Effect of the container terminal characteristics on performanc
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Vítor Caldeirinha1, J. Augusto Felício2 and Andreia Dionísio3
1 Centro de Estudos de Gestão; School of Economics and Management (ISEG)
2 School of Economics and Management; Technical University of Lisbon
3 CEFAGE-UÉ, Évora University (UÉ)
Effect of the container terminal characteristics on performance
, J. Augusto Felício2
and Andreia Dionísio3
Centro de Estudos de Gestão; School of Economics and Management (ISEG); Rua Miguel Lupi, 20.
1249-078 Lisbon; Email address: email@example.com; Phone: +351 - 213 970264
School of Economics and Management; Technical University of Lisbon; Rua Miguel Lupi, 20. 1249-078
Lisbon; Email address: firstname.lastname@example.org; Phone: +351 - 2133970264
CEFAGE-UÉ, Évora University (UÉ); Largo dos Colegiais, 2, Évora; ; Email address: email@example.com;
phone:+351 - 266740892
This paper focuses on the port and container terminal characteristics and evaluates its
contribution to performance measured by the efficiency, productivity, activity and customer
satisfaction. A Structural Equation Modeling (SEM) methodology was developed to determine
which factors are characteristics of a port and container terminal. A questionnaire was
submitted to senior managers of companies currently operating in twelve container terminals,
both Portuguese and Spanish, and 122 validated answers were obtained. The results confirm
the influence of the port and terminal characteristics on the terminal container performance
through of the efficiency, productivity, and activity level and customer satisfaction
Keywords: port characteristics, container terminal, terminal performance
Containerization and intermodality have experienced a fast growth in the last decades along
with hinterland expansion and increasingly transhipment operations held at intermediate
ports at crossing points of trade lanes. The container traffic growth has caused a great demand
for port container terminals and port congestion problems, demanding more investments in
new terminals, as well as it has intensified intra and interport competition between terminals.
Many container terminals are competing to become transhipment hubs as major shipping lines
and feeder networks tend to reorganize themselves. Also, the development of inland transport
accessibilities allowed a deeper penetration of ports over hinterlands.
For container cargo shippers and logistic chains, port and container terminal selection is made
according to their location, proximity to/from the market, port charges, freight rates,
turnaround time, cargo value and volume, liner services frequency and trade routes, although
his decision often depends on the overall network service organization and not on the port or
terminal per se (Yap & Notteboom, 2011). Besides the port strategic location, shippers and
shipping companies also look for port service reliability, service quality and lower costs per call,
lower charges and short turnaround times.
Containerization and intermodal transport were determinant for the changes operated in
ports in recent years. According to Cullinane et al. (2004), containerization has stimulated
shipping services globalization through the emergence of alliances and acquisitions in the liner
industry (horizontal integration). Furthermore, intermodality has led to powerful global logistic
door-to-door and other added-value service providers (vertical integration).
Inland transport infrastructures were expanded and large logistic areas were developed
creating interconnected bipolar systems with ports (Dias et al., 2010). Increasingly larger
vessels has not only lowered freight rates per container but, also, by serving a limited number
of major hub and gateway-ports, especially those with deeper inland penetration and wider
feeder connections, has intensified port competition for hinterlands and for main shipping
trade routes. As a result, shipping lines have wield greater bargaining power demanding ports
to have higher performance levels, better service quality and lower costs (Cullinane & Wang,
Container ports are important nodal points in the global logistic networks of containerized
freight transport (Baird, 2006) and those who were able to adapt to its requirements
succeeded in the logistic integration. The ability to provide logistic services has become an
important issue for the port survival, while creating value-added services and meeting
customer needs (Juang & Roe, 2010).
In a competitive environment, the performance of a container terminal is determined by
several factors, such as the market of the region where it is located, the physical and
organizational capacity, the integration in the logistic networks, the level of competition,
maritime and inland accessibilities, the type of handling equipment used at the quay and
parking areas, the liner shipping services and inland networks to which they are connected
(Tongzon & Heng, 2005).
Insufficient knowledge of the relationship between geographic location, physical infrastructure
("hard") and service ("soft") characteristics and the container terminal performance (Estache
et al., 2005) justifies this study. Furthermore, insufficient evaluation of the key determinants of
the container terminal performance, efficiency and productivity (Gonzalez & Tujillo, 2008), the
limited samples size of the determinant factors of port and container terminal performance
studies (Woo et al., 2011, Chang et al., 2008), limitations using structural equation modelling
methodology, only supported in factor analysis and without confirmatory analysis of the
structural model (Woo et al. May 2011, Chang et al. 2008).
This study attempts to understand the importance of port and terminal characteristics in
determining efficiency, productivity, customer satisfaction and activity level of a container
terminal. The objectives of this research are to analyse which characteristics of port and
container terminal are factors with influence on container terminal performance, measured by
the efficiency and productivity, terminal activity, and customer satisfaction. The main
questions addressed in this paper are: to understand why some container terminals are more
successful than others and how to successfully build a new container terminal. Previous
studies have not fully answered these questions with and holistic model.
