# Dynamic BIM Adoption Impact on Contract Cost Variance Factors Using PLS-SEM Techniques
## 1 Introduction  
The construction industry sector plays a role in the country's economy. It is a significant source of employment, investment, and economic growth. In many countries, the construction industry is the largest private sector employer. It is also a significant driver of innovation and technological development. The construction industry is a dynamic system that uses various technologies to produce structures and infrastructures. Particularly in developing countries, the construction sector contributes up to 10% to the gross domestic product (GDP) and around 50% to domestic fixed capital (DFC) [1]. However, the construction industry faces several issues related to different parties through the construction stages. Building information modeling (BIM) requires specialized training and skills, and transitioning from traditional design and construction methods to BIM can be challenging for some professionals and organizations [2]. However, BIM has recently solved several construction issues. One of the potential areas that BIM affects is Contract Cost Variance (CCV) [3], which is defined as the ratio of the difference between the winning bid and client estimation cost to the winning bid (CCV = (Winning bid-Client's estimation cost)/winning bid) [4]. The CCV is a crucial index, reflecting the cost overrun and final contract cost [5]. Most research carried out on CCV focuses on identifying the most important of CCV for different projects and countries [6; 7; 8; 9; 10]. Few studies have considered the influence of the factors' interdependencies on the CCV, such as [11]. However, the existing literature has not accounted for the temporal variation of CCV factors, making it challenging to understand their evolution over time. Previous studies have overlooked the dynamic influence of significantfactors on CCV over time, presenting a challenge in comprehending the evolving nature of these factors across time periods. Additionally, numerous studies have demonstrated the positive impact of Building Information Modeling (BIM) on improving construction cost estimation [12; 13; 14]. BIM's role in improving project management efficiency, accuracy, and coordination is well-documented. It reduces costs and risks associated with construction projects, although challenges such as regulatory development and stakeholder consensus remain [15]. Based on this foundation, the hypothesis of this study is formulated as follows: BIM positively influences the factors affecting construction costs during the pre-bid phase. As BIM adoption increases, the relative importance of these cost-related factors is expected to change over time. While the hypothesis primarily focuses on the association between BIM and construction costs during the pre-bid phase, it is important to note that BIM can influence various other variables beyond costs, such as project efficiency, quality, and collaboration. Expanding the hypothesis to include a broader set of variables affected by BIM implementation would provide a more comprehensive understanding of its overall impact on construction processes and is beyond the scope of the study. The main objective of this study is to assess the expected impact of BIM adoption in variation CCV factors over time. Two sub-objectives are developed in this study: (1) identify and rank the factors affecting CCV using a PLS-SEM casual model and (2) study the forecasted dynamic impact of BIM adoption on CCV factors over time. This paper provides valuable information to the stakeholders by providing insights into the critical factors affecting the accuracy of the contract cost with time at an early stage of a project. CCV factors and their impact are affected by the advancement of BIM technology, which helps improve design clarity, accuracy, and efficiency. The study assists decision-makers in taking appropriate and preventive measures to diminish CCV. Additionally, the paper introduces a new procedure to develop the relationship among the model's latent groups.