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  5. Strategizing with Generative AI: Productivity Gains in Social Media Marketing
 
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Strategizing with Generative AI: Productivity Gains in Social Media Marketing

Author(s)
Gastmann, Joel  
Bastos, Marco  
Uri
http://hdl.handle.net/10197/30906
Date Issued
2025-07-01
Date Available
2025-12-18T16:09:19Z
Abstract
This study explores the use of ChatGPT for social media marketing and its potential impact on employment. We take stock of the literature on productivity gain and automation of work to unpack the role of AI in devising such strategies. To this end, we carried out in-depth, semi-structured interviews with 20 social media professionals and a follow-up experiment using ChatGPT to generate social media marketing strategies for two companies. The results support the prospect of generative AI leading to increased productivity in the creation of strategies for social media as the experiments yielded high-quality output in terms of brand-fit, appropriate addressing of business challenges, and strategic flexibility. The results, however, also highlight the limitations in using off-the-shelf AI tools for marketing campaigns with intellectual property and data protection requirements. We conclude with an assessment of potential risks associated with data protection, the growing development of in-house AI tools by companies, and ethical considerations about the use of Generative AI.
Sponsorship
University College Dublin
Other Sponsorship
National Council for Scientific and Technological Development
Type of Material
Journal Article
Publisher
Taylor & Francis
Journal
Journal of Marketing Communications
Copyright (Published Version)
2025 the Authors
Subjects

Generative AI

ChatGPT

Social medial marketi...

Productivity gain

Automation

DOI
10.1080/13527266.2025.2525913
Language
English
Status of Item
Peer reviewed
ISSN
1352-7266
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Strategising with Generative AI.pdf

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Owning collection
Information and Communication Studies Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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