In simple words, enterprise environmental factors are those factors which have an impact on the success of a project. They can alter the results of aproject. These factors are not controllable by the project team. This can be either external factors or internal factors to the project and the effect of these environmental factors can be positive as well as negative (Belout & Gauvreau, 2004). Thus, it can either limit or enhance the capability of project team management. Some of the internal factors are: organisational culture, structure, geographic distribution of resources, infrastructure or resource availability. Some of the external factors are: marketplace conditions, financial considerations, physical environmental elements,legal restrictions, or social and cultural issues (Guo et al., 2016).
In the construction project, there were both external and internal enterprise environmental factors which affected the project. The external factor which affected the project was climate/ mother nature. It was observed that some of the plan was not completed due to the climatic conditions. On the other hand, the internal factor impacting the outcome of the construction project was related to availability of human resources, that is, lack of availability of labors. These were the two enterprise environmental factors (EEFS) which affected the construction project.
There are several techniques which can be used to analyse the qualitative or quantitative impact of enterprise environmental factors on the project (Thamhain, 2013). The most appropriate tool which can be used for the analysis of both qualitative and quantitative impact on the construction project is logistic regression.
Logistic regression analysis refers to a model which uses a logistic function to determine anexplained variable that is binary. This statistical model is required to model the probability of a specific event. For example, pass or fail, healthy or sick, win or lose. This is not limited only to two values, rather it can be extended to include several classes of events. In the case of binary logistic model, the dependentvariable has two values, for example pass or fail, that is expressed as an indicator variables. The label given to two values are 0 and 1. Thus, in a logistic regression, binary variable is regressed on other independent variables which can be qualitative or quantitative in nature. The coefficients of independent variables in a logistic regression will imply the impact of those independent variables on the dependent variable (Austin & Merlo, 2017).
In construction project, to evaluate either the qualitative or quantitative impact of enterprise environmental factors, the most appropriate is to use the logistic regression. This is because logistic regression is a predictive analysis. The dependent variable can measure the success of a project by using proxy for it. The independent variables are the external and internal factors which have an influence on the success of a project. In our case, the independent variables will be climatic conditions and availability of labors.
Austin, P. C., & Merlo, J. (2017).Intermediate and advanced topics in multilevel logistic regression analysis. Statistics in medicine, 36(20), 3257-3277.
Belout, A., &Gauvreau, C. (2004). Factors influencing project success: the impact of human resource management. International journal of project management, 22(1), 1-11.
Guo, T., Wang, G., & Wang, C. (2017). Empirical Study on the Effect of Environmental Factors on Enterprise Growth-Comparative Analysis of Chinese Large Scale Industrial Enterprises and Small/Medium Industrial Enterprises. EURASIA Journal of Mathematics, Science and Technology Education, 13(11), 7549-7559.
Thamhain, H. (2013). Managing risks in complex projects. Project management journal, 44(2), 20-35.
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