Are the ‘Illnesses’ of Traditional Likert Scales Treatable?

Zsuzsanna E. Tóth, Gábor Árva, Rita V. Dénes

Abstract


Purpose: The main aim of this paper is to introduce the development and the application of a fuzzy rating scale in measuring customer satisfaction which are to be demonstrated through a healthcare example in order to illustrate how the proposed methodology is able to enhance the reliability of traditional Likert scale-based evaluations.

Methodology/Approach: The proposed methodology is built on fuzzy sets the membership function of which is composed of two sigmoid functions by applying Dombi’s conjunction operator. The possible ‘values’ of the linguistic variable expressing customer satisfaction are to be expressed by these functions which can also be linked to the level of organizational performance allowing the illustration of the mainly nonlinear relationship between the provided and perceived service performance.

Findings: The application of the proposed fuzzy rating scale confirms its ability to reflect the unambiguity of human ratings as well as the context-dependency of ratings resulting in a more precise representation of human judgements.

Research Limitation/implication: The presented methodology may be viewed as a viable approach in any kind of service quality evaluations where Likert-type scales are traditionally applied to handle its weaknesses.

Originality/Value of paper: The proposed methodology is not only able to reflect the satisfaction of customers and the organizational performance simultaneously, but the expectations of customers related to the desired level of performance can also be incorporated into the establishment of the scale yielding to more reliably supported managerial decisions.

Keywords


fuzzy number; Likert scale; healthcare; service quality; patient satisfaction

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References


Akdag, H., Kalaycı, T., Karagöz, S., Zülfikar, H. and Giz, D., 2014. The evaluation of hospital service quality by fuzzy MCDM. Applied Soft Computing, [e-journal] 23, pp.239-248. DOI: 1016/j.asoc.2014.06.033.

Al-Borie, H.M. and Damanhouri, A.M.S., 2013. Patient’s satisfaction of service quality in Saudi hospitals: A SERVQUAL analysis. International Journal of Health Care Quality Assurance, [e-journal] 26(1), pp.20-30. DOI: 10.1108/09526861311288613.

Alhashem, M.A., Alquraini, H. and Chowdhury, R.I., 2011. Factors influencing patient satisfaction in primary healthcare clinics in Kuwait. International Journal of Health Care Quality Assurance, [e-journal] 24(3), pp.249-262. DOI: 10.1108/09526861111116688.

Arab, M., Tabatabaei, M., Rashidian, A., Rahimi, A. and Zarei, E., 2012. The effect of service quality on patient loyalty: a study of private hospitals in Tehran, Iran. Iranian Journal of Public Health, 41(9), pp.71-77.

Battisti, F., Nicolini, G. and Salini, S., 2010. The Rasch model in customer satisfaction survey data. Quality Technology & Quantitative Management, [e-journal] 7(1), pp.15-34. DOI: 10.1080/16843703.2010.11673216.

Behdioğlu, S., Acar, E. and Burhan, H.A., 2017. Evaluating service quality by fuzzy SERVQUAL: a case study in a physiotherapy and rehabilitation hospital. Total Quality Management & Business Excellence, [e-journal] 30(3/4), pp.1-19. DOI: 10.1080/14783363.2017.1302796.

Benoit, E., 2013. Expression of uncertainty in fuzzy scales based measurements. Measurement, [e-journal] 46(9), pp.3778-3782. DOI: 10.1016/j.measurement.2013.04.006.

Bertolini, M., Bevilacqua, M., Ciarapica, F.E. and Giacchetta, G., 2011. Business process re‐engineering in healthcare management: a case study. Business Process Management Journal, [e-journal] 17(1), pp.42-66. DOI: 10.1108/14637151111105571.

Büyüközkan, G., Çifçi, G. and Güleryüz, S., 2011. Strategic analysis of healthcare service quality using fuzzy AHP methodology. Expert Systems with Applications, [e-journal] 38(8), pp.9407-9424. DOI: 10.1016/j.eswa.2011.01.103.

Calcagnì, A. and Lombardi, L., 2014. Dynamic Fuzzy Rating Tracker (DYFRAT): a novel methodology for modeling real-time dynamic cognitive processes in rating scales. Applied Soft Computing, [e-journal] 24, pp.948-961. DOI: 10.1016/j.asoc.2014.08.049.

