Jang-EffectLogisticsService-2013

The Effect of Logistics Service Quality on Customer Loyalty through Relationship Quality
in the Container Shipping Context
Author(s): Hyun Mi Jang, Peter B. Marlow and Kyriaki Mitroussi
Source: Transportation Journal , Vol. 52, No. 4 (Fall 2013), pp. 493-521
Published by: Penn State University Press
Stable URL: https://www.jstor.org/stable/10.5325/transportationj.52.4.0493
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The Effect of Logistics Service Quality on Customer
Loyalty through Relationship Quality in the Container
Shipping Context
Hyun Mi Jang, Peter B. Marlow, and Kyriaki Mitroussi
Abstract
The objective of this research is to explore the role of logistics service
quality in generating shipper loyalty, considering relationship quality in
the unique context of container shipping. This is to fill the gaps revealed
in the current understanding of ocean carrier–shipper relationships,
particularly the lack of studies attempting to investigate shippers’ future
intentions to use the same carrier as opposed to the previous studies
that focused on carrier selection criteria or on shippers’ satisfaction
with the service attributes. Soft concepts such as customer loyalty and
logistics service quality have been increasingly explored in a variety of
industries to offer further insight into the relationship issues. However, it was discovered that relatively few studies on this topic have been
conducted in the context of maritime transport. The theoretical model
is tested on data collected through a postal questionnaire survey of 227
freight forwarders in South Korea. Structural equation modeling (SEM)
is employed to rigorously examine relationships among the extensive
set of key variables simultaneously in a holistic manner. The findings
demonstrate that container shipping lines should develop a high level of
logistics service quality as well as relationship quality in order to attain
higher (beyond mere satisfaction) levels of shippers’ loyalty.
Keywords
Logistics service quality, customer loyalty, relationship quality, container
shipping, structural equation modeling
Hyun Mi Jang
Lecturer
Dongseo University
Email: jangh0911@gdsu.dongseo.ac.kr
Peter B. Marlow
Associate Dean/Professor
Logistics and Operations Management
Cardiff University
Kyriaki Mitroussi
Senior Lecturer
Logistics and Operations Management
Cardiff University
Transportation Journal, Vol. 52, No. 4, 2013
Copyright © 2013 The Pennsylvania State
University, University Park, PA
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Introduction
Today’s competitive environment is characterized by shorter product and
technology life cycles, the globalization of markets, and high uncertainties
in supply and demand. Operated in this environment, shippers, namely
manufacturers and retailers, have globalized their production systems,
adopted a “Just-in-Time” philosophy, and outsourced a number of activities with the aim of minimizing total costs and maximizing customer value.
This widespread adoption of the supply chain management approach by
shippers is affecting the crucial success factors in transport and logistics,
both for individual logistics service providers, such as logistics specialists,
freight forwarders and carriers, and for clusters of organizations, such as
ports (Carbone and Gouvernal 2007). Despite the diversity of transport services, maritime transport in the global freight trade is of major significance
in terms of tonnage as it handles approximately 90 percent of the global
total tonnages. In particular, the importance of container liner shipping
to international trade cannot be ignored as 60 percent of general cargo is
globally containerized (Wang 2006). Acciaro emphasized the significance
of this industry by saying “Without the development of containerization
and the liner shipping industry, globalization could not have taken place
the way we know it nowadays” (2010, p. 55).
As the role of container shipping lines has evolved in global supply
chains, they are required to pay more attention to managing relationships
with their partners, particularly with shippers. Shippers are spending
more time finding qualified carriers to help increase market share as well as
achieve higher levels of customer satisfaction. Qualified carriers therefore
must meet more stringent criteria relating to employee, equipment, facility
capability, and system compatibility with the highest performance and
pricing consistency. This is supported by the findings of Carbone and Gouvernal’s (2007) study conducted with maritime experts, which shows that
“selecting the key logistics service providers” and “establishing long-term
relationships with customers” are vital for container shipping lines to
achieve a higher degree of supply chain integration. Evangelista (2005) also
pointed out that container shipping lines are increasingly forced to focus
on shippers’ demand first to attract their attention, meet their expectations, and further develop stronger connections with them.
Considering the significance of the containerized trade served by
shipping lines to global economies and the recent changes and severe
competition in maritime transport, it is crucial for container shipping
lines to put more effort into utilizing their logistics service capabilities for
strengthening the long-term relationships with their shippers to attain
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Jang, Marlow, Mitroussi: The Effect of Logistics Service Quality \ 495
their loyalty. However, as compared to other industries, relatively few
studies have been conducted on these relationship issues, which focus on
customer loyalty together with logistics service quality in the container
shipping context. It was noted that many studies in maritime transport have
been conducted to evaluate the importance or satisfaction shippers attached
to their ocean carriers (e.g., Brooks 1990; Kent and Parker 1999; Lu 2003a;
Matear and Gray 1993). From these it is difficult to understand whether they
can continue the business with existing customers as sometimes satisfied
shippers leave for other ocean carriers, while dissatisfied shippers stay with
them for several reasons. In other words, simply satisfying shippers does not
guarantee that they are always loyal. In contrast, in marketing and supply
chain literature, a multitude of studies have been conducted to explore customer loyalty in depth with other important concepts such as relationship
quality and the switching barrier (e.g., Chen and Wang 2009; Davis and Mentzer 2006; Liu, Guo, and Lee 2011; Rauyruen and Miller 2007).
Moreover, the simple investigation would have produced limited
results since other factors may influence customer loyalty, and also there
are different kinds of customer loyalty such as spurious loyalty, which
is different from true loyalty. Therefore, a more complex link needs to
be examined, including factors such as relationship quality, to produce
more sophisticated results. Against this background, the objective of
this research is to identify the effect of logistics service quality in creating customer loyalty through relationship quality for better understanding of the relationship issues in the context of container shipping.
By exploring these concepts simultaneously and also dividing them
into several subconstructs, more complexity is added in understanding of ocean carrier–shipper relationships. It is important to provide
empirical evidence to improve container liners’ understanding of the
relationship issues with their shippers. The findings can also be utilized
to segment the shipper groups according to their loyalty types so that
their logistics service capabilities are strategically managed for each
group and strong relationships are pursued with shippers who are more
loyal to them.
