Abstract
The problem with evaluating investment projects is that there are many factors that determine the degree of their successful conclusion. Consequently, there has been an active debate for years as to which critical success factors (CSFs) contribute most to the performance of construction projects. This is because the practice of empirical research is based on two steps: first, researchers choose a particular model from the space of all possible models, and second, they act as if the chosen model is the only one that fits the data and describes the phenomenon under study. Hence, there are many CSF lists that can be found in the literature, owing to the uncertainty at the model selection stage, which is usually ignored. Alternatively, model averaging accounts for this model uncertainty. In this study, the Bayesian model averaging and data from a survey of Polish construction managers were used to investigate the potential of 28 factors describing a diverse set of characteristics in explaining the performance of construction projects in Poland. Determinants of successful completion of investment projects are categorized by their level of evidential strength, which is derived from posterior inclusion probabilities (PIPs), i.e., providing strong, medium and weak evidence.
Highlights
Construction is one of the focal sectors of the Polish economy, with a significant share in GDP
The common practice in empirical research is based on selecting a single model from an infinite space of all possible models
The Bayesian model averaging (BMA) approach allows us to analyze the whole set of all possible models, which results in estimating the most accurate model parameters
Summary
Construction is one of the focal sectors of the Polish economy, with a significant share in GDP. In 2019, the construction sector in Poland employed 980 thousand people, which accounted for 6.1% of all Polish workers. According to Sobieraj [5] and Górny [6], the key factors negatively affecting the implementation of investment projects in Poland are primarily of a human nature. They are related to the unavailability of workers with high qualifications and appropriate professional experience; seasonality of employment combined with the lack of properly trained staff; and high rotation of personnel, leading to the employment of young and inexperienced workers. On top of all of this is the complexity of construction projects, resulting in an increased risk of accidents on sites and budgetary constraints and ensuing recruitment of workers who accept lower wages and whose professional skills are rather dubious [5,7,8]
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