TY - GEN
T1 - Impacts of Content Emotions and Visuals on Brand Communication
AU - Chen, Yijia
AU - Zhu, Yingpeng
AU - Liu, Matthew Tingchi
AU - Loi, Edmund H.N.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
PY - 2026
Y1 - 2026
N2 - Brands and enterprises have increasingly relied on user engagement with social media content for product promotion; however, research on how social media posts effectively capture user attention remains limited. The present study addresses this gap by examining the influence of visual complexity and textual emotional tone—used as independent variables in this analysis—on consumer engagement (measured through likes, comments, saves, and shares) across different combinations. The findings indicate that both factors significantly affect engagement, with posts characterized by high visual complexity and a positive emotional tone yielding particularly strong outcomes. Data collected through simulated questionnaire surveys (N = 740 valid responses after excluding incomplete or biased data) and actual content published on the RedNote (also known as Xiaohongshu) platform consistently support the study’s two core hypotheses. Grounded in Media Richness Theory (MRT), the Elaboration Likelihood Model (ELM), and Dual Process Theories (DPT), this research extends the existing literature by elucidating the synergistic effects of visual and emotional elements in user-generated content (UGC). These results provide actionable insights for marketers, content creators, and platform designers seeking to optimize social media content strategies.
AB - Brands and enterprises have increasingly relied on user engagement with social media content for product promotion; however, research on how social media posts effectively capture user attention remains limited. The present study addresses this gap by examining the influence of visual complexity and textual emotional tone—used as independent variables in this analysis—on consumer engagement (measured through likes, comments, saves, and shares) across different combinations. The findings indicate that both factors significantly affect engagement, with posts characterized by high visual complexity and a positive emotional tone yielding particularly strong outcomes. Data collected through simulated questionnaire surveys (N = 740 valid responses after excluding incomplete or biased data) and actual content published on the RedNote (also known as Xiaohongshu) platform consistently support the study’s two core hypotheses. Grounded in Media Richness Theory (MRT), the Elaboration Likelihood Model (ELM), and Dual Process Theories (DPT), this research extends the existing literature by elucidating the synergistic effects of visual and emotional elements in user-generated content (UGC). These results provide actionable insights for marketers, content creators, and platform designers seeking to optimize social media content strategies.
KW - Consumer engagement
KW - Elaboration Likelihood Model (ELM)
KW - Media Richness Theory (MRT)
KW - Social media optimization
KW - Textual emotional tone
KW - User-Generated Content (UGC)
KW - Visual complexity
KW - Xiaohongshu (RedNote)
UR - https://www.scopus.com/pages/publications/105037458778
U2 - 10.1007/978-3-032-16288-5_30
DO - 10.1007/978-3-032-16288-5_30
M3 - Conference contribution
AN - SCOPUS:105037458778
SN - 9783032162878
T3 - Smart Innovation, Systems and Technologies
SP - 419
EP - 437
BT - Marketing and Smart Technologies - Proceedings of ICMarkTech 2025
A2 - Reis, José Luís
A2 - Ruiz-Mafé, Carla
A2 - Peter, Marc K.
A2 - Reis, Luís Paulo
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Marketing and Technologies, ICMarkTech 2025
Y2 - 27 November 2025 through 29 November 2025
ER -