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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.
We examine to what extent a transaction relation-based value network maturity status of New Technology-Based Firms (NTBFs) is related to their survival. A specific challenge of NTBFs is their lack of market-orientation, which is why the maturity of the ties they form towards the market in terms of customers, financiers, personnel and partners is supposed to be a strong indicator for survival. We analyze a sample of 170 NTBFs by capturing their value network status from business plans and defining their survival status using secondary research. Simple statistical tests and regressions suggest that the official registration of the business is a pre-step for survival that requires industry-specific value network dimension strengths. A sub-sample survival analysis shows that for all NTBFs that have reached registration, regardless of their industry, a stronger customer value network maturity dimension prevents from failure and is thus a significant predictor for survival. Moreover, the analyses partly support the idea that NTBFs from the IT sector are less dependent on a strong value network in the financier dimension to survive. The results are of relevance for both practitioners and researchers in the innovation system: a better understanding of the factors impacting on NTBF survival can help to provide more tailored support services for young firms, increase the effectiveness of resource allocations, and provide a basis for further research.