Tan, G.W.H., Ooi, K.-B., Leong, L.-Y. and Lin, B. (2014) Predicting the Drivers of Behavioral Intention to Use Mobile Learning: A Hybrid SEM-Neural Networks Approach. Computers in Human Behavior, 36, 198-213.
https://doi.org/10.1016/j.chb.2014.03.052

has been cited by the following article:

• TITLE: Predicting Online Travel Adoption Intention of an Indian Consumer: A SEM-Neural Network Approach

  AUTHORS: Chakravarthi Koundinya

  KEYWORDS: Online Ticketing, Online Travel, Neural Network Technology Adoption, Online Adoption Intention

  JOURNAL NAME: Theoretical Economics Letters, Vol.9 No.2, February 26, 2019

  ABSTRACT: Online channel has redefined the way businesses were conducted and many e-travel firms were the first to exemplify. The purpose of this paper is to determine the key factors that influence consumers’ intention to adopt online channel for train travel. The extended model incorporates the basic ingredients of TAM, TPB and Shim’s model along with several external variables such as trust, convenience and involvement. Data were collected from 514 online travel customers. In the first phase, structural equation modeling was employed to determine the significant variables and in the second phase, the neural network model was used to rank the relative importance of the significant predictors identified by SEM. The results indicated that Trust, Perceived Usefulness, Convenience and Website as significant factors. Among significant ones, neural networks suggested “convenience” as the highest importance. The study indicates few marketing approaches that can help travel service providers to formulate optimal marketing strategies.