Insights into the impact of visual and textual information on investment decision-making: A multimodal business plan analysis via deep representation learning

Business plans (BPs) serve as crucial communication tools between entrepreneurs and investors, but there is controversy in existing research regarding the actual impact of BP’s quality on investment decision. To investigate how the visual and textual features of BP impact investors’ investment decisions, we develop a flexible computational framework to represent the visual and textual information in business plans and design a series of indicators to measure the corresponding information quality. Specifically, we propose three quality indicators, namely $V_{BP}$, $T_{BP}$, and $I_{BP}$, based on the deep representations of the BP’s visual feature, text information, and brief introduction. Through Logit Regression analysis of 4597 business plans and their corresponding 42,533 decision-making samples from an online investment platform, we find BP’s quality significantly influences initial investment decisions. Visual quality and textual introduction quality exhibit significant effects (p < 0.05). We also reveal the moderating effects of investor risk preferences. Our computational modeling and empirical evidence provide key insight into decision mechanisms. This is the first investigation that utilizes deep pre-trained models to comprehensively model BP’s multimodal features within the investment decision-making domain. The proposed quality indicators can enable scalable, unbiased evaluation to address the evolving needs of decision-makers in an increasingly complex and data-rich environment.