Journal of Theoretical and Applied Electronic Commerce Research

Online review images shape consumer perceptions by offering visual cues of product quality and use. Existing studies focus on aesthetics or object presence but overlook the functional balance among image types. This study introduces the Holistic Image Proportion (HIP)—the ratio of holistic to detailed review images—as a key determinant of visual information completeness. Using deep learning (ResNet-101) to classify over 240,000 images from 4450 clothing products, we find an inverted U-shaped relationship between HIP and sales: a balanced mix (HIP ≈ 0.5) maximizes performance. A follow-up experiment confirms that balanced image composition enhances perceived completeness, which fully mediates its effect on purchase intention. Review sentiment further moderates this relationship, amplifying the effect under positive sentiment. This research extends information completeness theory to visual data, highlighting that completeness emerges from functional image composition rather than quantity or aesthetics, offering new insights for multimodal persuasion and e-commerce design.

The growth of e-commerce has led to a rapid increase in user-generated content (UGC), attracting scholars’ attention as a new data source for investigating customer requirements. However, existing requirements analysis methods fail to integrate three critical requirement indicators: stated importance, derived importance, and performance. Using only one or two of these indicators inevitably has its limitations. This paper proposes a novel framework for analyzing and prioritizing customer requirements based on multi-source UGC. First, customer requirements are extracted from online reviews and questions & answers using non-negative matrix factorization. Next, aspect-level sentiment analysis and multi-source data fusion are employed to calculate dual importance and performance. Specifically, we developed an improved importance–performance analysis (IPA) model, named dual importance–performance analysis (Du-IPA), which integrates the three indicators to classify requirement types in a 3D cube with corresponding improvement strategies. Finally, by combining the three indicators, an improved prospect value and PROMETHEE-II are proposed using prospect theory to prioritize CRs for product service improvement. The effectiveness of the proposed method is demonstrated through a case study of fresh food in online retail.

Green brands are increasingly leveraging electronic commerce and interactive marketing to enhance persuasive efforts in new media environments. However, empirical research on the role of linguistic rhetoric in these interactive settings remains limited. This study examines whether the use of homophonic puns in green brand advertising increases green purchase intention on digital platforms, and how visual salience moderates this effect. Across four online experiments (total N = 1382), using stimuli modeled on social media feeds and mobile commerce interfaces, we manipulated advertising copy to include either homophonic puns or non-pun alternatives and varied the level of interface salience. The results indicate that homophonic puns significantly increase green purchase intention, primarily through the mechanism of affiliative humor, which proved stronger than alternative humor pathways. Moreover, visual salience moderates this effect: high-salience layouts amplify humor perception and purchase intention, while low-salience conditions yield no significant effects. This study advances the field of interactive marketing by proposing a language–emotion–behavior framework and introducing a cross-modal synergy mechanism that links linguistic rhetoric with visual attention in digital media contexts. The findings provide both theoretical insights and practical implications for e-commerce platforms, particularly in optimizing advertising copy, attention design, and message placement strategies to align pun-based messages with high-salience executions, thereby enhancing green brand engagement.