Australian manufacturing firms benefit from knowledge sharing

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Innovative knowledge sharing, supply chain integration
and firm performance of Australian manufacturing
firms
Prakash J. Singh
a
& Damien Power
a
a
Department of Management & Marketing, The University of Melbourne, Melbourne,
Australia
Published online: 02 Dec 2013.
To cite this article: Prakash J. Singh & Damien Power (2014) Innovative knowledge sharing, supply chain integration and
firm performance of Australian manufacturing firms, International Journal of Production Research, 52:21, 6416-6433, DOI:
10.1080/00207543.2013.859760
To link to this article: http://dx.doi.org/10.1080/00207543.2013.859760
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Innovative knowledge sharing, supply chain integration and firm performance of Australian
manufacturing firms
Prakash J. Singh* and Damien Power
Department of Management & Marketing, The University of Melbourne, Melbourne, Australia
(Received 18 September 2012; accepted 21 October 2013)
A number of reports show that innovation in Australian manufacturing firms is declining. We propose that better
knowledge sharing practices can assist these firms to become more innovative. In this paper, we examine this proposition
by empirically testing the relationship between knowledge sharing practices within and between trading partners as a
framework for integration, and testing for the effect of these practices on firm performance. Data were collected from
418 organisations in the manufacturing industry in Australia to assess the degree to which innovative knowledge sharing
practices provide a competitive advantage to Australian firms. Structural equation modelling approach to data analysis
was used. It was found that the three innovative knowledge sharing constructs (internal knowledge integration,
knowledge integration with customers and 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 as an innovation promoting higher firm
performance levels. Managers of manufacturing firms in Australia specifically, and others more generally, can use this as
a way to conceptualise how their firms can develop internal integration and collaborative relationships with their trading
partners.
Keywords: supply chain management; knowledge; knowledge-based view; collaboration; integration; manufacturing
industry
1. Introduction
A number of recent reports show that Australian manufacturing firms have been declining in terms of their innovativeness
(Cutler et al. 2008; Green et al. 2009; Samson 2010). A number of recommendations have been made to remedy this
situation. Many of these centre on macro-level policy changes (Cutler et al. 2008). In this paper, we take a different tact
by proposing a firm-level operational concept related to innovative knowledge management practices that these
manufacturing firms can use to generate competitive advantage.
The rationale for this proposition is based on some developments in the supply chain management field. Specifically,
the development of long-term relationships based on collaboration between trading partners has become a central theme
of research in the area of supply chain management (Monczka et al. 1998; Bensaou 1999; Johnston et al. 2004). A
parallel-related theme has been that of integration (Frohlich and Westbrook 2001; Droge, Jayaram, and Vickery 2004).
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
(Vickery et al. 2003; Gunasekaran and Ngai 2004; Kulp, Lee, and Ofek 2004); 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, Handfield, and Ragatz 2005; Jayaram and
Pathak 2013); and sharing information to facilitate coordination of decisions across trading partner networks (Sahin and
Robinson 2005).
This body of research highlights that the integration of systems, processes and strategy is important for supply chain
trading partners to realise the benefits of closely linking supply to demand. These benefits, however, are not necessarily
realised easily or without risk (Chen, Sohal, and Prajogo 2013). 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,
*Corresponding author. Email: pjsingh@unimelb.edu.au
© 2013 Taylor & Francis
International Journal of Production Research, 2014
Vol. 52, No. 21, 6416–6433, http://dx.doi.org/10.1080/00207543.2013.859760
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the ability of trading partners to share, integrate and leverage knowledge takes the form of an enabling innovation by
which such risks can be identified, managed and/or mitigated (Hult, Ketchen, and Slater 2004).
This paper builds on and extends the work of previous authors in emphasising the important role knowledge plays
in facilitating effective management of the supply chain (Hult et al. 2006; Ketchen and Hult 2007; Breja, Banwet, and
Iyer 2010; Rebolledo and Nollet 2011; Cai et al. 2013; Yang 2013). As the development of coherent strategies to enable
closer integration with trading partners provides a potential source of competitive advantage for Australian
manufacturing firms, finding innovative ways to facilitate such integration becomes critical. Testing the potential for a
knowledge-based approach to integration is therefore the objective of this paper.
