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The impact of cloud-enabled process
integration on supply chain performance
and firm sustainability: the moderating
role of top management
Himanshu Shee and Shah Jahan Miah
College of Business, Victoria University, Melbourne, Australia
Leon Fairfield
Metcash, Laverton North, Australia, and
Nyoman Pujawan
Department of Industrial Engineering, Sepuluh Nopember Institute of Technology, Surabaya, Indonesia
Abstract
Purpose –Theorising from the intersection of supply chain and information systems (IS) literature, this study aims to investigate supply chain
integration (SCI) as a multidimensional construct in the context of cloud-based technology and explores the effect of cloud-enabled SCI on supply
chain performance, which will eventually improve firm sustainability from a resource-based view (RBV). In addition, the moderating effect of top
management is explored.
Design/methodology/approach –Using cross-sectional survey data collected from a sample of 105 Australian retail firms, this study used
structural equation modelling to test the hypothesised relationship of cloud-enabled SCI with performance in a theoretical model.
Findings –Results show that cloud-based technology has positive effect on SCI, and the cloud-enabled SCI is positively related to supply chain
performance which eventually influenced firm sustainability. Further, top management intervention moderates the relationship between supplier
and internal integration with supply chain performance. But it is found to have no moderating effect on the relationship between customer
integration and supply chain performance.
Practical implications –Recognising the potential benefits of emerging cloud-based technologies reported in this study, retail managers need
to understand that higher order SCI requires the support of cloud-based technology to improve supply chain performance and firm
sustainability.
Originality/value –This research extends prior research of information and communication technologies-enabled SCI and its effect on supply
chain performance which overly remains inconsistent. In addition, IS literature abounds with discussion on cloud computing technology per
se, and its adoption in supply chain is overly rhetoric. This study fills this gap by conceptualising the multiple dimensions of SCI enabled by
cloud-based technology and the way it affects supply chain and firm sustainable performance. Investigating SCI in context of cloud-based
technology is a unique contribution in this study. The moderating effect of top management in this decision also adds to the current body of
literature.
Keywords Information systems, Australia, Supply chain integration, Cloud technology, SCM performance
Paper type Research paper
1. Introduction
Today supply chains compete against other chains in an
increasingly competitive business environment (Hult et al.,
2007), thereby forcing the firms to join hands in a partnership
(Whipple and Frankel, 2000). Such a partnership promotes
efficient consumer response by fulfilling the right product to the
right customer delivered in full and on time (Rahman and
Bullock, 2005;Russell, 2000). Efficient logistics movement
and timely sharing of demand and supply information is likely
to be achieved through adequate integration of business
partners (Lee et al.,1997). Supply chain processes and their
The current issue and full text archive of this journal is available on
Emerald Insight at: www.emeraldinsight.com/1359-8546.htm
Supply Chain Management: An International Journal
23/6 (2018) 500–517
© Emerald Publishing Limited [ISSN 1359-8546]
[DOI 10.1108/SCM-09-2017-0309]
The authors are thankful to Victoria University and Metcash Australia for
funding this empirical research, which is a part of a larger project (vide
grant No: CRGS 14/15, Dt.5/12/2014). The authors are grateful to the
anonymous reviewers for their time and valuable feedback to improving
the overall quality of the paper. The authors sincerely thank the editor for
facilitating the review process. The authors acknowledge Ian Sadler for his
constructive feedback to improve the paper.
Received 27 September 2017
Revised 14 January 2018
3 June 2018
29 July 2018
Accepted 30 July 2018
500
effective integration, as defined by Chen et al. (2009, p. 27),
refer to “linking major business functions and business
processes within and across firmsintoacohesiveandhigh-
performing business model”. Supply chain process
integration endorses the flow of information, materials and
funds with other supply chain partners for effective decision-
making (Rai et al., 2006). Further, demand management has
received an increased attention in supply chain because of
rapid growth in the use of technologies that enable firms to
synchronise supply and demand by accurate inventory
replenishment and order fulfilment (Coyle et al., 2016).
While global supply chains, with longer lead times, pose a
special challenge, this technology enables the firms to sense
market changes and make adjustment to expected customer
requirement.
Although information and communication technologies
(ICT) are well-recognised to virtually integrate supply chain
processes (Giménez and Lourenço, 2008), the current
literature represents supply chain integration (SCI) using
traditional information technology (Gunasekaran et al.,
2004;Prajogo and Olhager, 2012), e-business (e.g.
e-commerce, e-procurement, e-collaboration) (Giménez and
Lourenço, 2008) and e-supply chain (Yao et al., 2007)by
using barcoding, one-to-one electronic data interchange
(EDI), warehouse management systems (WMS) and MRP/
MRP-II over the internet (Paulraj et al., 2008;Zhang et al.,
2011). Apart from improving operational and strategic
capability, connectivity and visibility and deployment of
state-of-the-art technologies (Prajogo and Olhager, 2012;Rai
et al., 2006;Shi and Yu, 2013), such integration lacks a
dynamic response of new technology adoption as enabler of
such mechanism.
While a supply chain can improve the performance
through transaction cost efficiencies and coordination
effectiveness using traditional ICT technologies such as
enterprise resource planning (ERP) and transaction
processing systems (Yao et al., 2007), the emergence of
cloud technology (Marston et al., 2011) poses challenges to
transitioning from the current ICT legacy to superior cloud-
based technologies. Literature reveals limited empirical
studies so far, except a study published by Wu et al. (2013).
Cloud technology is defined as an “ICT-enabled service
model where hardware and software services are delivered
on-demand to end-user customers over Internet in a self-
service fashion quite independent of devices and locations”
(Marston et al.,2011, p.177). Jede and Teuteberg (2015)
state that cloud technology is still in its infancy particularly
in supply chain, thereby calling for an empirical
investigation to better support managers to understand its
potential in cross-firm logistics process integration. As the
use of cloud technology is receiving growing interest among
many firms, and not yet fully adopted in industry practices,
this study investigates the adoption intention decision rather
than its effectiveness in real life use.
Although there are many studies that have investigated
SCI issues using ICT as backbone of information flow
between functions that are distributed across players
(Prajogo and Olhager, 2012;Rai et al., 2006;Yu, 2015;
Zhao et al., 2011), most of them focussed on SCI as a single
construct having its mixed effect (positive or not) on
performance. Few other empirical studies (Yu, 2015;Zhao
et al., 2011) have reported the effect of multi-dimensions of
ICT-enabled SCI on performance. While ICT capability
can enhance the collaboration and reduce the cost of
coordination by integrating these sub-dimensions of SCI
(Shi and Yu, 2013), limited studies have systematically
investigated the potential use of cloud technology as an
enabler and its effect on SCI and subsequently on
performance. A few empirical studies however recently have
reported the cloud technology adoption in supply chain
context (Bruque-Cámara et al.,2016;Cegielski et al., 2012;
Wu et al., 2013). For example, Bruque-Cámara et al. (2016)
reveal a positive effect of community cloud development on
the informational-physical integration of supply chain and
on operational performance. But they have not considered
the external integration such as integrating supplier and
customer using the cloud-based technology. Wu et al.
(2013) report that firms’characteristics (e.g. business
processes complexity, organisational culture, existing
information systems and compatibility) do influence the
decision to adopt cloud-based technology. Cegielski et al.
(2012) investigate firm’s information processing
requirements and capability and find the positive effect on
firms’intention to adopt cloud computing. However, these
studies did not consider cloud technology adoption as a
potential enabler of SCI as a multidimensional construct
and its effect on performances. Thus, this study is unique in
way that it examines SCI in the context of cloud technology
as an enabler. This research therefore argues for SCI using
the cost-effective pervasive cloud-based technology.
In addition, previous studies have measured performance
as a single construct (Prajogo and Olhager, 2012;Rai et al.,
2006;Shi and Yu, 2013), but the effect of SCI on
performance remains mixed and inconsistent (Zhang et al.,
2011, p. 1232). A few other studies have used operational
and firm level performance separately but concurrently
(Bruque-Cámara et al., 2016;Wiengarten and Longoni,
2015;Yu, 2015). Also a limited number of studies to date
have investigated operational and firm performance in
hierarchical order, except a study by Fawcett et al. (2011)
who find a positive effect of operational performance on
customer satisfaction, growth and profitability which is
quite different from the cloud-enabled framework in this
study. We examine the effect of cloud technology on SCI
that will influence supply chain performance and firm
sustainable performance (i.e. economic, environmental and
social) in hierarchical order. This conceptualises the
possibility that cloud-enabled SCI will enhance the supply
chain performance initially which will then influence the
firm’s sustainability. Supply chain performance, as
proposed by the Supply Chain Council, is measured using
five dimensions, i.e. reliability, responsiveness, flexibility,
efficiency, cost and an efficiency indicator (Shepherd and
Günter, 2010). Thus, the question is whether the cloud-
enabled SCI has the potential to improve supply chain
performance which leads eventually to sustainable firm
performance.
