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

作者：

Highlights：

• We extended IRT with perceived novelty and socio-demographic variables.

• A two-staged SEM-ANN approach was used to rank the normalized importance.

• The ANN model predicts m-wallet resistance with 76.4 % accuracy.

• Education and perceived novelty have negative effects on m-wallet resistance.

• Usage, risk, value & tradition barriers have positive effects on m-wallet resistance.

摘要

•We extended IRT with perceived novelty and socio-demographic variables.•A two-staged SEM-ANN approach was used to rank the normalized importance.•The ANN model predicts m-wallet resistance with 76.4 % accuracy.•Education and perceived novelty have negative effects on m-wallet resistance.•Usage, risk, value & tradition barriers have positive effects on m-wallet resistance.

论文关键词：Mobile wallet resistance,Innovation resistance theory,Perceived novelty,Socio-demographics,Artificial neural network

论文评审过程：Received 26 April 2019, Revised 25 November 2019, Accepted 26 November 2019, Available online 5 December 2019, Version of Record 24 February 2020.

论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2019.102047