INTRODUCTION

As the world economy becomes more integrated through an accelerated process of globalisation of production, consumption and services, the market place for an increasing number of shippers and receivers is now simply the globe. In international shipping and logistics, the relentless striving for greater economies of scale, global coverage, higher efficiency and improved service quality have leveraged competition for cargo and services to a global market level.

Yet, however much globalisation has impacted various segments of international shipping and logistics, the port industry has lagged behind other infrastructure and logistics sectors in embracing global changes. For many decades, the combination of the institutionally rigid structures and spatially conventional arrangements meant that neither cross-industry integration nor cross-border expansion was possible. With the process of port privatisation and deregulation being widely implemented during the last two decades or so, barriers to global port operations have started to be lifted gradually and new operating port structures have emerged. Global port operators (GPOs) can be defined as those actors who extend their activities to international port operations with a view of establishing globe-spanning network services. Four current types of market players can be listed under the GPOs umbrella:

Terminal-operating shippers (TOS)

Shippers involved directly, or through subsidiaries, in the management of terminals mainly for non-containerised cargo operations such as for handling oil and car shipments. Against the trend of logistics outsourcing, many global shippers have decided to retain full control over their distribution channels, including such activities as transport and port operations. Global firms such as Shell, Cargill and Hyundai own their own fleet of vessels (industrial shipping) or operate them through long-term lease (bareboat chartering), and so is the case for dedicated terminals, warehousing and retail outlets.

Terminal-operating shipping lines (TOSL)

Ocean carriers operating a range of port facilities (predominantly container terminals) either through single or joint long-term lease and concession agreements. Depending on the nature of the agreement, terminals are operated either on a dedicated or common-user basis although variations to these arrangements exist, for instance when a dedicated terminal provides services to other members of the shipping alliance the terminal-operating carrier belongs to. The management of such terminals is usually separated from that of the shipping line (COSCO terminals) and is sometimes undertaken by established subsidiaries, for example, APM Terminals, P&O Ports (now part of DP World) and APL Eagle Marines.

Terminal-operating port authorities (TOPA)

Service operating port authorities such as Singapore and Dubai ports expanding their activities, usually through new organisational entities (PSA International and DP World, respectively) to ports and terminals beyond their initial spatial bases.

Terminal-operating companies (TOC)

Firms, other than shippers, ocean carriers or port authorities, whose origins are in logistics operations, property development or any other related business venture but have expanded their activities into international port operations and management. Firms such as HPH, Eurogate, SSA Marine, ICTSI, ABP and the former CSXWT (bought by DP World) belong to this category.

Although consolidation practices in the industry date back to the early 1980s, and in some cases even well back to the 1970s, strong interest from academia has only taken place over the last decade or so. Even though, much of the literature to date is largely descriptive (Airriess, 2001; Heaver et al, 2001; Juhel, 2001; Notteboom, 2002; De Souza et al, 2003, Midoro et al, 2005; Slack and Fremont, 2005) and there is a lack of empirical research (Figure 1).

Figure 1.

Variations of predominant channel structures in global shipping and ports.

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Channel conflict and power between ports and shipping lines is certainly a major factor that can explain current and future consolidation versus footloose strategies in the industry. Unfortunately, little or no attention has been devoted to investigating these relationships, in particular in the context of contemporary structuring of port institutional and management systems. This study seeks to fill this gap in the literature and applies the marketing approach of channel management theory to investigate various relationships between shipping lines and ports, focusing in particular on sources and impact of conflict and the interplay between consolidation arrangements and footloose mobility. To achieve this, we developed and tested a structural equations model (SEM) of how channel relationships impact port-related consolidation strategies and performance levels of shipping lines, which in turn would affect mobility and footloose decisions. The remainder of the paper is structured as follows. The next section reviews the potential of conflict and channel structure in international shipping and logistics. The further sections describes the research approach and methodology undertaken for this study and reports on the different stages of the empirical analysis and presents the results of the measurement and structural models. The final section concludes with a summary and suggestions for future research.