Carlucci, D., Renna, P. and Schiuma, G., 2013. Evaluating service quality dimensions as antecedents to outpatient satisfaction using back propagation neural network. Health Care Management Science, [e-journal] 16(1), pp.37-44. DOI: 10.1007/s10729-012-9211-1.

de la Rosa de Sáa, S., Gil, M.Á., González-Rodríguez, G., López, M.T. and Lubiano, M.A., 2015. Fuzzy rating scale-based questionnaires and their statistical analysis. IEEE Transactions on Fuzzy Systems, [e-journal] 23(1), pp.111-126. DOI: 10.1109/TFUZZ.2014.2307895.

Dombi, J., 2009. Pliant Arithmetics and Pliant Arithmetic Operations. Acta Polytechnica Hungarica, 6(5), pp.19-49.

Garrard, F. and Narayan, H., 2013. Assessing obstetric patient experience: a SERVQUAL questionnaire. International Journal of Health Care Quality Assurance, [e-journal] 26(7), pp.582-592. DOI: 10.1108/IJHCQA-08-2011-0049.

Gil, M.Á. and González-Rodríguez, G., 2012. Fuzzy vs. Likert scale in statistics. In: E. Trillas, P.P. Bonissone, L. Magdalena and J. Kacprzyk, eds. 2012. Combining experimentation and theory. Berlin, Heidelberg: Springer. pp.407-420. DOI: 10.1007/978-3-642-24666-1_27.

Grondahl, V.A., Wilde-Larsson, B., Karlsson, I. and Hall-Lord, M.L., 2013. Patients’ experiences of care quality and satisfaction during hospital stay: a qualitative study. European Journal for Person Centered Healthcare, [e-journal] 1(1), pp.185-192. DOI: 10.5750/ejpch.v1i1.650.

Haque, A., Sarwar, A.A.M., Yasmin, F. and Anwar, A., 2012. The impact of customer perceived service quality on customer satisfaction for private health centre in Malaysia: a structural equation modeling approach. Information Management and Business Review, 4(5), pp.257-267.

Hesketh, B., Pryor, R., Gleitzman, M. and Hesketh, T., 1988. Practical applications and psychometric evaluation of a computerized fuzzy graphic rating scale. Advances in Psychology, [e-journal] 56, pp.425-454. DOI: 10.1016/S0166-4115(08)60493-8.

Hu, H-Y., Lee, C-H. and Yen, T-M., 2010. Service quality gaps analysis based on Fuzzy linguistic SERVQUAL with a case study in hospital out‐patient services. The TQM Journal, [e-journal] 22(5), pp.499-515. DOI: 10.1108/17542731011072847.

Iversen, H.H., Holmboe, O. and Bjertnæs, Ø.A., 2012. The Cancer Patient Experiences Questionnaire (CPEQ): reliability and construct validity following a national survey to assess hospital cancer care from the patient perspective. British Medical Journal, [e-journal] 2(5), p.e001437. DOI: 10.1136/bmjopen-2012-001437.

Jónás, T., Tóth, ZS.E. and Árva, G., 2018. Applying a fuzzy questionnaire in a peer review process. Total Quality Management & Business Excellence, [e-journal] 29(9/10), pp.1228-1245. DOI: 10.1080/14783363.2018.1487616.

Kessler, D.P. and Mylod, D., 2011. Does patient satisfaction affect patient loyalty?. International Journal of Health Care Quality Assurance, [e-journal] 24(4), pp.266-273. DOI: 10.1108/09526861111125570.

Kuzmanovic, M., Savic, G., Popovic, M. and Martic, M., 2013. A new approach to evaluation of university teaching considering heterogeneity of students’ preferences. Higher Education, [e-journal] 66(2), pp.153-171. DOI: 10.1007/s10734-012-9596-2.

Li, Q., 2013. A novel Likert scale based on fuzzy sets theory. Expert Systems with Applications, [e-journal] 40(5), pp.1609-1618. DOI: 10.1016/j.eswa.2012.09.015.

Lin, C.J. and Wu, W.W., 2008. A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, [e-journal] 34(1), pp.205-213. DOI: 10.1016/j.eswa.2006.08.012.

Liou, T.S. and Chen, C.W., 2006. Subjective appraisal of service quality using fuzzy linguistic assessment. International Journal of Quality & Reliability Management, [e-journal] 23(8), pp.928-943. DOI: 10.1108/02656710610688149.

Lozano, L.M., García-Cueto, E. and Muñiz, J., 2008. Effect of the number of response categories on the reliability and validity of rating scales. Methodology, [e-journal] 4(2), pp.73-79. DOI: 10.1027/1614-2241.4.2.73.