This study, therefore, aims to extend the current
understanding of cloud technology as an enabler of SCI and
explores the effect of multidimensional cloud-enabled SCI on
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
501
supply chain performance, which will eventually influence
firm sustainability. The perspective of the resource-based
view (RBV) underpins this research. This research is limited
to one specific, increasingly relevant cloud technology
services that was recently drawn to the attention of supply
chain managers and academics. Our view builds on the
foundation of work of Rai et al. (2006) on “ICT-enabled
integration for firm performance”by extending it to adopt
and implement the cloud-based technology in supply chain
process integration. Further, “top management initiative”
as moderator adds a new dimension and fills a gap in Rai et
al.’s ICT-enabled model.
The remainder of the paper proceeds with background
theory on resource-based view (RBV). Then we use a tenet of
this theory to create a theoretical research framework as the
basis of our investigation and hypotheses development. The
methodology section includes survey instrument, data
collection and the analysis technique. Then, we discuss our
findings with theoretical and practical implications. Finally, the
conclusion section presents a short summary and limitations of
the research.
2. Background theory
A literature review shows limited scholarly research on the
applications of cloud technology in supply chain process
integration. While its evolution goes back to the past few
years with much research around cloud applications
(Marston et al., 2011), there is an equally urgent need to see
its potential applications, particularly in supply chain process
management. As part of this study, an online database search
using key words like “cloud”and “supply chain integration”
revealed only 235 articles, published after year 2000,
retrieved mostly from IS journals with limited research
published in supply chain main stream journals. Published
literature however seen to be leaning towards cloud
computing (Leavitt, 2009;Marston et al., 2011),
technological innovation and adoption strategy for improving
performance (Attaran, 2017;Wu et al.,2013), its privacy and
security issues (Leavitt, 2009) and information processing
capability (Cegielski et al., 2012). This implies that limited
cloud computing related studies have been published in
supply chain journals. Nevertheless, a few studies have drawn
the attention to adopt cloud technology as strategic initiative
(Autry et al., 2010;Cegielski et al., 2012). While comparing it
with the traditional ICT-enabled SCI in a review, Cegielski
et al. (2012, p.185) claim that, “cloud computing supports
scalable on-demand computing power, rapid deployment,
and reduced support infrastructure while facilitating lower
cost of ownership”. Cloud is less of a technology type but
more of a paradigm shift in ICT capability building strategy
that needs further investigation in SCI context (Jede and
Teuteberg, 2015). While literature investigates cloud as more
of a technological shift (Jede and Teuteberg, 2015), there is a
need to empirically investigate how cloud-based technology
can help achieving the supply chain integration.
2.1 Resource based view
The Resource based view (RBV) is a popular theory deemed to
be appropriate foundation for this research. We argue how
cloud-based technology can create supply chain capability in a
firm that can be a source of competitive advantage. RBV claims
that physical resources (e.g. ICT and technical personnel) can
serve as source of competitive advantage only if they
outperform the equivalent assets of competitors (Barney,
1991). Nevo and Wade (2010) view ICT-enabled resources as
having a greater strategic potential than other resources of the
organisation in isolation. But the effect of ICT investment on
firm performance has received a mixed result in literature
(Zhang et al., 2011). Investment in ICT, in general, is argued as
one form of capacity building for enhancing competitive
advantage (Bharadwaj, 2000). Bharadwaj (2000) argues that
ICT capability building is not just a separate investment but
needs to be assessed for its compatibility with other resources
within the firm to understand its positive effect on the firm’s
sustainable performance (Liang et al.,2010). Therefore, firms
need to choose those emerging technologies that align with
its supply chain strategy (Wu et al.,2013). ICT capability is a
rent generating resources whereby the procurement of goods
and their distribution creates a set of complementary resources
unique to the firm that are inimitable by rival firms. Cloud
computing in the form of software as a service (SaaS),
infrastructure as a service (IaaS) and/or platform as a service
(PaaS) offer several computing advantages over a traditional
ICT system (Marston et al., 2011;Wu et al., 2013). Realising
the current potential of ICT capability along with emergence of
cloud-based computing (i.e. perceived as hybrid cloud), we
believe that virtual cloud adoption is an effective way of
resource building. If ICT applications (e.g. electronic data
interchange (EDI), warehouse management systems (WMS))
can span across organisational functions (i.e. technological
breadth) linking key suppliers and customers, we believe that
cloud-based computing services will be a cost-effective option
because on-premises investment is insignificant. Characterised
by its “on-demand and scalable computing power, rapid
deployment capability, minimal infrastructure needs and low
cost”(Wu et al.,2013, p. 30), cloud technologies can be a great
resource of competitive advantage for supply chain operations
in small, medium and large firms. We argue that cloud-based
technology is a resource that enables supply chain integration of
partners, hence creating a competitive advantage over other
firms.
2.2 Limitations of current information and
communication technologies drive the emerging cloud
adoption decision
Data collection as well as data processing is increasingly vital
for business. This suggests that firms obtain data to process for
decision-making (Jede and Teuteberg, 2015;Wu et al.,2013).
Data are getting more prominent and meaningful to firms
because the emerging big-data analytics explore accurate,
timely and relevant information along supply chain (Kache
et al.,2017;Schoenherr and Speier-Pero, 2015). The existing
ICT systems are able to support timely decisions (Wu et al.,
2013), in relation to procurement, production, distribution,
storage, retailing and after-sales services within business
processes that become complex with the multiplicity of
product volume and varieties. Involvement of multiple
partners in an outsourcing arrangement further adds to this
complexity of connectivity, tracing, tracking and visibility
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
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(Gunasekaran et al., 2015;Taylor, 2014). However,
Fawcett et al. (2007) positthatwillingnesstoshare
information is critical in information processing via ICT
connectivity. It is likely that limitation of the current
traditional ICT drives the decision to adopt a cloud-based
platform to facilitate supply chain functions (Cegielski et al.,
2012). The dated one-to-one EDI link for information
sharing is inefficient to meet hyper-dynamic markets in a
regional supply chain ecosystem (Taylor, 2014). We
consider cloud adoption as a construct in this study where
intention to adopt, or combine with current ICT, is a likely
enabler of digital integration. Supplier process, customer
process and internal processes are considered as three
supply chain processes proposed to be integrated for
information exchange via cloud services.
The decision to adopt cloud technology, we believe, is
contingent upon two aspects: limitations of the current ICT
and the ever-evolving benefits of emerging cloud-based
technology in a supply chain environment. Both aspects, of
course, point in the same direction of cloud adoption
decision. The limitation of current ICT can be audited by
checking its application functionality, that is, the extent of
flexibility and responsiveness of the current ICT to
accommodate the complex business requirements without
much change to existing hardware and software systems
(Byrd and Turner, 2000). The supply chain partners,
however, vary in their capacity to manage supply chain
processes (e.g. supplier and customer relationship,
forecasting, order fulfilment and after-sales services)
(Croxton et al., 2001), which are overly dependent on the
ICT backbone. The varying degree of ICT infrastructure is
generally characterised by its support for business processes,
compatibility with the business requirement, scalable to
quickly meet incremental changes, reliability after the
changes and self-sufficiency in terms of on-premises
software availability (Byrd and Turner, 2000;Wu et al.,
2013). Current ICT is likely to encounter serious limitations
of these characteristics when it is applied to a supply chain
environment. From the RBV perspective, ICT are resources
(e.g. technology resources, technical and managerial skills
and IT business resources) (Barbosa et al., 2018)andany
variation at the partner level may cause a bottle neck in
offering cross-organisational connectivity, visibility and data
transfer. Use of telephone, fax, text messaging, emailing and
basic spreadsheets are the commonly used lower order
communication channels for small retailers for ordering
merchandise to their large suppliers. Large retailers are
relatively better off with higher order ICT legacy in terms of
investment and infrastructure in most of the traditional but
higher order ICT services namely enterprise resource
planning (ERP), advanced planning and optimisation
(APO) for supply chain, customer relationship management
(CRM), supplier relationship management (SRM),
electronic data interchange (EDI), vendor managed
inventory (VMI), predictive data analytics (PDA) and global
positioning systems (GPS).
Nonetheless, current practice appears to be disjointed
because of inadequate ICT facilities for small and medium
suppliers as well as retail customers which creating a bottleneck
in information sharing and exchange. Further, the traditional
approach to information sharing via a one-to-one EDI link is no
longer appropriate in the current hyper-dynamic market where
the chain partners come from extended enterprises beyond
borders (Taylor, 2014). This disparity in the level of
communications remains all time issues in a supply chain
transaction. While all partners are more or less self-sufficient in
their ICT capability, they face real challenges in supply chain
operations when it comes to businesses across company
borders.