2. Literature review and hypotheses
2.1 Collaboration in the supply chain
Many theories have been developed to explain how and why firms can best organise inter-firm relationships. Transaction
cost economics (TCE) 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, 1985). In the supply chain management literature, this paradigm has been
described as the ‘arm’s length’ model (Dyer, Cho, and Chu 1998). In fact, this approach to supplier relationships is still
widely endorsed as acceptable practice (Kaufman, Wood, and Theyel 2000). The rationale for this strategy has been to
counteract the possibility of opportunistic behaviour of trading partners (Williamson 1975, 1985), or to neutralise
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) (Oliver 1990; Fisher 1996). 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 and Baden-Fuller 1995; Grant 1997; Grant and Baden-Fuller 2004), and that firm
performance is directly linked to building capabilities through interacting with heterogeneous sources of knowledge
(Kogut and Zander 1992; Kogut 2000).
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, 1961). Management of the supply chain as a system rather than many individual parts promotes
innovative sharing of information (and in some cases assets) between organisations, recognising areas of common interest
and combined competitive advantage (van Donk and van der Vaart 2005; Peterson, Handfield, and Ragatz 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 inter-organisational
relationships, the systems view of the supply chain promotes the importance of integration between the firm, avenues of
supply and channels of distribution. This innovative view of the role and influence of a firm in an industry provides chal-
lenges 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 to the bounded nature of their rationality (Simon 1957). Managers may
understand the innovative potential from collaboration with multiple trading partners in order to mitigate against the struc-
tural dynamics of the supply chain (Lee, Padmanabhan, and Whang 1997a, 1997b). At the same time, however, they are
confronted with the risk of incurring additional transaction costs through collaboration.
A specific innovation source of particular interest to scholars modelling the dynamics of supply chain interactions has
been the leverage that more effective knowledge exchange offers (Forrester 1958, 1961; Lee, Padmanabhan, and Whang
1997a, 1997b). One outcome of the early modelling of industrial systems was the recognition that supply chains are
‘dynamically complex’, characterised 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, Padmanabhan, and Whang 1997a, 1997b). Sterman (1989) describes this phenomenon as being driven by irrational
International Journal of Production Research 6417

human behaviour resulting from a misunderstanding of real demand. Lee, Padmanabhan, and Whang (1997a) 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 potentially reliable and timely information and the
leverage innovative knowledge sharing can provide. Understanding of dynamically complex situations lies in under-
standing interrelationships and processes (Senge 1990), knowledge of which is more likely to be gained through closer
collaboration in a supply chain. Further, use of this information represents a powerful form of innovation for building
knowledge that is of value to a network of firms (Jarillo 1988), making the economics of collaboration attractive. At the
same time, possible motivations for opportunistic behaviour could also be neutralised. 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.
2.2 KBV of the firm
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 and Baden-Fuller 1995). This perspective builds on the Resource-Based
View (Penrose 1959; Barney 1991) 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 promotes building innovative capability through the building
of competencies and improving absorptive capacity. As firms’ employees are involved in accessing knowledge through
boundary spanning activities, recent empirical studies have shown the capacity for organisational 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 inter-organisational relationships (Grant and Baden-Fuller 1995; Heiman and
Nickerson 2002; Grant and Baden-Fuller 2004), solution choice based on problem complexity (Nickerson and Zenger
2004), and collaborative supply chain practice (Hult, Ketchen, and Arrfelt 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 cannot 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 a structural governance framework promoting innovative thinking and practice. From a KBV perspective,
collaboration between trading partners represents, on one level, a factor minimising 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 recognised (Hult,
Ketchen, and Slater 2004; Hult et al. 2006; Yang 2013).
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 potential
source of innovative practice and competitive advantage (Grant and Baden-Fuller 1995). In this sense, the KBV perspec-
tive 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 innovative capability for competitive advantage (Kogut 2000).
