Advertisement

What Factors do Affect Freight Volume of the Steel Industry in Korea?: A Structural Equation Modeling Approach

  • Seungjin Shin
  • Sangho Choo
  • Kangdae Lee
  • Dongjoo Park
Transportation Engineering
  • 9 Downloads

Abstract

One of the preliminary requirements for freight transportation modeling is to understand the factors that affect the freight volume. In this study, we attempt to investigate the relationship between various factors and freight volumes using a structural equation model. From a review of the existing literature, we formulate our research hypotheses and identify the factors. We propose a research model describing the relationship between the factors and freight volume. Finally, we estimate a structural equation model using the survey data of 97 private steel companies in Korea. From the developed models, we find that all the three characteristics (i.e., location, agglomeration, and other) have positive effects on steel freight volume. Location and other characteristics have a more significant effect than agglomeration characteristics because these characteristics have a direct effect, whereas agglomeration characteristics have an indirect effect.

Keywords

freight volume Structural Equation Modeling (SEM) steel industry 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arvis, J. F., Mustra, M, A., Ojala, L., Shepherd, B., and Saslavsky, D. (2010). Connecting to Compete 2010: Trade Logistics in the Global Economy. The World Bank, Washington, D.C.CrossRefGoogle Scholar
  2. Bartik, T. J. (1985). “Business location decisions in the united states: Estimates of the effects of unionization, taxes, and other characteristics of states.” Journal of Business and Economic Statistics, vol. 3, no. 1, pp. 14–22, DOI: 10.2307/1391685.MathSciNetGoogle Scholar
  3. Bayraktar, E., Demirbag, M., Koh, S. C., Tatoglu, E., and Zaim, H. (2009). “A causal analysis of the impact of information systems and supply chain management practices on operational performance: evidence from manufacturing SMEs in Turkey.” International Journal of Production Economics, vol. 122, no. 1, pp. 133–149, DOI: 10.1016/j.ijpe.2009.05.011.CrossRefGoogle Scholar
  4. Bentler, P. M. (1990). “Comparative fit indexes in structural models.” Psychological bulletin, vol. 107, no. 2, pp. 238–246, DOI: 10.1037/0033-2909.107.2.238.CrossRefGoogle Scholar
  5. Bollen, K. A. (1989). Structural equation with latent variables. Wiley, New York.CrossRefzbMATHGoogle Scholar
  6. Brouwer, A. E., Mariotti, I., and Ommeren, J. (2004). “The firm relocation decision: An empirical investigation.” Annuals of Regional Science, vol. 38, no. 2, pp. 335–347, DOI: 10.1007/s00168-004-0198-5.CrossRefGoogle Scholar
  7. Browne, M. W. and Cudeck, R. (1992). “Alternative ways of assessing model fit.” Sociological Methods and Research, vol. 21, no. 2, pp. 230–258, DOI: 10.1177/0049124192021002005.CrossRefGoogle Scholar
  8. Chan, F. T., Chan, H. K., Lau, H. C., and Ip, R. W. (2006). “An AHP approach in benchmarking logistics performance of the potal industry.” Benchmarking: An International Journal, vol. 13, no. 6, pp. 636–661, DOI: 10.1108/14635770610709031.CrossRefGoogle Scholar
  9. Chen, L. and Notteboom, T. (2014). “A cost perspective on the location of Value–added Logistics services in supply Chains.” International Journal of Logistics Systems and Management, vol. 18, no. 1, pp. 24–48, DOI: 10.1504/IJLSM.2014.062121.CrossRefGoogle Scholar
  10. Choi, M. and Choi, T. (2016). “Agglomeration, productivity, and highgrowth firms in the manufacturing sector of South Korea.” International Journal of Urban Sciences, vol. 21, no. 1, pp. 58–71, DOI: 10.1080/12265934.2016.1195278CrossRefGoogle Scholar
  11. Choi, Y. and Yim, H. (2008). “Analysis of determinants on industrial location classified by scale of company.” Korea Real Estate Academy Review, vol. 34, pp. 67–80.Google Scholar
  12. Choo, S. and P. L. Mokhtarian. (2005). “Does telecommunications affect passenger travel, or Vice Versa? Structural equation Modelings of Aggregate U.S. Time Series Data Using Composite Indices.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., vol. 1926, pp. 224–232, DOI: 10.3141/1926-26.Google Scholar