Lubiano, M.A., de Sáa, S.D.L.R., Montenegro, M., Sinova, B. and Gil, M.Á., 2016. Descriptive analysis of responses to items in questionnaires. Why not using a fuzzy rating scale?. Information Sciences, [e-journal] 360, pp.131-148. DOI: 10.1016/j.ins.2016.04.029.

Lupo, T., 2013. A fuzzy ServQual based method for reliable measurements of education quality in Italian higher education area. Expert Systems with Applications, [e-journal] 40(17), pp.7096-7110. DOI: 10.1016/j.eswa.2013.06.045.

Lupo, T., 2016. A fuzzy framework to evaluate service quality in the healthcare industry: An empirical case of public hospital service evaluation in Sicily. Applied Soft Computing, [e-journal] 40, pp.468-478. DOI: 10.1016/j.asoc.2015.12.010.

Naidu, A., 2009. Factors affecting patient satisfaction and healthcare quality. International Journal of Health Care Quality Assurance, [e-journal] 22(4), pp.366-381. DOI: 10.1108/09526860910964834.

Parasuraman, A, Zeithaml, V.A. and Berry, L.L., 1988. SERVQUAL: A multiple item scale for measuring customer perceptions of service quality. Journal of Retailing, 64(1), pp.12-40.

Ramsaran-Fowdar, R.R., 2008. The relative importance of service dimensions in a healthcare setting. International Journal of Health Care Quality Assurance, [e-journal] 21(1), pp.104-124. DOI: 10.1108/09526860810841192.

Roberge, D., Tremblay, D., Turgeon, M.È. and Berbiche, D., 2013. Patients’ and professionals’ evaluations of quality of care in oncology outpatient clinics. Supportive Care in Cancer, [e-journal] 21(11), pp.2983-2990. DOI: 10.1007/s00520-013-1872-x.

Singh, A. and Prasher, A., 2019. Measuring healthcare service quality from patients’ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, [e-journal] 30(3/4), pp.1-18. DOI: 10.1080/14783363.2017.1302794.

Sowa, J.F., 2013. What Is the Source of Fuzziness?. In: R. Seiging, E. Trillas, C. Moraga and S. Termini, eds. 2013. On Fuzziness. Berlin, Heidelberg: Springer. pp.645-652. DOI: 10.1007/978-3-642-35644-5_31.

Tóth, ZS.E., Jónás, T. and Dénes, R., 2018. Service quality evaluations in healthcare based on flexible fuzzy numbers - Patients and employees in the focus. In: S.M. Dahlgaard-Park and J.J. Dahlgaard, eds. 2018. 21st QMOD International Conference on Quality and Service Sciences. Wales, UK, 22-24 August 2018. Lund: Lund University Library Press. pp. 972-983.

Tóth, ZS.E., Surman, V. and Árva, G., 2017. Challenges in course evaluations at Budapest University of Technology and Economics. In: B. Zafer, Y.M. Melis and X.T. Roslind, eds. 2017. 8th ICEEPSY - International Conference on Education and Educational Psychology. Porto, Portugal, 11-14 October 2017. Porto: Future Academy. pp.629-641. DOI: 10.15405/epsbs.2017.10.60

Tsai, H-Y., Chang, C-W. and Lin, H-L., 2010. Fuzzy hierarchy sensitive with Delphi method to evaluate hospital organization performance. Expert Systems with Applications, [e-journal] 37, pp.5533-5541. DOI: 10.1016/j.eswa.2010.02.099.

Vandamme, R. and Leunis, J., 1993. Development of a multiple-item scale for measuring hospital service quality. International Journal of Service Industry Management, [e-journal] 4(3), pp.30-49. DOI: 10.1108/09564239310041661.

Woldegebriel, S., Kitaw, D. and Rafele, C., 2015. Application of Fuzzy Logic for Prioritizing Service Quality Improvement in Healthcare. International Journal of Scientific and Engineering Research, 6(5), pp.530-537.

Yeh, C.H. and Kuo, Y.L., 2003. Evaluating passenger services of Asia-Pacific international airports. Transportation Research Part E: Logistics and Transportation Review, [e-journal] 39(1), pp.35-48. DOI: 10.1016/S1366-5545(02)00017-0.

Yesilada, F. and Direktor, E., 2010. Health service quality: a comparison of public and private hospitals. African Journal of Business Management, 4(6), pp.962-971.




DOI: http://dx.doi.org/10.12776/qip.v24i2.1439

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