KNOWLEDGE BASED VIEW OF SUPPLY CHAIN INTEGRATION
Prakash J. Singh
Department of Management & Marketing The University of Melbourne VIC 3010
AUSTRALIA Ph: +61 (0)3 8344 4713 Fax: + 61 (0)3 9349 4293 Email: pjsingh@unimelb.edu.au
Damien Power
Department of Management & Marketing The University of Melbourne VIC 3010
AUSTRALIA Ph: +61 (0)3 8344 3737 Fax: + 61 (0)3 9349 4293 Email: damien@unimelb.edu.au
KNOWLEDGE BASED VIEW OF SUPPLY CHAIN INTEGRATION
ABSTRACTThe aims of this paper are to empirically test the relationship between knowledge sharing practices within and between trading partners as a framework for integration, and to test for the effect of these practices on firm performance. Data was collected from 418 organizations in the manufacturing industry in Australia. Structural equation modelling approach to data analysis was used. It was found that the three knowledge related constructs (internal knowledge integration, knowledge integration with
customers, knowledge integration with suppliers) were strongly inter-related, providing
a case for knowledge based integration of firms with their trading partners. Further, these three exogenous constructs collectively explained about a third of the variance in the endogenous construct (firm performance). The relationships identified provide support for the efficacy of knowledge based collaboration. Managers can use this as a way to conceptualize how their firms can develop internal integration and collaborative relationships with their trading partners.
Category of paper: Research paper.
INTRODUCTION
The development of longer term relationships based on collaboration between trading partners has become a central theme of research in the area of supply chain management (Bensaou, 1999; Johnston et al., 2004; Monczka et al., 1998). A parallel related theme has been that of integration (Droge et al., 2004; Frohlich and Westbrook, 2001). Research in this area has focused on a range of integration modes including: linking logistics systems and methods with marketing strategy (Alvarado and Kotzab, 2001); cross-functional integration in a supply chain context (Pagell, 2004); integration through connecting trading partners’ information systems to promote transparency and information flow (Gunasekaran and Ngai, 2004; Kulp et al., 2004; Vickery et al., 2003); the use of internet technologies as an enabler of integration (Garcia-Dastugue and Lambert, 2003; Zeng and Pathak, 2003); achieving integration through coordinated design of products, processes and the supply chain (Peterson et al., 2005); and sharing information to facilitate coordination of decisions across trading partner networks (Sahin and Robinson, 2005).
This body of research highlights that integration of systems, processes and strategy is important for supply chain trading partners to realize the benefits of closely linking supply to demand. These benefits, however, are not necessarily realized easily or without risk. In particular, pursuing supply chain integration involves collaboration that can blur the boundaries of the firm such that the economics of the relationship become subject to the good will of the participants, and to their ability to control costs associated with coordination. Against this background, the ability of trading partners to share, integrate and leverage knowledge becomes a plausible mechanism by which such risks can be identified, managed and/or mitigated (Hult et al., 2004).
This paper builds on and extends the work of previous authors in emphasizing the important role knowledge plays in facilitating effective management of the supply chain (Hult et al., 2007; Hult et al., 2006; Ketchen and Hult, 2007). As the development of coherent strategies to enable closer integration with trading partners becomes a potential source of competitive advantage, understanding how best to facilitate such integration becomes critical. Testing the potential for a knowledge based approach to integration is therefore the objective of this paper.
LITERATURE REVIEW AND HYPOTHESES Collaboration in the Supply Chain
Many theories have been developed to explain how and why firms can best organize inter-firm relationships. Transaction cost economics is based in the concept of bounded rationality (Simon, 1957), or the cognitive limits that constrain managers when choosing trading partners whom they can trust. This leads to the assumption that all relations with trading partners are subject to the risk of opportunistic behaviour (i.e., placing self–interest before the relationship, or being deceptive and dishonest in the service of your own interests), particularly if the interests of parties are also assumed not to be aligned (Williamson, 1975; Williamson, 1985). In the supply chain management literature, this paradigm has been described as the “arm’s length” model (Dyer et al., 1998). In fact, this approach to supplier relationships is still widely endorsed as acceptable practice (Kaufman et al., 2000). The rationale for this strategy has been to counteract the possibility of opportunistic behaviour of trading partners (Williamson, 1975; Williamson, 1985), or to neutralize bargaining power of suppliers and/or customers (Porter, 1980; Porter and Millar, 1985).
This theory has more recently been modified to accommodate the existence of networks and other hybrid collaborative governance forms (Jarillo, 1988; Williamson,
1991). Other theoretical perspectives have also emerged to explain why closer ties with trading partners provide strategic benefits that outweigh these risks (Barringer and Harrison, 2000). Resource dependence theory would frame this relationship between trading partners as being governed by one firm seeking to control the resource(s) (Thorelli, 1986), or by cultivation of a partnership with the aim of gaining access to the resource(s) (Fisher, 1996; Oliver, 1990). Strategic choice theory would suggest firms collaborate in pursuit of either growth through increasing market power (Harbison and Pekar, 1998), or efficiency through shared risk and economies of scale (Powell, 1990). The knowledge based view (KBV) of the firm would suggest that collaboration provides access to strategic knowledge (Grant, 1997; Grant and Baden-Fuller, 1995; Grant and Baden-Fuller, 2004), and that firm performance is directly linked to building capabilities through interacting with heterogeneous sources of knowledge (Kogut, 2000; Kogut and Zander, 1992).