2.3 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 (Narasimhan and Kim 2001; Vickery et al. 2003; Droge, Jayaram, and Vickery
2004; Chong et al. 2013). 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, Lee, and
Ofek 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, Maull, and
6418 P.J. Singh and D. Power

Ellis 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, Roth, and Dean
2003; Vachon and Klassen 2007). 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 organisational processes
creating conditions conducive to the development of competitive advantage (Rosenzweig, Roth, and Dean 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) and 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, 1961; Sterman 1989), and more recently in supply chain manage-
ment studies (Lee, Padmanabhan, and Whang 1997a, 1997b). Implied in the latter is that accessing, assimilating and
effectively transferring knowledge as widely as possible across the span of a supply chain builds innovative capability
and underwrites the potential benefits of collaboration (Breja, Banwet, and Iyer 2010; Liao et al. 2011). 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 the nature of the network and the relationships within it have been hypothesised to be related to more
effective knowledge transfer and creation of competitive capability (Grant and Baden-Fuller 1995; Kogut 2000; Heiman
and Nickerson 2002).
2.4 Synthesis & hypotheses
The effectiveness of integration between a group of organisations 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), neutralised (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-firm resource, the
management of which provides firms with a method of improving the operational effectiveness of the system and a
potential source of competitive advantage.
Supply chains can be characterised as systemic in nature and thus must be managed as systems in order maximise
their effectiveness (Forrester 1958, 1961; Sterman 1989; Senge 1990). 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, Padmanabhan, and Whang 1997a; Lee, So, and Tang 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; Kyläheiko et al. 2011). Collaboration
between trading partners is therefore a strategy that can be employed to both facilitate the innovative flow of
information (Grant and Baden-Fuller 1995, 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, 2002). The first of
our hypotheses capture these relationships by proposing that innovative 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 internal knowledge integration.
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 knowledge integration with suppliers.
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, 2002; Heikkila
2002; Rosenzweig, Roth, and Dean 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 organisational processes promoting competitive advantage (Rosenzweig, Roth, and
Dean 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 can be innovative in integrating 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, So, and Tang 2000; van Donk and van der Vaart 2005;
Peterson, Handfield, and Ragatz 2005; Vereecke and Muylle 2006). These previous studies, however, have been general
in their definition and in operationalising of the concept of ‘integration’, rather than specific in taking a KBV perspec-
tive. They also provide support for the evidence from the KBV body of literature where relationships and network
dynamics have been hypothesised to be related to knowledge transfer effectiveness and competitive capability (Grant
and Baden-Fuller 1995; Kogut 2000; Heiman and Nickerson 2002). As such our second set of hypotheses proposes that
innovative 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.
Knowledge
integration with
customers
Firm
performance
Internal
knowledge
integration
H2a
H2b
H2c
H1a
H1b
H1c
Knowledge
integration with
suppliers
Collaborative knowledge integration
Figure 1. The underlying theoretical model.

Hypothesis 2c: There is a significant positive effect of the extent of knowledge integration with suppliers and firm
performance.
Figure 1 is the theoretical model that provides a summary of the key concepts and hypotheses.
3. Research method
3.1 Study participants
Data for the empirical testing of the above hypotheses were obtained with a postal questionnaire conducted over two
stages involving manufacturing industry organisations 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 manufacturing plant. A total of 1053 plants were approached to participate in
this study. The eventual response rate of 41.3% (n = 418) was obtained.
Non-response bias was assessed by assessing the differences between respondents to the two phases of the survey.
Statistical analysis (t-tests) of responses between the two groups showed little difference. Given that the latter group
would have been non-respondents if they had not been sent reminder notes, the lack of differences between the two
groups suggest that non-response bias was not a significant issue.
Table 1 shows the number of employees and annual revenue turnover of the plants that participated in this study.
The study involved predominantly small plants with a majority having less than 100 employees and $A10 million
($A1 = $USD1.07) in annual revenue. These plants were mainly from the machinery and equipment manufacturing
(26%) and metal products (17%) manufacturing industry sub-categories.
3.2 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). This instrument was pre-tested with eight practitioners and academicians, and a
pilot test within 21 organisations to ensure that errors were within tolerable limits.
