Possibilistic linear systems and their application to the linear regression model
Fuzzy Sets and Systems
On assessing the H value in fuzzy linear regression
Fuzzy Sets and Systems
Determining objective weights in multiple criteria problems: the critic method
Computers and Operations Research
Assessment of learner satisfaction with asynchronous electronic learning systems
Information and Management
Computers and Industrial Engineering
Expert Systems with Applications: An International Journal
Critical success factors for e-learning acceptance: Confirmatory factor models
Computers & Education
Fuzzy regression model of R&D project evaluation
Applied Soft Computing
Determining factors of the use of e-learning environments by university teachers
Computers & Education
Guidelines for the development of e-learning systems by means of proactive questions
Computers & Education
An integrated decision making approach for ERP system selection
Expert Systems with Applications: An International Journal
Impact of media richness and flow on e-learning technology acceptance
Computers & Education
Expert Systems with Applications: An International Journal
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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A sound decision methodology for evaluating and selecting e-learning products should consider multiple and conflicting criteria and the interactions among them. In this paper, a decision framework which employs quality function deployment (QFD), fuzzy linear regression and optimization is presented for e-learning product selection. First, a methodology for determining the target values for e-learning product characteristics that maximize overall customer satisfaction is presented. The QFD framework is employed to allocate resources and to coordinate skills and functions based on customer needs. Differing from earlier QFD applications, the proposed methodology employs fuzzy regression to determine the parameters of functional relationships between customer needs and e-learning product characteristics, and among e-learning product characteristics themselves. Finally, the e-learning product alternatives are evaluated and ranked with respect to deviations from the target product characteristic values. The potential use of the proposed decision framework is illustrated through an application on e-learning products provided by the universities in Turkey.