Therefore, a well-integrated ICT platform for digital
access of information has become a basic need of the
business today. The emerging cloud computing solution is
likely to take on this challenge for firms. The cloud-based
solution provides a clear advantage over traditional ICT
because it gives competitive advantage over other forms of
technologies (Bharadwaj, 2000). Marston et al. (2011)
emphasise cloud as a possible new class of ICT application
that delivers every possible service (e.g. Infrastructure as a
Service (IaaS), Platform as a Service (PaaS) and Software as
a Service (SaaS)). IaaS provides a full computer
infrastructure, PaaS offers partial or full application
development access to users and SaaS delivers complete
applications such as CRM (customer relationship
management), SRM (supplier relationship management)
and enterprise resource management via internet (Leavitt,
2009). We, therefore, argue in favour of cloud technology
services for the next level of digital integration in supply
chain.
Despite the technological challenges (e.g. security, privacy,
platform reliability, extra bandwidth cost and vendor locked-
in issue), Leavitt (2009) argues that cloud computing is here
to stay and will grow more than on-premises IT investment
because of its advantages, namely cost saving, easy scalability
and high availability, particularly in software as service
(SaaS). Vendors such as Amazon Web services, Oracle SaaS
platform, Cisco, Hewlett-Packard, Salesforce Automation,
NetSuite and Google Apps have made a foray into this
space. They help firms to save cost on on-premises
investment in software, hardware, system networking,
physical space, power consumption and skilled personnel.
This frees up firms to focus clearly on core competencies
(Prahalad and Hamel, 1990). Cloud computing services
thus provide a provision of thin clients (i.e. services accessed
via internet), grid computing (i.e. virtual organisation of
computers) and utility computing (i.e. pay as you use)
(Leavitt, 2009).
3. Research framework and hypotheses
development
The theoretical framework of cloud-enabled supply chain
integration and hypotheses development are discussed and
presented in Figure 1. The rational for hypotheses development
is discussed in the following sections.
3.1 Relationship between cloud-based technology and
supply chain integration
The definition of cloud computing provided earlier in
introduction section is adapted from Marston et al. (2011)
because of its business perspective and therefore more
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
503
appropriate for supply chains. The users of cloud services, by
definition, will pay for the Web services (i.e. client-server
communication) as they continue in using its functions,
which are quite independent of the locations of their
transactions. Marston et al. (2011) have identified five key
advantages of cloud computing: immediate access to
hardware and corporate computing software without any
upfront investment; lower IT barriers to innovation through
ubiquitous online applications, like Facebook and YouTube;
scaling up/down the self-services use as need arises; business
analytics to understand customers and other partners in a
supply chain dealing with terabytes of data; and low cost of
entry for SMEs to benefit from business analytics. Another
perspective is that when the big businesses are resting on their
legacy of a robust ICT platform and it appears to be self-
reliant, they may also choose to adopt it for selective projects
that go beyond their current capacity when it comes to timely
delivery. However, small and medium enterprises (SMEs) are
the potential adopters of the emerging cloud services (i.e.
SaaS) because of easy access, low cost, pay-as-you-go
perspectives for their information processing and decision-
making. Regardless of the advantages, adoption of the
“cloud”may depend on the information processing
requirement and in-house capacity available in a firm (Wu
et al., 2013) and compatibility with existing ICT and security
issue both internal and external information flow (Gangwar
et al., 2015). While a small proportion of SMEs have achieved
SCI using conventional ICT applications on piece-meal
basis, many are yet to realise the potential benefits of cloud-
based technologies for a higher level of performance in
logistics process integration.
We believe that the benefits from cloud adoption may flow
to both the focal firm and to its trading partners. The
perceived benefits to suppliers (e.g. purchase order and
inventory status at supplier), perceived benefits to customers
(e.g. sales order and inventory status at customers) and
perceived benefits internal to the firm (e.g. data accuracy for
sales and operations planning) are likely to influence the
intention to adopt cloud services. The cloud-enabled supplier
and customer integration will probably support a higher level
of information backbone (i.e. resource building and
capability) that can facilitate timely information sharing and
use. Thus, we believe that intention to adopt cloud services
will be likely to strengthen the integration of supplier process,
customer process and internal processes within firm.
Therefore, we propose the firsthypothesisas:
H1. The level of adoption and use of cloud technology
affect supply chain integration positively (i.e.
supplier, customer and internal).
3.2 Relationship between cloud-enabled process
integration and supply chain performance
A well-integrated platform powered by cloud services enables
real-time data transfer among partners in an extended chain.
This is possible as the cloud operates on an internet protocol
that facilitates timely communication with suppliers and
customers. Seamless information flow in relation to sales
forecast, production planning, order tracking and tracing,
delivery status and stock level can occur when the logistics
processes of key suppliers and customers are integrated, using
cloud in this case. Logistics activities within a firm, mainly
around inventory turnover, safety stock, order processing,
labour cost, accurate pricing etc. are likely to be well supported
via cloud services. Cloud adoption, similar to traditional ICT,
will be likely to facilitate information flow (Lee et al.,1997),
material flow (Stevens, 1990), funds flow (Mabert and
Venkataramanan, 1998) and demand information flow (Coyle
et al.,2016) among partners in a supply chain. As information
distortion remains a recurring issue in a supply chain, the cloud
technology may be able to mitigate this effect, known as
bullwhip effect (Lee et al., 1997;Shee and Kaswi, 2016),
thereby improving the inventory situation at each partner along
the chain. When demand-supply is in perfect sync, cloud
services can manage a series of activities among chain partners
to deliver efficient consumer response by replenishing
inventory in more efficient way (Harris et al., 1999). Just-in-
time is an effective replenishment programme between a
manufacturer and suppliers. Cloud is believed to deliver better
buyer-supplier collaboration by using the internet. Even ICT
has the capability to do this currently through wide range of
technologies (e.g. WMS, ERP, EDI) (Autry et al.,2010).
Cloud-based services, however, are believed to do it in an
improved way (e.g. reduced cost, scalability, flexibility,
mobility and shared resources)(Marston et al.,2011;Zhang
et al.,2010).
Cloud services are believed to simplify the mixed and
inconsistent results of SCI-performance saga in the literature
Figure 1 Conceptual model of cloud adoption and performance relationship
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
504
(Flynn et al., 2010;Wiengarten and Longoni, 2015;Zhang
et al., 2011). RBV theory perspective however argues that
ICT resources, in combination with other organisational
capabilities, can drive the firm towards its performance goal
(Bharadwaj, 2000;Liang et al., 2010). Where ICT
infrastructure is easy to replicate, cloud adoption will
possibly offer a competitive edge over others (Wu et al.,
2013). The greater the width (involving suppliers and
customers) and depth of integration (i.e. operational
coordination to joint collaboration) via cloud technology,
the greater the positive effect would be on supply chain
performance across cost, quality, delivery and flexibility
dimensions (Banchuen et al., 2017). Joint operations
through integration of suppliers and customers using cloud-
based technology is likely to improve supply chain
performance significantly. Cost, quality, delivery and
flexibility are the four commonly used operational indicators
used in this study (Schoenherr and Swink, 2012).
Therefore, the second hypothesis can be stated as:
H2. Cloud-enabled supply chain integration will have a
positive effect on supply chain performance.
3.3 Relationship between cloud-enabled supply chain
performance and sustainable firm performance
While literature is established with ICT or digitally enabled
supply chain integration (Cegielski et al., 2012), cloud
technology adoption in SCI for performance improvement is
still in its infancy (Jede and Teuteberg, 2015). Literature shows
firm performance as a single construct to measure operational
performance and business performance together (Flynn et al.,
2010;Huo et al., 2014;Rai et al.,2006). This study measures
the performance in two levels and hierarchical order. First, the
effect of cloud-enabled SCI on supply chain (SC) performance,
followed by the second, the effect of the SC performance on
firm sustainability. Firm sustainability uses triple bottom line
(TBL) concept to assess the economic, environmental and
social performance (Elkington, 1997). Thus, SC performance
is treated as an antecedent to firm level sustainability. The
triple bottom line perspective further argues that
environmental and social sustainability performance are as
importantaseconomicperformance(Elkington, 1997).
Pagell and Wu (2009,p.88)positthat“supply chain
performance should be measured not just by profits, but also
by the impact of the chain on ecological and social systems”.
In line with the supply chain configuration that drives
development of environmental and social capabilities, it can
eventually impact the environmental and social performance
at the firm level (Parmigiani et al.,2011). This study
therefore measures sustainability dimensions at firm level
rather at supply chain level. Environmental sustainability
refers to a supply chain that has limited emissions and
engagement in activities that can save firm’s ecosystem
(Vachon and Mao, 2008). Social sustainability cares about
well-being, health and safety of employees and stakeholders
engaged in the firms’supply chain (Kleindorfer et al.,2005).
