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25,1 The impact of supply chain
relationship dynamics on
Michael Smurﬁt Graduate School of Business, University College Dublin,
London Business School, London, UK, and
Sean de Burca
Michael Smurﬁt Graduate School of Business, University College Dublin,
Purpose – The purpose of this paper is to investigate how the dynamics of supply chain (SC)
relationships impact on manufacturing performance.
Design/methodology/approach – A conceptual framework was developed incorporating
dimensions of SC relationship dynamics and manufacturing performance. Structural equation
modelling was used to test the model with data collected using a postal questionnaire from 200
suppliers in the electronics sector in the Republic of Ireland.
Findings – There was mixed support for the impact of SC relationship dynamics on manufacturing
performance. Hypotheses in respect of cost and quality were supported but those in respect of
ﬂexibility and delivery were not.
Research limitations/implications – Using single informants and focal customers in research
Practical implications – The process of forming and developing SC relationships can be complex
and requires ﬁrms to be competent in areas such as negotiation skills, conﬂict management,
anticipating problems/ﬁnding solutions in advance and joint problem solving. The selection of such
managers should be driven by the need to ﬁnd individuals who possess supply chain management
skills and relational competencies.
Originality/value – The nature of the relationship between measures of manufacturing performance
has been addressed by two dominant theories: the cumulative or “sandcone” theory and the “trade-off”
theory. Findings provide only partial support for this theory with respect to quality and cost. In
contrast, the model is more aligned with the “trade-off” theory.
Keywords Supplier relations, Performance management, Ireland, Electronic engineering
Paper type Research paper
Empirical research in the area of supply chain (SC) relationships has primarily sought
to explain the nature of relationship processes rather than their effect on
International Journal of Operations & manufacturing or business performance (Styles and Ambler, 2000). As a result,
Production Management there is a considerable body of work focusing on the interaction between the various
Vol. 25 No. 1, 2005
pp. 6-19 dimensions of SC relationships (such as trust, commitment, adaptation, communication
q Emerald Group Publishing Limited and collaboration) but far less on the impact of SC relationship dimensions on
DOI 10.1108/01443570510572213 manufacturing performance. In contrast, there is a considerable body of empirical
research that has examined the impact of manufacturing practices on manufacturing Supply chain
performance (Ferdows and De Meyer, 1990; Pearson et al., 1995; Voss et al., 1995;
Ahmed et al., 1996; Flynn et al., 1999; Lau, 1999; Cua et al., 2001). Accordingly, this
paper posits the following research question:
RQ1. How do the dynamics of SC relationships impact on manufacturing performance?
The remainder of this paper is structured as follows: ﬁrst, we ﬁrst review the 7
theoretical context and outline our hypotheses; second, we describe our methodology:
third we develop and test a model of SC relationships and manufacturing performance;
fourth, we reﬂect on the implications of our study and conclude with some suggestions
for future research.
Theoretical background and hypotheses
Researchers have used a number of different theoretical frameworks in order to explain
the nature of SC relationships. These include transaction cost theory, political economy
theory, social exchange theory and resource dependence theory (Robicheaux and
Coleman, 1994). These theoretical frameworks have all contributed to the modelling of
SC relationships both in their identiﬁcation of the underlying dimensions of
relationships and their selection of appropriate units of analysis (such as ﬁrm, dyad or
network). In addition, different but complementary streams of research have appeared
in the somewhat diverse areas of industrial and business-to-business marketing,
channel management, relationship marketing, operations management, supply chain
management, logistics and purchasing. They include the IMP (Industrial Marketing
and Purchasing Group) Interaction Model, Network Models, Channel Models, Process
Models and Partnership Models. The primary focus of these approaches has been on
the nature of relationship processes rather than the effect of relationships on
manufacturing or business performance. We wish to address this gap in the context of
the sequence of how relationships evolve and their impact (if any) on manufacturing
According to Dwyer et al. (1987), relationships evolve through ﬁve general phases:
(4) Commitment; and
(5) Dissolution (which we do not include for the purpose of this study).
