The recent economic downturn has intensified the need for cooperation among carriers in the container shipping industry. Indeed, carriers join inter-firm networks for several reasons such as achieving economies of scale, scope, and the search for new markets. In this paper we apply network analysis and construct the Cooperative Container Network in order to study how shipping companies integrate and coordinate their activities and to investigate the topology and hierarchical structure of inter-carrier relationships. Our data set is comprised of 65 carriers that provide 603 container services. The results indicate that the Cooperative Container Network (CCN) belongs to the family of small world networks. This finding suggests that the most cooperative companies are small-to-medium-size carriers that engage in commercial agreements in order to reduce costs and, when in partnership with larger carriers, these cooperative companies are able to compete, especially against the largest carriers. However shipping companies with high capacity engage in cooperation with other carriers by merely looking for local partners in order to increase their local and specialized market penetration.
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In this paper the terms “company” and “firm” are used interchangeably.
The density is calculated as the ratio between number of links and maximum number of links for the case of a complete graph with the same number of nodes.
The shortest path is the minimum distance (number of links) that separates two nodes.
A path (also known as a cycle) is a walk that connects two or more nodes. The path is closed if the start and end node of a walk both coincide.
Ahuja G (2000) Collaboration networks, structural holes, and innovation: a longitudinal study. Adm Sci Q 45(3):425–455
Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97
Alix Y, Slack B, Comtois C (1999) Alliance or acquisition? Strategies for growth in the container shipping industry, the case of CP ships. J Transp Geogr 7:203–208
Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512
Barros CP (2005) Decomposing growth in Portuguese seaports: a frontier cost approach. Marit Econ Logist 7:297–315
Baum JAC, Shipilov AV, Rowley TJ (2003) Where do small worlds come from? Ind Corp Chang 12(4):697–725
Bergantino AS, Veenstra AW (2002) Interconnection and co-ordination: an application of network theory to liner shipping. Int J Marit Econ 4:231–248
Biggart N, Guillen M (1999) Developing difference: social organization and the rise of the auto industries of South Korea, Taiwan, Spain and Argentina. Am Sociol Rev 64:722–747
Blondel VD, Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:1742–5468
Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424:175–308
Brooks MR, Blunden RG, Bidgood CI (1993) Strategic alliances in the global container transport industry. In: Rerck R (ed) Multinational strategic alliances. International Business Press, London, pp 221–250
Brown L, Rugman A, Verbeke A (1989) Japanese joint ventures with western multinationals: synthesizing the economic and cultural explanations of failure. Asia Pac J Manag 6:225–242
Caves R, Porter ME (1977) From entry barriers to mobility barriers: conjectured decisions and contrived deterrence to new competition. Q J Econ 91:241–267
Chen J, Yahalom S (2013) Container slot co-allocation planning with joint fleet agreement in a round voyage for liner shipping. J Navig 66(4):589–603
Cisic D, Komadina P, Hlaca B (2007) Network analysis applied to Mediterranean liner transport system. In: Proceedings of the International Association of Maritime Economists Conference, Athens
Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111–066117
Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703
Dittrich K, Duysters G, de Man AP (2007) Strategic repositioning by means of alliance networks: the case of IBM. Res Policy 36(10):1496–1511
Drewry Shipping Consultants (2005) Ship operating costs. Annual market review and forecast, London
Ducruet C, Notteboom T (2012) The worldwide maritime network of container shipping: spatial structure and regional dynamics. Glob Networks 12(3):395–423
Ducruet C, Lee SW, Ng AKY (2010) Centrality and vulnerability in liner shipping networks: revisiting the Northeast Asian port hierarchy. Marit Policy Manag 37(1):17–36
Erdos P, Renyi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 12:17–61
Farmer JD, Geanakoplos J (2008) The virtues and vices of equilibrium and the future of financial economics. arXiv:0803.2996
Ferrari C, Parola F, Benacchio M (2008) Network economies in liner shipping: the role of the home markets. Marit Policy Manag 35(2):127–143
Financial Times (2013) ‘Big three’ container shipping groups plan alliance, 18 June
Frémont A, Soppé M (2004) Les stratégies des armateurs de lignes régulières en matière de dessertes maritimes. Belgéo 4:391–406
Garcia-Pont C, Nohria N (2002) Local versus global mimetism: the dynamics of alliance formation in the automobile industry. Strateg Manag J 23:307–321
