Research paper

Supply chain integration and efficiency
performance: a study on the interactions
between customer and supplier integration
Pamela Danese
Department of Management and Engineering, University of Padova, Vicenza, Italy, and

Pietro Romano
Department of Electrical, Managerial and Mechanical Engineering, University of Udine, Udine, Italy
Abstract
Purpose – This research intends to investigate whether there are synergies that a firm could or should exploit by simultaneously implementing
customer and supplier integration. In particular, the aim is to analyze the impact of customer integration on efficiency, and the moderating role of
supplier integration.
Design/methodology/approach – This study analyzes data from a sample of 200 manufacturing plants. Two hypotheses are tested through a
hierarchical regression analysis. Customer and supplier integration constructs consider items related to different aspects of the integration (e.g. sharing
of production plans and customers’ forecasts, feedback on performance, communication on quality considerations and design changes, joint quality
improvement efforts, close contact, partnerships). The focus of the integration clearly extends beyond the dyad, as it includes the integration of focal
operations upstream and downstream, with both suppliers and customers.
Findings – Supplier integration positively moderates the relationship between customer integration and efficiency, whereas the analyses do not
support the hypothesis that in general customer integration positively impacts on efficiency. They also reveal that, when supplier integration is at a low
level, customer integration can even produce a reduction in efficiency.
Practical implications – Efficiency performance optimization requires levering simultaneously on customer and supplier integration to foster their
interaction, rather than investing and acting on customer integration only. In addition, before deciding whether to invest in customer integration,
managers should ascertain the level of supplier integration, since it acts as a prerequisite for the successful implementation of customer integration.
Originality/value – Compared with previous studies investigating the main impact of customer and supplier integration on a company’s performance,
this research analyzes a model that considers the interaction effect between these integration strategies. This provides a number of original implications
for the interpretation of the relationship between customer and supplier integration and efficiency.
Keywords Supplier relations, Supply chain management, Performance measures, Customer relationship management
Paper type Research paper

nowadays is considered an important determinant to maintain
a competitive advantage over competitors.
Numerous studies have explored the concept of SC
integration in different research areas such as information
processing (Lee et al., 1997; Lee et al., 2000; Zhao et al.,
2002), inventory planning and logistics (Ganeshan et al.,
2001; Disney and Towill, 2002; Romano, 2009; Danese,
2011), or partnership/relationships (Carter et al., 2000; Fynes
et al., 2005). The dominant belief is that SC integration is a
useful approach to improve various measures of firm
performance (Fabbe-Costes and Jahre, 2008; van der Vaart
and van Donk, 2008; Singh and Power, 2009; Ou et al., 2010;
Wiengarten et al., 2010). However, some authors argue that
performance improvements are not assured if SC integration

1. Introduction
Over the years the attention of practitioners and academic
literature on integration practices between supply chain
partners has significantly grown (van der Vaart and van Donk,
2008). The intensification of global competition and the
demand for better customer service have considerably
increased the need for integration between companies.
Consequently, supply chain (SC) integration, aimed at
coordinating processes along the supply chain seamlessly,
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1359-8546.htm

Supply Chain Management: An International Journal
16/4 (2011) 220– 230
q Emerald Group Publishing Limited [ISSN 1359-8546]
[DOI 10.1108/13598541111139044]

Received: August 2010
Revised: November 2010
Accepted: January 2011

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Supply chain integration and efficiency performance

Supply Chain Management: An International Journal

Pamela Danese and Pietro Romano

Volume 16 · Number 4 · 2011 · 220 –230

programs are not accompanied by the implementation of a
coherent mix of supply chain management (SCM) initiatives
encompassing, for instance, lead time reduction, supplier
network rationalization, production network reconfiguration,
development of partnerships, etc (de Treville et al., 2004;
Danese et al., 2006; Kim, 2006). In particular, it has been
proposed that implementing integration both upstream and
downstream is better than concentrating the firm’s efforts on
integrating customers or suppliers only (Frohlich and
Westbrook, 2001; Rosenzweig et al., 2003).
Despite this recognition, SCM literature does not
empirically examine the way these integration practices
“interact” and whether their simultaneous presence can
determine a positive additional synergistic effect on
companies’ performance. Several authors distinguish
between integration with customers and suppliers and
investigate the main impact of each integration activity on
companies’ performance (Lee et al., 2007). A key question
however is: are there synergies that a firm could or should
exploit by simultaneously implementing customer and
supplier integration?
This paper intends to contribute to filling this gap by
focusing on efficiency performance. In particular, the aim is to
analyze the impact of customer integration on efficiency, and
the moderating role of supplier integration. In fact, SCM
literature suggests that customer integration, as well as
reducing inventory and manufacturing costs, usually
determines other additional costs, due to the frequent plan
modifications required to follow customer needs (Disney and
Towill, 2002). Supplier integration could be a useful tool to
limit the negative implications of customer integration on
costs, thus amplifying its positive effect on efficiency. Our
intent is to open an interesting debate on this issue, by
introducing explanations on how efficiency is not improved by
using just customer integration practices more extensively, but
by simultaneously integrating upstream.
The paper is organized as follows. First, it analyzes existing
literature on the impact of customer integration on efficiency
and the moderating role of supplier integration, and then
discusses the research hypotheses this study intends to
examine. The following section introduces the sampling
frame, measures and data collection. This is followed by
analyses and discussion of the theoretical and managerial
implications of this study. Finally, conclusions are drawn
which summarize the results found.

