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.