Online Marketing Trends and Purchasing Intent: Advances in Customer Satisfaction through PLS-SEM and ANN Approach

Abstract

Purpose: The research aims to discern the factors of online marketing that influence consumer intention and enhance satisfaction, particularly in the context of Bangladesh.

Methods: The study uses quantitative data, targeting respondents from urban areas and cities from various socio-economic classes. This study uses a two-stage structural equation modeling-artificial neural network approach. Initially, the analysis utilized the PLS-SEM method to assess the structural model. Finally, the analysis utilized the ANN approach to check the robustness of the findings.

Results: The study's findings reveal that factors such as convenience, comparison, ease of use, and variety seeking significantly influence customer satisfaction in online shopping. Conversely, promotional activities and customer service were found to have less impact on customer satisfaction. Customers anticipate prompt and efficient service, and a failure to meet these expectations can strain the customer-seller relationship.

Practical implications: This study presents an alternative business model without the need for physical store visits. However, despite the growth of internet technology in Bangladesh and its potential to provide products and services at lower costs, convincing customers to shop online remains a challenge for online traders in the country.

Originality: This research offers a unique perspective on the dynamics of online marketing and consumer satisfaction in Bangladesh, shedding light on the factors that drive or deter online shopping in a developing nation using a two-staged SEM-ANN approach. This provides actionable knowledge for decision-makers in online service provision, aligning with the quantitative methodology’s characteristic of Decision Sciences.