(Lee et al., 2007), or by defining SC integration as a unique
concept that includes both upstream and downstream
integration (Vickery et al., 2003; Kim, 2006).
Focusing our attention on the relationship between
customer integration and efficiency performance, several
arguments can be found in the literature to support the
existence of a positive link. For instance, Lee et al. (1997)
identify the potential causes of the “bullwhip effect” (i.e. the
natural tendency of decentralized decision-making to amplify,
delay and distort demand information moving upstream in a
make-to-stock supply chain) and recommend strategies for
counteracting its effect. Suggested remedies include sharing
point-of-sales data and operational alignment to final demand
of channel member activities. These practices reduce system
uncertainty and, in turn, costs (Chen et al., 2000; Bayraktar
et al., 2009; Ryu et al., 2009; Coppini et al., 2010;
Wangphanich et al., 2010). Lee et al. (2000) demonstrated
that information sharing lowers costs from 12-23 percent,
while Gavirneni et al. (1999) reported cost reductions of 1-35
percent, when sharing retailers’ demand data. Zhao et al.
(2002) found that information sharing and order coordination significantly impact on supply chain performance
in terms of total costs; while Lin et al. (2002) demonstrated
that sharing demand information allows greater inventory cost
savings to be achieved.
A further important aspect of customer integration is the
development of partnership relationships with customers
(Power, 2005). These can promote cooperation, openness of
communication and a problem-sharing attitude. Companies
working in close contact can share information on unexpected
problems that can occur locally and, on the basis of these,
activities are adjusted accordingly. Customers could provide
the producer with feedbacks on quality and delivery
performance, or involve the manufacturer in their quality
improvement efforts. Thus, partnership relationships
guarantee more efficient problem solving and the creation of
inter-company decision-making routines (Flynn and Flynn,
1999; Frohlich and Westbrook, 2001).
From the above arguments, it follows that there is a
theoretical foundation as well as emerging evidence for a
general positive relationship between customer integration
and efficiency performance.
However, although the potential benefits of customer
integration, and in general SC integration, seem compelling, a
recent academic debate has arisen on the actual positive
impact of integration on company performance. A couple of
literature reviews on SCM survey-based research have argued
that studies on the effect of SC integration on performance
are not unanimous and that caution is advisable (FabbeCostes and Jahre, 2008; van der Vaart and van Donk, 2008).
In particular, some authors maintain that the nature of SC
integration is complex and more knowledge is needed on its
relation to performance. For instance, Stock et al. (2000)
conclude that external integration does not necessarily
provide performance benefits in all cases. Sezen (2008)
found that information sharing and SC integration were not
significantly related to flexibility, efficiency and output
performance (i.e. customer satisfaction and profit). Cousins
and Menguc (2006) argue that whilst SC integration clearly
has its benefits, it also has costs and “in some cases it has the
reverse effect” (p. 616). Coherently, Das et al. (2006) report
that collaborating with other partners can cause increased
costs of coordination and inflexibility.

2. Literature review and research hypotheses
2.1 Relationship between customer integration and
efficiency
In a recent literature review on survey-based SCM studies,
Fabbe-Costes and Jahre (2008) argue that authors generally
agree that stronger linkages and a higher degree of integration
across organizational boundaries lead to better performance
for the focal organization and its supply network. Some
researchers have limited their analyses to integration with
customers (Closs and Savitskie, 2003; Fynes et al., 2005;
Sahin and Robinson, 2005) or suppliers (Das et al., 2006;
Handfield et al., 2009; Jain et al. 2009; Lawson et al., 2009;
Vachon et al., 2009; Wagner and Krause, 2009) in order to
ascertain their distinct contribution to performance. Instead,
in other studies, authors take a broader perspective by
considering integration with both customers and suppliers
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Volume 16 · Number 4 · 2011 · 220 –230

With regard to the relationship between customer
integration and efficiency, Disney and Towill (2002) have
investigated the issue of inventory nervousness in downstream
integrated systems, and its negative effect on costs. They
claim that, in integrated systems, continuously recalculating
inventory control parameters according to demand signals
causes fluctuations in target inventory levels or in production
quantities. Therefore, a slow reaction to demand signals can
result in a more stable inventory level and a reduction in
production quantity fluctuations. In a further work, Disney
et al. (2004) suggest that Vendor Managed Inventory (VMI)
systems should not be too complex in order to improve the
dynamics of supply chains. When testing different
information sharing practices, they show that, although
players have information available, very complex decision
making can result in increased inventory costs.
Therefore, while the general link between customer
integration and efficiency is widely acknowledged,
inconsistencies across previous studies suggest the need for
further research and hypothesis testing. Hence, we intend to
analyze the following hypothesis:

upon what they call an “arc of integration” (representing the
relative direction – toward suppliers and/or customers – and
degree of integration activity), and demonstrates that the
strategy based on the greatest arc of integration guarantees the
best performance. However, they do not distinguish between
the contribution of customer and supplier integration.
Starting from this research, over the years, several studies
have been developed. By extending Frohlich and Westbrook’s
(2001) research, Rosenzweig et al. (2003) studied the
relationship between SC integration and multiple measures
of business performance and the mediating effect of
competitive capabilities. Zailani and Rajagopal (2005)
analyzed the concept of arcs of integration in the US and
the East Asian context. Based on an extensive literature
review of multiple aspects of SC integration and performance,
they conclude that most companies integrate their
organization with suppliers and customers by pursuing a
strategy with a broad arc of integration, and that companies
with the greatest arcs of supplier and customer integration
have the highest rate of performance improvement. Kannan
and Tan (2010) do not only recognize the need to integrate
both suppliers and customers, but also analyze the positive
impact of a broad span of integration (i.e. beyond the
immediate network) on several performance dimensions.
The abovementioned studies reveal that, even though
customer and supplier integration can be considered as two
distinct concepts that differ with regard to the direction of
integration, they are in fact strictly related, since
SC integration requires that companies be simultaneously
integrated upstream and downstream, and significant benefits
can be achieved when a firm is integrated with both customers
and suppliers.
Starting from these considerations, we believe that an
interesting opportunity to better understand the impact of SC
integration on companies’ performance lies in the
examination of possible interaction effects between customer
and supplier integration. As demonstrated by Lee et al.
(2007), each of these integration strategies could directly
influence the level of performance achieved. However, when
implemented together, they could also interact by
determining an additional positive effect on performance.
Although the literature does not explicitly test the existence of
this interaction effect on performance, the abovementioned
studies illustrate the importance of integrating downstream
and upstream simultaneously.
In particular, what has not been tested in the extant
literature and is plausible is that absence of supplier
integration is likely to significantly weaken the effect of
customer integration on efficiency performance. For instance,
it is well known that the benefits due to bullwhip-effect
reduction are maximized when an high level of customer
integration is accompanied by a high level of supplier
integration, as suppliers can align their production plans
with those of final customers (Lee et al., 1997). In addition,
supplier integration can help to limit the negative impact of
the nervousness effect caused by customer integration, as the
upstream network is able to efficaciously react in the event of
demand changes without creating significant inefficiencies
(e.g. rush orders), or the manufacturer and suppliers can
rapidly reach an agreement on how to modify production and
purchasing plans to satisfy the demand, when it suddenly
changes (Danese et al., 2009). However, the role of supplier
integration as moderator of the customer integration-

H1.

Customer integration is positively related to efficiency
performance.

2.2 The interaction effect of customer and supplier
integration
Both van der Vaart and van Donk (2008) and Fabbe-Costes
and Jahre (2008) conclude that a possible explanation for the
mixed results emerging from SCM studies on the relationship
between SC integration and performance can be that studies
use different definitions of SC integration and levels of
analysis.
In particular, studies do not only differ with regard to the
items applied to measure integration practices, but also for
the direction and span of the integration. As explained above,
several SCM studies focus on supplier or customer
integration. Instead, by recognizing the importance for
companies to integrate upstream and downstream
simultaneously, some authors include in their studies both
these dimensions. In particular, Fabbe-Costes and Jahre
(2008) distinguish between:
.
studies that test the relationship between customer and
supplier integration and performance separately; and
.
studies that consider integration of suppliers – focal
company – customers without differentiating between
upstream and downstream integration.
For instance, Lee et al. (2007) investigate the distinct
contribution of integrating internally with customers and
suppliers to improve different dimensions of performance,
such as cost containment and reliability.
Instead, Kim (2006) builds an aggregate construct of SC
integration and, by analyzing a sample of 623 manufacturing
Korean and Japanese organizations, demonstrates that the
level of SC integration positively impacts on firm
performance, in terms of market and financial performance,
and customer satisfaction.
Also the seminal work of Frohlich and Westbrook (2001) on
“arcs of integration” is classified by Fabbe-Costes and Jahre
(2008) among those SCM studies that do not differentiate
between customer and supplier integration. In fact, this study
defines five distinct classes of supply chain strategies based
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Supply Chain Management: An International Journal

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Volume 16 · Number 4 · 2011 · 220 –230

efficiency relationship has not been empirically proved. In
other words, there is a lack of empirical evidence proving that
the presence of supplier integration enhances the impact of
customer integration on efficiency, whereas the absence of
supplier integration weakens it. We feel that shedding light on
this point would provide sturdy arguments to explain the
inconsistencies across previous studies on the relationship
between customer integration and efficiency. For these
reasons, this research intends to study the following
hypothesis:

process engineer, new product development manager etc). In
order to raise measurement reliability, each questionnaire was
administered to different respondents within each plant.
Researchers involved in HPM project asked the CEOs (or a
coordinator within each plant) to provide the name and
contact addresses of the respondents for each questionnaire,
and to distribute the questionnaires received by individual
visits or by post to the respondents. Within the research
group, for each country, a group of researchers and a person
in charge of data collection were identified. Each group had to
provide assistance to the respondents, to ensure that the
information gathered was both complete and correct. We were
responsible for collecting data from plants located in Italy.
The items used in the present research were targeted
toward plant managers, inventory managers and plant
superintendents, or their immediate subordinates working in
direct contact with customers and suppliers. Respondents
were specifically asked to give answers on SCM practices
adopted and performances obtained. In particular, as in other
survey-based SCM studies (see van der Vaart and van Donk,
2008), both supplier and customer integration of focal firms
were examined, by asking respondents from these firms about
integration practices adopted with suppliers and customers
respectively. Since there were multiple respondents within
each plant for each item, an average was taken to obtain a
single value per item per plant.
Respondents from a total of 266 plants returned the
questionnaires, but 66 plants were excluded from the analyses
because they provided incomplete responses about the items
used in this study. Accordingly, the analysis that follows and
all reported statistics were based on a sample of 200 plants. As
shown in Table II, the sample is stratified to approximate
equal distribution across all three sectors. We use industry (as
well as size) as a control variable later in the analysis to test
whether this had any impact on efficiency performance.