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 per-
formance was used (see Table 2). Some of these items have been used in other studies (Singh 2008; Singh and Power
2009; Singh, Power, and Chuong 2011). 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.
4. Data analysis procedures and results
4.1 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.
Table 1. Number of employees and approximate annual turnover of plants that participated in the study.
Number of employees Number of plants
1–100 320
101–250 60
251+ 35
No response 3
Total 418
Approximate annual turnover
Less than $10 M 210
$10–50 M 140
Greater than $50 M 40
No response 28
Total 418

4.1.1 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.
4.1.2 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 have excessive non-normality.
4.1.3 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
Table 2. Constructs and associated items.
Construct Item label and description*
1. Knowledge integration
with customers
CR1: The organisation is aware of the requirements of its customers.
CR2: The organisation measures customer satisfaction.
CR3: Processes and activities of the organisation 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 organisation has systematic processes for handling complaints.
CR7: Misunderstandings between customers and organisation about orders are rare.
CR8: Customers contribute to the development of the organisation’s values.
2. Internal knowledge
integration
IOP1: The organisation 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 organisation has an ‘open’ culture where a sense of trust results in strong relationships
between people.
IOP5: Collection methods used ensure that data are reliable and valid.
IOP6: Key data are presented to different levels of the organisation 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.
3. Knowledge integration with
suppliers
SI1: The organisation seeks long-term stable relationships with suppliers.
SI2: The interests of suppliers were considered when values of the organisation were developed.
SI3: The organisation 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.
4. Firm performance FP1: Inventory levels.
FP2: Profits.
FP3: Demand for the products made by the organisation.
FP4: Perceived product quality by customers.
FP5: Time for new product development.
FP6: Delivery performance.
FP7: Market share.
*
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 organisations with respect to the various measures of performance, using a five-point
scale with 1 representing ‘very satisfactory’ and 5 representing ‘very dissatisfactory’.

Table3.Inter-itemPearsoncorrelationcoefficientsanddescriptivestatisticsofitems.
CR1CR2CR3CR4CR5CR6CR7CR8IOP1IOP2IOP3IOP4IOP5IOP6IOP7IOP8SI1SI2SI3SI4SI5SI6FP1FP2FP3FP4FP5FP6FP7
1.Knowledgeintegrationwithcustomers
CR11
CR20.480**
1
CR30.496**
0.543**
1
CR40.336**
0.578**
0.483**
1
CR50.370**
0.474**
0.514**
0.661**
1
CR60.250**
0.329**
0.298**
0.317**
0.360**
1
CR70.348**
0.272**
0.398**
0.290**
0.389**
0.299**
1
CR80.271**
0.345**
0.378**
0.417**
0.402**
0.211**
0.288**
1
2.Internalknowledgeintegration
IOP10.312**
0.379**
0.436**
0.416**
0.302**
0.160**
0.345**
0.342**
1
IOP20.258**
0.405**
0.408**
0.363**
0.383**
0.190**
0.269**
0.315**
0.395**
1