Liang et al. (2010), from meta-analysis, find that firms’
technological resources can improve supply chain performance
via efficiency improvement but may not influence firm financial
performance directly. Amidst the mixed and inconsistent
results in the literature (Flynn et al., 2010), it is unclear whether
cloud-enabled supply chain performance will be able to affect
firm performance positively. We believe that the cloud-enabled
supply chain performance is perceived to have positive effect on
sustainable firm performance. Thus, the third hypothesis is
formulated as:
H3. Cloud-enabled supply chain performance will have a
positive effect on a firm’s sustainable performance (i.e.
economic, social and environmental).
3.4 Top management initiative as moderator
Firms often adopt ICT that helps to improve operational cost
efficiency which demonstrates each firm’s readiness for
information technology applications in the supply chain
(Khalifa and Davison, 2006;Parasuraman, 2000;Richey et al.,
2007). Teo et al. (2003) support this view of technology
adoption through normative pressure from top management
and employees. Wu et al. (2013) point out technological
turbulence in the industry that forces top management to
consider the possible adoption of technology. Technological
turbulence refers to the rate of change of technology in a
market environment (Autry et al., 2010). The ICT adoption
literature has acknowledged the role of top management
initiative and support in relation to ICT adoption in the
supply chain (García-Sánchez et al., 2017;Salwani et al.,
2009;Tarofder et al.,2017). Salwani et al. (2009) suggest
that perceptions of top management play critical role in the
ability of technological innovation to create value in firms. A
firm’s adoption of cloud services is determined by perceived
external pressure (i.e. coercive and mimetic) and internal
pressure from employees and top management (i.e.
normative). Jaworski and Kohli (1993) argue that top
management response to changing market competition calls
for adoption of new products and services (i.e. cloud in this
case) that are likely to match the strategic needs of suppliers
and customers.
Top management support is assessed by top management’s
initiatives and willingness to take the risk of investing in
cloud computing services (Premkumar and Ramamurthy,
1995;Yao et al., 2007). Supply chain technology, which is
cost effective, easier to use and implement, can impel the
firms to seek its assistance for demand and supply
management. Autry et al. (2010) however reveal that the
“ease of use”is not the consideration, rather “perceived
usefulness”drives technology adoption in more realistic
way.Firmsaremorelikelytoadoptcloudservicesasa
strategic means to achieving optimum performance (i.e. cost
efficiency and service effectiveness). While digital
integration is researched to a greater length, the role of cloud
computing as an innovative application is yet to see its
potential. Adoption of the cloud leads to firm’s capability
enhancement based on RBV perspective. When partners are
digitally connected and data become more visible between
them, this enhances the information processing power that
helps to make quality decisions. Thus, cloud technologies
are likely to enhance supply chain performance by
establishing process connectivity, data visibility and
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
505
information exchange among the partners and within the
internal functions. Therefore, we hypothesise that top
management pressure will moderate the relationship
between cloud-enabled SCI and SC performance:
H4. Top management support for cloud technology
adoption positively moderates the relationship
between SCI and SC performance.
3.5 Control variable
When it comes to cloud adoption in supply chain management,
larger firms may differ from small and medium ones in their
current ICT capability and information processing capability
(i.e. connectivity and modularity of system). So firm size,
operationalised by the number of employees, was controlled for
because larger firms may have more resources and likely to
leverage higher order ICT compared to small firms (Wu et al.,
2013). We extend this view to the context of cloud as an ICT
artefact. Therefore, without positing a hypothesis, we propose
firm size as a control variable.
4. Methodology
4.1 Population and sampling frame
We used a Web-based survey approach to collect data (Ansari
and Kant, 2017). This type of survey is a cost-effective way to
collect data from managers (Fawcett et al.,2007). The target
population for this study was selected from a list of small and
medium to large firms registered with the Chartered Institute of
Logistics and Transport in Australia (CILTA- www.cilta.com.
au). A sample was drawn by filtering through a list of over 3,000
CILTA-registered members holding medium to senior position
in supply chain, logistics and operations, as well as information
and communication technology (ICT). This yielded a list of
180 professionals with their emails. A second source of about
50 companies was drawn from Australia Stock Exchange
(ASX) database (www.asx.com.au) comprising retailing, food
and beverages category to match up with categories drawn from
CILTA. The third source was the Web-based survey via
LinkedIn social media. About 100 professionals were identified
in medium to senior management position in the area of
manufacturing, wholesale and retaining and ICT. Fan and Yan
(2010) claim that pre-emailing invitation to potential
participants is likely to yield more responses. The participants
from these sources accessed an online link to completing the
survey via the Qualtrics platform, a popular software to launch
and administer questionnaires and collect data directly in a
SPSS file format.
CILTA sample returned merely 10 responses (response rate
of 5.5 per cent), ASX sample returned only five responses
(response rate 10 per cent) and LinkedIn yielded 91 responses
resulting in a response rate of 91 per cent. Though the earlier
responses were relatively smaller we decided to retain them
because those respondents were from logistics companies. A
reminder via email after a couple of weeks even did not evince
any extra responses from CILTA and ASX, but it worked well
with LinkedIn contacts. No more than one participant
responded from a company. In most cases the supply chain
manager consulted the IT manager or vice versa while
responding to the questionnaire. A total 105 questionnaires
were collected from the three sources (total 330) that resulted
in a response rate of 31.8 per cent. The data were collected in
final quarter of 2015. As an individual company was used as the
unit of analysis, we checked that each response represented a
separate company.
The sample size remains a vexing point of discussion while
using covariance-based structural equation modelling (CB-
SEM) for data analysis. Hazen et al. (2015) although report
that CB-SEM is inherently a “large sample”analysis
approach, they also find that about 36 per cent articles have
sample size less than 200, with a few even less than 100 cases.
Further, lots of recent Monte Carlo simulation studies report
sample sizes of 40, 90, 150 and 200 where there is no
difference noticed in effect size for small sample (Goodhue et
al., 2007). Most supply chain research use small sample size
(i.e. less than 100) if the population of interest is restricted in
size (Barrett, 2007). Westland (2010) reveals that 80 per cent
of the meta-study articles draw conclusions from samples
which too small to be significant. Also, studies using the
partial list square (PLS)-SEM approach reported varying
sample size around 100. Benitez-Amado and Walczuch
(2012) used 63 cases; Inman et al. (2011) analysed 107 cases;
Klein and Rai (2009) reported 91 cases; and Rai et al. (2006)
used 110 cases. Given that cloud adoption is just developing
within the businesses, the population is restrictive in size.
Therefore, the sample size of 105 is considered to be
adequate to analyse the theoretical model with moderate level
of data fitting.
4.2 Survey instrument
Multi-item measures used in this study applied a five-point
Likert scale ranging: 1-strongly disagree, 3-neither agree nor
disagree and 5-strongly agree. These items were adapted from
previously validated measures in the literature. Before
administering the survey to the full sample, feedback was
supplied by three academics from supply chain and logistics
discipline, two industry professionals from logistics and IT and
five research scholars from the supply chain and logistics
discipline. The feedback from these experts ensured content
and face validity of the questionnaire. After incorporating their
feedback, the final version was entered in Qualtrics software
and a link was created for distribution to the full sample drawn
from the CILTA, Australian Stock Exchange and LinkedIn
sources.
The construct “cloud adoption”is conceptualised as having
two sub-constructs: cloud application functionality and cloud
adoption intention. Wu et al. (2013) propose these two as
inversely related but find their relationship as non-significant.
This means that the higher the application functionality, the
less is the chance to adopt cloud technology. Application
functionality measures the current ICT capacity and the way it
can adopt any incremental changes without jeopardising the
underlying ICT systems. From the perspective of this study, we
adapted three items on “cloud adoption intention”from Wu
et al. (2013) and developed two new items such as “the upfront
benefits realised for customer and supplier management”and
“already subscribed to cloud on ongoing cost”. The twelve
items to assess cloud-enabled internal integration were adapted
from Yao et al. (2007). We adapted four items to measure
cloud-enabled supplier integration and four items for cloud-
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Himanshu Shee et al.
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Volume 23 · Number 6 · 2018 · 500–517
506
enabled customer integration from the earlier study by
Wiengarten and Longoni (2015). The construct top
management, used as moderator, was adapted from Yao et al.
(2007). The six items to assess supply chain performance,
seven items to assess financial performance, six items to assess
social performance and seven items to assess environmental
performance were adapted from Wiengarten and Longoni
(2015). The items, whether adapted or newly developed, went
through psychometric test for reliability and validity check that
established the questionnaire as valid and reliable for future
use. All measures are presented in Appendix.
While measuring the reliability and validity of the constructs
by confirmatory factor analysis (CFA) using AMOS 21, some
items were dropped from each construct to make the model fit
the data well. Details are provided in Section 5.4. After few
stages of modification and purification undertaken in the path
model, their remained three items to measure cloud adoption
intention (five items under application functionality were
dropped), two items to measure customer integration and three
items to measure supplier integration and internal integration.
