Innovation Performance in Buyer-Supplier Relationships:
The Role of Inter-Firm Absorptive Capacity
Alwin R. Akkerman
a, b, c, daUniversity of Groningen, Faculty of Economics and Business, P.O. Box 800, 9700 AV, Groningen, The
Netherlands.
bMSc Marketing Master Thesis, 8th of January 2016.
cKoningin Wilhelminaplein 194, 1062 KR, Amsterdam, The Netherlands. Tel: + 31 (0) 6 12 45 44 33,
e-mail: alwinakkerman@gmail.com, student number: s1906569.
dFirst supervisor: H. J. Berger, tel. +31 (0) 50 363 3249, e-mail: j.berger@rug.nl.
MANAGEMENT SUMMARY
With its aim to advance the understanding of inter-firm absorptive capacity (ACAP) and its effect on innovation, this study takes the perspective of the knowledge-based view (KBV) and investigates in what way and to what extent inter-firm ACAP influences innovation performance in buyer-supplier relationships.
Literature on mainly ACAP, inter-firm relationships, knowledge-based theory, and innovation are reviewed and combined to create its theoretical framework. This study deliberately takes the multidimensionality of the ACAP construct into account and distinguishes between a relationship’s potential- and realized absorptive capacity. Its empirical research makes use of a large dyadic data set of 166 matched-pair buyer-supplier relationships obtained from key informants from both sides of the
relationship. The method used for its statistical analysis is Partial Least Squares Structural Equation Modeling (PLS-SEM).
Empirical results underpin the necessity of conceiving ACAP as a multidimensional construct, since its underlying dimensions show a contrasting impact on the positive effects of R&D expenditures’ and complementarity’s effect on innovation
performance. Furthermore, findings reveal substantial differences between buyer and supplier’s perspectives on multiple dimensions. From a KBV perspective, the results are discussed, and scientific and managerial implications for decision-makers
operating a buyer-supplier relation are provided, after which this study provides interesting directions for future research.
PREFACE
My master thesis deals with the complex subject of inter-firm absorptive capacity. Within buyer-supplier relationships, I wanted to shed light and provide interesting insights in absorptive capacity’s relation to innovation. I hope that the readers of this paper can follow my train of thought, even without prior knowledge on this subject. I want to thank you, pa en ma, for supporting me through thick and thin. I would certainly not be where I am now without your unconditional help and love.
To my brother and friends; without you, I would have handed in my thesis two and a half years ago. Thank you guys.
For the fallen. They shall grow not old, as we that are left grow old. Age shall not weary them, nor the years condemn. At the going down of the sun and in the morning, we will remember them.
1.
INTRODUCTION
The past two decades it has become widely evident that knowledge lies at the basis of the creation and preservation of competitive advantage. Especially the ability of firms to innovate has become increasingly important as firms with high innovative performance often show to have higher profitability, greater market value, superior credit ratings, and higher survival probabilities (Grant, 1996; Volberda, Foss, and Lyles, 2010). Current models of innovation show that firms increasingly adopt strategies that make use of external sources of knowledge in order to achieve and sustain innovation (Laursen and Salter, 2006). Knowledge from outside the boundaries of an organization can prove to be valuable sources of competitive advantage, and the ability to exploit externally acquired knowledge is often critical to a firm’s innovative performance (Cohen and Levinthal, 1990; Zahra and George, 2002; Lin et al., 2012). Hence, the ability of firms to absorb external knowledge, known as absorptive capacity (ACAP), has become increasingly important.
The aim of this study is to advance the understanding of inter-firm ACAP and its effect on innovation. Zahra and George (2002) define ACAP as “a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability”. These four capabilities play different but complementary roles and are divided in potential ACAP (PACAP), and realized ACAP (RACAP). PACAP makes a firm open to acquiring and assimilating external knowledge and corresponds to explorative learning, whereas RACAP comprises the transformation and exploitation activities of external knowledge and corresponds to exploitative learning (Berger, 2015).
Veugelers, 2006). This study will examine whether ACAP moderates these direct relationships.
The contribution of this paper is threefold. First, this paper extends and builds upon the study of Berger (2015) by taking into consideration the multidimensionality of ACAP, as proposed by Zahra and George (2002), in an inter-firm setting. So far, apart from Berger (2015), empirical research on an inter-firm level did not measure the construct of ACAP (Jap, 1999; Lane, Salk, and Lyles, 2001), or measured only a part of the multidimensional construct (Selnes and Sallis, 2003; Johnson, Sohi, and Grewal, 2004). Jansen, Van den Bosch, and Volberda (2005) did take into account the multidimensionality ACAP, however, on an intra-firm level.
Second, this study takes the perspective of the KBV as a framework to explain and predict inter-firm relationships relating to ACAP. The KBV focuses on knowledge as the most valuable resource of an organization and as a key determinant of sustained competitive advantage (Grant, 1996). Furthermore, it considers the importance of external knowledge within the firm’s environment and conceptualizes it as a resource that can be acquired, transferred, and integrated (Judge et al., 2015). Firms that collaborate increasingly focus on the integration of knowledge resources and knowledge creation for sustained competitive advantage, and knowledge management is destined to become a critical activity in supply chain interactions (Malhotra, Gosain, and El Sawy, 2005). Because ACAP is crucial in developing and increasing a firm’s knowledge base, the perspective of the KBV seems justified (Volberda et al., 2010).
supplier, and of attributes that are closely related to the identity of their own firm, different perceptions are formed.
This paper is structured as follows: first, in the theoretical framework the KBV perspective and relevant concepts of this research are defined and further elaborated upon using current literature. Furthermore, hypotheses are formulated, and the conceptual model is explained. Second, the method section describes the nature of the dyadic buyer-supplier data gathered by Berger (2015) and its measures, after which the statistical procedure adopted for this study is explained. Third, the results section comprises the measurement model, in which validity and reliability analyses are conducted, and the structural model, which estimates the relationships between the variables. Fourth, the discussion summarizes the results of the statistical tests and provides scientific and managerial implications, subsequently the limitations of this research are addressed and implications for future research are given.
2.
THEORETICAL FRAMEWORK
performance is explained. Finally, with its goal to act as a visual guideline for this study, a conceptual model of this research is presented.
