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CEFAGE-UE, Universidade de Évora, Palácio do Vimioso, Lg. Marquês de Marialva, 8, 7000-809 Évora, Portugal Telf: +351 266 706 581 - E-mail: [email protected] - Web: www.cefage.uevora.pt CEFAGE-UE Working Paper 2013/13 Effect of the container terminal characteristics on performance Vítor Caldeirinha 1 , J. Augusto Felício 2 and Andreia Dionísio 3 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 performanc

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Page 1: Effect of the container terminal characteristics on performanc

CEFAGE-UE, Universidade de Évora, Palácio do Vimioso, Lg. Marquês de Marialva, 8, 7000-809 Évora, Portugal

Telf: +351 266 706 581 - E-mail: [email protected] - Web: www.cefage.uevora.pt

CCEEFFAAGGEE--UUEE WWoorrkkiinngg PPaappeerr

22001133//1133

EEffffeecctt ooff tthhee ccoonnttaaiinneerr tteerrmmiinnaall cchhaarraacctteerriissttiiccss oonn ppeerrffoorrmmaannccee

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)

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Effect of the container terminal characteristics on performance Vítor Caldeirinha1, J. Augusto Felício2 and Andreia Dionísio3

1.

Centro de Estudos de Gestão; School of Economics and Management (ISEG); Rua Miguel Lupi, 20. 1249-078 Lisbon; Email address: [email protected]; Phone: +351 - 213 970264

2. School of Economics and Management; Technical University of Lisbon; Rua Miguel Lupi, 20. 1249-078

Lisbon; Email address: [email protected]; Phone: +351 - 2133970264 3.

CEFAGE-UÉ, Évora University (UÉ); Largo dos Colegiais, 2, Évora; ; Email address: [email protected];

phone:+351 - 266740892

Abstract

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

JEL R42

1. Introduction

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.

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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,

2006).

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.

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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

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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

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performance and service quality, particularly when large vessels call demanding for large space

areas.

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. Methodology

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).

Figure 1 – Research model

Continental port location

Regional port location

Land accessibility

Continental position-port

Port dynamics

Continental position-port

Maritime accessibility

Continental position-port

Terminal maritime services

Continental position-port

Logistic integration and terminal organization

Port and

terminal

characteristics

Customer satisfaction

Terminal activity

Terminal efficiency and productivity

H3 H4 H5

Container

terminal

performance

H2

H1a

H1b

H1c

H1d

H1e

H1f H1g

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Based on the theoretical background and research model, the following assumptions are

established:

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

container terminal;

Hypothesis 1c: The port inland accessibility is an important characteristic of a port and

container terminal;

Hypothesis 1d: The port dynamics is an important characteristic of a port and container

terminal;

Hypothesis 1e: The maritime accessibility is an important characteristic of a port and container

terminal;

Hypothesis 1f: The maritime terminal services are an important characteristic of a port and

container terminal;

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

characteristics;

Hypothesis 3: The terminal productivity and efficiency are representative measure of

performance;

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 terminal

performance

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

al., 2000

Shippers’ satisfaction Robinson, 2002; Liu et al., 2009

Shipowners’ satisfaction Liu et al., 2009

Freight forwarder and shipping agents’

satisfaction

Liu et al., 2009; Magala and Sammons, 2008

Continental port

location

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

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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

networks

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,

2007

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

Terminal maritime

services

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

terminal organization

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".

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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

Lisboa Bilbao

Setúbal Tarragona

Sines Algeciras

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.

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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

perception.

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

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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 545.67; 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 1 (2

nd

Level)

2 (2nd

Level) 3 4 6 6 7 8 9 10 11 12

Port and terminal Characteristics

(2nd

Level) 1 0.56 0.75

Container terminal performance

(2nd

Level) 2 0.52 0.78 0.74

Logistic Integration and terminal

organization 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

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leading shipping companies, terminal organization and adaptation to the logistic network

requirements.

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=574,354; 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

(GoF), χ2=210,215; χ2/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,

R2=0.51 for Customer satisfaction and R2=0.57 for Container Terminal Characteristics. The

result has a Goodness-of-fit (GoF), χ2=97.462; 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.

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12

Figure 1 – Structural Model

0,78 0,54

0,85

0,84 0,51 0,73

0,84

0,55

0,75 0,43 0,55 0,78

0,78 0,71 0,76

0,59 0,73

0,72 0,74

0,71 0,6 0,66 0,56 0,37 0,71

0,81 0,77 0,75 0,85

0,59 0,61

0,55 0,52 0,72

0,75 0,55 0,31

0,96 0,58 0,7

0,33 0,64

0,82

0,86 0,58

0,9

0,64 0,34

0,71

0,72

0,75

0,66

0,62

Port and terminal

characteristics

Maritime accessibility

Regional port location

Logistic integration and

terminal organization

Land Access

Port dynamics

Terminal maritime service

Continental port location

Customer satisfaction

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

(0,61)

Shipowners´ satisfaction(0,57)

Shippers´ satisfaction(0,53)

Terminal Productivity (0,50)

Terminal Efficiency(0,72)

Container flows to/from hinterland (0,50)

Transhipment container traffic (0,4)

Rail access to dry inland terminals (0,52)

Container terminal

performance

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13

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

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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.

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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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