While some authors prefer to use at least three items to measure
one factor (Anderson and Gerbing, 1988;Bollen, 1989),
Kline (2005) suggests that two items are sufficient. Further,
Kenny (1979, p. 143) suggests the rule of thumb about the
number of items is: “two might be fine, three is better, four is
best and anything more is gravy”. Therefore, the two items
measuring customer integration under the common construct
“cloud-enabled integration”is not an issue. Also, top
management influence, supply chain performance,
environmental performance, social and financial performance
were measured by three items each. All the measures used in
final path model are listed in Appendix with Cronbach’salpha
reliability for the respective constructs.
5. Data analysis
5.1 Demography
The respondents are comprised of 89 per cent male and 11
per cent female with 48.6 per cent having work experience <4
years, 25.7 per cent having 4-8 years and 25.7 per cent having
>9 years in the firms from where they responded. The
respondents represent 12.4 per cent small firms with <19
employees, 26.6 per cent medium firms with 20-250
employees and 61 per cent large firms with >200 employees
(ABS, 2017). The annual sales revenue was AU$500k or less
for 7.6 per cent firms, AU$500k to AU$9.9m for 13.4 per
cent firms, AU$10m to 49.99m for 20 per cent firms, AU$50
to AU$199.99 for 16.2 per cent firms and >AU$200 for 42.8
per cent firms. Currently 1 AUD = 0.772456 USD (www.xe.
com). The respondents hold position ranging between
General- and Deputy General manager/MD/CEO/Vice-
president (22.9 per cent), Middle Manager/IT Manager
(61.9 per cent) and others 15.2 per cent. The survey involves
18.1 per cent manufacturing, 21.2 per cent wholesale and
retailing, 26.7 per cent transport and storage and 6.7 per cent
IT services. These respondents are involved in organisational
decision-making to a moderate to considerable extent (64.5
per cent) and to a low extent (12.4 per cent).
The use of cloud computing is not a new concept but its
applications in supply chain are in their infancy. As the business
cases are developing slowly, the chain partners are watching the
benefits and usefulness of cloud adoption while concerned
about the technology’s maturity. To capture the extent to
which Australian SMEs value cloud adoption to enhance
supply chain process integration through connectivity, visibility
and data analytics, the survey has a section on “cloud currently
in use”or “plan to adopt within a year”. Descriptive statistics
reveal that 51-64 per cent respondents are engagedwith light to
heavy activities in the areas of inventory information (51 per
cent), demand forecasting (55 per cent), obtaining order status
(64 per cent) and tracking order (64 per cent) from suppliers
using cloud-based technology. The activities with customers
vary from 55 per cent for inventory information, 50.5 per cent
for demand forecasting, 61 per cent for order status and 62 per
cent for tracking information. Further, the results reveal that
13-19 per cent will adopt cloud with suppliers and 14-19 per
cent with customer within a year. However, the respondents
report that they have no plan to adopt cloud in next one year
with customers (22-31 per cent) and suppliers (23-30 per
cent). In summary, the results indicate that 37-49 per cent
respondents are yet to adopt cloud as the time elapses.
Therefore, this study on cloud adoption decision is quite
timely.
5.2 Test of non-response bias and common method bias
Non-response bias can pose a host of problems in survey
research within the supply chain domain (Wagner and
Kemmerling, 2010). One method to check for this is to inspect
for significant differences between the responses received in the
early and late waves of the survey (Armstrong and Overton,
1977). Therefore, non-response bias was examined by
comparing the demographic of the early and late respondents
(e.g. number of employees, work experience, position) using
two-way t-test. The results yielded no significant differences in
the responses (p<0.05) indicating no concern of non-response
bias in the data.
Given that the data were perceptual and collected via a
single source at one point in time, we used Harmon’sone-
factor test to assess common method bias. The exploratory
factor analysis (EFA) based model showed more than one
factor with the eigenvalue greater than 1. The six factors so
generated thus accounted for cumulative 68.11 per cent of
the total variance, while the first factor only accounts for
25.45 per cent of the total variance. We used confirmatory
factor analysis (CFA) based on Harman’ssinglefactortestin
which all the manifest variables were modelled as the
indicators of a single factor that demonstrated a poor model
fit with Chi-square (
x
2
)(n= 105) = 1015.446, df = 275, p=
0.000,
x
2
/df = 3.693, GFI = 0.493, NFI = 0.359, TLI =
0.357, CFI = 0.411, RMSEA = 0.160 and SRMR = 0.1579.
The poor model fit indicates the absence of common method
bias in the data.
5.3 Data analysis techniques
The descriptive statistics and the participant profiles were
assessed using SPSS 21. The full measurement model and
structural model were analysed by SEM-AMOS 21 for
Windows. The hypotheses testing was accomplished in two
steps: direct effect and moderation effect. First, we investigated
the direct effect of cloud technology adoption on supply chain
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
507
integration (SCI). Then we tested the perceived effect of
cloud-enabled SCI on the performance improvement at the
level of supply chain and at the firm level. As a first test of
hypotheses, we aggregated the item scores (average value) of
first order latent variables into the respective observed
variables. The averaging of items here reduces the
complexity of parameter estimate resulting in a smaller chi-
square and degrees of freedom (difference between sample
moments and parameters estimate). Further, any negative
error variance at the construct level gets smoothened
providing the covariance matrix is a positive definite. Using
the observed variables, we estimated the path coefficients
between cloud adoption and SCI (H1), SCI and supply
chain (SC) performance (H2) and SC performance and firm
sustainable performance (H3).
In the second step, a moderator analysis was performed
(H4). The influence (moderation effect) of top management
initiative on the relationship between three integration types
and SC performance was analysed by the interaction effect of
the two latent variables via their factor scores. To get the value,
a factor analysis with varimax rotation was undertaken and a
standardised factor score on the first factor was saved. For
example, factor score of the supplier integration process was
multiplied by the factor score of top management initiative to
give an interaction-effect variable such as supplierInt x top
management. A similar calculation was done for the
moderation effect of top management on customer integration
(CustInt x top management) and internal integration (InterInt
x top management).
5.4 Psychometric assessment reliability and validity
tests using measurement model
All perceptual measures were assessed for reliability and
validity. The psychometric properties of the nine latent
constructs involving 44 items were evaluated simultaneously in
CFA using AMOS 21 (refer Appendix for all measurement
items). A full measurement model involving all latent
constructs was tested for its fitness with data. The goodness-of-
fit indices indicated a moderate fit with the data: Chi-square =
331.894, p= 0.02, df = 263, Chi-square/df = 1.262, GFI =
0.810, NFI = 0.769, TLI = 0.924, CFI = 0.938,
RMSEA = 0.050, PClose = 0.482, SRMR = 0.0656. The
model could have been improved further to get the indices
satisfied with the threshold values for better fit but it was
stopped at this stage to retain a critical construct i.e. customer
integration process (i.e. Cust_int). The customer integration
is believed to be equally important as supplier integration and
internal integration in a supply chain. So, this construct was
retained compromising the model fit indices to moderate
level. This is similar to the earlier study presented by Li et al.
(2006). While more stringent test is undertaken in path
model to check the goodness-of-indices satisfying their
respective specified values, it is okay to compromise with the
indices at moderate level. The test of reliability and validity
further support this assertion.
5.4.1 Reliability
The reliability of the measures in the final model was examined
through Cronbach’s alpha and composite reliability (CR)
values. The results showed that all Cronbach’s alpha values
were higher than 0.7 (except for Cust_Int (0.68) and cloud
(0.67)) which indicated satisfactory levels of internal
consistency (Hair et al.,2010;Nunnally and Bernstein, 1994).
The CR values were also higher than 0.7 (except for Cust_Int
(0.68) and cloud (0.67) and thus indicated desirable levels of
item reliability achieved (Hair et al.,2010). These results
suggested the internal consistency of the items which cluster in
the constructs and the item reliability in measuring the
constructs. The value of average variance extracted (AVE)
should be greater than 0.5. According to Nunnally and
Bernstein (1994), 0.4 is acceptable.
The item reliabilities were further confirmed by the factor
loadings. All of the factor loadings exceeded 0.50 and
significant (t>1.96, p<0.05), except for three indicators
under cloud adoption intention. However, the factor loading of
these three indicators above 0.4 is acceptable as suggested by
Hair et al. (2010). The inter-construct correlation coefficients,
Mean (M), standard deviation (SD), Cronbach’s alpha, CR
and AVE are presented in Table I.
5.4.2 Validity
Convergent validity was supported in an examination of the
AVE for each construct. AVE represents the amount of item
variation explained by the construct. All the AVE values were
above 0.5, except for construct cloud adoption (0.41) and Ext_
pressure (0.47). These values indicated that most of the item
variations were explained by the latent factor structures
(Bagozzi and Yi, 1988;Fornell and Larcker, 1981). The
convergent validity of the constructs was, therefore, confirmed.
Nunnally and Bernstein (1994) recommend the need for AVEs
to be greater than 0.4, Cloud and Ext_pressure were thus
retained in the analysis.