H2.

Supplier integration positively moderates the
relationship between customer integration and
efficiency performance.

Table I summarizes the main issues discussed in the literature
review, that inspired the two hypotheses underlying this
research.

3. Methods
3.1 Data collection and sample
This study uses data from the third round of the High
Performance Manufacturing (HPM) project data set. Data
were collected in 2007 by an international team of researchers
working in different universities all over the world. During the
2005-2007 timeframe they collaborated to update the HPM
questionnaire and decide how to organize data collection (e.g.
how to contact plants and respondents). Data include
responses from manufacturing plants operating in
machinery, electronic and transportation components
sectors (SIC codes: 35, 36 and 37, respectively) and located
in different countries (i.e. Finland, US, Japan, Germany,
Sweden, Korea, Italy, Austria and Spain). The plants in the
HPM study were randomly selected from a master list of
manufacturing plants in each of the countries. All plants
represented different parent corporations, and had at least
100 employees.
Participating plants received a batch of questionnaires,
targeted at the respondents who were the best informed about
the topic of the specific questionnaire. In total 20 recipients
were involved in each plant (plant manager, HR manager,

3.2 Research variables and measures
The items used in the present research are a subset of the
whole HPM survey. In the HPM questionnaire, multiple
measurement items for each latent construct are used because
they provide a greater degree of reliability than single items.

Table I Main issues discussed in the literature review
Main issues
CI positively affects efficiency
performance

The positive impact of CI on
efficiency is questionable

SI can strengthen the effect of CI
on efficiency performance

Studies

Information sharing and alignment to final demand reduce
the bullwhip effect
Partnership relationships guarantee more efficient
problem solving
SCM survey-based studies are not unanimous
SC integration does not have a significant or even a
negative impact on performance
CI amplifies the nervousness effect
Integration with both customers and suppliers is important
to maximize performance
Bullwhip-effect reduction is maximized when a high level
of CI is accompanied by a high level of SI
SI can help to mitigate the negative effect of nervousness

223

Lee et al. (1997); Gavirneni et al. (1999); Lee et al.
(2000); Lin et al. (2002); Zhao et al. (2002); Coppini et al.
(2010)
Flynn and Flynn (1999); Frohlich and Westbrook (2001);
Power (2005)
Fabbe-Costes and Jahre (2008); van der Vaart and van
Donk (2008)
Stock et al. (2000); Cousins and Menguc (2006); Das
et al. (2006); Sezen (2008)
Disney and Towill (2002); Disney et al. (2004)
Frohlich and Westbrook (2001); Rosenzweig et al.
(2003); Zailani and Rajagopal (2005); Kim (2006);
Kannan and Tan (2010)
Lee et al. (1997)
Danese et al. (2009)

Supply chain integration and efficiency performance

Supply Chain Management: An International Journal

Pamela Danese and Pietro Romano

Volume 16 · Number 4 · 2011 · 220 –230

and customer integration. Two factors with eigenvalues above
1 were extracted and there was comforting evidence for both
convergent and discriminant validity. All the items load onto
their intended constructs and have high factor loadings (i.e.
more than 0.50), thus reflecting high construct validity.
Further, there are no cross-loadings greater than 0.40,
providing evidence of discriminant validity.
Finally, Cronbach a-values for the three constructs exceed
0.70, indicating high reliability (Nunnally, 1994).
Thus, three multi-item constructs were identified:
1 customer integration (CI);
2 supplier integration (SI); and
3 efficiency (EFF).

Table II Sample characteristics

Total number of plants
Electronics
Machinery
Transportation components
Mean number of hourly and salaried
personnel
Percentage of large-size plants
(more than 250 employees)

n

%

200
67
68
65

33.5
34
32.5

639
64

As to the efficiency (EFF) construct, it should be noted that
the items considered measure the efficiency performance of
the focal firm – as do several survey-based papers on SCM
(van der Vaart and van Donk, 2008) – but also the benefits
beyond focal firms’ boundaries (see Zhao et al., 2002; Kim
et al., 2010).
All the items were measured using multi-item perceptual
scales with values ranging from 1 to 7.
Table IV shows basic statistics for the three constructs, each
obtained by averaging the relevant items.

Given the lack in the SCM literature of a standard scale for
measuring supplier and customer integration, and efficiency
performance (within and beyond focal firms’ boundaries),
firstly we identified the central dimensions of integration and
efficiency usually mentioned in the literature. Then, we
followed established guidelines for scale development and
examined the measurement model through exploratory
methods (Hair et al., 2006).
Each construct with its block of items was first factor
analyzed. This was done to assure the internal rule of
unidimensionality. Table III reports the items comprising
each construct, and the outputs of SPSS 17.0 obtained by
factor analyzing the items of each construct separately, along
with reliability test results using Cronbach’s a. Convergent
validity is demonstrated since, for each construct only one
component with an eigenvalue above 1 is identified, the
variance explained is above 50 percent, and factor loadings
are all above 0.50 (Hair et al., 2006).
Then, as suggested by Hair et al. (2006), we ran a principal
component analysis with Varimax rotation on the complete set
of items that compose the two independent variables: supplier

4. Data analysis and results
4.1 Hierarchical regression
The central issue this research intends to investigate is not
only whether customer integration positively affects efficiency,
but also whether customer and supplier integration interact by
determining a positive significant effect on efficiency
performance. In particular, this research intends to
investigate whether supplier integration can act as
moderator in the customer integration-efficiency relationship.