The rest of this article consists of four sections. The next section
provides a review of the literature on key concepts to produce research
hypotheses. We then describe the methodological context, including data
collection, analysis methods, sample, and measurement of variables. The
discussion of results and findings of the survey follows. Conclusions drawn
from the analyses and strategic implications for container liner shipping
companies are discussed in the final section.
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Theoretical Background
Fundamentals and Measurement of Logistics Service Quality
In the context of maritime transport, as global competition has intensified
over the past decades, container shipping lines are facing more sophisticated
demands and increased expectations from shippers. According to Gibson,
Sink, and Mundy (1993), shippers’ transport management has shifted from
selecting different carriers for each service and/or route to negotiating
with fewer carriers that provide a wide range of services under long-term
relationships. In addition, the primary value sought by shippers has been
diverted from “price” to “service quality.” Baird (2003) indicated that shippers
actually demand a total value-added service package instead of one or two
services from carriers. Consequently, integrated logistics services provided
by container shipping lines have become highly significant when selecting
carriers.
To understand how the logistics service has been examined in maritime
transport-related studies, 23 studies published since 1990 have been examined from three perspectives: 12 studies from a shipper’s perspective,
5 studies from a carrier’s perspective, and 6 studies from both carriers and
shippers’ perspectives to identify their service perception gap (e.g., Brooks
1990; Casaca and Marlow 2005; Lu 2003a, 2003b). From this comprehensive
review, “prompt response to problems and complaint” was discovered to be
used most frequently in maritime transport studies, followed by “on-time
pick-up and delivery” and “knowledge and courtesy of sales personnel.”
However, it should be noted that service priorities differ between shippers depending on the nature of their cargoes and businesses. Shippers’
requirements are also changing over time. As such, even though a number
of empirical studies have been conducted to select and evaluate logistics
service in maritime transport, each shows different results.
In an effort to evaluate logistics service, marketing tools using customer
perceptions of provider performance have begun to be applied in logistics
research (Stank, Goldsby, and Vickery 1999). Logistics service quality (LSQ)
is an instrument to measure customer perceptions of the value created
for them by logistics services. Logistics service can be used to create customer and supplier value through service performance, influence satisfaction and customer loyalty, and increase market share (Daugherty, Stank,
and Ellinger 1998). In addition, the importance of two aspects of logistics
service has been widely underlined in logistics and supply chain research
(e.g., Davis-Sramek et al. 2009): operational logistics service quality (OLSQ)
and relational logistics service quality (RLSQ).
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Conceptualization and Operationalization of Relationship Quality
Due to the uncertainty stemming from the complexity of the logistics services
provided by container shipping lines, it is of major importance to manage relationships with shippers effectively and maintain high-quality relationships to
reduce uncertainty. Relationship quality was shown to be a better predictor of
customer loyalty than service quality. Furthermore, the intangibility of relationship quality makes it difficult to be duplicated by competitors, thus providing a sustainable competitive advantage to the firm (Roberts, Varki, and Brodie
2003). The concept of relationship quality arises from theory and research in
the field of relationship marketing and has been employed extensively in logistics literature (e.g., Fynes, Voss, and de Búrca 2005; Panayides and So 2005) and
marketing literature to investigate relationships between buyers and sellers
(e.g., Boles, Johnson, and Barksdale 2000) and customers and service personnel/firms (e.g., Hennig-Thurau, Gwinner, and Gremler 2002). Nonetheless, it
was found that, except for a study by Bennett and Gabriel (2001), research has
yet to address the relationship quality in the context of maritime transport.
Relationship quality has generally referred to an overall construct based
on all previous experiences and impressions the customer has had with
the service provider (Hennig-Thurau and Klee 1997). Previous research has
revealed that constructs of relationship quality have a direct impact on customer retention, customer loyalty, and long-term orientation between firms.
In addition, relationship quality plays a vital role in reducing uncertainty in
many service contexts and the potential of service failures customers face
(Qin, Zhao, and Yi 2009). Although there is a lack of consensus in defining
and measuring relationship quality due to the fact that a variety of relationships exist across the range of customers and business markets, relationship quality, similar to service/product quality, can best be operationalized
as a higher-order and multidimensional construct. From the previous studies, it can be inferred that “satisfaction,” “trust,” and “commitment” seem to
be most commonly utilized as dimensions of relationship quality.
Fundamentals and Measurement of Customer Loyalty
It is becoming clear that customer loyalty, mainly with regard to relationships between suppliers and their customers, is a key construct in
marketing and service-related research. While a considerable number
of studies have been conducted, customer loyalty is still too complex
to be defined in a simple way (Jacoby and Kyner 1973). In a review of the
literature, Davis-Sramek, Mentzer, and Stank (2008) argued that more
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than 20 different definitions of customer loyalty had been identified, most
of which were described in terms of the method of measurement, rather
than an explicit explanation of what it is and what it means. For instance,
Maignan, Ferrell, and Hult (1999, 459) delineated it as “the non-random
tendency displayed by a large number of customers to keep buying products from the same firm over time and to associate positive images with the
firm’s products.” Davis-Sramek, Mentzer, and Stank (2008, 785) summarized that “customer loyalty has been defined in terms of repeat purchasing, a positive attitude, long-term commitment, intention to continue the
relationship, expressing positive word-of-mouth, likelihood of not switching, or any combination of these.” This richness of definitions demonstrates that the researcher is required to decide the particular dimensions
of customer loyalty and the way to deal with their interrelatedness when
conducting empirical studies.
Oliver (1999) pointed out that customer loyalty evolves and consists of four
stages: cognitive loyalty, affective loyalty, conative loyalty, and action loyalty.
Each stage has its own vulnerabilities, depending on the nature of the customer’s
commitment. Previous literature suggests that there are different types of customer loyalty, and so the phrase “customer loyalty” may refer to different things.
According to Jones and Sasser (1995), there are two kinds of customer loyalty:
true long-term and false loyalty, of which the latter makes customers stay loyal
when they are not. False loyalty is attributed to various reasons, such as high
switching costs, government regulations that limit competition, proprietary
technology that restricts alternatives, and strong loyalty-promotion programs.
To clarify customers’ true loyalty, previous studies highlighted the fact that attitudinal loyalty should be combined with behavioral loyalty (e.g., Oliver 1999).
Following this stream, thus, customer loyalty is proposed as a composite concept combining both behavioral and attitudinal loyalty in this study.
