E-learning is a cutting-edge learning and teaching approach that improves education and learning processes in an online environment [1, 2]. E-learning platforms, which are except in time and place, provide training and learning opportunities and play a key role in promoting new methods of teaching . It is noted that numerous developing nations have introduced and implemented e-learning for education. It can involve a full focus on the learning platform, integrated e-learning, or a conventional blackboard classroom with e-learning.
Despite its benefits, the complete and effective implementation of E-learning has yet to be achieved . If E-learning is effectively incorporated in the education sector, several potential advantages can be seen. Previous research has shown that critical success factors (CSFs) play a key role in the successful implementation of e-learning. Besides, it's also been shown that essential factors of different dimensions can have different effects on the e-learning system [5–7]. Thus, it is important that the analysis and resource allocation of the E-learning CSFs be specifically examined and that a proposed framework of the success factors of E-learning is presented.
Figure 1 shows the basic framework of this research. The e-learning system relies on various success factors from several viewpoints, such as framework, organisational alignment, instructor, and student support. Therefore, to make it more competitive and efficient, the effects of critical success factors (CSFs) on the E-learning system must be critically evaluated. Interpretive Structural Modelling (ISM) was used in this current paper to study the diversified aspects of various dimensions of the web-based E-learning system. Through the literature review, the current paper quantified the CSFs along with their 8 factors associated with the web-based e-learning framework and was further analysed. Besides, each factor's effect was successfully derived. From the literature review, eight key E-learning barriers are identified which curb the efficiency of the E-learning system. After identifying the barriers, the influencing power of each barrier in the e-learning is found based on the driving and dependent power calculated in the MICMAC analysis. There are various techniques available for providing a structure-based relationship of the enablers, such as The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Analytical Hierarchical Process (AHP), but they cannot provide a quantified view of the inter-relationships. Hence, for the quantification of the interrelationships of any sophisticated system, an integrated MICMAC-based ISM approach may be applied. Therefore, based on expert and academic opinions, the ISM technique is used for developing contextual relations between the variables. Based on this, a complex relationship is converted into a simpler relationship. Furthermore, this paper is divided into five sections: a literature review in Sect. 2, research methodology and numerical illustration in Sect. 3, discussion of findings in Sect. 4, and conclusion in Sect. 5.