Identifying target customer needs for a Master’s Degree Program in Industrial Engineering by conjoint analysis and Kano model

N. Phumchusri, Mookarin Thongoiam
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引用次数: 0

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.
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通过联合分析和卡诺模型确定工业工程硕士学位课程的目标客户需求
面对日益激烈的招生竞争,客户满意度已成为许多院校战略工作的关键因素。本文从新产品开发的角度出发,基于目标学生的需求,提出了一种系统的方法来确定工业工程硕士学位课程的客户需求。该方法包括两种方法:基于选择的联合分析和Kano模型。采用联合分析的方法,探究该项目各属性的重要分值,即专业集中度、课时、研究类型、教学语言、教学形式、学费。此外,还确定了每个属性中级别的流行程度。潜在类别模型用于识别不同类型的目标客户。结果表明,两个不同的细分市场有不同的偏好。需求和偏好的异质性主要表现在专业集中偏好水平以及学费等其他属性上。其他属性,如跨学科、合作项目、工作经验要求和团队(有出席/缺席选项),通过Kano模型进行分析,以确定它们的类别,即它们的重要性。本研究在应用这两种方法来获得消费者对教育行业新硕士课程开发的感知洞察方面,是文献上的先驱。
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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