An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports

Abstract

Ports play a critical role in the economy of many countries and regions. Failure or unreliability of port services can significantly influence port customers—shipping lines and cargo owners—and result in their dissatisfaction. However, what constitutes port service quality (PSQ) and its influence on the satisfaction of port customers has not been well investigated in the literature. Therefore, this study investigates the concept of PSQ and its influence on customer satisfaction in the case of Korean container ports. Following a literature review, a conceptual model of PSQ and its influence on customer satisfaction is proposed. The model was validated through a survey of 313 members of the Korean Port Logistics Association (KPLA). Partial least squares structural equation modeling (PLS-SEM) was conducted to confirm the PSQ dimensions and to examine their relationship with customer satisfaction using SmartPLS 3.2.1 software. PSQ is found to be a five-factor construct, and its management, and image and social responsibility factors have significant positive effects on customer satisfaction. In addition to its academic contribution, this study also contributes to management practices because port managers can use the PSQ scale to measure their customers’ satisfaction and justify investments in the quality management of port services.

Full text

The Asian Journal of Shipping and Logistics 31(4) (2015) 437-447
Contents lists available at ScienceDirect
The Asian Journal of Shipping and Logistics
Journal homepage: www.elsevier.com/locate/ajsl
http://dx.doi.org/10.1016/j.ajsl.2015.08.008
2092-5212/© 2015 The Korean Association of Shipping and Logistics, Inc. Production
and hosting by Elsevier B.V. All rights reserved.
An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean
Container Ports
Gi Tae YEOa , Vinh V. THAIb, Sae Yeon ROHc
a Professor, Incheon National University, Korea, E-mail:ktyeo@incheon.ac.kr (First Author)
bSenior Lecturer, RMIT University, Australia, E-mail:vinh_thai_2000@yahoo.com (Corresponding Author)
cResearch Fellow, Nanyang Technological University, Singapore, E-mail:SROH@ntu.edu.sg
A R T I C L E I N F O
Article history:
Received 9 July 2015
Received in revised form 30 November 2015
Accepted 1 December 2015
Keywords:
Port Service Quality
Customer Satisfaction
Container Port
Korea
A B S T R A C T
Ports play a critical role in the economy of many countries and regions. Failure or unreliability of
port services can significantly influence port customers—shipping lines and cargo owners—and
result in their dissatisfaction. However, what constitutes port service quality (PSQ) and its influence
on the satisfaction of port customers has not been well investigated in the literature. Therefore, this
study investigates the concept of PSQ and its influence on customer satisfaction in the case of
Korean container ports. Following a literature review, a conceptual model of PSQ and its influence
on customer satisfaction is proposed. The model was validated through a survey of 313 members of
the Korean Port Logistics Association (KPLA). Partial least squares structural equation modeling
(PLS-SEM) was conducted to confirm the PSQ dimensions and to examine their relationship with
customer satisfaction using SmartPLS 3.2.1 software. PSQ is found to be a five-factor construct,
and its management, and image and social responsibility factors have significant positive effects on
customer satisfaction. In addition to its academic contribution, this study also contributes to
management practices because port managers can use the PSQ scale to measure their customers’
satisfaction and justify investments in the quality management of port services.
Copyright © 2015 The Korean Association of Shipping and Logistics, Inc. Production and hosting by
Elsevier B.V. All rights reserved. Peer review under responsibility of the Korean Association of Shipping
and Logistics, Inc.
1. Introduction
Ports are well known as playing an important role in multimodal
transport systems and international supply chains, apart from their
traditional role as clusters of economic activities. Ports engage in various
activities: loading/discharging cargo onto/from vessels; providing value-
added services such as labeling, packaging, cross-docking, and others; and
acting as warehouse and distribution centers (World Bank, 2007). Ports
add more value to shipments that are in the port area by further integrating
themselves into value chains. Many ports are increasingly being perceived
as integrated and inseparable nodes in their customers’ supply chains.
Ports play a critical role in the effective and efficient management of

438 An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports
product and information flow in the supply chain because these transport
nodes are important and indispensable. Any failure or unreliability in
ports’ services results in unhappy customers as a result of the disruption in
the smooth movement of these flows in the next stage of the supply chain.
This role of ports in the supply chain is increasingly being viewed in both
the academic literature and management practices.