This paper has been organized in the following way: after the introduction, the theoretical
background and methodology are presented. Then, results obtained are analysed, followed by
discussion and conclusions. Finally, contributions for future knowledge, study limitations and
guidelines for future research are proposed.
2. Theoretical background
Both the economic performance of the nearby region and the proximity to industrial and
urban centres are essential determinants to understand the container terminal performance.
Geographical location is relevant when explaining the container terminal performance. The
terminal selection is largely driven by local economy development, because production and
consumption centres enhance container flows (Tongzon, 2002; Cheon, 2007).
The proximity of a container terminal to the European economic core is largely regarded to
influence performance. The north European ports, in the range of Le Havre-Hamburg have
been serving important and increasingly extended hinterlands, efficiently taking advantage of
economies of scale and, by doing so, they were driven to compete with south ports in their
hinterland regions. Some south European ports emerged as intermediary hubs connecting
other continents with European ports, assuming a transhipment role (Notteboom, 2010).
The proximity to the Mediterranean Sea is an important locational factor of performance
because it is where Asia-Europe global container shipping networks cross, selecting
Mediterranean ports as hub ports, for concentrating cargo flows from the hinterland and from
feeder ports and serving northern European ports, including Atlantic ones (including North
America, South America and Africa ports). Notteboom (2011) refers that the proximity to
major shipping networks is an important factor in the terminal selection decision. The main
hubs tend to have common characteristics, such as excellent nautical accessibility, proximity to
important hinterlands and along main navigation routes or at the crossing points of North-
South and East-West routes, connecting trade flows (Notteboom & Rodrigue, 2009; 2010).
Most port authorities and operators have made significant infrastructure investments in order
to reduce operational costs and improve service quality, which are important factors that
influence terminal performance (Cullinane & Wang, 2009). Furthermore, investments in inland
accesses are very important to expand the hinterland and contribute to improve terminal
performance. Inland accessibility and terminal hinterland are driven by transport costs,
alternative modes, capacity and quality of inland connections and transport service quality, as
well as integration on the main land transport networks or at the crossroads of inland trade
routes. Turner, Windle and Dresner (2004) examined the impact of hinterland and maritime
accessibilities on performance and Gaur (2005) identified factors that affect the terminal
performance, including maritime access and hinterland connectivity.
Intermodality allows the coordination between different logistic service providers and
transport operators, maritime or inland or at the maritime/land interface. Some added value
logistic functions, such as pre-assemblies, preparation and customization, labelling, packaging
and distribution, are performed at major ports and terminals. In order to assure port
integration in logistic networks, many ports provide specialized logistic services within the port
area, which are determinant for container terminal performance.
The reputation of a port and terminal is very important for terminal performance. Cheo (2007)
considered port marketing strategies, including communication and image, as essential to
attract new liner services and traffic. Pando et al (2005), Pardali & Kounoupas (2007) and
Cahoon (2007) examined the importance of marketing tools for port performance, which
includes communication as a way to change the port reputation. Notteboom (2011) identified
several factors related to port demand, including the quality of port services, port reputation
and marketing initiatives of the port community.
Moreover, De Langen (2004) argued that coordination between the active players of both
hinterland network and port is necessary. The port service quality depends on the
performance of many players, including terminal operators, freight forwarders, container
operators and port authority and that influences the overall terminal performance. Though,
the quality of port services such as towage, pilotage and port authority services can
significantly affect the terminals’ choice by shipowners and shippers.
The maritime accessibility limits the vessels’ size and the capacity of the terminal and, thus,
the type and number of quay equipment used per vessel and the terminal width needed.
Therefore, maritime accessibility affects the terminal efficiency by limiting the vessels’ size, the
freight rates per container and quay productivity per ship. As pointed out by Tongzon (2002)
and Wiegmans (2003), the nautical accessibility is a determinant factor of terminal efficiency.
The maritime accessibility defines the markets served and the level of maritime services
provided to port users. The vessel size calling a container terminal has a large influence on the
hierarchical set of shipping lines network that calls the terminal and, consequently, is a key
factor of its performance.
The frequency of vessel calls gives shippers more options and greater flexibility, which are
determinant factors in the terminal selection process and that leads to improved terminal
performance (Tongzon, 2002). Strategic alliances among shipping companies and global logistic
networks (that also include shipping maritime services) shape the network configuration and
the set of container terminals to call (Tongzon & Heng, 2005). That is why the integration in
maritime global supply chains is a critical issue for container terminals, especially in the global
carriers’ networks and global terminal operator’s ones. A terminal served by worldwide liner
shipping networks shows better performance and greater efficiency levels.
