Incentivizing Collaborative BIM-Enabled Projects by Chen-Yu Chang

Author:Chen-Yu Chang [Неизв.]
Language: eng
Format: epub
ISBN: 9781628256246
Publisher: Independent Publishers Group

The Model

Conceptually, an incentivization system to the project is similar to an engine in a car. The effectiveness of the incentivization system is a decisive factor for project performance. However, how well incentivization could work does not only depend on the measures taken, but also on the delivery environment in which it is embedded. Actually, from a theoretical point of view, there are two dominant factors (Chang, 2015): The first-order alignment should be secured by choosing the appropriate delivery system and the second-order alignment by selecting right incentivization measures (e.g., the risk-sharing ratio; see Chang, 2014d). According to an empirical investigation of UK infrastructure projects, these two factors together can account for 75% of the variations in project outcome (Chang & Mills, 2016) and thus they could be the most important pillars upon which the framework can rest. Instead of directly modeling them, the strategy chosen was to measure the result of these two factors. In other words, the delivery system and incentive system in the project are treated as exogenous (Figure 19) and take the result as input to the model. The reason for choosing this strategy was based on the 10 case studies conducted for Chinese BIM-enabled projects prior to the survey. It is found that few explicit financial rewards were used, but a broad range of intangible factors could translate into long-term financial benefits to motivate BIM participation. China has a unique business environment with a high diversity of local practices, so discovering the comprehensive list of motivators and their effects on BIM participation could entail another research. To make the analysis manageable, this research chooses to focus on the effect of incentivization created by a project's delivery environment instead of the measures taken. The exogeneity of these factors is indicated by dotted lines in Figure 19. This effect could be affected by behavioral biases (H1). The questions concerning behavioral biases in the questionnaire are all phrased in a way to make the high score representative of a bias source's strong positive impact on the effectiveness of incentivization, so the presence of (positive) biases could strengthen the effectiveness of incentivization (H2). The effect of these factors could work forward to influence the effectiveness of BIM on project performance (H3), and the acceptance of advanced incentivization features (H4). In the meantime, the experience with incentivization in the project could have a direct impact on BIM experts’ perceptions of the essential characteristics of an advanced incentivization system.

Download

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we'll remove relevant links or contents immediately.