We now use this framework to develop our conceptual model of SC dynamics as
follows. Awareness refers to A’s recognition that B is a feasible exchange partner;
however, interaction between the parties has not yet transpired. Exploration refers to
the search and trial phase in relational exchange and communication is a key feature of
this phase. Communication is “the formal as well as informal sharing of meaningful
and timely information between ﬁrms” (Anderson and Narus, 1990, p. 44). Frequent
and timely communication is important because it assists in resolving disputes and
aligning perceptions and expectations (Morgan and Hunt, 1994). Effective
communication is therefore essential for successful relationships (Monczka et al.,
1995). There are three aspects of communication behaviour that are important in
relationships: the quality of the communication, the form of information sharing and
IJOPM the extent to which both parties jointly engage in planning and goal setting (Mohr and
25,1 Nevin, 1990). Expansion is the next phase in relationship development and refers to the
continual increase in beneﬁts obtained by exchange partners. With increased
communication, parties begin to establish trust. Trust has been deﬁned as “the ﬁrm’s
belief that another company will perform actions that will result in positive actions for
the ﬁrm, as well as not take unexpected actions that would result in negative outcomes
8 for the ﬁrm” (Anderson and Narus, 1990, p. 45). Assuming the relationship continues to
develop and communication increases, the level of trust between both parties will grow
(Anderson and Narus, 1990). Formally, this gives:
H1. Communication has a positive effect on trust.
Commitment is the next phase of relationship development and refers to an implicit or
explicit pledge of relational continuity between exchange partners. Communication
and trust continue to grow during this phase leading to increased co-operation and goal
congruence. Increasing levels of trust reduce the speciﬁcation and monitoring of
contracts, reduce uncertainty and provide material incentives for co-operation (Hill,
1990). This co-operation includes collaboration on matters such as quality, product and
process design, information systems and value analysis. Once trust is established, both
parties learn that coordinated joint efforts will lead to outcomes that exceed what either
would have achieved acting independently. This gives:
H2. Trust has a positive effect on co-operation.
The co-operation which arises from the presence of trust during the expansion phase
now begins to impact on the commitment phase. As the relationship develops and
expectations continue to grow, the parties begin to bond themselves in such way as to
encourage their continued investment in the relationship (i.e. they become committed).
Such bonding is frequently by way of exchanging transaction speciﬁc investments (or
adaptation). Williamson (1983) argues that the exchange of transaction-speciﬁc
investments (or adaptation) results from both increased trust and co-operation that
gives credibility of commitment to the relationship. This gives:
H3. Trust has a positive effect on adaptation (transaction-speciﬁc investments).
H4. Co-operation has a positive effect on adaptation (transaction-speciﬁc
Adaptation is frequently bilateral in nature. Suppliers adapt to the needs of speciﬁc
important customers and customers adapt to the capabilities of speciﬁc suppliers
(Heide and John, 1988). Adaptations are important for a number of reasons. First, they
can represent considerable investments by one or both parties. Second, they may be of
critical importance for the conduct of business. Third, the investments frequently
cannot be transferred to other SC relationships. Adaptations can have signiﬁcant
consequences on the long-term performance because they enhance the competencies
and attractiveness of a particular supplier/customer. More speciﬁcally, adaptation
involving process and product design, value analysis, cost targeting and design of
quality control and delivery systems can directly impact on the key manufacturing
performance variables: cost, quality, ﬂexibility and delivery (Heide and John, 1990). In
H5. Adaptation has a positive effect on cost. Supply chain
H6. Adaptation has a positive effect on quality. relationship
H7. Adaptation has a positive effect on ﬂexibility.
H8. Adaptation has a positive effect on delivery.
