Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach

By Lai-Ying Leong, Teck-Soon Hew, Keng-Boon Ooi, June Wei. International Journal of Information Management, 2020

498 citations recorded by the source corpus.

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.

Subfields: Information Systems and Management, Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/c6977b6b04bdb3ed2f98aef011f503c9fe8f5b13

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems and Management

1. Shalini Talwar
2. Puneet Kaur
3. Manish Talwar
4. Amandeep Dhir

In Management Information Systems

1. Amandeep Dhir
2. Shalini Talwar

In Strategy and Management

1. Amandeep Dhir
2. Puneet Kaur
3. Shalini Talwar

Citations in the corpus

• Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development (2020, background): “Simlarly, while investigating these three non-adopter groups for onine shopping, Lian and Yen (2013) revealed that opponents and ejectors had a higher level of barriers compared to postponers.”