Existing studies have researched the importance of ports in regional and
national economies and their changing roles in the context of logistics and
supply chain management. The literature relating to the measurement of
port efficiency and port choice in the logistics and supply chain context is
also well developed. Despite the aforementioned importance, what
constitutes port service quality (PSQ) and its effect on port customers’
satisfaction has yet to be well investigated. In this paper, we aim to
address these gaps in the literature by proposing and validating a
conceptual model of PSQ and by examining the causal relationship
between PSQ and customer satisfaction. This paper is organized as
follows. First, a literature review is provided, followed by the proposed
conceptual PSQ model. The research methodology is described next,
followed by analyses and discussions on the findings of this study. Finally,
concluding comments, including implications for academia and
management and future research directions, are outlined.
2. Literature Review
2.1. Service Quality and Port Service Quality
Throughout the literature, a universal approach to the definition of the
concept of quality and its associated dimensions has never been a reality,
even though the research agenda has existed for quite some time.
Although quality is an exclusive concept, overwhelming studies exist on
the subject of quality in the service industry with both concurring and
conflicting views (e.g. Anderson and Sullivan, 1993; Bolton and Drew,
1991; Gupta and Zeithanml, 2006; Maarten et al. 2015; Rust et al. 1999;
Van Doorn and Verhoef, 2008). The SERVQUAL model is one of the
initial and most commonly used tools to measure service quality
(Parasurman et al., 1988) and consists of five dimensions: tangibles,
reliability, responsiveness, assurance, and empathy. Hopkins et al. (1993)
evaluated cognitive service quality in the logistics sector using
SERVQUAL model, and identified the meeting of customer expectations
being the fundamental requirement for customer satisfaction. However,
various scholars criticized the SERVQUAL model despite its pervasive
application. For example, Cronin and Taylor (1992) proposed the
SERVPERF model, which considers only actual performance and, thus,
eliminates the expectation component present in the SERVQUAL model.
Another common critique of the SERVQUAL model was that its
dimensions lack dimensional stability (Carman, 1990), which is limited to
applications in the five service industries (Parasuraman et al., 1985, 1988).
Many researchers who questioned whether the SERVQUAL model can be
applied to all service industries as a generic scale suggested that industry-
specific measurement determinants be required to provide more accurate
measurements (Babakus and Boller, 1992; Caro and Garcia, 2007;
Ladhari, 2008; Van Dkyke et al., 1997).
In addition, the SERVQUAL model arguably neglects the service
encounter outcome because it was designed to only address the service
delivery process (Baker and Lam, 1993). Grönroos (1984) developed a
model consisting of the three dimensions of technical quality, functional
quality, and corporate image, which effectively consider the service
outcome component when measuring the quality of a service. Technical
quality describes how the customer obtains the service and functional
quality describes the service achieved in the end. Meanwhile, corporate
image influences the perception of quality in a positive, neutral, or
negative manner. Lehtinen and Lehtinen (1991) emphasized the
importance of this attribute by proposing a model including the three
dimensions of physical quality, interactive quality, and corporate quality.
In the most recent literature, SERVQUAL has been pointed out as not
being a universal tool to measure service quality in specific contexts, such
as in B2B services (Benaziü and Došen (2012), corporate banking (Guo et
al., 2008), supply chains (Seth et al., 2006), and others. Further studies on
various service industries using the conceptualization and measurement
instrument of SERVQUAL also indicated that it is not applicable for all
industries or in all socio-cultural and economic environments. Indeed,
various authors also found that the dimensions of service quality indicated
in SERVQUAL are either too many or too few for the specific context of
their research.
Despite numerous studies on service quality measurement in various
industries, little research has been conducted in the maritime industry in
general and ports in particular. Rather than focusing on detailed service
quality measurements, most maritime-related literature researched the
issue of carrier and port selection. Among a few relevant studies in this
respect, Ugboma et al., (2004) found that all five SERVQUAL
dimensions were valid. Meanwhile, efficiency, timeliness, and security
were found by Lopez and Poole (1998) to contribute to the quality of port
services. Brady and Cronin (2001) identified the aspects of service quality
including “rational quality”, “result quality”, and “physical environmental
quality”. This study further developed sub-factors of the port service
quality, for example, the “relational quality” includes port sales, customer
relations and distribution network, while the “exogenous quality”
indicates the volume of cargo flows, hinterland, and the size of free trade
zones (FTZ) (Cho et al. 2010). Ha (2003) identified a group of port service
quality factors, including “ready information availability of port-related
activities,” “port location,” “port turnaround time,” “facilities available,”
“port management,” “port costs,” and “customer convenience.” On
another note, separate measurement tools of port service quality
comprising “endogenous quality,” “exogenous quality,” and “relational
quality” were also developed (Cho et al., 2010). They explored the effects
of port service quality on customer satisfaction, loyalty and referral
intentions. A few subsequent studies focusing on the efficiency and
service quality of Asian ports (Lee, 2000; Song and Yeo, 2004) have
utilized these frameworks and evaluated customers’ reaction to various
factors of service quality (Cho et al. 2010). However, these studies
neglected the critical dimension of social responsibility, which can
enhance or damage the image or reputation of organizations and, hence,
the perceived quality of their services. This fact is particularly important
in the context that many ports around the world are now attempting to
implement green port initiatives.
