 2012).
2.3 SUPPLIER PROTECTIVENESS
However, academic literature suggest that some partners are less open and transparent with their knowledge (Hamel, 1991; Simonin, 2004). A consequence of this is, that when firms have a strong learning intent from the supplier, this will reduce the enthusiasm of the supplier to provide their knowledge (Easterby-Smith et al., 2008). This phenomenon is also referred as the degree of supplier protectiveness (Simonin, 1999). Academic literature suggest that there are several types of protective mechanisms and can range from contractual obligations, patent protection and using informal shielding practices aimed at protecting the core competencies of a supplier (Inkpen and Beamish, 1997). In NPD projects, the extent of knowledge transfer depends on the willingness of the supplier to share their knowledge with the buying firm or other external organizations (Pisano, 1990).
2.4 KNOWLEDGE AMBIGUITY
According to the characteristics of knowledge, the ability of the buying firm to understand the factors that contribute to success of knowledge transfer is referred as causal ambiguity (Lippman et al., 1982). Causal ambiguity makes it hard for a firm to identify the knowledge which they wish to transfer from suppliers (Grant, 1996; Spender, 1996). Simonin (1999) defines causal ambiguity as the lack of understanding of the logical linkages between actions and outcomes, inputs and outputs, and causes and effect that are related to technological or process know-how. It is said that causal ambiguity has a central role within knowledge transfer between firms. Causal ambiguity hampers knowledge transfer between companies because causal ambiguity makes it harder for companies to identify the knowledge they wish to transfer from suppliers (Grant, 1996; Spender, 1996). Another aspect of knowledge ambiguity is that when a supplier’s competitive advantage is based on knowledge that in itself is causally ambiguous, it is often difficult for the buying company to receive inter-firm knowledge transfers (Reed and DeFillippi, 1990). However, knowledge ambiguity contributes to protect knowledge from being imitated but also hinders knowledge transfer within and between firms (Coff et al., 2006). The common view of knowledge ambiguity is that it is suggested to negatively affect organizational knowledge transfer (van Wijk et al., 2008). Further in this study, causel ambiguity will be seen as knowledge ambiguity.
2.5 ABSORPTIVE CAPACITY
The last determinant is absorptive capacity. In general, firms find it often hard to absorb new knowledge, especially knowledge from outside the firm (Lawson and Potter, 2012). In this research absorptive capacity is defined as the ability to value, assimilate and apply new knowledge and has a central role in determining the extent of knowledge transfer beyond the firms’ boundaries (Cohen and Levinthal, 1990). According to the current state of absorptive capacity within a buyer-supplier relationship, little research has focused on this aspect and especially when suppliers become involved in a firm’s NPD project (Ettlie et al., 2006).
Furthermore, He et al. (2011) supported the view that trust facilitates knowledge transfer in a buyer-supplier partnership.
To conclude, all the constructs are summed up in the table below. It gives an overview of the constructs, how the constructs are defined and which references are used.
Constructs Definition References
Supplier protectiveness Reduce the enthusiasm of the supplier to provide knowledge through protective mechanisms which can range from contractual obligations, patent protection and using informal shielding practices aimed at protecting the core competencies of a supplier.
Easterby-Smith et al., 2008
Inkpen and Beamish, 1997
Learning intent The desire and will of the buying firm to learn new knowledge from its supplier.
Lawson and Potter, 2012
Absorptive capacity The ability to value, assimilate and apply new knowledge.
Cohen and Levinthal, 1990
Knowledge ambiguity The lack of understanding of the logical linkages between actions and outcomes, inputs and outputs, and causes and effect that are related to technological or process know-how.
Simonin, 1999
Inter-firm knowledge transfer The movement of a body of the supplier’s knowledge, skills, ideas and experience to the buying firm.
3 THEORETICAL FRAMEWORK
Because the study of Lawson and Potter (2012) will be used as an input for this research, the hypotheses which are listed below in the hypotheses section are tested in another environment (The Netherlands). So with these hypotheses results become more meaningful and the outcomes will be much harder to claim to be statistically significant. This will automatically fill the literature gap which will be filled in by this research.
3.1 HYPOTHESES
When suppliers become more protective towards their the knowledge, the buying firm can adapt by developing strategies and practices that increase the transferability of (sticky) technological knowledge (Szulanski, 1996; von Hippel, 1994). Argote et al. (2003) states that of these practices one of the most influential is likely to be improving the firm’s absorptive capacity within the buyer-supplier relationship. For example: A buying firm can adapt practices, routines and orientation from their suppliers in order to achieve knowledge transfer (Lawson and Potter, 2012). This implies that supplier protectiveness acts as a trigger for buying firms to increase their absorptive capacity in assimilating the supplier’s technological knowledge. This leads to the following hypothesis:
H1.Higher levels of supplier protectiveness are positively related to the degree of inter-firm knowledge transfer in NPD projects.