POWER, CONFLICT AND CHANNEL STRUCTURE

The literature on channel management has its roots in marketing management, and latterly in logistics and supply chain management (SCM). A channel can be loosely defined as a set of organisations that have banded together for trade, distribution and/or marketing purposes. In logistics management, channels are often reduced to the physical routes taken by goods as they move from producers to customers; hence only transport, warehousing and logistics providers are usually part of the logistics channel. However, a channel may include non-commercial organisations such as government agencies or involve other route configurations such as for non-physical flow movements, for example, promotion, negotiation and financing. In the context of this paper, we adopt a marketing channels perspective whereby a channel is defined as 'the network of organisational contacts a firm operates to achieve its distribution objectives'. Note the focus on 'contactual' rather than contractual relationships in a channel structure, meaning that an organisation can be a channel member without necessarily holding a direct contractual arrangement with the firm. There are numerous examples of contactual relationships in international shipping and logistics channels, for instance when using the services of third parties in a multimodal transport operation. Two other distinctive features of the marketing channel approach are worth underlining: (a) its focus on channel control and (b) the appreciation of conflict between organisations. In particular, the conflict attribute differentiates the marketing channel approach from the supply chain approach, the latter requiring cooperative relationships and integration of organisations. Both approaches deal, however, with channel relationships between independent entities and must not be confused with strategies of vertical or horizontal integration, which are a common practice in international shipping and logistics.

The marketing and supply chain literature depicts different types of channel structures ranging from very fragmented to fully integrated channels. While supply chain theory suggests collaborative arrangements within and across channels, relationships between various members of the international logistics channel have been more adversative than collaborative. Traditional channel relationships in the industry have for long been marked by 'arm's length' arrangements and the quasi-dominance of shipping lines vis-à-vis other channel members, mainly individual ports and terminals. However, recent studies on the subject suggest that global shippers and retailers are increasingly gaining control of the international logistics channel (Gibson et al, 2002; Nir et al, 2003; Bichou and Gray, 2004; Bichou, 2006). Some researchers have studied shipper-carrier relationships within the context of logistics performance and supply chain collaboration (Lai, 2004; Lu, 2003; Lemoine and Dagnaes, 2003) whereas others have examined channel relationships in the intermodal and distribution industry (Taylor and Jackson, 2000). However, little has been done to examine adversative relationships and the behaviour of channel members in view of recent concentration practices in the container port industry. Adversative relationships are associated with specific aspects of channel behaviour, such as conflict, control and power. It should be pointed out that channel power and conflict in the context of this paper must not be confused with interpersonal power and conflict relationships as studied in the field of organisational behaviour and management.

Channel conflict occurs when one member of the channel interferes with another member's objective with the purpose of bringing harm or achieving gains at the latter's expense. The literature depicts different sources of channel conflict including goal incompabilities, role incongruities, resource scarcities, perceptual dissimilarities and expectational differences (Stern, 1969). Such conflicts may take place at different levels of channel structure, with three main types being identified by the marketing literature namely horizontal channel conflicts inter-type channel conflicts, and vertical channel conflicts. In this paper, we focus on the latter type of channel conflicts.

Evidence of goal incompabilities in international logistics occurs between ports seeking maximum utilisation of their assets (berths, cranes, yards, etc) versus shipping lines in quest of the shortest time in port. Ports and ocean carriers may also get into conflict because of resource scarcities, for instance when dedicated terminals are allocated to a single shipping line, hence pushing other carriers to operate via ports elsewhere. Similar footloose arrangements take place in opposite situations such as when shipping lines desert a port because they could not have dedicated berths there, for instance when Maersk/Sealand decided to shift their transhipment operations from the port of Singapore to the port of Tanjung Pelepas in Malaysia. Another source of channel conflict is role incongruities. For example, a transhipment port may consider regular customers (carriers, freight forwarders, shippers, etc) as partners while they may view the role of the port as being similar to that of any other stopover point. For more discussion on such sources of channel conflicts in shipping and ports, the reader is referred to Haralambides (2002) and Haralambides et al (2002).

Perceptual and expectational differences are also a source of conflict in international logistics. An instance of conflicts resulting from perceptual differences is when a port displays generous pricing promotional tools in an attempt to attract more lines but fails to appreciate that such discounts are seen by shipping lines as a small fraction of the total cost incurred by them, including for the time-in-port cost. Similar perceptual differences occur in instances where carriers offer discounted freight rates to shippers. For conflicts caused by expectational differences, examples include situations where a port sets specific operational arrangements and targets (number of cranes per vessel, average crane move per hour, minimum reporting-time-to-gate, holidays and working day pattern, etc) that are not approved of by ocean carriers and other port customers.

