Uncovering unobserved heterogeneity bias: Measuring mobile banking system success
作者：
Highlights：
• This study examines mobile banking system use using log data and survey data of the same users to determine the success of the system.
• Using both objective and subjective measures of system success to measure system success provides more reliable understanding with enhanced insights.
• Cluster Analysis algorithm was used to classify users into multiple categories before determining their success factors from the IS Success model.
• Though a priori segmentation using data analytics, our proposed approach provides an integrated methodology to address the heterogeneity bias of the IS success model with to improve the explanatory power of the model.
摘要
•This study examines mobile banking system use using log data and survey data of the same users to determine the success of the system.•Using both objective and subjective measures of system success to measure system success provides more reliable understanding with enhanced insights.•Cluster Analysis algorithm was used to classify users into multiple categories before determining their success factors from the IS Success model.•Though a priori segmentation using data analytics, our proposed approach provides an integrated methodology to address the heterogeneity bias of the IS success model with to improve the explanatory power of the model.
论文关键词：System usage,IS success model,Subjective/objective measures,Sample segmentation,Mobile banking and clustering
论文评审过程：Received 24 April 2019, Revised 5 June 2019, Accepted 12 July 2019, Available online 19 August 2019, Version of Record 19 August 2019.
论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2019.07.005