IOP30.290**
0.302**
0.315**
0.318**
0.274**
0.129**
0.289**
0.331**
0.372**
0.301**
1
IOP40.354**
0.256**
0.425**
0.323**
0.336**
0.123*
0.391**
0.373**
0.481**
0.344**
0.388**
1
IOP50.303**
0.297**
0.295**
0.254**
0.361**
0.173**
0.272**
0.256**
0.294**
0.394**
0.219**
0.282**
1
IOP60.258**
0.338**
0.420**
0.335**
0.362**
0.214**
0.252**
0.343**
0.329**
0.462**
0.277**
0.343**
0.625**
1
IOP70.367**
0.377**
0.480**
0.426**
0.379**
0.241**
0.431**
0.317**
0.422**
0.457**
0.336**
0.555**
0.480**
0.496**
1
IOP80.362**
0.205**
0.342**
0.273**
0.283**
0.114*
0.319**
0.288**
0.330**
0.319**
0.366**
0.497**
0.304**
0.307**
0.587**
1
3.Knowledgeintegrationwithsuppliers
SI10.366**
0.249**
0.329**
0.284**
0.305**
0.090.239**
0.201**
0.348**
0.273**
0.250**
0.374**
0.289**
0.313**
0.357**
0.370**
1
SI20.252**
0.247**
0.324**
0.299**
0.257**
0.0820.166**
0.384**
0.342**
0.369**
0.179**
0.326**
0.260**
0.342**
0.294**
0.198**
0.447**
1
SI30.183**
0.161**
0.252**
0.197**
0.291**
0.136**
0.176**
0.215**
0.259**
0.320**
0.127**
0.228**
0.291**
0.334**
0.301**
0.230**
0.362**
0.331**
1
SI40.215**
0.225**
0.311**
0.211**
0.312**
0.0660.244**
0.233**
0.234**
0.336**
0.194**
0.233**
0.281**
0.383**
0.302**
0.171**
0.336**
0.338**
0.510**
1
SI50.138**
0.121*
0.219**
0.190**
0.218**
0.0290.148**
0.281**
0.223**
0.370**
0.134**
0.199**
0.153**
0.247**
0.187**
0.185**
0.264**
0.363**
0.283**
0.501**
1
SI60.155**
0.204**
0.261**
0.224**
0.216**
00.180**
0.363**
0.311**
0.324**
0.208**
0.252**
0.159**
0.266**
0.219**
0.149**
0.389**
0.538**
0.266**
0.458**
0.562**
1
4.Firmperformance
FP10.202**
0.213**
0.236**
0.227**
0.166**
0.0530.189**
0.241**
0.203**
0.252**
0.210**
0.250**
0.258**
0.210**
0.297**
0.212**
0.140**
0.115*
0.108*
0.197**
0.160**
0.193**
1
FP20.118*
0.101*
0.0910.184**
0.0880.0490.0820.098*
0.112*
0.120*
0.114*
0.108*
0.161**
0.122*
0.211**
0.161**
0.134**
0.099*
0.0580.0430.0560.0810.346**
1
FP30.109*
0.0780.0670.139**
0.0690.0560.0210.117*
0.0920.150**
0.105*
0.0810.226**
0.157**
0.164**
0.103*
0.127**
0.124*
0.110*
0.0840.109*
0.116*
0.275**
0.487**
1
FP40.231**
0.144**
0.218**
0.199**
0.242**
0.112*
0.262**
0.157**
0.274**
0.207**
0.219**
0.380**
0.222**
0.228**
0.355**
0.333**
0.258**
0.183**
0.272**
0.240**
0.114*
0.135**
0.187**
0.124*
0.163**
1
FP50.202**
0.230**
0.263**
0.231**
0.204**
0.183**
0.181**
0.0870.186**
0.270**
0.218**
0.134**
0.226**
0.198**
0.291**
0.230**
0.148**
0.117*
0.261**
0.171**
0.128**
0.0950.308**
0.275**
0.231**
0.141**
1
FP60.237**
0.238**
0.298**
0.166**
0.235**
0.200**
0.290**
0.141**
0.175**
0.213**
0.172**
0.239**
0.309**
0.261**
0.328**
0.201**
0.220**
0.188**
0.224**
0.273**
0.130**
0.163**
0.350**
0.124*
0.149**
0.211**
0.303**
1
FP70.153**
0.144**
0.118*
0.277**
0.203**
0.0930.128**
0.0730.136**
0.182**
0.121*
0.137**
0.176**
0.163**
0.156**
0.129**
0.156**
0.107*
0.103*
0.0940.120*
0.111*
0.204**
0.417**
0.492**
0.191**
0.124*
0.107*
1
Descriptivestatistics
Mean1.722.352.062.222.081.742.172.552.332.792.322.492.242.392.482.111.772.751.892.212.672.892.332.652.271.842.812.062.54
Std.
dev.
0.6171.0040.7810.9260.830.6930.8860.8590.9780.9560.8631.0420.8030.8590.8790.7490.6860.9080.7490.7990.9510.9020.8581.0230.9020.6450.9640.7980.908
**
Correlationissignificantatthe0.01level(2-tailed).
