E-Learning Products Adoption by Sem and CMB Multivariate Technique of Data Mining


  • Sri Tulasi T. Department of Business Administration, Kalasalingam Business School, Kalasalingam Academy of Research and Education, Krishnankoil 626126, Tamil Nadu, India
  • Inayath Ahamed S. B. Department of Business Administration, Kalasalingam Business School, Kalasalingam Academy of Research and Education, Krishnankoil 626126, Tamil Nadu, India


Marketing challenges, e-learning, adoption


The contemporary education landscape has witnessed a rapid surge in the utilization of e-learning products, ushering in new possibilities for accessible and flexible learning experiences. However, the successful adoption of these products hinges upon overcoming diverse marketing challenges. This study delves into the intricate interplay between marketing challenges associated with e-learning products and their subsequent impact on adoption intentions. Through an extensive exploration of perceived quality and credibility, technical obstacles, pricing strategies, lack of awareness, and learner engagement, this research sheds light on the multifaceted hurdles that impede the effective promotion of e-learning solutions. By employing a comprehensive analysis, the study reveals a profound relationship between these challenges and the intentions of learners to embrace e-learning products. The findings underscore the necessity for targeted strategies to navigate these challenges, fostering an environment conducive to wider e-learning adoption and the evolution of education paradigms. The research unveiled that the primary marketing obstacles linked to e-learning products encompass perceived quality and credibility, technical hurdles, pricing strategies, lack of awareness, and learner engagement. Moreover, the findings conclusively substantiated a noteworthy influence of these factors on the intentions of adopting e-learning products.


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How to Cite

Tulasi T., S. ., & S. B., I. A. (2024). E-Learning Products Adoption by Sem and CMB Multivariate Technique of Data Mining. International Journal of Intelligent Systems and Applications in Engineering, 12(15s), 276–285. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4745



Research Article