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Tavakoli A, Aliei M. Modeling the Performance of Humanitarian Supply Chain Management with the Help of Partial Least Squares (PLS). jorar 2019; 11 (1) :1-10
URL: http://jorar.ir/article-1-468-en.html
URL: http://jorar.ir/article-1-468-en.html
Assistant Professor, Department of Management, School of Economics and Administrative Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
Abstract: (2441 Views)
INTRODUCTION: Increasing the incidence of natural disasters around the world has led to increased concerns about the social and economic development of developed countries. Natural disasters are inevitable, but they can be taken to reduce their negative impacts on countries. Organizations involved in managing these crises must regulate their supply chain and make the necessary changes to improve the performance of the humanitarian supply chain.
METHODS: Data analysis by partial least squares method (PLS) was performed using smart-pls2 software and a researcher-made questionnaire with 25 questions that examined six structures. In this research, 320 questionnaires have been distributed. In the statistical population of the survey, there are military centers, fire brigades, Omer Crescent population, emergency 115, crisis management and renovation committee in the parliament, governorate, municipalities, supply chain managers, Active practitioners in this field, subject specialists (reference persons and perpetrators in this field), and other members of the organizations involved in rescue and rescue operations in Tehran (randomly selected from the 22 areas). Eventually, 193 people were involved in relief and rescue operations. The reliability of the model has been investigated and verified through three ways of evaluating factor load coefficients, Cronbach's alpha coefficients, composite reliability (CR). The average variance extracted (AVE) values are greater than 0.5, and CR values are larger than AVEs. That means convergent validity. Also, the mean of the AVE for each structure is greater than 0.50 and this is a sign of convergent validity.
FINDINGS: For the model, GOF is 0.56; that means the model has a great fit. The statistics above show that the proposed model is suitable for data collection. In general, the proposed model confirms the relationship between the use of information technology, mutual trust, flexibility, agility, adaptability and performance of the humanitarian supply chain. Fit statistics have four indicators: Goodness of Fitness (GFI) of 0.92, Fitted Goodness Index (AGFI) of 0.9, Root RMSEA error of 0.04, and 360.88 x 360 times. Finally, the ratio of k2 to the degree of freedom for 1.38 is obtained, indicating the good of fitness of the model fit with the data. With the aid of p-values and T-Values 8 hypotheses were confirmed in this modeling.
CONCLUSION: The results show that the agility and flexibility of organizations in the humanitarian supply chain are related to the use of information technology and organizational trust, which in turn affects performance.
METHODS: Data analysis by partial least squares method (PLS) was performed using smart-pls2 software and a researcher-made questionnaire with 25 questions that examined six structures. In this research, 320 questionnaires have been distributed. In the statistical population of the survey, there are military centers, fire brigades, Omer Crescent population, emergency 115, crisis management and renovation committee in the parliament, governorate, municipalities, supply chain managers, Active practitioners in this field, subject specialists (reference persons and perpetrators in this field), and other members of the organizations involved in rescue and rescue operations in Tehran (randomly selected from the 22 areas). Eventually, 193 people were involved in relief and rescue operations. The reliability of the model has been investigated and verified through three ways of evaluating factor load coefficients, Cronbach's alpha coefficients, composite reliability (CR). The average variance extracted (AVE) values are greater than 0.5, and CR values are larger than AVEs. That means convergent validity. Also, the mean of the AVE for each structure is greater than 0.50 and this is a sign of convergent validity.
FINDINGS: For the model, GOF is 0.56; that means the model has a great fit. The statistics above show that the proposed model is suitable for data collection. In general, the proposed model confirms the relationship between the use of information technology, mutual trust, flexibility, agility, adaptability and performance of the humanitarian supply chain. Fit statistics have four indicators: Goodness of Fitness (GFI) of 0.92, Fitted Goodness Index (AGFI) of 0.9, Root RMSEA error of 0.04, and 360.88 x 360 times. Finally, the ratio of k2 to the degree of freedom for 1.38 is obtained, indicating the good of fitness of the model fit with the data. With the aid of p-values and T-Values 8 hypotheses were confirmed in this modeling.
CONCLUSION: The results show that the agility and flexibility of organizations in the humanitarian supply chain are related to the use of information technology and organizational trust, which in turn affects performance.
