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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, Yogesh Dwivedi Orcid Logo

Technological Forecasting and Social Change, Volume: 175, Start page: 121345

Swansea University Author: Yogesh Dwivedi Orcid Logo

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DOI (Published version): 10.1016/j.techfore.2021.121345

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 t...

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Published in: Technological Forecasting and Social Change
ISSN: 0040-1625
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa58739
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first_indexed 2021-11-22T13:06:54Z
last_indexed 2023-01-11T14:39:32Z
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spelling 2022-10-26T10:49:55.3805984 v2 58739 2021-11-22 Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis d154596e71b99ad1285563c8fdd373d7 0000-0002-5547-9990 Yogesh Dwivedi Yogesh Dwivedi true false 2021-11-22 BBU 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. Journal Article Technological Forecasting and Social Change 175 121345 Elsevier BV 0040-1625 Mobile advertising; Behavioral targeting advertising; Mobile commerce; TAM; TAM2; Artificial neural network 1 2 2022 2022-02-01 10.1016/j.techfore.2021.121345 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2022-10-26T10:49:55.3805984 2021-11-22T13:03:47.7858671 Faculty of Humanities and Social Sciences School of Management - Business Management Guoqiang Wang 1 Garry Wei-Han Tan 2 Yunpeng Yuan 3 Keng-Boon Ooi 4 Yogesh Dwivedi 0000-0002-5547-9990 5 Under embargo Under embargo 2021-11-23T08:30:52.2416141 Output 847960 application/pdf Accepted Manuscript true 2023-05-14T00:00:00.0000000 Distributed under the terms of a Creative CommonsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). true eng https://www.creativecommons.org/licenses/by-nc-nd/4.0/
title Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
spellingShingle Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
Yogesh Dwivedi
title_short Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
title_full Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
title_fullStr Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
title_full_unstemmed Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
title_sort Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis
author_id_str_mv d154596e71b99ad1285563c8fdd373d7
author_id_fullname_str_mv d154596e71b99ad1285563c8fdd373d7_***_Yogesh Dwivedi
author Yogesh Dwivedi
author2 Guoqiang Wang
Garry Wei-Han Tan
Yunpeng Yuan
Keng-Boon Ooi
Yogesh Dwivedi
format Journal article
container_title Technological Forecasting and Social Change
container_volume 175
container_start_page 121345
publishDate 2022
institution Swansea University
issn 0040-1625
doi_str_mv 10.1016/j.techfore.2021.121345
publisher Elsevier BV
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
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description 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.
published_date 2022-02-01T04:15:30Z
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score 11.012678

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