The origins of supply chain management as a set of practices and a valid area of enquiry lie in the recognition that the competitiveness of firms is tied to the way industrial systems are configured and how firms interact within such systems (Forrester, 1958; Forrester, 1961). Management of the supply chain as a system rather than many individual parts promotes the sharing of information (and in some cases assets) between organizations, recognizing areas of common interest and combined competitive advantage (Peterson et al., 2005; van Donk and van der Vaart, 2005; Vereecke and Muylle, 2006). This approach, rather than focusing on the risks associated with opportunism, takes the opposite view that closer collaboration with trading partners represents an opportunity. Rather than just focusing on interorganizational relationships, the systems view of the supply chain promotes the importance of integration between the firm, avenues of supply, and channels of distribution. This extension of the role and
influence of a firm in an industry provides challenges for existing theory and practice. Transaction cost theory provides a range of governance options enabling differing levels of integration and collaboration (Williamson, 1991). The costs inherent in the nature of the relationship, however, may not be easily identifiable to managers due their “bounded rationality” (Simon, 1957). Managers may understand the need to pursue collaboration with multiple trading partners in order to mitigate against the structural dynamics of the supply chain (Lee et al., 1997a; Lee et al., 1997b). At the same time, however, they are confronted with the risk of incurring additional transaction costs through collaboration.
Of particular interest to scholars modelling the dynamics of supply chain interactions has been the leverage that more effective knowledge exchange offers (Forrester, 1958; Forrester, 1961; Lee et al., 1997a; Lee et al., 1997b). One outcome of the early modelling of industrial systems was the recognition that supply chains are “dynamically complex”, characterized by situations where cause and effect are separated, and difficult to associate, in both time and space (Senge, 1990). Under these conditions delays occur (e.g., in both physical movement of goods and the transfer of information relative to such flows), leading to what has become known as the “bullwhip effect” (Lee et al., 1997a; Lee et al., 1997b). Sterman (1989) describes this phenomenon as being driven by irrational human behaviour resulting from a misunderstanding of real demand. Lee et al. (1997) believe that practices such as demand forecast updating, order batching, price fluctuation and rationing and shortage gaming are the main drivers.
Where these differing views converge is in identifying the importance of reliable and timely information and the leverage knowledge can provide. Understanding of dynamically complex situations lies in understanding interrelationships and processes (Senge, 1990), knowledge of which is more likely to be gained through closer
collaboration in a supply chain. Further, if they can use this information to build knowledge that is of value to a network of firms (Jarillo, 1988) the economics of collaboration become attractive. At the same time, possible motivations for opportunistic behaviour could also be neutralized. In this sense collaboration between trading partners can be justified as a strategy for altering the dynamics of supply chain systems such that knowledge of the interrelationships within the system allows for more coherent and effective management.
Knowledge Based View of the Firm
The knowledge based view (KBV) of the firm defines knowledge as the resource with the highest strategic value that can be generated, acquired and applied within and between firms (Grant, 1997). This perspective builds on the Resource Based View (RBV) (Barney, 1991; Penrose, 1959) by suggesting that knowledge promotes competitive advantage because knowledge resources have characteristics consistent with either; a) developing capabilities that are rare, valuable, imperfectly imitable and non-substitutable (Barney, 1991), or; b) being of themselves largely intangible resources consistent with possessing these characteristics. The KBV of the firm also supports the building of competencies through improving absorptive capacity. As firms’ employees are involved in accessing knowledge through boundary spanning activities, recent empirical studies have shown the capacity for organizational learning is increased (Teigland and Wasko, 2003). Further, the KBV has been applied to problems of definition of firm boundaries (Grant and Baden-Fuller, 1995), governance of interorganizational relationships (Grant, 1997; Grant and Baden-Fuller, 2004; Heiman and Nickerson, 2002), solution choice based on problem complexity (Nickerson and Zenger, 2004), and collaborative supply chain practice (Hult et al., 2007).
The implications at the firm level are important because the value of a firm is not just a function of its constituent parts (Kogut, 2000). As Kogut points out, knowledge that resides outside of a firm can not be assumed to be “public”, and in fact may be embedded in the rules and norms of the relationships between firms. Knowledge externally held (if not a “public good”) could therefore be expected to have characteristics similar to those of tacit knowledge in individuals (being difficult to codify and often having an important social context). It could also need to be supported by “credible rules” and “sanctioning mechanisms” (explicit codification of rules and conditions of engagement) (Kogut, 2000) that provide an explicit structural governance framework. From a KBV perspective, collaboration between trading partners represents on one level a factor minimizing the cost and time for effective transfer of knowledge between firms, and at a deeper level a potential significant source of value. As such, the value of knowledge as a strategic resource enabling more effective management of the supply chain has been recognized (Hult et al., 2006; Hult et al., 2004).
