Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis

The study investigates the antecedents that affect consumers' acceptance of behavioral targeting advertising (BTA) services by extending technology acceptance Model 2 (TAM2) with perceived risk. A two-stage PLS-SEM-artificial-neural-network (ANN) predictive analytic approach was adopted to analyze the collected data, of which PLS-SEM was first applied to test the hypotheses, followed by the ANN technique to detect the nonlinear effect on the model. A total of 475 usable self-administered questionnaires were collected, and the results showed that only the relationship between the image and perceived usefulness (PU) was not supported. As per Model B, the ranking of subjective norms (SN) and PU between the PLS-SEM and ANN model does not match each other, implying that hidden attributes may exist in affecting the role of SN and PU under the practical context of which the relationship between variables may not fully be explained by a linear perspective. The finding is beneficial for advertising practitioners and software developers who wish to optimize BTA results. Theoretically, the study extends TAM2 in the context of advertising, which is a neglected research area. Methodologically, the study is the first to apply TAM2 using the hybrid PLS-SEM-ANN in the context of advertising.

Automated summary

This study investigates the factors that affect consumers' acceptance of behavioral targeting advertising (BTA) services by extending the technology acceptance Model 2 (TAM2) with perceived risk. The results show that only the relationship between image and perceived usefulness (PU) was not supported. In practical situations, hidden attributes may affect the role of subjective norms (SN) and PU, and the relationship between variables cannot be fully explained by a linear perspective.