As an intermodal link in the logistic networks (Robinson, 2002), ports are facing fierce
competition while trying to satisfy their requirements. Port terminals increasingly seek to
improve service quality and hinterland connectivity in order to meet the logistic network
demands (Notteboom & Winkelmans, 2004). The access to vast hinterlands is regarded as a
key factor of success of European ports (De Langen, 2004). Besides improving the service
quality, ports and terminals should also contribute to improve competitiveness and
performance of the supply chains in which they are integrated (Tongzon et al., 2009). Due to
the intermodal nature of the container transport network, terminals must necessarily be an
efficient and effective connection point between different transportation modes.
Robinson (2002) reported that port choice has become a decision made within the entire
network and therefore the competition is no longer between ports but rather between supply
chains, which calls for a wider approach beyond port and terminal selection criteria. This
means that shippers tend to choose the logistic networks which fulfil their requirements in
terms of costs, transit times, efficient handling, productivity and reliability, connectivity and
interoperability (Tongzon et al., 2009).
The customer focus is a critical issue for container terminal performance, because terminals
need to show flexibility/agility in adapting new requirements and market changes, making the
necessary adjustments to meet increased customer demands. In addition, a well-organized
terminal layout can improve the terminal productivity and capacity and, consequently, affect
performance and service quality, particularly when large vessels call demanding for large space
Panayides and Song (2009) also identified information systems, communication and informal
relations in the supply chain as essential to performance, productivity and competitiveness of
supply chains and port networks. Information and communication systems can improve the
efficiency of supply chain operations contributing to achieve its purposes (Cachon & Fisher,
2000). Furthermore, information sharing is regarded as an effective way to contribute to
improve container terminal integration in the supply chains. It allows companies to improve
safety, reliability in a faster synchronized process with impacts in terms of costs and service
quality (Zhao et al. 2002), because information systems avoid duplication of documents,
maintain data integrity along the transport chain and reduce costs.
The type of terminal manager who take into primary consideration the customers' demands
and their logistic networks requirements and the type of organizational structure adopted are
both key elements that affect all the terminal services. The type of terminal organization, more
or less formal, flexible or rigid, hierarchical or flattened, is crucial for terminal agility in order to
meet not only customer demands, but also inland logistic and shipping networks’ demands. A
flexible container terminal organizational structure is relevant when there is a need to quickly
adapt to customer requirements (Liu et al., 2009).
3.1- Research model and hypotheses
The research model is based on the definition of a global and holistic conceptual model
including different constructs –“hard”, “soft” and port and terminal location – and attempts to
establish a relation between the port and container terminal characteristics and terminal
performance, measured by the efficiency and productivity, activity, and customer satisfaction
Figure 1 – Research model
Continental port location
Regional port location
Terminal maritime services
Logistic integration and
Terminal efficiency and
Based on the theoretical background and research model, the following assumptions are
Hypothesis 1a: The port location at continental level is an important characteristic of a port
and container terminal;
Hypothesis 1b: The port location at regional level is an important characteristic of a port and
Hypothesis 1c: The port inland accessibility is an important characteristic of a port and
Hypothesis 1d: The port dynamics is an important characteristic of a port and container
Hypothesis 1e: The maritime accessibility is an important characteristic of a port and container
Hypothesis 1f: The maritime terminal services are an important characteristic of a port and
Hypothesis 1g: The logistic integration and terminal organizational structure are important
characteristics of a port and container terminal;
Hypothesis 2: The container terminal performance is strongly influenced by port and terminal
Hypothesis 3: The terminal productivity and efficiency are representative measure of
Hypothesis 4: The terminal activity is a representative measure of performance;
Hypothesis 5: The terminal customer satisfaction is a representative measure of performance.
3.2- Constructs and variables
Based on the literature and the results of the exploratory analysis conducted with data
obtained from the survey, the port and terminals characteristics can be narrowed to seven
constructs, namely port location at continental and regional level, inland accessibility, port
dynamics, maritime accessibility, terminal maritime services, logistic integration and terminal
organization (Table 1).