Research Hypotheses
A conceptual model is developed to determine how and to what extent
logistics service quality (LSQ) and relationship quality (RQ) toward customer
loyalty (CL) are related. By dividing these major concepts into subconstructs
(i.e., LSQ—operational logistics service quality and relational logistics service
quality, RQ—satisfaction, trust and commitment, and CL—attitudinal loyalty
and behavioral loyalty), more sophisticated interrelationships can be explored
for the first time in the container shipping context. Before establishing a
research model, the definition of each construct is described in table 1 to avoid
possible confusion. The assumed relationships among those key concepts are
described in figure 1.
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The theoretical foundations for the relationships depicted in figure 1
are summarized by the following 16 hypotheses. First, logistics service
quality has two dimensions that together can create a strong incentive for
container shipping lines to gain customer loyalty: OLSQ and RLSQ. The
first dimension of logistics service quality, OLSQ is an internal or operations-oriented dimension, involving such features as on-time delivery and
short transit time. The second dimension of logistics service quality, RLSQ
reflects an external or market-oriented dimension, which involves the firm’s
ability to sense and understand customer needs through relationships created by customer service personnel. Stank, Goldsby, and Vickery (1999)
argued that these two constructs are the co-varying antecedents of satisfaction and loyalty. This means firms that tend to be more progressive operationally also tend to be more aware of customer needs and wants, and vice
versa. In this study, the model depicted in figure 1 portrays RLSQ as an
Table 1/Definition of Key Concepts
Operational logistics service
quality
Davis-Sramek, Mentzer, and
Stank 2008, p. 783
The perceptions of logistics
activities performed by service
providers that contribute to
consistent quality, productivity,
and efficiency
Relational logistics service
quality
Davis-Sramek, Mentzer, and
Stank 2008, p. 783
The perceptions of logistics
activities that enhance
service firms’ closeness to
customers, so that firms can
understand customers’ needs
and expectations and develop
processes to fulfill them
Satisfaction
Howard and Sheth 1969, p. 145
The buyer’s cognitive state
of being adequately or
inadequately rewarded for the
sacrifices he has undergone
Trust
Schurr and Ozanne 1985, p.
940
The belief that a partner’s word
or promise is reliable and
a party will fulfill his/her
obligations in the relationship
Commitment
Dwyer, Schurr, and Oh 1987,
p. 19
An implicit or explicit pledge of
relational continuity between
exchange partners
Attitudinal loyalty
Chaudhuri and Holbrook 2001,
p. 83
The level of customers’
psychological attachments and
attitudinal advocacy toward
the service provider/supplier
Behavioral loyalty
Chaudhuri and Holbrook 2001,
p. 83
The willingness of average
business customer to repurchase
the service and the product
of the service provider and to
maintain a relationship with the
service provider/supplier
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Figure 1 Main Research Model
antecedent to OLSQ because RLSQ allows container shipping lines to gain
insights into what shippers need and want. This proposition was supported
in the supplier-buyer relationship studies (e.g., Stank et al. 2003) and
manufacturer-retailer relationship studies (e.g., Davis-Sramek, Mentzer,
and Stank 2008). Therefore, this link will be tested in the container shipping
context.
Hypothesis 1. In the carrier-shipper relationship, RLSQ has a
positive impact on OLSQ.
Empirical studies in marketing, operations, and logistics show
considerable support for links between operational and relational
performance and customer satisfaction (e.g., Davis-Sramek et al. 2009).
In particular, in the logistics and supply chain context, both operational
and relational performance of logistics service was proved to positively
affect customer satisfaction. Satisfaction was revealed as one of the dimensions of relationship quality. Thus, considering only satisfaction with
logistics service quality may provide limited results. In addition, from
the interviews, trust and commitment, unexplored concepts in maritime
transport, were revealed to be important due to the uncertainty in maritime transport. Accordingly, relationship quality comprising satisfaction,
trust, and commitment will be investigated.
Hypothesis 2. In the carrier-shipper relationship, RLSQ has a
positive impact on SA.
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Hypothesis 3. In the carrier-shipper relationship, RLSQ has a
positive impact on TRU.
Hypothesis 4. In the carrier-shipper relationship, RLSQ has a
positive impact on COM.
Hypothesis 5. In the carrier-shipper relationship, OLSQ has a
positive impact on SA.
Hypothesis 6. In the carrier-shipper relationship, OLSQ has a
positive impact on TRU.
Hypothesis 7. In the carrier-shipper relationship, OLSQ has a
positive impact on COM.
When analyzing future intentions, Garbarino and Johnson (1999) argued
that three factors of relationship quality, namely overall customer satisfaction, trust, and commitment, can be separately identified and interacted
differently for different types of customers. Specifically, they hypothesized
that overall customer satisfaction may influence trust and, in turn, trust
may impact commitment. These two paths were confirmed to be significant. These relationships were also supported by Caceres and Paparoidamis’s
study (2007). In addition, trust was emphasized to play a major role in creating commitment in the relationship process (Morgan and Hunt 1994). As
there were no empirical studies which investigate these inter-relationships
in the context of container shipping transport, they will be tested as follows:
Hypothesis 8. In the carrier-shipper relationship, SA has a
positive impact on TRU.
Hypothesis 9. In the carrier-shipper relationship, TRU has a
positive impact on COM.
The main research model includes relationship quality as a
determinant of both aspects of customer loyalty. Customer loyalty is
proposed as a composite concept combining both attitudinal loyalty and
behavioral loyalty to enable maximum explanatory power of the construct
and prevent limited results. Relationship quality together with switching
barriers was proven to have positive effects on customer loyalty (Liu, Guo,
and Lee 2011). Rauyruen and Miller (2007) studied how relationship quality can influence customer loyalty in the business-to-business (B2B) context through two levels of relationship quality (relationship quality with
employees of the supplier and relationship quality with the supplier itself
as a whole) that comprises four different dimensions (i.e., service quality,
satisfaction, trust, and commitment). Given the theory and evidence of
past research on relationship quality and customer loyalty, it is possible
to lay out the following six hypotheses.
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Hypothesis 10. In the carrier-shipper relationship, SA has a
positive impact on AL.
Hypothesis 11. In the carrier-shipper relationship, TRU has a
positive impact on AL.
Hypothesis 12. In the carrier-shipper relationship, COM has a
positive impact on AL.
Hypothesis 13. In the carrier-shipper relationship, SA has a
positive impact on BL.
Hypothesis 14. In the carrier-shipper relationship, TRU has a
positive impact on BL.
