Differential effects of visual complexity in firm-generated content on consumer engagements: a deep learning approach

Abstract

Purpose

This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Design/methodology/approach

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

Findings

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Originality/value

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

Citation

Wang, F., Yue, M., Yuan, Q. and Cao, R. (2024), "Differential effects of visual complexity in firm-generated content on consumer engagements: a deep learning approach", Marketing Intelligence & Planning, Vol. 42 No. 4, pp. 684-703. https://doi.org/10.1108/MIP-12-2022-0570