Advertisement

Health Care Management Science

, Volume 22, Issue 1, pp 106–120 | Cite as

Service quality benchmarking via a novel approach based on fuzzy ELECTRE III and IPA: an empirical case involving the Italian public healthcare context

  • Concetta Manuela La FataEmail author
  • Toni Lupo
  • Tommaso Piazza
Article
  • 146 Downloads

Abstract

A novel fuzzy-based approach which combines ELECTRE III along with the Importance-Performance Analysis (IPA) is proposed in the present work to comparatively evaluate the service quality in the public healthcare context. Specifically, ELECTRE III is firstly considered to compare the service performance of examined hospitals in a noncompensatory manner. Afterwards, IPA is employed to support the service quality management to point out improvement needs and their priorities. The proposed approach also incorporates features of the Fuzzy Set Theory so as to address the possible uncertainty, subjectivity and vagueness of involved experts in evaluating the service quality. The model is applied to five major Sicilian public hospitals, and strengths and criticalities of the delivered service are finally highlighted and discussed. Although several approaches combining multi-criteria methods have already been proposed in the literature to evaluate the service performance in the healthcare field, to the best of the authors’ knowledge the present work represents the first attempt at comparing service performance of alternatives in a noncompensatory manner in the investigated context.

Keywords

Service quality benchmarking Service quality management Healthcare quality ELECTRE III IPA Fuzzy set theory 