Short Reports or Letters: Research Article |
Subject:
حمایت روانی در سوانح
References
1. Kovacs G, Spens K. Humanitarian logistics in disaster relief operations. Int J Phys Distrib Logist Manag 2007; 37(2): 99-114. [DOI:10.1108/09600030710734820]
2. Tomasini RM, Van Wassenhove LN. From preparedness to partnerships: Case study research on humanitarian logistics. Int Trans Oper Res 2009; 16(5): 549-59. [DOI:10.1111/j.1475-3995.2009.00697.x]
3. Yamada S, Gunatilake RP, Roytman TM, Gunatilake S, Fernando T, Fernando L. The Sri Lanka Tsunami Experience. Disaster Manag Response 2006; 4(2): 38-48. [DOI:10.1016/j.dmr.2006.01.001]
4. Rogers P, Burnside-Lawry B, Dragisic J, Mills C. Collaboration and communication: Building a research agenda and way of working towards community disaster resilience. Disaster Prev Manag 2016; 25(1): 75-90. [DOI:10.1108/DPM-01-2015-0013]
5. Lummus RR, Vokurka RJ, Duclos LK. Delphi study on supply chain flexibility. Int J Prod Res 2005; 43(13): 2687-708. [DOI:10.1080/00207540500056102]
6. Katayama H, Bennett D. Agility, adaptability and leanness: A comparison of concepts and a study of practice. Int J Prod Econ 1999; 60-61: 43-51. [DOI:10.1016/S0925-5273(98)00129-7]
7. Andrade AD, Urquhart C. Unveiling the modernity bias: A critical examination of the politics of ICT4D. Information Technology for Development 2012; 18(4): 281-92. [DOI:10.1080/02681102.2011.643204]
8. Hu Q, Kapucu N. Information communication technology utilization for effective emergency management networks. Pub Manag Rev 2016; 18(3): 323-48. [DOI:10.1080/14719037.2014.969762]
9. Clemons EK, Row MC. Sustaining IT Advantage: The role of structural differences. MIS Quarterly 1991; 15(3): 275-92. [DOI:10.2307/249639]
10. Powell TC, Dent-Micallef A. Information technology as competitive advantage: The role of human, business, and technology resources. Strat Mgmt J 1997; 18(5): 375-405.
https://doi.org/10.1002/(SICI)1097-0266(199705)18:5<375::AID-SMJ876>3.0.CO;2-7 [DOI:10.1002/(SICI)1097-0266(199705)18:53.0.CO;2-7]
11. Kivunike FN, Ekenberg L, Danielson M, Tusubira FF. Perceptions of the role of ICT on quality of life in rural communities in Uganda. Info Technol Develop 2011; 17(1): 61-80. [DOI:10.1080/02681102.2010.511698]
12. Ye F, Wang Z. Effects of information technology alignment and information sharing on supply chain operational performance. Comput Ind Eng 2013; 65(3): 370-7. [DOI:10.1016/j.cie.2013.03.012]
13. Youn SH, Yang MG, Kim JH, Hong P. Supply chain information capabilities and performance outcomes: An empirical study of Korean steel suppliers. Int J Inf Manage 2014; 34(3): 369-80. [DOI:10.1016/j.ijinfomgt.2014.01.008]
14. Zaheer A, McEvily B, Perrone V. Does Trust Matter? Exploring the effects of interorganizational and interpersonal trust on performance. Organization Science 1998; 9(2): 123-251. [DOI:10.1287/orsc.9.2.141]
15. Bordoloi SK, Cooper WW, Matsuo H. Flexibility, adaptability, and efficiency in manufacturing systems. Prod Oper Manag 1999; 8(2): 133-50. [DOI:10.1111/j.1937-5956.1999.tb00366.x]
16. Swafford PM, Ghosh S, Murthy N. The antecedents of supply chain agility of a firm: Scale development and model testing. J Oper Manag 2006; 24(2): 170-88. [DOI:10.1016/j.jom.2005.05.002]
17. Adeagbo A, Daramola A, Carim-Sanni A, Akujobi C, Ukpong C. Effects of natural disasters on social and economic well being: A study in Nigeria. Int J Disaster Risk Reduct 2016; 17: 1-12. [DOI:10.1016/j.ijdrr.2016.03.006]
18. Albright EA, Crow DA. Learning in the aftermath of extreme floods: Community damage and stakeholder perceptions of future risk. Risk Hazards & Crisis in Public Policy 2015; 6(3): 308-28. [DOI:10.1002/rhc3.12085]
19. Harte W, Sowman M, Hastings P, Childs I. Barriers to risk reduction: Dontse Yakhe, South Africa". Disaster Prev Manag 2015; 24(5): 651-69. [DOI:10.1108/DPM-03-2015-0056]
