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

Guoqiang Wang, Garry Wei-Han Tan, Yunpeng Yuan, Keng-Boon Ooi and Yogesh K. Dwivedi

Technological Forecasting and Social Change, 2022, vol. 175, issue C

Abstract: 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.

Keywords: Mobile advertising; Behavioral targeting advertising; Mobile commerce; TAM; TAM2; Artificial neural network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:175:y:2022:i:c:s0040162521007769

DOI: 10.1016/j.techfore.2021.121345

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