TY - JOUR
T1 - Predicting determinants of internet banking adoption
T2 - A two-staged regression-neural network approach
AU - Sharma, Sujeet Kumar
AU - Govindaluri, Srikrishna Madhumohan
AU - Al Balushi, Shahid M.
N1 - Publisher Copyright:
© Emerald Group Publishing Limited.
PY - 2015/7/20
Y1 - 2015/7/20
N2 - Purpose – The purpose of this paper is to explore the main determinants of Internet banking users on the basis of literature of technology acceptance model (TAM). Understanding and predicting main determinants of Internet banking is an important issue for banking industry and users. Design/methodology/approach – Service quality and trust were incorporated in the TAM together with demographic variables. The data were collected using Google Docs from 110 Omani Internet banking users. A two-staged regression-neural network model was applied to understand and predict Internet banking adoption. Findings – The results obtained from multiple linear regression model were compared with the results from neural network model to predict Internet banking adoption and the performance of latter model was found to superior. The neural network model was able to capture relative importance of all independent variables, service quality, trust, perceived usefulness, perceived ease of use, attitude and demographic variables, whereas perceived ease of use and demographic variables were not significant predictors of Internet banking adoption as per the regression model. Practical implications – This study provides useful insights with regard to development of Internet banking systems to banking professionals and information systems researchers in Oman and similar emerging economies. Originality/value – This study is probably the first attempt to model Internet banking adoption in Gulf Cooperation Council using a predictive rather than explanatory focus. The majority of studies in Internet banking adoption in Oman and elsewhere usually utilize modeling methods suited for explanatory purposes.
AB - Purpose – The purpose of this paper is to explore the main determinants of Internet banking users on the basis of literature of technology acceptance model (TAM). Understanding and predicting main determinants of Internet banking is an important issue for banking industry and users. Design/methodology/approach – Service quality and trust were incorporated in the TAM together with demographic variables. The data were collected using Google Docs from 110 Omani Internet banking users. A two-staged regression-neural network model was applied to understand and predict Internet banking adoption. Findings – The results obtained from multiple linear regression model were compared with the results from neural network model to predict Internet banking adoption and the performance of latter model was found to superior. The neural network model was able to capture relative importance of all independent variables, service quality, trust, perceived usefulness, perceived ease of use, attitude and demographic variables, whereas perceived ease of use and demographic variables were not significant predictors of Internet banking adoption as per the regression model. Practical implications – This study provides useful insights with regard to development of Internet banking systems to banking professionals and information systems researchers in Oman and similar emerging economies. Originality/value – This study is probably the first attempt to model Internet banking adoption in Gulf Cooperation Council using a predictive rather than explanatory focus. The majority of studies in Internet banking adoption in Oman and elsewhere usually utilize modeling methods suited for explanatory purposes.
KW - Internet banking
KW - Neural network
KW - Oman
KW - Regression
KW - Service quality
KW - TAM
UR - http://www.scopus.com/inward/record.url?scp=84934760144&partnerID=8YFLogxK
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U2 - 10.1108/MRR-06-2014-0139
DO - 10.1108/MRR-06-2014-0139
M3 - Article
AN - SCOPUS:84934760144
SN - 2040-8269
VL - 38
SP - 750
EP - 766
JO - Management Research Review
JF - Management Research Review
IS - 7
ER -