Annotated Bibliography

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 [Accessed 20 August 2025].

Annotation

This article examines how consumers perceive and accept AI-generated advertising, focusing on verisimilitude (realism), vitality, and synthesis. The authors argue that when AI visuals appear overly artificial, they evoke eeriness that undermines acceptance and weakens brand trust. Conversely, visuals with higher realism and vitality increase consumer comfort and willingness to engage.

For my Intervention 1, this study provided the theoretical backbone. It supported my rationale that realism is a decisive factor in determining consumer acceptance of AI-generated visuals. I designed my experiment to test whether participants’ emotional responses—warmth, authenticity, and brand congruence—shift when exposed to highly realistic vs. synthetic AI images. The results confirmed Gu et al.’s findings: realistic AI images reduced resistance and even seemed more natural than over-retouched human photos, while “too perfect” outputs triggered discomfort.

The value of this article lies in clarifying boundaries for acceptance: realism and vitality are essential to preserving consumer trust, while excessive synthesis risks undermining emotional connection. It not only framed my intervention design but also highlighted that AI visuals must retain human warmth and imperfection to avoid eroding brand–consumer relationships.

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2. Rindfleisch, A. & O’Hern, M. (2015)

Reference

Rindfleisch, A. and O’Hern, M., 2015. Customer co-creation: a typology and research agenda. In: N.K. Malhotra (ed.) Review of Marketing Research. Emerald Group Publishing, pp.289–318. doi:10.1108/S1548-643520150000012009. Available at: https://experts.illinois.edu/en/publications/customer-co-creation-a-typology-and-research-agenda [Accessed 7 August 2025].

Annotation

This book chapter develops a typology of customer co-creation, distinguishing between “content contribution” and “content selection.” It argues that consumers should not be treated as passive recipients of marketing outputs but as active collaborators who help co-create brand value.

The framework is directly relevant to my Intervention 3, where participants are not only asked to evaluate AI-generated visuals but also to actively contribute their preferences (e.g., scene, model type, colour). By combining “contribution” and “selection,” the experiment mirrors the co-creation model, allowing me to investigate whether participatory involvement enhances emotional connection to AI-generated brand visuals.

The value of this source is that it moves my project from merely identifying “boundaries not to cross” (Intervention 2) to exploring mechanisms for “repairing emotional connection” through consumer participation. It highlights that emotional resonance may not only depend on avoiding harmful practices but also on inviting consumers into the creative process. This broadened my research from defensive strategies to proactive, engagement-based solutions.

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3. Rezwana, J. & Maher, M.L. (2022)

Reference

Rezwana, J. and Maher, M.L., 2022. Identifying ethical issues in AI partners in human–AI co-creation. arXiv preprint arXiv:2204.07644. Available at: https://arxiv.org/abs/2204.07644 [Accessed 7 August 2025].

Annotation

This paper introduces the COFI framework for human–AI co-creation, focusing on ethical and design challenges. It demonstrates that systems enabling two-way interaction, where AI can respond to and incorporate user input, are perceived as more reliable, personalised, and trustworthy by users. The study underscores that interactivity is central to enhancing user experience and emotional trust.

This was particularly influential for Intervention 3. Inspired by the COFI framework, I designed an experiment in which participants could input their creative ideas (e.g., colours, scenes) and see them materialised in AI-generated outputs. The goal was to test whether this participatory process—shifting users from passive viewing to active involvement—would increase warmth, authenticity, and brand congruence.

The value of this article lies in its clear articulation of how two-way communication between humans and AI can strengthen reliability and trust. It directly shaped my research design, allowing me to test participatory involvement not only as an engagement tool but also as a potential emotional safeguard mechanism for brands using AI-generated content.