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The impact of entrepreneurship orientation on project performance: A machine learning approach

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  • Sabahi, Sima
  • Parast, Mahour Mellat

Abstract

Recent studies in project management have shown the important role of entrepreneurship orientation of the individuals in project performance. Although identifying the role of entrepreneurship orientation as a critical success factor in project performance has been considered as an important issue, it is also important to develop a measurement system for predicting performance based on the degree of an individual's entrepreneurial orientation. In this study, we use predictive analytics by proposing a machine learning approach to predict individuals' project performance based on measures of several aspects of entrepreneurial orientation and entrepreneurial attitude of the individuals. We investigated this relationship using a sample of 185 observations and a range of machine learning algorithms including lasso, ridge, support vector machines, neural networks, and random forest. Our results showed that the best method for predicting project performance is lasso. After identifying the best predictive model, we then used the Bayesian Information Criterion and the Akaike Information Criterion to identify the most significant factors. Our results identify all three aspects of entrepreneurial attitude (social self-efficacy, appearance self-efficacy, and comparativeness) and one aspect of entrepreneurial orientation (proactiveness) as the most important factors. This study contributes to the relationship between entrepreneurship skills and project performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.

Suggested Citation

  • Sabahi, Sima & Parast, Mahour Mellat, 2020. "The impact of entrepreneurship orientation on project performance: A machine learning approach," International Journal of Production Economics, Elsevier, vol. 226(C).
  • Handle: RePEc:eee:proeco:v:226:y:2020:i:c:s0925527320300098
    DOI: 10.1016/j.ijpe.2020.107621
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