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International Journal of Production Research
Vol. 48, No. 23, 1 December 2010, 7025–7042
Supply chain information technologies and organisational
initiatives: complementary versus independent effects
on agility and firm performance
S.K. Vickerya*, C. Drogea, P. Setiab and V. Sambamurthya
a
The Eli Broad Graduate School of Management, Michigan State University,
East Lansing, MI, USA; bSam M. Walton College of Business,
University of Arkansas, Fayetteville, AR, USA
(Received 26 April 2009; final version received 14 September 2009)
This research investigates the roles of supply chain information technologies
(SCIT) and supply chain organisational initiatives (SCOI) in engendering agility
and business performance in manufacturing firms. We examine two competing
models, both of which incorporate agility as a mediator between the use of SCIT
or SCOI and firm performance; the models differ in how the impacts of SCIT and
SCOI are manifest. In one model, SCIT and SCOI are hypothesised to have
separate effects on agility, which then impacts firm performance; in the second
model, complementarities, or the interaction of SCIT and SCOI, impacts agility
directly. Structural equation modelling results show that agility is full mediator,
related to firm performance in both models. Further, the model with complementary interactions fits better. These results have implications for how
manufacturing firms can position their investments in SCIT and SCOI to
enhance agility and overall performance.
Keywords: resource complementarities; firm performance; agile manufacturing;
supply chain management; business information systems
1. Introduction
Though earlier empirical research had demonstrated that firms’ information technology
(IT) investments are associated with productivity, profitability and consumer welfare
(Hitt and Brynjolfsson 1996), more recent research has sought insights into how and why
these impacts occur (Weill and Aral 2006). One stream of research suggests that the
benefits of IT are transmuted into positive organisational effects through synergistic
combination with complementary investments (Barua and Mukhopadhyay 2000, Zhu and
Kraemer 2002). Another stream of research suggests that IT investments and assets impact
business processes and/or capabilities, and these in turn engender organisational
performance (Sambamurthy et al. 2003, Ray et al. 2005, Tanriverdi 2006). Finally,
a third stream of research suggests that the extent of IT use is a critical antecedent
to performance; in other words, without adequate levels of use, it would be difficult to
observe the performance impacts of IT (Devaraj and Kohli 2003).
This research examines the roles of supply chain information technologies (SCIT) and
supply chain organisational initiatives (SCOI) in engendering agility and firm performance.
*Corresponding author. Email: vickery@msu.edu
ISSN 0020–7543 print/ISSN 1366–588X online
ß 2010 Taylor & Francis
DOI: 10.1080/00207540903348353
http://www.informaworld.com
7026
S.K. Vickery et al.
MODEL 1 (M1): DIRECT IMPACT OF SCIT
Supply chain
information
technologies
(SCIT)
H1a
H2
Firm
performance
Agility
H1b
Supply chain
organisational
initiatives
(SCOI)
MODEL 2 (M2): IMPACT OF COMPLEMENTARITY OF SCIT WITH SCOI
Complementarity
(interaction of
SCIT and SCOI)
H1
H2
Agility
Firm
performance
Figure 1. Alternate models of SCIT and SCOI impacts.
The context is operations and supply chain processes in manufacturing firms. The goal of
this research is to examine how the use of SCIT and SCOI impacts agility and the
performance of manufacturing firms. Two streams of research underlie the development
of two contrasting conceptual models. First, research on the business value of IT or supply
chain initiatives provides insights about SCIT or SCOI and either agility or firm
performance. An important question emerges: do SCIT and SCOI have separate effects
or does the impact depend on complementarities between SCIT and SCOI? The second
stream of research provides insights about direct versus indirect effects on firm performance
and leads to the following question: are the effects of SCIT and SCOI on firm performance
direct, or do they manifest themselves indirectly through capabilities such as agility?
To address these fundamental questions, our research contrasts two models of firm
performance: (1) separate direct effects of SCIT and SCOI on agility, which in turn
impacts firm performance (Model 1 in Figure 1); and (2) the complementary interactions
of SCIT and SCOI affecting agility, which in turn affects firm performance (Model 2 in
Figure 1) (Melville et al. 2004). Agility is modelled as a complete mediator in both cases
(no direct effects are shown in Figure 1); however, we test whether it is indeed the case that
there are no direct effects.
