【公开学术报告】Miniature Storytelling in Crowdfunding: The Role of Visual Congruence and Verbal Distinctiveness

发布时间:2025-02-27

Miniature Storytelling in Crowdfunding: The Role of Visual Congruence and Verbal Distinctiveness

Speaker: Wang Rui (George Washington)

Date & Time: Tue. 18th, March 2025, from 9:30 AM to 11:00 AM (Beijing Time)

Zoom Meeting ID:  84353820719 (Password: 451289)

Join via the Link: https://us02web.zoom.us/j/84353820719.

ABSTRACT

Entrepreneurial narratives play a critical role in mobilizing resources, especially for social ventures engaging impact-driven audiences. While increased attention has been paid to entrepreneurial stories shared through online platforms, existing literature has largely overlooked the audience’s sequential engagement with these stories and their multimodal composition. Building upon the concept of optimal distinctiveness and theories on communication modes, we theorize how the distinctiveness of visual and textual elements differ in appealing to the audience. Furthermore, we theorize the relative impact of an initial miniature story—the succinct entrepreneurial story usually leveraging visuals along with verbal text to communicate the essence of an entrepreneurial endeavor—compared to a subsequent fuller story. Analyzing borrowing campaigns pitched on Kiva over 16 years, both mini-pitches (in the form of image-text pairs) and fuller descriptions, we find that visual category congruence and verbal category distinctiveness are positively related to funding success, but only verbal category distinctiveness leads to faster funding. Moreover, we find that mini pitches have a greater impact on accelerating the funding process than fuller stories. Our findings caution against assuming that audiences equally engage in each fuller entrepreneurial story and assuming visual and verbal modes of entrepreneurial narratives have similar impacts on audience support.

Keywords:

Entrepreneurial storytelling; category congruence and distinctiveness; multimodal information; crowdfunding; computer vision


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