Balancing in-house and outsourced logistics services: effects on supply chain agility and firm performance

Abstract

The purpose of this study is to explore the role of logistics strategies (flexibility, collaboration, and differentiation) as antecedents of supply chain agility (SCA) and financial performance. Toward this end, we develop an integrative model based on the strategy–structure–performance paradigm, the resource-based view, and the relational view. Using structural equation modeling, we test the model with survey data gathered from 279 companies. The findings indicate that logistics strategies influence financial performance both directly and indirectly through SCA, providing empirical evidence for SCA’s central role in helping firms quickly sense and respond to changing environments.

Full text

Balancing in-house and outsourced logistics services:
Effects on supply chain agility and firm performance
Taewon Hwang
Valdosta State University
Sung Tae Kim*
skim6@stmarytx.edu
St. Mary’s University
* Corresponding author
Working Paper
3rd submission to Service Business: An International Journal
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Balancing in-house and outsourced logistics services: Effects on supply chain agility and
firm performance
Abstract The purpose of this study is to explore the role of logistics strategies (flexibility,
collaboration, and differentiation) as antecedents of supply chain agility (SCA) and financial
performance. Toward this end, we develop an integrative model based on the strategy-structure-
performance (SSP) paradigm, the resource-based view (RBV), and the relational view. Using
structural equation modeling, we test the model with survey data gathered from 279 companies.
The findings indicate that logistics strategies influence financial performance both directly and
indirectly through SCA, providing empirical evidence for SCA’s central role in helping firms
quickly sense and respond to changing environments.
Keywords: supply chain agility, logistics capabilities, relational view
1 Introduction
In today's rapidly changing environments, firms should reinvent themselves continuously by
establishing a strong network of internal and external relationships (Barratt and Barratt 2011).
Firms with non-integrated business processes and weak collaborative relationships are unlikely
to be successful in fiercely competitive global markets (Barratt 2004). Thus, an increasing
number of firms are recognizing the importance of supply chain agility (SCA). SCA is generally
defined as a strategic ability to quickly sense and respond to changes via the effective integration
of supply chain relationships (Fayezi et al. 2017). With the shift of competition from the firm-
level to the supply chain level, SCA is increasingly being viewed as a strategic lever that leads to
an organization’s success and prosperity (Sharma et al. 2017). Fast fashion retailers such as
H&M and Zara have incorporated agility into their supply chains through postponed production
processes, state-of-the-art sorting, and material handling technologies (Li et al. 2008). Another
example is Apple, which seeks to make its supply chain more agile with limited overstock of
inventory. To meet a variety of customer needs, Apple maintains tight control over its supply
chain from design to sales, constantly balancing the products’ characteristics with its respective
supply chains (Kouvelis and Milner 2002).
Although researchers are aware of the importance of SCA in managing today’s networked
enterprises, a review of the literature on SCA reveals significant shortcomings. First, many
frameworks incorporating SCA lack sound theoretical underpinnings, providing a fragmented
and incomplete understanding of how SCA interacts with other factors (Li et al. 2008; Gligor et
al. 2013; Gligor 2014). This study intends to contribute to bridging this knowledge shortfall by
developing an integrative SCA framework based on three theoretical perspectives: the strategy-
structure-performance (SSP) paradigm, the resource-based view (RBV), and the relational view.
The framework also responds to recent calls for the use of complementary theories in supply
chain management (SCM) research (Halldórsson et al. 2015). These calls encourage SCM
researchers to select one theory as the main theory and then complement it with one or several of
the other theories; one particular theory alone may not be enough to explain complex supply
chain issues (Skjoett-Larsen 1999; Halldórsson et al. 2007). This study attempts to empirically
demonstrate the value of a combined view driven by multiple theories in developing a SCA
framework.
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Second, although the impact of SCA on firm performance has been relatively well
studied, there is only a small amount of empirical research on factors affecting SCA (Swafford et
al. 2006; Braunscheidel and Suresh 2009; Gligor and Holcomb 2012a). This study proposes
logistics strategies as antecedents of SCA because developing effective logistics operations that
span the key boundaries of a supply chain is a pre-requisite for being nimble and responsive to
changing markets (Mentzer et al. 2004; Gligor and Holcomb 2012b; Lee and Van Wyk 2015).
Specifically, this study focuses on three logistics strategies: flexibility, collaboration, and
differentiation. The first two logistics strategies are based on the relational view, which
emphasizes the value of resources created through inter-firm relationships (Klein and Rai 2009).
These externally oriented strategies involve the use of logistics service providers (LSPs).
Flexibility refers to a firm’s logistics strategy to change in response to unexpected needs through
the effective use of LSP services (Sinkovics and Roath 2004; Fayezi et al. 2017). Collaboration
refers to a firm’s logistics strategy to build shared processes where the firm works together with
its LSP to achieve business goals (Stank et al. 2001; Stefansson 2006). While the first two
strategies are externally oriented, the third strategy is internally oriented. Differentiation refers to
a firm’s logistics strategy to gain differentiation through in-house logistics. It is based on the
RBV, which highlights the internal development of a firm’s unique resources (Gligor and
Holcomb 2014). The rationale for including both internal and externally oriented logistics
strategies is based on the view that logistics outsourcing should not be an all-or-nothing decision
(Millen et al. 1997). In logistics outsourcing, it has been shown that using a combination of in-
house and a LSP is the most effective way to reap the business value from logistics (Langley and
Holcomb 1992). For example, a recent survey of European consumer goods companies revealed
that over three quarters of these companies utilize both LSPs and the internal logistics
department to manage at least one logistics function (Wilding and Juriado 2004). Following this
perspective, this study aims to gain insights into how both internal and external logistics
strategies influence SCA as well as financial performance. Our research model is empirically
tested with survey data gathered from 279 companies by using structural equation modeling
(SEM).
The paper is organized as follows. Section 2 elaborates the theoretical underpinnings of
our research model. Section 3 illustrate our research model and develop our hypotheses
accordingly. Section 4 presents our research methodology, including a discussion of the data
collection. Section 5 presents the results of our research. Section 6 describes theoretical and
practical implications of the results. Section 7 concludes with limitations and suggestions for
further research.