Gardellin V, Das SK, Lenzini (2011) Cooperative vs. non-cooperative: self-coexistence among selfish cognitive devices. In: World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2011 I.E. International Symposium on a (pp. 1–3). IEEE
Gimeno J (2004) Competition within and between networks: the contingent effect of competitive embeddedness on alliance formation. Acad Manag J 47(6):820–842
Goerzen A, Beamish PW (2005) The effect of alliance network diversity on multinational enterprise performance. Strateg Manag J 26(4):333–354
Gomes-Casseres B (1996) The alliance revolution: the new shape of business rivalry. Harvard University Press, Cambridge
Graham MG (1998) Stability and competition in intermodal container shipping: finding a balance. Marit Policy Manag 25:129–147
Grandori A, Soda G (1995) Inter-firm networks: antecedents, mechanisms and forms. Organ Stud 16(2):183–214
Grønhaug K (1989) Knowledge transfer: the case of the Norwegian technology agreements. OMEGA Int J Manag Sci 17(3):273–279
Hoetker G, Mellewigt T (2009) Choice and performance of governance mechanisms: matching alliance governance to asset type. Strateg Manag J 30(10):1025–1044
Hoffmann J (2010) Shipping out of the economic crisis. Brown J World Aff 16(2):121–130
Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193–218
Hüttenrauch M, Baum M, Vielfalt E (2008) Die dritte Revolution in der Automobilindustrie. Springer, Berlin
Imai A, Nishimura E, Papadimitriou S, Liu M (2006) The economic viability of container mega-ships. Transp Res E 42:21–41
Koluza P, Kolzsch A, Gastner MT, Blasius B (2010) The complex network of global cargo ship movements. J R Soc Interface 7:1093–1103
Koza MP, Lewin AY (1999) The coevolution of network alliances: a longitudinal analysis of an international professional service network. Organ Sci 10(5):638–653
Lam JSL, van de Voorde E (2011) Scenario analysis for supply chain integration in container shipping. Marit Policy Manag 38(7):705–725
Li SX, Huang Z, Zhu J, Chau PYK (2002) Cooperative advertising, game theory and manufacturer–retailer supply chains. OMEGA Int J Manag Sci 30:347–357
Lorange P (2001) Strategic re-thinking in shipping companies. Marit Policy Manag 28(1):23–32
Márquez-Ramos L, Márquez-Ramos I, Pérez-García E, Wilmsmeier G (2011) Maritime networks, services structure and maritime trade. Netw Spat Econ 11:555–576
Midoro R, Pitto A (2000) A critical evaluation of strategic alliances in liner shipping. Marit Policy Manag 27(1):31–40
Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67:026126–026139
Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices, arXiv:physics/0605087v3
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113–026128
Newman MEJ, Strogatz SH, Watts DJ (2003) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64:026118 (1–17)
Notteboom T, Rodrigue JP (2012) The corporate geography of global container terminal operators. Marit Policy Manag 39(3):249–279
Notteboom T, Rodrigue JP, De Monie G (2010) The organizational and geographical ramifications of the 2008–09 financial crisis on the maritime shipping and port industries. In: Hall PV, McCalla B, Comtois C, Slack B (eds) Integrating seaports and trade corridors. Ashgate, Surrey
Panayides PM, Cullinane K (2002) Competitive advantage in liner shipping: a review and research agenda. Int J Marit Econ 4:189–209
Panayides PM, Wiedmer R (2011) Strategic alliances in container liner shipping. Res Transp Econ 32:25–38
Parola F, Satta G, Caschili S (2013) Unveiling cooperative networks and ‘hidden families’ in the container port industry. Marit Policy Manag 40. doi:10.1080/03088839.2013.782442
Patibandla M, Petersen B (2002) Role of transnational corporations in the evolution of a high-tech industry: the case of India’s software industry. World Dev 30(9):1561–1577
Phelps C, Schilling MA (2005) Interfirm collaboration networks: the impact of small world connectivity on firm innovation. Academy of Management Proceedings 2005 (1). Academy of Management
Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithm Appl 10(2):191–218
Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110–016124
Rimmer PJ (1998) Ocean liner shipping services: corporate restructuring and port selection/competition. Asia Pac Viewpoint 39(2):193–208
Roijakkers N (2003) Inter-firm cooperation in high-tech industries: a study of R&D partnerships in pharmaceutical biotechnology. PhD thesis, Maastricht University, Maastricht
Rosegger G (1992) Cooperative strategies in iron and steel: motives and results. OMEGA Int J Manag Sci 20(4):417–430
Rowley T, Behrens D, Krackhardt D (2000) Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strateg Manag J 21:369–386
Saramäki J, Kivelä M, Onnela JP, Kaski K, Kertész J (2007) Generalizations of the clustering coefficient to weighted complex networks. Phys Rev E 75:027105–027109
Satta G, Parola F, Ferrari C, Persico L (2013) Linking growth to performance: insights from shipping line groups. Marit Econ Logist 15(3):349–373
Solomonov R (1951) Rapport A: connectivity of random nets. Bull Math Biophys 13:107–117
Soppé M, Parola F, Frémont A (2009) Emerging inter-industry partnerships between shipping lines and stevedores: from rivalry to cooperation? J Transp Geogr 17(1):10–20