Data gathered through a field survey of the top independently owned first tier suppliers
to US automotive OEMs is used to test the hypotheses. The fiercely competitive auto
industry is well known both for its emphasis on supply chain management overall and for
its early adoption and sophisticated use of supply chain information technologies such as
electronic data interchange (EDI), computerised production systems and company-wide
information systems (see, e.g., Rassameethes et al. 2000).
International Journal of Production Research
7027
The paper is organised as follows. First, we draw upon prior theory and research to
motivate the study’s models. This is followed by a definition of model constructs and the
development of the research hypotheses. Next, we describe the research methodology.
Finally, the paper presents the results of our analysis and discusses implications for
research and practice.
2. Conceptual background
2.1 Separate effects versus complementarities
Early concerns about the ‘productivity paradox’ (Solow 1987) can be contrasted with
subsequent studies finding positive effects of IT on firm outputs (Brynjolfsson and Hitt
1996, Melville et al. 2004). Outputs were measured in various ways including productivity,
business profitability, and consumer surplus (Hitt and Brynjolfsson 1996). Other studies
have found evidence of the positive impacts of IT at the firm level (e.g., Mukhopadhyay
et al. 1997). This suggests examining a model in which IT (in our case, SCIT) is a separate
antecedent, which together with SCOI impacts agility and firm performance (Model 1,
Figure 1).
Melville et al.’s (2004) review of research on the business value of IT proposes an
integrative model wherein complementarities between IT and other resources are the focus.
A complementary set of resources could create super additive synergies (Milgrom and
Roberts 1995, Barua and Whinston 1998). Thus, given the value of SCIT and the value
of complementary SCOI, the combined value should be greater than that of the two
separately due to synergies (Wade and Hulland 2004). Thus we examine a second model
encompassing complementarities (Model 2, Figure 1). The first important overall question
is: do SCIT and SCOI have separate effects (Model 1), or are complementarities key
(Model 2)?
2.2 Capabilities as mediator
A second important issue is whether agility is a complete mediator (as in Figure 1) or
whether there are also direct effects to firm performance that bypass agility; direct effects
are not shown in Figure 1.
Makadok (2003) claims that resource-picking processes are associated with investments in factor inputs, whereas capability-building processes are associated with the
integration of complementary resources into significant differentiating capabilities.
Similarly, Grant (1996) argues for a hierarchy of capabilities in which higher-order
capabilities are derived from integrating lower-order capabilities and resources;
higher-order capabilities engender lasting differential advantage because they are truly
rare, valuable, and inimitable. There is some research that supports such mediating roles
of a variety of capabilities. Barua et al. (2004) show that higher-order capabilities such as
supplier and customer side digitisation mediate the impact of IT on firm performance.
Similarly, Tanriverdi (2006) examines how knowledge capabilities mediate the same link.
Banker et al. (2006) examine the mediating role of just-in-time and supplier participation
capabilities, whereas Rai et al. (2006) demonstrate that process integration mediates the
link between IT infrastructure and firm performance.
We propose that agility is such a higher-order business capability. Agility has
been defined differently by various scholars (see, e.g., Narasimhan and Das 2000,
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S.K. Vickery et al.
Sambamurthy et al. 2003). D’Aveni (1994) defines agility as the firm’s ability to detect and
seize market opportunities with speed and surprise (‘sense and respond’). Sometimes the
focus is specifically on supply chain agility; for example, Swafford et al. (2006) define it as
the ability to ‘react speedily to marketplace changes’ (p. 182), while Yusuf et al. (2004)
emphasise the ability ‘to respond, real time to the unique needs of customers and markets’
(p. 379) (see also Shaw et al. 2005). Correspondingly, we define agility as rapid
responsiveness to the needs and wants of customers and potential customers. Note that
our definition not only emphasises responsiveness to current and future customers, but
also the speed of the response. Our agility construct encompasses five measures of a first
tier supplier’s ability to effectively and quickly respond to current and potential OEM
customers: customer responsiveness, product modification flexibility, new product
introduction speed, manufacturing speed, and delivery speed. Our research views agility
in relation to interactions with customers, orchestration of internal operations, and rapid
interfacing across boundaries.
