Uncovering the themes and trends in crowdfunding research using Latent Dirichlet Allocation

Abstract

AbstractCrowdfunding (CF) has become a significant force in the entrepreneurial landscape, offering an innovative alternative to traditional financing channels for startups and projects. As the field expands, it is crucial to systematically analyze the existing literature to identify key themes, patterns, and emerging areas of interest. To achieve this goal, this study investigates the CF literature using latent Dirichlet allocation (LDA)-based topic modeling based on 1,678 publications extracted from the Scopus database. The review reveals significant growth in CF research, with top journals spanning diverse disciplines. Eight main topics are identified, including CF campaign success and financing, donation-based CF, social effects of CF, entrepreneurial projects and rewards in CF, financial and fintech aspects of CF, CF project success and performance, P2P lending models and credit risk assessment, and equity CF and venture capital. Several research directions are suggested for each topic to advance the CF field. The theoretical and practical implications are also discussed. To the authors’ best knowledge, this study represents the first systematic analysis of the CF literature using the LDA approach, offering a comprehensive and up-to-date overview of this field and highlighting emerging areas of interest and potential research directions.