Hypothesis 15. In the carrier-shipper relationship, COM has a
positive impact on BL.
Customer loyalty is conceptualized as the causal relationship between
attitudinal and behavioral loyalty. Dick and Basu (1994) viewed customer
loyalty as an attitude-behavior causal relationship in their framework.
Bandyopadhyay and Martell (2007) also proved that behavioral loyalty
is affected by attitudinal loyalty across many brands of the toothpaste
category. In the same way, whether attitudinal loyalty truly leads to the
behavioral loyalty in the container shipping context will be examined.
Hypothesis 16. In the carrier-shipper relationship, AL has a
positive impact on BL.
Methodology
Data Collection and Analysis Methods
This empirical research employs a questionnaire survey for data collection.
By using a survey strategy, a large amount of data from a sizable population can be collected in a highly economical way. To select the sample for a
questionnaire survey, it should be decided whether both types of customers
in container shipping, beneficial cargo owners (BCOs) and freight forwarders, should be included as previous studies affirmed that BCOs and freight
forwarders have different priorities in terms of criteria for not only carrier
selection but also mode choice and port selection (e.g., D’este and Meyrick
1992). In addition, there is a possibility of conflict in transportation channels since various participants have their own objectives. In this regard, it
was determined to involve only one customer type.
Based on both the literature review and semi-structured interviews,
freight forwarders, the intermediaries as both the decision-makers and
the buyers in maritime transport, are selected as the survey samples. This
is because, compared to shippers, they are more service-oriented in their
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decision making (D’este and Meyrick 1992), and have more expertise and
experience over wider range of traffic (Tongzon 2002). Murphy and Daley
(2001) described freight forwarders as trade specialists capable of providing
a variety of functions to facilitate the movement of shipments. Most studies
demonstrate that direct shippers or carriers were mainly employed as a sample in maritime transport–related studies, but intermediaries, particularly
freight forwarders who play a central role in choosing transportation, were
relatively ignored in the marketplace. Martin and Thomas (2001) noted that
container shipping lines will continue to regard freight forwarders as their
key customer base. To analyze the data gathered, structural equation modeling (SEM) is considered as the most appropriate analytical tool. SEM gives
a deeper understanding of the causal relationships of multiple constructs
in the conceptual model as applied in logistics studies, such as Vickery
et al. (2004) and Lin et al. (2005).
Sample
Ocean freight forwarders in South Korea were the samples of the questionnaire survey. The number of freight forwarders varies slightly with different
organizations and different materials. This may be attributed to the fact
that the characteristics of relatively easy entry and exit from this industry
by many small- and medium-sized freight forwarders make it difficult to
estimate the exact number of freight forwarders. This was supported by our
semi-structured interviews and the previous studies by Bird and Bland (1988)
and Sakar (2010). Considering this, nonprobability sampling was deemed to
be appropriate for the current study. Specifically, convenience and purposive sampling were selected for the present study. Convenience sampling
has been chosen due to the easy accessibility and proximity to the researcher,
and purposive sampling because it uses the knowledge and experience of
the researcher to obtain a representative sample of the population based on
the researcher’s evaluation. A total of 1,017 ocean freight forwarders were
selected from the 2011 Maritime and Logistics Information Directory published
by the Korea Shipping Gazette as a sample for the empirical research.
The questionnaire survey was conducted over one month (February
2011). The five-page Korean language questionnaire, accompanied by a cover
letter, a letter of recommendation, and a postage-paid return envelope,
was mailed to the potential respondents. Table 2 illustrates the response
rate of the mail survey. The total response rate was 23.21 percent (227/978),
which is higher than those of the previous empirical studies. To check any
potential nonresponse bias, the nonresponse bias was estimated using
procedures recommended by Armstrong and Overton (1977) and Lambert
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Table 2/Questionnaire Response Rate
Total
1
Number distributed
1,017
2
Nondeliverable
39
3
Effectively delivered (1 − 2)
978
4
Total responses
233
5
Discard
6
Effective questionnaires (4 − 5)
7
Response rate
6
227
23.21%
and Harrington (1990). The last quartile of respondents was assumed to be
most similar to nonrespondents as their replies took the longest time and
most effort to obtain. The responses given by the last quartile were compared with those provided by the first quartile, and the results show that a
nonresponse bias is not a concern in this study.
The characteristics of the respondents were analyzed by identifying
their position, and work experience in the ocean freight forwarding industry and in the current firm, as revealed in table 3. The analysis demonstrates
a variety of demographic backgrounds among the respondents, and significant variance in response was also noted.
Even though it was proved that almost half of the respondents have
a lower position in their firm and fewer than six years of work experience both in the industry and the current firm, it would be appropriate
to include them in this present study as they are the people who directly
deal with liner shipping companies. This implies that they had sufficient
business experiences with the liner shipping companies that allow them to
provide reliable and accurate answer to the survey questions. Tables 4 and 5
summarize the results of eight questions relating to the respondents’ firms.
Significant variance in responses of their firm information was identified,
suggesting that the sample is representative of the population.
Measures
The measurement items for evaluating logistics service quality, relationship
quality, and customer loyalty were mainly adopted from prior research and
supplemented with a result of the qualitative interviews. A comprehensive
review of the literature and interviews with practitioners were used to
ensure the accuracy and validity of the questionnaire instrument. Each
variable was measured using a five-point Likert scale, where 1 corresponds
to “strongly disagree” and 5 “strongly agree.” The final measurement items
employed in this study are presented in the appendix.
