Advertisement

Evaluating the impact of service quality on the dynamics of customer satisfaction in the telecommunication industry of Jorhat, Assam

  • Syed Abou Iltaf Hussain
  • Debasish Baruah
  • Bapi Dutta
  • Uttam Kumar Mandal
  • Sankar Prasad Mondal
  • Thuleswar Nath
Article
  • 7 Downloads

Abstract

Service quality acts as an antecedent to customer satisfaction (CS). Evaluation of service quality in an enterprise is vital to improve productivity and increase CS. Usually, it is difficult to rate service quality due to the presence of vagueness in the available information as well as impreciseness in the physical nature of the problem. The comprehensive intention of this paper is to present a robust modified SERVQUAL based multi-criteria decision making (MCDM) method to evaluate the quality of service and its interaction with the dynamics of CS. The proposed evaluation model is a hybrid model, which integrates three popular tools of decision making at the different stage. At first stage, service quality assessment SERVQUAL with the statistical tool has employed to identify appropriate factors affecting the service quality of the telecommunication network. In the next stage, to find the appropriate weight of the different factors in the evaluation process, fuzzy Rasch method is utilized. The final stage involves the selection of the service provider with the most contented customer based on fuzzy MCDM method. Moreover, a new risk minimizing evaluative model is proposed for the study. The strength of the proposed approach is its practical applicability and ability to provide solution under partial or lack of quantitative information. The proposed model is applied for evaluating the service quality of the telecommunication industry of Jorhat, Assam with respect to 256 participants on 15 criteria. Finally, sensitivity analysis is conducted to evaluate the robustness of the proposed approach.

Keywords

Multi-criteria decision-making Telecommunication service provider selection Customer satisfaction SERVQUAL analysis Statistical analysis Risk minimization 

Notes

Acknowledgements

The authors are very grateful and express their sincere gratitude to the reviewers for giving their valuable time in reviewing the paper. The authors also like to express their sincere gratitude to the editor and chief-editor for providing the opportunity to revise the manuscript.

Compliance with ethical standards

Conflict of interest

The study is taken up as a project for partial fulfillment of Masters of Engineering degree from Jorhat Engineering College, Jorhat, Assam, India. The project is neither partially nor completely funded by any government or private institute to the best of knowledge of the authors.