Discriminant validity was observed by comparing the square
root of AVE value of a construct with the inter-correlation
coefficients of the remaining constructs. The square root of
AVE for each construct that exceeds the correlations associated
with the remaining constructs indicates that the latent
construct can explain more of the variances in its items than it
shares with another construct (Fornell and Larcker, 1981).
Diagonal values in Table II represent the square root of AVE
values and the correlations are shown below the diagonal. The
diagonal values are more than the correlation associated with
the remaining constructs, confirming that there is no issue of
discriminant validity.
The discriminant validity is further confirmed by following
Bagozzi and Yi (1988)’s nested model comparison method
using full measurement model. This helps to compare the chi-
square of the first model (i.e. model 1) with covariance between
constructs is free to estimate, with the second model (i.e.
model 2) in which the covariance is constrained to 1. If the chi-
square test between the two models yields significant difference
(p<0.05), then the pair of construct is said to meet the
discriminate validity criterion. All models are found to have
significant chi-square difference at p<0.05, confirming the
constructs as intended. Therefore, discriminant validity is not
an issue in this study.
5.5 Structural model
Figure 2 shows the path diagram for the structural relationship.
The goodness-of-fit indices of the structural model is assessed
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
508
Table I Correlation coefficients of all constructs, Cronbach alpha, CR and AVE (n= 105)
No. of
items 1 2 3 4 5 6 7 8 9 Mean SD
Cronbach
alpha CR
b
AVE
a
1. Cust_int 2 1.0 4.167 0.587 0.68 0.68 0.52
2. Top Mgmt 3 0.268** 1.0 3.321 0.850 0.72 0.72 0.47
3. Social 3 0.232* 0.310** 1.0 3.454 0.687 0.78 0.78 0.55
4. Cloud adoption 3 0.408** 0.512** 0.440** 1.0 3.743 0.756 0.67 0.67 0.41
5. Environ-ment 3 0.189 0.100 0.029 0.213 1.0 2.918 0.820 0.85 0.85 0.65
6. SC_Performance 3 0.323** 0.463** 0.480** 0.571 0.220** 1.0 3.489 0.700 0.81 0.81 0.59
7. Supp_int 3 0.706** 0.231** 0.096 0.510 0.064 0.241** 1.0 4.210 0.553 0.79 0.78 0.54
8. Financial 3 0.290** 0.346** 0.606** 0.519 0.133 0.545** 0.172 1.0 3.476 0.714 0.79 0.80 0.56
9. Inter_int 3 0.519** 0.167 0.257** 0.345 0.003 0.140 0.362** 0.141 1.0 4.083 0.633 0.85 0.85 0.65
Notes: **p<0.01; *p<0.05;
a
AVE: Average variance extracted X
l
2.hX
l
21X
u
ðÞ
i;
b
CR: Composite reliability X
l
2.hX
l
2
1
X
u
ðÞ
i(Fornell and Larcker, 1981)
Table II Discriminant validity test
1 2 3 4 5 6 7 8 9 Mean SD
1. Cust_int 0.72 4.167 0.587
2. Top Mgmt 0.268** 0.69 3.321 0.850
3. Social 0.232* 0.310** 0.74 3.454 0.687
4. Cloud 0.408** 0.512** 0.440** 0.64 3.743 0.756
5. Environment 0.189 0.100 0.029 0.213 0.81 2.918 0.820
6. SC_Performance 0.323** 0.463** 0.480** 0.571 0.220** 0.77 3.489 0.700
7. Supp_int 0.706** 0.231** 0.096 0.510 0.064 0.241** 0.73 4.210 0.553
8. Financial 0.290** 0.346** 0.606** 0.519 0.133 0.545** 0.172 0.75 3.476 0.714
9. Inter_int 0.519** 0.167 0.257** 0.345 0.003 0.140 0.362** 0.141 0.81 4.083 0.633
Notes: *p<0.05; **p<0.01;Diagonal values are square root of AVEs. Values below the diagonal are correlation coefficients
Figure 2 Structural model with path coefficients
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
509
using AMOS-SEM analysis. Previous studies have used
x
2
test
along with other measures such as
x
2
/df, NFI, CFI, RMSEA
and SRMR, to assess the model fitness (Hair et al., 2010;
Jöreskog and Sörbom, 1982). This analysis yielded
x
2
=
73.877, df = 51, p= 0.020,
x
2
/df = 1.449, NFI = 0.834, CFI =
0.940, RMSEA = 0.066, PCLOSE = 0.212, Standardised
RMR = 0.078 (value close to 0 is preferable). Though the
x
2
test (
x
2
= 73.877, df = 51, p<0.05) was unable to determine
the goodness-of-fit level of the model, Bollen-Stine bootstrap
(p= 0.234) was assessed to support this model at p>0.05
(Bollen and Stine, 1992;Hazen et al.,2015). The goodness-of-
fit indices fit the data well with values close to their specified
limit. However, few paths like CustInt TopMgmt !
SC_Performance, firm sustainability !environmental factor
and firm sustainability !Firm size are found to be non-
significant. The standard estimate and critical ratios (t-values)
are presented in Table III.
The results support for the research model indicating that all
the four hypotheses (i.e. H1,H2,H3 and H4) were found
positively significant and supported. The predictive power of
the model is assessed by variance or R
2
values (Rai et al.,2006).
R
2
values indicate the amount of variance in the construct
explained by its indicator(s). Results show that cloud adoption
intention has positive effect on SCI (i.e. H1 supported) and
explains 21 per cent variance in SCI. The SCI has positive and
significant effect on supply chain performance (i.e. H2
supported). Both SCI and top management together explain 20
per cent of the variance in supply chain performance. Further,
supply chain performance has positive and significant effect on
firm sustainability performance (i.e. H3 supported) that in turn
explains 45 per cent of variance in firm sustainability. The top
management as moderator significantly influences the
relationship of supplier integration process (0.30, p<0.05)
and internal integration process (0.22, p<0.05) on supply
chain performance. However, it could not influence customer
integration process to have a positive influence on supply
chain performance because the participants perceived this as
less likely from cloud adoption perspective. So, hypothesis
H4 waspartlysupported.ThisissimilartoastudybyYu
(2015) who, using ICT-enabled SCI, finds that customer
integration has no positive effect on financial and operational
performance.
5.6 Competing path model
Moreover, the model was assessed for an alternative
competing model as a matter of comparison with the
proposed theoretical model. An additional direct path from
cloud technology adoption to SC performance was assessed
for its significant effect. This path was found significant
(0.34) at p<0.001, causing the path between cloud-enabled
SCI and SC performance non-significant (0.17 at p>0.05).
Because the study objective was to investigate the positive
influence of cloud-enabled SCI on SC performance, the
original model in Figure 1 was therefore retained as the
preferred model.
Li et al. (2006) suggest using Chi-square difference test
(CDT) to compare the difference between chi-square statistics
values and the degrees of freedom (df) of the hypothesised
model and each of the competing models. To compare the
models, the chi-square value of the competing model is
subtracted from the hypothesised model and the respective
df. The chi-square statistics table is then used to assess
whether the chi-square difference is significant,thatisthe
calculated p-value should be less than 0.05 (|t| >1.96),
which should indicate that the hypothesised model is
preferred. Alternatively, one can use MS Excel function
[CHISQ.DIST.RT] to calculate p-values. If the difference is
non-significant,thatiscalculatedp-value is more than 0.05
(|t| <1:96), the competing model is preferred (Werner and
Schermelleh-Enge, 2010). For the chi-square difference of
12.045 (= 73.877-61.832) and Ddf = 1, the calculated p-
value is 0.0005, which is less than 0.05 and significant. So,
the original hypothesised model is preferred.
6. Discussion
Drawing on the literature at the intersection of supply chain
integration and IS stream, this research provides a robust
test of the resource-based view (RBV) while improving the
supply chain performance through cloud-enabled supply
chain integration (SCI). We modelled the research based on
cloud technologies as an additional resource for the firms.
Accordingly, the relationship between cloud capability, SCI
and performance are grounded in the RBV. Specifically, the
cloud capability could facilitate and complement existing
resources both within and across the firm boundaries. The
results indicate that the cloud capability is likely to help to
achieve SCI efficiency to increase supply chain performance
(i.e. cost, quality and delivery performance), which in turn
has a positive effect on firm sustainability (i.e. financial,
environmental and social). Further, the study has used top
management intervention as moderator to investigate its
influence on cloud adoption strategy for SCI and reveals
that cloud-enabled SCI can affect both performances
positively.
The emergence of cloud technologies in the IS domain has
not only revolutionised the computing world in itself but
also made its significant impact on business applications
Table III Path analysis standardised regression weights, C.R. (t-value),
(n=105)
Std.
estimate S.E. C.R.
