Int. J. Production Economics 153 (2014) 24–34
Contents lists available at ScienceDirect
Int. J. Production Economics
journal homepage: www.elsevier.com/locate/ijpe
Exploring relationships among IT-enabled sharing capability, supply
chain flexibility, and competitive performance
Yan Jin a,n, Mark Vonderembse b, T.S. Ragu-Nathan b, Joy Turnheim Smith a
a
b
Elizabeth City State University, Elizabeth City, NC 27909, USA
The University of Toledo, OH, USA
art ic l e i nf o
a b s t r a c t
Article history:
Received 22 October 2012
Accepted 21 March 2014
Available online 1 April 2014
This research explores the mechanism through which IT infrastructure enables superior firm performance by empirically examining the links among IT-enabled sharing capability, supply chain flexibilities
(as measured by a manufacturing firm's product development flexibility, production flexibility, logistics
flexibility, suppliers' flexibility, and the flexibility of the supply base), and competitive performance.
This study expands the research on IT's impact on competitive performance by focusing on IT-enabled
sharing capability and the indirect effect of this capability on firm performance. Most prior research
focused on the technical aspects of IT infrastructure and tested direct relationships.
In this research, a large-scale survey was used to collect 198 responses from U.S. manufacturers to
investigate this framework. Structural Equation Modeling was used to examine and test the measurement and structural models. The results indicated that IT-enabled sharing capability is associated with
flexibilities in a manufacturer's supply chain, which in turn are associated with the firm's competitive
performance. This finding suggests that a firm should focus on flexibilities in the supply chain to improve
its performance. IT-enabled sharing capability is an antecedent for improving these flexibilities.
Longitudinal research, multiple respondents, and techniques for improving response rate should be
considered in future research to provide more robust results.
& 2014 Elsevier B.V. All rights reserved.
Keywords:
IT-enabled sharing capability
A manufacturer's supply chain flexibility
Extended resource based view (ERBV)
Dynamic view of RBV
1. Introduction
A recent survey of corporate boards of directors by the Gartner
Group found that 52% believe that maintaining competitive
performance is of extremely high importance (Lopez, 2011). The
same survey, however, indicated that there is no real consensus
about how that maintenance of competitive performance is
related to IT capabilities. IT infrastructure itself does not differentiate a firm from its competitors because IT applications are
becoming increasing standardized (Zhang and Dhaliwal, 2009).
However, greater firm performance and sustainable competitive
performance can be achieved when IT infrastructure is used to
meet customer-determined organizational needs. IT infrastructure
can create a strong positive impact on the effectiveness of a
manufacturing firm's supply chain when it enables the firm's
IT-enabled sharing capability. These sharing capabilities can help
n
Correspondence to: WH223, School of Business and Economics, Elizabeth City
State University, Elizabeth City, NC 27909, USA. Tel.: þ 1 252 335 8533;
fax: þ1 252 335 3491.
E-mail addresses: [email protected] (Y. Jin),
[email protected] (M. Vonderembse),
[email protected] (T.S. Ragu-Nathan), [email protected] (J.T. Smith).
http://dx.doi.org/10.1016/j.ijpe.2014.03.016
0925-5273/& 2014 Elsevier B.V. All rights reserved.
the firm to create unique, difficult to imitate, and non-substitutable
capabilities (Prajogo and Olhager, 2012).
In order to discuss information sharing capabilities, it is
important to understand what these capabilities entail. Information sharing capabilities encompass two aspects: (1) the capability
a firm has of dealing with intangible information that exists within
all of the relevant parts of the firm itself and among suppliers,
clients and distribution networks that the firm has, and (2) the
firm's capability of constructing a tangible network to communicate both internally among various areas of the firm and
externally with supply chain members (the integration of the IT
systems to support the connections of information) (Keen, 1991).
An example of the first aspect would be the ability of a salesperson
to access production scheduling databases and raw material
availability in order to give a customer an estimated completion
date for a project. An example of the second aspect of the
IT-enabled sharing capability would be a system that allowed a
firm to see which of its suppliers could best meet price and
delivery needs, which of the firm's production facilities were
available for the order and which allowed the firm's customers
to access production lead times and status of order production.
While IT-enabled sharing capability adds little to a firm's
competitive performance without the actual practice of information sharing, a firm's IT-enabled sharing capability both enhances
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
the likelihood of the use of that practice and adds value by
improving the level of information processed and the value of
that information. Within the firm, these sharing capabilities allow
an order loaded into the sales system to generate orders for the
necessary raw materials and, upon receipt of expected delivery
information facilitated by the external relationships with suppliers, to schedule the necessary production time and space, and to
reserve space in a delivery vehicle using the appropriate shipping
method. In order for this model to work, however, information
sharing capabilities must exist that allow the information to pass
up and down the chain so that the firm can remain in communication with both suppliers and customers. IT-enabled sharing
capability helps to create an information-based platform that
facilitates flexibility on multiple levels – within the firm, with
suppliers, and in relationships between the manufacturing firm
and its suppliers (Lummus et al., 2003). These flexibilities in a
firm's supply chain enhance competitive performance, allowing
the firm to meet specific customer needs regarding quality,
delivery dependability, and time-to-market better (Gosling et al.,
2010; Swafford et al., 2006).
Prior research has generally tested the relationship between IT
infrastructure and firm performance as a direct link, but with
inconsistent results. In contrast, this research explores the idea
that this relationship may be indirect, positing that IT-enabled
sharing capability may influence the manufacturer's other capabilities and help it build competitive performance (Bhatt et al.,
2010). With respect to context, prior studies have included both
service and manufacturing firms. Because there may be a difference in how these firms develop and implement IT infrastructure,
including service and manufacturing firms in the same study may
have confounded the results as well.
In addition, prior studies focused on the technical elements of
IT infrastructure, such as specific applications like EDI that become
industry standards, have not supported a link between IT infrastructure and firm performance (e.g., Jeffers, 2010; Peng et al.,
2011; Ray et al., 2005). These elements, however, represent the
capacity of IT infrastructure to perform specific functions rather
than the ability of IT infrastructure to facilitate information sharing
and improve decision making. The capacity of IT infrastructure
depends primarily on the decision to invest. It therefore cannot be
the basis for a competitive performance since it is easily copied.
In contrast, the capability of IT infrastructure depends on successfully infusing IT infrastructure into the organization, which is more
difficult to imitate (Zhang and Dhaliwal, 2009). Thus, although
companies could invest in the same IT infrastructure, the application
or implementation of IT infrastructure would typically be different
for different firms, making it the basis for a competitive performance.