2.1. Knowledge-Based View
This study frames its research using the knowledge-based theory of the firm; the knowledge-based view (KBV). The KBV focuses on knowledge as the most valuable resource of an organization and as a determinant of sustained competitive advantage (Grant, 1996). The KBV perspective can be especially fruitful because of its compatibility with the resource-based view (RBV). “To the extent that it focuses upon knowledge as the most strategically important of the firm's resources, the KBV is an outgrowth of the RBV (Grant, 1996).” The RBV of the firm sees an organization as a cluster of idiosyncratic resources and capabilities and optimal deployment of these resources is critical to sustainable competitive advantage (Grant, 1996). Where the RBV sees knowledge as one of many organizational resources, the KBV sees knowledge as the most strategically significant.
According to the KBV, a firm’s overall state of knowledge can vary, and firms with more extensive knowledge have an advantageous position to perform in a knowledge-based global economy (Grant, 1996; Judge et al., 2015). Because the KBV is a theory of the firm, its primary focus is at knowledge and learning within the boundaries of the firm. However, the KBV also considers the importance of external knowledge within the firm’s environment and conceptualizes it as a resource that can be acquired, transferred, and integrated (Judge et al., 2015). Firms that collaborate in a supply-chain increasingly focus on the integration of these external knowledge resources in order to achieve and sustain innovation, and thus, competitive advantage (Malhotra et al., 2005; Laursen and Salter, 2006).
2.2. Innovation Performance
network resources. Network resources are assets either described as social capital, or network capital, both facilitating knowledge flows across partnering firms. According to network theory, certain network positions give advantageous access to knowledge flows (Greve, 2009).
Innovation and knowledge seem to be two inseparable concepts. Ahuja and Katila (2001) put forward that the innovativeness of an organization is an outcome of an expansion of its knowledge base. Where organizations can increase their own knowledge base through a series of investments in knowledge over time, these organizations can also increase their knowledge base through the acquisition of external knowledge bases (Ahuja and Katila, 2001). Cohen and Levinthal (1990) state that these sources of knowledge outside of the firm’s boundaries are key to the innovation process. Furthermore, the ability of firms to internalize externally acquired knowledge can influence the extent to which they achieve higher innovation performance from collaborations (Laursen and Salter, 2006; Lin et al., 2012). Here, ACAP plays a critical role (Cohen and Levinthal, 1990).
In this study, innovation performance consists of two learning performance measures, explorative- and exploitative learning performance. The necessity for this distinction between explorative- and exploitative learning performance becomes evident in the following sections.
2.3. R&D Expenses
The term expenses requires some nuance because even though expenses are usually seen as costs, firms view R&D expenses as an investment rather than an expense or cost and often is used to signal positive future prospects towards investors (He and Tian, 2014). The importance of investing in R&D is will established in literature. For instance, research has shown that R&D investments “generate persistent profits, high stock returns, superior market value, and overall has a strong relation with increased profitability (McAlister, Srinivasan, and Kim, 2007).”
most value for firms. They state that it is the generation of technological capabilities with the use of R&D that make it possible for firms to create superior products or improve the production and distribution processes in order to gain competitive advantage and meet new needs of customers (Mizik and Jacobson, 2003). Accordingly, we expect that the R&D expenditures of both buyer and supplier will have a positive influence on their innovation performance.
Hypothesis 1a: R&D Expenses will be positively related to Explorative Learning
Performance.
Hypothesis 1b: R&D Expenses will be positively related to Exploitative Learning
Performance.
2.4. Complementarity
Inter-firm complementarity is considered to be a key driver of the formation of exploitative commercial partnerships (Colombo, Grilli, and Piva, 2006). Complementarity is defined as “the degree to which the firms are able to fill out, or complete, each other’s performance by supplying capabilities, knowledge, and resources (Jap, 1999)”. Lin, Yang, and Arya (2009) found that resource complementarity has a positive influence on the performance of partnerships. Moreover, firms should always search for partners that are sufficiently differentiated in order to acquire new or complementary capabilities and missing elements. These complementary resources can be leveraged and integrated to create synergy, and ultimately competitive advantage.
increase with increasing relatedness, but beyond some optimum innovation performance will decrease with increasing relatedness. Therefore, situations where these knowledge bases are too related or too unrelated need to be avoided. The reason why knowledge bases too related to each other would negatively impact innovation performance is because of a lack of complementarity (Ahuja and Katila, 2001). According to Cassiman et al., (2005) firms with complementary technologies before a merger and acquisition (M&A) will increase their R&D productivity after the M&A. Furthermore, there has been found a positive influence of complementary assets on new product development, for example because it allows incumbent firms to benefit from creative new entrant firms (Rothaermel and Hess, 2001). Next to resources, and as stated in previous paragraph, strong empirical evidence was found that complementarity between a firm’s internal knowledge and its acquired external knowledge positively affects innovation performance (Cassiman and Veugelers, 2006).
Partnering firms can experience negative effects because they are too little or too much related. Firms that are too little related have difficulties integrating each other’s knowledge because the knowledge bases are too distant, whereas firms that are too similar are unable to benefit from each other’s knowledge base because of a lack of new and dissimilar inputs. Too much complementarity, on the other hand, is expected not to harm a partnership because the more partnering firms are able to complete each other’s performance by supplying capabilities, knowledge, and resources, the better. Accordingly, it is expected in this study that complementarity will positively affect innovation performance in buyer-supplier relations.
Hypothesis 2a: Complementarity will be positively related to Explorative Learning
Performance.
Hypothesis 2b: Complementarity will be positively related to Exploitative Learning
2.5. The Multidimensional Variable ACAP
Cohen and Levinthal (1990) defined ACAP as “the ability to value, assimilate, and apply new knowledge”. This early definition shows that ACAP has to be to be considered as a variable with multiple dimensions. Zahra and George (2002) identified four important dimensions of ACAP. They define ACAP as “a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic organizational capability.” The four dynamic capabilities of ACAP – acquisition, assimilation, transformation, and exploitation – have different but complementary roles and show a distinction between a firm’s potential (PACAP) and realized (RACAP) ACAP.