A further extension of the implied nature of much of the knowledge that exists in relationships (or what Kogut terms “networks”) is that if we accept that transfer will be costly and difficult, the same conditions serve to limit imitation (by competitors). As such, the distribution of such knowledge across multiple heterogeneous sources becomes a source of competitive advantage (Grant and Baden-Fuller, 1995). In this sense, the KBV perspective provides support for the proposition that collaboration is an effective strategy for accessing knowledge distributed amongst trading partners. Access to diverse sources of knowledge, therefore, promotes growth of the knowledge base (for the firm and/or the network) and builds competitive advantage (Kogut, 2000).
Supply Chain Integration
Common themes covering supply chain integration include cooperation, collaboration, information sharing, trust, partnerships, shared technology, and a fundamental shift away from managing individual functional processes, to managing integrated chains of processes (Droge et al., 2004; Narasimhan and Kim, 2001; Vickery et al., 2003). Integration of information technologies through development of standards and connection of legacy systems has also been identified as an important driver of potential performance improvements (Kulp et al., 2004).
An emergent theme has been to re-define the supply chain as a “demand chain” to reflect the importance of customer focus and to highlight the importance of end-to-end coordination between supply and demand (Williams et al., 2002). This has led to the examination of integration between trading partners from a more holistic perspective with the emphasis being on trying to determine the nature, importance and influence of integration across multiple tiers of the chain (Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002; Heikkila, 2002; Rosenzweig et al., 2003). The findings of these studies vary, but some unifying themes emerge including: in rapidly growing industries trading partners can achieve efficiency and higher levels of customer satisfaction through a positive feedback loop between collaboration, information flows and the positive impact this has on the relationship (Heikkila, 2002); high levels of integration intensity lead to the embedding of capabilities in organizational processes creating conditions conducive to the development of competitive advantage (Rosenzweig et al., 2003); integration using web-based technologies was most effective for manufacturers when it included linking technologies with both suppliers and customers concurrently (Frohlich and Westbrook, 2002); the wider the span and degree of integration activity across the supply chain (i.e., for a manufacturer the extent to which the integration with
trading partners extends both upstream and downstream in the supply chain), the stronger is the link to performance improvement (Frohlich and Westbrook, 2001).
Implied in these results is the recognition of the systemic nature of supply chains as initially identified and discussed in the systems dynamics literature (Forrester, 1958; Forrester, 1961; Sterman, 1989), and more recently in supply chain management studies (Lee et al., 1997a; Lee et al., 1997b). Implied in the latter is that accessing, assimilating and effectively transferring knowledge as widely as possible across the span of a supply chain underwrites the potential benefits of collaboration (Hult et al., 2003). For both of the above reasons, the findings of these studies have a strong resonance with seminal papers from within the KBV body of literature where both the nature of the network and the relationships within it have been hypothesized to be related to more effective knowledge transfer and creation of competitive capability (Grant and Baden-Fuller, 1995; Heiman and Nickerson, 2002; Kogut, 2000).
Synthesis
The effectiveness of integration between a group of organizations operating within a supply chain, therefore, could be expressed in terms of the quality and quantity of knowledge being exchanged, and the effectiveness of coordination. The risks associated with transaction costs increasing may in fact be either mitigated (e.g., by reducing the limits of rationality through knowledge exchange, the total cost of transactions is reduced), neutralized (e.g., the supply chain system becomes a coherently functioning entity), or made tolerable (e.g., total system cost is reduced such that local increases in costs can be tolerated). As such, knowledge becomes an important inter-firm and intra-inter-firm resource, the management of which provides inter-firms with a method of improving the operational effectiveness of the system, and a potential source of competitive advantage.
Hypotheses
Supply chains can be characterized as systemic in nature and thus must be managed as systems in order maximize their effectiveness (Forrester, 1958; Forrester, 1961; Senge, 1990; Sterman, 1989). The systemic nature of the supply chain is such that knowledge may reside in multiple locations (Grant and Baden-Fuller, 1995), be in different forms (Kogut and Zander, 1992), and possess a value based on the coordination capabilities of the network (Kogut, 2000). Knowledge provides both a motivation for, and a key element of, collaboration between supply chain partners with the potential for enabling more effective integration (Lee et al., 1997a; Lee et al., 2000). Knowledge held within a network of trading partners, however, is only as valuable as the capability of the network to transfer, process and leverage it (Grant and Baden-Fuller, 1995; Heiman and Nickerson, 2002). Collaboration between trading partners is therefore a strategy that can be employed to both facilitate the flow of information (Grant, 1997; Grant and Baden-Fuller, 2004), and/or provide coordination through governance (Jarillo, 1988). Recent empirical studies support the systems view of the supply chain by incorporating a “demand chain” perspective reinforcing the value of integration across both demand and supply (Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002). The first of our hypotheses capture these relationships by proposing that knowledge integration in a supply chain is a function of the extent of knowledge integration with customers and suppliers, as well as the extent of such integration within a firm (see Figure 1). It is proposed that:
Hypothesis 1a: There is a significant positive relationship between the extent of knowledge integration with customers and the extent of knowledge integration with suppliers.