Our research models agility as a mediator between the use of SCIT and/or SCOI and
firm performance. This is consistent with Swafford et al. (2008) who model supply chain
agility (as well as supply chain flexibility) as mediators of the effects of information
technology integration on competitive business performance. Their focus on the ability
of IT to coordinate activities within functions and across global supply chains (i.e., IT
integration) is conceptually distinct from our focus on complementarities in an attempt
to identify supermodular synergies between IT and organisational initiatives (Milgrom
and Roberts 1990, 1995). Nevertheless, we have followed their lead in modelling agility as
a direct antecedent of firm performance because agility enables firms to acquire, integrate
and reconfigure resources and dynamically position themselves competitively. Our two
research models do not differ in conceptualising agility as mediator; i.e., both specify
complete as opposed to partial mediation. We now turn to a detailed discussion of the
constructs and hypotheses.
3. Construct definitions
3.1 Use of supply chain information technologies (SCIT)
SCIT facilitates the flow and processing of information across both functional areas and
firm boundaries to effectively connect activities and processes. Supply chain activities
require the collaboration of many intra-organisational units (marketing, sales, operations,
production, and logistics). Increasingly, these activities involve networked firms, and
therefore, coordination is a critical determinant of supply chain effectiveness. Also,
inter-organisational units or networked firms need effective communication and
monitoring to reduce the risk of exploitation in the relationship (Stroeken 2000). Prior
research shows that SCIT can decrease both coordination costs and transaction risk, thus
engendering the cooperation and coordination necessary for leveraging supply chain
initiatives (Nooteboom 1992, Clemons et al. 1993).
Recent literature has highlighted the importance of the three specific information
technologies examined in this study: computerised production systems (CPrdSy),
integrated information systems (IntInSy), and integrated electronic data interchange
(I-EDI) (Hill and Scudder 2002, Banker et al. 2006). Consistent with Devaraj and Kohli
(2003), our focus is on the extent of use of these technologies. Taken as a set, these three
technologies facilitate coordination and integration across a variety of ‘boundaries’ in an
International Journal of Production Research
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extended enterprise: they help to coordinate and integrate activities within manufacturing
(computerised production systems); they facilitate interaction across functional areas
(integrated information systems); and they support and promote collaboration between
a firm and external partners (integrated EDI). These three IT applications correspond to
Wade and Hulland’s (2004) classification of information system resources as ‘inside-out’
(computerised production systems), ‘spanning’ (integrated information systems), and
‘outside-in’ (integrated EDI) (see also Banker et al. 2006).
3.2 Use of supply chain organisational initiatives (SCOI)
Supply chain initiatives strengthen linkages and enhance coordination within and between
the various functions in the firm’s value chain and across the value chains of the firm
and its external partners (Porter 1985). The objective is to integrate activities, functions,
and systems (both internal and external to the firm) to create processes in which materials,
products, and information flow seamlessly across the supply chain in a manner that
competitors cannot easily match (Frohlich and Westbrook 2001). A growing literature
touts strategic value in supply chains engendering competitive performance (see, e.g.,
Eisenhardt and Tabrizi 1995, Peterson et al. 2005). In the twenty-first century, competition
is increasingly defined as occurring between rival supply chains rather than rival firms
(Li et al. 2005).
This study focuses on the use of three types of supply chain initiatives. The first is
supplier initiatives (SupInt), or the use of inter-organisational supplier development,
supplier partnering, and just-in-time (JIT) purchasing for achieving integration with
suppliers. Supplier development, which enhances supplier capability and performance
(Krause et al. 2000), can include supplier evaluation and diagnosis, site visits by buyers,
joint kaizen teams, and supplier certification programmes (Handfield et al. 2000, Prahinski
and Benton 2004). Supplier partnering involves treating the supplier as a strategic
collaborator (Monczka et al. 1998), and seeks to include all participants throughout the
product life cycle so that each can provide input to the other’s processes. Goals often
include joint product design and/or access to supplier technological capabilities (see, e.g.,
Narasimhan and Das 1999). Buying firms typically engage in supplier partnering and/or
supplier development activities with suppliers expected to deliver JIT in support of an
overall lean strategy (see, e.g., Germain and Droge 1997). JIT purchasing ensures small,
frequent, on time and reliable deliveries of quality parts (Kaynak 2005, Goffin et al. 2006).
