Examining the Roles of Metainformational Cues in Crowdfunding Success from the Elaboration Likelihood Model Perspective
This study employs the Elaboration Likelihood Model (ELM) to investigate how six metainformational cues – number of updates, founder’s experience, media usage, number of investors, project text length, and psychological capital language – influence crowd-funding success.
Metainformational cues are essential information for demonstrating a crowdfunding project and play a critical role in investors’ investment decision-making. While prior research has focused on intrinsic project information, the role of supplementary metainformational cues remains underexplored, particularly through the lens of dual-processing theories like ELM.
This study collected data from 188 crowdfunding projects from Kickstarter, a popular crowdfunding platform. A regression analysis was conducted to test relations between the six metainformational cues and crowdfunding success through the central and peripheral routes.
Our research findings extend ELM’s application to crowdfunding, demonstrating how intuitive cues reduce investor uncertainty and drive decisions. Moreover, the study advances crowdfunding literature by empirically validating a novel framework that bridges metainformation and persuasion theory, offering actionable insights for founders and platforms.
Regression analysis reveals that central route metainformational cues including the number of updates, founder's experience, and use of media are positively correlated with crowdfunding success. The three metainformational cues on the peripheral route, i.e., number of investors, project text length, and psychological capital language, also have a positive relationship with crowdfunding success.
Founders should prioritize frequent updates, integrate multimedia (e.g., videos/images), and highlight past successes to build trust. Investors can rely on crowd signals (e.g., backer counts) as decision heuristics.
Researchers could deepen the theoretical framework and combine the ELM with signal theory to explore the dynamic interaction effects of metainformational cues, explore cultural differences in the effectiveness of cues, and use advanced natural language processing (NLP) for language analysis.
The findings empower underrepresented entrepreneurs, simplify investment decisions for novices, and promote transparent crowdfunding ecosystems, potentially boosting economic innovation.
Future research can track investor browsing behavior through eye-tracking experiments, identify real-time switching mechanisms between central and peripheral routes, test sustainability-related cues (such as Environmental, Social, and Governance statements), and validate results on non-western platforms (such as Taobao).
Back