2 Background
2.1 The strategy-structure-performance (SSP) paradigm
SSP predicts that strategy dictates organizational structure, which in turn, influences
performance. Chandler (1962) was one of the first scholars to consider the relationship between
strategy and structure. He investigated the growth of U.S. companies and found that the
companies implemented a divisional organizational structure as they grew through a strategy of
product diversification. Rumelt (1974) also showed that firms diversifying into related
businesses outperformed firms diversifying into unrelated businesses or firms vertically
integrated with limited diversification options. Subsequent studies have supported these findings
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by reporting that firms with certain strategy and structure combinations significantly performed
better than others without such combinations (Williamson 1975; Teece 1981). Galbraith and
Nathanson (1978) and Miles and Snow (1978) argued that the establishment of strong alignment
between strategy and structure should be a baseline requirement for successful businesses. They
further attempted to extend SSP by introducing the strategy-structure-processes-performance
(SSPP) paradigm, which includes process related factors such as resources allocation processes,
cross-departmental decision processes, and integration mechanisms.
In the field of logistics and SCM, many researchers have adopted the SSP framework
(Defee and Stank 2005; Nakano 2015). Early efforts were largely invested on developing
conceptual frameworks, which suggested that performance is likely to be higher when the firm’s
strategy and structure are in line with the unique capabilities inherent in the firm’s logistics
practices (Chow 1995; Stank and Traichal 1998; Stock et al 1998). Many subsequent works were
performed to test these models empirically. Rodrigues et al. (2004) demonstrated that firms that
pursue strategies of relationship development and select an appropriate organizational structure
are likely to enhance logistics performance. Defee et al. (2010) developed the concepts of supply
chain leadership and supply chain followership and investigated their impact on supply chain
structural and performance outcomes. Daugherty et al. (2011) found that decentralization and
formalization indirectly influence market performance through logistics service innovation
capability. Spillan et al. (2013) examined whether logistics strategies in the US and China
contribute to organizational effectiveness. Patel et al. (2013) described how strategic supply
chain orientation exerts its influence on structural supply chain orientation and performance
outcomes. Recognizing a growing scholarly interest in this subject, Nakano and Akikawa (2014)
conducted a review of SCM studies based on SSP/SSPP. They claimed that the use of the SSP
paradigm can enrich the field of SCM. Their argument is also supported by Gligor (2014) who
reviewed SCA frameworks.
2.2 The resource-based view (RBV) and the relational view
The RBV defines a strategic asset as a resource that is rare, valuable, non-substitutable, and
difficult to imitate (Barney 2001). A strategic asset can be both tangible (e.g., building,
equipment) and intangible (e.g., reputation, strategy). The RBV posits that firms that can
accumulate such strategic assets are likely to create sustainable competitive advantage. Firms
that establish distinctive competencies through unique combinations of strategic assets can
achieve advantage over competitors and earn above-normal rates of return (Acedo et al. 2006).
In the field of logistics and SCM, the RBV has been widely used to explore the value of
strategic logistics practices (Olavarrieta and Ellinger 1997; Wong and Karia 2010; Gligor and
Holcomb 2014). Daugherty et al. (2001) focused on resource commitment in a reverse logistics
context and investigated the impact of reverse logistics implementation on firm performance.
Autry et al. (2005) explored the impact of warehouse management system capabilities on
organizational performance. Richey et al. (2007) emphasized the role of logistics service
technology support as a core competency that predicts superior performance outcomes. Barratt
and Oke (2007) argued that a distinctive supply chain visibility would allow firms to obtain a
sustainable competitive advantage. Guang et al. (2012) addressed the importance of green supply
chain management as a strategic asset that contributes to firm performance.
However, the RBV has been criticized for its internally oriented view, which focuses on
resources within the firm and makes comparisons with the industry to determine whether the
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firm holds comparative advantages (Priem and Butler 2001). This reasoning has led to a
complementary perspective, called the relational view (Dyer and Singh 1998). It states that a
firm’s competitiveness not only arises from internal resources but also depends on inter-firm
relationships (Lee et al. 2001). Because a firm’s key resources may span firm boundaries and be
embedded in interfirm relationships, it is crucial for a firm to relate its activities to those of other
firms for better performance. Common relational resources include: (1) investments in relation-
specific assets; (2) substantial knowledge exchange; (3) complementary resources; and (4)
effective governance (Dyer and Singh 2004). Thus, the relational view requires that the
relationship itself be specified as the focal unit of analysis to include various external business
partners such as parts suppliers, distributors, logistics service providers, etc.
With the growing trend toward the integration of internal and external activities for global
competitiveness, the relational view has been used as a theoretical basis for many logistics and
SCM studies (Cousins et al. 2006; Sanders et al. 2011). Paulraj et al. (2008) proposed inter-
organizational communication as a relational competency, which delivers significant strategic
advantages for supply chain partners. Klein and Rai (2009) highlighted the importance of
relation-specific assets in logistics partnerships. They argued that buyer and supplier strategic
information flows represent the exchange of complementary strategic resources, which improve
the relationship-specific performance for both parties. Chen et al. (2013) examined the impacts
of relational assets such as trust and knowledge exchange on supply chain integration. Wieland
and Wallenburg (2013) investigated the influences of communication, collaboration, and
information integration on supply chain resilience.
2.3 The concepts of agility and flexibility in this study
Agility can be defined as "the ability to cope with unexpected challenges, to survive
unprecedented threats of business environment, and to take advantage of changes as
opportunities"(Sharifi and Zhang 1999, p. 9). Agility has been noted as a necessary component
that enables firms to establish competitive advantage in dynamic marketplaces. The concept of
agility has been adopted in many areas of management, such as manufacturing (Yusuf et al.
1999), organizational behavior (Dove, 2002), and information systems (Lu and Ramamurthy
2011). Furthermore, increasing global competition is not limited to the traditional boundaries of
the individual firm but has advanced to include a network of interdependent firms, stimulating a
great deal of research on SCA (Fayezi et al. 2017; Sharma et al. 2017). Fisher (1997) was one of
the first researchers to consider agility in the context of supply chains. Subsequent works further
refined this concept by highlighting SCA as a business-wide capability (Swafford et al. 2006).
