HomeJournal of Interdisciplinary Perspectivesvol. 2 no. 3 (2024)

Service Quality Assessment Tool in a State University in Northern Mindanao

Richard Ian Mark T Necosia | Isaias S Sealza

Discipline: Education

 

Abstract:

Higher education institutions (HEIs) worldwide are increasingly being recognized as integral components of the service industry. However, established models for assessing service quality, such as SERVQUAL and HiEduQual, have primarily focused on foreign higher education systems. This study explored the unique context of a Philippine State University. It aims to localize existing quality assurance mechanisms by developing a tool to evaluate service quality from the viewpoint of undergraduate students. The results offer valuable insights into evolving service quality assessment practices within Philippine state universities and colleges (SUC), serving as a template for refinement and adaptation in similar contexts. 708 undergraduate students answered the initial 52-item questionnaire. After initial data analysis, only 630 cases were subjected to further analysis using Principal Component Analysis (PCA) and Confirmatory Factor Analysis (CFA). This resulted in a seven-factor model comprising 31 indicators, exhibiting favorable model fit indices (RMSEA = 0.039, CMIN/DF = 2.073, PCFA = 0.785, PNFI = 0.751, CFI = 0.951). These factors encompassed the following dimensions: ease of doing business, leadership quality, teacher quality, knowledge services, activities, e-governance, and continuous improvement. The findings demonstrated strong internal consistency and reliability across all scale factors. Convergent and discriminant validity were also confirmed. It is recommended that SUCs consider adopting the localized tool in their internal quality assessment procedures to complement existing service quality assessment mechanisms. As the tool is specifically tailored to students’ perspectives as primary end users of SUC services, further research can focus on integrating the results of the study to develop a multi-stakeholder internal quality assessment tool or framework to meet evolving needs and expectations.



References:

  1. Abrigo, M. M. (2021). If you pay peanuts, you get monkeys? Education spending and schooling quality in the The Philippines. Philippine Institute for Development Studies, (2021-27).
  2. Alnaami, N., Al Haqwi, A., & Masuadi, E. (2020). Clinical Learning Evaluation Questionnaire: A Confirmatory Factor Analysis. Advances in Medical Education and Practice, 11, 953-961. https://doi.org/10.2147/AMEP.S243614
  3. Anderson, C., & Zeithaml, C. P. (1984). Stage of the product life cycle, business strategy, and business performance. Academy of Management Journal, 27(1), 5-24.
  4. Bentler, P. M. (1990). "Comparative Fit Indexes in Structural Models. Psychological Bulletin, 107(2), 238-246.
  5. Bera, S., & Rao, K. V. (2011). Estimation of origin-destination matrix from traffic counts: the state of the art.
  6. Black, W. C., Anderson, R. E., Babin, B. J., & Hair, J. F. (2019). Multivariate Data Analysis. Cengage. ISBN: 978-1-4737-5654-0
  7. Buuren, S. v. (2018). Flexible Imputation of Missing Data (2nd ed.). CRC Press, Taylor & Francis Group.
  8. Buzzell, R. D., & Gale, B. T. (1987). The PIMS principles: Linking strategy to performance. New York: Free Press.
  9. Calenge, C., Darmon, G., Basille, M., Loison, A., & Jullien, J. M. (2008). The factorial decomposition of the Mahalanobis distances in habitat selection studies. Ecology, 89(2), 555-566.
  10. Clewes, D. (2003). A Student-centred Conceptual Model of Service Quality in Higher Education. Quality in Higher Education, 9, 69-85. 10.1080/13538320308163
  11. Darawonga, C., & Sandmaung, M. (2019). Service quality enhancing student satisfaction in international programs of higher education institutions: a local student perspective. Journal of Marketing for Higher Education. https://doi.org/10.1080/08841241.2019.1647483
  12. De Maesschalck, R., Jouan-Rimbaud, D., & Massart, D. L. (2000). The mahalanobis distance. Chemometrics and intelligent laboratory systems, 50(1), 1-18.
  13. Field, A. (2005). Discovering Statistics Using SPSS. London SAGE Publication.
  14. Field, A. (2013). Discovering Statistics using SPSS (4th ed.). London: SAGE.
  15. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–88. 10.2307/3150980.
  16. Galeeva, R. B. (2016). SERVQUAL application and adaptation for educational service quality assessments in Russian higher education. n. Quality Assurance in Education, 24(3), 329-348. doi:10.1108/QAE-06-2015-0024
  17. Ghorbani. (2019). Mahalanobis distance and its application for detecting multivariate outliers. Facta Universitatis, Series: Mathematics and Informatics, 583-595.
  18. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate Data Analysis (3rd ed.). New York: Macmillan.
  19. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage.
  20. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
  21. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modeling: guidelines for determining model fit. Electron. J. Bus. Res. Methods, 6(1), 53-60.
  22. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  23. Kaiser, H. F. (1974). An index of factorial simplicity. psychometrika, 39(1), 31-36.
  24. Kinanti, E. S., Ritchi, H., & Handoyo, S. (2020). Factor Analysis of Service Performance in Higher Education Institutions. Journal of Accounting Auditing and Business, 3(1), 1-13. Google scholar. http://dx.doi.org/10.24198/jaab.v3i1.24733
  25. Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling (4th ed.). Guilford Publications.
  26. Lang, K. M., & Little, T. D. (2018). Principled Missing Data Treatments. Prevention Science, 19, 284-294. https://doi.org/10.1007/s11121-016-0644-5
  27. Latif, K. F., Latif, I., Sahibazada, U. F., & Ullah, M. (2017). In search of quality: measuring Higher Education Service Quality (HiEduQual). Total Quality Management and Business Excellence, 30(7-8), 768-791. DOI: 10.1080/14783363.2017.1338133
  28. Latif, K. F., Latif, I., Sahibzada, U. F., & Ullah, M. (2017). In search of quality: measuring Higher Education Service Quality (HiEduQual). Total Quality Management & Business Excellence. 10.1080/14783363.2017.1338133
  29. Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to Classify, Detect, and Manage Univariate and Multivariate Outliers, With Emphasis on Pre-Registration. International Review of Social Psychology, 32(1), 1-10. https://doi.org/10.5334/irsp.289
  30. Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology, 74, 150-156.
  31. MacCallum, R. C., Widaman, K. F., Preacher, K. J., & Hong, S. (2001). Sample size in factor analysis: The role of model error. Multivariate Behavioral Research, 36, 611-637.
  32. MacCallum, R. C., Widaman, K. F., Zhang, S., & Hong, S. (n.d.). Sample size in factor analysis. Psychological Methods, 4(1), 84-99.
  33. Mindrila, D. (2010). Maximum likelihood (ML) and diagonally weighted least squares (DWLS) estimation procedures: A comparison of estimation bias with ordinal and multivariate non-normal data. International Journal of Digital Society, 1(1), 60-66.
  34. Netemeyer, R. G., & Bearden, W. O. (2003). Scaling Procedures: Issues and Applications. Sage.
  35. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.
  36. Pamatmat, F. V., Dominguez, L. L., Pamin, C. D., & Daran, A. M. (2018). Service Quality Dimensions Of A Philippine State University And Students’ Satisfaction: Bridging Gaps To Excellence. International Journal of Advanced Research, 6(7), 673-681. 10.21474/IJAR01/7411
  37. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985, Autumn). A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49(4), 41-50. https://doi.org/10.2307/1251430
  38. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988, Spring). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1). https://psycnet.apa.org/record/1989-10632-001
  39. Peña, D., & Prieto, J. F. (2001). Multivariate outlier detection and robust covariance matrix estimation. Technometrics, 43(3), 286-310.
  40. Pett, M. A., Sullivan, J. J., & Lackey, N. R. (2003). Making Sense of Factor Analysis: The Use of Factor Analysis for Instrument Development in Health Care Research. SAGE Publications.
  41. Pohan, M. (2013). How education breaks the cycle of poverty: An inter-regional study of Indonesian households [Unpublished Dissertation].
  42. Schafer, J. L., & Graham, J. W. (2002). Missing data: our view of the state of the art. Psychological methods, 7(2), 146.
  43. Streiner. (1994). Figuring out factors: the use and misuse of factor analysis. Canadian Journal of Psychiatry, 39(3), 135-140.
  44. Stryzhak, O. (2020). The relationship between education, income, economic freedom, and happiness. (75th ed.). SHS Web Conference.
  45. Tabachnick, B. G., & Fidell, L. S. (2007). Using Multivariate Statistics. Pearson Education.
  46. Verardi, V., & Dehon, C. (2010). Multivariate outlier detection in Stata. The Stata Journal, 10(2), 259-266.
  47. Xiang, S., Nie, F., & Zhang, C. (2008). Learning a Mahalanobis distance metric for data clustering and classification. Pattern recognition, 41(12), 3600-3612.
  48. Yuan, K. H., & Bentler, P. M. (2001). Effect of outliers on estimators and tests in covariance structure analysis. British Journal of Mathematical and Statistical Psychology, 54(1), 161-175.
  49. Zamanzadeh, V., Ghahramanian, A., Rassouli, M., Abbaszadeh, A., & Alavi, H. (2015). Design and Implementation content validity Study: development of an instrument for measuring patient-centered communication. J Caring Sci., 4(5), 165-178. https://www.semanticscholar.org/paper/Design-and-Implementation-Content-Validity-Study%3A-Zamanzadeh-Ghahramanian/3053b1128d4fb9e9d0c790261602662ca19af5a1
  50. Zeithaml, V. A. (2020). Service quality, profitability and the economic worth of customers: What we know and what we need to learn. Journal of the Academy of Marketing Science, 28(1), 67-80.