Figure 1 shows our conceptual model in a structural equation model operationalising 9
The instrument used to test the stated hypotheses was a mail survey. A questionnaire
based on existing ﬁve-point Likert measurement scales anchored by strongly
agree/disagree for the relationship constructs and superior/inferior to competitors for
the performance constructs was initially drafted (see Appendix). While it could be
argued that objective scales are more insightful we have used subjective scales because
of the multi-sectoral nature of our survey. In addition, ﬁrms can be reluctant to disclose
exact performance ﬁgures (Ward and Duray, 2000); however, managers
well-acquainted with performance data can provide an accurate subjective
assessment (Choi and Eboch, 1998). Indeed, past research indicates that managerial
evaluations correspond closely to objective data obtained from both internal and
external sources (Dess and Robinson, 1984; Venkatraman and Ramanujam, 1986).
This draft questionnaire then was pre-tested with academics and practitioners to
check its content validity and terminology and modiﬁed accordingly. The modiﬁed
questionnaire was then pilot tested to check its suitability and appropriateness for the
target population before mailing. To encourage completion, respondents were
promised, and received, a summary of the research ﬁndings. Two repeat mailings of
the instrument were carried out to improve the overall response rate. For the purposes
of this study, we adopted the approach used by Sako et al. (1994), where respondents
were asked to reply to questions with respect to the basis of the most important or focal
Structural equation model
25,1 The population chosen for this study were manufacturing companies in the electronics
sector in the Republic of Ireland. The reasons for focussing on this sector are twofold.
First, electronics has emerged as a leading sector in the Republic of Ireland in terms of
adopting manufacturing and SC practices and is not subject to the same level of
regulation as other comparable sectors such as pharmaceuticals and chemicals
10 (Dicken, 1998). As such, the sector is predominantly inﬂuenced by competitive rather
than regulatory forces. Second, the sector is heterogeneous in terms of sub-sectors and
In order to establish the size of the survey population, databases from the Irish
Trade Board, the National Standards Association of Ireland, the Industrial
Development Authority and Kompass Ireland were consulted. This produced an
initial listing of 821 companies. Telephone contact was established with each of these
companies and the key informant was also identiﬁed at this stage. The key informant
was identiﬁed by enquiring as to which single individual was responsible for, and
capable of responding to questions on SC relationships and manufacturing
performance. This step was taken in order to improve the quality and quantity of
responses as well as to reduce the impact of potential inaccurate recall, hindsight bias
and subconscious attempts to maintain self-esteem that can occur from using a single
informant (Kumar et al., 1993). From the initial frame of 821 companies, 283 were
removed from the sample as they had either gone into liquidation or were service
rather than manufacturing plants. Each of the remaining 538 companies was then sent
a copy of the questionnaire. A total of 202 questionnaires were returned, of which 200
were usable giving an overall response rate of 38 per cent.
Analysis and ﬁndings
The degree to which the sample is representative of the population was addressed by
carrying out a series of standard chi-square goodness-of-ﬁt tests with respect to
employee numbers, plant ownership and plant age (see Table I). For each of the
characteristics, we found no signiﬁcant difference between the population percentages
and the sample percentages. This suggests that the sample response proﬁle is not
signiﬁcantly different from the population proﬁle and that the sample is broadly
representative on key variables.
The descriptive data collected (plant size, ownership) conﬁrmed much of what is
already known about the electronics sector in Ireland in terms of industry structure. On
the one hand, the majority of companies are relatively small, independently owned
indigenous operations, and, on the other, there are a smaller number of larger plants
that are subsidiaries of overseas companies (Table I).
Conﬁrmatory factor analysis and structural path model
We conducted a single-step analysis with the simultaneous estimation of both
measurement and structural models to test our hypotheses using AMOS 4. The
covariance matrix used for data input is shown in the Appendix. The overall ﬁt
statistics for the model (x2¼ 509.52, df ¼ 516, x2/df¼ 1.72, p , 0.001, GFI ¼ 0.83,
AGFI ¼ 0.80, CFI ¼ 0.87, RMSEA ¼ 0.06) demonstrate an acceptable level of overall
ﬁt (Bollen, 1989).