Hypothesis 1b: There is a significant positive relationship between the extent of knowledge integration with suppliers and the extent of internal knowledge integration. Hypothesis 1c: There is a significant positive relationship between the extent of knowledge integration with customers and the extent of internal knowledge integration.
The findings of recent studies where a “demand chain” or holistic (system wide) perspective on integration has been taken have pointed toward a positive relationship with firm performance (Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002; Heikkila, 2002; Rosenzweig et al., 2003). The major themes identified have included: improved efficiency and higher levels of customer satisfaction through a positive feedback loop between collaboration and information flows (Heikkila, 2002); embedding of capabilities in organizational processes promoting competitive advantage (Rosenzweig et al., 2003); integration of codified knowledge by manufacturers using web-based technologies being positively related to performance when technologies were linked to suppliers and customers concurrently (Frohlich and Westbrook, 2002); and for a manufacturer the extent to which the integration with trading partners extends both upstream and downstream being positively related to performance improvement (Frohlich and Westbrook, 2001). The results of these studies indicate that the extent to which a firm integrates processes and systems with trading partners will have a direct effect on performance of the firm. As such, they reinforce arguments in the literature supporting the potential for collaboration to build competitive advantage (Lee et al., 2000; Peterson et al., 2005; van Donk and van der Vaart, 2005; Vereecke and Muylle, 2006). These previous studies, however, have been general in their definition and in operationalizing of the concept of “integration”, rather than specific in taking a KBV perspective. They also provide support for the evidence from the KBV body of literature where relationships and network dynamics have been hypothesized to be related to
knowledge transfer effectiveness and competitive capability (Grant and Baden-Fuller, 1995; Heiman and Nickerson, 2002; Kogut, 2000). As such our second set of hypotheses proposes that knowledge integration (i.e., internal knowledge integration and knowledge integration with customers and suppliers) in a supply chain has a direct positive effect on firm performance. Formally, it is proposed that:
Hypothesis 2a: There is a significant positive effect of the extent of knowledge integration with customers on firm performance.
Hypothesis 2b: There is a significant positive effect of the extent of internal knowledge integration on firm performance.
Hypothesis 2c: There is a significant positive effect of the extent of knowledge integration with suppliers and firm performance.
RESEARCH METHOD Study Participants
Data for the empirical testing of the above hypotheses was obtained with a postal questionnaire conducted over two stages involving manufacturing industry organizations in Australia using the JAS-ANZ Register (Standards Australia, 2004). The respondents to the survey were senior managers (general, operations, quality, production, etc). This register is a database of all plants registered to various management meta-standards, including quality, environmental, risk, safety, etc. The unit of analysis was the plant with a sample size of 1053 and response rate of 41.3% (n=418).
Non-response bias was assessed by assessing the differences between respondents to the two stages of the survey. Statistical analysis (t-tests) of responses between the two groups showed little difference.
Table 1 is a cross-tabulation of number of employees and annual revenue turnover of the plants that participated in this study. The study participants were predominantly small plants with almost half having less than 100 employees and $A10 million in annual revenue. These plants were mainly from the machinery and equipment manufacturing (26 percent) and metal products (17 percent) manufacturing industry sub-categories.
--- Insert Table 1 here --- Measurement Instrument
The measurement instrument used in this study was derived from a large study (146 items) of quality and operational management practices (Singh, 2003). Theis instrument was pre-tested with eight practitioners and academicians and a pilot test within 21 organizations to ensure error (Malhotra and Grover, 1998) was acceptable..
For this paper, a subset of the items (measured on five point Likert scales) relevant to the key constructs of knowledge integration with customers, internal knowledge integration, knowledge integration with suppliers and firm performance was used (see Table 2). Some of these items have been used in other studies (Singh, 2008; Singh and Power, 2009). In the current study, these items are interpreted in a different theoretical light. The four constructs along with their associated items, and together with the scales that were used, are shown in Table 2.
--- Take in Table 2 here ---
DATA ANALYSIS PROCEDURES AND RESULTS Psychometric Properties of Measurement Models
A series of tests were performed to ensure that the three constructs had sound psychometric properties. These tests were for face validity, multicollinearity, reliability, convergent and discriminant validity, and common methods bias.
Face Validity. The lists of items assigned to the constructs were arrived at through a review of the literature (some of which are referred to in the Literature Review section earlier). This provided evidence to accept that the constructs and their associated items had sufficient grounding in the literature and therefore had face validity.