Potential ACAP (PACAP) embodies two of the four dimensions of ACAP, acquisition and assimilation. Acquisition is the capability of a firm to identify and acquire externally generated knowledge critical to its operations (Zahra and George, 2002). Assimilation stands for the routines and processes of a firm that allow it to analyse, process, interpret, and understand externally obtained information (Zahra and George, 2002). The dimensions acquisition and assimilation constitute PACAP and corresponds to explorative learning (Berger, 2015). Explorative learning focuses on the search and acquisition of new recourses and capabilities (March, 1991).
PACAP and RACAP coexist at all times and separately play a necessary but insufficient role to achieve long-term performance (Zahra and George, 2002). For example, a firm can be very competent at acquiring and assimilating external knowledge but missing the skills to transform and exploit this newly acquired knowledge for profit generation. In turn, firms are not able to exploit external knowledge if it can’t acquire and assimilate this knowledge. As such, PACAP is needed to provide RACAP with knowledge inputs and RACAP is needed to leverage PACAP in order to improve performance. “The distinction between PACAP and RACAP suggests that externally acquired knowledge undergoes multiple iterative processes before the recipient firm can successfully exploit it to achieve a competitive advantage (Zahra and George, 2002).” Furthermore, the distinction between the two dimensions can give insights into why certain firms are more efficient using ACAP than others, and is vital in assessing their unique contributions to innovation and competitive advantage (Zahra and George, 2002).
2.5.1. ACAP and R&D Expenditures
The study of Cassiman and Veugelers (2006), regarding complementarity between the activities of innovation strategies, uses data from the Community Innovation Survey on Belgian manufacturing firms. The researchers focus their attention on a firm’s own R&D and externally acquired knowledge and provide econometric evidence for complementarity between these two innovation activities. These activities complement each other in a way that a firm’s internal know-how will increase with the marginal return to external knowledge acquisition strategies and vice versa (Cassiman and Veugelers, 2006). The greater a firm’s ACAP, the greater its ability to acquire, assimilate, transform and exploit external knowledge sources.
Hypothesis 3a: PACAP will positively moderate the relationship between R&D
Expenses and Explorative Learning Performance.
Hypothesis 3b: RACAP will positively moderate the relationship between R&D
Expenses and Exploitative Learning Performance.
2.5.2. ACAP and Complementarity
As mentioned before, the extent to which firms are able to internalize externally acquired knowledge can influence their ability to achieve higher innovation performance from collaborations (Lin et al., 2012). Firms need to possess a certain degree of ACAP in order to adequately acquire, assimilate, transform, and exploit external capabilities, knowledge, and resources that are complementary to their own. Accordingly, it is expected that the effect of complementarity on innovation performance in buyer-supplier relationships will increase in strength at higher levels of inter-firm ACAP.
Hypothesis 4a: PACAP will positively moderate the relationship between
Complementarity and Explorative Learning Performance.
Hypothesis 4b: RACAP will moderate positively the relationship between
2.6. Conceptual Model
The hypotheses result in the conceptual model below in Figure 2.1. As stated before, PACAP conforms to explorative learning, and RACAP conforms to exploitative learning. In turn, exploitative and explorative learning performance relate to innovation performance.
Figure 2.1. The conceptual model
3.
METHOD
This section begins with examining the nature of the large dyadic dataset of 166 matched-paired buyer-supplier relationships this study analyses. Furthermore, the measures that are used in the survey to collect data on all relevant variables and the statistical procedures adopted for this study are discussed.
3.1. Data
for involvement and knowledgeability of the respondents by determining their working position, number of years they have been involved in the relationship, and the percentage of time spent on the relationship. All buyer respondents are located in the Netherlands, whereas the supplier respondents are located all over the world. The study of Oosterhuis et al., (2013) make use of data gathered from 89 dyadic buyer-supplier relationships and show that perceptions between respondents of buyer and supplier firms differ either when they possess different information or when they are asked about subjects related to their own organization’s identity. An important implication of their study is that future research should take into account these perceptual differences. Therefore, this study tests its hypotheses for the buyer and supplier database separately.
3.2. Measures
This study uses first-order and second-order constructs. “First-order constructs are latent constructs that have observed variables as indicators. Second-order constructs have other latent (first-order) constructs as their indicators (Berger, 2015)”.
While it has to be noted that R&D expenditures per employee has proven to be more stable and less sensitive to effects of business cycles, accounting manipulations, and asset sales, the variable R&D expenditures was measured as a percentage of sales over the last three years (Baysinger, Kosnik, and Turk, 1991) (Appendix 1.1.). However, instead of measuring R&D spending as an absolute number, it is not uncommon to measure R&D as proportion of sales in order to obtain data relative to the firm’s size (Graves, 1988). Berger’s (2015) measure of complementarity was first developed by Jap (1999), measuring complementary competencies of a buyer-supplier dyad, and further modified by Lambe, Spekman, and Hunt (2002) to form a reflective three-item measure of complementarity (Appendix 1.8.).
Innovation performance of the relationship was measured by explorative learning performance (associated with PACAP) and explorative learning performance (associated with RACAP). Both learning performance measures, in their own way, measure innovation performance. Explorative learning performance relates to a relationship’s joint creation of new expertise on dimensions of manufacturing and production, product development, technology, marketing, and management (Appendix 1.6.). Exploitative learning performance relates to the relationship’s performance in terms of lowered costs, flexibility, improved product quality, synergies in sales and marketing, new product development, reaction speed to changes in the market, and return on investment (Appendix 1.7.).
As Ahuja and Katila (2001) denoted, innovation performance can be measured in terms of innovative inputs, such as R&D expenditures, or in terms of innovative outputs, such as patenting frequency. However, the innovative performance measures used in this study are based on a degree of synergy realization rather than more removed and potentially ambiguous based measures. Where accounting-based measures do not distinguish between performance resulted from the relationship and individual firm performances, synergy realization focuses solely on the performance of the buyer-supplier relationship. Both these measures of innovation are formative multi-item constructs (Berger, 2015).
separately. Subsequently, PACAP was measured by combining the (first-order) dimensions of acquisition and assimilation. RACAP was measured by combining the (first order) dimensions of transformation and exploitation. This results in both PACAP and RACAP being second-order formative constructs. The following four paragraphs discuss the measures of the dimensions of ACAP.