Characteristic Population (%) Sample (%) x2
No. of employees
Fewer than 20 21.9 16.5
20 but fewer than 50 41.2 40.0
50 but fewer than 100 15.6 20.5
100 but fewer than 200 11.0 11.5 11
200 or more 10.3 11.5 NS
Irish 55.0 52.0
UK 5.0 2.5
Other European 14.0 14.5
USA 20.5 25.0
Japan 2.0 3.5
Other 3.5 2.5 NS
Less than 5 years 10.8 14.0
6 but less than 11 years 18.5 22.0
11 but less than 20 years 47.1 42.0
20 but less than 50 years 21.2 19.0 Table I.
50 years or more 2.4 3.0 NS Population and sample
Note: NS ¼ Not signiﬁcant proﬁles
Conﬁrmatory factor analysis (CFA) was performed to evaluate the measurement
properties of the model constructs. The factor loadings (l), standard errors, t-values and
Cronbach a values are shown in Table II. All but one of the items (i.e. A1) have high
(l . 0.60) and signiﬁcant (t . 1.96) loadings (Chin, 1998). However, we retain A1, given
its centrality to our argument and also that its factor loading (0.50) is reasonably high
and also signiﬁcant ( p , 0.01). In addition, the reliability of each scale was satisfactory
with Cronbach a values of at least 0.70 achieved in all cases (Nunally, 1978) (Table II).
Table III shows the standardized path estimates (g), standard errors and t-values for
the path (structural) model. With the exception of H7 and H8, all path estimates are
both high (g . 0.20) and signiﬁcant (t . 1.96) (Chin, 1998). The results thus provide
empirical support for all but two of our hypotheses. We reﬂect on our ﬁndings and
their implications in the next section (Table III).
Discussion and implications
We reﬂect on our results with regard to ﬁrst, SC relationship dynamics and second, the
effect of SC dynamics on manufacturing performance. Using Dwyer et al.’s (1987)
relationship development process incorporating the sequential phases of awareness,
exploration, expansion and commitment we developed a conceptual framework which
posited that the initial activity in relationship development was communication. As
communication grows in terms of frequency and intensity, trust develops which in turn
leads to increased co-operation and adaptation. Our analysis provides empirical
support for this sequence of events in relationship development. In addition, our results
are consistent with previous studies that have focussed on speciﬁc phases of
relationship development such as communication ! trust (Anderson and Narus,
1990), trust!co-operation (Styles and Ambler, 2000), and co-operation!adaptation
IJOPM Construct Standardised loading l Standard error t-value a
CM2 0.65 0.12 7.22
CM3 0.80 0.15 8.18
12 CM4 0.62 0.12 6.97
T2 0.68 0.12 8.24
T3 0.84 0.14 9.62
T4 0.75 0.11 8.92
Q2 0.75 0.17 5.63
F2 0.65 0.38 2.97
CL2 0.71 0.16 7.14
CL3 0.78 0.16 7.39
CL4 0.60 0.13 6.34
A2 0.82 0.24 6.69
A3 0.63 0.22 5.97
A4 0.77 0.24 6.61
C2 0.83 0.35 4.43
Table II. D1* 0.99
Conﬁrmatory factor D2 0.84 0.28 2.53
analysis and reliabilities Note: * The corresponding parameter is set to 1 (unstandardised) to ﬁx the scale of measurement
path estimate Standard
Hypothesis Path g error t-value Result
H1 Communication ! trust 0.79 0.12 6.57 Supported
H2 Trust ! co-operation 0.43 0.16 4.34 Supported
H3 Trust ! adaptation 0.36 0.10 3.51 Supported
H4 Co-operation ! adaptation 0.34 0.06 2.47 Supported
H5 Adaptation ! quality 0.61 0.20 4.56 Supported
H6 Adaptation ! cost 0.52 0.16 3.63 Supported
Table III. H7 Adaptation ! ﬂexibility 0.38 0.14 1.82 Not supported
Path model coefﬁcients H8 Adaptation ! delivery 0.13 0.12 1.58 Not supported
(Wilson and Mummalaneni, 1986). Our ﬁndings are also consistent with those of Supply chain
Humphries and Wilding (2003). relationship
The managerial implications in respect of SC relationship dynamics are that ﬁrms
need to identify at what stage they are at in terms of relationship development and act
accordingly. As with personal relationships, the process of forming and developing SC
relationships can be complex and requires ﬁrms to be competent in areas such as
negotiation skills, conﬂict management, anticipating problems/ﬁnding solutions in 13
advance and joint problem-solving. Managers can monitor and measure relationship
building using a staged model that covers awareness, exploration, expansion and
commitment. The selection and training of such managers should be driven by the
need to ﬁnd individuals who not only possess supply chain management skills but also
relational competencies and an extensive personal network (or the ability to develop
one). Recognising that SC relationships are close, trusting and requiring investment is
a different perspective from viewing them for the purposes of manipulation. However,
the development and nurturing of these skills is still at a relatively low level in respect
of our study population. Indeed, training and development programmes in the
electronics sector still focus on skills such as sales training rather than relationship
management (Fynes and Ainamo, 1998).