Correlation Coefficients and Descriptive Statistics. The inter-item Pearson correlation coefficients are shown in Table 3. These coefficients are low to moderate in magnitude. If inter-item correlations are greater than 0.9, the possibility that multicollinearity could be existing is high (Hair Jr. et al., 2006). As none of the coefficients is greater than 0.9, multicollinearity related problems did not appear to be present. Table 3 also shows the mean and standard deviation values of all the items. These values suggest that the item measures did not suffer from excessive non-normality.
--- Take in Table 3 here ---
Reliability. The Cronbach’s alpha reliability coefficients for the constructs were: knowledge integration with customers 0.833; internal knowledge integration 0.835; knowledge integration with suppliers 0.797; and firm performance 0.703. These coefficients exceeded the minimum threshold level of 0.7 for acceptable reliability (Hair
Jr. et al., 2006) for all the constructs. Therefore, the items used reliably estimated the constructs.
Convergent and Discriminant Validities. Convergent and discriminant validities were both assessed using a confirmatory factor analysis (CFA) model testing approach. The CFA model is a structural equation model (SEM) where the constructs are all co-varied with each other. The SEM analysis was performed with the AMOS® 5.0 software package. The maximum likelihood (ML) estimation technique was used to fit the models to the data. All other procedural requirements for successful reflective SEM analysis as described in Hair Jr. et al. (2006) were implemented.
A number of commonly reported indices for assessing the goodness-of-fit of SEM models with data were obtained for the CFA model. These were as follows: χ2(371)
= 1167 with p-value < 0.001; χ2/df = 3.146; goodness-of-fit index (GFI) = 0.825; adjusted goodness-of-fit index (AGFI) = 0.795; Tucker-Lewis index (TLI) = 0.792; comparative fit index (CFI) = 0.810; root mean square residual (RMR) = 0.054; and, root mean square error of approximation (RMSEA) = 0.072.
Each of the these fit measures was evaluated to assess the level of fit obtained. In the case of the χ2 measure, the p-value associated with the χ2 measure would need to be higher than 0.05 for good fit. However, this fit measure has a tendency to produce negative results with sample sizes greater than 200 (Hair Jr. et al., 2006). Since the sample size in the current study was 418, this particular measure of goodness of fit was disregarded. The χ2/df value suggested “acceptable” fit. Conventional cut-off criteria for indices of fit are considered by some researchers to be excessively stringent (Hair Jr. et al., 2006; Marsh et al., 2004; Schermelleh-Engel et al., 2003; Sharma et al., 2005). For example, Sharma et al. (2005, pp.941-942) suggests that for datasets with more than 24 items and sample size of around 200, “more liberal” cut-off values (e.g., 0.8) should be
used for indices such as GFI and TLI. Applying these criteria to GFI, AGFI, TLI, CFI, RMR, and RMSEA values obtained for the CFA in the current study, we assess the fit to be adequate.
All the parameters associated with the CFA are shown in Table 4. As these results show, the convergent validity of the constructs was generally supported; all the estimated factor loadings of items on constructs were significant (at p-values < 0.001), the signs were all positive and only one was below 0.4, with the minimum being +0.369, and average of +0.598. Further, from the squared multiple correlation values, the variances of the items explained by their constructs were reasonably high (with the average being 37 percent). As for discriminant validity, correlations between the constructs were mostly moderate (with the average correlation coefficient being +0.593), suggesting that items assigned to one construct were not significantly highly loading on others.
--- Take in Table 4 here ---
Common Methods Bias. Since all items were measured using a five-point Likert scale and responses were received from a single individual in the plant, there is some possibility that common methods bias could be present. To test for this, Harmon’s one factor test using a confirmatory approach (Podsakoff et al., 2003) was performed. This involved testing a one factor congeneric model (Joreskog, 1971), where all 31 items were loaded onto a single ‘common factor’ construct. The SEM results of this test indicated that common methods bias was unlikely to be present, with the goodness-of-fit indices for this model indicating much poorer goodness-of-fit with data in absolute terms, and also being worse than the CFA and hypothesized models. (Results for the hypothesized
model are provided in the next section.) The indices for Harmon’s one factor model were: χ2(377) = 1741, p-value < 0.001; χ2/df = 4.617; GFI = 0.749; AGFI = 0.711; TLI =
0.649; CFI = 0.674; RMR = 0.062; and, RMSEA = 0.093. SEM Results for the Structural Model
Evaluation of Goodness-of-Fit Indices. The hypothesized model as presented in Figure 1 consists of constructs (which are estimated with items) and multiple inter-dependent relationships between these constructs. To assess these hypothesized relationships, the SEM analysis procedure was again used. Since the number of relationships specified in the hypothesized model is exactly the same as that in the CFA, the fit indices are therefore the same for the two models (i.e., χ2
(371) = 1167 with p-value
< 0.001; χ2/df = 3.146; GFI = 0.825; AGFI = 0.795; TLI = 0.792; CFI = 0.810; =RMR = 0.054; and, RMSEA = 0.072). Based on the assessment of fit indices for the CFA, it can be concluded that the hypothesized model has an “adequate” level of empirical support.