Staropoli C (1998) Cooperation in R&D in the pharmaceutical industry—the network as an organizational innovation governing technological innovation. Technovation 18(1):13–23
Stopford M (2009) Maritime economics, 3rd edn. Routledge, London
Stuart TE, Hoang H, Hybels R (1999) Interorganizational endorsements and the performance of entrepreneurial ventures. Adm Sci Q 44:315–349
Sullivan BN, Tang Y (2012) Small-world networks, absorptive capacity and firm performance: evidence from the US venture capital industry. Int J Strateg Chang Manag 4(2):149–175
Sydow J, Windeler A (1998) Organizing and evaluating interfirm networks: a structurationist perspective on network processes and effectiveness. Organ Sci 9(3):265–284
Tang LC, Low JMW, Lam SW (2011) Understanding port choice behavior—a network perspective. Netw Spat Econ 11(1):65–82
UNCTAD (2012) Review of maritime transport 2012. United Nations Publication, Geneva
Watts D, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442
Zaheer A, Bell GG (2005) Benefiting from network position: firm capabilities, structural holes, and performance. Strateg Manag J 26(9):809–826
S.C. and F.M. acknowledge the financial support of the Engineering and Physical Sciences Research Council (EPSRC) under the grant ENFOLD-ing: Explaining, Modelling and Forecasting Global Dynamics, reference EP/H02185X/1.
We propose the application of a network community detection analysis in order to identify how carriers cluster together in the container shipping industry.
In this study we present the results of the application of the Spinglass method (Reichardt and Bornholdt 2006). We also test a number of other methods (Table 3), but we propose the partition provided by the Spinglass algorithm because it offers the highest value of modularity Q (Newman and Girvan 2004) among the methods tested. The modularity Q provides a measure to discern if a partition is valid to unfold the community structure in a network.
The six methods tested provide us with partitions of similar values of modularity Q, unless the number of clusters provided by each method ranges between 4 and 9. In order to evaluate how similar these partitions look, we propose the application of a quantitative index, the Adjusted Rand Index (ARI) proposed by Hubert and Arabie (1985). The ARI compares two partitions T and W of the same data set. The first partition T is used as a reference partition. Classes in partition W are in turn evaluated according to the following formulation:
- a :
is number of pairs of elements belonging to the same class both in T and W.
- b :
is number of pairs of elements belonging to the same class in T and to different clusters in W.
- c :
is number of pairs of elements belonging to different classes in T and to the same cluster in W.
- d :
is number of pairs of elements belonging to different classes both in T and W.
- n :
is number of elements of the partitions.
The ARI ranges between 0 and 1 (perfect similarity). Table 4 shows the level of similarity of the partitions provided by the six methods of community detection. We order the table to have a comparative analysis from the best method (as proved by the modularity function Q) to the lower value. A high level of agreement can be detected among all the partitions (Table 4), thus confirming that apart from small variations, the partition provided by the Spinglass method is reliable.
By comparing the Spinglass partitioning (Table 5) with the results proposed in Table 4, as well as the composition of official alliances, we can see that community 2 comprises all members of the CKYH alliance. The membership of this cluster appears to be quite heterogeneous, as it is composed of 17 carriers with an average degree k of 11 (standard deviation of 7.9). The other carriers are minor companies, as they do not belong to the first 15 companies in terms of shipped freight volumes. The members of the other official alliance, i.e. G6, are spread over the other clusters.
In order to clarify the relationships between the network structure and the cluster organization of carriers, Table 6 shows some statistics on the average degree and standard deviation of the carriers belonging to each cluster, and the list of the three leading carriers in terms of network connections. Most clusters show similar values of average degree k and standard deviation. There are a few leaders in each group (dominant carriers) while weakly connected carriers compose the remainder of the clusters’ population (high values of standard deviation). Finally, Fig. 7 depicts the cluster membership of the carriers in our sample as provided by the Community Spinglass method. The size of each node is drawn as a function of the number of connections, while the colours correspond to node membership in the five clusters.
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Caschili, S., Medda, F., Parola, F. et al. An Analysis of Shipping Agreements: The Cooperative Container Network. Netw Spat Econ 14, 357–377 (2014). https://doi.org/10.1007/s11067-014-9230-1
- Container shipping line
- Cooperative agreement
- Small world network
- Complex network analysis