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1. Initial Plan and Research Intention At the beginning of October, I created a four-week action plan that aimed to move my Unit 4 project smoothly from research review to intervention testing.The plan outlined a clear linear path — building the framework, designing the experiment, conducting the test, and analysing the results. However, as the…

1. Rediscovering My Question At the beginning of Unit 3, my central research question was: “How can fashion brands ensure that AI-generated visuals maintain — rather than weaken — emotional connection with consumers?” This question arose from my curiosity and concern about the growing adoption of AI within fashion’s creative production.While AI imagery offers efficiency…

1. Gu, C., Jia, S., Lai, J., Chen, R. and Chang, X. (2024) Reference Gu, C., Jia, S., Lai, J., Chen, R. and Chang, X., 2024. Exploring consumer acceptance of AI-generated advertisements: From the perspectives of perceived eeriness and perceived intelligence. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), pp.2218–2238. doi:10.3390/jtaer19030108. Available at: https://www.mdpi.com/0718-1876/19/3/108…

1. Research Evolution My inquiry began with an interest in cultural stereotypes and AI-driven creative control (Blog 1). I explored how contemporary Chinese aesthetics are often reduced to outdated symbols in global visual discourse, and experimented with generative AI to reconstruct more emotionally resonant narratives. Through tutorial feedback (Blog 2), I realised my scope was…

I recently came across an article by Rindfleisch and O’Hern, who proposed a Co-creation model encompassing both user-contributed content and user-selected content. This framework emphasizes that consumers are not merely passive recipients but active collaborators in shaping brand visual experiences (Rindfleisch & O’Hern, 2015). In the second intervention, I examined the boundaries that brands should…

https://24042236.myblog.arts.ac.uk/files/2025/08/AI视觉内容与情感链接测试AIVisualsEmotionalConnectionTest报告2.docx 1. Sample Overview The sample was relatively diverse but skewed toward young to middle-aged men with moderate fashion interest. 2. Key Findings Brand Representativeness (Q5) Emotional Appeal (Q6) Four Dimensions (Q7–Q10) Image A consistently led across all four dimensions, particularly realism and warmth. Attitude Change After Disclosure (Q12) Ranking of Influencing Factors (Q14) Consumers…

BackgroundIn Intervention 1, I found that when brands adhere to certain boundaries while using AI tools, they can enhance efficiency while mitigating the negative effects on emotional connection with consumers. Building on this finding, I designed Intervention 2 to identify which visual elements most significantly affect consumers’ emotional acceptance and to define the emotional boundaries…

Research Question How can fashion brands ensure that AI-generated visual content preserves, rather than undermines, consumers’ emotional connection to the brand? Rationale This first intervention aimed to test whether consumers’ emotional acceptance of AI-generated advertising could shift rapidly when exposed to specific visual elements. Prior research suggests that consumer acceptance of AI advertising is shaped…

In my interviews with fashion practitioners, they highlighted that one of the biggest challenges when using AI generation tools is ensuring visual consistency, particularly in terms of brand tonality, colour accuracy, and emotional expression. The topic of emotional expression especially drew my attention, as I had already noticed instances where consumers found it difficult to…

In my research process, I conducted an expert interview with a professional who has hands-on experience using AI tools to generate brand visuals. My goal was to understand the real challenges brands face when integrating AI into their creative workflows, and how these challenges affect brand visual identity. For citation purposes, I will reference this…