The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
An Integrated SEM-Neural Network Approach for Predicting Mobile Banking Determinants of Adoption in Turkey
Abstract
Higher penetration of the most widely used mobile technology applications and 3G and 4G mobile networks have led to the higher usage of smartphones for mobile banking activities in recent times. Data were collected from 395 mobile banking users and analyzed using an innovative two-staged regression and neural network (NN) model. In the first stage, structural equation modeling was employed to test the research hypotheses and identify significant antecedents influencing mobile banking acceptance. In the second stage, the significant antecedents obtained from the first stage were input to a neural network model for ranking. The results revealed that autonomous motivation and perceived ease of use are the two main predictors influencing mobile banking acceptance. Theoretical and practical implications of findings are discussed. Policy makers can find significant results in this chapter for implementing future service design. Limitations and future research scope are also discussed.
Related Content
|
Vinod Kumar, Himanshu Prajapati, Sasikala Ponnusamy.
© 2023.
18 pages.
|
|
Sougatamoy Biswas.
© 2023.
14 pages.
|
|
Ganga Devi S. V. S..
© 2023.
10 pages.
|
|
Gotam Singh Lalotra, Ashok Sharma, Barun Kumar Bhatti, Suresh Singh.
© 2023.
15 pages.
|
|
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma.
© 2023.
16 pages.
|
|
R. Soujanya, Ravi Mohan Sharma, Manish Manish Maheshwari, Divya Prakash Shrivastava.
© 2023.
12 pages.
|
|
Nimish Kumar, Himanshu Verma, Yogesh Kumar Sharma.
© 2023.
22 pages.
|
|
|