References

  1. 1.
    Awasthi, A., Chauhan, S. S., Omrani, H., & Panahi, A. (2011). A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality. Computers & Industrial Engineering, 61(3), 637–646.CrossRefGoogle Scholar
  2. 2.
    Al-Aali, A., Khurshid, M. A., Nasir, N. M., & Al-Aali, H. (2011). Measuring the service quality of mobile phone companies in Saudi Arabia. King Saud University Journal-Administrative Sciences, 22(2), 43–55.Google Scholar
  3. 3.
    Ali, A. (2017). Service quality and customers’ satisfaction level in telecommunication sector: A comparative study of Kingdom of Saudi Arabia & India. International Journal of Applied Business and Economic Research, 15(25), 521–527.Google Scholar
  4. 4.
    Dahiya, K., & Bhatia, S. (2015). Customer churn analysis in telecom industry. In 4th International conference on reliability, infocom technologies and optimization (ICRITO) (trends and future directions) (pp. 1–6). IEEE.  https://doi.org/10.1109/icrito.2015.7359318.
  5. 5.
    Yadav, R. K., & Dabhade, N. (2013). Impact of services quality on customer satisfaction of mobile users—A case study of Airtel. International Journal of Innovative Researcher & Studies, 2(5), 139–163.Google Scholar
  6. 6.
    Chaudhary, A., & Uprety, I. (2013). Identification of telecom service quality dimensions in India with fuzzy analysis. Global Journal of Management and Business Studies, 3(5), 467–474.Google Scholar
  7. 7.
    Chaudhary, A., & Uprety, I. (2016). Analysis of telecom service quality factors with analytic hierarchy process and fuzzy extent analysis: A case of public sector unit. International Journal of Business and Systems Research, 10(2–4), 162–185.CrossRefGoogle Scholar
  8. 8.
    Baruah, D., Nath, T., & Bora, D. (2015). Impact of service quality dimensions on customer satisfaction in telecom sector. International Journal of Engineering Trends and Technology (IJETT), 27(2), 111–117.CrossRefGoogle Scholar
  9. 9.
    Grnroos, C. (1990). Service management and marketing: Managing the moments of truth in service competition. New York: Jossey-Bass.Google Scholar
  10. 10.
    Hirmukhe, J. (2012). Measuring internal customers’ perception on service quality using SERVQUAL in administrative services. International Journal of Scientific and Research Publications, 2(3), 1–6.Google Scholar
  11. 11.
    Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of Retailing, 64(1), 12–40.Google Scholar
  12. 12.
    Liou, J. J., Yen, L., & Tzeng, G. H. (2010). Using decision rules to achieve mass customization of airline services. European Journal of Operational Research, 205(3), 680–686.CrossRefGoogle Scholar
  13. 13.
    Khan, M. A. (2010). An empirical assessment of service quality of cellular mobile telephone operators in Pakistan. Asian Social Science, 6(10), 164–177.CrossRefGoogle Scholar
  14. 14.
    Dalvi, P. K., Khandge, S. K., Deomore, A., Bankar, A., & Kanade, V. A. (2016). Analysis of customer churn prediction in telecom industry using decision trees and logistic regression. In Symposium on colossal data analysis and networking (CDAN) (pp. 1–4). IEEE.  https://doi.org/10.1109/cdan.2016.7570883.
  15. 15.
    Ajmal, K., & Han, Y. (2010). An analysis of the telecommunications business in China by linear regression. https://pdfs.semanticscholar.org/74a6/92a7e2b96bcb42cfd5c53cdd64d4bc51afb6.pdf.
  16. 16.
    Oghojafor, B. E. A., Mesike, G. C., Omoera, C. I., & Bakare, R. D. (2012). Modelling telecom customer attrition using logistic regression. African Journal of Marketing Management, 4(3), 110–117.Google Scholar
  17. 17.
    Garín Muñoz, T., Perez Amaral, T., Gijón, C., & López, R. (2012). Customer satisfaction of mobile-internet users: An empirical approximation for the case of Spain. Journal of Reviews on Global Economics, 2, 442–454.Google Scholar
  18. 18.
    Olatokun, W. M., & Ojo, F. O. (2016). Influence of service quality on consumers’ satisfaction with mobile telecommunication services in Nigeria. Information Development, 32(3), 398–408.  https://doi.org/10.1177/0266666914553316.CrossRefGoogle Scholar
  19. 19.
    Upal, M. (2008). Telecommunication service gap: Call center service quality perception and satisfaction. Communications of the IBIMA, 3, 18–27.Google Scholar