Our ﬁndings in respect of the relationship between the dynamics of SC relationships
and manufacturing performance are more mixed. More speciﬁcally, our ﬁndings suggest
that adaptation (or investment in transaction-speciﬁc investments) leads to an
improvement in product quality (H5) and a reduction in product cost (H6) but has no
effect on ﬂexibility (H7) or delivery performance (H8). In this regard it is useful to think
of quality efforts as reducing variance while ﬂexibility efforts accommodate variance
(Jayaram et al., 1999). For example, if managing the dynamics of a relationship are
focused on achieving very high conformance to speciﬁcations, it can be more difﬁcult to
achieve performance on ﬂexibility dimensions such as rapidly accommodating a sudden
increase in demand (volume ﬂexibility) or frequent or rapid changes in the product mix
(variety ﬂexibility). Accommodating these sources of variation can be more challenging
when conformance standards are higher and managers are highly focused on achieving
them as compared to when quality standards are lower. This could also explain the lack
of support for H8 (delivery) as accommodating variance is critical here also.
What then are the implications of our study for the understanding manufacturing
performance? The nature of the relationship between measures of manufacturing
performance has been addressed by two dominant theories: the cumulative or
“sandcone” theory and the “trade-off” theory. The “sandcone” theory, as developed by
Ferdows and De Meyer (1990) is based on the proposition that competencies are
cumulative rather than mutually exclusive. They suggest that lasting improvements in
performance always involve the same sequence of ﬁrst quality improvement, followed
by dependability, speed and ﬁnally cost efﬁciency. Our ﬁndings provide only partial
support for this theory with respect to quality and cost. In contrast, our model is more
aligned with Skinner’s (1969) “trade-off” theory. This theory is based on the
proposition that trade-offs between measures of manufacturing performance occur
because factories are technology-based systems which are limited in what they can do
with their equipment, materials, information systems and management systems. As a
result, trade-offs will occur. Our ﬁndings support this view. In this regard, Skinner
(1992, p. 21) argues that trade-offs are dynamic and notes that “in many
IJOPM technologically-based relationships between outcomes, generally called trade-offs, the
25,1 variables may run in parallel or counter to one another at different levels of amplitude”.
How the changing dynamics of a SC relationship impact on this is an interesting
avenue for future research.
Finally, the items used to measure adaptation (see Appendix) primarily reﬂect an
emphasis on adapting production processes and tooling to match the requirements of the
14 customer. It is not surprising that that such adaptations lead to improvements by
reducing variation and improving quality. As a result, the cost of poor quality is reduced
and the ﬁrm becomes more cost competitive. On the other hand, it would appear that
investment in transaction-speciﬁc investments has no effect on ﬂexibility or delivery
performance. This may very well be explained by the fact that none of the items used to
measure adaptation had an explicit delivery/ﬂexibility dimension. As such, the construct
items could be expanded to include such dimensions in future studies.
Limitations and future research
The limitations associated with this study primarily relate to the use of the focal or “most
important” customer and reliance on supplier perceptions of SC relationships. Using the
concept of the focal customer has a number of disadvantages. First, the approach
assumes the existence of a single focal relationship whereas, in practice, a company may
have a selection of equally important customer relationships that inﬂuence one another.