References

  1. 1.
    Ebner K, Urbach N, Mueller B (2016) Exploring the path to success: a review of the strategic IT benchmarking literature. Inf Manag 53(4):447–466.  https://doi.org/10.1016/j.im.2015.11.001 Google Scholar
  2. 2.
    Tickle M, Mann R, Adebanjo D (2016) Deploying business excellence – success factors for high performance. Int J Qual Reliab Manag 33(2):197–230.  https://doi.org/10.1108/IJQRM-10-2013-0160 Google Scholar
  3. 3.
    Adebanjo D, Abbas A, Mann R (2010) An investigation of the adoption and implementation of benchmarking. Int J Oper Prod Manag 30(11):1140–1169.  https://doi.org/10.1108/01443571011087369 Google Scholar
  4. 4.
    Dattakumar R, Jagadeesh R (2003) A review of literature on benchmarking. BIJ 10(3):176–209.  https://doi.org/10.1108/14635770310477744 Google Scholar
  5. 5.
    Barrows CW, Vieira ET Jr, Di Pietro RB (2016) Increasing the effectiveness of benchmarking in the restaurant industry. Int J Process Manag Benchmark 6(1):79–111.  https://doi.org/10.1504/IJPMB.2016.073327 Google Scholar
  6. 6.
    Voss CA, Åhlström P, Blackmon K (1997) Benchmarking and operational performance: some empirical results. Int J Oper Prod Manag 17(10):1046–1058.  https://doi.org/10.1108/01443579710177059 Google Scholar
  7. 7.
    Yasin MM (2002) The theory and practice of benchmarking: then and now. BIJ 9(3):217–243.  https://doi.org/10.1108/14635770210428992 Google Scholar
  8. 8.
    Ranerup A, Norén L (2015) How are citizens' public service choices supported in quasi-markets? Int J Inf Manag 35(5):527–537.  https://doi.org/10.1016/j.ijinfomgt.2015.05.002 Google Scholar
  9. 9.
    Keehley P, MacBride SA (1997) Can benchmarking for best practices work for government? Qual Prog 3(3)Google Scholar
  10. 10.
    Angst C, Agarwal R, Gao G, Khuntia J, McCullough JS (2014) Information technology and voluntary quality disclosure by hospitals. Decis Support Syst 57(1):367–375.  https://doi.org/10.1016/j.dss.2012.10.042 Google Scholar
  11. 11.
    Ancarani A, Capaldo G (2001) Management of standardised public services: a comprehensive approach to quality assessment. Manag Serv Qual: Int J 11(5):331–341.  https://doi.org/10.1108/09604520110404059 Google Scholar
  12. 12.
    Lupo T, Delbari SA (2017) A knowledge-based exploratory framework to study quality of Italian mobile telecommunication services. Telecommun Syst.  https://doi.org/10.1007/s11235-017-0380-6
  13. 13.
    Anand G, Kodali R (2008) Benchmarking the benchmarking models. Benchmarking 15(3):257–291.  https://doi.org/10.1108/14635770810876593 Google Scholar
  14. 14.
    Anderson K, Mcadam R (2007) Reconceptualising benchmarking development in UK organisations: the effects of size and sector. Int J Product Perform Manag 56(7):538–558.  https://doi.org/10.1108/17410400710823615 Google Scholar
  15. 15.
    Zadeh LA (1996) Fuzzy logic computing with words. IEEE Trans Fuzzy Syst 4(2):103–111.  https://doi.org/10.1109/91.493904 Google Scholar
  16. 16.
    Curcurù G, Galante GM, La Fata CM (2012) A bottom-up procedure to calculate the top event probability in presence of epistemic uncertainty. In Proceedings of the 11th international probabilistic safety assessment and management conference and the annual European safety and reliability conference 2012, PSAM11 ESREL, HelsinkiGoogle Scholar
  17. 17.
    Certa A, Hopps F, Inghilleri R, La Fata CM (2017) A Dempster-Shafer theory-based approach to the failure mode, effects and criticality analysis (FMECA) under epistemic uncertainty: application to the propulsion system of a fishing vessel. Reliab Eng Syst Saf 159:69–79.  https://doi.org/10.1016/j.ress.2016.10.018 Google Scholar
  18. 18.
    Roy B (1978) ELECTRE III: un algorithme de classements fonde sur une representation floue des preference en presence de criteres multiples. Cahiers de CERO 20(1):3–24Google Scholar
  19. 19.
    Martilla JA, James JC (1977) Importance-performance analysis. J Mark 41(1):77–79.  https://doi.org/10.2307/1250495 Google Scholar
  20. 20.
    Wong P, Ng PML, Mak CKY, Chan JKY (2016) Students’ choice of sub-degree programmes in self-financing higher education institutions in Hong Kong. High Educ 71(4):455–472.  https://doi.org/10.1007/s10734-015-9915-5 Google Scholar
  21. 21.
    Chang S-F, Chang J-C, Lin K-H, Yu B, Lee Y-C, Tsai S-B, Zhou J, Wu C, Yan Z-C (2014) Measuring the service quality of e-commerce and competitive strategies. Int J Web Serv Res 11(3):96–115.  https://doi.org/10.4018/ijwsr.2014070105 Google Scholar
  22. 22.
    Wong MS, Hideki N, George P (2011) The use of importance-performance analysis (IPA) in evaluating Japan's e-government services. J Theor Appl Electron Commer Res 6(2):17–30Google Scholar