Sharp et al. (1999) suggested that SCA represents the ability of a supply chain to quickly react to
external changes. Ismail and Sharifi (2006) conceptualized it as the capability of supply chain
members as a whole to dynamically respond to rapid market shifts. Li et al. (2008) presented a
unified definition of SCA, which serves as the basis for this study. They described SCA as the
result of integrating an alertness to environmental changes (opportunities/challenges) with the
ability to use resources in responding to such changes in a timely and adjustable manner.
Building on this foundation, some researchers developed measurement instruments for SCA (Li
et al. 2009; Gligor et al. 2013). Others developed SCA frameworks to explore the role of SCA in
broader supply chain networks (Eckstein et al. 2015; Gligor et al. 2016).
Although the discussions so far indicate that there is a general agreement on the
definition of SCA, it should be noted that the term agility has been used interchangeably with
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flexibility in the supply chain context (Fayezi et al. 2017). As suggested by Bernardes and Hanna
(2009), this confusion was partly due to the fact that both terms were often identified as key
characteristics of competitive supply chains. In fact, flexibility has been examined not just at the
firm’s level but also at the supply chain level (Closs et al. 2005). From a supply chain
perspective, a single department or firm cannot provide the breadth and intensity of flexibility
required to integrate a variety of value activities across the supply chain (Stevenson and Spring
2007). Thus, flexibility has become increasingly integrated with numerous supply chain
initiatives such as supplier flexibility and supply network flexibility (Liao et al. 2010). Among
the different aspects of flexibility, this study focuses on flexibility developed through logistics
outsourcing relationships. LSPs manage, control, and deliver logistics activities on behalf of
client firms (Hertz and Alfredsson 2003). Today, LSPs are more than just transportation
providers; they allow client firms to access to essential resources and address unanticipated
market needs (Green et al. 2008). Many researchers recognized flexibility as one of the most
distinctive benefits of partnering with LSPs (Lieb and Butner 2007; Hartmann and De Grahl
2011). This research suggests that flexibility obtained from LSPs enables a firm to improve its
SCA, viewing flexibility as an antecedent of SCA. This perspective is in line with previous
research that hypothesized the flexibility–agility association (Swafford et al. 2006; Braunscheidel
and Suresh 2009).
3 Review of literature and hypotheses development
We build on the theoretical lenses described above, as well as the supporting literature presented
in this section, in order to develop an integrative framework shown in Figure 1. The framework
posits that both internal and external logistics strategies influence financial performance directly
and indirectly through SCA. The overall structure of the framework is based on the SSP
paradigm, which links strategy-structure-performance (Defee and Stank 2005). According to
SSP, a supply chain structure centers on the design of a firm’s network of relationships through
which its supply chain is administered (Nakano and Akikawa 2014). SSP also denotes that a
supply chain structure may be adapted to support the changing needs of supply chain members
(Nakano 2015). Considering the boundary spanning nature of logistics operations, it is likely that
a firm’s logistics strategies facilitate developing an agile supply chain structure, which in turn
leads to superior performance.
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3.1 Flexibility
Maintaining an agile supply chain is important for every firm, especially given the complexities
and volatilities in the business environment (Fayezi et al. 2017). Such uncertainty necessitates
more commitment among supply chain members for greater flexibility (Mentzer et al. 2001;
Young et al. 2003). The relational view describes flexibility as the willingness of firms to allow
variations as situations change (Chen et al. 2015). Flexibility facilitates adaptation to unexpected
changes, allowing firms to adjust to new circumstances without relying on renegotiations
(Skipper and Hanna 2009). Thus, the relational view treats flexibility as one of the key
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noncontractual elements in inter-firm relationships (Gopal and Koka 2012). In the context of
logistics outsourcing, flexibility has been considered a major relational asset that a firm can
obtain from the interactions with its LSP (Lieb and Butner 2007; Hartmann and De Grahl 2011).
A LSP strives to tailor the services based on constantly shifting market expectations, helping
client firms improve strategic agility across their supply chain (Barve et al. 2008; Qureshi et al.
2008). For instance, a LSP provides more shipping flexibility with firms by suggesting a variety
of certified carriers (Lee et al. 2013). Also, if a disaster occurs, a LSP helps firms reroute
shipments quickly at nearly any point in the supply chain. These examples show that flexibility
enabled by a LSP results in higher SCA. The following research hypothesis is thus proposed:
H1a: Flexibility is positively related to SCA.
Another potential impact of flexibility is that it can lower the client firm’s costs associated with
securing additional resources during crisis conditions (Vanovermeire et al. 2014). A LSP usually
has facilities that store items from multiple firms, each of whom pays for only the space it uses
(Cho et al. 2008). In this way, firms can improve their return on invested capital without paying
for an entire facility dedicated to inventory that often fluctuates over time (Gotzamani et al.
2010). This cost efficiency is a compelling advantage for firms that cannot fill an entire facility
all year around (Sinkovics and Roath 2004). In addition, a LSP offers flexible, multi-modal
transportation options, helping firms maximize efficiency and reduce costs (Meixell and Norbis
2008). Further evidence is presented by several empirical studies showing the positive
relationship between flexibility and firm performance (Closs et al. 2005; Chen et al. 2015).
Hence, the following hypothesis is offered:
H1b: Flexibility is positively related to financial performance.
