Linking Critical Success Elements To Delay Mitigation And Time Optimization In Construction Projects In Chennai

Construction projects play a vital role in economic development and infrastructure growth.  however, delays in project completion continue to be one of the most critical challenges affecting time, cost, and quality performance. Previous studies have identified that delays are caused by multiple factors such as poor planning, design changes, financial issues, resource shortages, and ineffective communication among stakeholders (Shehu 2021; Zaman et al. 2022; Department of Building Engineering and Management, School of Planning and Architecture, New Delhi, India et al. 2024; Lokeshwaran M and Aswin Bharath A 2023; Seki and Patriadi, 2025). These delays lead to cost overruns, productivity loss, disputes among stakeholders, and reduced client satisfaction (Ajayi and Chinda 2022;Nigeria et al. 2025; Sinaga and Husin 2021). In addition, the increasing complexity of construction projects, involving multiple stakeholders and dynamic working conditions, further intensifies the problem of schedule deviations (Alafeef 2024; Saibal Kumar Saha 2023; Seki and Patriadi 2025). Recent studies also indicate that delay factors are interrelated and create a chain effect, making delay management more complex (Saibal Kumar Saha 2023; Ajayi and Chinda 2022). Furthermore, emerging atypical factors such as regulatory changes, environmental constraints, and geopolitical influences are not adequately considered in traditional delay models (Fashina et al. 2021). At the same time, several researchers have focused on identifying critical success factors (CSFs) that contribute to successful project delivery. These include planning and scheduling efficiency, leadership, stakeholder coordination, communication, quality management, and resource optimization (Shehu 2021; Ovalle et al. 2024; Maw et al. 2025; Moza and Paul 2024). Effective implementation of these elements has been shown to improve project performance and reduce delays ( Surahman et al. 2025; Oluwatosin 2024; Sinaga and Husin 2021). For example, proper time management processes such as activity sequencing, duration estimation, and schedule control significantly enhance timely project completion (Sinaga and Husin 2021), while quality management practices help reduce rework and defects, indirectly minimizing delays (Oluwatosin 2024). However, studies also reveal that critical success factors are context-dependent and vary based on project type, location, and stakeholder perception, indicating the absence of a universal framework (Moza and Paul 2024).

In terms of time optimization, methods such as Critical Path Method (CPM), Critical Chain Project Management (CCPM), and Building Information Modelling (BIM) have demonstrated significant improvements in project scheduling. Studies report that CPM can reduce project duration by up to 44% through efficient activity sequencing and resource allocation ( Surahman  et al. 2025; Seki and Patriadi, 2025), while CCPM integrated with BIM improves coordination and achieves time savings of over 30% (Anastasiu et al. 2023). Additionally, delay mitigation strategies such as improved communication, financial management, and technology adoption have been widely recommended (Abd Aziz et al. 2022). Despite these advancements, most studies have examined delay factors, success elements, and optimization techniques independently, without establishing a comprehensive relationship among them. Although extensive research exists, there is a clear gap in integrating critical success elements, delay factors, and time optimization into a unified analytical framework. Existing studies primarily focus on delay identification(Bhaumik et al. 2025; Amit Moza et al. 2024; Seki and Patriadi, 2025), success factor evaluation (Ovalle et al. 2024; Moza and Paul 2024), or optimization techniques (Anastasiu et al. 2023; Seki and Patriadi, 2025) in isolation. There is limited research that quantitatively examines how critical success elements influence delay reduction and subsequently improve time optimization. Moreover, stakeholder-based variations and the role of emerging atypical factors are not adequately incorporated into existing models (Fashina et al. 2021). This fragmentation limits the practical application of research findings in real-world project environments. To address this gap, the present study aims to evaluate the effect of critical success elements on delay management and time optimization in construction projects through an integrated analytical approach. The study focuses on identifying key success elements, examining their relationship with major delay factors, and assessing their impact on project time performance using statistical techniques. The novelty of this research lies in developing a comprehensive framework that links critical success elements, delay factors, and time optimization within a single model, supported by empirical data and stakeholder perspectives. The study is conducted in the context of the Indian construction industry, with a specific focus on Tamil Nadu, particularly the Chennai region, which represents a rapidly developing urban construction environment. Primary data for this study were collected through a structured questionnaire survey using a Google Forms platform, targeting construction professionals including contractors, consultants, engineers, and project managers. The region is characterized by high project density, increasing complexity, and frequent schedule delays, making it a suitable case for evaluating delay management practices. The insights derived from this study are expected to provide practical and scalable solutions applicable to similar developing construction environments.