Second, there might not necessarily be a uniform perception within a supplier company
as to which customer is the most important. Hence, using a single key informant could
produce some bias in our study. Third, respondents were not given a speciﬁc set of
criteria on which to base their selection of a focal customer relationship in our study;
instead, they were asked to respond with respect to their “most important customer
relationship”. This approach was adopted so as to facilitate a ﬂexible interpretation of
the term “most important”. Equally, however, such ﬂexibility can give rise to bias if
respondents used a wide variety of ways to deﬁne “most important”.
Relying on supplier perceptions of analysis is also a limitation. It can be argued that
the perceptions of relationship in our study are somewhat one-sided in that they
represent the views of just one party and ignore the views of customers. This limitation
implicitly suggests a signiﬁcantly different research design based on the relationship
dyad (in itself, not without difﬁculties in terms of sample size, dyad access,
conﬁdentiality and accuracy of response).
As is often the case, longitudinal research could provide valuable contributions to
theory development and reﬁnement in the ﬁelds of SC relationships. There is a
signiﬁcant temporal dimension to how buyer-seller relationships develop. Accordingly,
tracking the development of SC relationships could help clarify cause and effect
relationships between variables. A research design, described by Anderson (1995) as
“cross-sectional research which is longitudinal in character” presents a potentially
interesting approach to data collection in this regard. This involves identifying critical
indicators of each stage of SC relationship development from a set of relationship
dyads at pre-ordained points in time. These critical indicators could then be used as a
basis on which to separate the relationships into those that are at similar stages of
development. These sub-samples could then be analysed separately, and the effects of
temporal constructs assessed empirically. Empirical studies of the customer’s
perspective would complement and add to the ﬁndings of this study. Finally, future
research could examine issues such as customer perceptions of the dimensions of Supply chain
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Construct measures and sources (anchored by a strongly agree/disagree ﬁve-point scale). Please
also see Table AI for sample covariances (estimates):
(1) Quality performance (customer satisfaction) (Voss and Blackmon, 1994):
Q1 – Frequency of customer complaints.
Q2 – Adequacy of customer complaint tracking/feedback systems.
(2) Delivery performance (Choi and Eboch, 1998):
D1 – Speed of delivery relative to competitors.
D2 – Percentage of orders delivered on-time.
(3) Cost performance (Fynes and Voss, 2001):
C1 – Unit cost of product relative to competitors.
C2 – Unit cost of product over life cycle.
(4) Flexibility performance (Dixon, 1992):
F1 – Volume ﬂexibility.
F2 – Variety (product line) ﬂexibility.
(5) Communication (Heide and John, 1992):
CM1 – Exchange of information in this relationship takes place frequently and
informally, and not only according to a pre-speciﬁed agreement.
CM2 – In this relationship, any information that might help the other party will be
provided for them.
CM3 – Both parties in the relationship will provide proprietary information if it can
help the other party.
. CM4 – Both parties keep each other informed about events or changes that may
affect the other party.
(6) Co-operation (Morgan and Hunt, 1994):
CL1 – We co-operate extensively with this customer with respect to product design.
. CL2 – We co-operate extensively with this customer with respect to process design.
CL3 – We co-operate extensively with this customer with respect to forecasting and
CL4 – We co-operate extensively with this customer with respect to quality practices.
(7) Adaptation (Heide and John, 1992): Supply chain
A1 – Gearing up to deal with this customer requires highly specialised tools and relationship
A2 – Our production system has been tailored to meet the requirement of this
A3 – We have made signiﬁcant investments in tooling and equipment that are
dedicated to our relationship with this customer. 19
A4 – Our production system has been tailored to produce the items supplied to this
(8) Trust (Larzelere and Huston, 1980):
. T1 – Based on your past and present experience, how would you characterise the
level of trust your ﬁrm has in its working relationship with this customer.
T2 – We feel that this customer can be counted on to help us.
T3 – We feel that we can trust this customer completely.
T4 – This customer has a high level of integrity.