Table III Validity test of measures

Factor

Item

Customer integration (CI)

We frequently are in close contact with our customers
Our customers give us feedback on our quality and delivery performance
We consider our customers’ forecasts in our supply chain planning
Our customers do not have access to our production plans (reverse scored)
We work as a partner with our customers
Our customers involve us in their quality improvement efforts
KMO ¼ 0.719 (Bartlett’s test of sphericity, p , 0.001)
We share our production plans with our suppliers
We emphasize openness of communications in collaborating with our suppliers
We maintain cooperative relationships with our suppliers
We maintain close communications with suppliers about quality considerations and
design changes
We actively engage suppliers in our quality improvement efforts
KMO ¼ 0.759 (Bartlett’s test of sphericity, p , 0.001)
Indicate your opinion about how your plant compares with its competitors in your
industry with regard to: unit cost of manufacturing
Indicate your opinion about how your plant compares with its competitors in your
industry with regard to: inventory turnover
Capacities are balanced in our supply network
We have large in-process inventories between different operations (reverse scored)
KMO ¼ 0.737 (Bartlett’s test of sphericity, p , 0.001)

Supplier integration (SI)

Efficiency (EFF)

Variance
explained
(%)

Cronbach
a

0.80
0.81
0.61
0.72
0.62
0.70

50.60

0.72

0.53
0.66
0.80

53.66

0.73

52.17

0.70

Factor
loading

224

0.81
0.81

0.69
0.75
0.71
0.74

Supply chain integration and efficiency performance

Supply Chain Management: An International Journal

Pamela Danese and Pietro Romano

Volume 16 · Number 4 · 2011 · 220 –230

Table IV Basic statistics and correlation analysis
Variables
1 Customer integration (CI)
2 Supplier integration (SI)
3 Efficiency (EFF)

Mean S.D.
5.09
5.31
4.54

Range

Table V Hierarchical regression analysis
Correlations
SI
EFF

Control variables Main effects Interaction effect
Model 0
Model 1
Model 2

0.52 3.39-6.28 0.437 * 0.244 *
0.50 3.75-6.70
–
0.455 *
0.80 2.25-6.64
–
–

Constant
Size
Electronics
Transp. com.
CI
SI
SIXCI
R2
R2 adjusted
DR2
F value of DR2

Note: *Significant at the 0.01 level (Pearson probabilities)

We employed a hierarchical regression procedure, by using
SPSS 17.0 (linear regression module; entering method: by
blocks). Firstly, control variables (i.e. firm size and sector)
were considered in the regression model. The firm size (SIZE)
was measured by the number of employees (hourly and
salaried personnel). The sector was inserted in the regression
model by creating dummy variables. The form of dummy
variable coding used was “indicator coding”, which means
that the regression coefficients for the dummy variables
represent the deviation from the comparison group. The
machinery sector was arbitrarily taken as the baseline/
comparison group.
Then, the main independent variables – i.e. CI, SI – were
introduced as a block, followed by the interaction term. The
following equation describes the moderated regression tested
in this study:
EFF ¼ b0 þ b1 · CI þ b2 · SI þ b3 · CI · SI þ 1

4.390 * * *
1.83E-4
20.027
0.123

4.459 * * *
1.10E-4
20.113
0.104
0.090
0.657 * * *

0.053
0.036
0.053
3.203 *

0.222
0.199
0.169
18.613 * * *

4.386 * * *
1.13E-4
2 0.106
0.116
0.060 *
0.696 * * *
0.565 * *
0.250
0.224
0.028
6.352 * *

Notes: The values reported are unstandardized regression coefficients;
* p-value , 0:05 level; * *p-value , 0.01 level; * * *p-value , 0.001 level;
VIF (variance inflation factor) below 1.501; Tolerance above 0.666

In all the models, for all the independent variables the VIF is
lower than 1.501 and tolerance is greater than 0.666, which
are coherent with the recommended threshold values (Hair
et al., 2006). Also the bivariate correlation of CI and SI with
the interaction term confirms that multicollinearity is not a
problem in model 2 (correlation between CI *SI and CI is
0.036 ( p-value: 0.617), and between CI *SI and SI is 2 0.042
( p-value: 0.552)).

ð1Þ

As suggested by Jaccard and Turrisi (2003) and Brambor et al.
(2006), when the b3-coefficient of the product term CI · SI is
statistically significant, and R2 increases when this term is
introduced in the model, the existence of a moderated effect
on the CI-EFF relationship is demonstrated. As
recommended by several authors, to address the problem of
multicollinearity, we mean-centered the independent variables
(Jaccard and Turrisi, 2003). Then, we examined
multicollinearity diagnostics, by checking the variance
inflation factor (VIF) and tolerance values.
Table V reports the results of the hierarchical regression
analysis.
Model 0 represents the first step of the hierarchical
regression. The control variables industry and size do not
result as significantly related to efficiency performance. Also
in the models 1 and 2 similar results on the effect of control
variables on the response variable are found. Thus, we can
conclude that when introducing the additional variables in
models 1 and 2, the effect of the control variables appears
stable. More interestingly, when the independent variables: CI
and SI are added to the regression model (model 1), we can
note that SI is significantly related to efficiency performance,
whereas CI is not. Thus, we found that hypothesis H1 is not
confirmed, and thus in general it is not possible to conclude
that CI always improves efficiency.
Model 2 reports the interaction result, along with changes
occurring to the main variables when the product term was
introduced. The significant and positive b3 coefficient
suggests that it is possible to confirm the existence of a
positive interaction effect between CI and SI. Additional
support is the significant change in R2 from model 1 to 2
(0.028). Therefore, H2 is fully supported.