Acquisition refers to the capability of the relationship to identify and acquire external knowledge that is critical to its operations. Components relevant to acquisition are ‘prior investments’, ‘prior knowledge’, ‘intensity’ (new connections), ‘speed’ (of learning), and ‘direction’ (quality of learning) (Zahra and George, 2002). The indicators of acquisition, measured by Berger (2015), are formative indicators that cause the construct. It has to be noted, however, that prior investments and prior knowledge were excluded from the construct, as the research model of Berger (2015) used both components as antecedents of ACAP (Appendix 1.2.).
Assimilation refers to the routines and processes of the relationship that allow it to analyse, process, interpret, and understand externally obtained information. Its relevant component is ‘understanding’ (interpretation, comprehension, learning) (Zahra and George, 2002). Assimilation’s indicators are of a reflective nature which means that the causal arrows point from the construct to the indicators (Appendix 1.3.).
Transformation refers to the capability of the relationship to develop and refine the routines that facilitate combining existing knowledge and the newly acquired and assimilated knowledge. Relevant components of transformation are ‘internalization’ and ‘conversion’ (Zahra and George, 2002). Transformation is a formative construct and its indicators cause the construct. For the measurement of transformation, informants answered questions related to the relationship’s ability to relate acquired knowledge to what is already known, and how knowledge is shared and transferred in the relationship (Berger, 2015) (Appendix 1.4.).
(core competencies) and ‘implementation’ (harvesting resources) (Zahra and George, 2002). The construct of exploitation measures the relationship’s ability to commercialize new external knowledge (Berger, 2015) (Appendix 1.5.).
3.3. Statistical Procedure
For the statistical procedure Partial Least Squares (PLS), a variance-based Structural Equation Model (SEM), is used. PLS-SEM shares a lot of similarities with multiple regression analysis and because of this characteristic it is particularly valuable for explanatory research purposes. It is the preferred method over covariance based SEM because of its superiority to handle models that include formative constructs (Hair et al., 2014). PLS-SEM makes a distinction between the measurement model, which relates the constructs to their measures, and the structural model, which relates the constructs to each other, and provides rigorous tests of construct reliability, convergent validity, and discriminant validity (Jarvis et al., 2003; Fornell and Larcker, 1981).
The statistical tests of this study are done using SmartPLS, one of the leading statistical software tools for PLS-SEM. Because of the sample size of n = 166, bootstrapping is the resampling method of choice. Bootstrapping is a nonparametric procedure and involves repeated random sampling with replacement from the original sample, creating a bootstrap sample. With this sample, standard errors are obtained for hypotheses testing (Hair, Ringle, and Sarstedt, 2011). Bootstrapping with sample sizes under n = 100 is discouraged because standard error bias and variability can become highly inflated (Kock, 2015; Nevitt and Hancock, 2001).
4.
ANALYSIS AND RESULTS
The typical procedure for PLS-SEM analysis is to first estimate the measurement model and subsequently the structural model (Chin, 1998). The measurement model, also referred to as the outer model, is used to evaluate the relationships between the indicators and their corresponding latent construct. The structural model, also referred to as the inner model, estimates the relationships between the latent constructs (Hair et al., 2014). It is necessary to proper specify of the measurement model before any meaning can be assigned to the analysis of the structural model (Jarvis et al., 2003).
4.1. Measurement Model
Table 4.1. Construct reliability and convergent validity of reflective scales. Constructs/indicators:
Buyer and Supplier Buyer Supplier
Standard loadings: Cronbach's alpha: Composite reliability: Standard loadings: Cronbach's alpha: Composite reliability: Standard loadings: Cronbach's alpha: Composite reliability: Assimilation 0,790 0,856 0,806 0,865 0,766 0,843 ASSIM1 0,637 0,620 0,601 ASSIM2 0,785 0,794 0,786 ASSIM3 0,821 0,810 0,822 ASSIM4 0,766 0,810 0,734 ASSIM5 0,668 0,699 0,640 Exploitation 0,767 0,844 0,766 0,856 0,763 0,841 EXPL1 0,691 0,675 0,693 EXPL2 0,692 0,652 0,736 EXPL3 0,785 0,837 0,718 EXPL4 0,851 0,850 0,852 EXPL5 0,568 0,562 0,574 Complementarity 0,728 0,845 0,749 0,843 0,693 0,820 COMPL1 0,827 0,816 0,846 COMPL2 0,837 0,837 0,834 COMPL3 0,743 0,793 0,638
Table 4.1. shows the convergent validity of the measurement model for reflective scales only. Because formative scales influence the latent construct, correlation among them is not assumed or required. These causal indicators are not invalidated by low internal consistency. The same validity evaluation approach as for reflective scales is therefore not appropriate for evaluating formative scales (Diamantopoulos, Riefler, and Roth, 2008; Jarvis et al., 2003).
composite reliability scores of all constructs are greater than 0.7 indicating acceptable convergent validity (Esposito Vinzi et al., 2010; Hair et al., 2014).