  20. 20.
    Loke, S. P., Taiwo, A. A., Salim, H. M., Downe, A. G., & Petronas, U. T. (2011). Service quality and customer satisfaction in a telecommunication service provider. International Conference on Financial Management and Economics, 11, 24–29.Google Scholar
  21. 21.
    Arokiasamy, A. R. A. (2013). The impact of customer satisfaction on customer loyalty and intentions to switch in the banking sector in Malaysia. The Journal of Commerce, 5(1), 14.Google Scholar
  22. 22.
    Gunarathne, U. W. H. D. P. (2014). Relationship between service quality and customer satisfaction in Sri Lankan hotel industry. International Journal of Scientific and Research Publications, 4(11), 1–7.Google Scholar
  23. 23.
    Sivanesan, R. (2013). A comparative study on subscribers attitude and perception of BSNL and AIRTEL services in Kanyakumari district. International Journal of Commerce, Business and Management, 2(2), 95–104.Google Scholar
  24. 24.
    Zhao, L., Chen, Y., & Schaffner, D. W. (2001). Comparison of logistic regression and linear regression in modeling percentage data. Applied and Environmental Microbiology, 67(5), 2129–2135.CrossRefGoogle Scholar
  25. 25.
    Gasiea, Y., Emsley, M., & Mikhailov, L. (2010). Rural telecommunications infrastructure selection using the analytic network process. Journal of Telecommunications and Information Technology, 2, 15–29.Google Scholar
  26. 26.
    Oh, Y., Suh, E. H., Hong, J., & Hwang, H. (2009). A feasibility test model for new telecom service development using MCDM method: A case study of video telephone service in Korea. Expert Systems with Applications, 36(3), 6375–6388.CrossRefGoogle Scholar
  27. 27.
    Jyh-Fu Jeng, D., & Bailey, T. (2012). Assessing customer retention strategies in mobile telecommunications: Hybrid MCDM approach. Management Decision, 50(9), 1570–1595.CrossRefGoogle Scholar
  28. 28.
    Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), B-141.CrossRefGoogle Scholar
  29. 29.
    Cagdac Arslan, M., Catay, B., & Budak, E. (2004). A decision support system for machine tool selection. Journal of Manufacturing Technology Management, 15(1), 101–109.CrossRefGoogle Scholar
  30. 30.
    Tabucanon, M. T. (1988). Multiple criteria decision making in industry (Vol. 8). Amsterdam: Elsevier Science Ltd.Google Scholar
  31. 31.
    Chen, S. J., & Hwang, C. L. (1992). Lecture notes in economics and mathematical systems. Berlin: Springer.Google Scholar
  32. 32.
    Miller, G. A. (1956). The magic number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 91–97.CrossRefGoogle Scholar
  33. 33.
    Olson, D. L. (1996). Decision aids for selection problems. Berlin: Springer.CrossRefGoogle Scholar
  34. 34.
    Sun, S. (2002). Assessing computer numerical control machines using data envelopment analysis. International Journal of Production Research, 40(9), 2011–2039.CrossRefGoogle Scholar
  35. 35.
    Badi, I., Abdulshahed, A., & Shetwan, A. (2018). A case study of supplier selection for steelmaking company in Libya by using combinative distance-based assessemnt (CODAS) model. Decision Making: Applications in Management and Engineering, 1(1), 1–12.Google Scholar
  36. 36.
    Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–356.CrossRefGoogle Scholar
  37. 37.
    Molinari, F. (2016). A new criterion of choice between generalized triangular fuzzy numbers. Fuzzy Sets and Systems, 296, 51–69.CrossRefGoogle Scholar
  38. 38.
    Van Broekhoven, E., & De Baets, B. (2006). Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions. Fuzzy Sets and Systems, 157(7), 904–918.CrossRefGoogle Scholar
  39. 39.
    Linacre, J. M. (1994). Likert or Rasch?, Rasch Measurement. Transactions, 8(2), 356.Google Scholar
  40. 40.
    Lubiano, M. A., Salas, A., de Sáa, S. D. L. R., Montenegro, M., & Gil, M. Á. (2017). An empirical analysis of the coherence between fuzzy rating scale-and Likert scale-based responses to questionnaires. In M. B. Ferraro, P. Giordani, B. Vantaggi, M. Gagolewski, M. Ángeles Gil, P. Grzegorzewski & O. Hryniewicz (Eds.), Soft methods for data science (Vol. 456, pp. 329–337). Cham: Springer.CrossRefGoogle Scholar
  41. 41.