4.2 Additional analyses for interpreting moderated
regression
By considering the coefficients of Table V (model 2), we can
calculate that the marginal effect of CI on EFF depends on
SI, according to the following formula:

›EFF
¼ 0:060 þ 0:565 · SI
›CI

ð2Þ

where the variable SI is centered. The test of significance of
the coefficient in equation (2) takes the form of a t test, where
the standard error is a function of SI (Brambor et al., 2006).
We have verified that the t test is significant at a 0.05 level for
the values of SI lower than 2 0.74, and greater than 0.28 (see
the Appendix for further details).
Figure 1 shows how the marginal effect of CI varies when SI
increases. It is easy to see that CI has a stronger effect on
efficiency performance when the level of SI is high. Further,
we can note that for low levels of SI, the effect of CI could
even be negative. This means that, when companies are not
integrated upstream, not only are the benefits of CI lost, but
also a contrasting effect emerges that risks the achievement of
good levels of efficiency. Between the values of 2 0.74 and
0.28, the marginal effect of CI is not significant.
The research sample reveals that the centered variable SI
varies from 21.56 to 1.39; 39 percent of sample units falls
below the SI critical value of 2 0.1. Below this value, CI
seems to have a negative influence on efficiency performance
(see Figure 1). However, this situation is extreme (i.e.
companies with very low level of SI), since most units in the
sample are above the critical value of 2 0.1, and the remaining
units show a SI mean value of 20.50.
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Figure 1 The influence of supplier integration on the marginal effect of customer integration

To further gain an intuitive understanding of the interaction
effect between CI and SI, we computed and graphed through
MS-Excel the slope of efficiency performance on CI at a few
different values of SI. A suggested strategy is to evaluate the
effects of CI on performance at “low”, “medium”, and “high”
values of SI, where “low” might be defined as one standard
deviation below the mean SI score, “medium” as the mean
value, and “high” as one standard deviation above the mean
(Cohen and Cohen, 1983). Starting from the coefficients of
model 2 in Table V, and by using the three mentioned values
of the variable SI, three linear equations of efficiency
performance, depending on CI, were created. The visual
patterns of Figure 2 confirms that the effect of CI on
efficiency is greater when SI increases; while its effect is
negative when SI is at a low level.
Figure 2 highlights what Jaccard and Turrisi (2003) classify
as “disordinal interaction” or “crossover interaction”, namely
an interaction in which the regression line that regresses y
onto the focal independent variable (i.e. CI) for a given level
of the moderator (e.g. low level of SI) intersects with the
correspondent regression line for a different level of the
moderator (e.g. high level of SI). This type of interaction is
very useful for decision making in different situations. For
instance, it suggests that a high level of CI (right side of
Figure 2) should be accompanied by a high level of SI. On the
other hand a low level of CI (left side of Figure 2) should be
accompanied by a low level of SI. However, the research
sample reveals that this is a rare situation. In fact, only one
unit in the sample falls below the CI critical value of 2 1.23
(i.e. intersection point). Below the intersection point, a low
level of SI appears to be more convenient (see Figure 2).

First, these findings provide new insights to better
understand the controversial opinions and results found on
the CI-efficiency link. Differently from those studies
demonstrating that CI significantly improves efficiency
(Gavirneni et al., 1999; Lee et al., 2000; Lin et al., 2002;
Zhao et al., 2002), this research suggests that efficiency
improvements cannot be achieved merely by improving CI.
There is a significant impact on efficiency through CI only
under certain conditions in terms of supplier integration.
In particular, Figures 1 and 2 reveal that the positive impact
of CI on efficiency increases with rising levels of integration
between the company and its suppliers. A possible
explanation of this positive synergistic effect of SI and CI
can be found in studies on bullwhip effect and systems
dynamics (Towill et al., 1992; Lee et al., 1997; Mason-Jones
and Towill, 1999). When the company is closely coupled with
customers, it can transfer to integrated suppliers all the
information they need to align their production and shipping
plans to the final market demand. Thus, the co-presence of SI
and CI makes it possible to reduce inventories not only at the
customer-producer interface, but also in the supplier network.
On the contrary, when SI is low, CI can even penalize
efficiency performance. In fact, as explained above, CI can
cause a “nervousness” effect, arising when companies seek to
follow market signals and demand changes, that can
negatively affect cost performance (Disney and Towill,
2002). As illustrated in some case studies on SC integration
(Danese et al., 2009), SI can help to mitigate the negative
impact of the nervousness effect, thus making CI beneficial in
terms of cost improvements. Supplier integration can
facilitate coping with this effect as it allows the
manufacturer to be rapidly updated on the progress of its
orders at the supplier’s plant and to decide jointly with the
supplier the most appropriate plan modifications in order to
accommodate final customer requests. Instead, when SI is
low, lack of collaboration with suppliers and uncertainty
about order progress at the supplier’s plant force the
manufacturer to inefficiently resort to rush deliveries from
suppliers and large amounts of safety stocks to anticipate
customer requests and chase demand.

