Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Adoption of Mobile Devices/Services — Searching for Answers with the UTAUT
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Value-based Adoption of Mobile Internet: An empirical investigation
Decision Support Systems
Determinants of adoption of mobile games under mobile broadband wireless access environment
Information and Management
Organisational influences on e-commerce adoption in a developing country context using UTAUT
International Journal of Business Information Systems
Predicting tourism loyalty using an integrated Bayesian network mechanism
Expert Systems with Applications: An International Journal
Adoption of 3G services among Malaysian consumers: an empirical analysis
International Journal of Mobile Communications
Integrating TTF and UTAUT to explain mobile banking user adoption
Computers in Human Behavior
International Journal of Mobile Communications
An empirical analysis of the determinants of 3G adoption in China
Computers in Human Behavior
Predictive analytics in information systems research
MIS Quarterly
A neural network approach to predicting price negotiation outcomes in business-to-business contexts
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
M-commerce has continued to grow at an explosive rate. This purpose of this paper is to examine the predictors of m-commerce adoption by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The extended model incorporates additional constructs such as perceived value, trust, perceived enjoyment and personal innovativeness. A non-linear, non-compensatory model is developed to understand the predictors of m-commerce adoptions. Online survey was used to collect data from 140 Chinese users. Neural network analysis was used to predict m-commerce adoption, and the model was compared with the results from regression analysis. The neural network model outperformed the regression model in adoption prediction, and captured the non-linear relationships between predictors such as perceived value, trust, perceived enjoyment, personal innovativeness, users demographic profiles (e.g. age, gender and educational level), effort expectancy, performance expectancy, social influence and facilitating conditions with m-commerce adoption. This study applied neural network to provide further understanding of m-commerce adoption decisions based on a non-linear, non-compensatory model. The UTAUT model was also extended to examine consumer information systems such as m-commerce. The m-commerce study conducted in this research is in China, one of the fastest growing m-commerce markets in the world.