    Meads, D. M., & Bentall, R. P. (2008). Rasch analysis and item reduction of the hypomanic personality scale. Personality and Individual Differences, 44(8), 1772–1783.CrossRefGoogle Scholar
  42. 42.
    Rasch, G. (1960). Studies in mathematical psychology: I. Probabilistic models for some intelligence and attainment tests. Oxford, England: Nielsen & Lydiche. http://psycnet.apa.org/record/1962-07791-000.
  43. 43.
    Wampold, B. E. (1999). The promising advantages of Rasch. Rasch Measurement Transactions, 13(2), 695.Google Scholar
  44. 44.
    Wright, B. D., & Masters, G. N. (1982). 1982: Rating scale analysis. Chicago: MESA Press.Google Scholar
  45. 45.
    Wright, B., & Stone, M. (1979). Best test design. Chicago, IL: MESA Press.Google Scholar
  46. 46.
    Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43(4), 561–573.CrossRefGoogle Scholar
  47. 47.
    Lu, J., Jain, L. C., & Zhang, G. (Eds.) (2012). Risk management in decision making. Handbook on decision making (pp. 3–7). Berlin: Springer.  https://doi.org/10.1007/978-3-642-25755-1.CrossRefGoogle Scholar
  48. 48.
    Huang, J. H., & Peng, K. H. (2012). Fuzzy Rasch model in TOPSIS: A new approach for generating fuzzy numbers to assess the competitiveness of the tourism industries in Asian countries. Tourism Management, 33(2), 456–465.CrossRefGoogle Scholar
  49. 49.
    Fisher, W. P., Jr., & Luce, R. D. (1995). Fuzzy truth and the Rasch model. Rasch Measurement Transactions, 9(3), 442.Google Scholar
  50. 50.
    Kahraman, C., Onar, S. C., & Oztaysi, B. (2015). Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8(4), 637–666.CrossRefGoogle Scholar
  51. 51.
    Doumpos, M., & Zopounidis, C. (2002). Multicriteria decision aid classification methods (Vol. 73). Berlin: Springer.Google Scholar
  52. 52.
    Mahdipoor, H. R. (2006). Flow pattern recognition in tray columns with MADM (multiple attribute decision making) method. Computers & Chemical Engineering, 30(6–7), 1197–1200.CrossRefGoogle Scholar
  53. 53.
    Shanian, A., & Savadogo, O. (2006). A material selection model based on the concept of multiple attribute decision making. Materials and Design, 27(4), 329–337.CrossRefGoogle Scholar
  54. 54.
    Cicek, K., & Celik, M. (2010). Multiple attribute decision-making solution to material selection problem based on modified fuzzy axiomatic design-model selection interface algorithm. Materials and Design, 31(4), 2129–2133.CrossRefGoogle Scholar
  55. 55.
    Chauhan, A., & Vaish, R. (2012). Magnetic material selection using multiple attribute decision making approach. Materials and Design, 1980–2015(36), 1–5.CrossRefGoogle Scholar
  56. 56.
    Mandal, U. K., Kulavi, S., & Hussain, S. A. I. (2017). A hybrid approach of AHP-cross entropy model to select bearing material under MCDM environment. In International conference on future trends and challenges in mechanical engineering (FTCME).Google Scholar
  57. 57.
    Weber, C. A., Current, J. R., & Benton, W. C. (1991). Vendor selection criteria and methods. European Journal of Operational Research, 50(1), 2–18.CrossRefGoogle Scholar
  58. 58.
    Degraeve, Z., Labro, E., & Roodhooft, F. (2000). An evaluation of vendor selection models from a total cost of ownership perspective. European Journal of Operational Research, 125(1), 34–58.CrossRefGoogle Scholar
  59. 59.
    De Boer, L., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 7(2), 75–89.CrossRefGoogle Scholar
  60. 60.
    Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, 202(1), 16–24.CrossRefGoogle Scholar
  61. 61.
    Renganath, K., & Suresh, M. (2016). Supplier selection using fuzzy MCDM techniques: A literature review. In IEEE international conference on computational intelligence and computing research (ICCIC) (pp. 1–6). IEEE.  https://doi.org/10.1109/iccic.2016.7919590.
  62. 62.
    Vasiljević, M., Fazlollahtabar, H., Stević, Ž., & Vesković, S. (2018). A rough multicriteria approach for evaluation of supplier criteria in automotive industry. Decision Making: Applications in Management and Engineering, 1(1), 82–96.Google Scholar
  63. 63.