3.2 Collaboration
The relational view states that a firm should pursue collaboration because it is often difficult for
a single firm to possess all the resources needed to cope with changing business environments
(Dyer and Singh 1998). In logistics outsourcing relationships, a firm depends on its LSP, which
provides resources that the firm may not have (Min 2013). Establishing relationships with a LSP
has been considered one of the most effective ways to create a relational network (Selviaridis and
Spring 2007). For instance, over 60 percent of Fortune 500 firms have at least one contract with
a LSP and there is a growing trend of greater LSP involvement (Lieb and Lieb 2010). Previously,
LSP services were relatively limited in scope, covering traditional transportation and
warehousing functions. Due to globalization and ever-increasing customer expectations, a LSP
has extended its scope to include a broad set of supply chain activities (Yeung et al. 2006). A
LSP now serves as an advocate on behalf of client firms, creating a collaborative supply chain
environment (Wong and Karia 2010; Zacharia et al. 2011). For example, a LSP offers value-
added services such as proof of delivery, order tracking and tracing, and real‐time monitoring of
warehouses (Zeimpekis and Giaglis 2006). Firms engaging in collaboration with a LSP can
leverage key supply chain data from such services, sensing and responding faster than
competitors to changing market opportunities (Halldórsson and Skjoett-Larsen 2004; Wieland
and Wallenburg 2013). These arguments lead to the following hypothesis:
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H2a: Collaboration is positively related to SCA.
Previous studies highlighted the financial benefits gained through collaboration with a LSP
(Rajesh et al. 2011). Collaboration enables a LSP to better understand unique business needs and
develop tailored service offering, helping client firms achieve greater cost-efficiency (Wong and
Karia 2010). Compared to firms that view outsourced logistics functions as non-value-added
activities, firms that closely work with their LSP are more likely to improve their performance
(Chen et al. 2010). Further evidence is provided by the 21st Annual Third-Party Logistics Study
revealing that 44 percent of clients and 86 percent of LSPs suggest that working with others,
even competitors, can lead to logistics cost and service improvements (Third-Party Logistics
Study 2017). This survey also pointed out that close collaboration between LSPs and clients has
become a necessity to data-driven era where big data analytics are increasingly used to improve
business outcomes (Third-Party Logistics Study 2017). For example, sales data from a client
firm can be integrated with its LSP data such as geographical data, weather data, and commodity
trends. This collaborative approach can improve sales forecast accuracy and boost sales, helping
firms increase financial performance (Tyan et al. 2003). Accordingly, this study proposes the
following hypothesis:
H2b: Collaboration is positively related to financial performance.
As discussed above, firms collaborate to achieve SCA and further improve business outcomes.
Such collaborative relationships also facilitate the exchange of resources across firms, driving
the redesigning of business processes (Bowersox 1990). Thus, firms that establish strong
collaborative relationships with others are likely to exhibit greater flexibility (Sabath and
Fontanella 2002). This view is shared by numerous SCM studies that highlighted the role that
collaboration plays in enhancing information visibility (Ahmed et al. 1996; Wang and Wei 2007).
Zhang et al. (2002) suggested that value chain flexibility can be improved when every member
knows the effect of its actions on others in the supply chain. Liao et al. (2010) investigated the
benefits of supplier relationship management practices such as supplier strategic alliances and
supplier development. They found that these collaborative efforts promote information sharing
and coordination, enabling the focal manufacturer to foster supplier flexibility. In the context of
logistics outsourcing, a LSP strives to offer superior services to improve its relationships with
client firms. Thus, collaboration with a LSP is likely to help firms achieve greater flexibility in
adapting to rapid changes. This proposed relationship is consistent with Hartmann and De
Grahl’s (2011) work that adopted the relational view to examine the impact of collaboration on
flexibility in the logistics outsourcing context. Based on the above discussion, this study suggests
the following hypothesis:
H2c: Collaboration is positively related to flexibility.
3.3 Differentiation
Although the use of LSP services has become a key part of corporate strategies, it should be
noted that there are many challenges associated with LSP services (Selviaridis and Spring 2007).
In particular, LSP services tend to be one-size-fits-all, failing to address the specialized needs of
client firms (Wilding and Juriado 2004; Zacharia et al. 2011). Some firms dissatisfied with LSP
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services have taken previously outsourced logistics functions back in-house and differentiated
their logistics programs (Van Laarhoven et al. 2000; Gadde and Hulthén 2009). The RBV offers
a theoretical foundation for examining the impact of such efforts on SCA and firm performance
(Daugherty 2011; Ralston et al. 2013). The RBV suggests that a firm’s superior performance
depends on the ability to differentiate itself from competitors (Barney 1991). Following the RBV
framework, many logistics and SCM studies showed that logistics differentiation allows firms to
realize a variety of beneficial business outcomes (Kemppainen and Vepsäläinen 2007). The first
stream of research identifies customer value as a key outcome of differentiated logistics. For
example, Langley and Holcomb (1992) suggested that many firms create customer value through
unique and distinctive logistical services. Pirttilä and Huiskonen (1996) developed a process
model to help firms conduct a cost-of-service analysis in logistics service differentiation. They
stressed that successful internal differentiation of logistics services requires a balance between
customer satisfaction and the cost of services offered. This view was echoed by Van der Veeken
and Rutten (1998) who proposed a framework for differentiating logistics offerings to distinct
customer groups. Wang and Lalwani (2007) showed how e-business can be combined with
logistics differentiation strategies to deliver customized logistics services. Taken together, these
studies clearly indicated that firms move beyond physical goods to create differentiated logistics
services for specialized requirements. In other words, customized logistics services through
differentiation strategy enable firms to effectively respond to varying customer needs. Therefore,
it is hypothesized that:
H3a: Differentiation is positively related to SCA.
The second stream of research focused on the impact of differentiated logistics on overall
logistics/ firm performance. Fugate et al. (2010) operationalized logistics performance as a
construct composed of logistics efficiency, effectiveness, and differentiation. They found that
logistics differentiation is the most influential dimension of logistics performance, which is
positively associated with organizational performance. They argued that firms should compare
their logistics activities with those of their competitors. Fugate et al. (2012) demonstrated that a
firm’s ability to learn logistics-oriented knowledge is critical to successful logistics-based market
differentiation, which leads to superior organizational performance. Ralston et al. (2013)
reported a positive relationship between logistics service differentiation and logistics
performance. They also showed that logistics service differentiation is influenced by both
logistics innovativeness and logistics salience. In summary, there is strong evidence that logistics
differentiation is a significant predictor of a firm’s financial performance. The above
considerations motivate the following hypothesis:
H3b: Differentiation is positively related to financial performance.
