An Analysis of Shipping Agreements: The Cooperative Container Network

Abstract

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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Notes

  1. 1.

    In this paper the terms “company” and “firm” are used interchangeably.

  2. 2.

    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.

  3. 3.

    The shortest path is the minimum distance (number of links) that separates two nodes.

  4. 4.

    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.

References

  1. Ahuja G (2000) Collaboration networks, structural holes, and innovation: a longitudinal study. Adm Sci Q 45(3):425–455

    Article  Google Scholar 

  2. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97

    Article  Google Scholar 

  3. 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

    Article  Google Scholar 

  4. Barabasi AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  Google Scholar 

  5. Barros CP (2005) Decomposing growth in Portuguese seaports: a frontier cost approach. Marit Econ Logist 7:297–315

    Article  Google Scholar 

  6. Baum JAC, Shipilov AV, Rowley TJ (2003) Where do small worlds come from? Ind Corp Chang 12(4):697–725

    Article  Google Scholar 

  7. Bergantino AS, Veenstra AW (2002) Interconnection and co-ordination: an application of network theory to liner shipping. Int J Marit Econ 4:231–248

    Article  Google Scholar 

  8. 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

    Article  Google Scholar 

  9. Blondel VD, Guillaume J, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 10:1742–5468

    Google Scholar 

  10. Boccaletti S, Latora V, Moreno Y, Chavez M, Hwang DU (2006) Complex networks: structure and dynamics. Phys Rep 424:175–308

    Article  Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. 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

  16. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111–066117

    Article  Google Scholar 

  17. Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Drewry Shipping Consultants (2005) Ship operating costs. Annual market review and forecast, London

  20. Ducruet C, Notteboom T (2012) The worldwide maritime network of container shipping: spatial structure and regional dynamics. Glob Networks 12(3):395–423

    Article  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. Erdos P, Renyi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 12:17–61

    Google Scholar 

  23. Farmer JD, Geanakoplos J (2008) The virtues and vices of equilibrium and the future of financial economics. arXiv:0803.2996

  24. 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

    Article  Google Scholar 

  25. Financial Times (2013) ‘Big three’ container shipping groups plan alliance, 18 June

  26. Frémont A, Soppé M (2004) Les stratégies des armateurs de lignes régulières en matière de dessertes maritimes. Belgéo 4:391–406

    Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

  29. Gimeno J (2004) Competition within and between networks: the contingent effect of competitive embeddedness on alliance formation. Acad Manag J 47(6):820–842

    Article  Google Scholar 

  30. Goerzen A, Beamish PW (2005) The effect of alliance network diversity on multinational enterprise performance. Strateg Manag J 26(4):333–354

    Article  Google Scholar 

  31. Gomes-Casseres B (1996) The alliance revolution: the new shape of business rivalry. Harvard University Press, Cambridge

    Google Scholar 

  32. Graham MG (1998) Stability and competition in intermodal container shipping: finding a balance. Marit Policy Manag 25:129–147

    Article  Google Scholar 

  33. Grandori A, Soda G (1995) Inter-firm networks: antecedents, mechanisms and forms. Organ Stud 16(2):183–214

    Article  Google Scholar 

  34. Grønhaug K (1989) Knowledge transfer: the case of the Norwegian technology agreements. OMEGA Int J Manag Sci 17(3):273–279

    Article  Google Scholar 

  35. Hoetker G, Mellewigt T (2009) Choice and performance of governance mechanisms: matching alliance governance to asset type. Strateg Manag J 30(10):1025–1044

    Article  Google Scholar 

  36. Hoffmann J (2010) Shipping out of the economic crisis. Brown J World Aff 16(2):121–130

    Google Scholar 

  37. Hubert L, Arabie P (1985) Comparing partitions. J Classif 2:193–218

    Article  Google Scholar 

  38. Hüttenrauch M, Baum M, Vielfalt E (2008) Die dritte Revolution in der Automobilindustrie. Springer, Berlin

    Google Scholar 

  39. Imai A, Nishimura E, Papadimitriou S, Liu M (2006) The economic viability of container mega-ships. Transp Res E 42:21–41

    Article  Google Scholar 

  40. Koluza P, Kolzsch A, Gastner MT, Blasius B (2010) The complex network of global cargo ship movements. J R Soc Interface 7:1093–1103

    Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. Lam JSL, van de Voorde E (2011) Scenario analysis for supply chain integration in container shipping. Marit Policy Manag 38(7):705–725