  23. 23.
    Tseng M-L (2011) Importance-performance analysis of municipal solid waste management in uncertainty. Environ Monit Assess 172(1):171–187.  https://doi.org/10.1007/s10661-010-1325-7 Google Scholar
  24. 24.
    Lee Y-C, Wu H-H, Hsieh W-L, Weng S-J, Hsieh L-P, Huang C-H (2015) Applying importance-performance analysis to patient safety culture. Int J Health Care Qual Assur 28(8):826–840.  https://doi.org/10.1108/IJHCQA-03-2015-0039 Google Scholar
  25. 25.
    Dixit SK (2017) Integration of importance-performance analysis into the strategy of hospitals. Int J Healthc Manag 10(3):178–183.  https://doi.org/10.1080/20479700.2016.1241036 Google Scholar
  26. 26.
    Izadi A, Jahani Y, Rafiei S, Masoud A, Vali L (2017) Evaluating health service quality: using importance performance analysis. Int J Health Care Qual Assur 30(2):656–663.  https://doi.org/10.1108/IJHCQA-02-2017-0030 Google Scholar
  27. 27.
    Hou Y-H, Chang I-C, Chen C-F (2015) Application of an importance-performance analysis approach to evaluate an acupuncture treatment information system. CIN - Computers Informatics Nursing 33(1):37–42.  https://doi.org/10.1097/CIN.0000000000000111 Google Scholar
  28. 28.
    Bana e Costa CA, Oliveira MD (2012) A multicriteria decision analysis model for faculty evaluation. Omega 40(4):424–436.  https://doi.org/10.1016/j.omega.2011.08.006 Google Scholar
  29. 29.
    Certa A, Enea M, Lupo T (2013a) ELECTRE III to dynamically support the decision maker about the periodic replacements configurations for a multi-component system. Decis Support Syst 55(1):126–134.  https://doi.org/10.1016/j.dss.2012.12.044 Google Scholar
  30. 30.
    Roy B, Bouyssou D (1986) Comparison of two decision-aid models applied to a nuclear power plant siting example. Eur J Oper Res 25(2):200–215.  https://doi.org/10.1016/0377-2217(86)90086-X Google Scholar
  31. 31.
    Guitouni A, Martel JM (1998) Tentative guidelines to help choosing an appropriate MCDA method. Eur J Oper Res 109(2):501–521.  https://doi.org/10.1016/S0377-2217(98)00073-3 Google Scholar
  32. 32.
    Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Ser Sci 1(1):83.  https://doi.org/10.1504/IJSSCI.2008.017590 Google Scholar
  33. 33.
    Liberatore MJ, Nydick RL (2008) The analytic hierarchy process in medical and healthcare decision making: a literature review. Eur J Oper Res 189(1):194–207.  https://doi.org/10.1016/j.ejor.2007.05.001 Google Scholar
  34. 34.
    Morrissey AJ, Browne J (2004) Waste management models and their application to sustainable waste management. Waste Manag 24(3):297–308.  https://doi.org/10.1016/j.wasman.2003.09.005 Google Scholar
  35. 35.
    Hokkanen J, Salminen P (1997) Choosing a solid waste management system using multicriteria decision analysis. Eur J Oper Res 98(1):19–36.  https://doi.org/10.1016/0377-2217(95)00325-8 Google Scholar
  36. 36.
    Gavade RK (2014) Multi-criteria decision making: an overview of different selection problems and methods. Int J Comput Sci Inf Technol. 5(4):5643–5646Google Scholar
  37. 37.
    Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66Google Scholar
  38. 38.
    Büyüközkan G, Çifçi G (2012) A combined fuzzy AHP and fuzzy TOPSIS based strategic analysis of electronic service quality in healthcare industry. Expert Syst Appl 39(3):2341–2354.  https://doi.org/10.1016/j.eswa.2011.08.061 Google Scholar
  39. 39.
    Tsai H-Y, Chang C-W, Lin H-L (2010) Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance. Expert Syst Appl 37(8):5533–5541.  https://doi.org/10.1016/j.eswa.2010.02.099 Google Scholar
  40. 40.
    Büyüközkan G, Çifçi G, Güleryüz S (2011) Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Syst Appl 38(8):9407–9424.  https://doi.org/10.1016/j.eswa.2011.01.103 Google Scholar
  41. 41.
    Parasuraman A, Zeithaml VA, Berry LL (1985) A conceptual model of service quality and its implications for future research. J Mark 49(4):41–50.  https://doi.org/10.2307/1251430 Google Scholar
  42. 42.
    Sinimole KR (2012) Performance evaluation of the hospital services–a fuzzy analytic hierarchy process model. Int J Productivity Qual Manag 10(1):112–130.  https://doi.org/10.1504/IJPQM.2012.047944 Google Scholar
  43. 43.
    Baki B, Peker I (2015) An integrated evaluation model for service quality of hospitals: a case study from Turkey. J Multiple-Valued Logic Soft Comput 24:453–474Google Scholar
  44. 44.
    Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer-Verlag, New York.  https://doi.org/10.1007/978-3-642-48318-9 Google Scholar
  45. 45.