The second type of supply chain initiative focuses on intra-organisational initiatives
(IntraInt), defined as the use of enterprise-wide initiatives for collaboration including
cross functional teams, open organisational structures, and cross-departmental process
improvement. Business processes, which are cross-functional in nature, create value for a
firm’s customers (Porter 1985, Gerwin and Barrowman 2002). The most frequently cited
initiative for developing seamless business processes is the use of cross-functional teams,
which sometimes include suppliers and/or customers (Bishop 1999, Denison et al. 1996,
Guzzo and Dickson 1996, McDonough 2000). These teams engender cooperation/
collaboration, and forge linkages to reach win-win outcomes; they represent decentralised,
lateral decision mechanisms to speed decisions and increase ‘buy-in’ (Bishop 1999).
The organisational context or structure in which cross-functional teams operate can
impact their specific goals, composition, and effectiveness (Denison et al. 1996, Jassawalla
and Sashittal 2003). For example, in an ‘open organisation’, traditional ‘command and
7030
S.K. Vickery et al.
control’ structures are significantly reduced (Powell and Dent-Micallef 1997). Research
suggests that the relaxation of traditional hierarchies facilitate open horizontal
communications, decentralised decision-making, and team autonomy (Kessler et al.
2001, Jassawalla and Sashittal 2003).
Finally, the third type of supply chain initiative centres on operational initiatives
(OpInt) whose purpose involves streamlining and integrating manufacturing processes;
specifically, JIT manufacturing, group technology (GT), and cellular manufacturing.
These are fundamental elements of lean manufacturing (Womack et al. 1991, Liker 2004).
The core objectives of lean production are the integration of activities and tasks to
eliminate waste and increase responsiveness, and the continuous effort to simplify and
stabilise production processes (Fullerton and McWatters 2001). JIT manufacturing is
characterised by reduced setup times, small lot sizes, and a ‘pull’ mechanism (kanban
system). Group technology is related to cellular manufacturing since it ‘groups’ parts or
products having similar design or manufacturing characteristics into product families
for assignment to manufacturing cells (see, e.g., Song and Hitomi 1992). JIT and
GT/cellular manufacturing lower inventories and reduce non-valued added activities
such as waiting (Fullerton and McWatters 2001). Their strategic importance is highlighted
by Laugen et al. (2005) who classify them as ‘best’ manufacturing principles.
4. Research hypotheses
4.1 Model 1: separate effects of the use of SCIT and SCOI
The implementation and use of SCIT in organisations is often proposed and justified by
greater operational agility (Sambamurthy et al. 2003). The centralisation of organisational
information, ability to make organisation wide changes in data or information, processing
through a central repository, and communicating these changes across and within the
organisations are some of the key advantages of IT. Huber (1990) elaborates on the role
of IT in expediting decision-making, accessing and analysing market information, and
rapidly reconfiguring and recombining information. Thus, IT is likely to enhance reaction
to environmental changes. For example, rapid capture, compilation, analysis and
dissemination of sales data enable companies to respond to customer needs and market
place opportunities by addressing both the cost of discounted/unsold merchandise and the
opportunity cost of stock-outs. Weill et al. (2002) found a nuanced relationship between IT
infrastructure and strategic agility in leading enterprises: different types of strategic agility
require distinct patterns of IT-infrastructure capability. More recently, Overby et al. (2006)
introduced a conceptual model to describe the relationship of information technology to
enterprise agility. They argued that IT both directly and indirectly impacts enterprise
agility. Overall, the literature supports the following hypothesis:
H1a-M1 (Model 1): Greater use of SCIT will lead to higher agility for manufacturing
firms.
