Assessing Student Satisfaction and Improving Efficiency: A Study on University Cafeteria

References

• Tam R, Yassa B, Parker H, O’Connor H, Allman-Farinelli M. University students’ on-campus food purchasing behaviors, preferences, and opinions on food availability. Nutrition. 2017;37:7–13. doi:10.1016/j.nut.2016.07.007.
   PubMed Web of Science ®Google Scholar
• Deliens T, Clarys P, De Bourdeaudhuij I, Deforche B. Determinants of eating behavior in university students: a qualitative study using focus group discussions. BMC Public Health. 2014;14(1):53. doi:10.1186/1471-2458-14-53.
   PubMedGoogle Scholar
• Schulze MB, Martínez-González MA, Fung TT, Lichtenstein AH, Forouhi NG. Food-based dietary patterns and chronic disease prevention. BMJ. 2018:k2396. doi:10.1136/bmj.k2396.
   PubMedGoogle Scholar
• Twum FO, Peprah WK. The impact of service quality on students’ satisfaction. Int J Acad Res Bus Soc Sci. 2020;10(10):169–181. doi:10.6007/IJARBSS/v10-i10/7923.
   Google Scholar
• Cardozo RN. An experimental study of customer effort, expectation, and satisfaction. J Marketing Res. 1965;2(3):244–249. doi:10.1177/002224376500200303.
   Web of Science ®Google Scholar
• Howard J, Sheth J. The theory of buyer behavior. Published online January 1, 1969. doi:10.2307/2284311.
   Google Scholar
• Bourne PA. The perception of university students and workers on foodservice offered by the university’s cafeteria. J Adv Res Eng Edu. 2020;5:39–52.
   Google Scholar
• Serhan M, Serhan C. The impact of food service attributes on customer satisfaction in a rural university campus environment. Int J Food Sci. 2019;2019:2154548. doi:10.1155/2019/2154548.
   PubMedGoogle Scholar
• El-Said OA, Fathy EA. Assessing university students’ satisfaction with on-campus cafeteria services. Tour Manag Perspect. 2015;16:318–324. doi:10.1016/j.tmp.2015.09.006.
   Web of Science ®Google Scholar
• Imran K. Investigating university students’ satisfaction with on-campus cafeteria services: an empirical study in the perspective of a private university. Asian J Empir Res. 2018;8(6):225–237. doi:10.18488/journal.1007/2018.8.6/1007.6.225.237.
   Google Scholar
• Akbara AZ, Chua BL, Han H, Raposo A. Investigating international students’ perception of foodservice attributes in Malaysian Research Universities. Sustainability. 2021;13(15):8190. doi:10.3390/su13158190.
   Web of Science ®Google Scholar
• Mokoena ML. Outsourcing at Walter Sisulu University: Students’ Perceptions of an Outsourced Campus-Based Cafeteria [ thesis]. 2019. https://researchspace.ukzn.ac.za/handle/10413/17943. Accessed September 29, 2023.
   Google Scholar
• Hu C. An analysis of college students’ satisfaction with Online Food Delivery (OFD). Inf Syst Econ. 2022;3(3). doi:10.23977/infse.2022.030307.
   Google Scholar
• Almanza BA, Jaffe W, Lin L. Use of the service attribute matrix to measure consumer satisfaction. Hosp Res J. 1994;17(2):63–75. doi:10.1177/109634809401700207.
   Google Scholar
• Meyer MK. Students’ satisfaction with middle/junior high school foodservice and nutrition programs. J Am Diet Assoc. 1998;98(9):A105. doi:10.1016/s0002-8223(98)00683-x.
   Google Scholar
• Hoang ML. Students’ satisfaction with university lunch quality: case Fox Mamselli Restaurant at KAMK. http://www.theseus.fi/handle/10024/352327. Published 2020. Accessed July 24, 2023.
   Google Scholar
• Akter M, Sadekin MN, Patwary AK. An empirical study on students’ satisfaction from Mawlana Bhashani Science & Technology University, Bangladesh. Rev Econ Dev Stud. 2020;6(2):463–479. doi:10.47067/reads.v6i2.215.
   Google Scholar
• Raihen MN, Akter S, Sardar MN. Food satisfaction among students: a study of Present Public University students in Bangladesh. J Math Stat Stud. 2023;4(1):1–18. doi:10.32996/jmss.2023.4.1.1.
   Google Scholar
• Yang CC. A classification-based Kansei engineering system for modeling consumers’ affective responses and analyzing product form features. Expert Syst Appl. 2011;38(9):11382–11393. doi:10.1016/j.eswa.2011.03.008.