H2.Buyer firm learning intent is positively related to the degree of inter-firmknowledge transfer in NPD projects.
Absorptive capacity is seen as one of the most central determinant of a firm’s ability to facilitate knowledge transfer. It is argued that absorptive capacity is positively related to the degree of knowledge transfer (Easterby-Smith et al., 2008; Van Wijk et al., 2008). It is expected that absorptive capacity will directly improve the degree of inter-firm knowledge transfer. Absorptive capacity helps identifying the desired knowledge which resides within the supplier, develops the common knowledge required to assimilate the supplier’s knowledge and apply this external knowledge within the NDP project (Cummings and Teng, 2003). Thus, this leads to the following hypothesis:
H3. Absorptive capacity is positively related to the degree of inter-firm knowledge transfer in NPD projects.
Knowledge ambiguity is one of the most effective means of ensuring knowledge is hard and difficult to transfer and imitate (Inkpen and Beamish, 1997). In existing academic literature it is argued that higher levels of knowledge ambiguity result in a decreased level of inter-firm knowledge transfer (Reed and DeFillippi, 1990; Wilcox-King and Zeithaml, 2001). An example which is taken by Simonin (1999) is knowledge which is technologically complex in nature. This knowledge is complex and is likely to be causally ambiguous from a buying firm’s point of view and thus makes it hard to transfer the knowledge. Little research has focused on the role of knowledge ambiguity and knowledge transfer in the context of inter-firm NPD projects. To conclude, it is expected that a buying firm finds it difficult to understand the cause-and-effect relationship among the supplier’s knowledge contribution to the project, whilst the likelihood of receiving and integrating knowledge transfers from a supplier’s perspective will decrease. This will lead to the following hypothesis:
Because finding the right partner is crucial within collaboration in NPD projects, it is interesting to examine the effects of this relationship between finding a partner and inter-firm knowledge transfer within NPD projects. Academic literature suggest that the harder it is for a buying firm to find a right partner, the higher the R&D expenditures are to achieve transferability of knowledge between the two partners. However, when the partnership is established, these R&D expenditures will decrease because of trust between the buyer and supplier. Sherwood and Covin (2008) underpin this outcome. They argue that when a partnership is established, R&D expenditures in terms of inter-firm knowledge transfer will decrease. This will lead to the following hypothesis:
H5. The degree of finding the right partner is positively related to the degree of inter-firm knowledge transfer in NPD projects.
To test multicollinearity, an interaction model is developed between supplier protectiveness and the other variables: learning intent, absorptive capacity, knowledge ambiguity and find partnership. The following hypotheses are defined and will be examined to test whether supplier protectiveness has an effect on the learning intent of the buyer:
H6. A higher level of supplier protectiveness will increase the degree of learning intent of the buyer. H7. A higher level of supplier protectiveness will increase the degree of absorptive capacity of the buyer.
3.2 CONCEPTUAL MODEL
The conceptual model (figure 1) is focusing on the influence of motivation of knowledge transfer (learning intent and supplier protectiveness), characteristics of knowledge (knowledge ambiguity) and absorptive capacity on inter-firm knowledge transfer in NPD projects. The model is controlled for firm size and is based on firm-level. Hypotheses are developed exploring the relationships between these factors and what influence these variables have on de dependent variable: Inter-firm knowledge transfer in NPD projects. The conceptual model in figure 1, the hypotheses are visually presented. As discussed within the literature section, all hypotheses are expected to be positive and thus have a positive influence (+) on inter-firm knowledge transfer in NPD projects.
FIGURE 1 CONCEPTUAL MODEL
4 METHODOLOGY
In this section the methodology of the study is elaborated. First the survey research strategy is explained and why this strategy is suitable in order to answer the research question. Second, the sample selection is described. When the sample selection is described, the dataset and data collection is elaborated. Furthermore, the variables are described and visually reported within a table. At last, the data analysis and reliability & validity are discussed.
4.1 STRATEGY
This study follows a quantitative research strategy. In order to test the hypotheses which are derived from academic literature, a dataset had to be made. For this study, an original dataset from the CBS was chosen because it covered all the variables which were need to be tested to answer the hypotheses and eventually the research question. The researcher was not able to acquire qualitative data because this was beyond time limit and scope.
4.2 SAMPLE SELECTION
ongoing R&D activities. Within the electric-technical industry, 94 % of the firms have constantly or incidentally ongoing in-house R&D activities.
4.3 DATASET
The dataset which is used for this study is originally from the CBS. Characteristics of this dataset are the innovativeness of firms and the size of the firms. The dataset only allows firms to participate if the firms have 10 employees or more. Furthermore, a lot of aspects were measured. It was necessary to scale down the dataset because it involved more than thirty industries and over 15000 firms. So the industry which was chosen was the electric-technical industry. The reason behind this was that 187 firms were participating and all the data which was needed to do this survey was available. There are no missing data point, so this is beneficial for the outcome of this study. The table below shows certain characteristics of the sample.