3.4 SCA and financial performance
The relationship between SCA and firm performance has been investigated mainly from the
dynamic perspective of the RBV (Vickery et al. 2010). As an extension of the RBV, dynamic
capabilities represent a firm's ability to reconfigure existing resources to provide new sources of
competitive advantage (Teece 2007). Researchers adopting this perspective conceptualized SCA
as a dynamic capability to better understand how SCA influences firm performance (Gligor
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2016). Gligor and Holcomb (2012a) viewed SCA as a source of strategic leverage that is hard to
replicate. Their study revealed that SCA helps firms not only meet delivery deadlines but also
ensure accurate logistics services, supporting its positive impact on firm performance. Similarly,
Blome et al. (2013) claimed that SCA can be considered under the RBV as a unique strategic
asset, which facilitates cost efficient handling of supply chain disruptions and ultimately leads to
better operational performance. Eckstein et al. (2015) defined SCA as a dynamic capability,
which enables firms to sense and capitalize on environmental changes. They showed that SCA
positively influences cost and operational performance. Based on the above arguments, this study
proposes the following hypothesis:
H4: SCA is positively related to financial performance.
4 Research methodology
This study used a survey methodology to collect data and test the proposed model. In line with
previous SCA studies, this study examined the hypothesized relationships from the client
organizations’ perspective. The questionnaire was developed to examine a measurement model
which has five underlying constructs, such as collaboration, differentiation, flexibility, supply
chain agility, and financial performance as explained in the hypotheses development. These
constructs and measurement items were already tested in prior studies (Blome et al. 2013; Chen
et al. 2015; Gligor and Holcomb 2014; Hartmann and De Grahl 2011; Li et al. 2009; Lynch et al.
2010; Ralston et al. 2013). We adopted their items in this study and utilized additional items
found in Richey et al.’s paper (2012) to reinforce the measurement of the two constructs –
collaboration and differentiation.
The items were translated into Korean. After the translation, the questionnaire was
presented to experts from academia and industry to solicit their feedback regarding the survey
items. To make sure the translation was accurate and that the question meanings were not altered,
we used a double-translation method to translate the questionnaire. The original and back-
translated versions were compared for conceptual equivalence and translation errors and then
revised if necessary (Douglas and Craig 1983). To ensure face validity, the resulting version was
further refined on the basis of comments from the previously described pretests with experts. The
two English versions did not have any major differences. The scales used to measure this study’s
constructs were developed based on an in-depth literature review, and existing scales were used
wherever possible. Minor wording changes were made in order to adapt the scale to the specific
logistics outsourcing context. All items are measured using a five-point Likert-type scale. The
measurement scales and their sources are shown in Table 1.
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The data for this study was collected from Korean manufacturing firms. A mailing list of
logistics and SCM departments was compiled from the list of partner companies of Korea Trade-
Investment Promotion Agency (KOTRA). The survey was conducted in cooperation with a
research consulting firm. Approximately 1,000 companies were randomly selected from the list.
The respondents were selected from senior or middle managers who had direct responsibility and
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knowledge for logistics and SCM. It was because these high-ranking employees are likely to be
more reliable sources of information (Phillips 1981; Chiang et al. 2012). The survey team of the
consulting firm first called the logistics and SCM department of the selected companies for their
cooperation and then the questionnaire was sent to 400 firms that were willing to participate in
the survey. A total of 279 responses were received. If any omitted questions were found, the
survey team called the manager to complete the questionnaire. To evaluate the non-response bias,
we measured the difference between early and late responses of returned surveys. To do so, we
compared the measures of two key characteristics, such as firm size and business area using t-
tests (Armstrong and Overton 1977). We detected no significant differences, which indicate that
non-response bias does not appear to pose a problem to our study.
The responding companies represent largely 5 industries including electrical/electronic
(170), chemical (31), machinery (30), textile/footwear/apparel (26), and food/beverage (22). The
participating firms are mostly small and medium-sized enterprises (SMEs) and the median firm
size is 400 employees. Respondents’ job titles ranged from the employee in charge of SCM to
senior manager. Middle and senior managers were combined almost 70 percent, majority of the
job titles in charge of SCM. This result indicates that SCM of Korean small and medium-sized
manufacturers is under the supervision of higher-level workers with over-ten-year-experiences in
the industry. The demographic characteristics of responding firms are summarized in Table 2.
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5 Results
5.1 Analysis of reliability and validity
We tested the hypothesized relationships by using SEM according to Mplus version 6.12
(Muthén and Muthén 2011). The overall results regarding our hypothesized model are displayed
in Figure 2 and Table 6. The R2 values, which reflect the predictability of the model, are
important determinants of the strength of the model (Chin 1998). As shown in Figure 2, the R2
values were acceptable (28.3 percent for SCA and 54.1 percent for financial performance) when
compared to the values reported in the prior research.
The acceptability of the measurement model was examined by analyzing the convergent
validity, the discriminant validity, and the reliabilities of all constructs. Convergent validity
signifies that a set of measurement items represents one and the same underlying construct
(Brown 2006). It was examined in two ways. We first assessed composite reliability (CR) scores
for all constructs and then calculated the average variance extracted (AVE). Table 3 reports that
all constructs have CR scores greater than 0.7, which is the recommended minimal critical value
(Barclay et al. 1995). With respect to the AVE, on the other hand, all constructs exceeded 0.5, the
threshold of composite reliabilities as shown in Table 3, and all AVE estimates of the five
constructs were greater (Fornell and Larcker 1981). Moreover, the standardized factor loadings
of all individual measurement items presented in Table 4 were larger than 0.7. That is, the items
shared more variance with their respective latent variable than with error variance. In conclusion,
these results provided strong support for convergent validity.
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INSERT TABLE 3 ABOUT HERE
----------------------------------------------
----------------------------------------------
INSERT TABLE 4 ABOUT HERE
----------------------------------------------
We also compared the squared correlation coefficients between two latent constructs to
their AVE estimates (Fornell and Larcker 1981). According to this test, discriminant validity
exists if the items share more common variance with their respective construct than any variance
the construct shares with other constructs. Thus, the squared correlation coefficient between each
pair of constructs should be less than the AVE estimates for each individual construct.