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Table 3/Overall Profile of Survey Respondents
Total
Respondent
Variable
Category
Frequency
Percentage
Cumulative
1–3
55
24.2%
24.2%
4–6
56
24.7
48.9
7–9
33
14.5
63.4
10–12
41
18.1
81.5
13–15
20
8.8
90.3
16–18
6
2.6
93.0
19–21
10
4.4
97.4
22–24
4
1.8
99.1
25–27
1
0.4
99.6
28–30
–
–
100.0
Work experience
in the industry
(years)
Mean: 8.03
S.D.: 5.80
31–33
1
0.4
227
100.0%
1–3
92
40.5%
40.5%
4–6
74
32.6
73.1
Sum
Work experience
in the company
(years)
Mean: 5.18
S.D.: 4.04
7–9
31
13.7
86.8
10–12
19
8.4
95.2
13–15
6
2.6
97.8
16–18
2
0.9
98.7
19–21
1
0.4
99.1
22–24
1
0.4
99.6
100.0
25–27
1
0.4
227
100.0%
Vice president or
above
13
5.7%
5.7%
Director/vice
director
11
4.8
10.6
General manager
22
9.7
20.3
Assistant manager/manager
49
21.6
41.9
Section manager/
supervisor/chief
79
34.8
76.7
Staff
51
22.5
99.1
Other
2
0.9
100.0
227
100.0%
Sum
Respondents’
position
Sum
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506 / TRANSPORTATION JOURNAL™
Table 4/Overall Profile of Survey Respondents’ Firms (1)
Total
Respondents’
Firm Variable
Category
Company age
(years)
Percentage
a
Cumulative
Fewer than 5
years
22
9.8%
9.8%a
5–10 years
50
22.3a
32.1a
11–15 years
45
20.1a
52.2a
44
19.6
a
71.9a
a
89.3a
16–20 years
21–25 years
39
17.4
More than 25
years
24
10.7a
Missing
3
100.0a
227
100.0%a
Local firm
199
88.8%a
88.8%a
Foreign-owned
firm
21
9.4a
98.2a
Foreign-local firm
3
1.3a
99.6a
1
a
Sum
Ownership
pattern
Frequency
Other
Missing
Fewer than 5
19
8.4%
8.4%
5–10
17
7.5
15.9
11–20
43
18.9
34.8
21–40
28
12.3
47.1
41–50
15
6.6
53.7
51–100
26
11.5
65.2
100.0
More than 100
79
34.8
227
100.0
Less than 3
35
17.3%a
17.3%a
3–5
57
28.2a
45.5a
27
a
13.4
58.9a
11–15
11
5.4
a
64.4a
16–20
22
10.9a
6–10
21–25
1
75.2a
0.5
a
75.7a
a
81.2a
26–30
11
5.4
More than 30
38
18.8a
Missing
Sum
3
100.0a
Sum
Starting capital
invested in the
company (100
million Korean
Won)
100.0a
227
Sum
Full-time
employees
0.4
100.0a
25
227
100.0a
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Jang, Marlow, Mitroussi: The Effect of Logistics Service Quality \ 507
Total
Respondents’
Firm Variable
Company
location
Category
Frequency
Percentage
Cumulative
Seoul
105
46.5%a
46.5%a
Busan
97
42.9a
89.4a
Incheon
Daegu
0.4a
96.0a
2.7a
98.7a
3
a
Yes
1.3
100.0a
1
227
100.0a
126
57.0a
a
No
95
43.0
Missing
6
100.0a
57.0a
100.0a
Sum
227
11–20%
8
4.0a
4.0a
21–30%
25
12.4a
16.3a
31–40%
23
11.4a
27.7a
34
16.8
a
44.6a
a
62.9a
41–50%
51–60%
37
18.3
61–70%
36
17.8a
80.7a
22
10.9
a
91.6a
14
6.9
a
91–100%
3
1.5a
Missing
25
Sum
227
81–90%
a
95.6a
1
71–80%
Sum
0.9
6
Missing
The percentage
of the business
with the primary
liner shipping
company
in terms of
container
volumes
94.7a
a
Gwangju
Sum
Sum
2
5.3
Gyeonggi-do
Gyeongsang-do
Service contracts/
agreements
12
a
98.5a
100.0a
100.0a
Valid percent allowing for missing data.
Results of Analyses
Perceptions on Logistics Service Quality, Relationship Quality, and
Customer Loyalty
Table 6 demonstrates that all the mean values of the 14 items for logistics
service quality were above 3.0. For the nine-item scale used to measure
relationship quality, the overall mean of each subconstruct is 3.54 for satisfaction, 3.39 for trust, and 3.6 for commitment. The overall mean of relationship quality, 3.51, suggests that the respondents assess their relationship
quality with their primary liner shipping company positively. In addition,
the overall mean of behavioral loyalty (mean = 3.7) is revealed to be higher
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508 / TRANSPORTATION JOURNAL™
Table 5/Overall Profile of Survey Respondents’ Firms (2)
Total
Respondents’
Firm Variable
Major service
routes
Category
Frequency
Percentage
Cases (%)
North America
87
13.6
38.5%
Europe
101
15.8
44.7
Middle East
50
7.8
22.1
Central and
South America
43
6.7
19.0
Oceania
35
5.5
15.5
Southeast Asia
113
17.7
50.0
Japan
73
11.4
32.3
China
113
17.7
50.0
Russia
14
2.2
6.2
Africa
10
1.6
4.4
Other
Sum
1
0.2
0.4
640
100.0
283.2
than that of attitudinal loyalty (mean = 3.34). From this, it can be inferred
that the respondents tend to be dedicated to their primary liner shipping
company behaviorally rather than being emotionally attached to them.
Measurement Analysis
The data was analyzed in accordance with a two-step method where the
measurement model is first evaluated separately from the full structural
equation model. The measurement models are tested using confirmatory
factor analysis (CFA) by means of Analysis of Moment Structures (AMOS) to
confirm construct unidimensionality, reliability, and validity (see table 7).
The relations between the observed variables and the underlying variables
were postulated a priori based on both previous theoretical and empirical
studies and semi-structured interviews.