Channel power is closely associated with conflict as it can be either the cause of or the solution to it, and sometimes both. In the context of this paper, power is defined as the ability of one party to impact, control or change the market behaviour and objectives of another party. Because of specialisation and channel interdependence, it is believed that each channel member holds a certain degree of power over other members. Examples of channel power frequently observed in shipping and ports include coercive, reward, expert and legitimate power. Coercive and reward powers denote opposite ability of channel behaviour towards other members, respectively by punishing or rewarding them, for instance through demurrage and charges versus rebates and discounts. Channel members that have extensive coercive and reward power are global shippers, but they have for long chosen to focus on their core businesses and outsource key transport and logistics operations to global shipping and logistics providers. However, this trend is quickly changing since the past few years as we have witnessed a greater control being performed by global retailers over the international logistics channel (Bichou, 2006). Expert power stems from the degree of expertise and specialisation held by a channel member, for instance a shipping agent or a freight forwarder, and is seen by many as a counter-balance to extensive coercive powers usually held by channel leaders. Legitimacy is another source of power usually held by Governments. An instance where legitimate power has been used is the recent decision of the US authorities to block Dubai Ports World (DP World) from operating US ports as a result of its takeover of P&O Ports.

Power and conflict could be, either independently or concurrently, the cause as well as the consequence of variations in channel structures and configurations. In the context of the international consolidation of the port industry, conflicts may instigate vertical integration strategies leading to the emergence of GPOs, but other conflictual relationships could also result from such strategies. For instance, TOSLs may hold greater control over the logistics channel, but this may be undermined by rising powers of TOCs and TOPAs. An examination of structural shifts in channel conflict and power relationships is therefore needed in order to assess the impacts of consolidation arrangements on channel performance and mobility (footloose) behaviour of ocean carriers.

In this study, the authors investigate the extent of channel relationships between shipping lines and ports in the light of the international consolidation of the container-port industry. We focus on container ports because major consolidation arrangements mainly take place within this segment of the industry. An analytical framework is used to assess the impacts of channel relationships on consolidation arrangements and test whether the direction of the effect would result in an increasing or decreasing risk of commoditisation and footloose relocations.

RESEARCH DESIGN AND METHODOLOGY

The channel management literature involves abstract concepts (constructs) such as conflict, control, power, alienation, collaboration, integration, etc, each of which can be represented by latent variables or factors. However, since neither these factors nor the links between them are directly measurable, it is necessary to have a set of measures (measurable or manifest variables) that describe how such constructs depend on each other as well as on observed variables.

Structural equation modelling (SEM) is a powerful statistical technique that combines the measurement model and the structural model into a simultaneous statistical test. The main contribution of SEM is embedded within the paradigm of empirical analysis of causal relationships, while its ability to test complex hypotheses and unify several multivariate methods into one analytical framework makes it particularly attractive to researchers in the logistics, SCM and marketing disciplines. SEM requires researchers to consider an underlying model that links construct structural parameters (latent variables) with observed data items (measurable variables) to test hypotheses about those parameters. Figure 2 shows a general process for SEM, which we adopt in this study. For more on SEM and its application in transport logistics and SCM, the reader is referred to Rex (1998), Garver and Mentzer (1999), Kaplan (2000), Wisner (2003) and Byrne (2001).

Figure 2.

Diagram of conventional SEM process (from Kaplan, 2000).

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In order to analyse and test relationships between channel constructs, consolidation practices and the risk of footloose relocations, we developed a three-dimensional conceptual framework with one association and seven possible types of impacts, all translated into research hypotheses as formulated in Table 1. The theoretical model adopted in this study is based on expert judgement, a thorough literature review and the results of a case study undertaken by one of the authors. Hypotheses 1 to 5 in the model are based on widely reported literature on channel performance and relationships (see, for instance, McGuire and Staelin, 1986; Li, 2004; Zhoul et al, 2004; Rose and Shoham, 2004; Alberato-Sa, 2005; Heu and Sheu, 2005), whereas the relationships between consolidation and mobility are based on experts' judgement and the results of a case research undertaken for a leading GPO. The outcome of the case study suggests that in consolidated channel structures, mobility would reduce if shipping lines operate ports (as TOSL), but the risk of them being footloose would increase if they operate at ports run by TOCs or TOPA. Accordingly, we introduce hypothesis H6 whereby the overall level of consolidation practiced by shipping lines in container ports is believed to ultimately reduce the risk of footloose behaviour.