For example, a proprietary deployment of an IT infrastructure that
facilitates a manufacturing firm's just in time (JIT) inventory practices
may add flexibility to the production process by allowing the firm to
track the supplier's work in progress prior to the time of order. This
information would let the firm know if the lead time for a key
component is increasing, thus warning the firm that production may
have to slow down or the component may need to be ordered
elsewhere to meet promised delivery schedules. This information
would also allow the manufacturing firm to choose among suppliers,
based on projected delivery times as well as cost. If that process were
simply dependent on hardware compatibility, it would be relatively
simple for a competitor to duplicate the system. In contrast, a system
that garners information such as that described above must be
based on a culture of openness and collaboration that facilitates
and values communication between a manufacturer and its supplier.
Such a culture-supported infrastructure is more difficult to replicate
(Fawcett et al., 2011).
This paper addresses the value of the IT-enabled sharing
capability in creating that competitive performance. It contributes
25
to the IT infrastructure field in three ways. First, it focuses on the
IT-enabled sharing capability rather than on the technical aspects
of IT infrastructure. Second, it examines the indirect effect of the
IT-enabled sharing capability on firm's competitive performances
via a firm's supply chain flexibility. Third, it tests these hypotheses
sampling only the manufacturing industry, thereby avoiding the
potential for an industry confound that may exist when including
other contexts.
2. Theoretical background and research hypotheses
With increasing global competition, manufacturers seek ways
to build sustainable competitive performance in order to enhance
their competitive position. Competitive performance can come
from a variety of sources, such as differentiation of products and
services based on price, quality or service, but is most sustainable
when it is difficult to imitate. IT-enabled communication represents one potential source of differentiation. In a firm holding an
IT-enabled competitive performance, IT systems ensure timely
communication both internally and with external suppliers
(Akkermans and Horst, 2002; Yang et al., 2009). An example of
such a firm would be one where information regarding production
schedules was well communicated internally, allowing for appropriate staffing and resource utilization, while also being communicated clearly with suppliers, ensuring timely deliveries and evaluation
of the quality of raw materials.
As shown in Fig. 1, this research examines an element that is
vital to the firm's success, IT-enabled sharing capability. This
capability represents the extent to which manufacturers can
provide continuous information flows on a well-developed connection within the firm and between the firm and its suppliers.
IT-enabled sharing capability is directly associated with the flexibilities in a manufacturer's supply chain, including elements such
as product development flexibility, production flexibility, logistics
flexibility, suppliers' flexibility, and supply base flexibility. In turn
these supply chain flexibilities are directly associated with the
creation of competitive performances (Gosling et al., 2010). Thus,
the relationship between IT-enabled sharing capability and competitive performance is linked to the presence of flexibilities in a
manufacturer's supply chain.
2.1. The dynamic extension and the relational extension of RBV
As IT infrastructure becomes increasingly uniform, it is more
difficult for manufacturers to acquire unique IT hardware or
software that is a scarce heterogeneous resource. For this reason,
the dynamic extension and relational extension of the resource
based view (RBV), not the traditional RBV, provide the proper
theoretical lenses to defend the value of IT-enabled sharing
capability in maintaining the manufacturer's competitive position
(Bhatt et al., 2010; Byrd and Turner, 2001).
According to the dynamic extension of the RBV, the way a firm
uses or exploits its IT infrastructure could vary, thus generating the
dynamic organizational capabilities (e.g., IT-enabled sharing capability) that are unique and can create the superior performance
over time (Fawcett et al., 2011). For example, when a manufacturer
purchased a new ERP system, the ERP system per se does not
differentiate the firm from competitors because it is a commodity
that is readily available for purchase. What makes the difference is
how the firm integrates the individual technology (e.g., RFID) with
the ERP system and how the firm reconfigures the existing process
(e.g., order tracking) with the ERP system to synergize actions and
results. This integration and reconfiguration transform unified IT
infrastructure applications (e.g. ERP) into unique capabilities that
facilitate sharing information and streaming connections within
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Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
Research Model
00
Product
Production Logistics
Dev. Flex Flex
Flex
Annual Industry
Sales
Sector
CA1
ITSC2
ITSC3
IT-enabled
sharing
capability
H1
Supply chain
flexibility
H2
ITSC4
Competitive
advantage
CA2
CA3
ITSC6
Suppliers’
flexibility
Supply base
flexibility
H3
Fig. 1. The research model.
the firm and between the firm and its suppliers. Thus, they are the
strategic sources of sustainable competitive performance for the
firm (Duncan, 1995; Kayworth, et al., 2001).
The relational extension of the RBV incorporates the relational
view theory, named the extended resource based view (ERBV) by
Lewis et al. (2010), arguing that the unique capabilities of a firm
may also reside in the relationship with its suppliers (Dyer and
Singh, 1998). According to ERBV, a particular IT infrastructure may
develop sustainable competitive performances by generating a
relation-specific capability, which is difficult for competitors to
copy. For example, EDI is an openly available information technology that connects a firm with its suppliers and facilitates information sharing with them. Yet, the effectiveness of using EDI largely
depends on the characteristics of the specific buyer–supplier
relationship, such as the level of commitment to use the technology and the willingness to share information. These long-term
oriented characteristics foster specific investment in a particular
buyer–supplier relationship, which in turn improves both information sharing and the connectivity between manufacturer and
supplier (Fawcett et al., 2011). For example, the relationship could
include the ability for the manufacturer to check the supplier's
production lead time status before ordering or the use of procurement modules that automatically ordered necessary raw materials
on receipt of a customer order. A firm's IT-enabled sharing
capability generates the competitive performances over time,
because such capability is relation-specific and thereby a unique
resource.
2.2. IT-enabled sharing capability
Typically, technology by itself is not a rare or heterogeneously
differentiated resource. IT-enabled sharing capability, depending
on the way the IT is implemented, however, can be unique and
difficult to imitate (Radhakrishnan et al., 2008). Two aspects of
IT-enabled sharing capability have been used to explain how the
firm can exploit proprietary deployment of IT infrastructure to
enhance organization capability effectively.
The first aspect, named IT range, represents the extent to which
a manufacturing firm can provide the seamless information flow in
an accurate and timely manner within the firm and with its
suppliers (Bharadwaj, 2000; Closs et al., 2005; Keen, 1991). This
competitive information of a manufacturing firm can be classified
into three types – manufacturing information, logistics information,
and strategic information (Fawcett et al., 1996). The ability to access
this information gives the managers of the firm and its suppliers a
comprehensive picture of the situation and helps them make
appropriate decisions (Fawcett et al., 1996). In light of the dynamic
extension of the RBV, the ability to provide the needed information
regarding a firm's plans and operations, not the information per se,
is a dynamic capability of the manufacturer. Since IT range allows all
three types of this information to flow more smoothly – manufacturing information flowing through the company from sales to
purchasing; logistics from sales to production to shipping; and
strategic information from the firm's policy makers and research
and development to sales and manufacturing, it thus allows the
firm to react more nimbly to changing circumstances. When shared
with customers and suppliers, it further allows a more seamless
customer service experience which may also serve as a competitive
performance. In addition to the benefits to customer service,
knowing more about suppliers – such as knowing their prior
performance, expertise, and capabilities – is of value to the firm
since it helps the firm cooperate effectively with its suppliers. IT
range allows the firm to draw information from a variety of sources
to create a more complete set of facts on which decisions can be
made, making it relation-specific and therefore particularly valuable
as a source of competitive advantage.