According to the analysis, there were some low standardized regression weights, indicating inappropriate variables. As a result, six items that
had regression weights of lower than 0.50 were deleted from LSQ , two items
from RQ , and four items from CL. Within this analysis, both theoretical and
statistical considerations were incorporated as advised by Anderson and Gerbing (1988). The adjusted x2(x2/df) and other goodness-of-fit statistics in table 7
indicate that the model achieved a good fit to the observed data, thus satisfying the conditions of unidimensionality. All standardized regression weights
are greater than 0.60 and the critical ratios are significant p = 0.001, except for
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Jang, Marlow, Mitroussi: The Effect of Logistics Service Quality \ 509
Table 6/Descriptive Statistics for Key Concepts
Response scale (%)
Construct
Operational
logistics
service
quality
Relational
logistics
service
quality
Satisfaction
Trust
Commitment
Attitudinal
loyalty
Behavioral
loyalty
Indicators
(1)
(2)
(3)
(4)
(5)
Mean
SD
OLSQ01
0.4%
11.0%
34.4%
44.5%
9.7%
3.52
0.83
OLSQ02
0.4
6.6
44.1
41.0
7.9
3.49
0.76
OLSQ03
0.9
4.4
40.5
45.8
8.4
3.56
0.75
OLSQ04
0.4
9.3
34.4
48.5
7.5
3.53
0.78
OLSQ05
0.0
5.7
29.1
56.8
8.4
3.68
0.71
OLSQ06
0.4
9.4
42.2
42.6
5.4
3.43
0.76
OLSQ07
0.9
10.1
41.9
39.6
7.5
3.43
0.81
RLSQ08
0.9
16.7
37.9
40.5
4.0
3.30
0.82
RLSQ09
0.9
7.1
45.3
42.7
4.0
3.42
0.72
RLSQ10
0.9
9.7
37.4
43.2
8.8
3.49
0.82
RLSQ11
0.4
6.7
36.0
48.0
8.9
3.58
0.76
RLSQ12
2.6
16.3
45.4
29.5
6.2
3.20
0.88
RLSQ13
3.5
17.6
38.8
35.7
4.4
3.20
0.90
RLSQ14
0.9
10.1
40.5
43.2
5.3
3.42
0.78
SA01
0.0
4.9
40.3
48.7
6.2
3.56
0.69
SA02
0.4
4.4
45.1
44.7
5.3
3.50
0.69
SA03
0.0
5.3
40.5
47.1
7.0
3.56
0.70
TRU04
0.4
7.9
42.3
41.9
7.5
3.48
0.77
TRU05
0.0
5.7
44.9
44.1
5.3
3.49
0.69
TRU06
1.8
14.5
48.9
31.7
3.1
3.20
0.79
COM07
0.4
3.5
32.6
52.4
11.0
3.70
0.73
COM08
1.3
6.6
38.8
45.8
7.5
3.52
0.78
COM09
0.4
7.9
34.4
48.5
8.8
3.57
0.78
AL01
1.3
11.6
44.0
38.7
4.4
3.33
0.79
AL02
1.3
13.2
52.4
30.8
2.2
3.19
0.74
AL03
1.3
5.8
40.6
46.4
5.8
3.50
0.75
BL04
0.0
3.1
34.2
54.7
8.0
3.68
0.67
BL05
0.4
2.2
29.2
56.2
11.9
3.77
0.70
BL06
0.0
7.1
33.6
47.3
11.9
3.64
0.78
Note: OLSQ = Operational logistics service quality; RLSQ = Relational logistics service quality, SA =
Satisfaction; TRU = Trust; COM = Commitment, AL = Attitudinal loyalty; BL = Behavioral loyalty
one item in TRU construct with loading of 0.55 and one item in AL construct
with loading of 0.59, both of which, however, are significant at the 0.001 significant level and do not appear to harm the overall model fit. This demonstrates
adequate convergent validity. The values of Cronbach’s alpha (> 0.7), composite
reliability (> 0.7), average variance extracted (AVE > 0.5) indicate that construct
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510 / TRANSPORTATION JOURNAL™
Table 7/CFA Results for Measurement Model
Construct
OLSQ
RLSQ
SA
TRU
COM
AL
BL
Indicators
Standardized
Regression
Weight
Critical Ratio
(t-value)
Composite
Reliability
Average
Variance
Extracted
Cronbach’s
Alpha
OLSQ01
0.62
8.83***
0.91
0.58
0.85
OLSQ02
0.64
9.04***
OLSQ03
0.75
10.64***
OLSQ04
0.72
10.18***
OLSQ05
0.63
8.92***
OLSQ06
0.60
8.54***
OLSQ07
0.73
–
RLSQ08
0.70
10.52***
0.92
0.61
0.88
RLSQ09
0.68
10.10***
RLSQ10
0.75
11.26***
RLSQ11
0.75
11.20***
RLSQ12
0.76
–
RLSQ13
0.67
9.91***
0.93
0.82
0.86
0.88
0.71
0.77
0.90
0.75
0.83
0.82
0.60
0.72
0.88
0.72
0.79
RLSQ14
0.69
10.35***
SA01
0.83
13.80***
SA02
0.83
13.72***
SA03
0.81
–
TRU04
0.84
–
TRU05
0.86
14.33***
TRU06
0.55
8.37***
COM07
0.79
12.53***
COM08
0.84
–
COM09
0.74
11.70***
AL01
0.59
7.90***
AL02
0.60
8.08***
AL03
0.83
–
BL04
0.85
10.16***
BL05
0.73
9.40***
BL06
0.69
–
Overall Goodness-of-fit Indices
x2/df = 1.63
CFI = 0.94; TLI = 0.93; SRMR = 0.03
RMSEA = 0.05
validity was confirmed for the measurement models. Table 8 displays that the
correlation coefficients among the latent constructs do not exceed the cutoff
point of 0.85 advised by Kline (2005). The comparison between AVE and correlations also provides evidence of discriminant validity between the constructs.
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Jang, Marlow, Mitroussi: The Effect of Logistics Service Quality \ 511
Hypotheses Tests
The full hypothesized structural model is illustrated in figure 2. In this
figure, the error terms associated with observed and latent variables are
omitted for simplicity. Table 9 presents the parameter estimates of the full
structural model and exhibits the SEM results of the hypotheses testing.
The fit indices (x2/df = 1.67; CFI = 0.93; TLI = 0.92; SRMR = 0.03; RMSEA = 0.05)
are acceptable, implying that the estimated model has achieved a good fit.
According to table 9, all paths specified in the hypothesized model were
found to be statistically significant at different significance levels, except
for the following five hypothesized paths: OLSQ→TRU, RLSQ→COM,
SA→AL, SA→BL, and TRU→BL. In addition, notably, there is one negative relationship between TRU→BL, which is the opposite result to the
one hypothesized. This negative relationship may be because while theoretically trust can be assumed to have a positive effect on behavioral loyalty,
this result came from statistical data analysis and also is not very strong. In
addition, it can be assumed that trust can be related to behavioral loyalty
through other constructs, but not directly.
In terms of hypothesis testing, first, H1, H2, and H3 were supported
by the significant paths, RLSQ→OLSQ , RLSQ→SA, and RLSQ→TRU.
H5 and H7 were also supported by the significant paths, OLSQ→SA and
OLSQ→COM. In addition, H8 (SA→TRU) and H9 (TRU→COM) were
accepted. However, the hypothesis H10 (SA→AL) was not supported
among three paths between the construct of RQ and AL. While H15 was supported by the significant path, COM→BL, the other two paths, SA→BL and
TRU→BL, were rejected. Furthermore, H16 (AL→BL) was also accepted by
the significant path, AL→BL. In conclusion, 11 significant paths were identified in the structural model.