As previously discussed, supply chain initiatives focus on achieving integration within
functions, among functions, and between the firm and its external partners. These
practices optimise and coordinate linkages, and can change and/or reconfigure processes
by simplifying activities, eliminating delays, eliminating non-value added steps or
redundancies, and accelerating the flow of materials and products (see, e.g., Millson
et al. 1992). Such initiatives should position the company to better sense and rapidly
respond to changing customer requirements and market conditions. Thus, from a purely
International Journal of Production Research
7031
theoretical standpoint, it can be argued that supply chain initiatives engender agility
(see also Pasternack and Viscio 1998); there is also empirical support. Studies support
a positive relationship between specific supply chain initiatives (e.g., JIT purchasing) and
aspects of agility (e.g., delivery speed) (see Kaynak 2005); many individual practices are
associated with reduced cycle times, increased flexibility, and/or increased responsiveness,
which are all aspects of agility (Eisenhardt and Tabrizi 1995, Narasimhan and Das 1999,
Krause et al. 2000, Fullerton and McWatters 2001, Gerwin and Barrowman 2002). Thus:
H1b-M1 (Model 1): Greater use of SCOI will lead to higher agility for manufacturing
firms.
4.2 Model 2: SCIT and SCOI complementarities
Complementarities are said to exist when, ‘doing more of one thing increases the returns
on doing more of another’ (Milgrom and Roberts 1995, Barua and Mukhopadhyay 2000).
Research has proposed that the interaction of IT with other complementary resources
creates firm capabilities (Rai et al. 2006). Recent supply chain literature has proposed that
SCIT is the ‘backbone’ of a supply chain since it is used to acquire, process, and transmit
information among members for more effective coordination and decision-making
(Sanders and Premus 2002). Srinivasan et al. (1994), Lewis and Talalayevsky (1997),
and others identify SCIT as an essential component of supply chain management
activities.
Each SCIT in our study has a complementary organisational initiative. Thus we have
specified the following three complementarities, which together make up our
(SCIT) (SCOI) construct: (i) the interaction of (integrated EDI) (supplier initiatives),
(ii) (integrated information systems) (intra-organisational initiatives), and (iii) (computerised production systems) (operational initiatives). For the first set, integrated EDI
logically corresponds with supplier integration because EDI applications provide
inter-organisational capabilities for JIT purchasing, for the communication and collaboration necessary in supplier development, and for the close relationships requisite to
partnering (Srinivasan et al. 1994, Powell and Dent-Micallef 1997, Hill and Scudder 2002,
Mukhopadhyay and Kekre 2002). For the second set, integrated information systems
corresponds to intra-organisational initiatives because integrated IT enables crossfunctional teams and cross-departmental process improvement by, for example, ensuring
rapid access to common data bases and programs. Integrated IT enables an open
organisation wherein information rather than organisational structure constitutes the
skeletal frame and nervous system. Finally, computerised production systems corresponds
to operational initiatives because a lean system constituting JIT operations, cellular
manufacturing and group technology depends on information for ‘pull’ mechanisms
and scheduling, tracking and positioning inventory, and reducing non-value adding
activities such as waiting (for example). In a sense, information replaces inventory and
accelerates flows.
Overall, the complementary use of SCIT and SCOI enhances the efficacy of business
strategies, operations, organisational structures, competencies and culture (Barua and
Mukhopadhyay 2000); firms are better able to act quickly and respond to changing market
conditions (Bharadwaj 2000), gain strategic flexibility (Jarvenpaa and Leidner 1998) and
develop fast product life cycles. Hence, we propose that:
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S.K. Vickery et al.
H1-M2 (Model 2): Complementarities between the use of SCIT and SCOI will lead to
higher agility for manufacturing firms.
4.3 Agility and firm performance
It was earlier argued that agility is a mediator of model relationships. Agile firms are able
to effect more numerous and more complex competitive actions that help them gain
competitive advantage (Ferrier et al. 1999). As the agile firms capture rents out of these
competitive moves, they gain in financial performance over their competitors. Thus in
both models:
H2 (Models 1 and 2): Greater agility will lead to better financial performance.
Neither Model 1 nor Model 2 specify direct paths from the antecedents of agility to
firm performance; i.e., H1 and H2 together specify that agility is a complete (as opposed to
partial) mediator in either model.
