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Modeling Click Intention in E-Commerce Advertising a Hybrid PLS-SEM and ANN Approach: Modeling Click Intention in E-Commerce Advertising
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Author(s): Trung Quang Ngo (Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam)and Minh Duc Ly (Faculty of Commerce, Van Lang University, Ho Chi Minh City, Vietnam)
Copyright: 2026
Pages: 32
Source title:
Harnessing AI for Point-of-Sale Optimization
Source Author(s)/Editor(s): Nozha Erragcha (University of Jendouba, Tunisia)and Maher Toukabri (Northern Border University, Saudi Arabia & University of Jendouba, Tunisia & Laboratory ARBRE, University of Tunis, Tunisia)
DOI: 10.4018/979-8-3373-4392-1.ch010
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Abstract
This study explores the factors influencing consumers' intention to click on e-commerce advertisements using Partial Least Squares Structural Equation Modeling Artificial Neural Networks (PLS-SEM ANN). The main objectives of the research are to identify the key factors affecting click-through rates (CTR), assess the moderating role of privacy concerns, and predict the most significant factors influencing click behavior. A survey was conducted with 528 valid responses from consumers using e-commerce platforms such as Shopee, Lazada, and TikTok Shop, as well as companies operating on those platforms in Vietnam. The results show that Trust (T) and Information Quality (IQ) are the most significant predictors of click-through intention, with path coefficients of 0.352 (p < 0.01) and 0.770 (p < 0.01), respectively. Privacy Concern (PC) negatively affects click intention, with a path coefficient of -0.411 (p < 0.01), indicating that as privacy concerns increase, consumers are less likely to engage with advertisements. Additionally, Artificial Intelligence (AI) was found to have a positive impact on Trust, with a path coefficient of 0.656 (p < 0.01), enhancing the relevance and personalization of advertisements. Integrating PLS-SEM and ANN provides a comprehensive approach to modeling both linear and nonlinear relationships. The ANN model achieves an R2 value of over 0.85 for predicting click intention, demonstrating its ability to capture complex relationships that PLS-SEM may not identify. Importance-Performance Map Analysis (IPMA) shows that Trust (T) is the most important factor driving click behavior, with a performance score of 84%, followed by Information Quality (IQ) with a performance score of 65%. This study contributes significantly to understanding consumer behavior in the context of digital advertising.
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