The second aspect, named IT reach is the extent to which a
firm's IT network connects and supports various functions at
different levels within the firm, connects the firm with its
suppliers and supports the relationship between the firm and
its suppliers (Bharadwaj, 2000; Closs et al., 2005; Keen, 1991).
It does not necessarily reflect the raw number of functions connected and supported by IT infrastructure; rather it reflects the
value of these functions in improving the performance of the firm
and the supply chain. An IT network provides a shared foundation
that allows a firm to assess, link, exchange, and disseminate
available information as needed. Physical IT network hardware
(e.g., computers) by themselves do not give distinctive advantages
to a firm, because that hardware can be easily purchased on the
open market. Likewise, in most cases, the IT software is also readily
available for purchase and installation. However, as suggested by
the dynamic and relational extensions of the RBV, integrating
individual hardware and software components and making them
work cooperatively requires time, effort, understanding and commitment. Such integration is not easily duplicated by a competitor.
Thus IT reach may help a firm achieve improved competitive
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
performance through a sustainable competitive advantage
(Bharadwaj, 2000).
In addition, the two aspects of IT infrastructure capability
influence each other. An integrated IT network enhances transparency within the firm and within the firm's supply chain, so that
the manufacturer can promptly detect variations in its operations
as well as in suppliers' changes in production and delivery. As a
result, such a network provides the foundation for having accurate
and timely information. In return, information about the firm's
operation and suppliers' performance reinforces the value of the
integrated network garnered by both parties. Seamless information flow brings the latest updates regarding the manufacturer and
its suppliers to the attention of management of the relevant
entities. Based on this information, the manufacturer can make
any adjustments necessary to align the actions of the supply chain
with changing demand.
2.3. Flexibilities in supply chain
Flexibilities in a firm's supply chain serve as an intermediating
variable that links IT-enabled sharing capability to a firm's competitive performance. Flexibility is increasingly important in
accommodating uncertainty in the business environment (Koste
and Malhotra, 1999; Narasimhan et al., 2004). From the 1980s to
the early 2000s, flexibility research focused on how a firm's
flexible manufacturing and product development capabilities
could respond to environmental uncertainty and could enhance
firm performance (Koste and Malhotra, 1999; Narasimhan et al.,
2004; Stevenson and Spring, 2007). With the emergence of supply
chain management in the late 1990s, flexibility research expanded
to include a firm's supply chain components (e.g., logistics flexibility) and later to include suppliers' flexibility and the supply
base flexibility (Swafford et al., 2006). Based on Lummus et al.
(2003)'s conceptual model, these flexibility variables are discussed
as follows.
A manufacturing firm's flexibility, in a dynamic supply chain, is
important to successfully sustaining the firm's competitive positions and long-term profitability (Stevenson and Spring, 2007).
A manufacturing firm's flexibility is the firm's ability to adjust
its product development, logistics, and production processes
efficiently and effectively, so it can adapt to changes in the
environments, in particular changes in final customer demand
(Narasimhan et al., 2004). Flexibilities in product development and
production represent the capabilities of a manufacturer's management regarding new products and production processes (Koste
and Malhotra, 1999; Zhang et al., 2003). Logistics flexibility reflects
the abilities of the firm's procurement system to accommodate
various receipt and delivery requests accurately, quickly, and
efficiently (Prater et al., 2001). Product development, logistics,
and production are highly interrelated functions within the firm
(Prater et al., 2001). For example, production and logistics provide
a foundation to support product development. Without such a
foundation, the competitive performances generated by innovative products and by substantial modifications to existing products
will diminish quickly (Teece, 1986).
With the growing pressure on supply chains to respond quickly
and efficiently, the flexibility of each supply chain member, such as
suppliers' flexibility, rather than the manufacturer's flexibility alone
is important (Lau, 1999; Swafford et al., 2006). In the current
environment, where the level of vertical integration is limited, it is
difficult to imagine that a firm could accommodate customer
demand for product variety without the assistance of flexible
suppliers (Das and Abdel-Malek, 2003). Suppliers' flexibility is the
ability of vendors to efficiently and effectively adjust their operations to cope with a manufacturer's requests for components
27
needed to meet the final customers' demands (Das and AbdelMalek, 2003; Pujawan, 2004). This flexibility has positive impacts
on a manufacturer's product development, production, logistics,
and other capabilities (Lau, 1999). To a manufacturer, the most
important elements of suppliers' flexibility are order quantity and
product variety, which determine its ability to provide the right
amount and the right type of products in a timely manner
(Tachizawa and Thomsen, 2007). Because a manufacturer is striving to satisfy its customers on multiple competitive dimensions
simultaneously, it views suppliers' flexibility as a way of integrating both its needs and those of the customers.
Supply base flexibility is a firm's ability to change its buyer–
supplier linkage without high penalties (cost, time, and effort)
(Gosain et al., 2004; Lummus et al., 2003). Supply base flexibility
resides in the connection between the manufacturing firm and
supplier firms, not within these firms, as is the case with both a
manufacturing firm's flexibility and suppliers' flexibility. This
flexibility is important because supply chain performance depends
on the performance of each supply chain member and the
effectiveness of the connections among the members. Considering the different ways of varying a buyer–supplier relationship
(e.g., adding a new supplier, changing the closeness of the
relationship, switching purchasing orders to an alternative supplier), two focuses of flexibility (i.e., range and mobility) stand out
(Pujawan, 2004; Stevenson and Spring, 2007). First, the range of
supply base flexibility indicates the number of qualified suppliers.
When emergencies arise, the availability of qualified suppliers to
which orders can be switched is critical in maintaining the
expected manufacturing schedule. For example, the 2011 tsunami
in Japan and flooding in Thailand caused severe disruption for
some auto companies with key suppliers in those countries and
relatively few alternatives in other countries (Powell, 2011).
Second, mobility or responsiveness represents the manufacturer's
efficiency in developing new suppliers, adjusting supplier relationships, and switching purchasing orders. When rapid adjustments
are made at low cost, the competitive position of the supply chain
is maintained.