5. Research methodology
Data for our study was gathered through a questionnaire survey. The sampling frame
was the first tier suppliers to US car companies (OEMs) and the strategic business unit
(SBU) was the unit of analysis. Our population was the top 150 first tier suppliers; experts
from Automotive Industry Action Group (AIAG, with over 1000 members), provided the
list. An AIAG panel also ensured content validity and assisted in pretesting.
The questionnaire was mailed to CEOs and follow-up telephone calls were made.
If necessary, the respondent selected one SBU and forwarded the questionnaire to its
CEO. Following repeat calls, our final sample consisted of 57 SBUs (about 39% response)
with mean sales of $488.3 million (SD ¼ $646.8). North American OEMs accounted
for 83.9% of sales. Mean number of employees was 2862 and mean market share was
24.7%. We analysed response bias by comparing the 71 non-responding companies
with those who responded. Using available Dun & Bradstreet secondary data, we
found no statistically significant differences in average company sales or the number of
employees.
All items used to measure the SCOI and SCIT constructs were rated on ‘extent of use’
scales where 1 ¼ ‘extremely low use of initiative’ and 7 ¼ ‘extremely high use of initiative’.
Respondents could also select ‘not used’. Gerwin and Barrowman (2002) emphasise the
importance of measuring the extent to which a company has put an initiative into practice
in contrast to simply capturing whether it is used.
Agility and overall firm performance were measured relative to competitors on 7-point
scales (1 ¼ ‘worse in industry’; 7 ¼ ‘best in industry’). We also gathered objective data on
firm financial measures, but only for a subset due to reluctance of CEOs to divulge
financial data. Our analyses established significant correlations between the subjective
and objective measures, thereby providing evidence of the validity of subjective measures1.
Past research has, similarly, concluded that managerial assessments are consistent with
objective internal performance and also with external secondary data (Dess and Robinson
1984, Venkataraman and Ramanujam 1986).
The three items for complementarities were the product of SCIT with the
corresponding SCOI. To scale, each item was multiplied by a constant to reduce inflation
7033
International Journal of Production Research
due to multiplication. This method of scaling was preferred over the use of logarithmic
or other transformations, which may lead to non-linear transformations: multiplication
with a constant does not lead to any change in the results or the model fit2.
5.1 Analysis
We followed Anderson and Gerbing’s (1988) two-step approach whereby the structural
model is tested only after confirmatory factor analysis (CFA) reveals sound measurement
properties. Subsequently, a structural equation model (SEM) analysis was used for
hypothesis testing. Several indices were used to assess overall model fit. The Bollen index
(IFI; Bollen 1989) is robust, and the comparative fit index (CFI; Bentler 1990) is the most
stable fit index (Gerbing and Anderson 1992). Hu et al. (1992) suggested the root mean
square error of approximation index (RMSEA).
5.1.1 Measurement model (CFA or confirmatory factor analysis)
First, the items were univariate normally distributed according to individual skewness and
kurtosis results. Next, CFA analyses revealed good fit for the measurement items of SCIT,
SCOI, agility and firm performance (CFI ¼ 0.97; RMSEA ¼ 0.051; 2 ¼ 96.26, df ¼ 84).
These results, as well as the construct composite reliability (Fornell and Larcker 1981) are
in Table 1. Discriminant validity was tested at both item and construct levels. At the item
level, Lagrange multiplier (LM) cross loadings tests show that none is significant. Thus,
each item only loads on its respective construct. At the construct level, discriminant
validity was evaluated by sequentially restricting the correlations between the constructs to
Table 1. Construct, reliability, item loadings and t values.
Construct
Composite
reliability
Measurement items
Loading (l)
t value
Agility
0.79
New product introduction time
Manufacturing lead time
Delivery speed
Modification flexibility
Responsiveness to customers
0.489
0.737
0.806
0.519
0.725
3.600*
5.958*
6.720*
3.857*
5.829*
SCOI
0.71
Supplier initiatives
Intra-organisational initiatives
Operational initiatives
0.580
0.797
0.622
4.226*
6.089*
4.590*
SCIT
0.51
Integrated EDI
Integrated information systems
Computerised production systems
0.330
0.659
0.518
2.099*
0.412*
3.370*
Firm performance
0.95
Pre tax ROA
After tax ROA
Return on investment (ROI)
Return on sales (ROS)
0.984
0.978
0.856
0.826
10.219*
10.103*
8.014*
7.578*
Note: * indicates significance at 5% level.