Evaluation of Parameter Estimates. Table 5 shows the SEM output of the model with all the parameters presented in unstandardized form as well as in standardized form for the structural model. As the data in this table shows, there were no ‘offending’ (theoretically impossible) estimates present. Further, all the relationships were statistically significant and positive, as predicted in the hypothesized theoretical model. Also, the squared multiple correlation coefficient associated with the endogenous construct was 0.319, indicating that the three exogenous constructs accounted for about a third of the variance in performance.
--- Take in Table 5
The results of SEM analysis is shown in summary form in Figure 2. This figure provides the standardized regression and correlation coefficients between constructs and the squared multiple coefficient values for the endogenous construct.
--- Take in Figure 2
---
The regression and correlation data presented in Table 5 was further analyzed by examining the standardized effect sizes between constructs. Effect sizes measure the increase/decrease in the endogenous construct (in standard deviation units) when there is a one standard deviation increase in the exogenous construct. The standardized direct effects, indirect effects (calculated using the path analysis tracing rules described by Kline (2005)) and total effects of all the exogenous constructs on the endogenous construct of the model are shown in Table 6. A number of observations can be made. Firstly, all effects are positive. Secondly, two of the three direct effects are statistically insignificant. Thirdly, the two insignificant direct effects are compensated by significant indirect effects, leading to all three total effects being roughly equal in magnitude. ---
Take in Table 6
---
DISCUSSION AND CONCLUSIONS General Discussion
The results provide evidence supporting the contention that integration through collaboration between trading partners to facilitate access to, sharing of and leveraging knowledge explains a significant proportion of variance in performance within the group sampled. Further, the importance of approaching knowledge integration from an
holistic, system wide view is also supported highlighting the interdependence between internal, customer and supplier focused knowledge. Earlier studies have indicated the importance of knowledge as a strategic resource in supply chains (Hult et al., 2006; Hult et al., 2003), this relationship indicates that it is particularly important in enabling integrations through collaborative processes. These results also complement those of earlier studies where the relationship between extent of integration and performance was verified (Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002; Heikkila, 2002; Rosenzweig et al., 2003; Vickery et al., 2003), as well as those indicating that integration needs to be viewed holistically incorporating customer, internal and supplier processes (Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002).
They also extend the results of these studies by: (a) specifically incorporating and verifying the important role that knowledge plays in integration; and; (b) clarifying the relative importance of knowledge based integration at different levels of a supply chain. For H2(a, b and c), the results indicate (through the regression weights) that knowledge at each level of the chain plays an important role, and in particular highlights the relative importance of internal knowledge integration compared to that with suppliers and customers. The relative weights indicate that integration of knowledge within the firm is fundamental to extended integration of knowledge between trading partners. This is an important finding in the context of previous studies that have indicated that an outward facing approach is critical (Frohlich and Westbrook, 2001). Whilst not contradicting this view (i.e., the weights for all three constructs representing knowledge integration are strong and significant with customers and suppliers as well as internally), the relative weights indicate that internal integration is at least as important a factor.
For H1 (a, b and c), the combination of the three exogenous constructs (represented in the model as collaborative knowledge integration) are shown to explain 32% of the variance in firm level performance. Internal Knowledge Integration is shown to have the strongest (and only significant) direct effect on firm performance. This effect, however, does not of itself explain the 32% variance in firm performance. This variance is explained by the combination of the direct and indirect effects. Integration of knowledge with both customers and suppliers also contributes to this variance, but through their effect on internal knowledge integration rather than independently. As such, this finding further extends previous studies in this area (Frohlich and Westbrook, 2001) by showing that an outward facing approach (knowledge integration through trading partner relationships) finds real leverage at the firm level through internal knowledge integration.
The findings also provide a particular focus on integration through collaboration for the purpose of accessing, sharing and leveraging knowledge. Previous studies have used more general definitions of integration (access to systems, use of technology, common use of containers, use of third party logistics providers, “intensity” of integration, etc.) and reflect these in the nature of the items making up constructs (Frohlich and Westbrook, 2001; Rosenzweig et al., 2003). In this study, the focus has been on processes and methods whereby knowledge is made accessible and used by trading partners in a collaborative climate. As such, the high proportion of variance in firm performance explained serves to highlight the importance of knowledge collaboration as an integrative mechanism. An important research theme in this area has been the identification and analysis of the dynamics of supply chain systems and the isolation of the causes of these dynamics (Forrester, 1958; Forrester, 1961; Lee et al., 1997a; Sterman, 1989). An important element uniting these studies has been the
identification of the potential to alter these dynamics through shared information and managing the system rather than the individual firm. In particular, this has been identified as a strategy for reducing the “dynamic complexity” of systems (Senge, 1990). The findings relating to H1 provide evidence supporting the system wide view of integration and the potential for knowledge based collaboration to facilitate system effectiveness. The proposition that this translates at the firm level to a positive effect on performance as a result of the system being managed and coordinated through knowledge based collaboration is certainly plausible in light of these findings.
