The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach
Abstract
Abstract The advancement in mobile technology has enabled the application of the mobile wallet or m-wallet as an innovative payment method to substitute the traditional functions of the physical wallet. However, because of pro-innovation bias, scholars have a focus on the adoption of technology and very little attention has been given to the resistance of innovation, especially in the m-wallet context. This study addressed this absence by examining the inhibitors of m-wallet innovation adoption through the lens of innovation resistance theory (IRT). By applying a sophisticated two-staged structural equation modeling-artificial neural network (SEM-ANN) approach, we successfully extended the IRT by integrating socio-demographics and perceived novelty. The study has unveiled the noncompensatory and nonlinear relationships between the predictors and m-wallet resistance. Significant predictors from SEM analysis were taken as the ANN model’s input neurons. According to the normalized importance obtained from the multilayer perceptrons of the feed-forward-back-propagation ANN algorithm, we found significant effects of education, income, usage barrier, risk barrier, value barrier, tradition barrier, and perceived novelty on m-wallet innovation resistance. The ANN model can predict m-wallet innovation resistance with an accuracy of 76.4 %. We also discussed several new and useful theoretical and practical implications for reducing m-wallet innovation resistance among consumers.
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Metadata
- Authors
- Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Wei, J.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2020-04
- Volume
- 51
- Pages
- 102047
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W2992667364
- Cited by
- 528
- References
- 93
- FWCI
- 45.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
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- Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2019.102047
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