Within the sample, 186 NPD projects are successful completed. These projects were completed within a time-period of two years. Of these projects, 169 were product developments and 124 were service developments. This implies that firms can do products developments as well as service developments at the same time. The total R&D expenditures is 768 (measured in million ). Furthermore, within the questionnaire, firms were asked which partner was most valuable. It turned out to be that 23% valued the supplier as most valuable and 30% valued buying firms as most valuable. So overall, the data suggests that buyers are more important than suppliers within NPD projects. It is argued that buyers provide new knowledge new knowledge to the supplier and that this new knowledge is more important than from supplier to buyer. This is conflicting with the expectations of this study based on existing literature which claims that suppliers are one of the most frequent and important sources of new knowledge (Hult et al, 2006, Song et al, 2008).
SAMPLE CHARACTERISTICS
Description of characteristics Number In % of the sample NPD projects successful completed 186
NPD projects included goods 169 NPD projects included services 124
Most valuable partner was supplier 43 23%
Most valuable partner was buyer 56 30%
Number of innovators with patents 65 35%
Total R&D expenditures of the sample
768*
*Measured in million €
4.3.1 DATA COLLECTION
The existing data allows the researcher to measure any side effects. In existing literature it is often said that finding a strategic partner to start an NPD project, is hard to find. Within the analysis of the data, side effects will taken into account.
4.4 VARIABLES
This section describes the operationalization of the variables used in this study.
Supplier protectiveness is measured on the basis of which degree the supplier uses intentional policies to restrict knowledge transfer (Norman, 2002). This restriction will be measured on the basis of the application of patents and in which industry this application most often occurs. This is also referred as patent protection(Inkpen and Beamish, 1997).
Learning intent refers to the degree of acquiring external technological knowledge from the supplier (measured in €). So the firms are asked if they invest to acquire external knowledge from the supplier. Absorptive capacity is defined as the ability to value, assimilate and apply new knowledge and has a central role in determining the extent of knowledge transfer beyond the firms’ boundaries (Cohen and Levinthal, 1990). Furthermore, Cohen and Levinthal (1990) suggest that prior knowledge of the buying firm is key to understand external knowledge received from the supplier. This prior knowledge represents a set of learning or problem-solving capabilities but can differ in their learning outcome. The first will lead to the development of the capacity to assimilate existing knowledge, while the second one represents the capability to create new knowledge. Therefore, in this study the degree of qualified personnel within the firm will be taken into account to measure the absorptive capacity of the buying firms.
Knowledge ambiguity is defined as the lack of information about the technology of the partner/supplier. Knowledge ambiguity makes it hard for a firm to identify the knowledge which they wish to transfer from suppliers (Grant, 1996; Spender, 1996). Therefore, the variable is defined as the lack of information about the technology of the supplier.
Find partnership is defined as a type of collaboration in which two or more firms pool money, skills and other resources within NPD projects.
Inter-firm knowledge transfer is defined as the extent to which the buyer firm had captured their supplier’s technological know-how, reduced their technological reliance on the supplier and assimilated the supplier’s technology.
projects are defined as radical products and refers to fundamental and revolutionary changes in technology or products (Dewar and Dutton, 1986).
An overview of the variables is given in table 3 on the next page. This table describes the type of the variable (IV, DV, CV), definition, operationalization (how the variable is used and measured within the study), which references were useful and the scale of the variable (nominal, ordinal, interval or ratio).
4.5 DATA ANALYSIS
The data is analyzed by using a regression model and to see whether the hypotheses can be accepted or rejected. First of all the regression analysis will give a deeper insight whether the relationships between the independent variable and the dependent variable is positive or negative. This is measured by looking at the Beta-value. Furthermore, there will be looked at the significance of the relationship. If the significance is taken into account, much more can be said about the relationship and
4.6 RELIABILITY AND VALIDITY
According to the reliability of this study, the study reliable because although other instruments are used to measure the variables, there is a similarity with the outcomes of other studies using the same theoretical framework. So it is expected that the results of the study are reliable because the results are independent of other particular studies and therefore can be replicated in other studies and other environments. So the strategy which is used to determine the reliability is to repeat the theoretical framework which was used in the study of Lawson and Potter. Also more measurements were done in order to test the theoretical framework. This strategy will also increase the reliability of this study. Regarding to the validity of the study, the study is assumed to be valid. There is a clear explanation about the relationship between the study’s results and the way it has been generated.
Variable Type Definition Operationalization References Scale Supplier
Protectivene ss
IV Reduce the enthusiasm of the supplier to provide knowledge through protective mechanisms which can range from contractual obligations, patent protection and using informal shielding practices aimed at protecting the core competencies of a supplier.