Implications for Theory
The KBV of the firm defines knowledge as the resource with the highest strategic value that can be generated, acquired and applied within and between firms (Grant, 1997). The high proportion of variance in firm performance explained by collaborative knowledge integration in this study certainly provides strong empirical evidence that knowledge has high potential strategic value. Collaboration between trading partners as seen from a KBV perspective can minimize the cost and time for effective transfer of knowledge between firms (Grant, 2002), and/or represent of itself a potential significant source of value. Such value could reside in knowledge providing capabilities that are difficult to imitate (Nonaka et al., 2000), in the ability to generate economies of scale and/or scope (Grant, 2002), or in the relationships themselves such that “……networks constitute capabilities that augment the value of firms” (Kogut, 2000). For all three of these sources of value that the KBV proposes, the evidence from this study provides support.
The integrity of the constructs representing knowledge integration between trading partners (as shown in testing H1), combined with their explanation of a high proportion of the variance in firm performance (H2), provides a strong supportive
argument for the potential for knowledge based collaboration to create competitive advantage. It is implied in the nature of the model that the integration of knowledge at the three levels covered (suppliers, internal to the firm, customers), if governed by collaborative relationships, represents a set of capabilities difficult for competitors to replicate.
Production of goods and services (application of knowledge) require relationships between trading partners being used to access many different types of knowledge from many different sources (Kogut and Zander, 1992), whilst generation of knowledge requires specialization (Grant, 2002). Economies of scale and scope will be supported by the ability for firms to access both sources of knowledge. The results show that integration of knowledge from trading partners (whether it be specialized or based on the breadth of sources) has a positive effect on firm performance. The (cautious) implication is that this could provide circumstances conducive to the generation of economies of scale and/or scope.
The potential for knowledge to generate value within trading partner relationships such that the network itself represents a source of capability of value to a firm (Kogut, 2000) is also supported by the results. The results of H1 show that the integration of knowledge between trading partners (customers and suppliers with the firm) represents an holistic model the integrity of which is based on the combination of elements rather than the individual parts. In this sense, support is provided for the proposition (put forward by proponents of the KBV) that a collaborative knowledge based network can of itself represent a source of capabilities. Further, the results from H2 support the proposition that such networks can represent “……capabilities that augment the value of firms” (Kogut, 2000).
The knowledge based view of the firm proposes that the benefits of access to knowledge outweigh the potential for opportunism in inter-firm collaborations. The problem this creates is that it is arguably a source of increased transaction costs (Williamson, 1975) due to the bounded rationality of managers limiting in practical terms the ability of managers to choose appropriate trading partners with whom to collaborate (Simon, 1957). More recently, it has been examined as a complementary rather than an alternative source of explanation of governance arrangements (Heiman and Nickerson, 2002; Heiman and Nickerson, 2004; Nickerson and Zenger, 2004). These recent studies have provided evidence that indicates that the KBV and transaction cost economics (TCE) theories may not be irreconcilable alternatives. In particular by incorporating “……knowledge based attributes of transactions” such as tacitness and problem solving complexity, an explanation of where the need to overcome bounded rationality (through knowledge based collaborations) supports equity based collaboration (as predicted by TCE to be preferred where knowledge needs to be shared widely and/or accessed directly to mitigate the risk of opportunism) is proposed (Heiman and Nickerson, 2002). We argue that the results of this study provide indirect support for this view based on the integrity of the knowledge integration constructs (H1), and on the strength and significance of the relationship recorded with firm performance (H2). We have not measured specific constructs representing complexity and tacitness in this study. However, we contend that the nature of the relationships recorded in the model we have tested provide strong evidence of the importance of knowledge based collaboration in the management of inter-firm relationships. As such, the support for the efficacy of collaboration is significant, and thus the potential for such collaboration to be explained in terms of knowledge as a strategy for dealing with bounded rationality (as well as opportunism) is supported.
Implications for Practice
The results of this study have some important implications for managers when attempting to resolve the difficult issues associated with configuring inter-firm relationships. Firstly, there is clear evidence that the integration of knowledge through collaborative practices with both customers and suppliers provides substantial opportunities for firms to improve performance. This does not mean, however, that this is either the sole rational choice, nor that it will be the right choice in all cases and conditions. The case for being wary of opportunistic trading partners putting their perceived individual interests first is always going to carry weight. The choice will come down to the balance of risks, the importance of knowledge application to the firm, and the extent to which it is distributed across trading networks. What it does mean is that there is compelling evidence for managers to consider how, with whom, and when they can best facilitate knowledge exchange and learning. Further, the importance of getting these processes right internally is also highlighted. Managers wishing to promote effective knowledge exchange with trading partners need to focus first on creating the conditions internally to facilitate this. The evidence suggests that the effectiveness of collaboration based on integration of knowledge pivots on the effectiveness of internal processes supporting such collaboration.