Thai (2008) developed and validated a measurement model (ROPMIS)
to explore the concept of service quality in maritime transport. This model
consists of the following six dimensions: resources, outcomes, process,
management, and image and social responsibility. This model
incorporated newly developed elements, such as management-, image-,
and social responsibility-related quality dimensions, on the basis of a
comprehensive review of various service quality dimensions and factors
in previous studies. Compared with the SERVQUAL model, the ROPMIS
model is more applicable to the maritime industry because it incorporates
the image and social responsibility aspects that are critically important in

An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports 439
this industry. The author suggested that these factors could be revised for
specific sub-sectors in the maritime industry, such as ports, even though
the model was supposed to be generally applicable to maritime transport
service. The current research adopted this model and revised the
operationalized measurement items specific to the port sector.
2.2. Service Quality and Customer Satisfaction
Essentially, customer satisfaction is the sense that customers get when
they experience service that fulfills or surpasses their expectation.
Primarily in marketing, satisfaction is defined as the global evaluation of
relationship fulfillment by a firm (Dwyer and Oh, 1987) or the positively
affected state resulting from the assessment of a firm’s working
relationship (Farrelly and Quester, 2005; Gaski and Nevin, 1985).
Satisfaction is also one of the most important elements to explain any type
of relationship among participants (Sanzo et al., 2003) and a consumer’s
fulfillment response (Oliver, 1997).
Generally, customer satisfaction is known as an outcome of service
quality, which means that it is related to the quality of the products or
services provided to the customer in a positive manner. The level of
customer satisfaction is also believed to be enhanced, along with an
increased level of perceived quality of the product or service. In particular,
customer satisfaction is considered to be an intrinsic variable that explains
returning customers and their post-behaviors of purchasing products and
services (Oliver, 1980; Lee, 2000; Szymanski, and Henard, 2001).
Numerous studies in many service sectors confirmed the positive
relationship between service quality and customer satisfaction (Brady and
Robertson, 2001; Cronin and Taylor, 1994; Parasuraman et al., 1994) with
some conflicting evidence (Rosen and Suprenant, 1998). The few studies
in the transportation sector, including aviation (Anderson et al., 2009) and
high-speed railways (Cao and Chen, 2011), revealed a positive
relationship between service quality and customer satisfaction.
Nevertheless, research in the maritime sector on this relationship,
particularly in the context of ports, is scant and the subject deserves
further investigation.
2.3. Social Responsibility and Customer Satisfaction
It is nowadays believed that socially responsible firms, which
contribute both economically and ethically to the society and local
communities they serve, are better positioned to grow in terms of
reputation and revenues (Drobetz et al. 2014). The benefits corporate
social responsibility (CSR) for companies, including increased profits,
customer loyalty, trust, positive brand attitude and combating negative
publicity, are well-documented (e.g. Brown and Dacin, 1997; Drumwright,
1996; Maignan and Ferrell, 2001; Murray and Vogel, 1997; McDonald
and Rundle-Thiele, 2008; Sen and Bhattacharya, 2001; Sen et al. 2006).
Maignan and Ferrell (2004) identified a number of studies on CSR
programs’ positive effects on customers. Studies by Barone et al. (2000),
Berger and Kanetkar (1995), and Creyer and Ross (1997) established that
consumers are willing to actively support companies committed to cause-
related marketing, environmentally-friendly practices, or ethics. Murray
and Vogel (1997) investigated the effect on consumers of combined
programs of socially responsible business practices, cause promotions,
community volunteering, corporate social marketing, as well as pro-active
economic factors and consumer protection. Lua and Bhattacharya (2006)
identified a direct positive path between CSR and customer satisfaction on
Fortune 500 companies. However, they have also identified instances
where CSR did not always lead to customer satisfaction indicating that
there is a need to better understand the relationship between satisfaction
and CSR.
