Abstract

In the rapidly evolving landscape of digital philanthropy, budget transparency has emerged as a cornerstone of digital marketing strategies to foster donor trust. However, the prevailing assumption that "more disclosure is always better" remains underexamined, particularly regarding how disclosure strategies interact with specific budget structures. Leveraging a large-scale dataset from the Tencent Lejuan platform and employing Large Language Models (LLMs) to quantify unstructured textual features, this study empirically investigates the main effects of budget components—administrative overhead and direct costs—on fundraising performance, and explores the asymmetric moderating roles of disclosure quality (semantic explanation) and quantity (item count). The findings reveal three key insights. First, donors exhibit significant "administrative overhead aversion" and a corresponding "direct cost preference." Second, disclosure strategies demonstrate a crucial "signal fit" mechanism: for administrative overhead, high-quality semantic explanations serve as a vital justification signal, significantly alleviating its negative impact, whereas merely increasing disclosure quantity is less effective. Third, conversely, for direct costs, increasing disclosure quantity (itemized granularity) acts as a powerful verification signal that boosts fundraising performance, while extensive semantic explanations provide limited marginal utility due to information redundancy. This study theoretically delineates the boundary conditions of transparency in digital charity and proposes a differentiated disclosure framework tailored to specific budget attributes, offering actionable guidelines for philanthropic organizations to optimize digital trust-building.

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