5. Discussion
Our analyses empirically demonstrate that SI positively
moderates the relationship between CI and efficiency (H2),
whereas they do not support that in general CI significantly
and positively impacts efficiency (H1). These results have
both theoretical and managerial implications.
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Volume 16 · Number 4 · 2011 · 220 –230

Figure 2 Efficiency slope at low, medium and high levels of supplier integration

Results found on the CI-efficiency link and the moderating
effect of SI can have interesting managerial implications. An
important hint for managers is that efficiency performance
optimization requires levering simultaneously on CI and SI to
foster their interaction, rather than investing and acting on CI
only. In fact, when CI is not accompanied by a high level of
SI, this latter can act as a barrier that hinders the positive
impact of CI on efficiency. This is because the absence of
integration with suppliers does not allow the company to
efficaciously manage the nervousness effect caused by CI.
Therefore managers should be aware of this effect, that could
offset their efforts to improve efficiency through CI.
Finally, by clarifying what should be the impact of CI on
efficiency and the role of SI, this study offers guidance for
managers facing the selection of the most appropriate mix and
sequence of SC integration initiatives, in order to maximize
efficiency improvements. After having achieved good levels of
CI, companies should direct their efforts towards SI, by
extending the integration upstream, rather than continuing
investing on CI. For instance, from model 2 it is easy to
calculate that starting from a situation with a 6-level of both
CI and SI, a one-point increment in CI increases efficiency
performance by 8.6 percent; whereas a one-point increment
in SI increases performance by 22.8 percent. Moreover,
before deciding to invest on CI, managers should ascertain
what the level of SI is. In fact, SI paves the way for the
successful implementation of CI, because it can help to limit a
series of problems (e.g. nervousness of plans) that can offset
CI benefits.

and Jayaram et al. (2010), SC integration could go beyond the
immediate network, by involving second/third tier customers/
suppliers. However, we think that it could be difficult to
evaluate the involvement of these actors or the integration
between second and first (or third and second) tier suppliers/
customers by collecting information from respondents within
the focal firms. An ad hoc survey-based research should be
designed to evaluate the integration of the total supply
network that should collect information from different
respondents within plants positioned in different tiers of the
same supply network. In line with Seuring’s (2008)
suggestions, we think that collecting data from supply
chains (i.e. at least two, or three or more stages of the
supply chains) could be very useful.
A further opportunity for future research lies in the analysis
of possible additional moderating effects on the CI-efficiency
relationship, in order to increase the understanding of how to
optimize cost reduction. In SCM literature, it is widely
recognized that performance can be significantly improved
when SCM implementation encompasses an appropriate
bundle of initiatives ranging from physical and relational
supply network reconfiguration to inter-organizational process
re-engineering (de Treville et al., 2004; Danese et al., 2006;
Kim, 2006; Li et al., 2009). Extending the analysis of
moderated effects would be very interesting because of its
implications for management.
Finally, future studies could further investigate some
interesting indications which emerged in this research. For
instance, a remarkable and counterintuitive hint resulting
from this study concerns the role of SI when CI is low. As
highlighted in the left side of Figure 2, companies
characterized by extremely low degrees of integration with
their customers seem to be more efficient when integration
with suppliers is also low. It could be argued that integration
with suppliers in absence of integration with customers is not
likely to guarantee low stock and efficiency. In fact, suppliers’
plans are aligned with those of the manufacturer, but not with
final demand; and at the same time integrating suppliers
brings with it significant coordination costs. However, this
situation (i.e. CI extremely low and SI at a high level) is

5.1 Limitations of this study and future research
This research also exhibits a series of limitations to be taken
into consideration that suggest some proposals for future
research.
First, as in several other surveys on SCM (Frohlich and
Westbrook, 2001; Kim, 2006; Lee et al., 2007), in this study
we collected information on the level of integration of the
focal firm with its suppliers and customers. This limits the
focus of this research to the immediate network of the focal
company. Instead, as suggested by Kannan and Tan (2010)
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Volume 16 · Number 4 · 2011 · 220 –230

particularly rare and extreme (i.e. only one plant in our
sample), and thus further research is necessary to corroborate
this finding.