Table 4.2. Convergent validity formative scales. Constructs/indicators:
Buyer and Supplier Buyer Supplier
Indicator
weights: p-value: VIF:
Indicator
weights: p-value: VIF:
Indicator
weights: p-value: VIF:
Acquisition ACQUIS1 0,313 0,000 1,274 0,329 0,000 1,305 0,311 0,000 1,403 ACQUIS3 0,284 0,000 1,197 0,302 0,000 1,122 0,260 0,000 1,350 ACQUIS4 0,405 0,000 1,449 0,418 0,000 1,515 0,395 0,000 1,516 ACQUIS5 0,367 0,000 1,468 0,377 0,000 1,373 0,356 0,000 1,673 Transformation TRANSF1 0,343 0,000 1,417 0,328 0,000 1,376 0,329 0,000 1,392 TRANSF4 0,253 0,000 1,211 0,223 0,000 1,402 0,277 0,000 1,155 TRANSF5 0,022 0,651 2,801 0,019 0,808 2,745 0,094 0,169 3,202 TRANSF6 0,260 0,000 4,477 0,295 0,004 2,659 0,244 0,002 2,815 TRANSF7 0,336 0,000 1,820 0,351 0,000 2,145 0,281 0,000 1,679 TRANSF8 0,169 0,001 2,156 0,141 0,050 2,728 0,191 0,033 1,769 Explorative LP E'PLOR LP1 -0,099 0,676 2,095 0,234 0,390 1,834 -0,129 0,799 2,561 E'PLOR LP2 0,025 0,909 1,941 0,031 0,883 1,756 -0,009 0,984 2,322 E'PLOR LP3 0,835 0,000 1,996 0,668 0,010 1,783 1,148 0,015 2,328 E'PLOR LP4 0,336 0,137 1,882 0,308 0,586 2,155 -0,004 0,988 1,801 E'PLOR LP5 0,046 0,868 1,963 0,033 0,957 2,466 -0,104 0,826 1,772 Exploitative LP E'PLOIT LP1 0,013 0,887 1,43 0,078 0,439 1,537 -0,103 0,577 1,359 E'PLOIT LP2 0,156 0,109 1,679 0,122 0,251 1,691 0,224 0,105 1,725 E'PLOIT LP3 0,024 0,785 1,382 -0,014 0,906 1,403 0,198 0,104 1,396 E'PLOIT LP4 0,160 0,060 1,439 0,146 0,233 1,688 0,311 0,161 1,301 E'PLOIT LP5 0,267 0,039 1,698 0,286 0,097 1,883 0,321 0,092 1,78 E'PLOIT LP6 0,091 0,377 1,891 0,066 0,688 2,331 -0,094 0,645 1,868 E'PLOIT LP7 0,658 0,000 1,456 0,692 0,000 1,395 0,588 0,000 1,693 PACAP (2nd) ACQUIS 0,496 0,000 1,715 0,504 0,000 1,627 0,487 0,000 1,445 ASSIM 0,605 0,000 1,715 0,606 0,000 1,627 0,644 0,000 1,445 RACAP (2nd) TRANSF 0,558 0,000 1,997 0,532 0,000 1,862 0,331 0,016 1,919 EXPL 0,525 0,000 1,997 0,559 0,000 1,862 0,847 0,000 1,919
Note: (2nd) = second-order construct
Exploitative LP also show inadequate p-values. These, however, were not removed from the model to ensure content validity; that the construct is still adequately measuring the theoretical domain it needs to capture (Peter et al., 2007; Bollen and Lennox 1991). Removal of a causal indicator may remove a unique part of the composite latent construct and change the theoretical meaning of the variable (Jarvis et al., 2003).
Were multicollinearity between indicators is desirable for reflective scales, excessive multicollinearity for formative scales can destabilize the model and is very undesirable. High VIF values for formative indicators would suggest that these measure the same aspect of the construct, where researches would want all formative indicators to measure a different aspect of the construct. All VIF values showed to be lower than the threshold of 3.3 (Cenfetelli and Bassellier, 2009; Peter et al., 2007).
Table 4.3a. Squared roots of the average variances extraced (AVE) and correlation matrix (first-order constructs)
Buyer and Supplier
1 2 3 4 5 6 7 1. Acquisition 0,707 2. Assimilation 0,609 0,732 3. Transformation 0,516 0,639 0,741 4. Exploitation 0,521 0,612 0,659 0,707 5. Complementarity 0,263 0,440 0,429 0,332 0,799 6. Explorative LP 0,296 0,346 0,287 0,314 0,309 0,768 7. Exploitative LP 0,376 0,501 0,488 0,459 0,492 0,540 0,643
Table 4.3b. Squared roots of the average variances extraced (AVE) and correlation matrix (second-order constructs)
Buyer and Supplier
1 2
1. PACAP 0,892
2. RACAP 0,703 0,919
second-order constructs used in this study. Here, the diagonal coefficients represent the squared roots of the AVE’s, while the off-diagonal coefficients represent the correlation coefficients. Discriminant validity is considered acceptable when the square root of the AVE’s of each latent variable is larger than the correlations among the latent variables (Fornell and Larcker, 1981). As seen in Table 4.3a. and Table 4.3b, each latent construct has a higher square root of AVE than any of the correlations with other latent constructs. Both tables display the results for the combined database, the separate buyer and supplier database showed the same statistical patterns (Appendix 3). It is concluded from the previous four tables that the measurement model is sufficiently strong for further analysis and interpretation of the structural model.
4.2. Structural Model
As explained in section 3.1, the structural model was assessed for the buyer database and supplier database separately. Table 4.4. shows the explained variance and predictive validity measures of the independent variables, and the full collinearity measures (VIFs) of all latent variables. The variance explained (R-square) and the predictive validity (Stone-Geiser Q-square) of the dependent variables range from 0,09 to 0,51. The full collinearity test assesses both vertical (predictor-predictor) and lateral (predictor-antecedent) collinearity (Kock and Lynn, 2012). All VIF values are below the threshold of 3,3, which indicates no multicollinearity issues between the variables in both the buyer database and supplier database (Cenfetelli and Bassellier, 2009; Kock and Lynn, 2012).
Table 4.4. Full collinearity VIFs, explained variance, and predictive validity measures (PLS-SEM).
Full collinearity VIFs Explained variance Predictive quality
(R-square) (Stone-Geiser Q-square)
Buyer Supplier Buyer Supplier Buyer Supplier
R&D Expenditures 1,045 1,033 - - - -Complementarity 1,445 1,193 - - - -PACAP 1,919 1,845 - - - -RACAP 2,216 1,610 - - - -Explorative LP 1,292 1,417 0,182 0,167 0,137 0,094 Exploitative LP 2,214 1,817 0,511 0,266 0,493 0,192
For the estimation of the path coefficients, a bootstrap with n = 5000 subsamples was run, providing significance levels on all structural paths (Hair et al., 2011). This amount clearly exceeds the recommended amount of 200 by Chin (1998). Path weighting was the chosen structural model weighting approach. As the only weighting scheme to take into account the directionality of the structural model, path weighting is often the weighting scheme of choice in testing causal relations (Chin, 1998).