*
Correlationissignificantatthe0.05level(2-tailed).

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.
4.1.4 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 these fit measures was evaluated to assess the level of fit obtained. There is no strong consensus on how
well these indices describe model data fit. Bollen and Long (1993) and others have suggested a graduated list of terms.
These terms include: ‘perfect’, ‘strong’, ‘acceptable’, ‘adequate’, ‘marginal’, ‘weak’, ‘mediocre’, ‘poor’ and ‘no fit’. The
general recommendation for good fit are that p-value associated with the χ2
measure should be greater than 0.05; GFI,
AGFI, TLI and CFI values should be close to 1.0; and, RMR and RMSEA values should be close to 0.0. In our CFA
model, the χ2
statistic p-value is 0.000, suggesting poor fit. However, this fit measure has a tendency to produce nega-
tive 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 of 3.146 suggested ‘acceptable’ fit, this being
close to the conventional threshold value of 3.0 (Schermelleh-Engel, Moosbrugger, and Muller 2003; Hair Jr. et al.
2006; Schreiber et al. 2006). For the other measures of fit, Hu and Bentler (1999) recommend that conventional cut-off
values for strong fit are between 0.9 and 0.95 for indices such as GFI, AGFI, TLI and CFI; and 0.05 to 0.08 for RMR
and RMSEA. Applying these cut-off criteria in our CFA model results, we could conclude that fit is good for RMR and
RMSEA, but poor for GFI, AGFI, TLI and CFI. However, the conventional cut-off criteria for indices of fit are consid-
ered by some researchers to be excessively stringent (Schermelleh-Engel, Moosbrugger, and Muller 2003; Marsh, Hau,
and Wen 2004; Sharma et al. 2005; Hair Jr. et al. 2006; Millsap 2007; Chen et al. 2008; Jackson, Gillaspy, and
Purc-Stephenson 2009; Martinez-Lopez, Gazquez-Abad, and Sousa 2013). Less stringent cut-off criteria where factors
such as model complexity, sample size and number of observed variables are taken into account have been proposed by
Sharma et al. (2005) and Hair et al. (2006). For example, Sharma et al. (2005, 941–942) suggests that for data-sets with
more than 24 items and sample size of around 200, ‘more liberal’ cut-off values of around 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. Our results and fit assessment is similar to many studies
in the operations management area (Tan 2001; Frohlich 2002; Hult, Ketchen, and Nichols 2002; Douglas and Fredendall
2004). For example, Hult et al. declared ‘moderate but acceptable model fit’ (Hult, Ketchen, and Nichols 2002, 581)
based on CFI = 0.84, AGFI = 0.86 and RMSR = 0.08.
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%). 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.
4.1.5 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 fit with data in absolute terms, and also being worse than the CFA and hypothesised models.

(Results for the hypothesised 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. Common methods bias was further assessed using the marker variable method suggested by
Williams, Hartman, and Cavazotte (2010). This method involves including a latent factor that is theoretically unrelated
to the substantive constructs in our study, then performing several types of SEM analysis to determine if significant
levels of common methods bias is present in the measurement items. In our analysis, we included a marker construct
consisting of four items that measured the general business environmental conditions. Our results suggested that
common methods bias was unlikely to be present to a significant extent. Williams, Hartman, and Cavazotte’s (2010)
method also enables quantifying the amount of method variance associated with the measurement of the latent variables.
This reliability decomposition analysis showed that the method components accounted for 5, 4 and 6% of the total
Table 4. ML estimates for parameters of CFA model.