Suppliers' flexibility and supply base flexibility correlate with
each other and are associated to the firm's flexibility. These two
types of flexibilities are an asset specific to the manufacturing firm,
which both adds value to the supplier–manufacturer relationship
(Dyer and Singh, 1998) and is imperfectly imitable and imperfectly
suitable as well as rare (Lavie, 2006). A manufacturer with a high
supply base flexibility (e.g., a strong ability to identify a new
supplier or make a quick transition if necessary) can determine the
best source to use to meet customer demand, even if that means
using a different supplier than would normally be used (Gosain
et al., 2004; Gosling et al., 2010). When a firm has a broad supply
base, it can use that base to respond to last-minute needs from
different quantities of supplies or different supplies altogether
without having any adverse effect on the performance of the
supply chain. This ability gives the firm a high level of suppliers'
flexibility. IT-enabled sharing capability facilitates supply base
flexibility and suppliers' flexibility by enhancing the depth of
information shared. This depth of information reflects the partnership nature of the relationship rather than merely the presence of
the supplier in the network. Further, the dynamic adaptability
created by these partnerships creates a high level of flexibility for
the manufacturer that helps it respond to various customers'
demands. For these reasons, a manufacturing firm's supply chain
flexibility is a higher order construct that includes product development flexibility, production flexibility, logistics flexibility, suppliers'
flexibility, and supply base flexibility (Mishra and Shah, 2009).
IT-enabled sharing capability influences all these flexibilities.
First, IT-enabled sharing capability influences the firm's flexibility
28
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
by (1) keeping the manufacturing firm updated with accurate and
timely information and (2) building the capability of collecting,
analyzing and disseminating information within the firm and with
suppliers in an efficient and effective way. Being able to continuously monitor its own operations and supply chain operations
allows the manufacturer to detect changes in the environment and
respond quickly by adjusting its actions (e.g., product design,
production, and logistics) (Fawcett et al., 1996). In addition, better
IT-enabled sharing capability also lowers the cost and reduces the
time needed for refining and reengineering the business process
(Duncan, 1995). As a result, the manufacturing firm will be able to
adjust its supply chain operations flexibly in response to the
change in customer demand.
Besides the influences on the firm's flexibility, IT-enabled
sharing capability also influences the flexibility of the firm's
suppliers and its supply base. Built on a connected platform,
well-integrated IT infrastructure and inter-organizational activities
(such as order release, tracking and expediting) enable a high level
of timely and accurate information exchange with its suppliers.
When shared through the network, information regarding downstream activities, such as product demand, the production process,
and distribution, can dramatically reduce the impact of uncertainty for upstream suppliers (Fawcett et al., 2011; Field and Meile,
2008). Being aware of what has happened in the manufacturer's
supply chain enables suppliers to be able to adjust their operations
and to be aligned better with the manufacturer's request, which is
a higher flexibility for suppliers. In addition, the manufacturer,
through its information sharing capabilities, will be able to access
information about the changes happening on a supplier site, such
as a production interruption. Should such an interruption occur,
the firm can look for a substitute supplier if needed by tapping
into its network of alternate suppliers (i.e. using its higher level of
supply base flexibility). In addition, the IT-enabled sharing capability enables the firm to know both the existing suppliers and
the potential qualified suppliers better, which makes the manufacturer efficient and effective in developing a new buyer–supplier
relationship or in strengthening the existing relationship (i.e. higher
level of supply base flexibility).
Therefore, it is hypothesized that IT-enabled sharing capability
has a direct positive relationship with the flexibilities in a
manufacturing firm's supply chain (Hypothesis 1).
2.4. Competitive performance
Competitive performance is used as a proxy for the desired
accounting measures, innovation performance, product performance
and sales performance (Shan and Jolly, 2010). Competitive performance refers to the outcome of competitive advantage, which
indicates the extent to which an organization is able to develop an
edge over its competitors (McGinnis and Vallopra, 1999). Various
measures such as cost, time, and quality have been discussed in prior
flexibility and IT-related literature. In this study, quality, dependable
delivery, and time-to-market are considered. Cost is not included
because cost and the other three measures represent two distinct sets
of measures (Krause et al., 2007). In this research, quality means to
compete by having products that consistently meet or exceed the
customer's expectations. Dependable delivery means to compete by
consistently delivering the right product to customers at the right
time. Time-to-market means to compete by providing the innovative
products faster to customers. These competitive advantages reflect the
manufacturer's ability to provide a high level of customer service
resulting in a competitive performance (Shepherd and Gunter, 2005),
which cannot be easily copied by other competitors and thus have
sustainable value.
A manufacturing firm's supply chain flexibility affects its
competitive performance in three ways. First, a manufacturer's
supply chain flexibility indicates the ability to maintain a high
level of performance across all facets and all changes in its
operations and in its supply base. This enables the manufacturer
to offer products with high quality and achieve the quality
competitive performance. Second, supply chain flexibility shows
the manufacturer's ability to work with suppliers to provide a
wide variation of products, very different production outputs, and
various deliveries, which gives the firm the ability to deliver the
right product to customers at the right time and achieve the
dependable delivery competitive performance. Third, a manufacturing firm with high supply chain flexibility means that the firm,
with the help from their flexible suppliers and the flexible supply
base, can quickly and cost-effectively introduce different new
products, modify existing products, adjust output volumes and
product mixes, and change logistics systems. As a result, the firm
will be able to offer products with a swift response and achieve
the time-to-market competitive performance.
In some literature discussing a firm's competitive performance
(e.g., the cumulative capabilities), a firm's flexibility is the part of the
capabilities that also includes quality and delivery, where quality is the
foundation to achieve delivery and flexibility (Grobler and Grubner,
2006). We propose the relationship in a different direction for the
following reason. The flexibility, quality and delivery referred to in the
cumulative capabilities are a firm's internal manufacturing capabilities,
such as volume flexibility, mix flexibility, and manufacturing conformance. In contrast, this research looks at the supply chain flexibility as
having both an internal flexibility component that can lead to product
innovation (e.g., product development flexibility) and a component
involving interactions with suppliers which supports product innovation (e.g., logistics flexibility and supplier base flexibility, suppliers'
flexibility). We take this perspective because we believe that innovation is one of the key elements of competitive advantage along with
dependability and quality.
It is hypothesized that a manufacturing firm's supply chain
flexibility has a direct positive relationship with the firm's competitive performance (Hypothesis 2).
In this research, IT-enabled sharing capability can be viewed as the
internal ability a manufacturing firm has to exploit their integrated
information systems to share the information within the firm and
between the firm and its suppliers. ITSC is directly important to and
supportive of a manufacturer's supply chain flexibility, while ITSC's
development may indirectly help achieve other capabilities as well.
