Performance Evaluation of IT Industries Using SERVQUAL, DEA and FMCDM
الموضوعات :Alireza Alinezhad 1 , Ramin Hakimian 2
1 - Department of Industrial and Mechanical Engineering
Qazvin Branch, Islamic Azad University, Qazvin, Iran,
2 - Qazvin Branch, Islamic Azad University,
Qazvin, Iran
الکلمات المفتاحية: Data Envelopment Analysis (DEA), Information Technology (IT), Quality of Services (QOS), Servqual, Fuzzy Multi-Criteria Decision Making (FMCDM),
ملخص المقالة :
In developed countries, improvement has been made in service providing industries along with the improvements made in quality assurance and management skills and systems in the domain of manufacturing industries. Unfortunately, in developing countries, primary needs have led these countries to focus their attention illogically and solely on production and they disregard the quality of services. That is why this research aims at investigating service improvement in the domain of Information Technology. Nowadays, the role played by information technology and communication in economic growth is evident to everyone. Because of access to Internet, communication and information technology is considered one of the major elements in providing services and has improved the efficiency of this industry. Meanwhile, focusing on service quality is an efficient approach that results in customer orientation in an organization and is believed by our domestic organizations and companies to be an efficient method to satisfy customers’ demands and has been applied so many times. Presenting a compound approach, this research uses SERVQUAL as a tool for quality engineering of services, and multiple criteria decision making for ranking service providing branches, and data envelopment analysis for making improvement and benchmarking and it is an attempt to overcome these weaknesses and develop quality models in regard with service providing so that the current gap can be filled.
[1] Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manage Sci 1984;30;1078-92.
[2] Bouyssou D. Using DEA as a tool for MCDM: some remarks. J Oper Res Soc 1999;50;974-8.
[3] Cavana Robert, Y., Corbett Lawrence, M., & Lo, Y. L. (2007). Developing zones of tolerance for managing passenger rail service quality. International Journal of Quality & Reliability Management, 24(1), 7–31.
[4] Belton V, Vickers SP. Demystifying DEA: A visual interactive approach based on multi criteria analysis.J Oper ResSoc 1993;44;883-96.
[5] Cooper WW, Park KS, Yu G. IDEA and AR-IDEA: models for dealing with imprecise data in DEA. Manage Sci 1999;45;597-607.
[6] Charnes A, Cooper WW, Rhodes E. Measuring efficiency of decision making units. Eur J Oper Res 1978;2;429-44.
[7] Cook WD, Zhu J. Rank order data in DEA: a general framework. Eur J Oper Res 2006;174;1021-38.
[8] Doyle J and Green R. Data envelopment analysis and multiple criteria decision making. Omega 1993;21;713-5.
[9] Eboli, L., & Mazzulla, G. (2008). A stated preference experiment for measuring service quality in public transport. Transportation Planning and Technology,31(5), 509–523.
[10] Fick, G. R., & Ritchie, J. R. B. (1991). Measuring service quality in the travel and tourism industry. Journal of Travel Research, Fall, 2–9.
[11] Hensher, D. A., Stopher, P., & Bullock, P. (2003). Service quality- developing a service quality index in the provision of commercial bus contracts. Transportation Research Part A: Policy and Practice, 37(6), 499–517.
[12] Lovell CAK, Pastor JT. Target setting: An application to a bank branch network. Eur J Oper Res 1997;98;290-9.
[13] Lovell CAK, Pastor JT. Radial DEA models without inputs or without outputs. Eur J Oper Res 1999;118;46-51.
[14] Nathanail, E. (2008). Measuring the quality of service for passengers on the Hellenic railways. Transportation Research Part A: Policy and Practice, 42(1), 48–66.
[15] Opricovic S,Multicriteria Optimization of Civil Engineering Systems, Faculty of Civil Engineering, Belgrade. 1998
[16] Parasuraman A, Zeithaml VA, Berry LL. SERVQUAL: A multiple item scale for measuring consumer perceptions of service quality. J Retailing 1988;65;12-40.
[17] Ramanathan R. Data envelopment analysis for weight derivation and aggregation in the analytic hierarchy process. Com Oper Res 2006;33;1289-307.
[18] Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill.
[19] Stewart TJ. Relationships between data envelopment analysis and multicriteria decision analysis. J Oper Res Soc1996;47;654-65.
[20] Seol H, Choi J, Park G, Park Y. A framework for benchmarking service process using data envelopment analysis and decision tree. Expert Syst Appl 2007;32;432-40.
[21] Yeh, C. H., Deng, H., & Chang, Y. H. (2000). Fuzzy multicriteria analysis for performance evaluation of bus companies. European Journal of Operational Research, 126(3), 459–473.