Which degree the supplier uses intentional policies to restrict knowledge transfer. This restriction will be measured on the basis of the application of patents and in which industry this application most often occurs. This is also referred as patent protection. Norman, 2002 Inkpen and Beamish, 1997 Interval Learning Intent
IV The desire and will of the buying firm to learn new knowledge from its supplier.
Degree of acquiring external technological knowledge from the supplier Lawson and Potter, 2012 Interval Absorptive Capacity
IV The ability to value, assimilate and apply new knowledge.
A set of learning or problem-solving capabilities which can lead to the development of the capacity to assimilate existing knowledge, while the second one represents the capability to create new knowledge Cohen and Levinthal, 1990 Interval Knowledge Ambiguity
IV The lack of understanding of the logical linkages between actions and outcomes, inputs and outputs, and causes and effect that are related to technological or process know-how.
The lack of information about the technology of the partner/supplier. Knowledge ambiguity makes it hard for a firm to identify the
knowledge which they wish to transfer from suppliers
Simonin, 1999 Interval
Find partnership
IV Type of collaboration in which two or more firms pool money, skills and other resources.
Finding a partnership for buying firms to collaborate in NPD projects. Sherwood and Covin, 2008 Interval Inter-Firm knowledge transfer
DV The movement of a body of the supplier’s
knowledge, skills, ideas and experience to the buying firm.
R&D expenditures He et al., 2011 Interval
Size CV Size of the firm measured in number of employees.
Size of the firm measured in number of employees.
Lawson and Potter, 2012
Interval
5 RESULTS AND ANALYSES
In this section the results and the analyses of the data are presented. First, an overview is given of the variables which are used in this studied through the descriptive statistics. Second, a correlation matrix is presented in order to show whether the variables correlate with each other and whether the mutual relationships between the variables are significant or not.
5.1 CORRELATION MATRIX AND DESCRIPTIVE STATISTICS
The correlation matrix presents if the variables do have a correlation with each other. For example, it means that when a supplier’s protectiveness increases, the learning intent of the buying firm increase either.
First of all, the correlation coefficient is taken into account. All relationships show a positive value, which means there is a positive relationship. The level of significance show that almost every variable has a significant value. However, learning intent (IV) shows a weak relationship with inter-firm knowledge transfer in NPD projects in terms of R&D expenditures (DV). So there is not enough evidence to conclude the relationship between learning intent and the DV exists within the sample. This implies that this occurs by chance, and thus statistically insignificant. All other variables show a significant value lower P=0.05 and therefore it could be concluded that there is enough evidence to suggest that the correlation we observed does exist within the sample. Furthermore, the N=187 suggest that there are no missing values. This will increase the reliability of the sample.
M ean S.D. 1. 2. 3. 4. 5. 6. 2. Supplier protectiveness 0,3476 0,47748 0,749** 0,942** 0,514** 0,496** 3. Learning intent 0,2299 0,42193 0,795** 0,385** 0,371** 4. Absorptive capacity 0,3209 0,46806 0,484** 0,467** 5. Knowledge ambiguity 0,6684 0,47203 0,964** 6. Find partnership 0,6845 0,46597 *Correlation is significant at the 0,05 level (2-tailed) ** Correlation is significant at the 0,01 level (2-tailed)
0,23593
Variable
1. Inter-firm knowledge
transfer in NPD projects 0,9412 0,182* 0,137 0,172* 0,355** 0,368** TABLE 1 CORRELATION MATRIX AND DESCRIPTIVE STATISTICS
5.2 REGRESSION ANALYSIS
In this section an regression analyses is executed in order to examine if the independent variables influence the dependent variable, which are presented in the conceptual model. It is examined if the relationships are positive or negative and if the relationships show a significant value or not. Based on these results, the hypotheses will be accepted or rejected.
Table 2 presents the outcomes of the regression analysis of the dependent variable with the independent variables:
Model 3 represents the interaction model of supplier protectiveness with other independent variables
Control Firm size Independent Supplier protectiveness Learning intent Absorptive capacity Knowledge ambiguity Find partnership R R square Adjusted R square F N
Standardized regression coefficients are reported. * p < .05
** p < .01 *** p < .001
Variables
Results of Regression Analyses for inter-firm knowledge transfer in NPD projects as Dependent Variable from a buyer perspective
-0,065 -0,19 0,191 0,182* 0,137 0,749*** 0,172* 0,942*** 0,355*** 0,514*** 0,368*** 0,496*** 0,065 0,41 0,944 0,004 0,168 0,891 -0,001 0,141 0,889 0,796 6,074 372,557 187 187 187
Standardized regression coefficients are reported.