    Karadayi MA, Karsak EE (2014) Fuzzy MCDM approach for health-care performance assessment in Istanbul. In: Proceedings of 18th World Multi-Conference on Systemics, Cybernetics and Informatics WMSCI 2014, Orlando, United States, Volume 2. p 228–233Google Scholar
  46. 46.
    Opricovic S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455.  https://doi.org/10.1016/S0377-2217(03)00020-1 Google Scholar
  47. 47.
    Chang TH (2014) Fuzzy VIKOR method: a case study of the hospital service evaluation in Taiwan. Inf Sci 271:196–212.  https://doi.org/10.1016/j.ins.2014.02.118 Google Scholar
  48. 48.
    Andriosopoulos D, Gaganis C, Pasiouras FC, Zopounidis C (2012) An application of multicriteria decision aid models in the prediction of open market share repurchases. Omega 40(6):882–890.  https://doi.org/10.1016/j.omega.2012.01.009 Google Scholar
  49. 49.
    Certa A, Enea M, Galante GM, La Fata CM (2013b) A multistep methodology for the evaluation of human resources using the evidence theory. Int J Intell Syst 28(11):1072–1088.  https://doi.org/10.1002/int.21617 Google Scholar
  50. 50.
    Petrović M, Bojković N, Anić I, Stamenković M, Tarle SP (2014) An ELECTRE-based decision aid tool for stepwise benchmarking: an application over EU digital agenda targets. Decis Support Syst 59(1):230–241.  https://doi.org/10.1016/j.dss.2013.12.002 Google Scholar
  51. 51.
    El-Zein A, Tonmoy FN (2015) Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney. Ecol Indic 48:207–217.  https://doi.org/10.1016/j.ecolind.2014.08.012 Google Scholar
  52. 52.
    Giannoulis C, Ishizaka A (2010) A web-based decision support system with ELECTRE III for a personalised ranking of British universities. Decis Support Syst 48(3):488–497.  https://doi.org/10.1016/j.dss.2009.06.008 Google Scholar
  53. 53.
    Solecka K (2014) Electre III method in assessment of variants of integrated urban public transport system in Cracow. Transport Problems 9(4):83–96Google Scholar
  54. 54.
    Lupo T (2015) Fuzzy ServPerf model combined with ELECTRE III to comparatively evaluate service quality of international airports in Sicily. J Air Transp Manag 42:249–259.  https://doi.org/10.1016/j.jairtraman.2014.11.006 Google Scholar
  55. 55.
    Chang S-J, Hsiao H-C, Huang L-H, Chang H (2011) Taiwan quality indicator project and hospital productivity growth. Omega 39(1):14–22.  https://doi.org/10.1016/j.omega.2010.01.006 Google Scholar
  56. 56.
    Donabedian A (1990) The seven pillars of quality. Arch Pathol Lab Med 114(11):1115–1118Google Scholar
  57. 57.
    Li L, Benton WC (2003) Hospital capacity management decisions: emphasis on cost control and quality enhancement. Eur J Oper Res 146(3):596–614.  https://doi.org/10.1016/S0377-2217(02)00225-4 Google Scholar
  58. 58.
    Marshall GN, Ron DH, Rebecca M (1996) Health status and satisfaction with health care: results from the medical outcomes study. J Consult Clin Psychol 64(2):380–390.  https://doi.org/10.1037/0022-006X.64.2.380 Google Scholar
  59. 59.
    Headley DE, Miller SJ (1993) Measuring service quality and its relationship to future consumer behavior. Mark Health Serv 13(4):32–42Google Scholar
  60. 60.
    Frimpong NO, Nwankwo S, Dason B (2010) Measuring service quality and patient satisfaction with access to public and private healthcare delivery. Int J Public Sect Manag 23(3):203–220.  https://doi.org/10.1108/09513551011032455 Google Scholar
  61. 61.
    Neirotti P, Raguseo E, Paolucci E (2016) Are customers’ reviews creating value in the hospitality industry? Exploring the moderating effects of market positioning. Int J Inf Manag 36(6):1133–1143.  https://doi.org/10.1016/j.ijinfomgt.2016.02.010 Google Scholar
  62. 62.
    Strawderman L, Koubek R (2006) Quality and usability in a student health clinic. Int J Health Care Qual Assur 19(3):225–236.  https://doi.org/10.1108/09526860610661446 Google Scholar
  63. 63.
    Lee-Ross D (2008) An exploratory study of the contextual stability of SERVQUAL amongst three retail clusters in far North Queensland. J Place Manag Dev 1(1):46–61.  https://doi.org/10.1108/17538330810865336 Google Scholar
  64. 64.
    Senarat U, Gunawardena NS (2011) Development of an instrument to measure patient perception of the quality of nursing care and related hospital services at the national hospital of Sri Lanka. Asian Nurs Res 5(2):71–80.  https://doi.org/10.1016/S1976-1317(11)60015-7 Google Scholar
  65. 65.
    Lupo T (2016) A fuzzy framework to evaluate service quality in the healthcare industry: an empirical case of public hospital service evaluation in Sicily. Appl Soft Comput 40:468–478.  https://doi.org/10.1016/j.asoc.2015.12.010 Google Scholar
