When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness
Images contain more visual semantic information. Consumers first view multimodal online reviews with images. Research on the helpfulness of reviews on e-commerce platforms mainly focuses on text, lacking insights into the product attributes reflected by review images and the relationship between ima...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Theoretical and Applied Electronic Commerce Research |
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| Online Access: | https://www.mdpi.com/0718-1876/20/2/144 |
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| _version_ | 1849432755969982464 |
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| author | Chao Ma Chen Yang Ying Yu |
| author_facet | Chao Ma Chen Yang Ying Yu |
| author_sort | Chao Ma |
| collection | DOAJ |
| description | Images contain more visual semantic information. Consumers first view multimodal online reviews with images. Research on the helpfulness of reviews on e-commerce platforms mainly focuses on text, lacking insights into the product attributes reflected by review images and the relationship between images and text. Studying the relationship between images and text in online reviews can better explain consumer behavior and help consumers make purchasing decisions. Taking multimodal online review data from shopping platforms as the research object, this study proposes a research framework based on the Cognitive Theory of Multimedia Learning (CTML). It utilizes multiple pre-trained models, such as BLIP2 and machine learning methods, to construct metrics. A fuzzy-set qualitative comparative analysis (fsQCA) is conducted to explore the configurational effects of antecedent variables of multimodal online reviews on review helpfulness. The study identifies five configurational paths that lead to high review helpfulness. Specific review cases are used to examine the contribution paths of these configurations to perceived helpfulness, providing a new perspective for future research on multimodal online reviews. Targeted recommendations are made for operators and merchants based on the research findings, offering theoretical support for platforms to fully leverage the potential value of user-generated content. |
| format | Article |
| id | doaj-art-e0202d51637f463084e8e8e26cf8d2a4 |
| institution | Kabale University |
| issn | 0718-1876 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Theoretical and Applied Electronic Commerce Research |
| spelling | doaj-art-e0202d51637f463084e8e8e26cf8d2a42025-08-20T03:27:17ZengMDPI AGJournal of Theoretical and Applied Electronic Commerce Research0718-18762025-06-0120214410.3390/jtaer20020144When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review HelpfulnessChao Ma0Chen Yang1Ying Yu2College of Economics and Management, Zhejiang Normal University, Jinhua 321000, ChinaCollege of Economics and Management, Zhejiang Normal University, Jinhua 321000, ChinaCollege of Economics and Management, Zhejiang Normal University, Jinhua 321000, ChinaImages contain more visual semantic information. Consumers first view multimodal online reviews with images. Research on the helpfulness of reviews on e-commerce platforms mainly focuses on text, lacking insights into the product attributes reflected by review images and the relationship between images and text. Studying the relationship between images and text in online reviews can better explain consumer behavior and help consumers make purchasing decisions. Taking multimodal online review data from shopping platforms as the research object, this study proposes a research framework based on the Cognitive Theory of Multimedia Learning (CTML). It utilizes multiple pre-trained models, such as BLIP2 and machine learning methods, to construct metrics. A fuzzy-set qualitative comparative analysis (fsQCA) is conducted to explore the configurational effects of antecedent variables of multimodal online reviews on review helpfulness. The study identifies five configurational paths that lead to high review helpfulness. Specific review cases are used to examine the contribution paths of these configurations to perceived helpfulness, providing a new perspective for future research on multimodal online reviews. Targeted recommendations are made for operators and merchants based on the research findings, offering theoretical support for platforms to fully leverage the potential value of user-generated content.https://www.mdpi.com/0718-1876/20/2/144multimodal online reviewsmultimedia learning cognitive theoryperceived helpfulnesstext–image integrationfuzzy-set QCA |
| spellingShingle | Chao Ma Chen Yang Ying Yu When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness Journal of Theoretical and Applied Electronic Commerce Research multimodal online reviews multimedia learning cognitive theory perceived helpfulness text–image integration fuzzy-set QCA |
| title | When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness |
| title_full | When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness |
| title_fullStr | When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness |
| title_full_unstemmed | When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness |
| title_short | When Pixels Speak Louder: Unravelling the Synergy of Text–Image Integration in Multimodal Review Helpfulness |
| title_sort | when pixels speak louder unravelling the synergy of text image integration in multimodal review helpfulness |
| topic | multimodal online reviews multimedia learning cognitive theory perceived helpfulness text–image integration fuzzy-set QCA |
| url | https://www.mdpi.com/0718-1876/20/2/144 |
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