Table 1 – Constructs and variables
Construct Variables Authors
Container flows to/from the hinterland Sharma and Yu, 2009; Acochrane, 2008
Transhipment container traffic Acochrane, 2008; Onut et al., 2011
Terminal productivity Onut et al., 2011; Talley, 2006
Terminal Efficiency Chou, 2010; Acochrane, 2008; Onut et al., 2011; Turner et al.,
2004; Tongzon et al., 2009; Onut et al., 2011; Notteboom et
Shippers’ satisfaction Robinson, 2002; Liu et al., 2009
Shipowners’ satisfaction Liu et al., 2009
Freight forwarder and shipping agents’
Liu et al., 2009; Magala and Sammons, 2008
Distance to the centre of Europe Song and Yeo, 2004; Liu, 1995; Estache et al., 2001
Distance to the Mediterranean East/West
shipping trade lanes
Onut et al., 2011; Notteboom, 2011
Regional port location Distance to production centres Onut et al., 2011
Distance to hinterland markets Chou, 2010
Economic development of the region Chou, 2010; Onut et al., 2011; Zohil and Prijon, 1999, Cheo,
2007; Hung et al., 2010
Land access Rail access Juang and Roe, 2010; Onut et al., 2011; De Langen, 2004
Road access Juang and Roe, 2010; Tongzon, 2002, Wiegmans, 2003
Port integration on inland logistic
Juang and Roe, 2010; Onut et al., 2011; Woo et al., 2011;
Bichou e Gray, 2004
Rail access to dry inland terminals Juang and Roe, 2010; Chang et al., 2008; Bruce et al., 2008;
Tongzon et al., 2009; Panayedes and Song, 2011; Panayedes
and Song, 2009
Port dynamics Container terminal reputation Juang and Roe, 2010; Onut et al., 2011; Chang et al., 2008;
Cheo, 2007; Pando et al., 2005; Pardali and Kounoupas, 2007;
Cahoon and Hecker, 2007
Port reputation Juang e Roe, 2010; Onut et al., 2011; Chang et al., 2008; Cheo,
Port authority dynamics Van Der Horst and De Langen, 2008
Port community dynamics Van Der Horst and De Langen, 2008
Maritime accessibility Quay depth of the container terminal Wang and Cullinane, 2006
Water depth in port access Wang and Cullinane, 2006, Gaur, 2005; Turner et al., 2004
Number of shipping liner services from
the world’s top 10
Song e Yeo, 2004
Number of feeder and short-sea lines Chou, 2010; Veldman et al., 2011; Onut et al., 2011; Tongzon,
2002; Veldman and Buckmann, 2003; Hung et al., 2010
Number of intercontinental liner services Song and Yeo, 2004
Logistic integration and
Container Terminal manager Liu et al., 2009
Customer oriented Juang and Roe, 2010; Onut et al., 2011; Carbone e De Martino,
2003; Liu et al., 2009
Terminal Information system Carbone and De Martino, 2003; Panayedes and Song, 2009;
Cachon and Fisher, 2000; Zhao et al., 2002; Liu et al., 2009
Terminal organizational structure Bicou e Gray, 2004; Robinson, 2002; Liu et al., 2009
Container terminal layout The authors
Towage and pilotage service Juang and Roe, 2010; Hung et al., 2010
3.3- Data collection and measures
To evaluate the hypotheses a survey was sent to users of the main container terminals in
Portugal and Spain. Twelve major container terminals were selected, seven located in Portugal
and five in Spain, in a total of ten ports. A questionnaire was addressed about the importance
of each container terminal performance variable and about the importance of each
characteristic factor influencing container terminal performance for the port industry in
general, using a 5-point Likert scale (ranging from 1- not relevant to 5- very relevant). The
questionnaire was submitted to 1056 senior managers of companies currently operating in the
selected ports, with a positive response of 122 answers (12%), as shown in Table 2.
The component of the survey relating to the construct Container terminal performance was
based on the question "Please rate by degree of importance the following performance
indicators of container terminals Europeans, in general". The remaining variables were based
on the question "Please rate by degree of importance the following factors explaining the
performance of container terminals in Europe, in general".
Table 2 – Sample definition
Country Portugal Spain Total
Surveys sent 573 483 1,056
Valid answers 81 41 122
% 14 8 12
Ports Leixões Barcelona
Figueira da Foz Valencia
3.4- Statistical instruments
The structural equation model is a linear model that sets a relation between observed and
latent variables and between endogenous and exogenous variables, whether latent or
observed. It is divided in two sub-models: the measurement model and the structural one.
The measurement model defines how the latent variables are operationalized by the observed
ones, including exogenous variables and endogenous ones. The measurement model of
endogenous variables is defined as follows (Bollen, 1989):
y = η + Λy ɛ (1)
where, y is the vector (px1) of observed dependent p variables, Λy is the factor weight matrix
(pxr) of η in y, η is the vector (rx1) of dependent latent r variables and ɛ is the measurement
errors vector (px1) of y.
The measurement model of exogenous variables is defined by:
x = δ + ξ Λx (2)
where, x is the vector (qx1) of independent observed p variables, Λx is the factor weight matrix
(qxs) of ξ in x, ξ is the vector (sx1) of independent latent s variables and δ is the measurement
errors vector (qx1) of x. The structural model defines the causal relations between latent
variables, which can be defined by:
η = η + B + Γξ ς (3)
where, B is the matrix (rxr) of η coefficients of the structural model with Bii = 0, Γ is the matrix
(rxs) the x coefficients in the structural model, Σ is the vector (rx1) of r model residuals.