Homburg et al. (2013) organized the differentiate studies from supplier
versus customer perspectives and distinguishes findings from business-to-
consumer (B2C) versus business-to-business (B2B) contexts. In all B2C
context, it is evident that they have established a link between a firm’s
CSR activities and important consumer outcomes such as firm and
product evaluations, satisfaction, and loyalty (e.g. Bhattacharya and Sen,
2004; Brown and Dacin, 1997; Lichtenstein et al. 2004). CSR also is an
issue in B2B industries because these companies are often at the forefront
of engaging in CSR (Homburg et al. 2013). Levy (2010) also claimed that
CSR programs are vital for B2B companies. Existing research in the B2B
realm has typically focused on how firms implement CSR issues within
their business operations (Homburg et al. 2013). Researcher from a B2B
customer perspective has examined antecedents of a firm’s CSR
orientation by studying “purchasing social responsibility (e.g. Carter and
Jennings, 2004). Vaaland et al. (2008) indicated that whereas CSR is an
issue in relation to all business partners, the empirical studies focus on
consumer marketing and consumer responses, thereby excluding B2B
marketing.
The implementation of employee safety and enhancement of working
conditions as well as supporting community projects may result in
improvements to firm’s social performance and reputation (Gimenez et al.
2012). Relationship with the local community to promote positive image
and building trust through various efforts from port authorities have been
implemented (Saengsupavanich et al. 2009; Puig et al. 2015). Port
authorities take statutory duties to meet social and environmental
obligations whilst embedding CSR concept in port management systems
and undertaking routine operations and development projects
commercially (Pettit, 2008). Increased CSR reporting enhances firms’
transparency and lowers information costs on the part of investors,
potentially leading to positive financial effects (Drobetz et al. 2014).
Environmental management can reduce the negative effects of their
activities on the natural environment and enhance firm’s competitive
positions (Sharivastava, 1995). Success in addressing environmental
management could improve a firm’s image (Hick 2000) and provide new
opportunities for firms to enhance their capabilities (Hansmann and
Caludia, 2001).
3. Methodology
3.1. Conceptual Framework and Measures
As previously mentioned, we adopt the ROPMIS conceptual model that
was developed and validated by Thai (2008) to measure port service
quality. Because a close relationship exists between an organization’s
social responsibility profile and its perceived image in the market and
society, the image and social responsibility dimensions are combined into
a new dimension of image and social responsibility in our revised PSQ
model. Each PSQ dimension is measured using a number of variables that
are revised from the ROPMIS model to suit the specific context of ports.
For example, the measurement item “physical infrastructures” under the
resource-related dimension in the original ROPMIS model has been
expanded to include “physical infrastructures such as berths, yards,
warehouses, distribution centers and hinterland connection networks,”
which are the critical physical assets of a port. Meanwhile, customer

440 An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports
satisfaction is a well-developed construct in the existing literature, as are
measurement items used to assess the customer’s satisfaction of the
equipment and facilities, satisfaction of services, and overall satisfaction
(Anderson et al., 2009; Pantouvakis, 2010). Additionally, once customers
are satisfied with a service, the logical inference made is that they will
probably use the service and refer it to others (Cao and Chen, 2011). For
these reasons, these measurement items are also included in the customer
satisfaction construct.
The conceptual framework for this research is presented in Fig. 1and a
summary of measures is presented in Table 1.
Fig. 1. Conceptual framework of PSQ and customer satisfaction
Table 1
Constructs and measurement items
Research variables & measurement items
Code Author(s)
Resource- related PSQ
Revised
from Thai
(2008)
The port that we are using always has available
equipment and facilities to meet our requirements RESOU1
The equipment and facilities of the port that we are
using are modern and always function properly RESOU2
The port that we are using has strong and stable
financial stability RESOU3
The port that we are using has excellent shipment
track and trace capability RESOU4
The port that we are using excellent physical
infrastructure such as berths, yards, warehouses,
distribution centers, and hinterland connection
networks
RESOU5
Outcome- related PSQ
The port that we are using always provide fast
service OUTCO1
The port that we are using always provide service
in a reliable manner OUTCO2
The port we are using always provide service in a
consistent manner OUTCO3
The port that we are using always ensure safety
and security to our ships/shipments OUTCO4
The port that we are using always produce error-
free invoice and related documents OUTCO5
The port that we are using always offers
competitive price of service OUTCO6
The port that we are suing can always meet our
service requirements anytime and anywhere we
want
OUTCO7
Process-related PSQ
The staff in the port that we are using always
demonstrate professional attitude and behavior in
meeting our requirements
PROCE1
The staff in the port that we are using always
respond quickly to our enquiries and request PROCE2
The staff in the port that we are using always
demonstrate good knowledge our needs and
requirements
PROCE3
The level of ICT applications in customer service
at the port that we are using is comprehensive PROCE4
Management-related PSQ
The level of ICT applications in port operations
and management at the port that we are using is
comprehensive
MANAG1
The port that we are using demonstrates high level
of efficiency in operations and management MANAG2
The management in the port that w are using
always demonstrate good knowledge and
competence, including incident-handling capability
MANAG3
The management in the port that we are using
always demonstrate good understanding of our
needs and requirements
MANAG4
The port that we are using always collect our
feedback about their services and reflect on their
improvement
MANAG5
The port that we are using continuously improve
their customer-oriented operation and management
processes
MANAG6
Image and Social Responsibility-related PSQ
The port that we are suing demonstrates good
relationship with other ports and land transport
service providers
IMAGE1
The port that we are using possesses positive
reputation for reliability in the market IMAGE2
The port that we are using always emphasized on
operations and work safety IMAGE3
The port that we are using demonstrates good
record of operations and work safety IMAGE4
The port that we are using fulfill good social
responsibility to their employees and other
stakeholders
IMAGE5
The port that we are using always emphasizes on
environmentally responsible operations IMAGE6
The port that we are using has in place the
environmental management system IMAGE7
Customer Satisfaction
Anderson
et al.