Comparing the correlation coefficients given in Table 3 with the AVE estimates reported in Table
3, all squared correlations were smaller than the AVE for each individual construct. Moreover,
when it comes to the cross-loadings shown in Table 5, every item factor in the highlighted areas
exhibited strong loadings to the corresponding construct and low loadings to other constructs.
Therefore, these results collectively provided evidence of discriminant validity among the
theoretical constructs.
Reliability estimation was left for last because, in the absence of a valid construct,
reliability would not be meaningful (Koufteros 1999). Cronbach’s α values for all factors were
greater than 0.8, as shown in Table 4, which exhibited the internal consistency and validity of the
constructs as they were well above the suggested lower limit of 0.6 (Nunnally and Bernstein
1994). This result provided support for adequate construct reliability.
Lastly, to test the common method bias, we conducted a single common-latent-method-
factor test (see Appendix A). The fit of the measurement model with and without the common
method factor was nearly identical. Also, the weights of the standardized regression loadings
showed no significant changes. Thus, we concluded that common methods bias was not likely to
be a concern.
----------------------------------------------
INSERT TABLE 5 ABOUT HERE
----------------------------------------------
5.3 Hypotheses testing
The results of the structural model are shown in Table 6 and Figure 2. All fit indices were
indicative of a decent fitting model. Figure 2 indicates support for Hypotheses 1a, 1b, and 3a
with positive and significant effects: flexibility on SCA (b = 0.434; t = 7.481; p < 0.01),
flexibility on financial performance (b = 0.436; t = 7.205; P < 0.01), and differentiation on SCA
(b = 0.265; t = 3.963; p < 0.01). Collaboration was not found to have statistically significant links
to either SCA or financial performance, not supporting H2a (b = 0.033; t = 0.542; p = 0.588) and
H2b (b = -0.032; t = -0.609; p = 0.542). However, H2c was supported with positive and
significant correlation of collaboration with flexibility (b = 0.156; t = 2.496; p < 0.05). Although
H3a was supported, the results regarding H3b indicated that differentiation may not be able to
yield improved financial performance (b = 0.073; t = 1.232; p = 0.218). H4 was supported,
12

showing a positive relationship between SCA and firm’s financial performance (b = 0.413; t =
7.205; p < 0.01).
----------------------------------------------
INSERT TABLE 6 ABOUT HERE
----------------------------------------------
----------------------------------------------
INSERT FIGURE 2 ABOUT HERE
----------------------------------------------
6 Discussion
The present study made several important contributions. First, this study contributed to the
theoretical development of SCA in greater detail by developing an integrative model drawn from
multiple theories. Critics have pointed out that many of SCA frameworks seem rather ad-hoc and
theoretically unfounded, calling for more refined approaches (Gligor et al. 2013). In fact, this
concern has its roots in the field of SCM, which requires the use of complementary theories for
more in-depth understanding of its multi-faceted nature (Halldórsson et al. 2007; Halldórsson et
al. 2015). We addressed this issue by using three different theories, each of which touches upon
specific SCA issues. While the SSP paradigm offers a solid foundation on which to build our
nomological network of SCA, the RBV and relational view provide a basis for linking specific
logistics strategies to SCA and financial performance. As mentioned earlier, many authors have
acknowledged the importance of the SSP paradigm and its relevance to the logistics and supply
chain field (Defee and Stank 2005). SSP helps to explore, in a general sense, the effects that
logistics strategies have on firms. The RBV and relational view helps to provide more granular
insights into the mechanism that enables both internal and externally oriented logistics strategies
to translate into firm performance. We showed that SSP, combined with additional theories, can
provide a powerful theoretical lens through which to examine how different types of logistics
strategies affect firm performance both directly and indirectly through SCA.
Second, this study enriched the discipline’s knowledge on antecedents of SCA by
identifying specific logistics-related factors affecting SCA. The extant literature is still void in
terms of the precursors to SCA (Blome et al. 2013). As predicted, both flexibility and
differentiation had significant direct effect on SCA and financial performance. However, this
research found no direct effect of collaboration on SCA and financial performance. Collaboration
was related to them only indirectly through its effect on flexibility. One possible explanation of
this finding is that the benefits of collaboration with a LSP are not passed on to the entire supply
chain members if their needs are not properly addressed. This speculation is based on the
stakeholder theory, which states that each stakeholder has different values that the focal firm
should acknowledge (Parmar et al. 2010). Each supply chain member has its own objectives and
priorities, leading to potential conflicts and inefficiencies in the operations of the overall supply
chain. The best decision for the entire supply chain may not be the best one for a certain member
in the same network. The challenge for the focal firm is to identify the unique needs of supply
chain members and then balance them in a flexible manner. In the context of logistics
outsourcing, the benefits of a firm’s collaboration with a LSP appear to be manifest at the supply
chain level only when the firm is flexible enough to accommodate the different and changing
logistics requirements of supply chain members.
13

Third, this research highlighted the central role of SCA in transforming logistics
strategies into financial performance, providing further insights into the role of SCA in the age of
temporary advantage (D'Aveni et al. 2010). In today’s hyper competitive markets, few
advantages are sustainable over a long period of time. With rapid technological advances and
increasing globalization quickly making one’s current advantages irrelevant, the best way to be at
the top of the competition is to continually seek new forms of advantage through constant
adaptation. The results of this study showed that SCA enables firms to respond in a timely and
effective manner to such uncertainties when the right antecedents are in place. Thus, SCA is of
great practical importance to the field of SCM.
