An SEM-Neural Network Approach for Predicting Antecedents of Online Grocery Shopping Acceptance

Abstract

Customers today benefit from online grocery shopping (OGS) since it makes their lives easier and more pleasant. This study examines the extension of the technology adoption model with some new variables like visibility, economic values, habit, informativeness, and website trust and their influence on the intention to use online grocery shopping by Indian customers. A structured questionnaire was developed, and 342 responses were used for analysis. Two techniques were used: firstly, structural equation modelling (PLS-SEM) was used to determine which variables had a significant influence on the intention to use OGS; in the second phase, artificial neural network modelling (ANN) was used, under a deep learning approach, to classify the relative influence of the significant predictors obtained by PLS-SEM. The study’s findings suggest that the habit has the highest impact on users’ intention to use OGS, followed by perceived usefulness, perceived ease of use and website trust. On the other side, the results of neural network analysis confirmed many SEM findings. The study will help all online grocery service providers to build their services and strategies based on the tastes and needs of their customers.