Table 8/Comparing AVE and Interconstruct Correlations
OLSQ
RLSQ
SA
TRU
COM
AL
BL
0.58
0.47
0.49
0.44
0.36
0.20
0.22
RLSQ
0.69
0.61
0.42
0.49
0.36
0.31
0.17
SA
0.70
0.65
0.82
0.50
0.40
0.28
0.27
OLSQ
TRU
0.67
0.70
0.71
0.71
0.36
0.30
0.25
COM
0.60
0.60
0.63
0.60
0.75
0.29
0.37
AL
0.45
0.56
0.53
0.55
0.54
0.60
0.32
BL
0.47
0.42
0.52
0.50
0.61
0.57
0.72
Note: Diagonal elements are AVE; off-diagonal elements are correlations between constructs;
above-diagonal elements are the squared correlations estimates.
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Discussion of the Hypotheses
It was found that the impact of relational logistics service quality on
operational logistics service quality is positive and very strong from the
analysis (H1: Supported). While there are different studies which did not
specify this link (e.g., Davis-Sramek et al. 2009), assumed a moderating
effect between each of them (e.g., Zhao and Stank 2003), and assumed a
co-varying relationship between them (e.g., Stank, Goldsby, and Vickery
1999), it is generally supported that relational performance is an antecedent to operational performance by Mentzer, Flint, and Hult (2001),
Stank et al. (2003), and Davis-Sramek, Mentzer, and Stank (2008). From
this result, it can be inferred that once a container shipping line has
identified a shipper’s needs, it can better focus on the operational means
of meeting them.
Recently, firms have attempted to increase logistics service offering to
improve their competitive positioning, which is often evaluated in terms
of customer satisfaction with the services/products provided. The positive impact of logistics services on satisfying customers was consistently
recognized by Innis and La Londe (1994), Daugherty, Stank, and Ellinger
(1998), Mentzer, Flint, and Hult (2001), Mentzer, Myers, and Cheung
(2004), Saura et al. (2008), and Bienstock et al. (2008). Moreover, together
with these studies, several studies including Stank, Goldsby, and Vickery
(1999), Stank et al. (2003), Zhao and Stank (2003), Davis-Sramek, Mentzer,
Figure 2 The Structural Model and Significant Coefficients (Solid Lines)
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04/10/13 8:33 AM
Jang, Marlow, Mitroussi: The Effect of Logistics Service Quality \ 513
Table 9 The Results of the Hypotheses Test
Standardized Regression Weight
(Regression
Weight)
Critical ratios
(t-value)
Results of test
RLSQ→OLSQ
0.80 (0.82)
8.51***
Supported
H2
RLSQ→SA
0.29 (0.30)
2.66**
Supported
H3
RLSQ→TRU
0.28 (0.33)
2.72**
Supported
H4
RLSQ→COM
0.20 (0.20)
1.56
Not supported
H5
OLSQ→SA
0.58 (0.59)
5.04***
Supported
H6
OLSQ→TRU
0.10 (0.11)
0.79
Not supported
H7
OLSQ→COM
0.32 (0.32)
2.42*
Supported
H8
SA→TRU
0.56 (0.62)
4.93***
Supported
H9
TRU→COM
0.32 (0.29)
2.59**
Supported
H10
SA→AL
0.13 (0.13)
0.77
Not supported
H11
TRU→AL
0.36 (0.35)
1.98*
Supported
H12
COM→AL
0.34 (0.36)
3.05**
Supported
Hypotheses
H1
H13
SA→BL
0.01 (0.01)
0.07
Not supported
H14
TRU→BL
-0.08 (-0.07)
- 0.44
Not Supported
H15
COM→BL
0.38 (0.37)
3.34***
Supported
H16
AL→BL
0.57 (0.51)
4.17***
Supported
Note: ***p < 0.001, **p < 0.01, *p < 0.05
and Stank (2008), and Davis-Sramek et al. (2009) emphasized that not
only operational logistics service quality (delivering the right services/
products, with the right amount, to the right place, and at the right time),
but also relational logistics service quality (how contact personnel create
logistics service quality) have a positive effect on satisfaction. However,
satisfaction is only one subconstruct of relationship quality. Therefore,
it is difficult to judge the relationship quality comprehensively through
satisfaction alone. For this reason, this research has included trust and
commitment.
From the analysis, it was identified that operational logistics service
quality has a positive effect on satisfaction and commitment, but has no
influence on trust. This indicates that it is difficult to ensure shippers’ confidence in the carriers with only operational logistics service quality. On the
other hand, relational logistics service quality has a positive effect on satisfaction and trust, but has no influence on commitment. This also shows
that even though shippers are satisfied and further trust carriers with a high
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514 / TRANSPORTATION JOURNAL™
level of relational logistics service quality, they do not commit themselves
to those carriers. In conclusion, it was confirmed that H2, H3, H5, and H7
are supported from this research. In addition, the operational logistics service quality and relational logistics service quality of container shipping
lines are discovered to play a different role in creating the relationship quality between shippers and carriers. Based on these results, it can be argued
that satisfaction, trust, and commitment should all be considered to generate inclusive and sophisticated results.
Garbarino and Johnson (1999) demonstrated the different functions of
satisfaction, trust, and commitment in customer relationships. The interrelationships between them were also identified in marketing literature
(e.g., Caceres and Paparoidamis 2007; Morgan and Hunt 1994). Thus, it is
noteworthy to confirm these associations in maritime transport. Similar
to previous studies, it was verified that shippers’ satisfaction has a positive
effect on their trust and, in turn, shippers’ trust influences their commitment (H8 and H9: supported). These results also sustain the specification of
relationship quality into three subconstructs.
H10 through H15 dealt with the link between relationship quality and
attitudinal loyalty or behavioral loyalty. First, only trust and commitment
were revealed to have a positive impact on attitudinal loyalty and only
commitment was discovered to have a positive effect on behavioral loyalty.
In other words, shippers satisfied with container shipping lines show no
attitudinal or behavioral loyalty. Once shippers can trust their carriers,
they only become loyal attitudinally, but not behaviorally. In contrast, if
shippers become committed to their carriers, they turn into loyal customers both attitudinally and behaviorally. From this, it can be concluded
that simply satisfying shippers cannot guarantee gaining their loyalty.