  66. 66.
    Klir GJ, Yuan B (1999) Fuzzy sets and fuzzy logic, theory and applications. Prentice Hall P T R, Upper Saddle RiverGoogle Scholar
  67. 67.
    La Fata CM, Lupo T (2017) A combined fuzzy-SEM evaluation approach to identify the key drivers of the academic library service quality in the digital technology era: an empirical study. J Assoc Inf Sci Technol 68(10):2425–2438.  https://doi.org/10.1002/asi.23878 Google Scholar
  68. 68.
    Mishra AR, Db J, Hooda DS (2017) Exponential intuitionistic fuzzy information measure with assessment of service quality. Int J Fuzzy Syst 19(3):788–798.  https://doi.org/10.1007/s40815-016-0278-6 Google Scholar
  69. 69.
    Lee AR (1999) Application of modified fuzzy AHP method to analyze bolting sequence of structural joints. UMI Dissertation Services. A. Bell & Howell CompanyGoogle Scholar
  70. 70.
    Chang D-Y (1996) Application of the extent analysis method of fuzzy AHP. Eur J Oper Res 95(3):649–655.  https://doi.org/10.1016/0377-2217(95)00300-2 Google Scholar
  71. 71.
    Cronin JJ, Taylor SA (1992) Measuring service quality: a reexamination and extension. J Mark 56(3):55–68.  https://doi.org/10.2307/1252296 Google Scholar
  72. 72.
    Figueira J, Mousseau V, Roy B (2005) ELECTRE methods. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis, state of the art surveys. Springer, Berlin, pp 133–162.  https://doi.org/10.1007/0-387-23081-5_4 Google Scholar
  73. 73.
    Marzouk MM (2011) ELECTRE III model for value engineering applications. Autom Constr 20(5):596–600.  https://doi.org/10.1016/j.autcon.2010.11.026 Google Scholar
  74. 74.
    Certa A, Enea M, Galante G, La Fata CM (2009) Multi-objective human resources allocation in RD projects planning. Int J Prod Res 47(13):3505–3523Google Scholar
  75. 75.
    Figueira J, Roy B (2002) Determining the weights of criteria in the ELECTRE type methods with a revised Simos’ procedure. Eur J Oper Res 139(2):317–326.  https://doi.org/10.1016/S0377-2217(01)00370-8 Google Scholar
  76. 76.
    Mousseau V (1995) Eliciting Information Concerning the Relative Importance of Criteria. In: Pardalos PM, Siskos Y, Zopounidis C (eds) Advances in Multicriteria Analysis. Nonconvex Optimization and Its Applications, vol 5. Springer, Boston, MAGoogle Scholar
  77. 77.
    Certa A, Enea M, Galante GM, La Fata CM (2017) ELECTRE TRI-based approach to the failure modes classification on the basis of risk parameters: an alternative to the risk priority number. Comput Ind Eng 108:100–110.  https://doi.org/10.1016/j.cie.2017.04.018 Google Scholar
  78. 78.
    Damaskos X, Kalfakakou G (2005) Application of ELECTRE III and DEA methods in the BPR of Bank branch network. Yugoslav J Oper Res 15(2):259–276.  https://doi.org/10.2298/YJOR0502259D Google Scholar
  79. 79.
    Roy B (1991) The outranking approach and the foundations of ELECTRE methods. Theory and decisions 31(1):49–73.  https://doi.org/10.1007/BF00134132 Google Scholar
  80. 80.
    Lopez JCL, Noriega JJS, Chavira DAG (2017) A multi-criteria approach to rank the municipalities of the states of Mexico by its marginalization level: the case of Jalisco. Int J Inf Technol Decis Mak 16(2):473–513.  https://doi.org/10.1142/S0219622017500080 Google Scholar
  81. 81.
    Rogers M, Bruen M, Maystre L (2000) Electre and decision support. Kluwer Academic Publishers, London.  https://doi.org/10.1007/978-1-4757-5057-7 Google Scholar
  82. 82.
    Roy B (1993) Aide Multicritère à la Décision, Méthodes et Cas, Economica, ParisGoogle Scholar
  83. 83.
    Effective healthcare program, Stakeholder guide 2014. AHRQ Publication No. 14-EHC010-EF. http://www.effectivehelatcare.ahrq.gov. Accessed Feb 2014
  84. 84.
    Feng M, Mangan J, Wong C, Xu M, Lalwani C (2014) Investigating the different approaches to importance-performance analysis. Serv Ind J 34(12):1021–1041.  https://doi.org/10.1080/02642069.2014.915949 Google Scholar
  85. 85.
    De Borger B, Kerstens K, Costa A (2002) Public transit performance: what does one learn from frontier studies? Transp Rev 22(1):1–38.  https://doi.org/10.1080/01441640010020313 Google Scholar
  86. 86.
    Lupo T, Cusumano M (2017) Towards more equity concerning quality of urban waste management services in the context of cities. J Clean Prod 171:1324–1341.  https://doi.org/10.1016/j.jclepro.2017.09.194 Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Dipartimento dell’Innovazione Industriale e Digitale (DIID) – Ingegneria Chimica, Gestionale, Informatica, MeccanicaUniversità degli Studi di PalermoPalermoItaly
  2. 2.Università Ca’ FoscariVeneziaItaly

Personalised recommendations