Danese, P., Filippini, R., Romano, P. and Vinelli, A. (2009),
“Creating e-clusters: a new challenge for supply chain
management”, Supply Chain Forum: An International
Journal, Vol. 10 No. 1, pp. 78-90.
Das, A., Narasimhan, R. and Talluri, S. (2006), “Supplier
integration – finding an optimal configuration”, Journal of
Operations Management, Vol. 24 No. 5, pp. 563-82.
de Treville, S., Shapiro, R.D. and Hameri, A.P. (2004),
“From supply chain to demand chain: the role of lead time
reduction in improving demand chain performance”,
Journal of Operations Management, Vol. 21 No. 6,
pp. 613-27.
Disney, S.M. and Towill, D.R. (2002), “A procedure for the
optimization of the dynamic response of a vendor-managed
inventory system”, Computers & Industrial Engineering,
Vol. 43 Nos 1-2, pp. 27-58.
Disney, S.M., Naim, M.M. and Potter, A. (2004), “Assessing
the impact of e-business on supply chain dynamics”,
International Journal of Production Economics, Vol. 89 No. 2,
pp. 109-18.
Fabbe-Costes, N. and Jahre, M. (2008), “Supply chain
integration and performance: a review of the evidence”,
The International Journal of Logistics Management, Vol. 19
No. 2, pp. 130-54.
Flynn, B.B. and Flynn, J. (1999), “Information-processing
alternatives for coping with manufacturing environment
complexity”, Decision Sciences, Vol. 17 No. 3, pp. 249-69.
Frohlich, M. and Westbrook, R. (2001), “Arcs of integration:
an international study of supply chain strategies”, Journal of
Operations Management, Vol. 19 No. 2, pp. 185-200.
Fynes, B., de Burca, S. and Voss, C. (2005), “Supply chain
relationship quality, the competitive environment and
performance”, International Journal of Production Research,
Vol. 43 No. 16, pp. 3303-20.
Ganeshan, R., Boone, T. and Stenger, A.J. (2001), “The impact
of inventory and flow-planning parameters on supply chain
performance: an exploratory study”, International Journal of
Production Economics, Vol. 71 Nos 1-3, pp. 111-8.
Gavirneni, S., Kapusincski, R. and Tayur, S. (1999), “Value
of information in capacitated supply chains”, Management
Science, Vol. 45 No. 1, pp. 16-24.
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and
Tatham, R.L. (2006), Multivariate Data Analysis, 6th ed.,
Pearson Prentice-Hall, Upper Saddle River, NJ.
Handfield, R., Petersen, K., Cousins, P. and Lawson, B.
(2009), “An organizational entrepreneurship model of
supply management integration and performance
outcomes”, International Journal of Operations & Production
Management, Vol. 29 No. 2, pp. 100-26.
Jaccard, J. and Turrisi, R. (2003), Interaction Effects in Multiple
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CA.
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International Journal of Production Research, Vol. 47 No. 11,
pp. 3013-39.
Jayaram, J., Tan, K. and Nachiappan, S.P. (2010),
“Examining the interrelationships between supply chain
integration scope and supply chain management efforts”,
International Journal of Production Research, Vol. 48 No. 22,
pp. 6837-57.

6. Conclusion
This study intends to contribute to existing literature on SC
integration by investigating the impact of customer
integration on efficiency performance and the moderating
role of supplier integration. Results found highlight the need
for firms to simultaneously pursue integration with customers
and suppliers to improve efficiency performance. In fact, CI
alone is not enough to guarantee cost reductions; and if SI is
low, CI can even make efficiency worse. This provides a
number of original implications for the interpretation of the
relationship between CI and efficiency. In particular, we can
infer that CI can cause additional costs and problems that SI
can help to mitigate. Schedule nervousness is a typical effect
linked to customer integration, that can be efficaciously faced
through the integration of suppliers.

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Volume 16 · Number 4 · 2011 · 220 –230

Further reading
Gunasekaran, A., Lai, K. and Cheng, T.C.E. (2008),
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pp. 201-12.

Appendix. Significance of the marginal effect of
CI on EFF
With reference to the following equation describing
moderated regression:
y ¼ b0 þ b1 x þ b2 z þ b3 xz þ 1
the test of significance of the marginal effect
›y=›x ¼ b1 þ b3 z, takes the form of a t test, where the
standard error is a function of z and can be calculated such
that:
standard error ¼



varðb1 Þ þ z2 · varðb3 Þ þ 2 · z · covðb1 b3 Þ


1=2

where var(b1), var(b3) and cov(b1 b3) are the variances of b1,
and b3 and the covariance of b1,and b3, respectively.
Following these formulas, we have created and applied an
MS-Excel sheet, that automatically calculates the range of z
values, where this t test results significant at 0.05 level. The
input data of this MS-Excel sheet are: b1, b3, var(b1), var(b3),
and cov(b1 b3). All these input data can be obtained from
linear regression module of SPSS 17.0. In particular, they can
be extracted by selecting “covariance matrix” in the regression
coefficient menu of the “statistics” option.

About the authors
Pamela Danese is Assistant Professor of Operations & Supply
Chain Management at the University of Padova, Italy. She
graduated in Management and Engineering with first class

honours at University of Padova and completed her PhD in
Operations Management in cooperation with the Operations
Management research groups in the Department of
Management and Engineering at the same University and
the Vlerick Leuven Gent Management School (Belgium). She
teaches Supply Chain Management for the MS degree in
Management and Engineering, and on the PhD Programme
in Management and Engineering, University of Padova. She
also teaches Supply Chain Management at CUOA Business
School. Her principal research interests focus on Supply
Chain Management. She has published in: International
Journal of Production Economics, International Journal of
Operations & Production Management, International Journal of
Production Research, Interfaces, Journal of Manufacturing
Technology Management, Journal of Purchasing and Supply
Management, Supply Chain Forum: An International Journal.
Pamela Danese is the corresponding author and can be
conacted at: pamela.danese@unipd.it
Pietro Romano is Associate Professor of Supply Chain
Management and New Product Development at the
University of Udine (Italy). He graduated in Management
and Engineering and completed his PhD in Operations and
Supply Chain Management at the University of Padova. He
was among the founders and first Director of Master in Lean
Management at the Cuoa Business School. He is a member of
EurOMA, Production and Operations Management Society
(POMS), and Internatonal Purchasing and Supply Education
and Research Association (IPSERA) and was in the
Organizing Committee of the first Joint EurOMA-POMS
International Conference (Como – Italy). He has taken part
in several research projects on supply chain managementrelated topics and is author and co-author of more than 100
publications. He has published in the MIT Sloan Management
Review, International Journal of Production Economics,
International Journal of Operations & Production Management,
International Journal of Production Research, Interfaces,
International Journal of Logistics: Research and Applications,
Journal of Purchasing and Supply Management, Journal of
Manufacturing Technology Management, Supply Chain Forum,
and Total Quality Management.

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