Identifying Customer Attitudes Toward the Exploitation of Women in Ads Using Machine Learning

Shadi Abudalfa; Ameer Alzerei; Mohammed Salem

doi:10.1007/978-981-99-7798-7_15

Identifying Customer Attitudes Toward the Exploitation of Women in Ads Using Machine Learning

Shadi Abudalfa, Ameer Alzerei, Mohammed Salem^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

This chapter aimed to identify customer attitudes toward the exploitation of women in ads using machine learning. Primary data were gathered through empirical research, which included 343 questionnaires from Palestinian customers in the Gaza Strip. The findings indicated that consumers had negative attitudes toward the exploitation of women in ads, with a majority expressing dissatisfaction. The findings of this study provide insights for marketers and advertisers to develop more ethical and responsible advertising strategies that align with customer values and preferences. The study demonstrates the potential of machine learning techniques in identifying and analyzing customer attitudes toward social issues in advertising.

Original language	English
Title of host publication	Technical and Vocational Education and Training
Publisher	Springer
Pages	185-193
Number of pages	9
DOIs	https://doi.org/10.1007/978-981-99-7798-7_15
State	Published - 2024
Externally published	Yes

Publication series

Name	Technical and Vocational Education and Training
Volume	39
ISSN (Print)	1871-3041
ISSN (Electronic)	2213-221X

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Keywords

Advertising
Customer attitudes
Data clustering
Machine learning
Women exploitation

ASJC Scopus subject areas

Education

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-99-7798-7_15

Cite this

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abstract = "This chapter aimed to identify customer attitudes toward the exploitation of women in ads using machine learning. Primary data were gathered through empirical research, which included 343 questionnaires from Palestinian customers in the Gaza Strip. The findings indicated that consumers had negative attitudes toward the exploitation of women in ads, with a majority expressing dissatisfaction. The findings of this study provide insights for marketers and advertisers to develop more ethical and responsible advertising strategies that align with customer values and preferences. The study demonstrates the potential of machine learning techniques in identifying and analyzing customer attitudes toward social issues in advertising.",

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doi = "10.1007/978-981-99-7798-7\_15",

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Identifying Customer Attitudes Toward the Exploitation of Women in Ads Using Machine Learning. / Abudalfa, Shadi; Alzerei, Ameer; Salem, Mohammed.
Technical and Vocational Education and Training. Springer, 2024. p. 185-193 (Technical and Vocational Education and Training; Vol. 39).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Salem, Mohammed

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KW - Data clustering

KW - Machine learning

KW - Women exploitation

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Identifying Customer Attitudes Toward the Exploitation of Women in Ads Using Machine Learning

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this