Construct Item
Unstandardized output Standardized output
Factor loading
(std. error, p-value)
Item error variance
(standard error, p-value)
Factor
loading
Sq multiple
correlation
Knowledge integration with customers CR1 1.000a
0.250 (0.019, 0.000) 0.585 0.342
CR2 1.942 (0.177, 0.000) 0.517 (0.042, 0.000) 0.697 0.486
CR3 1.598 (0.139, 0.000) 0.278 (0.023, 0.000) 0.738 0.544
CR4 1.886 (0.172, 0.000) 0.394 (0.034, 0.000) 0.735 0.540
CR5 1.696 (0.154, 0.000) 0.315 (0.027, 0.000) 0.737 0.543
CR6 0.824 (0.110, 0.000) 0.391 (0.028, 0.000) 0.429 0.184
CR7 1.273 (0.144, 0.000) 0.573 (0.042, 0.000) 0.518 0.268
CR8 1.304 (0.143, 0.000) 0.515 (0.038, 0.000) 0.548 0.300
Internal knowledge integration IOP1 1.000a
0.307 (0.024, 0.000) 0.604 0.365
IOP2 1.019 (0.097, 0.000) 0.469 (0.039, 0.000) 0.630 0.397
IOP3 0.729 (0.083, 0.000) 0.392 (0.030, 0.000) 0.500 0.250
IOP4 1.154 (0.106, 0.000) 0.392 (0.030, 0.000) 0.655 0.429
IOP5 0.810 (0.082, 0.000) 0.547 (0.045, 0.000) 0.597 0.356
IOP6 0.948 (0.089, 0.000) 0.409 (0.037, 0.000) 0.653 0.426
IOP7 1.152 (0.096, 0.000) 0.505 (0.043, 0.000) 0.775 0.601
IOP8 0.771 (0.076, 0.000) 0.667 (0.062, 0.000) 0.609 0.370
Knowledge integration with suppliers SI1 1.000a
0.520 (0.050, 0.000) 0.589 0.347
SI2 1.472 (0.145, 0.000) 0.359 (0.027, 0.000) 0.655 0.429
SI3 1.016 (0.115, 0.000) 0.726 (0.057, 0.000) 0.548 0.300
SI4 1.326 (0.135, 0.000) 0.524 (0.041, 0.000) 0.671 0.450
SI5 1.474 (0.160, 0.000) 0.588 (0.051, 0.000) 0.627 0.393
SI6 1.571 (0.155, 0.000) 0.606 (0.046, 0.000) 0.704 0.496
Firm performance PF1 1.000a
0.549 (0.042, 0.000) 0.560 0.313
PF2 1.282 (0.169, 0.000) 0557 (0.040, 0.000) 0.602 0.362
PF3 1.127 (0.156, 0.000) 0.618 (0.048, 0.000) 0.655 0.360
PF4 0.496 (0.085, 0.000) 0.414 (0.031, 0.000) 0.600 0.136
PF5 0.935 (0.130, 0.000) 0.422 (0.033, 0.000) 0.369 0.217
PF6 0.692 (0.105, 0.000) 0.308 (0.027, 0.000) 0.417 0.174
PF7 1.011 (0.149, 0.000) 0.353 (0.027, 0.000) 0.534 0.286
Relationship Covariance (standard error, p-value) Correlation
coefficient
Knowledge integration with customers ↔ Firm performance 0.079 (0.015, 0.000) 0.455
Knowledge integration with customers ↔ Internal knowledge
integration
0.167 (0.022, 0.000) 0.783
Knowledge integration with customers ↔ Knowledge integration
with suppliers
0.079 (0.013, 0.000) 0.546
Internal knowledge integration ↔ Firm performance 0.16 (0.028, 0.000) 0.563
Internal knowledge integration ↔ Knowledge integration with
suppliers
0.153 (0.023, 0.000) 0.642
Knowledge integration with suppliers ↔ Firm performance 0.076 (0.016, 0.000) 0.391
a
Parameter fixed to enable structural equation modelling analysis, therefore not tested for statistical significance.

reliability values for the three substantive constructs in our model, these being knowledge integration with customers,
knowledge integration with suppliers and internal knowledge integration, respectively. The methods proportion for the
firm performance construct was 43%. While this value is high, it was not unexpected as there is a strong theoretical link
between firm performance and business environmental conditions. Overall, these tests suggest that common methods
bias was not present in our study in a significant manner.
4.2 SEM results for the structural model
4.2.1 Evaluation of goodness-of-fit indices
The hypothesised 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 hypothesised relationships, the SEM analysis
procedure was again used. Since the number of relationships specified in the hypothesised 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 hypothesised model has an ‘adequate’ level of
empirical support.