RESEARCH METHODOLOGY

The present study adopts a quantitative research approach to evaluate the relationship between critical success elements, delay factors, and time optimization in construction projects. The research is based on primary data collected through a structured questionnaire survey developed using Google Forms. The questionnaire was designed based on an extensive review of literature and includes key variables related to critical success elements, delay factors, and project performance indicators. A five-point Likert scale was used to capture the perception of respondents regarding the significance of each factor. The study area is focused on the construction industry in Tamil Nadu, India, particularly in the Chennai region, where rapid urban development and infrastructure expansion have led to increased project complexity and frequent delays. The target respondents include construction professionals such as contractors, consultants, site engineers, and project managers with relevant industry experience. A total of responses was collected through online distribution, ensuring a diverse representation of stakeholders across different project types. The collected data were exported from the Google Forms platform into a spreadsheet format and analyzed using statistical tools. Initially, data screening was performed to remove incomplete or inconsistent responses. Reliability analysis was conducted using Cronbach’s alpha to ensure internal consistency of the questionnaire items. Subsequently, Relative Importance Index (RII) was used to rank critical success elements and delay factors based on their significance. Exploratory Factor Analysis (EFA) was carried out to identify underlying factor groupings and reduce dimensionality of the variables. Correlation analysis was performed to examine relationships between critical success elements and delay factors. Further, regression analysis was used to evaluate the influence of critical success elements on delay reduction and time optimization. In addition, Analysis of Variance (ANOVA) was conducted to assess differences in perception among different stakeholder groups. The overall methodology integrates statistical analysis with stakeholder-based evaluation to develop a comprehensive understanding of how critical success elements contribute to effective delay management and improved time performance in construction projects.

RESULT AND DISCUSSION

1. Data Collection, Screening, and Reliability Analysis

 Data were collected from construction professionals across Tamil Nadu, including clients, contractors, consultants, design engineers, and academicians, to ensure a comprehensive representation of key stakeholders involved in project planning and execution. A structured questionnaire survey was administered using a snowball sampling approach, resulting in a total of 238 responses. After applying data screening procedures, 213 valid responses were retained for further analysis. The respondent profile demonstrates a balanced distribution, with contractors as 43.2% representing the largest group, followed by consultants as 27.2%, clients as 19.2%, design professionals as 7.0%, and academicians as 3.4%. This distribution reflects a diverse combination of practical, managerial, and technical expertise within the construction sector. The distribution of respondents across different stakeholder categories is illustrated in Figure 1, which highlights the dominance of contractor and consultant participation, consistent with their active involvement in project execution and coordination.

Figure 1 Respondent Distribution in construction survey

In addition to graphical representation, a summarized view of respondent characteristics and reliability measures is presented in Table 1, providing a concise overview of the dataset used for analysis.

Table 1. Respondent profile and reliability statistics

Prior to statistical analysis, the dataset was subjected to rigorous pre-processing to ensure data quality and suitability for multivariate analysis. Responses with significant missing values were excluded, while minor missing entries (less than 1.2%) were addressed using mean substitution. Outliers were identified through standardized z-score analysis, and extreme values beyond ±3.0 were removed to prevent distortion of results. These steps ensured that the dataset met the assumptions required for subsequent analytical techniques. The internal consistency of the measurement scales was assessed using Cronbach’s alpha, as presented in Table 1. The results indicate excellent reliability, with alpha values of 0.921 for Critical Success Elements and 0.936 for Critical Delay Factors, both significantly exceeding the recommended threshold of 0.70. These findings confirm strong internal coherence among the survey items and validate the robustness of the dataset for further factor extraction and relationship analysis. Overall, the data collection and screening process established a reliable foundation for examining the interaction between critical success elements and delay factors in construction projects.

2. Exploratory Factor Analysis (EFA)

Exploratory Factor Analysis (EFA) was carried out to group the large number of success and delay attributes into a smaller set of meaningful factors. This helps in simplifying the data and identifying the key dimensions that influence construction project performance. The analysis was performed using Principal Component Analysis (PCA) with Varimax rotation to obtain clearly interpretable factor groupings. Before performing the factor analysis, the suitability of the data was checked using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett’s Test of Sphericity.

Table 2. KMO and Bartlett’s Test Results

KMO values obtained were 0.894 for success attributes and 0.907 for delay attributes, which indicate that the sample size and correlations are adequate for factor analysis. Bartlett’s Test was also found to be statistically significant (p < 0.001), confirming that meaningful relationships exist among the variables. The results of these tests are summarized in Table 2, which confirms that the dataset is appropriate for factor extraction.

Figure 2 Scree Plot for Factor Extraction

Based on eigenvalues greater than 1 and factor loadings above 0.60, a total of six Critical Success Elements (CSEs) and seven Critical Delay Factors (CDFs) were extracted. These factors represent the key dimensions influencing delay management and time performance in construction projects. The scree plot shown in Figure 2 supports the selection of factors, where a clear change in slope is observed after the significant components, indicating the appropriate number of factors to retain and the extracted factors are summarized in Table 3. For clarity, only the main factor groupings and representative attributes are presented.