The structural equation model can be exploratory or confirmatory regarding the analysis of
latent variables or factors, aiming to determine the latent variables or to confirm their
existence and relationships with the observed ones. This methodology was used to confirm the
measurement model of latent factors explaining the container terminal performance, as well
as the latent variables of performance by using AMOS18 software.
4. Results and Analysis
By using a structural equation modelling methodology, a confirmatory analysis of the research
model and hypotheses was made. The variables collected were used to determine the latent
model and descriptive statistics (Table 3). Regarding the variables related to customer
satisfaction, productivity, efficiency and container traffic (activity), senior managers were
asked to classify by degree of importance each variable of container terminal performance.
The results showed high values between 3.54 for transhipment handling and 4.49 for
productivity and efficiency. In the questionnaire, senior managers were requested to evaluate
the importance of the port and terminal characteristics on performance. High values were
obtained between 3.43 for the distance to the centre of Europe and 4.49 for road accesses,
which confirm the influence of these factors on the terminal performance according to their
Table 3 – Descriptive Statistics
Min Max Mean Std. Deviation Skewness Kurtosis
Shippers’ satisfaction 1 5 4.04 .991 -.964 .421
Shipowners’ satisfaction 1 5 4.45 .794 -1.904 4.878
Freight forwarder and shipping agents’ satisfaction 1 5 3.96 .939 -.890 .888
Terminal Productivity 2 5 4.49 .730 -1.332 1.161
Terminal Efficiency 2 5 4.49 .707 -1.325 1.399
Container flows to/from hinterland 1 5 3.81 .903 -.438 .123
Transhipment container traffic 1 5 3.54 1.005 -.511 .040
Distance to the centre of Europe 1 5 3.43 .961 -.269 -.061
Distance to the Mediterranean trade lanes 1 5 3.50 .956 -.578 -.164
Distance to production centres 2 5 3.91 .761 -.420 .028
Distance to hinterland markets 2 5 3.92 .756 -.445 .104
Economic development of the region 2 5 3.86 .865 -.347 -.545
Rail access 1 5 4.16 .882 -1.194 2.099
Road access 3 5 4.49 .671 -.971 -.230
Port integration on logistic networks 2 5 4.30 .599 -.452 .710
Rail access to dry inland terminals 1 5 4.13 .918 -.982 .853
Port reputation 1 5 3.46 .825 -.227 -.103
Container Terminal reputation 2 5 3.66 .849 -.033 -.651
Port Authority dynamics 2 5 3.91 .843 -.416 -.393
Port community dynamics 2 5 3.87 .823 -.294 -.474
Water depth in the port access 1 5 4.21 .805 -.987 1.275
Quay depth of the container terminal 2 5 4.34 .736 -.773 -.288
No. of shipping liner services from top 10 2 5 3.79 .752 -.100 -.396
No. of feeder and short-sea liner services 2 5 3.80 .746 -.028 -.516
No. of intercontinental liner services 2 5 3.80 .738 .095 -.676
Terminal manager type 1 5 3.94 .893 -.593 .025
Customer oriented 1 5 4.48 .719 -1.573 3.538
Terminal information system 1 5 4.36 .804 -1.624 3.831
Terminal organization structure 2 5 4.31 .739 -.946 .730
Container terminal layout 2 5 4.25 .731 -.678 .063
Towage and pilotage service 1 5 3.71 .777 -.311 .418
Using the structural equation modelling, significant coefficients were obtained in what
concerns the relations between the latent variables and the observed ones (> 0.5). The
convergent validity of the model was confirmed (Anderson et al., 1987; Mantzer & Garver,
1999), which corroborates the adequacy of the model to input data. The results also confirm
the face validity of the latent variables, due to the fact that each variable showed consistency
with the concepts and definitions found in the literature and in the theoretical model. The
model aims to measure latent variables that are distinct and robust. The variance (R2
> 0.4) of
the latent variables is high, which indicates the robustness of the model, with the exception of
the variables port authority and port community dynamics and towage and pilotage service.
As Table 4 shows, the correlation between the latent variables is less than 0.85 and less than
the square root of the Average Variance Extracted (AVE) of the latent variables, which runs
across the table. This means that the latent variables are internally consistent and distinct from
each other, i.e., they are neither confused nor strongly correlated. The AVE values of the first-
level latent variables are always greater than 0.5. The results confirm the robustness of the
structural equation modelling and the latent variables used, i.e., the discriminant validity of
the model is demonstrated (Fornell & Larcker, 1981; Kline, 2005). The results also confirm the
unidimensionality of the structural equation modelling (Hair et al, 1998; Tabachnick & Fidell,
2001), with the following Goodness-of-fit indicators (GoF) of the measurement model of the
first survey, χ2
/df 1:44; IFI: 0.91 (> 0.9); CFI: 0.90 (> 0.9); RMSEA: 0.06 (<0.1). This
demonstrates the adequacy of the measurement model of the latent variables.