Model 1 Model 2
Control variable firm size Independent variables
Results of Regression Analyses for inter-firm knowledge transfer in NPD projects as Dependent Variable from a buyer perspective
Following prior research, the hypotheses are tested with an regression analysis. The control variable for this study is firm size. There was a second control variable for this study, but because a certain industry was chosen, the type of industry do not influence the overall model at all. So this variable was not taken into consideration. It turned out to be that firm size do not have an effect within the model of the study. The regression analysis show a negative Beta value for firm size, which means that firm size do not influence the results significantly.
H1 examines whether supplier protectiveness has an effect on inter-firm knowledge transfer in NPD projects. Supplier protectiveness is measured on the basis of the application of patents. Inkpen and Beamish (1997) referred to this phenomenon as patent protection. It is expected that a higher supplier protectiveness will increase the absorptive capacity of the buyer and therefore will increase the inter-firm knowledge transfer between buyer and supplier within NPD projects. Table 1 indicates that the model correlates with 0.182 with the dependent variable. The results suggest that supplier protectiveness was positively related to inter-firm knowledge transfer within NPD projects. This means that supplier protectiveness has an influence on inter-firm knowledge transfer in NPD projects measured in R&D expenditures. The Beta shows a positive value and this indicates that there is a positive relationship between supplier protectiveness and inter-firm knowledge transfer in NPD projects.
H2 argues that buyer firm learning intent is positively related to the degree of inter-firm knowledge transfer in NPD projects. First of all, table 1 indicates that the model correlates with 0.137 with the dependent variable inter-firm knowledge transfer within NPD projects. Table 2 indicates that the model is not significant because p > 0.05.This indicates that the learning intent does not explain the degree of inter-firm knowledge transfer within NPD projects.
H3 examines whether absorptive capacity is positively related to the degree of inter-firm knowledge transfer in NPD projects. Table 1 indicates that absorptive capacity correlates with 0.172 with the dependent variable inter-firm knowledge transfer within NPD projects. Table 2 indicates that the model is significant because p < 0.05.This indicates that higher educated personnel does explain the degree of inter-firm knowledge transfer within NPD projects in terms of R&D expenditures. The Beta shows a positive value and this indicates that there is a positive relationship between the independent and dependent variable. This means that higher educated personnel will lead to higher inter-firm knowledge transfer within NPD projects.
knowledge ambiguity will lead to a higher degree of inter-firm knowledge transfer within NPD projects. This is not in line with the fourth hypothesis.
At last, one side effect which is often discussed within academic literature is trouble of finding the right partner and establish a partnership and is examined by hypothesis 5. The data allows us to examine this effect within the electric-technical industry in the Netherlands. Table 1 indicates that finding a partner from a buyer’s perspective correlates with 0.368 with the dependent variable inter-firm knowledge transfer within NPD projects. This indicates that the model is significant because p < 0.05.This indicates that the degree of finding the right partner does explain the degree of inter-firm knowledge transfer within NPD projects in terms of R&D expenditures. The Beta shows a positive value and this indicates that there is a positive relationship between the independent and dependent variable.
To test multicollinearity, an interaction model is tested between supplier protectiveness and the other variables. This is tested with H6 (B=0.749;p<0.001), H7 (B=0.942;p<0.001), H8 (B=0.514;p<0.001) and H9 (B=0.496;p<0.001). All hypotheses were tested positively, which means that the interaction model is confirmed, This implies that a higher supplier protectiveness will trigger the buying firm to increase their absorptive capacity and therefore the R&D expenditures will increase to establish inter-firm knowledge transfer within NPD projects. Explicitly absorptive capacity show a high correlation with supplier protectiveness. Furthermore, the R square shows a value of 0.889. This implies that the model is extremely relevant (88,9%) and explains that supplier protectiveness has a high influence on the other independent variables. The control variable firm size has a positive Beta value and is significant and means that firm size matters within this model.
Hypotheses Accepted Rejected H1. Higher levels of supplier protectiveness are positively related to the degree of
inter-firm knowledge transfer in NPD projects.
X
H2. Buyer firm learning intent is positively related to the degree of inter-firm knowledge transfer in NPD projects.
X
H3.Absorptive capacity is positively related to the degree of inter-firm knowledge transfer in NPD projects.
X
H4.The degree of knowledge ambiguity is negatively related to the degree of inter-firm knowledge transfer in NPD projects.
X
H5.The degree of finding the right partner is positively related to the degree of inter-firm knowledge transfer in NPD projects.
X
H6.A higher level of supplier protectiveness will increase the degree of learning intent of the buyer.
X
H7.A higher level of supplier protectiveness will increase the degree of absorptive capacity of the buyer.
X
H8.A higher level of supplier protectiveness will increase the degree of knowledge ambiguity.
X
H9.A higher level of supplier protectiveness will increase the trouble of finding the right partner.
X
Does Firm Size influence the degree of inter-firm knowledge transfer in NPD projects?