    Bojanić, D., Kovač, M., Bojanic, M., & Ristic, V. (2018). Multi-criteria decision making in defensive operation of guided anti-tank missile battery: An example of hybrid model fuzzy AHP-MABAC. Decision Making: Applications in Management and Engineering, 1(1), 51–66.Google Scholar
  64. 64.
    Daim, T. U., Bhatla, A., & Mansour, M. (2013). Site selection for a data centre—A multi-criteria decision-making model. International Journal of Sustainable Engineering, 6(1), 10–22.CrossRefGoogle Scholar
  65. 65.
    Elsheikh, R. F. A. (2017). Multi-criteria decision making in hotel site selection. International Journal of Engineering Science Invention, 6, 15–18.CrossRefGoogle Scholar
  66. 66.
    Roy, J., Adhikary, K., Kar, S., & Pamucar, D. (2018). A rough strength relational DEMATEL model for analysing the key success factors of hospital service quality. Decision Making: Applications in Management and Engineering, 1(1), 121–142.Google Scholar
  67. 67.
    Chatterjee, P., Mondal, S., Boral, S., Banerjee, A., & Chakraborty, S. (2017). A novel hybrid method for non-traditional machining process selection using factor relationship and multi-attributive border approximation method. Facta Universitatis, Series: Mechanical Engineering, 15(3), 439–456.CrossRefGoogle Scholar
  68. 68.
    Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187.CrossRefGoogle Scholar
  69. 69.
    Chen, S. J., & Hwang, C. L. (Eds.) (1992). Fuzzy multiple attribute decision making methods. Fuzzy multiple attribute decision making (pp. 289–486). Berlin: Springer.  https://doi.org/10.1007/978-3-642-46768-4.CrossRefGoogle Scholar
  70. 70.
    Kharat, M. G., Kamble, S. J., Raut, R. D., Kamble, S. S., & Dhume, S. M. (2016). Modeling landfill site selection using an integrated fuzzy MCDM approach. Modeling Earth Systems and Environment, 2(2), 53.CrossRefGoogle Scholar
  71. 71.
    Kuo, Y. C., Lu, S. T., Tzeng, G. H., Lin, Y. C., & Huang, Y. S. (2013). Using fuzzy integral approach to enhance site selection assessment—A case study of the optoelectronics industry. Procedia Computer Science, 17, 306–313.CrossRefGoogle Scholar
  72. 72.
    Chen, V. Y., Lien, H. P., Liu, C. H., Liou, J. J., Tzeng, G. H., & Yang, L. S. (2011). Fuzzy MCDM approach for selecting the best environment-watershed plan. Applied Soft Computing, 11(1), 265–275.CrossRefGoogle Scholar
  73. 73.
    Efe, B. (2016). An integrated fuzzy multi criteria group decision making approach for ERP system selection. Applied Soft Computing, 38, 106–117.  https://doi.org/10.1016/j.asoc.2015.09.037.CrossRefGoogle Scholar
  74. 74.
    Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1–55.Google Scholar
  75. 75.
    Xu, Z. S. (2007). Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making. Control and Decision, 22(2), 215–219.Google Scholar
  76. 76.
    Xu, Z. S., & Jian, C. H. E. N. (2007). Approach to group decision making based on interval-valued intuitionistic judgment matrices. Systems Engineering-Theory & Practice, 27(4), 126–133.CrossRefGoogle Scholar
  77. 77.
    Pamučar, D., Božanić, D., Lukovac, V., & Komazec, N. (2018). Normalized weighted geometric Bonferroni mean operator of interval rough numbers—Application in interval rough DEMATEL-COPRAS. Facta Universitatis, Series: Mechanical Engineering.  https://doi.org/10.22190/fume180503018p.CrossRefGoogle Scholar
  78. 78.
    Hämäläinen, R. P., & Karjalainen, R. (1992). Decision support for risk analysis in energy policy. European Journal of Operational Research, 56(2), 172–183.CrossRefGoogle Scholar
  79. 79.
    Slovic, P. (1987). Perception of risk. Science, 236(4799), 280–285.CrossRefGoogle Scholar
  80. 80.
    Yurdakul, M., & Ic, Y. T. (2009). Application of correlation test to criteria selection for multi criteria decision making (MCDM) models. The International Journal of Advanced Manufacturing Technology, 40(3–4), 403–412.CrossRefGoogle Scholar
  81. 81.
    Burgess, T. F. (2001). A general introduction to the design of questionnaires for survey research. Information system services (pp. 1–27). Leeds: University of Leeds.Google Scholar
  82. 82.
    Bolarinwa, O. A. (2015). Principles and methods of validity and reliability testing of questionnaires used in social and health science researches. Nigerian Postgraduate Medical Journal, 22(4), 195.CrossRefGoogle Scholar
