As an increasing number of crowdfunding platforms recommend that entrepreneurs post multimodal data to improve data diversity and attract investors’ attention, it becomes necessary to study how functions of multimodal data take effect to predict fundraising outcomes (i.e., success or failure). There is a lack of research providing a comprehensive investigation of multimodal data in crowdfunding. Rooted in language and visual image metafunctional theories, we propose a framework to explore ideational, interpersonal, and textual metafunctions of multimodal data. We empirically examine the effectiveness of each metafunction, each modality, and their combination in predicting fundraising outcomes. The empirical evaluation shows the predictive utility of any metafunctions and metafunction combinations. The results also demonstrate that adding data modalities can help to improve the prediction performance