20. Kuipers SL, Boin RA. Building Joint crisis management capacity? Comparing civil security systems in 22 European countries. Risk, Hazards & Crisis in Public Policy 2015; 6(1): 21. [DOI:10.1002/rhc3.12070]
21. Robinson S, Murphy H, Bies A. Structured to partner: School district collaboration with nonprofit organizations in disaster response. Risk, Hazards & Crisis in Public Policy 2014; 5(1): 77-95. [DOI:10.1002/rhc3.12047]
22. Kunz N, Reiner G. A meta-analysis of humanitarian
23. logistics research. Journal of Humanitarian Logistics and Supply Chain Management, 2012; 2(2): 116-47. [DOI:10.1108/20426741211260723]
24. Kovacs G, Spens K. Humanitarian logistics and supply chain management: The start of a new journal. Journal of Humanitarian Logistics and Supply Chain Management 2011; 1(1): 5-14. [DOI:10.1108/20426741111123041]
25. Valero J, Jung K, Andrew S. Does transformational leadership build resilient public and nonprofit organizations? Disaster Prev Manag 2015; 24(1): [DOI:10.1108/DPM-04-2014-0060]
27. Lowry PB, Gaskin JE. Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. IEEE Trans Prof Commun 2014; 57(2): 123-46. [DOI:10.1109/TPC.2014.2312452]
28. Hall DJ, Skipper JB, Hanna JB. The mediating effect of comprehensive contingency planning on supply chain organisational flexibility. International Journal of Logistics Research and Applications 2010; 13(4): 291-312. [DOI:10.1080/13675561003749247]
29. Christopher M, Towill D. An integrated model for the design of agile supply chains. International Journal of Physical Distribution & Logistics Management 2001; 31(4): 235-46. [DOI:10.1108/09600030110394914]
30. Overstreet R, Hall D, Hanna J, Kelly Rainer R. Research in humanitarian logistics. Journal of Humanitarian Logistics and Supply Chain Management 2011; 1(2): 114-31. [DOI:10.1108/20426741111158421]
31. Chan TC, Killeen J, Griswold W, Lenert L. Information technology and emergency medical care during disasters. Acad Emerg Med 2004; 11(11): 1229-36. [DOI:10.1197/j.aem.2004.08.018]
32. Jefferson T. Evaluating the role of information technology in crisis and emergency management. VINE 2006; 36(3): 261-4. [DOI:10.1108/03055720610703542]
33. Telleen S, Martin E. Improving information access for public health professionals. J Med Syst 2002; 26(6): 529-43. [DOI:10.1023/A:1020244726109]
34. Hall D, Skipper J, Hazen B, Hanna J. Inter-organizational IT use, cooperative attitude, and inter-organizational collaboration as antecedents to contingency planning effectiveness. Int J Logist Manag 2012; 23(1): 50-76. [DOI:10.1108/09574091211226920]
35. Narasimhan R, Swink M, Kim SW. Disentangling leanness and agility: An empirical investigation. J Oper Manag 2006; 24(5): 440-57. [DOI:10.1016/j.jom.2005.11.011]
36. National Research Council. Reducing disaster losses through better information. Washington, DC: National Academies Press; 1999.
37. Lee HW, Zbinden M. Marrying logistics and technology for effective relief. Forced Migr Rev 2003; 18: 34-5.
38. Clay Whybark D. Issues in managing disaster relief inventories. Int J Prod Econ 2007; 108(1): 228-35. [DOI:10.1016/j.ijpe.2006.12.012]
39. Boyson S, Corsi T, Verbraeck A. The e-supply chain portal: A core business model. Transp Res E:
40. Log Trans Rev 2003; 39(2): 175-92. [DOI:10.1023/A:1022457229083]
41. Pettit S, Beresford A. Critical success factors in the context of humanitarian aid supply chains. Int J Phys Distrib Logist Manag 2009; 39(6): 450-68. [DOI:10.1108/09600030910985811]
42. Whang S. Information sharing in a supply chain. Int J Technol Manag 2000; 20(3/4): 373-87. [DOI:10.1504/IJTM.2000.002867]
43. Thomas A. Humanitarian logistics: Enabling disaster response. San Francisco, CA: Fritz Institute; 2003.
44. Blecken A. Humanitarian logistics: Modelling supply chain processes of humanitarian organisations. Bern, Switzerland: Haupt Verlag AG; 2010.