Table 4 – Consistency of latent variables, measurement model
Latent correlation Var. AVE
3 4 6 6 7 8 9 10 11 12
Port and terminal Characteristics
1 0.56 0.75
Container terminal performance
2 0.52 0.78 0.74
Logistic Integration and terminal
3 0.61 0.60 0.48 0.78
Land access 4 0.66 0.64 0.51 0.38 0.81
Maritime accessibility 5 0.82 0.67 0.54 0.40 0.42 0.91
Continental port location 6 0.76 0.66 0.51 0.39 0.42 0.44 0.87
Port dynamics 7 0.56 0.75 0.62 0.48 0.50 0.53 0.52 0.75
Maritime terminal services 8 0.84 0.60 0.46 0.36 0.38 0.40 0.39 0.47 0.92
Regional port location 9 0.69 0.68 0.53 0.41 0.43 0.45 0.44 0.53 0.40 0.83
Activity 10 0.58 0.55 0.56 0.33 0.35 0.37 0.36 0.44 0.33 0.37 0.76
Customer satisfaction 11 0.70 0.59 0.73 0.36 0.38 0.40 0.39 0.47 0.35 0.40 0.84 0.33
Efficiency and productivity 12 0.72 0.47 0.60 0.29 0.30 0.32 0.31 0.37 0.28 0.32 0.28 0.26 0.85
Note: SQRT(AVE) in diagonal
The measurement model involves three latent variables dependent on the container terminal
performance: Activity, Efficiency and Productivity and Customer Satisfaction. The results also
confirm the existence of seven latent exogenous variables or independent/ explanatory factors
of performance: Location (continental port location), Region port location, Land access, Port
dynamics, Maritime accessibility, Logistic integration and terminal organization and Maritime
services provided at the terminal.
This result validates the theoretical model under research considering the general perception
of the Iberian Peninsula terminal users. Therefore, container terminal performance depends
on the proximity to the centre of Europe and to the Mediterranean Sea, on the economic
importance of the region where the terminal is located, on the proximity to urban and
production centres, on the quality of road and rail accesses to the hinterland and logistic
platforms and on the port authority and community dynamics. It also depends on the port and
the terminal reputation, maritime access, number of intercontinental liner services of world
leading shipping companies, terminal organization and adaptation to the logistic network
Using the measurement of the structural equation model, we examined the causal
relationships between the latent variables, using a second-level latent variable Port and
terminal characteristics to explain the dependent variables of the model. As Figure 18 shows,
the coefficients explaining the latent dependent variables found were significant. In addition,
the results demonstrate that the structural model satisfies the unidimensionality criteria (Hair
et al., 1998; Tabachnick & Fidell, 2001), with the following indicators of reasonable adequacy:
Goodness-of-fit (GoF), χ2
/df=1404 ; IFI=0.908 (>0.9); CFI=0.905 (>0.9);
RMSEA=0.058 (<0.1). In the reflective model, the relationship between the second level latent
variable Port and Terminal characteristics and the first level exogenous latent variables has
high coefficients (β>0.5), which means that the hypothesis that the latter reflect a superior
variable is not rejected.
One limitation of this model is the small sample size, consisting of only 122 observations for a
large number of variables. In the SEM models, the suitable number of observations should be
about 10 times the number of observed variables. Thus, there should be only 12/13 observed
variables, but instead we have 31 variables. In order to confirm the results, the model has
been simplified maintaining the same constructs and the observed variables that have greater
importance for each latent variable in order to fulfil the criterion of the relationship between
sample size and the number of observed variables. The results are consistent of the latent
variables model, with the following indicators showing a good adequacy: Goodness-of-fit
/df=1356; IFI=0.942 (>0.9); CFI=0.94 (>0.9); RMSEA= 0.054 (<0.1). This
shows that the sample size does not influence the conclusions regarding the confirmation of
the research model.
To confirm the result, it was developed a formative SEM model, as opposed to previous
reflective model, which considers the latent variables as causes of the observed variables, a
less frequently used by researchers in SEM due to controversial issues that remain in the
conceptualization, estimation and validation of such models (Diamantopoulos et al., 2008;
Freeze and Raschke, 2007).
In the formative model, compared to the previous reflective, it was changed the direction of
the causal relationships between the first-level and second-level latent, and it was used the
factor analysis scores as observed variables in the model, since it is not possible to use
reflective latent variables in a formative model.
The results were very similar: R2
=0.33 for Efficiency and productivity, R2
=0.37 for Activity,
=0.51 for Customer satisfaction and R2
=0.57 for Container Terminal Characteristics. The
result has a Goodness-of-fit (GoF), χ2
/df=1,335; IFI= 0.919 (>0.9), CFI=0.914 (>0.9),
RMSEA=0.053 (<0.1), confirming the good adjustment. The formative model maintains the
findings of the reflective model.
