Advertisement

Methodological Demonstration of a Text Analytics Approach to Country Logistics System Assessments

  • Aseem KinraEmail author
  • Raghava Rao Mukkamala
  • Ravi Vatrapu
Conference paper
Part of the Lecture Notes in Logistics book series (LNLO)

Abstract

The purpose of this study is to develop and demonstrate a semi-automated text analytics approach for the identification and categorization of information that can be used for country logistics assessments. In this paper, we develop the methodology on a set of documents for 21 countries using machine learning techniques while controlling both for 4 different time periods in the world FDI trends, and the different geographic and economic country affiliations. We report illustrative findings followed by a presentation of the separation of concerns/division of labor between the domain expert and the text analyst. Implications are discussed and future work is outlined.

Keywords

Logistics Transport system evaluation Big data analytics Data mining Text mining Machine learning 

References

  1. Beugelsdijk S, Mudambi R, McCann P (2010) Place, space and organization: economic geography and the multinational enterprise. J Econ Geogr 10(4):485–493CrossRefGoogle Scholar
  2. Beugelsdijk S, Mudambi R (2013) MNEs as border-crossing multi-location enterprises: The role of discontinuities in geographic space. J Int Bus Stud 44Google Scholar
  3. Bird S (2006) NLTK: the natural language toolkit. Paper presented at the proceedings of the COLING/ACL on interactive presentation sessionsGoogle Scholar
  4. Bookbinder JH, Tan CS (2003) Comparison of Asian and European logistics systems. Int J Phys Distribut Logist Manag 33(1)Google Scholar
  5. Carter JR, Pearson JN, Peng L (1997) Logistics barriers to international operations: the case of the people’s republic of China. J Bus Logist 18(2):129–145Google Scholar
  6. Kinra A, Kotzab H (2008) Understanding and measuring macro-institutional complexity of logistics systems environment. J Bus Logist 29(1):327–346CrossRefGoogle Scholar
  7. Kinra A (2015) Environmental complexity related information for the assessment of country logistics environments: implications for spatial transaction costs and foreign location attractiveness. J Transp Geogr 43:36–47CrossRefGoogle Scholar
  8. McCann P, Mudambi R (2004) The location behavior of the multinational enterprise: some analytical issues. Growth Change 35(4):491–524CrossRefGoogle Scholar
  9. Memedovic O, Ojala L, Rodrigue JP, Naula T (2008) Fuelling the global value chains: what role for logistics capabilities. Int J Technol Learn Innov Dev 1(3):353–374CrossRefGoogle Scholar
  10. Min H (1994) Location analysis of international consolidation terminals using the analytic hierarchy process. J Bus Logist 15(2):25–44Google Scholar
  11. Mukkamala R, Hussain A, Vatrapu R (2014a) Fuzzy-set based sentiment analysis of big social data. In: Proceedings of IEEE 18th international enterprise distributed object computing conference (EDOC 2014), Ulm, Germany, pp 71–80. doi:1510.1109/EDOC.2014.1519. ISBN: 1541-7719/1514Google Scholar
  12. Mukkamala R, Hussain A, Vatrapu R (2014b) Towards a set theoretical approach to big data analytics. In: Proceedings of the 3rd IEEE international congress on big data 2014, Anchorage, United StatesGoogle Scholar
  13. Rodrigue J-P (2012) The geography of global supply chains: evidence from third-party logistics. J Supply Chain Manag 48(3):15–23CrossRefGoogle Scholar
  14. Rodrigue J-P, Notteboom T (2010) Comparative North American and European gateway logistics: the regionalism of freight distribution. J Transp Geogr 18(4):497–507CrossRefGoogle Scholar
  15. Vatrapu R, Mukkamala R, Hussain A (2014a) A set theoretical approach to big social data analytics: concepts, methods, tools, and findings. Paper presented at the computational social science workshop at the European conference on complex systems 2014Google Scholar
  16. Vatrapu R, Mukkamala R, Hussain A (2014b) Towards a set theoretical approach to big social data analytics: concepts, methods, tools, and empirical findings. Paper presented at the 5th annual social media & society international conference 2014Google Scholar
  17. Yang Y, Liu X (1999) A re-examination of text categorization methods. Paper presented at the proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  18. Zhang H, Li D (2007) Naïve Bayes text classifier. Paper presented at the granular computing, 2007. IEEE international conference on GRC 2007Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Aseem Kinra
    • 1
    Email author
  • Raghava Rao Mukkamala
    • 2
  • Ravi Vatrapu
    • 2
    • 3
  1. 1.Department of Operations ManagementCopenhagen Business SchoolFrederiksbergDenmark
  2. 2.Computational Social Science Laboratory, Department of IT ManagementCopenhagen Business SchoolFrederiksbergDenmark
  3. 3.Mobile Technology Laboratory, Faculty of TechnologyWesterdals Oslo ACTOsloNorway

Personalised recommendations