(2009),
Pantouva
kis
(2010),
Cao and
Chen
(2011)
Overall, we are satisfied with the facilities,
equipment and other infrastructures of the port that
we are using
SATIS1
Overall, we are satisfied with the management and
employees of the port that we are using SATIS2
Overall, we are satisfied with the service quality of
the port that we are using SATIS3
We will refer service of the port that we are using
to our business partners SATIS4
We will continue using services of the port we are
using SATIS5

An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports 441
3.2. Research Hypotheses
Because the relationship between service quality and customer
satisfaction is under-researched in the port sector, this study examines
how PSQ as a five-dimensional construct affects the satisfaction of port
customers. Hence, the following five hypotheses were developed:
H1: Resources-related PSQ positively influences customer satisfaction.
H2: Outcomes-related PSQ positively influences customer satisfaction.
H3: Process-related PSQ positively influences customer satisfaction.
H4: Management-related PSQ positively influences customer satisfaction.
H5: Image- and social responsibility-related PSQ positively influences
customer satisfaction.
3.3. Sampling and Data Collection
The survey was selected as the method of data collection in this study.
The mailing list covers all categories of the port’s customers, such as
shipping lines and cargo owners or their representatives, such as freight
forwarders or logistics service providing companies. The sampling frame
was constructed from Korea Port Logistics Association (KPLA) members,
and the total sampling approach was taken. The mailing list comprised
313 members of the KPLA. In South Korea, members of the KPLA
manage 28 ports. Questionnaires were posted to each company in the
mailing list from December 2012 to January 2013. The questionnaire,
which was preceded by a cover letter on the letterhead of the authors’
institutions, employed both fixed-alternative and opened-ended response
questions. It consisted of two sections in which respondents were asked to
indicate their attitude toward statements describing the service quality
factors of the port that their company uses most of the time in Korea, and
their satisfaction with that port’s services. The respondent’s attitude is
measured using a five-point Likert scale, ranging from 1 representing
“strongly disagree” to 5 representing “strongly agree.” The second section
asked demographic questions, such as the respondent’s business sector,
their designation, and work experience. Before distribution, the
questionnaire was pre-tested with a small group of academics and
shipping companies to ensure the language clarity and face validity of the
measurement constructs. The questionnaire survey was then administered
by post.
A follow-up request was sent two weeks after the initial mailing. By the
cut-off date, 103 questionnaires were returned from the KPLA. Among
the 103 responses obtained, 99 valid replies were used for further analysis.
The valid response rate was 31.6%. As for years in business, 38%.4 of the
respondents in the sample started their work between five and 10 years
ago, 23.2% engaged in business between 11 and 15 years ago, 17.2%
between 16 and 20 years ago, and 21.2% were in business for more than
20 years.
4. Analysis and Findings
4.1 Partial Least Square Structural Equation Modeling (PLS-SEM)
Analysis
The measurement of the five-factor PSQ and customer satisfaction
model was evaluated for overall fit using tests of reliability and
convergent and discriminant validity through partial least square structural
equation modeling (PLS-SEM) and SmartPLS 3.2.1 software. PLS is a
useful tool for structural equation modeling in applied research projects,
particularly with limited participants and skewed data distribution (Wong,
2011). PLS-SEM becomes a good analysis tool in the following situations
(Hwang et al. 2010; Wong, 2010):
1) The sample size is small;
2) Applications have little available theory;
3) Predictive accuracy is paramount; and,
4) The correct model specification cannot be ensured.
It is important to note that PLS-SEM is not appropriate for all kinds of
statistical analysis. There exist some weaknesses of PLS-SEM, including
(Wong, 2010):
1) High-valued structured path coefficients are needed if the sample
size is small;
2) Problem of multicollinearity if not handled well;
3) Since arrows are always single headed, it cannot model
undirected correlation;
4) A potential lack of complete consistency in scores on latent
variables may result in biased component estimation, loadings
and path coefficients; and,
5) It may create large mean square errors in the estimation of path
coefficient loading.
