Predicting m-commerce adoption determinants: A neural network approach

► Predictors of m-commerce adoption were examined in this research. ► The UTAUT model was extended by incorporating perceived value, trust, perceived enjoyment and personal innovativeness. ► Neural network was developed to examine the research model. ► Results showed that neural network can offer be...

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Vydané v:Expert systems with applications Ročník 40; číslo 2; s. 523 - 530
Hlavný autor: Chong, Alain Yee-Loong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Amsterdam Elsevier Ltd 01.02.2013
Elsevier
Predmet:
Applied sciences
Biological and medical sciences
China
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Consumer behaviour
Decision theory. Utility theory
Decisions
Determinants
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Linear inference, regression
M-commerce
Mathematical models
Mathematics
Neural network
Neural networks
Nonlinearity
Operational research and scientific management
Operational research. Management science
Probability and statistics
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Regression
Regression analysis
Sciences and techniques of general use
Social interactions. Communication. Group processes
Social psychology
Software
Statistics
Technology adoption
UTAUT
Technology adoption
Neural network
UTAUT
M-commerce
Consumer behaviour
Acceptance
Information system
Modeling
Innovation
Linear prediction
Confidence
Technology
Network analysis
Technology transfer
Electronic trade
Value analysis
Social perception
Regression analysis
User profile
Non linear model
Unified theory
Chinese
Explosives
Non linear effect
Mobile computing
ISSN:0957-4174, 1873-6793
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Shrnutí:► Predictors of m-commerce adoption were examined in this research. ► The UTAUT model was extended by incorporating perceived value, trust, perceived enjoyment and personal innovativeness. ► Neural network was developed to examine the research model. ► Results showed that neural network can offer better predictions than multiple regression model. ► Our approach helps decision makers formulate appropriate strategies for m-commerce implementation in the Chinese industry. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2012.07.068