Predicting Repurchase Behavior Based on Trust and Loyalty Indicators with Deep Learning Technology(3/3)

Project Details


AbstractPurpose –Perceived trust and loyalty are traditionally measured by questionnaires distributed to participants. In many cases, participants have low intention to fill lengthy surveys which cause studies ineffective and inefficient. To make matter worse, repurchase behavior is difficult to be measured with this approach. In this study, the data driven analysis technique is employed to define operational measures of trust and loyalty. The study is to show that data driven approach can be adopted to verify the relationship between trust and loyalty and both constructs can be the antecedents of repurchase behavior.Design/Methodology/approach –This study applies data driven analysis approach to extract the data from transaction logs and to formulate the operational definitions of trust and loyalty. SEM (structural equation modeling) statistic analysis is adopted to verify the conceptual model.Findings –The results show that the operational definitions can be used to form formative constructs of trust and loyalty and both constructs have significant impact on repurchase behavior.Research implication –This study shows that data driven analysis can be used to complement traditional questionnaire distribution approach to collect user opinions and verify models and even determine repurchase behaviors.Practical implication –Since the items of trust and loyalty are computed from logged data, the defined formula can be used as KPIs (Key Performance Indicators) for measuring and directing the behavior of relative personnels and departments to improve customers repurchase behaviors.Originality/value –This study utilizes data driven approach to formulate variables of trust and loyalty, verify the definitions, and confirm the relationships among trust, loyalty, and repurchase behavior through SEM.Keywords: Trust, Loyalty, Repurchase behavior, Data driven analysis, SEMPaper Type: Research paper
Effective start/end date1/08/2231/07/23

UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This project contributes towards the following SDG(s):

  • SDG 4 - Quality Education
  • SDG 8 - Decent Work and Economic Growth
  • SDG 9 - Industry, Innovation, and Infrastructure
  • SDG 12 - Responsible Consumption and Production


  • predict repurchase behavior
  • Recurrent Neural Network
  • Convolutional Neural Network
  • health retail
  • trust
  • loyalty


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