In spite of these limitations, PLS is useful for structural equation
modeling in applied research projects especially when there are limited
participants and that the data distribution is skewed (Wong, 2011). PLS-
SEM has been deployed in many fields, such as behavioral sciences (e.g.
Bass et al. 2003), marketing (e.g. Henseler et al. 2009), organization
(Sosik et al. 2009), management information system (e.g. Chin et al.
2003), and business strategy (e.g. Hulland, 1999). As mentioned in the
previous section, the fact that the current research had limited participants
(99 respondents) and was conducted only in Korean Container Ports
justifies the use of PLS-SEM research tool.
The first run of the PLS-SEM did not result in satisfactory construct
validity results. Hence, several PLS-SEM runs were subsequently
conducted to derive the best reliability and validity results. Through this
process, nine items which had indicator reliability lower than 0.4 were
deleted (Hulland, 1999): OUTCO5 (0.321), OUTCO6 (0.3663), IMAGE3
(0.164), IMAGE5 (0.370), IMAGE7 (0.336), MANAG3 (0.385),
MANAG4 (0.349), PROCE2 (0.266), and PROCE3 (0.373). The PSQ
measurement model and customer satisfaction based on PLS-SEM are
depicted in Table 2 and Fig. 2.
Table 2
Constructs and measurement items
Constructs Variables Loadings
Indicator
Reliability AVE CR
Resources
RESOU1 .658 .433
.531 .849
RESOU2 .782 .612
RESOU3 .674 .454
RESOU4 .818 .669
RESOU5 .697 .486
Outcomes
OUTCO1 .735 .540
.575 .871 OUTCO2 .804 .646
OUTCO3 .787 .619

442 An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports
OUTCO4 .716 .513
OUTCO7 .746 .557
Process PROCE1 .794 .630 .699 .822
PROCE4 .876 .767
Management
MANAG1 .780 .608
.534 .820
MANAG2 .776 .602
MANAG5 .652 .425
MANAG6 .707 .500
Image &
Social
Responsibility
IMAGE1 .738 .545
.518 .811
IMAGE2 .755 .570
IMAGE4 .737 .543
IMAGE6 .644 .415
Satisfaction
SATIS1 .750 .563
.606 .885
SATIS2 .766 .587
SATIS3 .810 .656
SATIS4 .763 .582
SATIS5 .801 .641
Fig. 2. PSQ and customer satisfaction model based on PLS-SEM
The results of PLS-SEM showed that the indicator reliability of all item
loadings are significant, higher than the recommended minimum
acceptable value of 0.40, and close to the preferred level of 0.7 (Hulland,
1999). The T-statistics result of the outer model is presented in Table 3,
which shows that all T-statistics are larger than 1.96 and statistically
significant (p = .000). Therefore, the outer model loadings could be said to
be highly significant.
Table 3
Model summary – T-Statistics of outer loadings
Estimates Standard Error T
IMAGE1ĸImage & Social
Responsibility .738 .071 10.410
IMAGE2ĸImage & Social
Responsibility .755 .047 16.094
IMAGE4ĸImage & Social
Responsibility .737 .064 11.519
IMAGE6ĸImage & Social
Responsibility .644 .107 6.002
MANAG1ĸManagement .780 .041 19.231
MANAG2ĸManagement .776 .060 13.038
MANAG5ĸManagement .652 .080 8.136
MANAG6ĸManagement .707 .102 6.940
OUTCO1ĸ Outcomes .735 .058 12.698
OUTCO2ĸ Outcomes .804 .034 23.372
OUTCO3ĸ Outcomes .787 .047 16.639
OUTCO4ĸ Outcomes .716 .061 11.707
OUTCO7ĸ Outcomes .746 .082 9.112
PROCE1ĸ Process .794 .062 12.868
PROCE4ĸ Process .876 .040 22.097
RESOU1ĸ Resources .658 .085 7.716
RESOU2ĸ Resources .782 .055 14.187
RESOU3ĸ Resources .674 .089 7.613
RESOU4ĸ Resources .818 .052 15.852
RESOU5ĸ Resources .697 .125 5.571
SATIS1ĸ Satisfaction .750 .055 13.569
SATIS2ĸ Satisfaction .766 .057 13.335
SATIS3ĸ Satisfaction .810 .035 22.878
SATIS4ĸ Satisfaction .763 .041 18.633
SATIS5ĸ Satisfaction .801 .042 18.838
To further confirm the validity and reliability of the PSQ and customer
satisfaction model, their convergent and discriminant validities were also
examined using composite reliability (CR), average variance extracted
(AVE), and the square root of AVE (Bagozzi and Yi, 1998; Fornell and
Larcker, 1981). Traditionally, Cronbach’s alpha is used to measure
internal consistency reliability; however, it tends to provide a conservative
measurement in PLS-SEM (Wong, 2011). Therefore, prior studies
suggested the use of composite reliability as a replacement (Bagozzi and
Yi, 1998; Hair et al., 2012). As Table 2 indicates, these values are larger
than 0.7, indicating a high level of internal consistency reliability among
all reflective constructs. For convergent validity, each construct’s AVE is
evaluated. Again, Table 2 shows that all of the AVE values are larger than
the acceptable threshold of 0.5, confirming convergent validity.