CloudAdopt fiCloud_enabled__Integration 0.46
***
0.068 4.87
Cloud_enabled__Integration fi
SC_performance 0.34
***
0.123 3.49
SC_performance fiFirm_sustainability 0.67
***
0.081 6.67
Firm_sustainability fiEnvironment 0.16 0.159 1.41
Firm_sustainability fiSocial 0.76
***
0.116 7.84
Firm_sustainability fiFinancial 0.79
***
0.140 7.82
Cloud_enabled__Integration fiInter_int 0.54
***
0.116 5.42
Cloud_enabled__Integration fiSupp_int 0.75
***
0.103 7.32
Cloud_enabled__Integration fiCust_Int 0.94
***
0.294 5.42
SuppInt 3TopMgmt fiSC_performance 0.30
**
0.127 2.08
InterInt 3TopMgmt fiSC_performance 0.22
**
0.083 2.15
CustInt 3TopMgmt fiSC_performance 0.21 0.149 1.33
Firm__sustainability fiFirmSize 0.02 0.624 0.20
Notes: **p<0.05; ***p<0.001
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
510
(Attaran, 2017). It appears that academic investigation of
the cloud applications to supply chain process integration is
lacking (Schoenherr and Speier-Pero, 2015). This study
therefore investigates whether cloud-enabled supply chain
partners are able to integrate their logistics processes to
attain the desired performance. The results of SEM
modelling using cross-sectional data suggest that cloud
adoption helps upstream suppliers and internal functions to
integrate their processes effectively. This, in turn, has a
direct influence on supply chain performance. The study
investigates the theoretical view point, supported by
empirical evidence, of how cloud-enabled SCI can influence
performance of both the supply chain and the firm. Results
show that an increase in supply chain performance is
positively linked to a significant increase in firm
sustainability. All four hypotheses in this study are
supported positively and significantly. The magnitude and
significant path coefficients provide an additional support
for the research model.
Earlier research investigates the firm’s performance in a
digitally enabled (e.g. EDI, RFID and SAP) supply chain
integration perspective (Prajogo and Olhager, 2012;Yu, 2015).
However, this study extends the analysis to cloud-enabled SCI,
where the chain partners powered by cloud services perceive
that they have an improvement in SC performance as well as in
firm sustainability. Cloud-enabled SCI is likely to facilitate the
firm through cross-functional collaboration by exchanging cost
effective information on sales forecasts, production plans, order
tracking and tracing, delivery status and stock levels. Though
traditional ICT is still helpful in SCI (Cegielski et al.,2012),
firms are increasingly facing the challenge to adopt state-of-
the-art technology that helps to integrate internal functions
with external members (Jede and Teuteberg, 2015). This
study perceives cloud services (i.e. SaaS specifically) as the
starting point to achieve significant advantages in stability
and flexibility in decentralised and wide-spread global supply
chain operations.
Cloud-based technology adoption is believed to offer
functional advantages over conventional ICT-enabled SCI in
relation to operational efficiency (e.g. inventory sharing,
order status and tracking, demand forecasting), time
compression, higher IT-performance (e.g. high-speed data
access, add-on services, customisability, latest hardware and
software, as well as service bundles) with a medium to low
security level (e.g. data access and data networks) (Jede and
Teuteberg, 2015). This study therefore offers new knowledge
at the intersection of supply chain integration and the IS
stream of literature. Further, the respondents perceive that
cloud technology can help enterprises to achieve higher
quality IT services with minimal on-premises investment in
the existing platform.
We believe academics and managers quite often fail to
consider the key role of top management who can influence the
cloud adoption decision within the firm. Hence, this study has
incorporated top management intervention in the decision to
adopt cloud computing within the firm. The influence that the
top management can have on the relationship of supplier,
customer and internal functions to the supply chain
performance is assessed. The results suggest that top
management pressure as moderator appears to have significant
influence. The interaction effects of supplier process and top
management pressure (
b
= 0.3); as well as internal functions
and top management pressure (
b
= 0.22) are significant.
However, the interactive effect between the customer
integration process and top management pressure on supply
chain performance does not appear to be significant.
Although customer integration is critical in supply chain
processes, participants perceive it to be less important than
supplier and internal integration. The sample participants
perceive that top management’s decision to adopt cloud
computing may not affect the customer integration process.
Rather, they perceive integration of suppliers and internal
functions more meaningful. This is similar to the argument
put forth by Song et al. (2005, p.271) that “resource
combination does not always lead to synergistic performance
impact”, as seen in this study in relation to customer side of
the business.
The model also tests the influence on the firm’s sustainable
performance when the SC performance is improved by cloud-
based technology. The results show a direct and significant
relationship between supply chain performance and firm
sustainable performance with
b
= 0.67 (p<0.001). Further,
firm size was controlled for firm sustainable performance but
found to be insignificant.
6.1 Implications for research and practices
Theoretically, this study extends prior literature in supply chain
integration and the IS stream in many ways. First, our study on
the interrelationship between cloud-based technologies, SCI as
a three-dimensional construct and performance (both supply
chain and firm) grounded on RBV is unique for revealing
insightful knowledge. As such, the study empirically extends
the RBV perspective and provides an avenue for cloud-based
technology within the SCI context to enhance current
understanding. Second, while the literature is rich with
ICT-enabled SCI in improving the operational and business
performance(Flynn et al., 2010), this study fills the gap
comprehensively in the SCI literature and enhances our
understanding of the effect of cloud-enabled SCI on
performance. Third, this research reduces the gap in
literature by promoting cloud-based technology on-board
which differs from few earlier relevant studies by Bruque-
Cámara et al. (2016) and Wu et al. (2013).Theyhave
investigated cloud adoption from the perspective of
organisational constraints, as well as physical and
informational integration context. Our empirical study
however has taken a step forward to suggest that cloud-
based technology, as a complement to ICT, can influence
the SCI as a three-dimension construct that will eventually
affect the performance of supply chain and firm in
hierarchical order. Fourth, while cloud technologies, as a
valuable but no-longer-rare resource, are offered by
Amazon, Google and IBM to scale up systems to meet the
required capacity to manage the information exchange (Wu
et al.,2013), the way cloud is used currently by a few firms
may create competitive advantage. This study argues for
other firms to develop competitive advantage from RBV
perspective to help achieving SCI for SC performance.
Fifth, literature about the relationship between ICT-enabled
SCI on operational performance is mixed and inconsistent
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
511
(Flynn et al.,2010;Wiengarten and Longoni, 2015). The
perceived positive effect of cloud-enabled SCI on SC
performance and firm sustainability offers a significant
contribution to literature that simplifies the earlier anomalies.
It signifies that SCI needs the support of cloud-based
technology to improve SC performance. Earlier research on
e-SCM using low-to-medium range traditional technologies
(e.g. EDI and WMS) has helped in achieving transaction
cost efficiency and coordination effectiveness in the supply
chain (Yao et al., 2007). However, this research on cloud
technology argues a way to achieve the same level of output
faster over wider geography in cost-effective way. Finally,
this empirical study has revealed top management as a
moderator that positively influences the relationship
between cloud-enabled SCI and SC performance through
interaction effect. This adds to literature over other studies
where top management is used as antecedent in the
technology adoption framework (Gangwar et al., 2015;
García-Sánchez et al., 2017;Tarofder et al., 2017).
Practically, the study has offered significant contributions in
favour of small and medium enterprises (SMEs) as well as large
ones. First, this study informs SME owners and senior
management team that higher order SCI needs the support of
cloud-based technology. SMEs tend to be the main
beneficiaries of cloud-based computing services because of
their limited resources that constrain their investment in
technology (such as cloud-based technologies and services).
The manufacturers, wholesalers and transport companies
need to consider its potential when they decide on any ICT
investment. Second, we therefore argue from the results that
top management (i.e. SME owners and senior management
team) can influence the decision to adopt cloud-based
technology to maximise supply chain performance. In doing
so, the senior managers and IT professionals should know
that they can save costly investment because they pay as they
use cloud-based technologies (i.e. utility functionality).
Third, although the greatest challenge to SME supply chains
is to improve their performance in a competitive environment
(Taylor, 2014), the results indicate that managers are more
likely to achieve superior supply chain performance through
pervasive cloud-based SCI of internal and external functions,
specifically in Asia Pacific (APAC) region. Such integration
has the potent to drive operational cost low, optimise
inventory and assets and monitor the visibility of products as
they move across borders. The data being collected from
Australian SMEs, the high-speed state-of-the-art NBN
(national broadband network) can help in faster data and
voice transfer via cloud technologies using NBN. As about 39
per cent of sample SMEs are willing to use cloud technology,
comprising manufacturing (18.1 per cent), wholesaling and
retailing (21.1 per cent) and transport and storage (26.7 per
cent), to track raw materials and shipment schedule, this
study confirmsthatitisaworthwhileinvestmenttoreapSCI
benefits.
Fourth, the large firms that continue to use legacy ICT
systems with mission-critical applications (i.e. technological
breadth) are also be the likely candidates for cloud migration.
