Valuing high-tech startups using traditional valuation models has continued to pose valuation challenges to entrepreneurs, investors as well as financial analysts. The complications in valuing startups are heightened by the variations in valuation methodologies and the absence of operational data. Identifying the appropriate methodology for valuing startups is crucial to establishing value and a prerequisite for accessing funding through mergers or acquisitions. The purpose of this study was to examine the effect of valuation methods on the likelihood of mergers and acquisitions of high-tech startup organizations in the Nigerian capital market. The theoretical underpinning of this study is rooted in valuation theory and mergers and acquisitions theories. The extent to which valuation methods impact the likelihood of securing funds through mergers and acquisitions was the overarching research question. Random sampling was used to obtain records of valuation methods and mergers and acquisitions that occurred between 2006 and 2016 from companies in the high-tech sector. A binary logistic regression model was used to test the impact of valuation methods on the likelihood of mergers and acquisitions of high-tech startups. The impact of valuation methods on the likelihood of mergers and acquisitions was found to be not statistically significant. The participants indicated a preference for specific valuation methods during negotiations for mergers and acquisitions. The findings have implications for positive social change via a reduction in the unemployment rate by encouraging startups with their innovation and entrepreneurship. This should help to facilitate the emergence of sound valuation methods for valuing high-tech startups in the Nigerian capital market.
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