Green retrofits, which improve the environment and energy efficiency of buildings, are considered a potential solution for reducing energy consumption as well as improving human health and productivity. They represent some of the riskiest, most complex, and most uncertain projects to manage. As the foundation of project management, critical success factors (CSFs) have been emphasized by previous research. However, most studies identified and prioritized CSFs independently of stakeholders. This differs from the reality, where the success of green retrofits is tightly interrelated to the stakeholders of projects. To improve the analysis from a stakeholder perspective, the present study proposed an innovative method based on a two-mode social network analysis to integrate CSF analysis with stakeholders. The results of this method can provide further understanding of the interactions between stakeholders and CSFs, and the underlying relationship among CSFs through stakeholders. A pilot study was conducted to apply the proposed method and assess the CSFs for green retrofits in China. The five most significant CSFs are identified in the management of green retrofit. Furthermore, the interrelations between stakeholders and CSFs, coefficient and clusters of CSFs are likewise discussed.

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Stakeholders strongly influence project success, particularly for complex projects with heterogeneous stakeholders, and hence, understanding their influence is essential for project management and implementation. This paper proposes an original model based on social network analysis (SNA), which first introduces critical success factors (CSFs) as intermediate variables between stakeholders and project success. The model can demonstrate the interrelation between stakeholders and CSFs, and the results can reveal how stakeholders influence project success. Green retrofit is a typical type of complex project. The stakeholder relationship in green retrofit projects is more complex than in new projects, since more stakeholders (e.g., tenants and facility managers) who have particular interrelations (e.g., lease contract and split incentives between owners and tenants) are involved. Therefore, a case study of green retrofit in China was conducted to illustrate how the proposed model works. The results indicated the priorities and similarities of stakeholders in green retrofit. Stakeholders are categorized into five clusters according to their relationship. Based on the results, the important role of stakeholders in green retrofit projects was discussed. The main contribution of this study is providing a novel method to reveal how stakeholders influence the success of complex projects.

BackgroundIn order to better assist medical professionals, this study aimed to develop and compare the performance of three models—a multivariate logistic regression (LR) model, an artificial neural network (ANN) model, and a decision tree (DT) model—to predict the prognosis of patients with advanced schistosomiasis residing in the Hubei province.Methodology/Principal findingsSchistosomiasis surveillance data were collected from a previous study based on a Hubei population sample including 4136 advanced schistosomiasis cases. The predictive models use LR, ANN, and DT methods. From each of the three groups, 70% of the cases (2896 cases) were used as training data for the predictive models. The remaining 30% of the cases (1240 cases) were used as validation groups for performance comparisons between the three models. Prediction performance was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Univariate analysis indicated that 16 risk factors were significantly associated with a patient’s outcome of prognosis. In the training group, the mean AUC was 0.8276 for LR, 0.9267 for ANN, and 0.8229 for DT. In the validation group, the mean AUC was 0.8349 for LR, 0.8318 for ANN, and 0.8148 for DT. The three models yielded similar results in terms of accuracy, sensitivity, and specificity.Conclusions/SignificancePredictive models for advanced schistosomiasis prognosis, respectively using LR, ANN and DT models were proved to be effective approaches based on our dataset. The ANN model outperformed the LR and DT models in terms of AUC.