CAPTURING INTANGIBLE WORTH: A BIBLIOMETRIC REVIEW OF START-UP PRE-MONEY VALUATION RESEARCH

Purpose. This paper aims to develop a comprehensive and structured overview of academic research on pre-money valuation in startups, with a specific focus on the integration of intangible assets into valuation models. It addresses persistent limitations in the literature, such as fragmented theoretical contributions, the dominance of traditional financial metrics, inconsistent use of alternative multiples, and the underexplored role of data-driven and machine learning (ML) techniques. Design/methodology/approach. Adopting a bibliometric methodology, the study analyses 170 peer-reviewed journal articles published between 2010 and 2024. The analysis integrates citation metrics, age-weighted citation rates (AWCR), co-citation networks, keyword co-occurrence, bibliographic coupling, and country-level productivity and impact. VOSviewer software was used to generate visual mappings of the literature’s intellectual structure. Findings. The bibliometric analysis reveals a diverse and evolving research landscape. Contributions range from traditional financial valuation models to alternative efficiency-based metrics and the emerging application of data-driven and machine learning techniques. The thematic coverage extends to issues such as crowdfunding signals, institutional and geographical influences, and the growing relevance of intangible assets – including human capital, user engagement, and ecosystem maturity – in shaping startup pre-money valuation. Originality/value. This is the first bibliometric review focused exclusively on startup pre-money valuation that systematically integrates performance analysis, structural relationships, and the role of intangible and ML-enabled indicators. It offers an evidence-based agenda for future research and contributes to the development of valuation models that better reflect the complexity and heterogeneity of early-stage ventures.