Performance Evaluation of IT Industries Using SERVQUAL, DEA and FMCDM

Subject Areas : Business Strategy

Alireza  Alinezhad ¹ (Department of Industrial and Mechanical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran,)
Ramin  Hakimian ² (Qazvin Branch, Islamic Azad University, Qazvin, Iran)

Received: 2014-01-13 Accepted : 2014-06-13 Published : 2016-07-01

Keywords: Data Envelopment Analysis (DEA), Information Technology (IT), Quality of Services (QOS), Servqual, Fuzzy Multi-Criteria Decision Making (FMCDM),

Abstract :

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.

References:

[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