Supply chain flexibility is less visible than the visible elements of
competitive performance that are seen by customers (e.g., quality,
dependable delivery and introduction time of new products). A manufacturing firm's flexibilities (i.e. product development flexibility,
production flexibility and logistics flexibility) and suppliers' flexibility
depend on the manufacturing firm's IT-enabled sharing capability
allowing it to gather information regarding customer interests and
supplier modifications. This IT-enabled sharing capability must further
allow the efficient and effective change of the supplier–manufacturer
connection (i.e. the flexibility of supply base) as a means of expanding
the variety of offerings or permutations of offerings by the manufacturing firm.
It is hypothesized that IT-enabled sharing capability has an
indirect positive relationship with a manufacturing firm's competitive performance through the firm's supply chain flexibility
(Hypothesis 3).
3. Research design and methodology
3.1. Measurement development
Items of IT-enabled sharing capability were developed based
on information technology literature (Bharadwaj, 2000; Closs et al.,
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
2005; Fawcett et al., 1996; Keen, 1991; Li, 2006; Li et al., 2005; Zhang
et al., 2006). Keen (1991) discusses IT infrastructure in terms of IT
reach and IT range and their impact on competitive advantage. Closs
et al. (2005) suggest that there are two elements that are of critical
interest here – timeliness and sharing between the firm and its
supplier. Fawcett et al. (1996) suggest that two major categorizations –
manufacturing information and logistics information. Li et al. (2005)
suggests the importance of accuracy and timely information. These
references translated into the inclusion of the first three ITSC items.
Li (2006) and Zhang et al. (2006) indicate the importance of the
connection of relevant parts in the manufacturing firm suggesting
items regarding the use of the IT system in a way that supports
competitive advantage for the firm.
All five flexibility variables were developed based on two
popular aspects of flexibility, range and mobility/adaptability
(e.g., Koste and Malhotra, 1999; Swafford et al., 2006). Range
represents the number of states an organization can adopt;
mobility is the ease of changing from one state to another in
terms of cost and time. From these two aspects, each flexibility
variable was developed from various flexibility literatures. The
items measuring product development flexibility came from
existing literature, which discussed the ability for new product
introduction and design change accommodation (e.g., Narasimhan
et al., 2004; Vickery et al., 1999). Production flexibility items were
built on the literature of volume and mix flexibilities (e.g.,
Swafford et al., 2006; Zhang et al., 2003). Logistics flexibility items
came from the concept of Zhang et al. (2002)'s physical supply and
purchasing flexibilities, with an emphasis on the ability of a firm's
inbound transportation to provide the needed materials and
suppliers. Suppliers' flexibility measures were based on Lau
(1999)'s analysis of a supplier's ability to change production
volume and variety. Supplier base flexibility items were extended
from Gosain et al. (2004)'s partnering flexibility regarding the ease
of replacing the existing supplier with a new supplier. We added
two items to reflect the different ways of changing a manufacturer's supply base.
Items focusing on the firm's competitive advantage, the potential to achieve the competitive performance, were modified from
performance measures used in prior research (e.g., Krause et al.,
2007). All variables were measured through managerial perceptions by using 5-point Likert scales (1 ¼strongly disagree to
5 ¼strongly agree).
Two most commonly used control variables, a firm's industry
sector (SIC code) and the number of employees, are used in the
research model. First, SIC codes are recoded as nine dummy
variables (SIC20, SIC25, SIC28, SIC30, SIC34, SIC35, SIC36, SIC37,
and SIC38) to classify the sample into 10 groups according to a
manufacturing firm's industry sector. Second, the number of
employees is recoded as three binary variables (EMP_1 for firms
under 100 employees; EMP_2 for firms with 100–249 employees;
and EMP_3 for firms with 250–999 employees) so that manufacturing firms in this research were grouped into four categories
according to the firm size. These variables are included to control
the effects of industry sector and firm size on competitive
advantage so that the research model is a complete model and
results are rigorous.
A questionnaire was developed after conducting a careful
literature review of IT infrastructure, supply chain flexibility, and
firm performance to ensure the initial content validity of instruments (Haynes et al., 1995). The questionnaire was pre-tested to
refine the content validity through consultation with professionals
and practitioners who have extensive knowledge in this field.
A Q-sort method was applied in a pilot study to assess the preliminary convergent validity and discriminant validity of the
instruments (Moore and Benbasat, 1991). Items were revised as
needed and the final version is given in Appendix A.
29
Table 1
Profile of respondents.
Frequency
Industries (SIC code)
Electronic/electrical equipment (36)
Industrial/commercial machinery (35)
Instruments and related products (38)
Chemicals (28)
Transportation equipment (37)
Furniture and fixtures (25)
Rubber and plastic products (30)
Fabricated metal products (34)
Food (20)
Others (39)
Total
Percentage (%)
60
30
25
20
16
11
9
9
7
11
30.30
15.15
12.63
10.10
8.08
5.56
4.54
4.54
3.54
5.56
198
100.00
3.2. Data collection
The data was collected from an online survey which took place
from September to December 2007. The study population included
supply chain professionals in various U.S. manufacturing companies. An initial invitation was sent to each of the potential
respondents by email with a link to the online survey website.
Two reminder emails were sent out afterwards. Respondents were
offered the research results as an incentive for participation and
provided with other options (e.g., mail or fax) for them to answer
the survey. Finally, 198 usable responses were received from a pool
of 6485, after excluding 1424 undeliverable email addresses. The
response rate (3.3%) of this research was comparable to recent
research in management literature (e.g., Beltran-Martin et al.,
2008), considering a large amount of emails were likely filtered
by security systems, a large number of emails were idle because of
relocation, and the response level of top managers continues to
decline (Baruch, 1999; Klassen and Jacobs, 2001).
Next, the external validity of the sample was examined based
on the sampling design, its representativeness, and sample size
(Short et al., 2002). First, the study population was collected from
three databases of executive contacts: RSA Teleservices, Lead411,
and the Council of Supply Chain Management Professionals
(CSCMPs), all three accounting for a large share of the total
business and total population of U.S. firms. In addition, the sample
covered more than nine manufacturing sectors with a good
balance (Table 1). The sample also covered manufacturing firms
in different sizes (58% with more than 1000 employees, 29% with
100–1000 employees, and 22% with fewer than 100 employees)
and different annual sales (54% with over $500 M, 36% with
$10–500 M, and 8% with less than $10 M). For these reasons, the
results could be generalized for small, medium, and large firms
across a variety of manufacturing industries. Moreover, respondents (top executives – 42%, senior production managers – 15%,
senior logistics managers – 16%, senior purchasing managers –
21%) would have knowledge of the capabilities of their companies
and suppliers, which is suitable for this research.