Table 1 - Research hypotheses.

Full table

The SEM model can be expressed either by a mathematical (equation) or a schematic (path diagram) representation, the latter being more popular among researchers. Variables that are determined by other variables are known as endogenous variables while variables that are not are known as exogenous variables. Exogenous variables are usually related to each other by non-directional relations (covariances) represented by double-headed arrows. In our model, power and conflict are the exogenous variables whereas consolidation, mobility and channel performance are endogenous variables. Figure 3 depicts the research structure and the relationships among the variables. The hypotheses specify both the direction of the effect and the nature of the relationship (+: increasing, -: decreasing).

Figure 3.

Research model.

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EMPIRICAL ANALYSIS

Sample and procedure

Potential participants were identified from an in-house established database of more than 2,000 container and multipurpose terminals worldwide. Container terminals being operated partially or wholly by one or a combination of GPOs and featuring an annual throughput of more than 50,000 TEUs (20-foot equivalent units) were selected to make up a sample of 428 terminals. During the first half of 2005, questionnaires were sent (in separate copies) to Marketing and Operations managers in each terminal (N=856), requesting their perceptions of different levels of channel relationships between shipping lines and ports, as well as on the impacts of consolidation arrangements on footloose practices by carriers. In all, 108 respondents returned the questionnaire. Follow-up reminders were sent to non-respondents and an additional 24 responses were received, which brings the total number of responses to 132 with a final response rate of 15%. For non-response bias test, we conducted an analysis based on the procedure described by Lambert and Harrington (1990) and did not find any noticeable pattern of non-bias among variables (Table 2).

Table 2 - Profile classification of and response rates of participants in the sample.

Full table

Measurement scales

The survey included 18 questions intended to measure the various constructs of channel relationships, and the interplay between channel structure (consolidation) and footloose arrangements. Each construct in the study was modelled as a latent variable and measured by several items on a seven-point Likert Scale (1: strongly disagree, 7: strongly agree) as shown in Table 3. Channel power was measured using well established literature scales to capture a firm's power over its suppliers and customers (see for instance Allan and Hardy, 1989; Dutta et al, 1999; Taylor and Jackson, 2000). For channel conflict, we used items that best capture the nature and extent of adversarial relationships, namely the number and frequency of disagreements, the extent of cooperative arrangements and information sharing and the reaction to each other's decisions. Consolidation arrangements were measured by asking participants to report on their respective terminal operators' market share and expansion strategies over the past 5 years. For mobility, measurable items include information about the mode and nature of concession or lease contracts, and a ranking of the degree of mobility. Finally, we measured channel performance by asking participants to report on the variations in the levels of performance before and after operational involvement of the GPO. In both mobility and channel performance, one item was dropped for each variable because CFA (confirmatory factor analysis) had shown a high correlation of its measurement error with other measurement errors.

Table 3 - Items overview and statistical measures.

Full table

Following the proposed SEM approach, we tested the measurement model before analysing the structural model. Convergence validity was assessed by calculating Cronbach Alpha for each construct and item-to-total correlations for each item. Furthermore, CFA was performed on all scales using covariance-based SEM. The results from the CFA show a high degree of reliability and convergent validity, apart from the two items removed as shown in italics in Table 3. For all scales, the different goodness-of-fit-criteria exceed the established requirements. Additionally, the constructs show discriminant validity according to the Fornell/Larcker criterion. Thus, all constructs qualify for use in testing and evaluating our hypotheses. Statistical measure tests are reported in Table 4.

Table 4 - Statistical measures for constructs.

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Structural model

The structural model based on the stated hypotheses was tested. The analysis was performed using LISERL 8 model (Jöreskog and Sörborm, 1994). All goodness-of-fit criteria as shown indicate that the model fits the data well. The model, as illustrated in Figure 4, shows five significant relationships and two non-significant relationships (paths with discontinued arrows), and can be written using the equations below:

Figure 4.