For the estimation of moderating effects, pair-wise multiplication of all indicators was not feasible because PACAP and RACAP (which have to be multiplied with R&D expenditures and complementarity) have formative indicators. These cannot be multiplied with other indicators, as they are not assumed to reflect the same underlying construct. These can be independent of each other and measure different components (Esposito Vinzi et al., 2010). Therefore, a two-stage PLS approach is used that first estimates latent variable scores of the variables, and subsequently builds the interaction term (X * M). This interaction term, along with the latent variable scores of X and M, are used as independent variables in a multiple linear regression on the latent variable scores of Y (Esposito Vinzi et al., 2010). For both the buyer and supplier database, the standardized path coefficients are shown in Table 4.5.
Table 4.5. Estimated direct and moderation effects.
Independent Dependent Buyer Supplier Multi-group analysis
beta: p-value: beta: p-value: beta: p-value:
Direct effects
R&D Expenditures Explorative LP 0,125 0,060 * 0,066 0,441 0,193 0,040 **
Exlpoitative LP 0,011 0,826 0,115 0,088 * 0,126 0,066 *
Complementarity Explorative LP 0,205 0,032 ** 0,168 0,098 * 0,042 0,377
Exploitative LP 0,300 0,000 *** 0,254 0,005 *** 0,068 0,262 Moderation effects
R&D * PACAP Explorative LP 0,048 0,525 0,094 0,355 0,112 0,191
R&D * RACAP Exlpoitative LP 0,090 0,166 0,034 0,680 0,044 0,667
Compl * PACAP Explorative LP 0,155 0,031 ** 0,051 0,380 0,093 0,833
Compl * RACAP Exlpoitative LP 0,122 0,046 ** 0,076 0,289 0,052 0,728
*** p ≤ 0,01; ** p ≤ 0,05; * p ≤ 0,1
4.5. Hypotheses Testing
For hypotheses testing, the estimated direct and moderation effects are both taken into consideration. First, the results for the ‘buyer only’ database, shown in the left column of Table 4.5. are discussed. Second, the results for the ‘supplier only- database are discussed, these are shown in the middle column of Table 4.5.
Buyer only database
As expected, R&D expenses positively affect explorative learning performance (beta = 0,125, p-value = 0,060). Contrary to expectations, R&D expenses showed no significant effect on exploitative learning performance (beta = 0,011, p-value = 0,826). Hence, hypothesis 1a is supported, and hypothesis 1b is left unsupported. Complementarity shows a significant positive effect on explorative learning performance (beta = 0,205, p-value = 0,032), as well as on exploitative learning performance (beta = 0,300, p-value = 0,000), therefore supporting hypotheses 2a and 2b.
exploitative learning performance (beta = 0,090, p-value = 0,166). The results of the ‘buyer only’ database are summarized in Table 4.6a.
Table 4.6a. Overview of (supported and rejected) hypotheses (PLS-SEM) ('buyer only').
Dependent Hypothesis
Independent (/interaction)
Explorative LP
R&D Expenditures H1a sup.
Complementarity H2a sup.
R&D * PACAP H3a rej.
CM * PACAP H4a sup.
Exploitative LP
R&D Expenditures H1b rej.
Complementarity H2b sup.
R&D * RACAP H3b rej.
CM * RACAP H4b sup.
Supplier only database
The estimated direct and moderation effects of the supplier database are depicted in the middle column Table 4.5. It reveals that R&D expenses positively affect exploitative learning performance (beta = 0,115, p-value = 0,088). However, contrary to expectations, R&D expenditures do not show to be significantly related to explorative learning performance (beta = 0,066, p-value = 0,441). Furthermore, the results reveal that complementarity positively affects explorative learning performance on the 0,1 level of significance (beta = 0,168, p-value = 0,098), and exploitative learning performance on the 0,01 level of significance (beta = 0,254, p-value = 0,005).
shows not to be significantly moderated by RACAP (beta = 0,076, p-value = 0,289). The results for the supplier only database are summarized in Table 4.6b.
Table 4.6b. Overview of (supported and rejected) hypotheses (PLS-SEM) ('supplier only').
Dependent Hypothesis
Independent (/interaction)
Explorative LP
R&D Expenditures H1a rej.
Complementarity H2a sup.
R&D * PACAP H3a rej.
CM * PACAP H4a rej.
Exploitative LP
R&D Expenditures H1b sup.
Complementarity H2b sup.
R&D * RACAP H3b rej.
CM * RACAP H4b rej.
5.
DISCUSSION
This chapter begins with a summary and discussion of the empirical findings of this study. All estimated effects refer to those presented in Table 4.5. Second, scientific and managerial implications are discussed. This chapter concludes with a discussion of the limitations of this paper and directions for future research.
5.1. Discussion
5.1.1. Direct effects
specific attributes. More specifically, buyers and suppliers significantly differ in their perception of the relationship’s performance (Ambrose et al., 2010; Oosterhuis et al., 2013).
Evidence is found for the hypothesized direct effects of complementarity on explorative- and exploitative learning performance. For both the buyer and the supplier database, the statistical tests revealed positive and significant relationships. Therefore, it can be concluded that the degree to which firms are able to fill out, or complete each other’s performance by supplying capabilities, knowledge, and resources, positively affects their explorative- and exploitative learning performance, and thus, their innovation performance.
5.1.2. Moderation effects
Contrary to expectations, for both databases, PACAP shows not to be a moderator on the relationship between R&D expenditures and explorative learning performance. Furthermore, RACAP shows not to be a moderator on the relationship between R&D expenditures and exploitative learning performance. We have to take into account that R&D expenditures is a firm-level measure (measured as a percentage of sales), whereas all other variables in the model are measures on an inter-firm level. For example, explorative learning performance measures to what extent the relationship has resulted in joint creation of expertise in different areas, and exploitative learning performance measures the relationship’s performance on multiple dimensions. This means that if one of the firms involved in the dyad would increase their R&D spending, its effect on the relationship’s innovation performance would not be affected by the relationship’s ACAP.
intuition and interpretation. Because intuition is uniquely an individual-level process in the subconscious, and complementarity in is related to group-level activities (i.e. between firms), a relation between the two is not likely (Sun and Anderson, 2010). Similar to the moderation effect of PACAP, RACAP shows to be a statistically significant moderator on the relationship between complementarity and exploitative learning performance, but only for the buyer database. Post-hoc analysis on the direct effects of complementarity on the dimensions of RACAP, transformation and exploitation, reveals no unexpected insights in terms of insignificant relations (Appendix 4). Again there is a difference in outcomes between databases. The differences are likely to be caused by a difference in perception between buyer and supplier on the measures of complementarity, ACAP, and innovation performance (e.g. Ambrose et al., 2010; Oosterhuis et al., 2013). Moreover, Berger (2015) found statistical evidence that complementarity, PACAP, and RACAP are perceived different between buyer and supplier.