The question that arises is whether the first-level latent variables reflects the second level
latent Port and terminal characteristics, or is this variable an agglomerated caused by
uncorrelated first-level variables. Another question is whether the second level variable makes
sense. The results shows strong correlation between latent variables of first level, and the
internal consistency of the second level variable, with AVE=0.56 and high β values explaining
all the latent variables at the first level, and demonstrating the existence of the reflective
second level latent variable Port and terminal characteristics.
Figure 1 – Structural Model
0,84 0,51 0,73
0,75 0,43 0,55 0,78
0,78 0,71 0,76
0,71 0,6 0,66 0,56 0,37 0,71
0,81 0,77 0,75 0,85
0,55 0,52 0,72
0,75 0,55 0,31
0,96 0,58 0,7
Port and terminal
Regional port location
Logistic integration and
Terminal maritime service
Continental port location
Efficiency and productivity
ActivityQuay deph of the ontainer terminal (0,91)
Water depth in the port access (0,56)
Distance to production centres (0,71)
Distance to hinterland markets (0,70)
Economic development of the region (0,50)
Container terminal manager (0,42)
Customer oriented (0,5)
Terminal information system (0,52)
Terminal organizational structure(0,56)
Container terminal layout (0,44)
Towage and pilotage service (0,38)
Rail access (0,57)
Road access (0,61)
Port integration on logistic network(0,35)
Port reputation (0,5)
Container Terminal reputation(0,65)
Port Authority dynamics (0,34)
Port community dynamics (0,30)
No. shipping liner services from top 10 (0,67)
No. of feeder and shortsea liner (0,74)
No. of intercontinental liner services (0,81)
Distance to the centre of Europe (0,62)
Distance to the Mediterranean trade lanes (0,72)
Freight forwarder and shipping agents´ satisfaction
Terminal Productivity (0,50)
Container flows to/from hinterland (0,50)
Transhipment container traffic (0,4)
Rail access to dry inland terminals (0,52)
5. Discussion and conclusions
The results of the study confirm the research model as a wider, more holistic approach of the
factors affecting the performance of a container terminal. They also confirm the existence of
several latent variables consistently related to Port and terminal characteristics. The results
demonstrate that the global holistic port performance model cannot be rejected and shows
that port and terminal characteristics are related to the performance of a terminal (AVE=0.52,
β=0.75, R2=0.56) and by such the basic hypothesis of the research model should not be
rejected. Results do not rejected hypothesis 2: the container terminal performance is strongly
influenced by the port and terminal characteristics,
The results reveal three latent endogenous variables of container terminal performance, which
are influenced by the Port and the terminal characteristics. The first one is Customer
Satisfaction, that is reflected in the observed variables Shipping agents and freight forwarders’
satisfaction, Shipowners’ satisfaction and Shippers’ satisfaction. The second one is Efficiency
and Productivity, which is reflected in the observed variables Terminal and Terminal efficiency.
Finally, the third one is the terminal Activity, that is reflected in the observed variables
Container flows to/from the hinterland and Transhipment container Traffic. Therefore, the
following hypothesis are not rejected: hypothesis 3: the container terminal efficiency and
productivity are important measures of performance, hypothesis 4: the container terminal
activity is an important measure of performance and hypothesis 5: the container terminal
customer satisfaction is a representative measure of performance.
In the Iberian Peninsula, the proximity to the Mediterranean Sea or to large consumption and
production centres affect container terminal activity level by triggering cargo flows with
resulting effects on efficiency, which in turn attract more shipping lines with global coverage,
thereby influencing customer satisfaction. Moreover, the proximity to the Mediterranean Sea
main Asia-Europe liner shipping routes has influence on the performance of a container
terminal, which is more likely to be chosen to become part of intercontinental liner services
network and handle transhipment operations. These findings support Notteboom (2011)
previous research that referred to the proximity along major navigation routes or at crossing
points of North/South and East/West trade routes as a determinant factor in terminal
selection enhancing performance levels (Notteboom & Rodrigue, 2009; 2010). In this context,
hypothesis 1a is not rejected: the port geographical location at continental level is an
important port and container terminal characteristic. And the findings are consistent with
those reported by Tongzon (2002) and Cheo (2007), who referred that the economy of the
region affects container trade flows according to its level of production and consumption.
Thus, hypothesis 1b is not rejected: The port location at regional level is an important
characteristic of the port and container terminal.
An adequate inland accessibility allows an expansion of the terminal’s hinterland, generating
not only an impact on its activity but also facilitating cargo flow with effects on customer
satisfaction. Therefore, the findings of Turner, Windle and Dresner (2004) and Gaur (2005)
about the impact of inland access connections on terminal performance are confirmed. The
hinterland accessibility allows terminal expansion beyond the seaport limits, thereby enlarging
their area of influence to inland terminals, where major cargo volumes are transported by
railway. The importance of motorway network connections to the hinterland is also
demonstrated. Thus, hypothesis 1c is not rejected: the inland port accessibility is an important
characteristic of a port and container terminal.