    Article  Google Scholar 

  43. 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

    Article  Google Scholar 

  44. Lorange P (2001) Strategic re-thinking in shipping companies. Marit Policy Manag 28(1):23–32

    Article  Google Scholar 

  45. 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

    Article  Google Scholar 

  46. Midoro R, Pitto A (2000) A critical evaluation of strategic alliances in liner shipping. Marit Policy Manag 27(1):31–40

    Article  Google Scholar 

  47. Newman MEJ (2003) Mixing patterns in networks. Phys Rev E 67:026126–026139

    Article  Google Scholar 

  48. Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices, arXiv:physics/0605087v3

  49. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:026113–026128

    Article  Google Scholar 

  50. Newman MEJ, Strogatz SH, Watts DJ (2003) Random graphs with arbitrary degree distributions and their applications. Phys Rev E 64:026118 (1–17)

    Article  Google Scholar 

  51. Notteboom T, Rodrigue JP (2012) The corporate geography of global container terminal operators. Marit Policy Manag 39(3):249–279

    Article  Google Scholar 

  52. 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

    Google Scholar 

  53. Panayides PM, Cullinane K (2002) Competitive advantage in liner shipping: a review and research agenda. Int J Marit Econ 4:189–209

    Article  Google Scholar 

  54. Panayides PM, Wiedmer R (2011) Strategic alliances in container liner shipping. Res Transp Econ 32:25–38

    Article  Google Scholar 

  55. 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

  56. 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

    Article  Google Scholar 

  57. 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

  58. Pons P, Latapy M (2006) Computing communities in large networks using random walks. J Graph Algorithm Appl 10(2):191–218

    Article  Google Scholar 

  59. Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E 74:016110–016124

    Article  Google Scholar 

  60. Rimmer PJ (1998) Ocean liner shipping services: corporate restructuring and port selection/competition. Asia Pac Viewpoint 39(2):193–208

    Article  Google Scholar 

  61. Roijakkers N (2003) Inter-firm cooperation in high-tech industries: a study of R&D partnerships in pharmaceutical biotechnology. PhD thesis, Maastricht University, Maastricht

  62. Rosegger G (1992) Cooperative strategies in iron and steel: motives and results. OMEGA Int J Manag Sci 20(4):417–430

    Article  Google Scholar 

  63. 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

    Article  Google Scholar 

  64. 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

    Article  Google Scholar 

  65. 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

    Article  Google Scholar 

  66. Solomonov R (1951) Rapport A: connectivity of random nets. Bull Math Biophys 13:107–117

    Article  Google Scholar 

  67. 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

    Article  Google Scholar 

  68. 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

    Article  Google Scholar 

  69. Stopford M (2009) Maritime economics, 3rd edn. Routledge, London

    Google Scholar 

  70. Stuart TE, Hoang H, Hybels R (1999) Interorganizational endorsements and the performance of entrepreneurial ventures. Adm Sci Q 44:315–349

    Article  Google Scholar 

  71. 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

    Article  Google Scholar 

  72. Sydow J, Windeler A (1998) Organizing and evaluating interfirm networks: a structurationist perspective on network processes and effectiveness. Organ Sci 9(3):265–284

    Article  Google Scholar 

  73. Tang LC, Low JMW, Lam SW (2011) Understanding port choice behavior—a network perspective. Netw Spat Econ 11(1):65–82

    Article  Google Scholar 

  74. UNCTAD (2012) Review of maritime transport 2012. United Nations Publication, Geneva

    Google Scholar 

  75. Watts D, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442

    Article  Google Scholar 

  76. Zaheer A, Bell GG (2005) Benefiting from network position: firm capabilities, structural holes, and performance. Strateg Manag J 26(9):809–826

    Article  Google Scholar 

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Acknowledgments

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.

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Correspondence to Simone Caschili.

Appendix

Appendix

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.

Table 3 List of community detection methods applied to identify clusters in the CCN

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:

$$ ARI=\frac{\left({}_2^n\right)\left(a+d\right)-\left[\left(a+b\right)\left(a+c\right)+\left(c+d\right)\left(b+d\right)\right]}{{\left({}_2^n\right)}^2-\left[\left(a+b\right)\left(a+c\right)+\left(c+d\right)\left(b+d\right)\right]} $$
(4)

Where:

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.

Table 4 Similarity matrix of the Adjusted Rand Index values between the community detection methods tested on the CCN

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.

Table 5 List of cluster carrier membership provided by the Spinglass method. We report the degree k of each carrier in brackets

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.

Table 6 List of community detection methods applied to identify clusters in the CCN
Fig. 7
figure7

A visualisation of the CCN and the partitions detected by the Spinglass algorithm

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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

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Keywords

  • Container shipping line
  • Cooperative agreement
  • Small world network
  • Complex network analysis