Finally, our results shed light on the value of balancing in-house and outsourced logistics
services, lending support to the view that outsourcing logistics functions does not have to be an
all or nothing proposition. In fact, this view is often left unaddressed in SCM research, largely
due to the increasing popularity of logistics outsourcing. However, this study made it clear that
the combined use of in-house and LSPs can be an effective means of realizing the business value
from logistics. Firms should effectively utilize external logistics services that would complement
their existing knowledge. They should leverage the benefits from logistics outsourcing without
completely submerging into it. At the same time, firms should consider the high costs associated
with building and maintaining logistics in-house. Investing in internal logistics systems that do
not serve as a differentiator would be unlikely to generate the long-term returns. In fact, our
research failed to find support for the hypothesis that differentiation increases financial
performance, suggesting that developing differentiated logistics in-house might be costly and
thus unproductive. It is imperative to evaluate the benefits of logistics insourcing with its costs to
determine the right proportion of outsourcing to insourcing. This issue has important managerial
implications. Managers should strive to find the appropriate balance between the different
logistics strategies of internalization and externalization. For example, managers should identify
firm-specific logistics requirements and determine whether such needs can be better met in-
house by conducting a cost-benefit analysis. This balanced approach is called tapered integration,
which occurs when firms pursue some of their activities simultaneously, both in-house and
through outsourcing (Harrigan 1985). The use of tapered integration has been found to be a
valuable strategy in many industries (Rothaermel et al. 2006). In addition, managers should be
aware that maintaining the right balance over a long period of time is even more challenging. It
is not a static process but a dynamic process involving continual adjustments. Managers need to
constantly reassess how logistics operations are structured and arranged in the pursuit of
sustainable superior performance.
7 Conclusion
SCA has emerged as one of the most important concepts in SCM research. However, questions
regarding its antecedents and consequences remain largely unanswered. We attempted to fill this
void by exploring the role of logistics strategies as antecedents of SCA and financial
performance. Our integrative model, including the SSP paradigm, the RBV, and the relational
view, was tested with data from 279 companies. This study made four main contributions. First,
we showed that the use of complementary theories can provide valuable insights into the
complex and multifaceted nature of SCA. Second, we identified specific logistics-related factors
as antecedents of SCA. Third, we found the central role of SCA in transforming logistics
strategies into financial performance. Forth, we provided empirical evidence that using a
14

combination of in-house and LSPs can be an effective way to maximize the business value from
logistics.
Although we did our best to advance our understanding of SCA, limitations exist. First,
the cross-sectional nature of the study design limits the extent to which causal inferences can be
made. Future research should address this limitation by using longitudinal data. Second, while
this study sought generalizability across multiple industries by using a nationally representative
sample of South Korean manufacturers, future researchers are encouraged to replicate this study
in other contexts and cultures. Third, our data were collected from a single party involved in a
specific relationship. Future research should address this limitation by focusing on inter-firm
relationships in dyads, triads and larger networks.
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Fig. 1 Research model
22

23

Fig. 2 Hypothesized structural model results
Fit indices: χ2 = 620.616 (d.f.= 289), χ2/d.f. = 2.147, CFI = 0.952, RMSEA = 0.062, SRMR = 0.072
** p < 0.01; * p < 0.05
24

Table 1 Measurement scales
Constructs Items Mean S.D.
Item-to-total
correlation
Flexibility (Chen et al. 2015; Hartmann and De Grahl 2011; Richey et
al. 2012)
Please indicate your level of agreement with the following statements (1 =
strongly disagree, 5 = strongly agree) about your flexible logistics strategies
developed through a LSP.
Working with our LSP,
FL1 we often develop flexible processes to respond to new market
changes 3.84 0.77 0.670
FL2 we are flexible in accommodating unexpected situations 3.60 0.84 0.673
FL3 flexibility in response to request for changes is a characteristic of
our relationship 3.64 0.87 0.765
FL4 it is likely that we will be open to modifying our agreements if
unexpected events occur 3.75 0.91 0.816
FL5 we are able to handle sudden changes well 3.69 0.92 0.785
Collaboration (Hartmann and De Grahl 2011; Richey et al. 2012)
Please indicate your level of agreement with the following statements (1 =
strongly disagree, 5 = strongly agree) about your collaborative logistics
strategies developed through a LSP.
Working with our LSP,
CB1 we work together to exploit unique opportunities in the market 3.81 0.85 0.827
CB2 we look for synergistic ways to do business together 3.87 0.86 0.873
CB3 we work together to develop new ideas 3.81 0.88 0.865
CB4 we continually share proprietary information each other 3.89 0.86 0.873
Differentiation (Lynch et al. 2010; Ralston et al. 2013)
Please indicate your level of agreement with the following statements (1 =
strongly disagree, 5 = strongly agree) about your differentiated logistics
strategies developed in-house.
We often develop...
DF1 new logistics strategies 3.11 1.04 0.814
DF2 highly valuable logistics strategies 3.08 0.99 0.864
DF3 highly differentiated logistics strategies 3.27 1.06 0.900
DF4 logistics strategies with distinctly different features from those of
competitors 3.33 1.05 0.825
DF5 logistics strategies for specialized needs 3.02 1.00 0.864
DF6 higher quality logistics strategies than competitors 3.47 1.03 0.790
Supply Chain Agility (Gligor and Holcomb 2014; Li et al. 2009)
Please indicate your level of agreement with the following statements (1 =
strongly disagree, 5 = strongly agree) about your supply chain agility.
We are able to...
AG1 adapt our products/services sufficiently fast to new customer
requirements 3.20 0.84 0.703
AG2 react sufficiently fast to new market changes 3.36 0.84 0.740
AG3 react to significant increases and decreases in demand as fast as 3.35 0.86 0.777
25

required by the market
AG4 adjust our product/service portfolio as fast as required by the market 3.21 0.86 0.808
AG5
react adequately fast to supply-side changes (e.g., compensate for
spontaneous supplier outages, delivery failures, and market
shortages)
3.38 0.83 0.745
AG6
detect strategic opportunities/challenges in a timely manner (e.g.
new competitor movement, new economic tendency, new
technology, and new market)
3.34 0.83 0.764
Financial Performance (Blome et al. 2013; Lynch et al. 2010)
Please indicate your level of agreement with the following statements (1 =
strongly disagree, 5 = strongly agree) about your financial performance.
We have increased our...
