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The Effect of Automated vs Non-Automated Advertising on Customer Response to Social Media Ads: The Mediator Role of Perceived Personalisation

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Thesis(1.33 MB)

Date

2025

Authors

Vienings, Daniel Roedolf

Supervisor

Tipgomut, Pornchanoke
Kim, Jungkeun

Item type

Thesis

Degree name

Master of Business

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Publisher

Auckland University of Technology

Abstract

The way advertising content is produced, tailored, and distributed has changed dramatically because of the rapid development of artificial intelligence (AI) in digital marketing, especially on social media platforms. AI-generated advertising raises questions about customer trust, perceived manipulation, and emotional discomfort, even though it has advantages, including improved targeting precision and real-time optimisation. These issues are consistent with the personalisation paradox, which holds that emotions of creepiness can coexist with enhanced relevance brought about by personalisation. Even though AI advertising is becoming increasingly popular, little empirical research has been done on how consumers react to and perceive ads produced by AI as opposed to those developed by humans. To address this gap, this study looks at how perceived personalisation, relevance, and creepiness influence consumer engagement (clicks, likes, comments, and shares) and ad avoidance. It also investigates how business experience, being part of a moderated mediation model, influences perceptions of personalisation. A conceptual model that accounts for the direct and indirect impacts of AI-generated advertising on consumer behaviour is tested in this study using a quantitative experimental approach. Three advertising conditions, AI-generated personalised ads, AI-generated non-personalised ads, and human-created non-personalised ads were used in a between-subjects online experiment. A structured survey was used to gather information from a sample of n = 150 participants. The study tested the hypothesised links between ad type, perceived personalisation, relevance, creepiness, and behavioural effects by evaluating the measurement and structural models using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that, when compared to non-AI (human-generated) content, AI-generated advertisements did not significantly increase perceived personalisation, indicating that algorithmic targeting by itself does not necessarily provide improved customer perceptions. However, perceived personalisation had a significant impact on both creepiness and relevancy, demonstrating the dual nature of personalised advertising. The personalisation paradox is reflected in this dynamic: while personalised content might increase engagement potential, it can also cause psychological discomfort if it is viewed as being overly intrusive. Additionally, perceptions of personalisation were not significantly moderated by business experience, casting doubt on the idea that exposure to digital marketing in the workplace automatically results in positive assessments of AI-driven content. This thesis makes theoretical contributions by extending the SNS-Post Processing Framework to AI-generated advertising contexts, providing insights into how cognitive and affective responses to personalisation influence behavioural outcomes. Additionally, it also contributes to the limited empirical research on ad avoidance as a defensive mechanism triggered by AI-driven targeting. To promote customer trust and engagement, the study offers practical advice to digital platforms and advertisers on how to strike a balance between AI automation, human creativity, ethical transparency, and emotional resonance.

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http://hdl.handle.net/10292/20217

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Masters Theses