7034
S.K. Vickery et al.
one and determining the significance of chi-square difference tests. Results support
discriminant validity for the constructs.
To test for common method bias, we used a CFA approach to Harman’s one-factor
test; the rationale for this test is that if common method variance exists, a single latent
factor would suffice (Podsakoff and Organ 1986). The fit of the one-factor model
(2 ¼ 224.93, 90 df) is worse than the fit of our measurement model (2 ¼ 96.26, 84 df),
suggesting that common method bias is not a serious threat.
5.1.2 Estimation methods
The two different structural models (Figure 2) were tested with SEM using maximum
likelihood (ML). ML has desirable properties as it generates unbiased, consistent and
efficient estimates along with a model test statistic (2ML ¼ ðn 1ÞF^ML ). ML properties
hold asymptotically and are derived assuming multivariate normality. Thus we tested for
the impact of sample size and normality. Various approaches can be used to assess model
MODEL 1 (M1): DIRECT IMPACT OF SCIT
I-EDI
Supply chain
information
technology
IntInSy
R2=0.133
2
R =0.466
CPrdSy
0.984
0.365*
0.185
Firm
performance
Agility
ROS
0.979
Supply chain
organisational
initiatives
IntraInt
PT-ROA
0.827
0.532
SupInt
ROI
0.856
AT-ROA
OpInt
ModFlx
NwPrT
CusRes
MfgLd
DelSpd
MODEL 2 (M2): IMPACT OF COMPLEMENTARITY OF SCIT WITH SCOI
I-EDI X
SupInt
IntInSy X
IntraInt
CPrdSy X
OpInt
0.821
R2=0.127
R2=0.405
0.364
Complementary
(interaction of
SCIT and SCOI)
0.636**
Agility
0.356*
Firm
performance
0.855
0.990 PT-ROA
0.854
0.978
0.939
ROI
ROS
AT-
0.558 0.531
ModFlx
NwPrT
0.699
CusRes
0.764 0.831
MfgLd
DelSpd
Figure 2. Results for alternate models.
Note: 1. Variance explained is indicated in over the construct; 2. Parameter coefficients reported are
all standardised and their significance is tested at p 5 0.05 level.
*, significant at p 5 0.01, **, significant at p 5 0.05.
International Journal of Production Research
7035
fit under various conditions (Nevitt and Hancock 2001). Bootstrap sampling in the case
of SEM is recommended when sample sizes are small (see Yung and Bentler 1996).
We performed non-parametric bootstrap simulation; results for different sample sizes
show no difference in fit statistics and any bias in standard errors is within the acceptable
range. Also, we did jackknife simulation to test for bias due to outliers. All results indicate
that our original analyses were robust.
6. Results
The overall fit statistics for Model 1 (separate effects) and Model 2 (interaction) are in
Table 2. Model 1 had 2 ¼ 98.91, 86 df. CFI and IFI were 0.97 (i.e., over 0.95; Bentler
1990); RMSEA was 0.052. Overall, Model 1 fits well, but Model 2 appears to fit better.
Model 2 had 2 ¼ 54.28, 52 df, and CFI and IFI exceeded 0.99. The RMSEA was 0.028,
about half of Model 1’s.
Parameter estimates are in Table 3 and Figure 2. Agility to firm performance is
significant in Models 1 and 2 (supporting H2). For Model 1, the direct separate paths from
SCIT and SCOI to agility were not significant (H1a, H1b were rejected); neither SCIT nor
SCOI impacts agility. However, Model 2 shows a significant impact of complementarity
SCIT SCOI on agility (H1 is supported).
Note that both Model 1 and Model 2 implicitly state that agility is a full mediator.
In order to rule out partial mediation, we tested a third model (Model 3 in Table 2,
last column) that posited direct linkages of SCIT and SCOI to firm performance (the
agility construct was dropped because neither SCIT nor SCOI predict it). Model 3 has
a poorer fit (CFI ¼ 0.93; IFI ¼ 0.93; RMSEA ¼ 0.08). Also Model 3 does not support
direct impacts on firm performanc