Effect of the container terminal characteristics on performanc

648 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
648
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
15
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Effect of the container terminal characteristics on performanc

  1. 1. CEFAGE-UE, Universidade de Évora, Palácio do Vimioso, Lg. Marquês de Marialva, 8, 7000-809 Évora, Portugal Telf: +351 266 706 581 - E-mail: cefage@uevora.pt - 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)
  2. 2. 1 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: vitorcaldeirinha@gmail.com; Phone: +351 - 213 970264 2. School of Economics and Management; Technical University of Lisbon; Rua Miguel Lupi, 20. 1249-078 Lisbon; Email address: jaufeli@iseg.utl.pt; Phone: +351 - 2133970264 3. CEFAGE-UÉ, Évora University (UÉ); Largo dos Colegiais, 2, Évora; ; Email address: andreia@uevora.pt; 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.
  3. 3. 2 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.
  4. 4. 3 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
  5. 5. 4 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
  6. 6. 5 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
  7. 7. 6 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
  8. 8. 7 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".
  9. 9. 8 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.
  10. 10. 9 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
  11. 11. 10 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 (2nd Level) 2 (2nd Level) 3 4 6 6 7 8 9 10 11 12 Port and terminal Characteristics (2 nd Level) 1 0.56 0.75 Container terminal performance (2 nd 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
  12. 12. 11 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.
  13. 13. 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
  14. 14. 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
  15. 15. 14 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.
  16. 16. 15 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.
  17. 17. 16 References Anderson, J.C., Gerbing, D.W., and Hunter, J.E. (1987). On the assessment of unidimensional measurement: Internal and external consistency, and overall consistency criteria. Journal of Marketing Research, 24, 432–437. Baird, A., (2006). Optimizing the container transhipment hub location in northern Europe. Journal of Transport Geography, 14(3), 195-214. Cachon, G. and Fisher, M. (2000). Supply chain inventory management and the value of shared information. Management Science, 46(8), 1032-1048. Cahoon, S. (2007). Marketing communications for seaports: a matter of survival and growth. Maritime Policy and Management, 34(2), 151-168. Chang, Y., Lee, S., and Tongzon, J. (2008). Port selection factors by shipping lines: Different perspectives between trunk liners and feeder service providers. Journal of Marine Policy, 32, 877–885. Cheon, S. (2007). Evaluating impacts of institutional reforms on port efficiency changes malquimist productivity index for world container ports. Post Doctoral research, University of California, Berkeley. Cullinane, K. and Wang, Y. (2009). Capacity-based measure of container port accessibility. International Journal of Logistics, 12(2), 103–117. Cullinane, K., Song, D. W., Ping, J., and Wang, T. F. (2004). An application of DEA windows analysis to container port production efficiency. Review of Network Economics 3, 184–206. De Langen, P. (2004). Governance in seaport clusters. Maritime Economics & Logistics, 6, 141– 156. Diamantopoulos, A., Riefler, P., and Roth, K.P. (2008). Advancing Formative Measurement Models. Journal of business research, 61(12), 1203-1218. Dias, Q., Calado, F., and Mendonça, C. (2010). The role of European «ro-ro» port terminals in the automotive supply chain management. Journal of Transport Geography, 18(1), 116-124. Estache, A., Perelman, S. and Trujillo, L. (2005). Infrastructure performance and reform in developing and transition economies: evidence from a survey of productivity measures. World Bank Policy Research Working Paper 3514. Fornell, C. and Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Freeze, R. and Raschke, R. L., (2007). An Assessment of Formative and Reflective Constructs in IS Research. ECIS 2007 Proceedings paper 171. Garver, M.S. and Mentzer, J.T. (1999). Logistics research methods: Employing structural equation modeling to test for construct validity. Journal of Business Logistics, 20, 33–57. Gaur, P. (2005). Port planning as a strategic tool: a typology. Institute of Transport and Maritime Management Antwerp, University of Antwerp. Gonzalez, M.M. and Trujillo, L. (2008). Reforms and infrastructure efficiency in Spain’s container ports. Transportation Research Part A, 42(1), 243-257. Hair, J.F., Jr., Anderson, R.E., Tatham, R.L., and Black, W.C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, New York: Prentice Hall. Juang, Y. and Roe, M. (2010). Study on Success Factors of Development Strategies for Intermodal Freight Transport Systems. Journal of the Eastern Asia Society for Transportation Studies, 8. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: The Guilford Press. Liu W., Xu H., and Zhao X. (2009). Agile service oriented shipping companies in the container terminal. Transportation, 24(2), 143-153. Notteboom, T. (2010). Concentration and the formation of multi-port gateway regions in the European container port system: an update. Journal of Transport Geography, 18, 567–583.
  18. 18. 17 Notteboom, T. (2011). An application of multi-criteria analysis to the location of a container hub port in South Africa. Maritime Policy and Management, 38(1), 51–79. Notteboom, T. and Rodrigue, J.P. (2009). The future of containerization: Perspectives from maritime and inland freight distribution. Geojournal, 74(1), 7-22. Notteboom, T. and Rodrigue, J.P. (2011). Global networks in the container terminal operating industry Part 2: The future direction of terminal networks, Port Technology International, PT50–03-2. Notteboom, T. and Winkelmans, W. (2004). Factual report – work package 1: Overall market dynamics and their influence on the port sector. Study commissioned by the European Sea Ports Organisation (ESPO). Panayides, P.M. and Song, D.W. (2009). Port integration in global supply chains: measures and implications for maritime logistics. International Journal of Logistics Research and Applications, 12(2), 133 — 145. Pando, J., Araujo, A., and Maqueda, F.J. (2005) Marketing management at the world’s major Pardali, A. and Kounoupas, E. (2007). The application of marketing strategies in the container seaport market, Proceedings of the 5th International Conference on Maritime Transport and Maritime History, Barcelona. Robinson, R. (2002). Ports as elements in value-driven chain systems: the new paradigm. Maritime Policy and Management, 29, 241-255. Tabachnick, B.G. and Fidell, L.S. (2001). Using multivariate statistics (4th ed.). New York: Harper Collins. Tongzon, J. (2002). Port choice determinants in a competitive environment. IAME, Conference, Panama. Tongzon, J. and Heng, W. (2005). Port privatization, efficiency and competitiveness: Some empirical evidence from container ports (terminals). Transportation Research Part A, 39, 405– 424. Tongzon, J., Chang, Y., and Lee, S. (2009). How supply chain oriented is the port sector? International Journal of Production Economics, 122, 21–34. Turner, H., Windle, R. and Desner, M. (2004). North American containerport productivity: 1984–1997. Transportation Research Part E, 40, 339–356. Wang, T.F. and Cullinane, K. (2006). The efficiency of European container terminals and implications for supply chain management. Maritime Economics & Logistics, 8, 82–99. Wiegmans, B. (2003). Performance conditions for container terminals. Maritime Economics & Logistics, 6, 276–277. Woo, S., Petit, S., and Beresford, A. (2011). Port evolution and performance in changing logistics environment. Maritime Economics & Logistics, 13 (3), 250. Yap, W. and Notteboom, T. (2011). Dynamics of liner shipping service. Maritime policy and Management, 38(5), 471-485. Zhao X., Xie J., and Zhang W.J., (2002). The impact of information sharing and ordering coordination on supply chain performance. Supply Chain Management, 7(1), 24-40.

×