5.2. Scientific and managerial implications
According to the knowledge-based view, knowledge is the most valuable asset of an organization that can be acquired externally through collaboration with other organizations. Inter-firm ACAP provides the ability to acquire, assimilate, transform, and exploit these external sources of knowledge, and is crucial for partnering firms in developing and increasing their knowledge base and innovation performance. Accordingly, this study developed and empirically tested a model in which PACAP and RACAP act as moderators on the direct effects of R&D expenditures and complementarity on innovation performance.
For the direct effects, results show that R&D expenditures and complementarity are significantly and positively related to both explorative- and exploitative learning performance. However, for the effects of R&D expenditures, these results were not shared by both databases. First, from the supplier perspective, R&D expenditures showed to have no effect on explorative learning performance. Second, from the buyer perspective, R&D expenditures showed to have no effect on exploitative learning performance.
Regarding the moderation effects, this study failed to find empirical evidence that both PACAP and RACAP act as moderators on the direct effects of R&D expenditures on explorative- and exploitative learning performance, respectively. However, evidence was found that PACAP and RACAP positively moderate the direct effect of complementarity on explorative- and exploitative learning performance, respectively. Again, these findings show to be subject to the characteristics of the database, as they were only found significant for the buyer database. These results have some meaningful scientific and managerial implications and its contribution is threefold.
that this impact is strengthened by a higher degree of ACAP. Therefore, managers operating in an inter-organizational context should aim to increase inter-firm ACAP in order to enhance their innovation performance. This result, however, was not found statistically significant for supplier firms.
According to Cohen and Levinthal (1990), the level of prior related knowledge is the primary determinant of ACAP. However, Van den Bosch and Van Wijk (2001) state that managers can have a strong effect on knowledge-related processes. Current literature has shown that managers can directly affect ACAP by providing information to potential adopters within the organization (Volberda et al., 2010). Furthermore, Minbaeva et al. (2003) show that the ability and motivation of individuals within an organization correspond to its ACAP. With practises targeted at increasing employee’s ability (e.g. training) and employee’s motivation (e.g. performance-based compensation), managers can improve the ACAP of their organizations. Buyers in a vertical inter-firm relationship should work more closely and intensively together with their supplier partners to apply these practises on an inter-firm level. This is necessary in order to develop and increase their inter-firm ACAP, and thus, their innovation performance.
Third, results revealed the effect of complementarity on innovation performance differs between the buyer and supplier perspective. Also, the direct effects of both R&D expenditures and complementarity on explorative- and exploitative learning performance show to differ between buyer and supplier. This is consistent with the studies of Ambrose et al. (2010) and Oosterhuis et al. (2013) that prove buyers and suppliers have significantly different perceptions across a range of dimensions of which they possess different information and/or that are closely related to the identity of their own firm.
5.3. Limitations and future research
In this paragraph, the limitations of this study and directions for future research are discussed.
First, because external knowledge complements a firm’s R&D, and vice versa, this study theorized that greater ACAP would increase R&D’s effect on innovation performance (Cassiman and Veugelers, 2006). Leaning too heavily on the possibility of a moderation effect by ACAP, this study disregarded an alternative theory: that of mediation. Berger (2015) empirically revealed PACAP and RACAP are positively related to the combined measures of explorative and exploitative learning performance. Furthermore, the results of this study showed significant positive effects of R&D expenditures on explorative learning performance (only for the buyer database) and exploitative learning performance (only for the supplier database). Hence, future research could investigate the possibility of mediation effects of PACAP and RACAP on the relationship between R&D expenditures and innovation performance.
al., (2007) found that a firm’s size positively affects the explorative- and exploitative learning performance. Moreover, the authors found empirical evidence that the age of a firm, as well as the industry it operates in, affect innovation performance. Because of the small size of subsamples regarding the different industries the dyads operate in, this study was not able to take into account whether its findings would be supported for each separate industry. Future research could, for instance, investigate whether this study’s findings differ, or prove to be consistent, across industries with different knowledge strategies. Furthermore, controlling for the effects of firm size, and the age of the firm may yield interesting results. Therefore, a more complex model needs to be developed.
Third, the model of this paper limits itself to the direct role of ACAP on current innovation performance within vertical relationships of buyer and supplier. In order to better understand the value of ACAP for innovation in an inter-firm setting, a broader network perspective may prove to be fruitful for future research. For instance, Walter et al. (2003) state that business relationships benefit from direct functions and indirect functions. Direct functions capture activities and resources for value creation within the relationship, without being dependent upon other relationships. Indirect functions include effects in the future and/or of connected relationships. Within the construct of indirect functions, the authors place a function of innovation development. Here, the suppliers support the buyer’s innovation activities by, for instance, passing on innovative ideas, components, or engage in collaborative development projects. In turn, buyer firms can utilize these resource inputs to engage in larger, riskier, and long-term innovative projects. Accordingly, investments in the creation of value through innovation may very well lead to greater future innovative performance. Furthermore, these activities can lead to an increase and development of their future knowledge base and ACAP, and can be used in other buyer-supplier relationships in which the supplier firm operates (Walter, Ritter, and Gemünden, 2001).
between buyer and supplier’s perceptions in order to fully understand its underlying reasons.
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APPENDIX 1: The questionnaire.
Appendix 1.1. R&D expendituresWhat is your firm’s average R&D spending (as a percentage of sales)
over the last three years? ……… %
Appendix 1.2. Acquisition
1. Please answer the questions below to indicate the relationship’s ability to recognize and acquire new external knowledge.