Secondly, in a manufacturing context in particular, the results highlight the potential value of knowledge based collaboration given that the application of knowledge in this sector is critical. Although managers may spend time and effort documenting processes and procedures to enable ease of transfer, there is always a proportion of the knowledge that is tacit and cannot be easily replicated. In this context, integration through knowledge sharing and collaboration becomes an important option, particularly where access to multiple sources of knowledge is required. In many
manufacturing environments, where products rely on multiple sources of both supply and distribution, such expertise resides in a diverse and distributed range of locations.
The understanding of the dynamics of inter-firm governance is fundamental to the effective management of the individual firm. The key issue confronting managers revolves around the balancing out of the interests of their particular set of stakeholders, and those of other firms with whom they deal. This research points to and highlights the important role that knowledge based collaboration plays in understanding this riddle, and in providing managers with alternatives when developing relationships with trading partners. On balance, the choice comes down to developing a clear understanding of the risk/reward relationship between opportunity and opportunism.
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Figure 1. The underlying theoretical model Knowledge integration with customers Firm performance Internal knowledge integration H2a H2b H2c H1a H1b H1c Knowledge integration with suppliers
Figure 2. Final form of the theoretical model. Results shown are standardized regression weights on single-headed arrows, standardized correlation coefficients on double-headed arrows and squared multiple correlation coefficient on firm performance construct.)
** Coefficient is significant at the 0.01 level (2-tailed)
Firm performance +0.031 +0.509** +0.047 +0.783** +0.642** +0.546** 0.319 Knowledge integration with customers Knowledge integration with suppliers Internal knowledge integration
Table 1. Cross tabulation of number of employees and approximate annual turnover of plant that participated in the study
Approximate annual turnover Total No response Less than
$10M $10M - $50M Greater than $50M Number of employees No response 2 0 0 1 3 1 - 100 20 204 89 7 320 101 - 250 4 6 42 8 60 251+ 2 9 24 35 Total 28 210 140 40 418
Table 2. Constructs and associated items Construct Item label and description*
Supporting literature 1. Knowledge
integration with customers
CR1: The organization is aware of the requirements of its customers.
CR2: The organization measures customer satisfaction.
CR3: Processes and activities of the organization are designed to increase customer satisfaction levels.
CR4: Customers are encouraged to provide feedback.
CR5: Customer feedback is used to improve customer relations, processes, products and services.
CR6: The organization has systematic processes for handling complaints.
CR7: Misunderstandings between customers and organization about orders are rare.
CR8: Customers contribute to the development of the organization’s values.
(Carr et al., 1997; Droge et al., 2004; Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002; Johnson, 1997; Karapetrovic and Willborn, 2001; Naveh et al., 2004; Sahin and Robinson, 2005; Terziovski et al., 2003; Vickery et al., 2003)
2. Internal knowledge integration
IOP1: The organization encourages participation of stakeholders (i.e. employees, owners, customers, suppliers, and the broader community) in its activities. IOP2: Performance of each of the stakeholders (i.e. customers,
employees, owners and suppliers) is measured against short and long term objectives.
IOP3: Employees work in teams.
IOP4: The organization has an ‘open’ culture where a sense of trust results in strong relationships between people. IOP5: Collection methods used ensure that data is reliable and
valid.
IOP6: Key data are presented to different levels of the organization in a way that enhances understanding of the issues.
IOP7: The communication system is effective.
IOP8: Employees freely communicate with others at the registered site.
(Anderson et al., 1999; Droge et al., 2004; Johnson, 1997; McAdam and McKeown, 1999; Naveh and Erez, 2004; Naveh and Marcus, 2005; Sahin and Robinson, 2005; Vickery et al., 2003; Withers et al., 1997)
3. Knowledge integration with suppliers
SI1: The organization seeks long-term stable relationships with suppliers.
SI2: The interests of suppliers were considered when values of the organization were developed.
SI3: The organization seeks assurance of quality from suppliers.
SI4: Suppliers are provided with information so that they can improve their quality and responsiveness.
SI5: Suppliers are involved in the development of new products.
SI6: The gains resulting from cooperation with suppliers are shared with them.
(Droge et al., 2004; Frohlich and Westbrook, 2001; Frohlich and Westbrook, 2002; Sahin and Robinson, 2005; Vickery et al., 2003)
4. Firm
performance FP1: Inventory FP2: Profits. levels.
FP3: Demand for the products made by the organization. FP4: Perceived product quality by customers.
FP5: Time for new product development. FP6: Delivery performance.
FP7: Market share.
(Gupta and Somers, 1996; Naveh and Marcus, 2004; Naveh et al., 2004; Tan et al., 1998; Terziovski et al., 2003; Terziovski et al., 1997;
Venkatraman and Ramanujam, 1986; Williams
et al., 1995)
*Survey respondents were asked to express their agreement with statements associated with constructs 1 to 3, on a five point scale with 1 representing ‘strongly agree’ and 5 representing ‘strongly disagree’. For items associated with construct 4, survey respondents were asked to express the satisfaction of the organizations with respect to the various measures of performance, using a five point scale with 1 representing ‘very satisfactory’ and 5 representing ‘very dissatisfactory’.