The sample participants in this research (comprising about 61
per cent large firms) will definitely reap the benefits of cloud
services in their supply chain operations through avoidance of
additional ICT investment and considerable maintenance
expenses. They can augment their peak capacity by using
cloud computing for temporary projects that demand
additional resources. With cloud services available, it is the
time for the large firms to audit their ICT capability in
relation to information exchange with suppliers, retailers and
transporters and its further processing for timely decision in
supply chain operations, specifically in the APAC region in
this research. Asking themselves a few questions may help
diagnose their current ICT resource limitations and their
capability to deal with inventory status, overseas shipment
tracking, warehouse delivery, transport scheduling and other
transactional data from geographically dispersed chain
partners. Finally, from the RBV perspective, a cloud
capability is perceived to be quite helpful when large firms do
business with small start-up firms with fewer resources, that
is, inadequate ICT capability. So, a secured, networked, cost-
effective cloud-based services have the potential to integrate
all partners for effective decision-making along the supply
chain.
7. Conclusion and limitations
There is a need for scholars and practitioners to understand the
emergence of cost-effective ubiquitous cloud-based technology
beyond traditional ones such as EDI, WMS and SAP (Lyytinen
and Rose, 2003;Wu et al.,2013). The cloud-based
technologies and services, delivered over the internet, offering
an option to use and pay-as-you-go metering system (Marston
et al., 2011). Cloud services (i.e. SaaS in specific) is the likely
game changer in sustaining the relationships among
manufacturers, suppliers, retailers and consumers for
logistics process integration. From the RBV perspective, we
therefore propose a model to extend the traditional ICT-
enabled SCI to create a cloud capability which is a likely to
enable better SCI which improves performance in both the
supply chain and the firm. The results show that cloud-
enabled SCI (e.g. sharing of demand information, inventory
status, production and delivery schedules) has a positive,
significant effect on supply chain performance that in turn
improves the firm’s sustainable performance. The results
reveal that top management initiative assists the likelihood of
adopting and using the cloud services.
The research has both theoretical and methodological
limitations. Theoretically, first, the intention to adopt cloud
is dependent on complexity of cloud services and its
implementation issues. Because complexity is inversely
proportional to ease of use, usefulness and adoption
intentions (Autry et al., 2010;Chau and Hu, 2001;Gangwar
et al.,2015), a new construct “cloud adoption complexity”
in a future study would reveal how the propensity to adopt is
affected by this complexity. Second, the external influence
of competitors, suppliers and customers in the relationship
between cloud-enabled SCI and SC performance was not
considered. The future research model can test this
moderation effect using larger sample size. Third, top
management initiative did not directly support customer
integration (i.e. interaction effect) to improve SC
performance. As customer integration facilitates cost-
effective market responsiveness, which is a measure of SC
Supply chain performance and firm sustainability
Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
512
performance, we suggest that this relationship should be
verified with a larger sample in future research.
Methodologically, first, the use of cross-sectional data in this
study limits the predictability of the results over time. The
results of this study can be used as pointer for exploring the
longitudinal influence of the antecedents on performance.
Second, the involvement of first-tier suppliers from Asia-Pacific
Region in the future survey will reveal the perceived benefits of
collaborative planning of information sharing using cloud
services. Third, firm size, operationalised by employee
numbers, was found to have no significant effect on firm
sustainability in this research. Our relatively small sample size
did not allow the sample to be split into small, medium and
large firms to test their cloud adoption strategy and the
influence that parameter may have on a firm’s sustainable
performance. A larger and more homogeneous sample in a
future study should be able to find out how firm size affects the
cloud adoption decision (Zhao et al.,2011). Further, types of
firm, categorised between family business and partnership, may
have different levels of cloud adoption and its effect on
performance. So, this can be considered in future research.
Fourth, similar future research with a larger sample size would
enable us to separate into sub-groups of services, that is SaaS,
PaaS and IaaS, where the results could vary from one group to
another providing additional benefit of cloud as well as hybrid
technologies which merge cloud-based technology with ICT
systems like ERP/CRM/SCM systems. Fifth, cloud adoption
intention retains three measurement items in the path model to
test its effect on performance improvement. These three items
represent the opportunist, futuristic and strategic nature of
cloud adoption and they satisfy the method’s requirement of at
least three items within a construct (Hair et al., 2010). While
these three items are quite speculative on cloud adoption, the
two dropped out items were originally intended to measure the
cloud services in action and in-house cloud capability of firms.
Therefore, such firms who have already used cloud-based
technologies for their businesses may be sampled in future
research to reveal how the cloud-enabled SCI improves their
SC performance in practice.
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Appendix
All measurement items (factor loading) used in SEM
modeling are presented below.
A. Use of cloud-enabled supply chains (descriptive
statistics)(Yao et al., 2007).
Please indicate whether you currently conduct or plan to
conduct within a year any of the following activities over the
cloud computing that is moving from technology currently in
use to cloud-enabled services and computing.
(1 = light activities; 2 = moderate activities; 3 = heavy
activities; 4 = plan to within a year; 5 = do not plan to within a
year):
a.1 Share inventory information with suppliers.
a.2 Conduct demand forecasting with suppliers.
a.3 Obtain order status from suppliers.
a.4 Track orders from suppliers.
a.5 Obtain inventory information from customers.
a.6 Conduct demand forecasting with customers.
a.7 Provide order status to customers.
a.8 Track orders to customers.
B. Cloud adoption intention (Alpha = 0.67)(Wu et al.,
2013).
Please circle the degree to which you agree or disagree with
the following statement regarding your business (1 = strongly
disagree, 3 = Neutral and 5 = strongly agree).
b.1 Given that my company had access to cloud computing
technology, I predict that my company would use it for higher
level of performance, i.e. opportunistic. 0.410
b.2 My company plans to adopt some form of cloud
computing (i.e. extend/enhance current technology resources)
in the next six months, i.e. futuristic. 0.448
b.3 My company has realised the potential and upfront
benefits of cloud computing in our operating supply chain for
accounting, customer and supplier management, other apps and
so on, i.e. strategic (newly developed item, retained). 0.460
b.4 Assuming that I had the ability to adopt some form of
cloud computing technology for my company, I intend to do
so for higher level of performance, i.e. in-house capability
(item adapted and dropped).
b.5 My company is already working on cloud computing via
subscription and on-going cost for the past six months or
more., i.e. in action (newly developed item dropped).
C. Integration process.
We are interested in determining what you perceive as the
benefits from integrated supply chain with cloud services.
Please answer each of the following questions by circling the
appropriate number (1 = strongly disagree; 5 = strongly
agree):
Customer integration process (Alpha = 0.68)(Wiengarten
and Longoni, 2015).
c.1 System coupling with key customers. 0.616
c.2 Developing collaborative approaches with key
customers. 0.641.
Supplier integration process (Alpha = 0.79)(Wiengarten
and Longoni, 2015).
c.3 Sharing information with key suppliers (about sales
forecast, production plans, order tracking and tracing, delivery
status, stock level). 0.655
c.4 System coupling with key suppliers. 0.760
c.5 Developing collaborative approaches with key suppliers.
0.746
Internal integration process (Alpha = 0.85) (Yao et al.,
2007).
c.6 Reduce paperwork when receiving customer orders.
0.802
c.7 Reduce labour costs in receiving orders from customers.
0.799
c.8 Reduce the cost of placing orders with suppliers. 0.697.
D. Top management initiative (Alpha = 0.72) (Yao et al.,
2007).
d.1 Because top management views our firm as a technology
leader. 0.631
d.2 Because top management is willing to take the risks
involved in adopting cloud. 0.750
d.3 Because top management is likely to invest the
necessary funds in information and communication systems.
0.551
E. Supply chain performance(Alpha = 0.81) (Wiengarten
and Longoni, 2015).
How is your current performance improved compared with
that of your main competitor(s) in the following chosen areas?
[Consider the average performance of the group of
competitors that are the direct benchmark for your business]
[1 and 2: Much lower, 3: Equal and 4 and 5: Much higher].
e.1 Unit manufacturing/service cost. 0.580
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Himanshu Shee et al.
Supply Chain Management: An International Journal
Volume 23 · Number 6 · 2018 · 500–517
516
e.2 Delivery reliability. 0.693
e.3 Conformance quality. 0.521
F. Firm sustainable performance (Wiengarten and Longoni,
2015).
Environmental performance (Alpha = 0.85).
f.1 Pollution emission and waste production levels. 0.747
f.2 Carbon emission in transportation. 0.835
f.3 Minimum loss to environment. 0.833
Social performance (how much changes have occurred)
(Alpha = 0.78).
f.4 Workers’motivation and satisfaction. 0.724
f.5 Occupational health and safety conditions. 0.653
f.6 Social well-being. 0.609
Financial performance (Alpha = 0.79).
f.7 Sales growth in the past three years. 0.651
f.8 Finding new revenue streams (e.g. new products, new
markets). 0.631
f.9 Relative market share. 0.705
Corresponding author
Himanshu Shee can be contacted at: Himanshu.Shee@vu.
edu.au
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