  13. Davis, F. W. and Manrodt, K. B. (1994). “Service logistics: An introduction.” International Journal of Physical Distribution and Logistics Management, vol. 24, no. 4, pp. 59–68, DOI: 10.1108/EUM0000000000393.CrossRefGoogle Scholar
  14. De Jong, G. and Ben Akiva, M. (2007). “A micro-simulation model of shipment size and transport Chain Choice.” Transportation Research Part B, vol. 41, no. 9, pp. 950–965, DOI: 10.1016/j.trb.2007.05.002.CrossRefGoogle Scholar
  15. De Jong, G., Ben Akiva, M., and Baak, J. (2010). Logistics Model in The Swdish National Freight Model System-version 2, Deliverable 6B for the SAMGODS GROUP.Google Scholar
  16. De Jong, G., Ben Akiva, M., Bexelius, S., Rahman, A., and Van De Voort, M. (2004). The Specification of Logistics in the Norwegian and Swedish Freight Model Systems, Rand Europe.Google Scholar
  17. Devereux, M. P., Griffith, R., and Simpson, H. (2004). Agglomeration, regional grants and firm location, The Institute for Fiscal Studies, London.Google Scholar
  18. Golob, T. F. (2003). “Structural equation Modeling for travel behavior research.” Transportation Research B, vol. 37, no. 1, pp. 1–25, DOI: 10.1016/s0191-2615(01)00046-7.CrossRefGoogle Scholar
  19. Gonzalez-Feliu, J. and Mercier, A. (2013). “A combined people-freight accessibility approach for urban retailing and leisure planning at strategic level.” In 5th International Conference on Urban Freight Transport, I-NUF 2013.Google Scholar
  20. Gonzalez-Feliu, J., Salanova Grau, J. M., and Beziat, A. (2014). “A location-based accessibility analysis to estimate the suitability of urban consolidation facilities.” International Journal of Urban Sciences, vol. 18, no. 2, pp. 166–185, DOI: 10.1080/12265934.2014.930673CrossRefGoogle Scholar
  21. Hanaoka, S. and Kunadhamraks, P. (2009). “Multiple criteria and fuzzy based evaluation of logistics performance for intermodal transportation.” Journal of Advanced Transportation, vol. 43, no. 2, pp. 123–153, DOI: 10.1002/atr.5670430204.CrossRefGoogle Scholar
  22. Henderson, J. V. (1986). “Efficiency of resource usage and city size.” Journal of Urban Economics, vol. 19, no. 1, pp. 47–70, DOI: 10.1016/0094-1190(86)90030-6.CrossRefGoogle Scholar
  23. Henderson, J. V. (2003). “Marshall’s scale economies.” Journal of Urban Economics, vol. 53, no. 1, pp. 1–28, DOI: 10.1016/S0094-1190(02)00505-3.CrossRefGoogle Scholar
  24. Holguín-Veras, J. and Thorson, E. (2000). “Trip Length distributions in commodity-based and trip-based freight demand modeling.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., vol. 1707, pp. 37–48, DOI: 10.3141/1707-05.CrossRefGoogle Scholar
  25. Hong, S. J. (2006). Choice of Location Regarding to the Type of Business and the Trait of Regions, Master Thesis, Department of Economics, The Graduate School Yonsei University, Korea.Google Scholar
  26. Hong, S., Malik, M. L., and Lee, M. K. (2003). “Testing Configural, metric, scalar, and latent mean invariance across genders in sociotropy and autonomy using non-western sample.” Educational and Psychological Measurement, vol. 63, no. 4, pp. 636–654, DOI: 10.1177/0013164403251332.MathSciNetCrossRefGoogle Scholar
  27. Ivanova, O., Tavasszy, L. A., and Manshanden, W. J. J. (2007). “On the Development of the New Version of the RAEM Model for the Netherlands (RAEM 3.0).” In Proceedings of the Jointed Congress of the European Regional Science Association (47th) and Association de Science Régionale de Langue Française (44th), Paris.Google Scholar
  28. Kancs, A. (2015). “Predicting European Enlargement Impacts: A Framework of Interregional General Equilibrium.” Eastern European Economics, vol. 39, no. 5, pp. 31–63, DOI: 10.1080/00128775. 2001.11041001.Google Scholar
  29. Kaplan, D. (2007). Structural equation Modeling, Sage, pp. 1089–1093. ISBN 9781412950589.Google Scholar