FP1 profit relative to your best competitors 3.32 0.87 0.712
FP2 market share relative to your best competitors 3.27 0.89 0.758
FP3 return on investment relative to your best competitors 3.27 0.95 0.823
FP4 return on sales relative to your best competitors 3.24 0.93 0.774
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Table 2 Characteristics of responding firms
Frequency Percentage
Industry Classification
Chemical 31 11.1
Electrical and electronic 170 60.9
Food and beverage 22 7.9
Machinery (except electrical and electronic) 30 10.8
Textile, footwear, wearing apparel 26 9.3
Total 279 100.0
Firm Size (Employees)
Less than 100 86 30.8
101 – 500 79 28.3
501 – 1,000 66 23.7
Greater than 1,000 48 17.2
Total 279 100.0
Respondents’ Job Title
Employee in charge 87 31.2
Middle manager 143 51.2
Senior manager 49 17.6
Total 279 100.0
Respondents’ Career in SCM (years)
Less than 5 26 9.3
5 – 10 74 26.5
11 - 15 160 57.0
More than 15 20 7.2
Total 279 100.0
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Table 3 Reliability (composite reliability and AVEs) and correlations among latent variables
Construct Composite
Reliability AVE FL CB DF AG FP
Flexibility (FL) 0.885 0.607 0.779
Collaboration (CB) 0.926 0.760 0.414 0.872
Differentiation (DF) 0.949 0.758 0.369 0.425 0.871
Supply Chain Agility (AG) 0.921 0.520 0.422 0.498 0.378 0.721
Financial Performance (FP) 0.904 0.655 0.398 0.485 0.250 0.397 0.809
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Table 4 Convergent validity (item loading)
Construct Items Standardized loading t-value Cronbach’s α
Flexibility FL1 0.716*** 21.371 0.895
FL2 0.753*** 24.667
FL3 0.849*** 36.303
FL4 0.799*** 29.358
FL5 0.771*** 26.045
Collaboration CB1 0.907*** 61.000 0.941
CB2 0.807*** 35.925
CB3 0.957*** 78.068
CB4 0.806*** 35.556
Differentiation DF1 0.848*** 46.182 0.949
DF2 0.892*** 63.009
DF3 0.924*** 85.269
DF4 0.851*** 47.128
DF5 0.890*** 63.440
DF6 0.815*** 38.103
Supply Chain Agility AG1 0.761*** 27.781 0.913
AG2 0.780*** 30.118
AG3 0.861*** 45.838
AG4 0.882*** 51.734
AG5 0.701*** 21.504
AG6 0.708*** 22.161
Financial Performance FP1 0.797*** 31.401 0.911
FP2 0.825*** 35.793
FP3 0.869*** 44.701
FP4 0.758*** 26.204
Note: Significant at *** 0.01 level
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Table 5 Cross-loading among variables
FL CB DF AG FP
FL1 1.000 .329 .237 .304 .223
FL2 .573 .233 .187 .336 .228
FL3 .703 .323 .246 .331 .297
FL4 .546 .341 .289 .338 .334
FL5 .656 .412 .248 .365 .327
CB1 .329 1.000 .389 .413 .371
CB2 .281 .589 .254 .245 .356
CB3 .271 .690 .266 .508 .350
CB4 .330 .702 .291 .503 .356
DF1 .237 .389 1.000 .327 .227
DF2 .267 .319 .758 .241 .176
DF3 .280 .385 .751 .272 .142
DF4 .295 .442 .812 .323 .247
DF5 .258 .356 .771 .329 .130
DF6 .285 .496 .720 .300 .321
AG1 .304 .413 .327 1.000 .350
AG2 .307 .395 .362 .683 .324
AG3 .300 .396 .353 .644 .342
AG4 .301 .282 .303 .689 .269
AG5 .304 .345 .277 .886 .255
AG6 .165 .362 .344 .625 .259
FP1 .223 .371 .227 .350 1.000
FP2 .241 .428 .206 .359 .801
FP3 .176 .349 .188 .317 .836
FP4 .294 .432 .189 .392 .680
30

Table 6 Results of path analyses and hypotheses tests.
Path (from-to) Path coefficient
(t-value)
Hypotheses
test results
H1a Flexibility  Supply chain agility 0.434 (7.481)* * Supported
H1b Flexibility  Financial performance 0.436 (7.205)** Supported
H2a Collaboration  Supply chain agility 0.033 (0.542) Not supported
H2b Collaboration  Financial performance -0.032 (-0.609) Not supported
H2c Collaboration  Flexibility 0.156 (2.496)*Supported
H3a Differentiation  Supply chain agility 0.265 (3.963) ** Supported
H3b Differentiation  Financial performance 0.073 (1.232) Not supported
H4 Supply chain agility  Financial performance 0.413 (7.205)** Supported
Fit indices: χ2 = 620.616 (d.f.= 289), χ2/d.f. = 2.147, CFI = 0.952, RMSEA = 0.062, SRMR = 0.072
** p < 0.01; * p < 0.05
31

Appendix A
Common Method Bias Analysis
Construct Indicato
r
Standardized Regression
Weights with Common
Latent Factor (A)
Standardized Regression
Weights without Common
Latent Factor (B)
A – B
Flexibility FL1 0.716 0.833 0.117
FL2 0.753 0.885 0.132
FL3 0.849 0.924 0.075
FL4 0.799 0.838 0.039
FL5 0.771 0.887 0.116
Collaboration CB1 0.907 0.842 -0.065
CB2 0.807 0.870 0.063
CB3 0.957 0.937 -0.020
CB4 0.806 0.904 0.098
Differentiatio
nDF1 0.848 0.869 0.021
DF2 0.892 0.898 0.006
DF3 0.924 0.926 0.002
DF4 0.851 0.923 0.072
DF5 0.890 0.894 0.004
DF6 0.815 0.820 0.005
Supply chain AG1 0.761 0.795 0.034
agility AG2 0.780 0.836 0.056
AG3 0.861 0.892 0.031
AG4 0.882 0.911 0.029
AG5 0.701 0.892 0.191
AG6 0.708 0.876 0.168
Financial FP1 0.797 0.796 -0.001
performance FP2 0.825 0.863 0.038
FP3 0.869 0.904 0.035
FP4 0.758 0.772 0.014
32