The port dynamics and the port and terminal reputation enhance the possibility of attracting
more cargo and vessels and therefor more activity, satisfying customers and generating cargo
concentration. The results confirms the importance of the port and terminal reputation to
performance as mentioned by Cheo (2007) within the marketing and communication
strategies. These findings corroborate those of Pando et al (2005), and Pardali Kounoupas
(2007) and Cahoon (2007), who studied the impact of port marketing tools and image on
performance. They are also consistent with those of Notteboom (2011), who focused on the
importance of port community dynamics, with respect to marketing initiatives, on port
reputation and terminal performance. Moreover, the findings suggest that the port authority
dynamics, by assuming a general coordinator role in the port, influence the performance of
terminals. Consequently, the hypothesis 1d is not rejected: the port dynamics is a relevant
characteristic of a port and container terminal.
Improved water depth allows for a concentration of larger vessels at the port, which, taking
advantage of economies of scale and cargo density, achieve higher productivity and efficiency
levels and this in itself attracts more traffic from direct liner services, satisfying customers. The
results support the conclusions of Tongzon (2002) and Wiegmans (2003), who studied the
importance of maritime accessibility as determinant to terminal efficiency. The maritime
accessibility determines the terminal’s access to the market and triggers the maritime services
provided to port users, as well as it shapes the terminal hierarchy in the shipping networks,
which is key factor determining performance. Hence, hypothesis 1e is not rejected: maritime
accessibility is a determinant characteristic of a port and container terminal.
This finding is also consistent with Tongzon (2002) previous research on the importance of
having frequent liner services calling a port, especially intercontinental ones, for shippers in
their terminal selection process and, thereby, influencing terminal performance. It is also
demonstrated that port integration in worldwide liner shipping network has influence on
performance (Tongzon & Heng, 2005). As in previous cases, hypothesis 1f is not rejected: the
maritime services provided are an important characteristic of a port and container terminal.
A better terminal management, logistic integration and attention to customer demands
enhance the interface between logistic chain transport modes in the port and attracts even
more cargo, affecting activity and customer satisfaction. Robinson’s conclusions (2002) are
confirmed, i.e., port choice becomes more a function of the entire supply network, which calls
for wider approach beyond the port and terminal. It is confirmed that customer focus is a
determinant factor influencing container terminal performance, by allowing a quick response
to changes in supply chain’s need in an ever-changing market and thus satisfying customers’
demand. In such context, information systems have become of great importance for container
terminals in facilitating the exchange and sharing of information/data and that should lead to
higher levels of integration in the supply networks. The results evidence the importance of
both type of organization and management focused on customers and logistic networks
anchored in the port, which are key determinants when a terminal needs to accommodate to
the logistics networks’ demands (Liu et al., 2009). In conclusion, hypothesis 1g is not rejected:
the logistic integration and organization of the terminal are an important characteristic of a
port and container terminal.
6. Contributions, Limitations and Future Research
This study proposes a wider, more holistic research model about container terminal
performance regarding customer satisfaction, productivity, efficiency and activity, based on
the port and the terminal characteristics. The study contributes to a better understanding of
ports and container terminals for having succeeded in concentrating in one research model
several elements from previous studies.
The results allow response the research question: to understand why some container
terminals are more successful than others and how to successfully build a new container
terminal. The more successful container terminals are located in Europe centre or in
Mediterranean axis, are near important markets and producers, have good rail and road
accesses, are located inside a dynamic port, have deep maritime access and important
maritime line services, have a customer focus management, integrated management system
and organization structure and services oriented to meet the needs of the logistic supply
chains. Geographic location, maritime access and port dynamics are considered the most
important factors determining the terminal performance. Proximity to inland cargo and
maritime axis, ability to receive big motherships with low cost per container and strong
support from port authorities and port community, are the main factor when considering
builds a new terminal.
One limitation of this study is the sample size compared to the number of variables used,
although it is representative of the population presently operating in the ports of the Iberian
Peninsula. The results are consistent but may be complemented with specific analysis to
evaluate the type and level of influence between the variables.
One question that might be asked is whether the structural equation modeling should be
reflective or formative. In other words, the latent variables resulting from the observed
variables reflect the port and the terminal characteristics or, on the contrary, are independent
variables that can be concentrated in a formative latent variable.
In light of our results, further research should go deeper in the analysis of this model applied
on Iberian and European port terminals, testing its validity in practice, including qualitative and
quantitative variables and other methods of analysis.
In future studies we intend to test in detail the formative SEM model, checking its theoretical
validity against the reflective model, and we intend to further test the multiple linear
regression model, directly linking each port and terminal characteristic to each terminal
performance variable, in order to assess in the detail contribution of each.
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