Second, the sample represented the population in terms of firm
size and annual sales, both frequently used in accessing the nonresponse bias. Differences between the 198 respondents and the
population1 were examined by using chi-squared tests (Short
et al., 2002) among four categories (under 100, 100–249, 250–
999, and more than 1000) for firm size and five categories (under
1
One source that provided a mailing list did not disclose information about
company size and annual sales. The number of contacts provided by this source is
less than 15% of the entire population. Therefore, we calculated the non-respondent
bias by comparing the respondents with the population from the other two sources
where the company size and annual sales information were available.
30
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
$10 M, $10–49 M, $50–99.9 M, $100–499.9 M, and more than
$500 M) for annual sales. The results (χ2 ¼ 2.98, df ¼3, p 40.25
for firm size, and χ2 ¼ 1.87, df ¼4, p4 0.5 for annual sales) indicated
no significant differences between the sample and the population;
thus, response bias is not an issue. Third, a sample size over 150 or
200 is commonly considered necessary to be able to perform a
stable and a rigorous SEM model (Hair et al., 2005), and the
sample size of 198 useable respondents met this need.
4. Measurement model results and structural model fits
4.1. Common method variance
Table 2
Descriptive statistic, factor loadings, critical ratio, and R2 from CFA.
Item
Mean
IT-enabled
ISC1a
ISC2
ISC3
ISC4
ISC5a
ISC6b
S.D
Factor loading
Sharing Capability (ITSC)
3.687
0.957
–
3.444
1.068
0.701
2.909
1.172
0.790
3.444
1.083
0.664
3.338
1.197
–
3.116
1.193
0.828
Competitive Advantage(CA)
4.525
0.674
CA1b
CA2
4.222
0.873
CA3
3.778
1.062
0.703
0.781
0.584
Critical ratio
R2
–
9.898
11.174
9.308
–
–
–
0.492
0.625
0.441
–
0.686
–
8.228
6.854
0.494
0.610
0.341
Because the survey was answered by a single informant,
common method variance was checked before conducting further
validations (Podsakoff et al., 2003). Common method variance
exists if a measurement model that includes all items shows a
good model fit. The common model-fit indexes were calculated to
judge the validity of this measurement model. Because no index
was close to the acceptable value (Byrne, 2001) (χ2 ¼ 1124.990,
df ¼190; RMR¼0.155; NFI ¼ 0.472, CFI¼0.513; RMSEA¼ 0.158, 90%
CI: 0.149–0.167), common method variance was unlikely to be
problematic.
Product Development Flexibility (PDF)
PDF1a
3.864
1.050
–
PDF2b
3.525
1.001
0.747
PDF3
4.030
0.842
0.681
PDF4
3.768
0.965
0.869
–
–
8.977
10.604
–
0.559
0.464
0.755
Production
PF1b
PF2
PF3a
PF4
0.803
0.890
–
0.751
–
13.044
–
11.108
0.645
0.793
–
0.564
Logistics Flexibility (LF)
LF1b
3.859
0.7999
LF2
3.576
0.952
0.882
0.848
–
11.322
0.777
0.720
4.2. Measurement model results
Supply Base Flexibility
SBF1b
3.717
SBF2
3.672
SBF3
3.419
(SBF)
1.023
1.002
1.104
0.745
0.847
0.836
–
11.000
10.935
0.554
0.718
0.699
Suppliers' Flexibility (SF)
SF1b
3.626
0.879
SF2
3.621
0.845
SF3a
3.737
0.826
SF4
3.581
0.838
0.814
0.821
–
0.631
–
10.186
–
8.426
0.663
0.675
–
0.398
Supply Chain Flexibility (SCF)
PDF
3.604
0.693
PF
3.598
0.744
LF
3.428
0.658
SF
3.528
0.655
b
SBF
3.093
0.709
0.745
0.792
0.742
0.605
0.583
5.619
5.959
5.930
5.203
–
0.555
0.627
0.550
0.366
0.340
A manufacturing firm's supply chain flexibility (SCF) was
measured by five first-order variables (i.e., product development
flexibility – PDF, production flexibility – PF, logistics flexibility – LF,
suppliers' flexibility – SF, and supply base flexibility – SBF). The
IT-enabled sharing capability (ITSC) and the competitive advantage (CA) were first-order variables. Confirmatory factor analyses
(CFA) were used to examine the unidimensionality and reliability
of seven first-order variables and one second-order variable. The
fit indexes of the measurement model (including all variables in
Table 2) indicate the model is acceptable (χ2 ¼271.299, df ¼181;
RMR¼0.059; NFI ¼0.873, CFI¼0.953; RMSEA ¼0.050, 90% CI:
0.037–0.062).
Table 2 shows descriptive statistics, factor loadings, critical
ratio and R2 from CFA. All item-to-factor loadings were greater
than 0.5 (po 0.001), indicating the acceptable scale unidimensionality (Ellis et al., 2010; Shah & Goldstein, 2006). Table 3 shows
Cronbach's alpha, average variance extracted (AVE), composite
reliability (CR), and the maximum shared variance (MSV), and
the average shared variance (ASV) for all scales. CR values
exceeded 0.7 cutoff for all variables, indicating the acceptable
reliability (Fornell and Larcker, 1981). AVE values of all variables
except CA and SCF surpassed the 0.5 cutoff. The AVEs of CA and
SCF were actually very close to the cutoff values. CR of each
variable is greater than its AVE, indicating that the convergent
validity is satisfied (Fornell and Larcker, 1981).
The discriminant validity was assessed by comparing the AVE
and the MSV for each variable (Fornell and Larcker, 1981). The MSV
for each variable was less than the AVE for that variable (Table 4),
which indicated the adequate discriminant validity for all variables
(Fornell and Larcker, 1981). The only exception is SCF and CA,
which MSV is close to but greater than its AVE. This MSV is the
squared value of the correlation between SCF and CA. Discriminant
validity for SCF and CA is then evaluated by the difference in the
variances (χ2 values, df ¼1) of a correlated model of these two
variables and a single factor model consisting of all items of two
variables. Δχ2 (20.8) is significant at p o0.001 level, supporting
the discriminant validity between SCF and CA (Anderson and
Gerbing, 1988).
a
b
Flexibility (PF)
3.687
0.984
3.631
1.023
3.975
0.920
3.742
0.878
Deleted item after purification in CFA.
Items with the regression weights of 1 in CFA.
Table 3
Descriptive statistic, Cronbach's alpha, CR, AVE, MSV, and ASV.
ITSC
CA
PDF
PF
LF
SF
SBF
SCF
Mean S.D.