For the discriminant validity test, Fornell and Larcker (1981) suggested
that the square root of AVE in each construct can be used to establish
discriminant validity if the value is larger than other correlation values
among the constructs. Table 4 illustrates the square root of AVE in bold
on its diagonal along with the correlations between the constructs. For
example, in the current study, the construct of management’s AVE is
found to be 0.534 (from Table 2), making its square root 0.731. This
number is larger than the correlation values in the management column
(0.556, 0.659, 0.692, and 0.649) and larger than the values in the
management row (0.604). A similar observation is also made for the
image and social responsibility, outcomes, process, resources, and
satisfaction constructs. These results indicate that the discriminant validity
is well established.

An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports 443
Table 4
Results of discriminant validity test (Fornell-Larcker criterion)
Image &
Social
Responsibi
lity
Management Outcome Process Resource Satisfact
ion
Image &
Social
Responsibilit
y
.720
Management .604 .731
Outcomes .522 .556
.758
Process .508 .659 .512
.836
Resources .538 .692 .660 .551 .729
Satisfaction .583 .649 .448 .532 .471 .778
4.2 The Impacts of PSQ on Customer Satisfaction
The bootstrapping process of the PLS-SEM analysis was applied to
generate T-statistics to significance test the model at the 95% confidence
level, with customer satisfaction as the dependent variable and the
extracted five factors of the PSQ model as predictors. The results of this
analysis are summarized in Table 5 and Table 6. The multiple R (R= 0701)
shows that ample correlation exists between the dependent variable
(customer satisfaction) and five predictors, and this correlation is
statistically significant (p = .000). With the exception of the resource
factor, the other four predictors have a positive influence on customer
satisfaction, but this causal relationship is only statistically significant for
two predictors, namely image and social responsibility, and management,
but is not for outcomes and process. Specifically, the management-related
PSQ factor has the strongest positive influence on customer satisfaction (ȕ
= .419), followed by the image and social responsibility-related PSQ (ȕ
= .276). Hence, the first three hypotheses are rejected, whereas the
remaining two hypotheses are supported at 5% significance level and the
path coefficient will be significant if the T-statistics is larger than 1.96.
Table 5
Model summary-coefficient of determination
Model R R Square Adjusted R Square Standard Error
C .701a .491 .463 .064
Note: a. Predictors: (Constant), RESOU, OUTCO, PROCE, MANAG, IMAGE
Dependent Variable: SATIS
Table 6
Model summary-T-Statistics of path coefficients (Inner model)
Estimates S.E.a t-value p-value Results
Resources ĺ
Satisfaction -.073 .114 .645 .519† Not
supported
Outcomes ĺ
Satisfaction .053 .097 .550 .583† Not
supported
Process ĺ Satisfaction .128 .091 1.409 .159† Not
supported
Management ĺ
Satisfaction .419 .141 2.98 .003* Supported
Image & Social
Responsibility ĺ
Satisfaction
.276 .104 2.641 .009* Supported
Note: a = S.E. is an estimate of the standard error of the covariance
* = Significant at p< .05 (t >±1.96)
† = Non-significant
It is interesting to note that the influence of resources- (0.645), process-
(1.409), and outcomes-related (0.55) PSQ factors is not significant at 5%
significance level. Therefore, the port’s customer satisfaction can be
enhanced by factors beyond the provision of physical equipment and
facilities, staff knowledge, and core port service outcomes to be delivered
to customers. Such enhancement of customer enhancement is particularly
important for port managers to note because the port customers’
satisfaction cannot be taken for granted simply on the basis of the
provision of adequate and good quality physical equipment and facilities.