45. Whitten GD, Green KW, Zelbst PJ. Triple-A supply chain performance. Int J Oper Prod Manag 2012; 32(1): 28-48. [DOI:10.1108/01443571211195727]
46. Stevenson M, Spring M. Flexibility from a supply chain perspective: Definition and review. Int J Oper Prod Manag 2007; 27(7): 685-713. [DOI:10.1108/01443570710756956]
47. Gall MD, Borg WR, Gall JP. Educational research: An introduction. London, UK: Longman; 1996.
48. Schneider B, Ashworth SD, Higgs AC, Carr L. Design, validity, and use of strategically focused employee attitude surveys. Pers Psychol 1996; 49(3): 695-405. [DOI:10.1111/j.1744-6570.1996.tb01591.x]
49. Smith NC, Dainty P. The management research handbook. London, UK: Routledge; 1991.
50. Flynn BB, Sakakibara S, Schroeder RG, Bates KA, Flynn EJ. Empirical research methods in operations management. J Oper Manag 1990; 9(2): 250-84. [DOI:10.1016/0272-6963(90)90098-X]
51. Hair JF, Bill B, Barry B, Anderson RE, Tatham RT. Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall; 2009.
52. Moon KK-L, Yi CY, Ngai EWT. An instrument for measuring supply chain flexibility for the textile and clothing companies. Eur J Oper Res 2012; 222(2): 191-203. [DOI:10.1016/j.ejor.2012.04.027]
53. Roh S, Beresford AK, Pettit SJ. Humanitarian aid logistics: Response depot networks. Proceedings of the 20th NOFOMA Conference; 2008 June 5-6; Helsinki, Finland.
54. Tatham P, Spens K. Towards a humanitarian logistics knowledge management system. Disaster Prev Manag 2011; 20(1): 6-26. [DOI:10.1108/09653561111111054]
55. Chin WW, Newsted PR. Structural equation modeling analysis with small samples using partial least squares. In Hoyle RH, Editor. Statistical strategies for small sample research. Thousand
56. Oaks: CA: Sage Publications. p. 307-41.
57. Gefen D, Straub D, Boudreau MC. Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems: 2000; 4(1): 1-77. [DOI:10.17705/1CAIS.00407]
58. Barclay DW, Higgins C, Thompson R. The partial last squares (PLS) approach to causal modelling, personal computer adoption and use as an illustration. Technol Stud 1995; 2(2): 285-309.
59. Bagozzi RP, Yi Y, Phillips LW. Assessing construct validity in organizational research. Adm Sci Q 1991; 36(3): 421-58. [DOI:10.2307/2393203]
60. Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J Mark Res 1981; 18(3): 382-8. [DOI:10.1177/002224378101800313]
61. Wetzels M, Odekerken-Schroder G, van Oppen C. Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly 2009; 33(1): 177-95. [DOI:10.2307/20650284]
62. Holguin-Veras J, Jaller M, Van Wassenhove LN, Perez N, Wachtendorf T. On the unique features of post-disaster humanitarian logistics. Journal of Operations Management 2012; 30(7): 494-506. [DOI:10.1016/j.jom.2012.08.003]
63. Cooper M, Lambert D, Pagh J. Supply chain management: More than a new name for logistics. Int J Logist Manag 1997; 8(1): 1-14. [DOI:10.1108/09574099710805556]
64. Dubey R, Ali SS, Aital P, Venkatesh VG. Mechanics of humanitarian supply chain agility and resilience and its empirical validation. International Journal of Services and Operations Management 2014; 17(4): 367-84. [DOI:10.1504/IJSOM.2014.059999]
65. Maiers C, Reynolds M, Haselkorn M. Challenges to effective information and communication systems in humanitarian relief organizations. Proceedings of the IEEE International Professional Communication Conference: (IPCC); 2005 July 10-13; Limerick, Ireland.
66. McEntire DA. Coordinating multi-organisational responses to disaster: Lessons from the March 28, 2000, Fort Worth tornado. Disaster Prev Manag 2002; 11(5): 369-79. [DOI:10.1108/09653560210453416]
67. Moshtari M, Gonealves P. Understanding the drivers and barriers of coordination among humanitarian organizations. Proceedings of the POMS 23rd Annual Conference 2011; 2012 Apr. 27-30; Chicago, IL.
68. Sheffi Y, Rice JB. A supply chain view of the resilient enterprise. MIT Sloan Manag Rev 2005; 47(1): 40-9.
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