Cronbach's
alpha
CR
AVE
Maximum
Average
shared variance shared
variance
3.440
2.857
3.604
3.598
3.428
3.528
3.093
2.657
0.837
0.690
0.751
0.855
0.848
0.796
0.848
0.858
0.846
0.733
0.812
0.857
0.856
0.802
0.851
0.824
0.561
0.482
0.592
0.667
0.749
0.578
0.657
0.488
0.274
0.555
0.467
0.466
0.250
0.316
0.247
0.555
0.913
0.421
0.693
0.744
0.658
0.655
0.709
0.405
0.144
0.268
0.248
0.275
0.181
0.242
0.172
0.407
Since SCF is a second-order factor, a target-coefficient (T) was
used to validate the existence of this higher order structure (Marsh
and Hocevar, 1985). T is a ratio of the chi-square of the first-order
model (correlating all five first-order flexibilities, PF, PDF, LF, SF,
and SBF) to the chi-square of the second-order model (loading all
five first-order flexibilities PF, PDF, LF, SF, and SBF onto the secondorder factor SCF). T value was 0.93, indicating that the major
variance among PF, PDF, LF, SF, and SBF was captured by the higher
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
order construct SCF (Segars and Grover, 1998). Therefore, the
second-order structure of SCF was justified.
4.3. Structural model fits and results of hypotheses
After constructs were validated, the hypothesized relationships
were tested in a structural model. The weighted scores were
calculated for PF, PDF, LF, SF and SBF. The composite scores were
used as the observed items for a manufacturing firm's SCF in the
structural model. ITSC and CA were first-order variables and
corresponding items were observed items for those variables in
the structural model. The results (Table 4) of the structural model
indicated a reasonable model fit (χ2 ¼296.991, df ¼171; RMR¼
0.044; CFI¼ 0.937, NFI ¼0.870; RMSEA ¼0.061, 90% CI: 0.049–
0.073). All hypotheses were supported.
Hypothesis 1. showed that IT-enabled sharing capability is positively associated with a firm's supply chain flexibilities (t ¼4.802
and standardized coefficient ¼0.403). ITSC reflects a manufacturing firm's ability to integrate different processes and streamline
information exchange across the different business functions and
with the supply chain firms. According to the dynamic extension
of RBV, developing ITSC is a process that transforms the manufacturing firm, and as such is unique and difficult for competitors
to imitate. Such a dynamic capability helps the manufacturing firm
know what to do at what time, which makes them more flexible.
In addition, shared information based on the integrated IT processes between the manufacturing firm and suppliers keeps the
upstream suppliers updated about the downstream activities in
the supply chain and allow supply chain members to communicate
more frequently and smoothly. Possessing the relation-specific
information may allow the suppliers to respond to the manufacturer's request quickly. Thirdly, in order to keep suppliers with
high flexibility in the supply base over a long period of time, the
manufacturing firm needs to be able to select and change the
supplier as needed. For example, if the manufacturer was able to
connect to the potential flexible suppliers, the manufacturing firm
could use that flexibility to identify and switch to a different, more
appropriate supplier quickly.
Hypothesis 2. showed that a manufacturing firm's supply chain
flexibility is positively related to the firm's competitive performance (t¼ 7.018 and standardized coefficient ¼0.805). When a
manufacturing firm is flexible to customer requests with the help
from flexible suppliers and the flexibility to change the supply
base, the firm is able to provide different options, to change from
one option to another efficiently and effectively, and to maintain
consistent performance regardless of the option chosen. With
these competences, the manufacturing firm will be able to
compete with other companies on quality, dependable delivery,
and time-to-market of product introduction, which all indicate a
higher potential to have enhanced competitive performance.
In order to test whether the effect of a manufacturing firm's
supply chain flexibility has a mediating effect or an indirect effect
on the ITSC–CA relationship, two structural models were tested
according to the procedure suggested by Baron and Kenny (1986)
and Holmbeck (1997). The first model is a direct effect model with
only ITSC and CA. The second model is a mediated model including
SCF as an intermediate variable. In the direct effect model, ITSC
showed an insignificant association with CA (t¼1.221 and standardized coefficient ¼0.438). Because of this insignificant relationship, the effect of SCF on the ITSC–CA relationship can only be an
indirect effect, but not a mediated effect. We continued to test the
effect of SCF in the mediated model. The association of ITSC and CA
is insignificant in the mediated model (t ¼ 0.411 and standardized coefficient ¼ 0.107) and H1 and H2 are both significant.
31
In addition, results showed that the direct effect between ITSC
and CA is insignificant at p ¼0.705 (estimates¼ 0.107) and the
indirect effect is significant at p ¼0.002 (estimates¼ 0.325). Therefore, ITSC is associated with CA indirectly through SCF but not
mediated by SCF, and thus hypothesis 3 is supported.
5. Discussion, conclusions and implications
Today, under pressure to sell more for less, manufacturers are
struggling to improve flexibility that is needed to achieve competitive performances in customer service. Because competition
exists not only at the firm level but also at the supply chain level,
the effectiveness of its suppliers including how they utilize
resources is essential for a firm to differentiate its performance
from its competitors (Fawcett et al., 2011). The dynamic and
relational extensions of RBV both indicated that the possession
of IT resources is not as important as the firm-specific or relationspecific capabilities that can be generated from these resources.
This research shows that sharing capability enabled from IT, i.e.,
firm-specific and relation-specific capability, has a direct impact
on the firm's supply chain flexibility, which includes a firm's
product development, production and logistics, suppliers' and
supply base flexibilities. In turn, the firm's supply chain flexibility
influences the firm's competitive advantage and ultimately the
firm's competitive performance. Thus a firm with a wide network
of suppliers that has sophisticated data interfaces that allow for
real-time calculation of shipping time, work in process status and
modification deadlines is better able to sustain a competitive
performance in customer service than a firm with fewer supplier
options and less information.
While prior research testing the direct relationship between IT
infrastructure and firm performance presented contradictory
results, this research contributes to the IT literature by presenting
an empirical path that may explain these conflicts by linking the
IT-enabled sharing capability to the firm's competitiveness
through flexibility. As shown in the direct model, IT-enabled
sharing capability has no direct association with competitive
advantage, which means that if a manufacturing firm with ITSC
cannot end up with a more flexible supply chain, the firm's
competitive performance would not be improved. Because the
flexible supply chain makes a manufacturing firm to be competitive in meeting customers' demand, a firm's CA is improved only if
a manufacturing firm supply chain (itself, its suppliers and its
supply base) responds flexibly to customers' requests (Hypothesis 2)
with the help from a well-integrated information system and
a streamlined information sharing process (Hypothesis 1). The
direct/indirect effects ITSC on CA in the mediated model also
confirm the indirect effect of supply chain flexibility on the
ITSC–CA relationship.
Another contribution of this research is to emphasize information sharing capacity rather than focusing on the technical
perspective of IT infrastructure. The IT-enabled sharing capability
may make the firm's competitive performances sustainable
because it is dynamic and is not easily copied by competitors.