The finding that the management-related PSQ factor has the strongest
positive influence on customer satisfaction is in line with the results from
Thai (2008), who found that this factor was also perceived as the most
important in delivering service quality in maritime transport. In this
research, customers are found to care about the level of ICT applications
in port operations, demonstrating a high level of efficiency in operations
and management, improving their services and considering customers’
feedback, and continuously improving customer-oriented operations and
management processes. With loadings of 0.780, 0.776, 0.652, and 0.707,
respectively, they are good indicators of the management-related PSQ.
Specifically, in this case, Korean port customers emphasized that the
application of ICT in customer service and port operations and
management would lead to a more positive impact on their satisfaction.
This finding is somewhat expected given the high level of ICT
applications in all aspects of businesses in Korea.
Moreover, it is noted that the image and social responsibility (t = 2.641,
p <0.05) PSQ factor also has a significant positive impact on customer
satisfaction, implying that an emphasis on the port’s corporate social
performance is an important service quality enabler. Additionally, with a
loading of 0.644, environmental responsible operations imply the
importance of environmental management—one of the important factors
for enhancing customer satisfaction.
5. Conclusion
5.1 Discussion and Implications
This paper contributes to the existing literature by exploring the
composition of the port service quality construct and investigating its
impact on customer satisfaction in the port sector. Port service quality was
found to be a five-dimensional construct consisting of items related to
resources, outcomes, process, management, and image and social
responsibility. This PSQ construct covers all aspects of port service
delivery. Additionally, along with services internally within the port and
externally between the port and its customers, social responsibility is
included—a particularly important aspect in the maritime industry. As
such, this finding is unique for the port sector because it introduced and
empirically validated the measurement of port service quality. The PSQ
model in this research lays the foundation for further studies on the
management and delivery of service quality in the port sector, a topic that
has not been well studied in the literature.
This study also confirmed that delivering a quality port service has a
significant positive impact on customer satisfaction. Specifically, the
management-related PSQ factor, followed by the image and social
responsibility-related PSQ factor, have the strongest influence on
customer satisfaction, whereas the impact from the resources-, outcomes-,
and process-related factors was not statistically significant. Other
literature also supports the result that port service quality has a significant
impact on customer satisfaction (Dehghan et al. 2012; Polyorat and

444 An Analysis of Port Service Quality and Customer Satisfaction: The Case of Korean Container Ports
Sophonsiri, 2010). By supporting the essentials in the relational marketing
domain, this research contributes to confirming the critical causal
relationship between the two dimensions of service quality and customer
satisfaction. In addition, this research also highlights the importance of
managing port service quality from an all-around approach and not by
simply focusing on the port’s physical resources.
Meaningful implications for port managers are also derived from the
findings of this study. First, port managers could understand the
dimensions and aspects of port service quality that customers (e.g.,
shipping lines, cargo owners, and their representatives) appreciate and
request through the current study of the validated PSQ model. Port
managers may use this understanding to develop a standard measurement
scale of PSQ to measure customer satisfaction. For long-term orientation,
applying the PSQ model could facilitate a comparison and benchmarking
between ports and enhance their service quality performance. Second,
because this study confirmed that service quality has a significant positive
impact on customer satisfaction, port managers should invest in the
quality of their port services because doing so is critical to retaining
existing customers and to attract potential customers. On a further note,
port managers should also pay attention to corporate social responsibility
and environmental management activities that could help enhance the
port’s image and, thus, perceived service quality and satisfaction in the
eyes of their customers.
5.2 Conclusion, Limitations, and Future Research
The impact of service quality on customer satisfaction in the port sector
lacks research. The results from this study reveal that PSQ is a construct of five
factors, and that enhanced PSQ positively influences customer satisfaction.
In terms of contributing to knowledge and practical applications, the
current study helps enhance the understanding of service quality as a
relational marketing tool, particularly in the context of the port sector.
The main purpose of the current study is not to evaluate the service
quality of each and every port but to explore the relationship between port
service quality and customer satisfaction in Korean container ports.
However, one of several limitations of this study is the generalization of
its findings. First, this study examined the port sector in Korea; hence, its
external validity could be limited. Researchers could overcome this
constraint by expanding future similar studies to cross-industry levels.
Although the current research questionnaire was focused on container
ports, it could be modified for other sectors in the port industry for future
research. Second, this study was only conducted at the preliminary level
of investigating the relationship between port service quality and customer
satisfaction, in which the latter was treated as a single construct. Hence,
future research which examines the influence of port service quality on
other important aspects, such as customer loyalty, word of mouth
intention, and repurchase intention, would be useful in view of customer
satisfaction as a mediating variable. Last but not least, future research
should adopt a larger sample size so that further tests on the relationship
between port service quality and customer satisfaction can be conducted
on various respondent’s groups.
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