  30. Kenny, D. A. and McCoach, D. B. (2003). “Effect of the number of variables on measures of fit in structural equation Modeling.” Structural equation Modeling 10, pp. 333–351, DOI: 10.1207/s15328007sem1003_1.Google Scholar
  31. Kim, S. T. and Roh, K. H. (2004). “An Analysis on regional innovation cluster estimation and its Effects on regional economic growth.” Journal of Korean Association of Applied Economics, vol. 6, no. 2, pp. 63–97.Google Scholar
  32. Kline, Rex (2011). Principles and Practice of Structural equation Modeling (Third ed.). Guilford. ISBN 9781606238769.zbMATHGoogle Scholar
  33. Lee, H. I. and Lee, B. S. (2002). “An analysis of firm relocation in the manufacturing industries: The case of the relocating firms in the capital region.” Journal of Korea Planners Association, vol. 37, no. 7, pp. 103–116.Google Scholar
  34. Lee, H. Y. and Park, W. S. (2011). “A study on the location factors of foreign direct investment firms in Korea.” Journal of the Korean Association of Regional Geographers, vol. 17, no. 1, pp. 58–74.Google Scholar
  35. Lee, M. Y. (2008). An Analysis of Location Preference Factors of Industrial Complex in Gyeonggi-do, Master Thesis, Urban and Regional Planning, The Graduate School of Engineering Yonsei University, Korea.Google Scholar
  36. Lee, S. K. and Kim, E. (2014). “The effects of highway investments on production costs in the Korean manufacturing sector.” International Journal of Urban Sciences, vol. 19, no. 2, pp. 182–191, DOI: 10.1080/12265934.2014.980301.CrossRefGoogle Scholar
  37. Leitham, S., Mcquaid, R., and Nelson, J. (2000). “The influence of transport on industrial location choice: A stated preference Experiment.” Transportation Research Part A, vol. 34, no. 7, pp. 515–535, DOI: 10.1016/S0965-8564(99)00030-0.Google Scholar
  38. Levesque, C., Zuehlke, A. N., Stanek, L. R., and Ryan, R. M. (2004). “Autonomy and competence in german and american university students: A comparative study based on Self-Determination Theory.” Journal of Educational Psychology, vol. 9, no. 1, pp. 68–84, DOI: 10.1037/0022-0663.96.1.68.CrossRefGoogle Scholar
  39. Lofgren, H. and Robinson, S. (1999). Spatial networks in multi-region computable general equilibrium models, No. 35. International Food Policy Research Institute (IFPRI).Google Scholar
  40. Mariotti, I. (2005). Firm Relocation and Regional Policy: A Focus on Italy, the Netherlands and the United Kingdom, University of Groningen, Groningen.Google Scholar
  41. Maskell, P. (1996). Localized low-tech learning in the furniture industry, Danish Research Unit for Industrial Dynamics (DRUID) Working Paper, No. 96–11, DOI: 10.2139/ssrn.51720.Google Scholar
  42. Mizokami, S. and Kakimoto, R. (2003), “An inter-regional freight demand model by spatial applied general equilibrium analysis.” Journal of the Eastern Asian Society for Transportation Studies, vol. 5, pp. 287–299.Google Scholar
  43. Mizutani, M., Tsuchiya, K., Akiyoshi, S., Koike, A., and Ishikawa, Y. (2006), “International freight transport demand forecasting by SCGE model.” In Intermediate Input-Output Meeting, at Sendai, Japan Google Scholar
  44. Morgan, K. and Cooke, P. (1998). The Associational Economic: Firms, Regions, and Innovation. Oxford University Press.Google Scholar
  45. Park, S. H. (2010). Productivity Analysis on the Classification of Enterprise Location, Gyeonggi Research Institute.Google Scholar
  46. Park, S. Y. and Kim, Y. J. (2005). “A study on characteristics and performance factors among middle and venture business in it industry cluster backgrounds.” The Korean Small Business Review, vol. 27, no. 4, pp. 111–133.Google Scholar
  47. Porter, M. E. (1998). Clusters and the New Economics of Competition, Harvard Business Review, Boston.Google Scholar
  48. Porter, M. E. (2000). “Location, competition, and economic development: local clusters in a global economic.” Economic Development Quarterly, vol. 14, no. 1, pp. 15–34, DOI: 10.1177/089124240001400105.CrossRefGoogle Scholar
  49. Regmi, M. B. and Hanaoka, S. (2013). “Location analysis of logistics centres in laos.” International Journal of Logistics Research and Applications, vol. 16, no. 3, pp. 227–242, DOI: 10.1080/13675567. 2013.812194.CrossRefGoogle Scholar
