Effective stakeholder management (ESM) is a critical success factor for projects. The increasing complexity in the relationships among stakeholders and their diverse characteristics, including power and interests makes the management of stakeholders increasingly challenging. To date, much of the literature has focused on the stakeholder analysis with very limited to the direct and indirect relationships between stakeholder characteristics (SC) and project performance (PP). Therefore, the aim of this study is to fill these research gaps by empirically examining (1) the relationship(s) between SC and PP and (2) the mediation effect of ESM on the above-mentioned relationships. Data analysis was conducted using structural equation modelling. The findings suggest that stakeholder legitimate behaviour (LB), opposing behaviour (OB), and conflicting interests affect the ability to achieve both sets of quantitative and qualitative PP negatively. ESM has been identified as a key element to eliminate the negative effects of the aforementioned behaviours on qualitative (and not quantitative) PP measures.
Incursions of Mimosa pigra L., a super-invasive plant, were detected in Hoa Vang district, Da Nang city, Vietnam. This invasive species posed threats to the local agricultural and natural areas, especially to Ba Na - Nui Chua Nature Reserve located in the district. In this study, a habitat model was developed to predict potential areas for the upcoming occurrences of the plant. Detected locations of the species were analyzed in association with seven environmental layers (15 m spatial resolution), which characterized the habitat conditions facilitating the plant incursion, to calculate a multivariate statistic, Mahalanobis distance (D2). Mimosa occurrences were divided into subsets of modelling (for model construction) and validating data (for selecting the best model from replicate runs). The model performance was tested using a null model of 1,000 random points and indicated a significant relationship between D2 values and mimosa occurrence. The D2 model performed markedly better than the random model. The null model in combination with the entire dataset of mimosa locations was also used to identify the threshold D2 value. Using that threshold value, 99.5% of existing mimosa locations were detected and 20.3% of the study area was determined as high-risk areas for mimosa occurrence. These identified high risk areas would make an important contribution to the local alien invasive species management. Given the potential threats to these species from illegal harvesting, that information may serve as an important benchmark for future habitat and population assessments. The spatial modelling techniques in this study can easily be applied to other species and areas.