Evaluating the growth potential of digital business weak signals through the lens of entrepreneurs

Abstract

Previous studies develop data mining methods for identifying weak signals of technological and business changes, but there remains a research gap in understanding how to track the evolution of digital business weak signals over time and evaluate their potential development into strong signals or remaining weak signals. To address this research gap, we analyze the technology and business profiles of digital entrepreneurial ventures from 2011 to 2022, using data extracted from CrunchBase. We employ a 6-year window and a keyword-based text mining approach to classify digital business weak and strong signals across two distinct time periods. Using logistic regression analysis, we examine factors, such as entrepreneurship intensity and venture capital funding, that may be related to the development of weak signals into strong trends. We also apply Word2vec to assess and rank the applications of digital business weak signals based on their significance and potential. Additionally, we use term frequency-inverse document frequency (TF-IDF) to measure the relevance and importance of digital business weak signals over time. Our study identifies several weak signal areas that entrepreneurs perceive as having high growth potential, including Web3, quantum computing, space travel and satellite communication, and the valorization of forestry and natural resources. Among these, Web3 has gained significant importance and relevance in digital business contexts over the past few years.