  50. Shin, S., Park, D. J., and Kim, J. J. (2016), “Estimation of steel production/consumption using multi-regional computable general equilibrium model.” The Korea Transport Institute, Journal of Transport Research, vol. 23, no. 3, pp. 21–41.Google Scholar
  51. Targa, F., Clifton, K., and Mahmassani, H. (2005). “Economic activity transportation access: An econometric analysis of spatial patterns.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., vol. 1932, pp. 61–71, DOI: 10.3141/1932-08.Google Scholar
  52. Tavasszy, L. A. (1996). Modelling European Freight Transport Flows. Delft University of Technology, Trail Research School, Delft.Google Scholar
  53. Tavasszy, L. A. (2008). Freight modelling-an overview of international experiences, Paper prepared for the TRB Conference on Freight Demand Modelling: Tools for Public Sector Decision Making, Washington, D.C., pp. 47–55.Google Scholar
  54. Tavasszy, L. A., Cornelissen, C. E., and Huijsman, E. (2001). “Forecasting the impacts of changing patterns of physical distribution on freight transport in Europe.” In 9th World Congress on Transport Research. CD-ROM. Elsevier.Google Scholar
  55. Tavasszy, L. A., Smeenk, B., and Ruijgrok, C. J. (1998a). “A DSS for modelling logistics Chains in freight transport policy analysis.” International Transactions in Operational Research, vol. 5, no. 6, pp. 447–459, DOI: 10.1016/s0969-6016(98)00045-8.CrossRefGoogle Scholar
  56. Tavasszy, L. A., Van de Vlist, M., Ruijgrok, C. J., and Van de Rest, J. (1998b). “Scenario-Wise analysis of transport and logistic systems with a SMILE.” In 8th World Conference on Transport Research.Google Scholar
  57. The Korea Transport Institute (2012). National Transportation Demand Survey and Database Establishment in 2012: Commodity Flow(O/D) Survey on the National Area.Google Scholar
  58. Tippayawong, K. Y., Patitad, P., Sopadang, A., and Enkawa, T. (2010). “Factors Affecting efficient supply chain operational performance of high and low technology companies in thailand.” Management Science and Engineering, vol. 4, no. 3, pp. 24–33.Google Scholar
  59. Tucker, L. R. and Lewis, C. (1973). “A reliability coefficient for maximum likelihood factor analysis.” Psychometrika, vol. 38, no. 1, pp. 1–10, DOI: 10.1007/BF02291170.CrossRefzbMATHGoogle Scholar
  60. Van den Heuvel, F. P., De Langen, P. W., van Donselaar, K. H., and Fransoo, J. C. (2013). “Spatial concentration and location dynamics in logistics: The case of a dutch province.” Journal of Transport Geography, vol. 28, pp. 39–48, DOI: 10.1016/j.jtrangeo.2012.10.001.CrossRefGoogle Scholar
  61. Wang, L. and Tamagawa, H. (2011). “The characteristics of rail freight transportation and provincial factors in China.” International Journal of Urban Sciences, vol. 15, no. 1, pp. 47–59, DOI: 10.1080/12265934.2011.580150.CrossRefGoogle Scholar
  62. Wang, Q. and Holguín-Veras, J. (2008). “Investigation of attributes determining trip chaining behavior in hybrid microsimulation urban freight models.” Transportation Research Record: Journal of the Transportation Research Board, Transportation Research Board of the National Academies, Washington, D.C., no. 2066, pp. 1–8, DOI: 10.3141/2066-01.CrossRefGoogle Scholar
  63. Yang, C. H., Motohashi, K., and Chen, J. R. (2009). “Are new technology-based firms located on science parks really more innovative?: Evidence from Taiwan.” Research Policy, vol. 38, no. 1, pp. 77–85, DOI: 10.1016/j.respol.2008.09.001.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers 2018

Authors and Affiliations

  • Seungjin Shin
    • 1
    • 2
  • Sangho Choo
    • 3
  • Kangdae Lee
    • 4
  • Dongjoo Park
    • 1
  1. 1.Dept. of Transportation EngineeringUniversity of SeoulSeoulKorea
  2. 2.Post-Doc. Dept. of Logistics ResearchThe Korea Transport InstituteSejongKorea
  3. 3.Dept. of Urban Design & PlanningHongik UniversitySeoulKorea
  4. 4.Dept. of PackagingYonsei UniversityGangwon-doKorea

Personalised recommendations