Table 3. Extracted Critical Success Elements and Delay Factors

Overall, the factor analysis reduced a large set of variables into a structured set of success elements and delay factors. These extracted factors provide a clear basis for further analysis, including ranking and evaluation of relationships between success elements and delay reduction.

3. Relative Importance Index (RII) Analysis

 Relative Importance Index (RII) method was used to identify and rank the most significant Critical Success Elements (CSEs) and Critical Delay Factors (CDFs) based on the responses obtained from construction professionals. This method helps in understanding which factors have the highest influence on project time performance by converting Likert scale responses into comparable index values. The RII values were calculated for each factor using the standard formula, and the results were used to prioritize the most important success elements and delay causes affecting construction projects. The ranking results are presented in Table 4, which shows the top factors based on their RII values. Among the Critical Success Elements, Client and Stakeholder Involvement, Planning, Scheduling and Control, and Leadership and Managerial Competence were identified as the most influential factors contributing to improved project timelines. These findings indicate that active client participation, proper planning, and strong leadership play a major role in reducing delays and improving coordination. Similarly, for Critical Delay Factors, the highest-ranked issues were related to approval delays, design changes, and poor coordination among stakeholders, highlighting the importance of timely decision-making and effective communication in project execution.

Table 4. Ranking of Critical Success Elements and Delay Factors based on RII

To provide a clearer understanding of the ranking pattern, the distribution of RII values for major factors is illustrated in Figure 3, which highlights the relative importance of each factor. The figure shows that a small group of key factors contributes significantly to project delays and time performance, emphasizing the need to focus on these areas for effective delay management.

Figure 3. Relative importance of key success and delay factors based on RII

Overall, the RII analysis provides a clear prioritization of factors influencing construction delays and time optimization. By focusing on the highest-ranked success elements and addressing the most critical delay factors, project stakeholders can improve planning efficiency, reduce uncertainties, and achieve better schedule performance.

4. Correlation Analysis between Critical Success Elements and Delay Factors

Correlation analysis was performed to examine the relationship between the identified Critical Success Elements (CSEs) and Critical Delay Factors (CDFs). The purpose of this analysis was to understand how improvements in specific success elements influence the reduction of construction delays. Pearson correlation coefficients were calculated to measure the strength and direction of relationships between the variables. The results of the correlation analysis are presented in Table 5, which shows the relationship between major CSEs and selected delay factors. The analysis indicates that most of the success elements have a negative and significant correlation with delay factors, suggesting that strengthening these elements helps in reducing delays in construction projects.

Table 5. Correlation between Critical Success Elements and Delay Factors

Results show that Client and Stakeholder Involvement has a strong negative correlation with approval-related delays with r value of -0.78, indicating that faster decision-making and active client participation significantly reduce approval bottlenecks. Similarly, Planning, Scheduling and Control demonstrates a strong relationship with resource-related delays with r value of -0.73, highlighting the importance of proper planning in avoiding material and labour shortages. Communication and Coordination shows the highest influence on coordination-related delays with r value of -0.81, confirming that effective information flow and team interaction are critical for minimizing misunderstandings and workflow disruptions.

Figure 4 Correlation strength between critical success elements and delay factors

In addition, Leadership and Managerial Competence also shows consistent negative correlations across multiple delay categories, indicating its overall importance in managing project execution. To provide a clearer visual understanding of these relationships, the strength of correlations between key variables is illustrated in Figure 4, where stronger negative values indicate greater delay reduction potential. Overall, the correlation analysis clearly shows that improvements in critical success elements are associated with a reduction in major delay factors. Among the identified elements, client involvement, planning efficiency, and communication effectiveness play the most significant roles in minimizing delays. These findings confirm that delay management in construction projects is not only dependent on controlling delay factors but also on strengthening key managerial and organizational practices. This relationship forms the basis for further analysis of stakeholder differences and the development of a structured delay mitigation framework.

3.5 ANOVA

Analysis of Variance (ANOVA) was carried out to examine whether there are significant differences in the perception of Critical Success Elements (CSEs) and their influence on delay factors among different stakeholder groups, namely clients, contractors, and consultants. Understanding these differences is important because each group plays a distinct role in construction projects, and variations in their perspectives can affect decision-making and project outcomes. The ANOVA results are summarized in Table 6, which presents the F-values and corresponding significance levels (p-values) for selected critical success elements. A significance level of p < 0.05 was considered as the threshold for identifying statistically meaningful differences among stakeholder groups.

Table 6. ANOVA results for stakeholder differences