4.2.2 Evaluation of parameter estimates
Table 5 shows the SEM output of the model with all the parameters presented in unstandardised form as well as in
standardised form for the structural model. As the data in this table show, there were no ‘offending’ (theoretically
impossible) estimates present. Further, all the relationships were statistically significant and positive, as predicted in the
hypothesised 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.
The results of SEM analysis are shown in summary form in Figure 2. This figure provides the standardised
regression and correlation coefficients between constructs and the squared multiple coefficient values for the endogenous
construct.
The regression and correlation data presented in Table 5 were further analysed by examining the standardised 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 standardised 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.
Table 5. Relationships between constructs.
Relationships
Unstandardised output
Standardised output
Regression coefficient
Regression
coefficient
Squared multiple
correlation
Knowledge integration with customers → Firm performance 0.041 (0.145, 0.780) 0.031 0.319
(Firm performance)Internal knowledge integration → Firm performance 0.413 (0.113, 0.000) 0.509
Knowledge integration with suppliers → Firm performance 0.056 (0.099, 0.571) 0.047
Covariance coefficient
Correlation coefficient
Knowledge integration with customers ↔ Internal knowledge
integration
0.167 (0.022, 0.000) 0.783
Knowledge integration with suppliers ↔ Internal knowledge
integration
0.153 (0.023, 0.000) 0.642
Knowledge integration with customers ↔ Knowledge
integration with suppliers
0.079 (0.013, 0.000) 0.546

5. Discussion and conclusions
5.1 General discussion
The results provide evidence supporting the contention that integration through collaboration between trading partners to
facilitate innovative means for 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, Ketchen, and Nichols 2003; Hult et al. 2006), this relationship indicates that it is particularly
important in enabling innovative 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, 2002; Heikkila 2002; Rosenzweig, Roth, and Dean 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, 2002).
Table 6. Estimates of standardised direct, indirect and total effects of the exogenous constructs on the endogenous constructs.
Exogenous construct
Endogenous construct: firm performance
Direct effect Indirect effect Total effect
Knowledge integration with customers 0.031 0.627 0.658
Internal knowledge integration 0.509 0.085 0.594
Knowledge integration with suppliers 0.047 0.367 0.414
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
Collaborative knowledge integration
Figure 2. Final form of the theoretical model. Results shown are standardised regression weights on single-headed arrows,
standardised 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).

They also extend the results of these studies by: (a) specifically incorporating and verifying the important role that
innovative knowledge sharing 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 integra-
tion 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 Integra-
tion 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 innovative leverage at the firm level through internal
knowledge integration.
The findings also provide a particular focus on the potential to build innovative capability via 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, Roth, and Dean 2003). In this study, the focus has been on processes and methods
whereby knowledge is made accessible and used in innovative ways 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 innovative integrative mechanism. An important research theme in this area has been the identifica-
tion and analysis of the dynamics of supply chain systems and the isolation of the causes of these dynamics (Forrester
1958, 1961; Sterman 1989; Lee, Padmanabhan, and Whang 1997a). 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.
5.2 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 and Baden-Fuller 1995). The high proportion of variance in firm
performance explained by collaborative knowledge integration in this study certainly provides strong empirical evidence
that knowledge represents a significant source of innovative potential with high strategic value. Collaboration between
trading partners as seen from a KBV perspective can minimise 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
innovative knowledge sharing practices providing capabilities that are difficult to imitate (Nonaka et al. 2000), 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 innovative capability and 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 innovative capabilities
difficult for competitors to replicate.

Production of goods and services as the outcome of the application of knowledge in a commercial context 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 specialisation (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 specialised 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 a
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 innovative 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, 2004; Nickerson and Zenger 2004). These recent studies have provided evidence that indicates that the KBV and
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 relation-
ship 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 pro-
vide 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.
5.3 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 under-
standing of the risk/reward relationship between opportunity and opportunism. Managers in Australian manufacturing
firms specifically, and others more generally, can use these innovation-related knowledge management practices to
improve their internal and external supply chain integration. This, in turn, can lead to improved financial performance
and competitive advantage.
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