Strongly disagree
Strongly agree 1 2 3 4 5 6 7 1.1. In the relationship, data on the state-of-the-art of relevant external
technologies are accessible when needed. ………
1.2. Relevant changes in the industrial environment catch us (the joint
parties in the relationship) by surprise. ………
1.3. In the relationship, technological advancements that are critical to
our operations are identified before they enter our markets. ………
1.4. In the relationship, our companies put a lot of effort into acquiring
new external knowledge. ………
1.5. In the relationship, we have relevant, continuous and up-to-date
information on our current and potential competitors. ………
Appendix 1.3. Assimilation
2. Please answer the questions below to indicate the relationship’s ability to make sense out of new external knowledge.
Strongly disagree
Strongly agree 1 2 3 4 5 6 7 2.1. In the relationship, we are not sufficiently capable of processing
newly acquired information on new technologies and innovations, which are useful or have proven potential.
……… 2.2. New developments are well understood due to the shared
interpretation efforts of members of our companies. ………
2.3. In the relationship, we quickly and professionally analyze and
interpret changing market demands. ………
2.4. We effectively make use of our joint employees’ level of
knowledge, experience and competencies to analyze and interpret external information.
……… 2.5. The atmosphere in the relationship stimulates productive
Appendix 1.4. Transformation
3. Please answer the questions below to indicate the relationship’s ability to relate newly acquired knowledge to what is already known.
Strongly
disagree Strongly agree 1 2 3 4 5 6 7 3.1. In the relationship, we easily integrate newly acquired external
knowledge into our common understanding of business-related
affairs. ………
3.2. For us it is not easy to see the connections among the pieces of
external knowledge held by different members of our companies. ………
3.3. In the relationship, an apparent incongruity between existing
knowledge and newly acquired knowledge leads to ambiguity. ………
3.4. In the relationship, we are able to transcend traditional mind-sets if
newly acquired knowledge demands it. ………
4. Please answer the questions below to indicate how external knowledge is shared and transferred in the relationship.
Strongly
disagree Strongly agree 1 2 3 4 5 6 7 4.1. Employees of both partners get acquainted well enough to know
who knows what. ………
4.2. Employees of both companies know where critical expertise
resides within the relationship. ………
4.3. In the relationship, employees actively exchange newly acquired
knowledge. ………
4.4. In the relationship, it is well known to which employee, or group of
employees, specific new knowledge needs to be transferred. ………
Appendix 1.5. Exploitation
5. Please answer the questions below to indicate the relationship’s ability to commercialize new external knowledge.
Strongly
disagree Strongly agree 1 2 3 4 5 6 7 5.1. In the relationship, both companies constantly discuss how to
better exploit knowledge. ………
5.2. Our customers can immediately benefit from new technological
knowledge learned in the relationship. ………
5.3. New knowledge is easily integrated into our common operations.
……… 5.4. The relationship’s capacity to use and exploit new knowledge
allows us to respond quickly to changes in the environment. ………
5.5. We are proficient in transforming new technological knowledge
Appendix 1.6. Explorative Learning Performance
6. To what extent has the interaction between you and your partner resulted in the joint creation of …
To no extent To a great extent 1 2 3 4 5 6 7 6.1. new manufacturing and production expertise?
……… 6.2. new product development expertise?
……… 6.3. new technological expertise?
……… 6.4. new marketing expertise?
……… 6.5. new managerial expertise?
………
Appendix 1.7. Exploitative Learning Performance
7. Please answer the questions below to indicate the relationship’s performance.
Strongly
disagree Strongly agree 1 2 3 4 5 6 7 7.1. The relationship with the other company has resulted in lower
logistics costs. ………
7.2. Flexibility to handle unforeseen fluctuations in demand has been
improved because of the relationship. ………
7.3. The relationship with the other company has resulted in better
product quality. ………
7.4. Synergies in joint sales and marketing efforts have been achieved
because of the relationship. ………
7.5. The relationship has a positive effect on our ability to develop
successful new products. ………
7.7. The relationship helps us to detect changes in end-user needs and
preferences before our competitors do. ………
7.7. Investments of resources in the relationship, such as time and
Appendix 1.8. Complementarity
8. Please answer the questions below to indicate the complementarity between both partners.
Strongly
disagree Strongly agree 1 2 3 4 5 6 7 8.1. We both contribute different resources to the relationship that help
us achieve mutual goals. ………
8.2. We have complementary strengths that are useful to our
relationship. ………
8.3. We each have separate abilities that, when combined together,
enable us to achieve goals beyond our individual reach. ………
APPENDIX 2: p-values indicator loadings.
Appendix 2.1. Construct reliability and convergent validity of reflective scales. Constructs/indicators:
Buyer and Supplier Buyer Supplier
Standard
loadings: p-value: Standard loadings: p-value: Standard loadings: p-value:
APPENDIX 3: Squared roots of the AVE’s and correlation matrices.
Appendix 3.1a. Squared roots of the average variances extraced (AVE) and correlation matrix (first-order constructs)
Buyer 1 2 3 4 5 6 7 1. Acquisition 0,681 2. Assimilation 0,680 0,747 3. Transformation 0,532 0,692 0,742 4. Exploitation 0,304 0,444 0,672 0,812 5. Complementarity 0,365 0,388 0,386 0,261 0,733 6. Explorative LP 0,382 0,613 0,267 0,533 0,494 0,733 7. Exploitative LP 0,555 0,705 0,546 0,369 0,271 0,553 0,666
Appendix 3.1b. Squared roots of the average variances extraced (AVE) and correlation matrix (second-order constructs)
Buyer
1 2
1. PACAP 0,898
2. RACAP 0,711 0,918
Appendix 3.2a. Squared roots of the average variances extraced (AVE) and correlation matrix (first-order constructs)
Supplier 1 2 3 4 5 6 7 1. Acquisition 0,713 2. Assimilation 0,551 0,711 3. Transformation 0,492 0,582 0,678 4. Exploitation 0,513 0,566 0,644 0,7 5. Complementarity 0,224 0,422 0,459 0,323 0,78 6. Explorative LP 0,289 0,299 0,293 0,311 0,35 0,88 7. Exploitative LP 0,389 0,383 0,448 0,438 0,45 0,562 0,662
Appendix 3.2b. Squared roots of the average variances extraced (AVE) and correlation matrix (second-order constructs)
Supplier
1 2
1. PACAP 0,87