This emphasizes the importance of securing their trust and commitment
together with their satisfaction. Furthermore, trust is only limited to shippers’ attitudinal loyalty, but it does not lead to creating behavioral loyalty.
Even though extant literature reveals contradictory positions on commitment and loyalty, this study follows the stream that these two represent
distinct concepts as supported by Beatty and Kahle (1988), and concludes
that only commitment can result in both attitudinal and behavioral loyalty
(H11, H12, and H15: supported).
Finally, it was highlighted by Dick and Basu (1994) that loyalty consists of both attitudinal and behavioral aspects in which Bandyopadhyay
and Martell (2007) discovered that attitudinal loyalty was positively related
to behavioral loyalty. In this research, this positive relationship was also
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confirmed, and consequently, it can be concluded that by attaining attitudinal loyalty, it is possible to make shippers behaviorally loyal to container
shipping lines (H16: supported).
Conclusions and Implications
The results of the study provide container shipping lines with a holistic understanding of the role of logistics service quality, relationship
quality, and customer loyalty in the container shipping context. The
findings also offer various contributions to theory and practice. First,
this study contributes to the body of knowledge in relationship marketing, logistics/SCM, and maritime transport in the business-to-business
context by expanding the existing studies on the relationship among
logistics service quality, relationship quality, and the customer loyalty.
It was pointed out that there were few studies on these relationships in
maritime transport studies as compared to other studies undertaken in
the logistics and supply chain research context (e.g., Daugherty, Stank,
and Ellinger 1998; Davis-Sramek, Mentzer, and Stank 2008; Rauyruen
and Miller 2007). Second, more complexity is added when these relationships are simultaneously explored in a holistic manner. Thus, the
results of this study provide a new research framework that offers a
more in-depth insight into logistics service quality, relationship quality, and customer loyalty. It should also be noted that all constructs
employed in this study are conceptualized to be multidimensional and
while some existing measures were used, several new measures were
added from the qualitative study.
Third, to the authors’ knowledge, this research specifically
contributes to the maritime transport studies in that it has scrutinized
the carrier-shipper relationships thoroughly focusing on customer loyalty for the first time. Previous studies on maritime transport were more
likely to identify carrier selection criteria or shippers’ satisfaction or the
importance they attached to service attributes (e.g., Casaca and Marlow
2005; Kent and Parker 1999). This may be attributed to the fact that companies regarded satisfaction as a customer’s future purchase intentions
as argued by Drucker (2004). However, this study shows that simply satisfying customers does not indicate that firms will retain their customers,
despite the importance of the critical role of satisfaction. Furthermore,
this study considers relationship quality together to explore the customer loyalty phenomenon in the container shipping context. Fourth,
considering the contributions to the industry, container shipping lines
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516 / TRANSPORTATION JOURNAL™
can utilize the results of this study to segment the shippers according to their loyalty types. In other words, understanding the loyalty
relationships with logistics services helps carriers to distinguish their
shippers’ segments and further decide the level of logistics services to
each group. It is almost impossible to satisfy every customer or market
segment, given that different shippers have different needs and desires.
For that reason, it is vital to manage their logistics service capabilities
strategically to each group as well as pursue stronger relationships with
shippers who are more loyal to them.
Despite the significant contributions mentioned above, this study
has several limitations. First, there is the generalizability issue in this
study since the data collected came from a limited sample within one
country, and within a limited time frame. Second, as this study has the
confirmatory purposes on the research model and hypotheses developed
before collecting the data, further relationships between the constructs
cannot be considered. Nor can the dimensions and structures of the constructs be modified or observed measures be added to. More important,
customer loyalty should be investigated to anticipate the market share as
supported by Daugherty, Stank, and Ellinger (1998) and Stank et al. (2003).
Additionally, the moderating effect can be analyzed on the basis of other
factors, including switching barriers, a firm’s characteristics, or cultural
differences.
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Appendix
Category
Logistics service quality
(LSQ)
Relationship
quality (RQ)
Customer
loyalty (CL)
Construct
Observation Variable (Indicator)
Operational
logistics
service
quality
(OLSQ)
OLSQ01: Flexible pricing policy in meeting competitor’s rates
OLSQ02: Short transit time
OLSQ03: Satisfactory service frequency
OLSQ04: The ability to provide the correct type and quantity of
equipment (e.g. container and chassis) consistently
OLSQ05: Good reputation and image
OLSQ06: Satisfactory terminal services
OLSQ07: Cargo space availability
Relational
logistics
service
quality
(RLSQ)
RLSQ08: Sales personnel’s willingness to respond promptly to
problems and complaints
RLSQ09: Sales personnel’s knowledgeability
RLSQ10: Sales personnel’s courtesy
RLSQ11: Sales personnel’s ability to develop a long-term
relationship
RLSQ12: Sales personnel’s personal attention and effort to
understand the individual situation
RLSQ13: Sales personnel’s frequency of calls
RLSQ14: Sales personnel’s effort to establish and respond to the
needs
Satisfaction
(SA)
SA01: Contentment of doing business with my primary liner
shipping company
SA02: Feeling that the decision to do business with my primary
liner shipping company was a wise decision
SA03: Satisfaction with the performance of my primary liner
shipping company
Trust (TRU)
TRU04: Belief that my primary liner shipping company keeps its
promises
TRU05: Belief that my primary liner shipping company is sincere
and trustworthy
TRU06: Belief that my primary liner shipping company does not
withhold certain pieces of critical information that might have
affected the decision-making
Commitment (COM)
COM07: Preference to do business with my primary liner shipping
company rather than with others
COM08: Willingness to put in more effort to do business with my
primary liner shipping company than others
COM09: Wish to remain a customer of my primary liner shipping
company as the relationship with them is enjoyable
Attitudinal
loyalty (AL)
AL01: Thinking that it is necessity as much as desire that keeps me
involved with my primary liner shipping company
AL02: Feeling that I am attached emotionally to my primary liner
shipping company
AL03: Feeling that my primary liner shipping company deserves my
loyalty to it
Behavioral
loyalty (BL)
BL04: Willingness to continue to do business with my primary liner
shipping company in the next year
BL05: Intention to do more business with my primary liner shipping
company, all things being equal
BL06: Thinking that my primary liner shipping company is my first
choice for liner shipping services
Note
This article is a revised version of an earlier paper presented at the 2012 International
Association of Maritime Economists–IAME, Taiwan, September 6–8.
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