Conclusion- the most influential variables of the new globalization model that will affect the maritime transport in the future will be cybersecurity threat, Industry 4.0 technologies and climate change.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Purpose- to identify the variables that define the new globalization model from the point of view of the container shipping and port industry and to rank these variables by their importance.
📊
Key Findings & Data:Findings- According to the findings, there are 9 basic variables that will affect maritime transport and the port in the future.
💡
Conclusion & Impact:Conclusion- the most influential variables of the new globalization model that will affect the maritime transport in the future will be cybersecurity threat, Industry 4.0 technologies and climate change.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Sea transport · globalization · cybersecurity
Abstract
Purpose- to identify the variables that define the new globalization model from the point of view of the container shipping and port industry and to rank these variables by their importance. Methodology- A research process involving both qualitative (semi-structured interview) and quantitative method (Fuzzy AHP) has used to collect and analyse data. Findings- According to the findings, there are 9 basic variables that will affect maritime transport and the port in the future. Conclusion- the most influential variables of the new globalization model that will affect the maritime transport in the future will be cybersecurity threat, Industry 4.0 technologies and climate change.
Esmer, S. (2018). THE FACTORS AFFECTING THE SEA TRANSPORTATION IN THE NEW GLOBALIZATION ERA. Journal of Management Marketing and Logistics, Volume: 5 Issue: 3. https://ajindex.com/articles/1592957
Abdel-Kader, M., Dugdale, D. (2001). Evaluating investments in advanced manufacturing technology: a fuzzy set theory approach. British Accounting Review, 33, 455-489.
Aggelakakis, J., Bernandino, M., Boile, P., Christidis, A., Condeco, M., Krail, A., Papanikolaou, M., Reichenbach. J. (2015). The future of the transport industry. European Commission Joint Research Centre Institute for Prospective Technological Studies.
Baas, S. M., Kwakernaak, H. (1977). Rating and ranking of multiple aspect alternatives using fuzzy sets. Automatica, 13, 47-58.
Bernardino, J., Vieira, J., Garcia, H. (2013). Factors of evolution of demand and methodological approach to identify pathways. FUTURE Deliverable D3.1. Project financed by the 7th Framework Programme.
Bhattacharya, A., Khanna, D., Schweizer, C., Bijapurkar, A. (2017). The new globalization: going beyond the rhetoric. Boston Consulting.
Boddy, C. R. (2016). Sample size for qualitative research. Qualitative Market Research: An International Journal, 19(4), 426–432.
Bortolan, G., Degani, R. (1985). A review of some methods for ranking fuzzy numbers. Fuzzy Sets and Systems, 15, 1-19.
Bryman, A. (2008). Social research methods. Oxford university press, 4th edition.
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655.
Chen, S. J., Hwang, C. L. (1992). Fuzzy multiple attribute decision making methods. In: Fuzzy Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems, 375. Springer, Berlin, Heidelberg.
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management review, 14(4), 532–550.
Ezzati, R., Allahvıranloo, T., Khezerloo, S., Khezerloo, M. (2012). An approach for ranking of fuzzy numbers. Expert Systems with Applications, 39, 690-695.
H. Deng, (1999). Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning, 21, 215-231.
Jain, R. (1976). Decision making in the presence of fuzzy variable. IEEE Trans Systems Man and Cybernet SMC, 6, pp. 698-703.
Johnson, R. B., Onwuegbuzie, A. J. (2004). Mixed methods research: a research paradigm whose time has come. Educational Researcher, 33(7), 14–26.
Kwong, C. K., Bai, H. (2003). Determining the importance weights for the customer requirements in QFD using a fuzzy AHP with an extent analysis approach. IEE Transactions, 35(7), 619-626.
Liou, T. S., Wang, M. J. (1992). Ranking fuzzy numbers with integral value. Fuzzy Sets and Systems, 50(3), 247-255.
Malhotra, N. K. (2008). Marketing research: an applied orientation, 5/e, Pearson Education India.
Rodrigue, J. P. (2006). Transportation and globalization. Encyclopaedia of Globalization, Edited by Roland Robertson and Jan Aart Scholte. Routledge.
Rodrigue, J. P. (2010). Maritime transportation: drivers for the shipping and port industries. International Transport Forum, Paris.
Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83–98.
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234–281.
Saxon, S., Stone, S. (2017). Container shipping, the next 50 years. McKinsey&Company. Travel, Transport & Logistics.
Sheu, J. B. (2004). A hybrid fuzzy-based approach for identifying global logistics strategies. Transportation Research, 40(1), 39-61.
Tavasszy, L. A., Ruijgrok, C. J., Thissen, M. J. P. M. (2003). Emerging global logistics networks: implications for transport systems and policies. Growth and Change 34(4), 456–472.