A hybrid approach to develop an analytical model for enhancing the service quality of e-learning

The digital content industry is flourishing as a result of the rapid development of technology and the widespread use of computer networks. As has been reported, the market size of the global e-learning (i.e., distance education and telelearning) will reach USD 49.6 billion in 2014. However, to retain and/or increase the market share associated with e-learning, it is important to maintain or increase service quality in this sector. This research was intended to develop an analytical model for enhancing the service quality of e-learning using a hybrid approach from the perspective of customers. The evaluation methodology integrates the three methods: rough set theory (RST), quality function deployment (QFD), and grey relational analysis (GRA). First, important criteria affecting service quality (referred to as customer requirements (CRs)) and relevant technical information (referred to as technical requirements (TRs)) for e-learning are compiled from an extensive literature review. Using the data regarding customer satisfaction collected from a questionnaire survey, RST is then used to reduce the number of attributes considered and to determine the CRs. Furthermore, in consultation with domain experts, QFD is used together with GRA to analyze the interrelationships between CRs (which represent the voice of customer (VOC)) and TRs (which represent the voice of the engineer (VOE)) and to create an order of priority for the TRs given the CRs based on objective weighting using the entropy value. An illustrative example is provided an empirical analysis of the students who participated in the e-learning program at a particular university. The results reveal that of the fourteen TRs, "Curriculum development" has the greatest effect on e-learning service quality, followed by "Evaluation", "Guidance and tracing", "Instructional design", and "Teaching materials". Both the CRs and the TRs may vary depending on the individual organization. Nevertheless, the proposed model can be a useful point of reference for e-learning service providers, helping them to identify the TRs that they can use to enhance service quality and to target vital CRs. (C) 2011 Elsevier Ltd. All rights reserved.