Computer vision in branding: A conceptual framework and future research agenda

Yaqiu Li, Hsin Hsuan Meg Lee and Lorena Blasco-Arcas

Journal of Business Research, 2025, vol. 193, issue C

Abstract: This study examines how computer vision transforms branding research by offering a typology of visual features and introducing an integrative CTV-CBBE framework that bridges computational processes and branding outcomes. Through an integrative literature review, we analyze the impact of computer vision across different levels of brand equity, highlighting a progression from single-level to integrative visual analysis, from single to multimodal approaches, and from static imagery to broader visuals. These advancements underscore the growing importance of computer vision in navigating dynamic, hyperconnected branding environments. Our findings contribute to assessing brand identity, enhancing product design, interpreting brand meaning, evaluating consumer sentiment, and improving engagement. To advance the field, we propose a future research agenda centered on leveraging underexplored visual features, generative artificial intelligence, and multimodality while aligning technical innovations with branding theories. This study offers a strategic roadmap for researchers and practitioners to harness computer vision to enhance branding strategies.

Keywords: Branding; Computer vision; Machine learning; Image analysis; Visual analysis; Deep learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:193:y:2025:i:c:s0148296325001523

DOI: 10.1016/j.jbusres.2025.115329

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