6 DISCUSSION
This study has examined the influence of supplier protectiveness, learning intent, absorptive capacity and knowledge ambiguity on the level of inter-firm knowledge transfer in NPD projects. The first hypothesis of this study suggest that there is a positive relationship between supplier protectiveness and inter-firm knowledge transfer in NPD projects. This implies that when a supplier is more protective, the buyer on the other hand will increase their R&D expenditures in order to have a transferability of inter-firm knowledge transfer. This increase of R&D expenditures indicates that there is a willingness of the buying firm to learn from the supplier. This is tested with H6 and confirms that when a supplier becomes more protective, the higher the learning intent of the buyer will be (B=0.749;p<0.001). When a buying firm wants to acquire the supplier’s knowledge about a certain technology, the supplier would respond by becoming protective of their technological know-how. Regarding this protective behavior, supplier implement defensive procedures in order to protect their knowledge from being imitated. These defensive procedures should ensure that it will reduce the opportunistic behavior of the buying firm (Williamson, 1975). On the other hand, this protective behavior of the supplier can act as a trigger to increase the R&D expenditures of the buying firm in order to create a higher level of inter-firm knowledge transfer. Furthermore, Lawon and Potter (2012) suggest a higher level of supplier protectiveness will lead to an increase of the absorptive capacity. To test this suggestion, H7 examined the interaction effect of supplier protectiveness on absorptive capacity. This suggestion is positively tested and the suggestion of Lawson and Potter (2012) is therefore confirmed.
H2 proposed that a buyers learning intent is positively related to the degree of inter-firm knowledge transfer in NPD projects. Knowledge is recognized as an important component of success in NPD projects, it is the buyer’s firm intention to learn which determines whether that knowledge is transferred (Pèrez-Nordtvedt et al., 2008;Tsang, 2002). Furthermore, to acquire knowledge from beyond the boundaries of the firm, the buying firm require commitment of resources, time and effort (Pucik, 1988). This implies that the learning intention in dependent of a number of factors and these factors need to be aligned, otherwise the willingness to learn from the supplier will not be successful. Learning intention do not only explain the willingness to learn of a firm. A greater learning intent focuses the firm’s strategies, practices and heightens the importance of learning new external knowledge as a strategic objective (MacInnis et al. 1991). So a greater learning intention will lead to an increase of inter-firm knowledge transfer, but will also define the firm’s strategy with regard to acquire external knowledge as a strategic objective.
higher willingness to learn from the supplier, the higher the degree of inter-firm knowledge transfer in terms of R&D expenditures. Because the model is not significant, this conclusion cannot be made.
H3 proposed that absorptive capacity is positively related to the degree of inter-firm knowledge transfer in NPD projects. Academic literature suggest that absorptive capacity helps identifying the desired knowledge which resides within the supplier, develops the common knowledge required to assimilate the supplier’s knowledge and apply this external knowledge within the NDP project (Cummings and Teng, 2003). Furthermore, academic literature underpin that absorptive capacity is associated with increased knowledge transfer from supplier to buyer within NPD projects. There are a large number of empirical studies that have found absorptive capacity to be positively related to inter-firm knowledge transfer, lower transfer costs and a faster speed of knowledge transfer (Cohen and Levinthal, 1990, Van Wijk et al., 2008). This study confirms that a positive relationship exist, but cannot confirm that a faster speed of knowledge transfer exist.
H4 proposed that knowledge ambiguity is negatively related to the degree of inter-firm knowledge transfer in NPD projects. Academic literature suggest that higher levels of knowledge ambiguity result in a decreased level of inter-firm knowledge transfer (Reed and DeFillippi, 1990; Wilcox-King and Zeithaml, 2001). So the results show the opposite of what is described within academic literature. First of all, causal (or knowledge) ambiguity makes it hard for a firm to identify the knowledge which they wish to transfer from suppliers (Grant, 1996; Spender, 1996). Simonin (1999) defines causal ambiguity as the lack of understanding of the logical linkages between actions and outcomes, inputs and outputs, and causes and effect that are related to technological or process know-how. Another aspect of knowledge ambiguity is that when a supplier’s competitive advantage is based on knowledge that in itself is causally ambiguous, it is often difficult for the buying company to receive inter-firm knowledge transfers (Reed and DeFillippi, 1990). The results show support for this interpretation under hypothesis 8 (B=0.514;P<0.001). Because when a supplier becomes protective towards their knowledge, a buying firm will increase in their R&D expenditures in order to achieve transferability of knowledge.
decrease. Sherwood and Covin (2008) found that trust reduces the cost and time of knowledge transfer. So trust is crucial between the buyer and supplier from a cost perspective. Trust was not measured within this study, but the data suggest that partnerships are already established. However, the data allowed the researcher to examine whether supplier and buyer had problems in order to establish this relationship. It turned out to be that buying firms as well as suppliers did have problems. Because this study measured inter-firm knowledge transfer in terms of R&D expenditures, this study indicates that the harder it was to find a partner, the higher the R&D expenditures were in order to find a partner. It could be said that in this stage there was no trust between buyer and supplier. When a partnership was set up, the level of trust rose and the R&D expenditures decreased. So again, this is in line with the conclusion of Sherwood and Covin (2008).
