Big data, technology capability and construction project quality: a cross-level investigation.

Purpose: Embracing big data has been at the forefront of research for project management. Although there is a consensus that the adoption of big data has significantly positive impact on project performance, far less is known about how this innovative information technology becomes an effective driver of construction project quality improvement. This study aims to better understand the mechanism and conditions under which big data can effectively improve project quality performance. Design/methodology/approach: Adopting Chinese construction enterprises as samples, the theoretical framework proposed in this paper is verified by the empirical results of the two-level hierarchical linear model. The moderated mediation analysis is also conducted to test the hypotheses. Finally, the empirical findings are validated by a comparative case study. Findings: The results show that big data facilitates the development of technology capability, which further produces remarkable quality performance. That is, a project team's technology capability acts as a mediator in the relationship between organizational adaptability of big data and predictive analytics and project quality performance. It is also observed that two types of project team interdependence (goal and task interdependence) positively moderate the mediation effect. Research limitations/implications: The questionnaire study from China only represents the relationship within a short time interval in the current context. Future studies should apply longitudinal designs to properly test the causality and use multiple data sources to ensure the validity and robustness of the conclusions. Practical implications: The value of big data in terms of quality improvement could not be determined in a vacuum; it also depends on the internal capability development and elaborate design of project governance. Originality/value: This study provides an extension of the existing big data studies and fuels the ongoing debate on its actual outcomes in project management. It not only clarifies the direct effect of big data on project quality improvement but also identifies the mechanism and conditions under which the adoption of big data can play an effective role. [ABSTRACT FROM AUTHOR]