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Text produced by entrepreneurs represents a data source in entrepreneurship research on venture performance and fund-raising success. Manual text coding of single variables is increasingly assisted or replaced by computer-aided text analysis. Yet, for the development of prediction models with several variables, such dictionary-based text analysis methods are less suitable. Natural language processing techniques are an alternative; however, the implementation is more complex and requires substantial programming skills. More work is required to understand how text analytics can advance entrepreneurship research. This study hence experiments with different artificial intelligence methods rooted in Natural Language Processing and deep learning. It uses 766 business plans to train a model for the automated measurement of transaction relations, a construct which is an indicator for new technology-based firm survival. Empirical findings show that the accuracy of construct measurement can be significantly increased with automated methods and improves with larger amounts of training data. Language complexity sets limits to the precision of automated construct measurement though. We therefore recommend a hybrid approach: making use of the inherent advantages of combining automated with human coding until the amount of training data is sufficiently large to substitute the human coding completely. The study provides insights into the applicability of different text analytics methods in entrepreneurship research and points at future research potential.
Despite the increased attention dedicated to research on the antecedents and determinants of new venture survival in entrepreneurship, defining and capturing survival as an outcome represents a challenge in quantitative studies. This paper creates awareness for ventures being inactive while still classified as surviving based on the data available. We describe this as the ‘living dead’ phenomenon, arguing that it yields potential effects on the empirical results of survival studies. Based on a systematic literature review, we find that this issue of inactivity has not been sufficiently considered in previous new venture survival studies. Based on a sample of 501 New Technology-Based Firms, we empirically illustrate that the classification of living dead ventures into either survived or failed can impact the factors determining survival. On this basis, we contribute to an understanding of the issue by defining the ‘living dead’ phenomenon and by proposing recommendations for research practice to solve this issue in survival studies, taking the data source, the period under investigation and the sample size into account.