[22] Yedla, S., & Shrestha, R. M. (2003). Multi-criteria approach for the selection of alternative options for environmentally sustainable transport system in Delhi. Transportation Research Part A: Policy and Practice, 37(8), 717–729.
[23] Yang T, Kuo C. A hierarchical AHP/DEA methodology for the facilities layout design problem. Eur J Oper Res 2003;147;128-36.
6. Appendixes
Table 3. Average weight gain fuzzy numbers extracted from
Table 2 and parameters used in the method of fuzzy VIKOR
|
Average |
Weighted Index |
|
|
Fuzzy Number |
Wi |
|
|
(0.65,0.81,0.905) |
0.799 |
Holding general sessions before project execution |
|
(0.63,0.79,0.885) |
0.779 |
Receiving recognition plan certificate |
|
(0.515,0.68,0.805) |
0.673 |
Special meetings of the directors circles |
|
(0.45,0.645,0.82) |
0.642 |
Presenting successful executive models in similar industries |
|
(0.56,0.745,0.875) |
0.736 |
Expert’s sufficient knowledge regarding the process |
|
(0.315,0.49,0.655) |
0.488 |
Considering special organizational processes |
|
(0.465,0.64,0.79) |
0.636 |
Designing processes having future development as a perspective |
|
(0.305,0.485,0.675) |
0.487 |
Developing executive modules for special processes |
|
(0.3,0.5,0.695) |
0.499 |
Possibility of adding other software systems |
|
(0.73,0.88,0.955) |
0.868 |
Preparing special reports required by the organization |
|
(0.485,0.665,0.81) |
0.659 |
Presenting pamphlet and other educational notes |
|
(0.33,0.53,0.73) |
0.530 |
Possibility of entering test information in educational copy |
|
(0.51,0.705,0.875) |
0.701 |
Presenting executive models in similar industries |
|
(0.48,0.665,0.82) |
0.660 |
Making educational activities proper for various knowledge level |
|
(0.33,0.475,0.625) |
0.476 |
Using useful opinions of users |
|
(0.67,0.845,0.955) |
0.834 |
Increasing number of practical education sessions |
|
(0.52,0.72,0.885) |
0.714 |
Experts with pertinent document and sufficient experience |
|
(0.4,0.585,0.76) |
0.583 |
Accessibility of experts in times other than working hours |
|
(0.4,0.595,0.785) |
0.594 |
Presenting course completion certificate |
|
(0.255,0.42,0.605) |
0.423 |
Preparing new reports by users |
|
(0.33,0.475,0.625) |
0.476 |
Connecting to other existing systems |
|
(0.45,0.645,0.82) |
0.642 |
Linking to other Microsoft facilities |
Table 4. Data Envelopment Analysis and its results
|
Reference Group |
Efficiency Score |
Empathy |
Assurances |
Responsiveness |
Reliability |
Tangibles |
|
|
7,14 |
95 |
3.3 |
4.4 |
2.6 |
1.6 |
2.9 |
DMU1 |
|
|
100 |
1.8 |
5.2 |
3.4 |
0.1 |
3.8 |
DMU2 |
|
6,17 |
54 |
2.3 |
1.0 |
1.2 |
0.1 |
2.5 |
DMU3 |
|
1,6,19 |
58 |
1.1 |
3.5 |
2.1 |
1.9 |
0.1 |
DMU4 |
|
|
100 |
2.6 |
2.9 |
3.5 |
2.7 |
2.5 |
DMU5 |
|
|
100 |
2.5 |
4.9 |
2.7 |
3.9 |
1.5 |
DMU6 |
|
|
100 |
3.8 |
1.9 |
3.2 |
0.7 |
3.2 |
DMU7 |
|
7,17 |
88 |
2.6 |
4.1 |
1.5 |
2.8 |
1.8 |
DMU8 |
|
|
100 |
4.5 |
5.1 |
4.4 |
2.6 |
3.9 |
DMU17 |
Table 5. model of decision making (for example DMU8)
and target setting for Benchmarking
|
Empathy |
Assurances |
Responsiveness |
Reliability |
Tangibles |
|
|
|
3.8 |
1.9 |
3.2 |
0.7 |
3.2 |
DMU 7 |
1 |
|
4.5 |
5.1 |
4.4 |
2.6 |
3.9 |
DMU 17 |
2 |
|
5.1 |
4.7 |
5.3 |
5.6 |
4.7 |
Improvement target |
3 |
|
2.6 |
4.1 |
1.5 |
2.8 |
1.8 |
DMU 8 |
4 |
|
2.5 |
0.6 |
3.8 |
2.8 |
2.9 |
Improvement required |
5 |