   Web of Science ®Google Scholar
• Tran HD, Li H. Sound event classification based on feature integration, recursive feature elimination and structured classification. In: 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2009. doi:10.1109/ICASSP.2009.4959549.
   Google Scholar
• Cronbach LJ. Internal consistency of tests: analyses old and new. Psychometrika. 1988;53(1):63–70. doi:10.1007/BF02294194.
   Web of Science ®Google Scholar
• Taherdoost H. Validity and reliability of the research instrument; how to test the validation of a questionnaire/survey in a research. SSRN J. Published online 2016. doi:10.2139/ssrn.3205040.
   Google Scholar
• Nkansah BK. On the Kaiser-Meier-Olkin’s measure of sampling adequacy | semantic scholar. Math Theory Model. 2018;8:52–76.
   Google Scholar
• Msuha B, Mdendemi T. Hypothesis testing for the association between categorical variables: empirical application of chi-square test. Math Theory Model. 2019;9(2). doi:10.7176/mtm/9-2-02.
   Google Scholar
• Xia S, Yang Y. A model-free feature selection technique of feature screening and random forest-based recursive feature elimination. Int J Intell Syst. 2023;2023:1–16. doi:10.1155/2023/2400194.
   Web of Science ®Google Scholar
• Jeon H, Oh S. Hybrid-recursive feature elimination for efficient feature selection. Appl Sci. 2020;10(9):3211. doi:10.3390/app10093211.
   Google Scholar
• Somogyi Z. Performance evaluation of machine learning models. In: Somogyi Z, ed. The Application of Artificial Intelligence: Step-By-Step Guide from Beginner to Expert. Springer International Publishing; 2021:87–112. doi:10.1007/978-3-030-60032-7_3.
   Google Scholar
• Lobo VGR, Fonseca TCO, Moura FAS. Bayesian cross-validation of geostatistical models. Spat Stat. 2020;35:100394. doi:10.1016/j.spasta.2019.100394.
   Web of Science ®Google Scholar
• Guyon IM, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–1182. doi:10.1162/153244303322753616.
   Google Scholar
• Sharma A, Moon J, Bailey-Davis L, Conklin M. Food choices and service evaluation under time constraints: the school lunch environment. Int J Contemp Hosp Manag. 2017;29(12):3191–3210. doi:10.1108/ijchm-06-2015-0269.
   Web of Science ®Google Scholar
• Chang ML, Suki NMM, Suki NBM. Students’ satisfaction with the university cafeteria: structural relationships of food quality, staff, price fairness, and ambiance. In: Omar R, Bahrom H de Mello G, eds. Islamic Perspectives Relating to Business, Arts, Culture and Communication. Springer Singapore; 2015:373–381. doi:10.1007/978-981-287-429-0_35.
   Google Scholar
• Mattuck R, Blanchet R, Wait AD. Data representativeness for risk assessment. Environ Forensics. 2005;6(1):65–70. doi:10.1080/15275920590913886.
   Web of Science ®Google Scholar
• Georganos S, Grippa T, Vanhuysse SG, Wolff E, Shimoni M, Lennert M. Optimizing classification performance in an object-based very-high-resolution land use-land cover urban application. In: Heldens W, Chrysoulakis N, Erbertseder T Zhang Y, eds. Remote Sensing Technologies and Applications in Urban Environments II. SPIE; 2017. doi:10.1117/12.2278482.
   Google Scholar
• Sechidis K, Papangelou K, Nogueira S, Weatherall J, Brown G. On the stability of feature selection in the presence of feature correlations. In: Brefeld U, Fromont E, Hotho A, Knobbe A, Maathuis M Robardet C, eds. Machine Learning and Knowledge Discovery in Databases. Springer International Publishing; 2020:327–342. doi:10.1007/978-3-030-46150-8_20.
   Google Scholar
• Ramezan CA. Transferability of Recursive Feature Elimination (RFE)-derived feature sets for support vector machine land cover classification. Remote Sens. 2022;14(24):6218. doi:10.3390/rs14246218.
   Google Scholar
• How to become a Health Education Specialist. https://www.nchec.org/news/posts/how-to-become-a-health-education-specialist. Accessed September 29, 2023.
   Google Scholar
• Gates DM, Remmel A, Adamson BJ, Hutt LJ. A collaborative approach to workforce transformation: food and nutrition services staff education. Can J Diet Pract Res. 2000;61:13–17.
   PubMed Web of Science ®Google Scholar
• Grandner MA, Kripke DF, Yoon IY, Youngstedt SD. Criterion validity of the Pittsburgh Sleep Quality Index: investigation in a non-clinical sample. Sleep Biol Rhythms. 2006;4(2):129–136. doi:10.1111/j.1479-8425.2006.00207.x.
   PubMedGoogle Scholar