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Reed, Catherine (2025) Artificial intelligence in digital marketing: a new model for revealing and mitigating bias (a case study of an international software company). PhD thesis, University of Gloucestershire. doi:10.46289/XDJM2408
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16446 Reed (2025) Artificial intelligence in digital marketing thesis.pdf - Accepted Version Available under License All Rights Reserved. Download (4MB) | Preview |
Abstract
Artificial Intelligence (AI) is transforming modern business and society. As a nascent technology its influence continues to grow across various sectors. In the field of marketing, the potential of AI adoption is particularly pronounced as it offers significant benefits in strategy, content creation, and data analysis. However, alongside these advantages, the increasing use of AI raises critical ethical concerns, particularly around the propagation of bias. This research explores the current and possible bias issues - and how they can be mitigated - in AI driven marketing practices within the context of a global software company. Two principal forms of AI are currently being leveraged in marketing: traditional AI and generative AI. Traditional AI is grounded in supervised, semi-supervised and unsupervised learning and predictive analytics, whilst generative AI employs large language models to produce dynamic and creative content. Although these technologies can improve efficiencies and personalisation, they simultaneously introduce challenges related to transparency, authenticity, and algorithmic fairness. Current literature identifies that marketing bias - historically shaped by human judgment - is being compounded by automated systems that inherit and even magnify existing inequalities through their coding, prompting, and deployment. Despite the critical implications, there is a lack of clear governance frameworks and practical guidance for marketers using AI in their activities. This research employs a qualitative, inductive research approach through an in-depth case study of a leading global software provider. Drawing on a systematic literature review and semi-structured interviews with marketing professionals, the research examines how bias is perceived, experienced, and addressed in AI enabled marketing. The research contributes to both theory and practice by identifying specific areas where bias can emerge in AI usage and proposes a practical, practitioner-informed model for revealing and mitigating such bias within digital marketing. This thesis builds upon the existing theory of bias propagation in marketing and examines them through a lens of how they can compound via the use of technology. It examines the current usage and implementation of AI within marketing activities and presents that humans are an essential element of successful AI implementation, usage and management within business. The thesis finds that the marketing customer journey and current marketing technology are of vital importance when structuring the implementation of AI in marketing. The developed model - and accompanying guide on how to operationalise the model - provides actionable insights for both industry professionals and academic researchers. It offers a structured path to more mindful AI implementation in marketing by emphasising the need for internal governance and ongoing training for marketing teams to responsibly interact with AI tools. This research has limitations, however, as it is based on one case-study company in one marketing industry sector, but it can serve as a foundation for future research development in this field of study.
| Item Type: | Thesis (PhD) | |||||||||
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| Subjects: | H Social Sciences > HF Commerce > HF5001 Business > HF5410 Marketing Q Science > QA Mathematics > QA76 Computer software |
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| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences | |||||||||
| Depositing User: | Rhiannon Goodland | |||||||||
| Date Deposited: | 29 Jul 2026 12:33 | |||||||||
| Last Modified: | 29 Jul 2026 12:33 | |||||||||
| URI: | https://eprints.glos.ac.uk/id/eprint/16446 |
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