Thus, results of this research also reveal important practitioner
implications for IT-enabled sharing capability. The firm should
invest in practices that enhance the IT-enabled sharing capability,
such as employee training to improve the effectiveness of using IT
applications or more communication approaches with suppliers to
foster an information sharing culture. These practices will improve
the information flow and advance the integration across different
departments within the manufacturing firm and among supply
chain members. The enhanced IT-enabled sharing capability
makes it easier for the manufacturer to trace the material flow
in its supply chain (Fantazy et al., 2009). These improvements
32
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
Table 4
Summary of hypotheses testing.
Mediated model
AMOS coefficient
ITSC-SCF
SCF-CA
ITSC-CA
SIC20-CA
SIC25-CA
SIC28-CA
SIC30-CA
SIC34-CA
SIC35-CA
SIC36-CA
SIC37-CA
SIC38-CA
EMP_1-CA
EMP_2-CA
EMP_3-CA
a
b
Direct effect model
t-value
0.403
4.802a
0.805
7.018a
0.107
0.411
0.052
0.670
0.183
2.218b
0.053
0.541
0.026
0.324
0.040
0.498
0.035
0.318
0.242
1.818
0.055
0.581
0.022
0.208
0.041
0.186
0.098
0.395
0.116
0.899
2
χ ¼ 296.991, df ¼171; CFI ¼0.937, NFI¼ 0.870; RMR ¼0.044;
RMSEA ¼0.061, 90% CI: 0.049–0.073
AMOS coefficient
t-value
–
–
–
–
0.438
1.221
0.108
1.102
1.101
0.938
0.043
0.343
0.045
0.441
0.055
0.539
0.014
0.103
0.237
1.414
0.035
0.296
0.052
0.398
0.093
0.307
0.179
0.525
0.043
0.249
χ2 ¼ 149.135, df¼ 73; CFI ¼0.947, NFI¼ 0.907; RMR¼ 0.045;
RMSEA ¼ 0.073, 90% CI: 0.056–0.089
p o 0.001.
p o0.05.
enable the firm to build flexibility and achieve multiple competitive objectives and as such both improve the bottom line for the
corporation. Such competency will bring more profits for the firm.
5.1. Limitations and future research
Although this research provides several significant contributions,
some limitations need to be addressed in future research. First, this
research is a cross-sectional study. Future longitudinal research may
provide further insights underlying relationships between ITenabled sharing capability, supply chain flexibility, and competitive
performance. Second, multiple respondents, multiple methods for
obtaining measures and randomizing the order of items can be used
in future research to moderate the mono-respondent problem.
Third, future research might attempt to improve the response rate
by using different media for data collection, reaching the intended
respondents via state-of-the-art techniques, shortening the questionnaire, and establishing collaborative relationships between
researchers and respondents (Dillman et al., 2009). Fourth, because
of the low reliability of competitive performance, a second-order
construct might be considered to better represent this variable. In
addition, the perceptual measures of business performance could be
added in the survey to test how IT-enabled sharing capabilities,
supply chain flexibilities, and other cumulative capabilities will
influence the manufacturing firm's performance. Moreover,
although the model fits are acceptable, they were not great. This
might be the result of some double-barreled items for IT-enabled
sharing capability. For example, “Our IT system provides accurate
and timely information” can be broken up into two items “Our IT
system provides accurate information” and “Our IT system provides
timely information”. Balanced items between the internal and the
external measures for IT-enabled sharing capability are recommended as well.
In addition to these methodological limitations, different
research models can be considered. Human skills are discussed
widely in IT infrastructure management (Ray et al., 2005). People
decide what information technology to use and how to use it.
Employee knowledge and ability constrains the IT-enabled sharing
capability. Examining the interdependence between human
capital and information technology and the effect of their interactions on the firm's supply chain flexibility and competitive
performances therefore may also be a fruitful area of research. In
addition, cost is an important competitive advantage (Sarmiento
et al., 2010). It is a good idea to test the influences of IT-enabled
sharing capability and flexibilities in the supply chain on cost
leadership in future research. Porter's cost vs. quality as sources of
competitive advantage and eventually competitive performance
(e.g., financial performance) can also be included in the future
model (Porter, 1985).
Overall, the implications of IT infrastructure to create IT-enabled
sharing capability suggest that the key to competitive performance
through IT is not reflected solely in network complexity or the
acquisition of cutting edge technology, but rather how firms use what
they have and exploit the advantages generated from partner relationships with both suppliers and customers.
Appendix A. List of items in survey questionnaire
IT-enabled Sharing Capability (ITSC)
ITSC1: Our IT system provides accurate and timely information
for manufacturing operationn
ITSC2: Our IT system provides accurate and timely information
for logistics operation
ITSC3: Our IT system provides accurate and timely information
for our suppliers' performances
ITSC4: Our IT system aligns different internal functions
ITSC5: Our IT system supports joint production planning and
scheduling among purchasing, manufacturing, marketing,
and distributionn
ITSC6: Our IT system supports the management of linkages
between our firm and our suppliers
Manufacturing Firm's Supply Chain Flexibility (SCF)
Product Development Flexibility (PDF)
PDF1: Our firm can introduce many different new productsn
PDF2: Our firm can introduce new products efficiently
PDF3: Our firm can implement many different product
modifications
PDF4: Our firm can implement product modifications
efficiently
Y. Jin et al. / Int. J. Production Economics 153 (2014) 24–34
Production Flexibility (PF)
PF1: Our firm's manufacturing system can operate at many
high and low production volumes
PF2: Our firm's manufacturing system can change production
volumes efficiently
PF3: Our firm's manufacturing system can accommodate
many different product mixesn
PF4: Our firm's manufacturing system can change product
mixes efficiently
Logistics Flexibility (LF)
LF1: Our firm's procurement system can fill many different
in-bound shipment requests
LF2: Our firm's procurement system can respond to changes
of in-bound shipment requests efficientlyn
Suppliers' Flexibility (SF)
SF1: Our suppliers can satisfy our firm's needs at many low
and high order quantities
SF2: Our suppliers can respond efficiently to changes in our
firm's order quantities
SF3: Our suppliers can produce a large number of various
products for our firmn
SF4: Our suppliers can respond efficiently to changes in our
firm's product variety
Supply Base Flexibility (SBF)
SBF1: Our firm can quickly identify a new supplier when
necessary
SBF2: Our firm can easily make adjustments in the current
relationship
SBF3: Our firm can switch to alternative suppliers efficiently
Competitive Advantage (CA)
CA1: Our firm competes with other firms by offering high
quality products to our customers
CA2: Our firm competes with other firms by offering
dependable delivery to our customers
CA3: Our firm competes with other firms by quickly
introducing product in the market
n
Deleted item after purification in the confirmative factor
analysis.
Appendix B. Supplementary material
Supplementary material associated with this article can be
found in the online version at http://dx.doi.org/10.1016/j.ijpe.2014.
03.016.
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