7 CONCLUSION
This conclusion section will discuss the outcomes of this study. First a small introduction is given in order to sum up the outcomes. Second, the research questions are answered and the main findings are presented in a descriptive manner. Furthermore, implications for theory are given and implications for practice are given.
7.1 INTRODUCTION
This conclusion is based on the results of this study and these results are compared with the outcomes of the study of Lawson and Potter (2012). Furthermore, the hypotheses which are tested and are accepted are compared to findings in academic literature. So the results of this study are compared with evidence and conclusion from other academic studies. But the main conclusion is that the theoretical framework of Lawson and Potter and the hypotheses are significant and the results prove that this study does have the same results in another environment. This implies that all the relationships within the conceptual model are proven to be positive (Beta value).
7.2 RESEARCH QUESTION AND MAIN FINDINGS
The research question of this study was as follows:
What is the influence of supplier protectiveness, learning intent, absorptive capacity and knowledge ambiguity on inter-firm knowledge transfer in NPD projects from a buyers perspective? In order to answer the research question as complete as possible, the main findings of this study will be presented in the sections below. This study provides a deeper insight in which factors influences inter firm knowledge transfer between firms in NPD projects in terms of R&D expenditures. The results are only applicable in the Netherlands and it could be compared with other countries within the same industry based on the variables which are tested.
Regarding to learning intent, results show a significant relationship. But the model is insignificant. If we take prior research into account, this relationship is considered as positive. Lawson and Potter (2012) stated that ‘’a strong intention to learn external knowledge represents the first step towards removing organizational barriers that hinder the transfer of knowledge across organizational boundaries’’.
A buyer’s learning intent is defined as the desire and will of the buying firm to learn new knowledge from its supplier (Lawson and Potter, 2012). Furthermore, knowledge is seen as an important component of success in NPD. But more important is the buyer’s firm intention to learn which determines knowledge is transferred or not (Pèrez-Nordtvedt et al., 2008;Tsang, 2002). Besides these findings within the literature, it is argued that the stronger the intention to learn, the higher the chance that knowledge will be transferred and therefore the buyer will learn from the supplier (Inkpen,2000; Tsang, 2002). If these findings are compared with the results of this study, similarities show up. The stronger the buyer’s learning intent, the more increasing R&D expenditures to transfer knowledge between buyer and supplier and this ultimately will lead to a higher transferability of knowledge. It can be concluded that although the model of learning intent is insignificant, it is supposed that there is a positive relationship between learning intent and inter-firm knowledge transfer within NPD projects.
Absorptive capacity is positively related to inter-firm knowledge transfer within NPD projects. This is in line with the expectations of this study, which is based on existing academic literature. Absorptive capacity was measured in terms of higher educated personnel within the buying firm. This study showed that the higher personnel of a buyer firm is educated, the higher the degree of knowledge transfer. Hence, a plausible explanation is that higher educated personnel have more skills and qualifications. This will lead to transfer new external knowledge into competences for the buying firm. Another logical explanation is that, a greater absorptive capacity in terms of higher skilled personnel, will lead to higher R&D expenditures. These explanations are confirmed with the results showed in this study. Furthermore, this study has proven (H7) that when a supplier acts protective towards their knowledge, this will act as a trigger for the buying firm to increase their absorptive capacity in order to achieve inter-firm knowledge transfer.
expenditures, it could explain the positive relationship. A plausible explanation would be that when knowledge is hard to understand for the buying firm, the buying firm will increase their R&D expenditures in order to create the transferability of knowledge between buyer and supplier. If we take supplier protectiveness into account, another logical explanation could be made. Because knowledge ambiguity contributes to protect knowledge from being imitated but also hinders knowledge transfer within and between firms (Coff et al., 2006), a buying firm will invest more in their R&D activities in order to understand the external knowledge gained from the supplier. This is confirmed by hypothesis 8 (B=0.514;p<0.001) which argues that a higher supplier protectiveness hinders knowledge transfer and the knowledge which needs to be transferred is ambiguous and therefore hinders inter-firm knowledge transfer. This study show also a positive relationship between supplier protectiveness and inter-firm knowledge transfer within NPD projects. Another example could be that when the knowledge which in itself ambiguous, the buying firm will invest more in order to understand the knowledge from the supplier. In response to this, the supplier will become more protective towards their knowledge and will lower the chance of transferring this knowledge. In conclusion, the results of both variables strengthen each other. As said before, this is in conflict with existing literature. An important issue to address to this conflicting result is the use of a different definition of the variable of inter-firm knowledge transfer within NPD projects. Because this is defined in R&D expenditures, a buying firm will put more effort in understanding the knowledge which the buying firm wants to gain from their supplier. This will lead to an increase of R&D expenditures for the buying firm because they have to invest more time and effort.
