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Article type: Research Article
Authors: Phumchusri, Naragain* | Thongoiam, Mookarin
Affiliations: Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
Correspondence: [*] Corresponding author: Naragain Phumchusri, Department of Industrial Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand. E-mail: [email protected].
Abstract: Customer satisfaction has become a key factor in strategic work of many institutions towards the increasing competition regarding student recruitment. This paper presents a systematic approach to identify customer needs for a Master’s Degree Program in Industrial Engineering based on target students’ needs in the view of new product development. The approach consists of two methods: Choice-based conjoint analysis and Kano model. Conjoint analysis is used to explore important scores of each attribute of the program, i.e., specialist concentration, class period, research type, teaching language, teaching format, and tuition fee. Also, the popularity of levels in each attribute are identified. Latent class model is used to identify different clusters of target customers. The result indicates two different segments of different preferences. The heterogeneity of needs and preference is characterized mainly in levels of specialist concentration preference as well as other attributes such as tuition fee. Other attributes such as interdisciplinary, cooperate program, work experience requirement and group (with presence/absence option) are analyzed by Kano model to identify their categories, i.e., how important they are. This research contributes in the literature as a pioneer in applying these two methods to gain customer perception insights about new Master’s curriculum development for education industry.
Keywords: Customer preference, discrete choice experiment, choice-based conjoint analysis, latent class analysis, kano model
DOI: 10.3233/MAS-221409
Journal: Model Assisted Statistics and Applications, vol. 18, no. 2, pp. 135-147, 2023
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