Extended Technology Acceptance Model to Predict Mobile-Based Money Acceptance and Sustainability: A Multi-Analytical Structural Equation Modeling and Neural Network Approach

This research is a pioneering study into the adoption of mobile-based money services for financial inclusion and sustainability in developing countries like Togo. Owing to their differences from more usual mobile-based banking and payment services, such technology is being aggressively promoted by providers of network telecommunication companies. However, the factors influencing its sustainable acceptance remain largely unknown. This paper extends the original Technology Acceptance Model (TAM), by integrating self-efficacy (SEMM), technology anxiety (TAMM), and personal innovativeness (PIMM). The research model is assessed with survey data of 539 actual and prospective mobile money users employing structural equation modeling–artificial neural networks (SEM–ANN) approach. A feed-forward-back-propagation (FFBP) multi-layer perceptron (MLP) ANN with significant predictors obtained from SEM as the input units and the root mean square of errors (RMSE) indicated that the ANN method achieves high prediction accuracy. The results present conclusive evidence that perceived ease-of-use (PEMM) is the most significant factor affecting consumers’ attitudes to mobile-based money. While perceived usefulness (PUMM) and PIMM affect adoption decisions, their impact is much lower. Consumer attitudes and intentions were found to have a significant relationship with TAM. SEMM and TAMM; however, they showed mixed results. These findings will be useful to retain prevailing users and attract new ones.