Structural model. Where is the vector relating to exogenous latent variables, the vector relating to endogenous latent variables, x the vector relating to manifest variables of exogenous latent variables , y the vector relating to manifest variables of endogenous latent variables , the vector of measurement error of x, the vector of measurement error of y, the parameter of directional relation between a latent variable and its indicators, the vector of errors in the equations (disturbance terms) corresponding to endogenous latent variables , the parameter of directional relation between two endogenous latent variables, the parameter of directional relation between an endogenous and exogenous latent variable, the parameter of non-directional relation (covariance) between two latent variables, the (m n) matrix of structural coefficients between exogenous latent variables and endogenous latent variables, Â the (m n) matrix of structural coefficients between endogenous latent variables, the (n n) matrix of variances and co-variances between exogenous latent variables, the (m 1) matrix of residual variances and co-variances of the endogenous latent variables, _x the (p m) matrix of factor loading for the x variables, _y the (q n) matrix of factor loading for the y variables, the (p p) matrix of measurement error variances and co-variances of the x variables, the (q q) matrix of measurement error variances and co-variances of the y variables, the (q p) matrix of measurement error co-variances between the x variables and the y variables, m the number of endogenous latent variables , n the number of exogenous latent variables , p the number of the x variables, q the number of the y variables.

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Analysis of results

The structural model shows a significant covariance relationship between power and conflict. Regarding the regression coefficients, power and conflict both show direct positive effects on the level of consolidation in port services but power only shows a minor direct effect, which suggests that consolidation strategies in ports have little to do with the level of power held by shipping lines over other channel members, including ports. The research findings reveal that the levels of conflict and power have different impacts on footloose relocations. While conflict has a significant direct impact, the level of power has no influence on mobility, a hypothesis rejected along with H5 on the impact of channel performance on mobility, which re-emphasises the lack of a true supply chain partnership in shipping and ports. There exists, however, an indirect effect of power on mobility (through consolidation), but this indirect effect has a low standardised value and is therefore not significant. In this respect, the model depicts a surprisingly low negative impact of the level of consolidation on footloose arrangements, meaning that even though shipping lines may incur a long-term financial commitment by owning or operating a container port or terminal, this does not reduce their attitude towards mobility or the risk of them being footloose.

The reason behind this apparently unclear relationship may be found in contractual agreements TOSL (as grantees/lessees) hold with governments and public port authorities (grantors/lessors), as well as in joint-venture and operational arrangements they hold with other port operators, be it with other shipping lines (TOSL), operating terminal companies (eg Hong Kong HIT-COSCO terminal) or local firms (Tangiers APMT-AKWA terminal). A further analysis of responses on the nature of concession arrangements shows that 93% are operated through BOT (Build-Operate-Transfer) and EOT (Equip, Operate, Transfer) agreements with an average period of 20–25 years, but also with the possibility to transfer ownership from as short as 5 years. We also know from our case study that there are arrangements (mostly tacit but sometimes explicit) between TOSLs and other joint-venture partners to transfer ownership between them should one party decides to pull out before expiration of the lease or concession period (Figure 5).

Figure 5.

Results of the causal model.

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CONCLUSION

This paper examined how the channel management approach of the marketing theory can be employed to investigate consolidation arrangements versus footloose relocations as being operated by global ocean carriers in the container port industry. We used SEM to model and validate a conceptual construct developed for the purpose of this study.

The results, which might be surprising in their clarity, even hold true in a longer term perspective, as they validate earlier results from a case study carried out by one author for a GPO. From a carrier (shipping line) standpoint, vertical integration strategies such as port ownership aim at closing the gap between the carrier and the shipper/receiver by eliminating the layers of intermediaries and other members in the distribution and logistics channel. From this perspective, port consolidation is nothing new but a further attempt from mega carriers to provide integrated and global logistics services.

Future research could expand and even refine the measurement constructs by analysing how far consolidation strategies of each category of GPO are driven by (1) the level of conflictual relationships each port operator holds with others, and (2) the degree of concentration practiced in this industry. There is also scope to apply this set of relationships to other channel venues, and explore the association between consolidation and mobility arrangements and other meaningful criteria such as supply chain trust, collaboration and partnership.

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