Another variable which was taken into account in this study is the fact that firms find it hard to find the right partner. The data allows the researcher to examine this effect (H5). Because academic literature has many different views on this aspect, it would be interesting to see whether buying firm’s find it hard to find a partner. The results show a positive relationship (B=0.368;p<0.01) between finding a partnership and inter-firm knowledge transfer in terms of R&D expenditures. A logical explanation for this relationship is that the harder it is for the buying firm to find a partner, the higher the R&D expenditures are for the transferability of knowledge. Another explanation could be the fact that when a buyer don’t have a partner, the buying firm needs to acquire external knowledge in order to develop new products. Hypothesis 9 proves that when a supplier acts protective towards their knowledge, it is hard for the buying firm to set up a collaboration with this supplier. So the buying firm has more trouble for finding a partnership (B=0.496;p<0.001). However, when a partnership is established it is argued that the R&D expenditures will decrease (Sherwood and Covin, 2008).
be seen as exogenous. Because model 2 shows a relevancy of 14,1% and the interaction model shows a relevancy of 88,9%, it can be concluded that the variables are not independent of each other. This is confirmed with existing literature and earlier discussed within the discussion section. The most important interaction effect is supplier protectiveness related to absorptive capacity (B=0.942;p<0.001). This implies that the more protective a supplier is towards their knowledge, the more the buying firm will invest in their absorptive capacity in order to achieve inter-firm knowledge transfer. This suggestion out of academic literature is proven by this study.
7.3 IMPLICATIONS FOR THEORY
The theoretical conclusion of this study could add value to existing literature. As an example, this study examines absorptive capacity in terms of high educated personnel. This study proves that high educated personnel contributes to inter-firm knowledge transfer in NPD projects. However, because inter-firm knowledge transfer is defined in terms of R&D expenditures, cost factors play an important role. Therefore, managers can achieve a higher degree of inter-firm knowledge transfer, but at the same time cost factors will increase.
7.4 IMPLICATIONS FOR PRACTICE
A number of managerial implications are apparent for this study. First of all, it is suggested that a buying firm has the willingness to invest in their R&D in order to build up a relationship with the supplier in order to cooperate in NPD. But when a supplier is too protective according to their knowledge, there is a chance that the buying firm will seek for another partner. When a partnership is established, it will be beneficial for the supplier as well as the buyer. A certain level of trust arises and this will decrease the cost of working together and transferring knowledge within NPD projects. On the other hand, it is suggested that the buying firm will invest more in their R&D in order to understand the technology of the supplier. So when a supplier becomes more protective, the buying firm will increase their R&D expenditures in order to achieve inter-firm knowledge transfer. Managers should keep in mind that trust plays a central role and establish a partnership which is beneficial for both parties. Second, the results show that higher educated personnel increase the absorptive capacity of a firm. This indicates that in order to create an environment where there is inter-firm knowledge transfer, managers do need to have qualified personnel. A logical explanation would be that managers have to train their personnel and collaborate with for instance universities in order to increase the buyer firm’s absorptive capacity.
7.5 LIMITATIONS AND FUTURE RESEARCH
study makes assumptions based on data which is gathered in two years from 2008 to 2010. Longitudinal methods would enable greater examination of the process of inter-firm knowledge transfer in NPD projects. Third, the operationalization of the variables which are used in this study. The variables are specifically defined. This means that the scope of this study is limited. It would be interesting to combine existing quantitative data with qualitative data to get a more deeper understanding of inter-firm knowledge transfer. Fourth, the study has only focused on knowledge transfer from a buyer’s perspective. It would be interesting to examine the supplier’s perspective with regard to their protectiveness of knowledge which is essential for the supplier’s competitive advantage. Fourth, the study makes use of specific operationalization of the variables. Inter-firm knowledge transfer within NPD projects is measured in terms of R&D expenditures. Prior research has a deeper and more qualitative operationalization of variables. This indicates that there is a more deeper understanding and more detailed reasons are given. As mentioned before and in order to improve this study, qualitative research can be done in order to improve this study. For example, interviews can be held with the firms within the Dutch electric-technical industry. It would be interesting for further research to examine the effects between de independent variables. Only the interaction model of supplier protectiveness and the other independent variables are tested. It would be interesting to look whether learning intent has an effect on absorptive capacity. For example, the influence of highly educated personnel, can increase the learning intent of the buying firm. A logical explanation for this would be that the more educated personnel a firm has, the more external knowledge can be absorbed, and therefore there will be a higher learning intent of the buying firm. The relationship between knowledge ambiguity and find partnership is also interesting. Because of time restrictions and the scope of this research, the researcher was not able to measure all interactions effects.
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