The impact of digital analytics capabilities on project efficiency: The moderating dimensions of cross functional coordination

Abstract

This study examines the impact of digital analytics on project efficiency, with particular emphasis on the moderating role of cross-functional coordination and the interaction effects of its underlying dimensions. Data were collected through an online survey of 438 employees engaged in project-based work and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that digital analytics has a significant positive effect on project efficiency, indicating that analytics-driven insights enhance decision-making, improve responsiveness, and reduce operational inefficiencies. However, the results also demonstrated that the effectiveness of digital analytics is significantly strengthened by cross-functional coordination. This suggests that the value of analytics is not fully realized unless organizations foster collaboration and alignment across departments. Further analysis highlights that specific dimensions of cross-functional coordination contribute differently to project efficiency, such as data-driven decision-making capabilities and communication skills emerged as the most influential factors, emphasizing the importance of timely decisions and effective information exchange. Employee empowerment and collaboration and knowledge sharing also play important roles by encouraging participation, innovation, and collective problem-solving. In contrast, management support and transparency of information sharing function as enabling conditions that support coordination but have a comparatively lower direct influence. Overall, the study underscores the importance of integrating digital analytics with strong organizational capabilities. It demonstrates that project efficiency can be significantly enhanced when data-driven insights are supported by effective coordination, communication, and collaboration mechanisms, offering valuable theoretical and practical implications for data-driven project management.