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An Optimal Level of Incongruency: User-Generated Content’s Effect on Engagement and Purchase Intention
Majedeh, Esmizadeh
Majedeh, Esmizadeh
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Abstract
This study examines the impact of incongruency in user-generated branded photos (UGBPs) on social media engagement and purchase intention. Incongruency, defined as the dissimilarity between UGBPs and a brand’s values, presents conflicting effects in marketing literature—while congruency enhances processing fluency and trust, incongruency evokes novelty and surprise. Leveraging advanced AI methodologies, we quantify incongruency without relying on subjective surveys by comparing textual descriptions of brand values and UGBPs using contextual embeddings. Our analysis, based on 5,717 Instagram posts, reveals that incongruency positively affects social media engagement but negatively impacts purchase intention. Further, we find that brand equity moderates these effects, with higher-equity brands experiencing diminished engagement benefits from incongruency. User posting styles also influence outcomes, mitigating the negative impact of incongruency on purchase intention. These findings suggest that incongruency offers a strategic advantage for lower-equity beverage brands by driving engagement, though at the potential cost of reduced purchase intention. This research highlights the utility of AI-driven metrics for analyzing latent constructs and provides actionable insights for brands incorporating UGBPs in their marketing strategies.
Description
These are the slides from a presentation given at Artificial Intelligence in Management 2025 on 03/22/2025.
Date
2025-03-22
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University of Kansas
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Keywords
User-Generated Content (UGC), AI and content strategy, Visual imagery analysis, Social media engagement, Large language model