Determinants Analysis of Ride-Hailing Application Acceptance: SEM - Neural Network Approach

Abstract


 
 
 
 Research aim : This research aimed to develop a technology acceptance model based on innovation and marketing perspective on ride-hailing services application.
 Design/Methode/Approach : This research is an explanatory quantitative research that use a primary data which collected from 303 ride-hailing services application users with questionnaire. The methods were used in this research is SEM - neural network methods. SEM is used to analysis the significant of effect of independent variable on dependent variable while neural network is used to identify the most importance independent variable on explaining dependent variable.
 Research Finding : First, the SEM analysis result shows that relative advantage, price, promotion and distribution have an impact on user satisfaction. In intention to use variable furthermore, The only influencing variables are user satisfaction and distribution. Second, the results of neural network analysis on user satisfaction model shows that promotion is the most importance variable in explaining user satisfaction with followed by relative advantage, price and distribution. In neural network model of intention to use, user satisfaction is the most importance variable with followed by distribution.
 Theoretical contribution/Originality : With the results of this study it can be said that there is an integration of aspects of innovation and marketing in explaining application user acceptance.
 Practitionel/Policy implication : This research can be one of the considerations for the management of online transportation services to make a decision related to the service quality development, especially from the aspect of customer-based innovation and marketing.
 Research limitation : The limitations in this study are limited to use of neural networks as a machine learning method and conceptual aspects of innovation and marketing in explaining application user acceptance.