  83. 83.
    Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98.  https://doi.org/10.1037/0021-9010.78.1.98.CrossRefGoogle Scholar
  84. 84.
    Agarwal, B. L. (1988). Basic statistics. New York: Wiley Eastern Limited.Google Scholar
  85. 85.
    Yang, Z., Fang, K. T., & Kotz, S. (2007). On the Student’s t-distribution and the t-statistic. Journal of Multivariate Analysis, 98(6), 1293–1304.CrossRefGoogle Scholar
  86. 86.
    Sayfi, P., & Nikbakht, M. (2016). Identification and ranking green supplier selection criteria using one-sample T-test and FANP methods: A case study for petrochemical industry. Journal of Modern Processes in Manufacturing and Production, 5(1), 53–67.Google Scholar
  87. 87.
    Bestetti, R. B., Couto, L. B., Restini, C. B., Faria, M., Jr., & Romão, G. S. (2017). Assessment test before the reporting phase of tutorial session in problem-based learning. Advances in Medical Education and Practice, 8, 181.CrossRefGoogle Scholar
  88. 88.
    Deoskar, A. A. (2009). A study of mobile services from customer’s perspective. Ph.D. Thesis in computer management under Faculty of Management, University of Pune.Google Scholar
  89. 89.
    Ayhan, M. B. (2013). A Fuzzy AHP approach for supplier selection problem: A case study in a gear-motor company. International Journal of Managing Value and Supply Chains, 4(3), 11–23.CrossRefGoogle Scholar
  90. 90.
    Gal, T. (1986). Shadow prices and sensitivity analysis in linear programming under degeneracy. OR Spektrum, 8, 59–71.CrossRefGoogle Scholar
  91. 91.
    Jansen, B., De Jong, J. J., Roos, C., & Terlaky, T. (1997). Sensitivity analysis in linear programming: Just be careful! European Journal of Operational Research, 101(1), 15–28.CrossRefGoogle Scholar
  92. 92.
    Lin, C. J., & Wen, U. P. (2003). Sensitivity analysis of the optimal assignment. European Journal of Operational Research, 149(1), 35–46.CrossRefGoogle Scholar
  93. 93.
    Hussain, S. A. I., Mandal, U. K., & Mondal, S. P. (2018). Decision maker priority index and degree of vagueness coupled decision making method: A synergistic approach. International Journal of Fuzzy Systems, 20(5), 1551–1566.CrossRefGoogle Scholar
  94. 94.
    Awasthi, A., Chauhan, S. S., & Goyal, S. K. (2010). A fuzzy multicriteria approach for evaluating environmental performance of suppliers. International Journal of Production Economics, 126(2), 370–378.CrossRefGoogle Scholar
  95. 95.
    Awasthi, A., & Kannan, G. (2016). Green supplier development program selection using NGT and VIKOR under fuzzy environment. Computers & Industrial Engineering, 91, 100–108.CrossRefGoogle Scholar
  96. 96.
    Turanoglu Bekar, E., Cakmakci, M., & Kahraman, C. (2016). Fuzzy COPRAS method for performance measurement in total productive maintenance: A comparative analysis. Journal of Business Economics and Management, 17(5), 663–684.CrossRefGoogle Scholar
  97. 97.
    Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565–9573.CrossRefGoogle Scholar
  98. 98.
    Önüt, S., Kara, S. S., & Işik, E. (2009). Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company. Expert Systems with Applications, 36(2), 3887–3895.CrossRefGoogle Scholar
  99. 99.
    Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011.CrossRefGoogle Scholar
  100. 100.
    Kumar, M., Tat Kee, F., & Taap Manshor, A. (2009). Determining the relative importance of critical factors in delivering service quality of banks: An application of dominance analysis in SERVQUAL model. Managing Service Quality: An International Journal, 19(2), 211–228.CrossRefGoogle Scholar
  101. 101.
    Wilson, A., Zeithaml, V. A., Bitner, M. J., & Gremler, D. D. (2012). Services marketing: Integrating customer focus across the firm (2nd ed.). McGraw Hill.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Production EngineeringNational Institute of Technology AgartalaJiraniaIndia
  2. 2.Department of Mechanical EngineeringJorhat Engineering CollegeJorhatIndia
  3. 3.The Logistic Institute-Asia PacificNational University of SingaporeSingaporeSingapore
  4. 4.Department of Natural ScienceMaulana Abul Kalam Azad University of TechnologyHaringhataIndia

Personalised recommendations