Wen, Yingting and Laporte, Sandra (2024) Experiential Narratives in Marketing: A Comparison of Generative AI and Human Content. Journal of Public Policy and Marketing.

Abstract

As generative AI technologies advance, understanding their capability to emulate human-like experiences in marketing communication becomes crucial. This research examines whether generative AI can create experiential narratives that resonate with humans in terms of embodied cognition, affect, and lexical diversity. An automatic text analysis reveals that while reviews generated by ChatGPT 3.5 exhibit lower levels of embodied cognition and lexical diversity compared with reviews by human experts, they display more positive affect (Study 1A). However, human raters struggle to notice these differences, rating half of the selected reviews from AI higher in embodied cognition and usefulness (Study 1B). Instances of hallucination in AI-generated content were detected by human raters. For social media posts, the more sophisticated ChatGPT 4 model demonstrates superior perceived lexical diversity and leads to higher purchase intentions in unbranded content compared with human copywriters (Study 2).
This paper evaluates the performance of large language models in generating experiential marketing narratives. The comparative studies reveal the models’ strengths in presenting positive emotions and influencing purchase intent while identifying limitations in embodied cognition and lexical diversity compared to human-authored content. The findings have implications for marketers and policymakers in understanding generative AI’s potential and risks in marketing.