Development of a Predictive Model Based on the Alignment Tool in the Early Stages of Projects: The Case of Saudi Arabia Infrastructure Projects

Abstract

The construction industry plays a substantial role in shaping the economies of many countries. Construction management faces various challenges that can lead to project failures, particularly in infrastructure projects struggling to meet cost and time requirements. Inadequate project planning and the intricate nature of construction projects can cause participants’ project goals to not align. It is crucial to address these challenges early in the planning stages to ensure project success. This research involved investigating previous studies to understand current practices for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared based on essential alignment issues affecting team alignment during pre-project planning. Participants rated the level of agreement with alignment issues and the overall success of a project they worked on. The study utilized descriptive and inferential analysis techniques to assess infrastructure project success rates and develop a predictive model driven by the alignment tool. Multiple linear regression techniques were used during the model’s development, and validation and reliability outputs were obtained. By evaluating all relevant stakeholders, the model generates a score to facilitate the pre-project planning process, increasing the likelihood of project success. The study found that the model’s predictive accuracy was 94%. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings.

1. Introduction

The construction sector stands out as one of the biggest extensive industries globally, demonstrating the progress of a nation’s economy. It plays a crucial role in employment, thereby contributing to a reduction in unemployment rates. Nevertheless, despite its pivotal significance, the sector encounters various obstacles like budget overruns, substantial failure rates, conflicts among involved parties, and project delays. Numerous scholars argue that the construction industry is intricately linked with economic and societal aspects [1]. It enables the development of vital infrastructure and structures essential for diverse economic operations, thus promoting a country’s economic advancement [2]. It is also responsible for creating and sustaining the building environment [3].

The construction industries in different countries face serious challenges. In developing countries, these challenges are associated with socioeconomic conditions, a lack of resources, problems in institutions, and an inability to solve urgent issues. Unfortunately, these problems have become more significant recently [4]. One of the biggest obstacles facing the construction industry is completing projects on time [5]. Any construction project depends on three interrelated variables, namely quality, cost, and time. Unfortunately, the construction industry is vulnerable to inappropriate risk assessment and a lack of accurate planning, negatively affecting the industry [6]. Delays are a critical issue worldwide, notably evident at the level of the Kingdom of Saudi Arabia (KSA), where many public construction projects have been implemented over the last three decades [7]. About 70% of public projects in the KSA have been delayed, which has led to substantial financial losses for public services [8]. A study reported that the land acquisition factor is the primary reason for the delay in Saudi Arabia. Other factors contributing to the delay are partly due to a lack of experience and poorly organized utilities, like service lines [9].

Alignment is crucial in the pre-project planning process and significantly improves project success. The Construction Industry Institute (CII) defines alignment as the condition in which the appropriate project participants work together to develop and achieve the main project goals [10]. The alignment process involves ensuring that all team members understand the project goals and agree to work toward them. Ultimately, everyone is focused on the same objectives [10]. Alignment is a crucial component in the pre-project planning phase and significantly impacts project outcomes. Various tools are available for use in the alignment process. One such tool is the alignment thermometer, which is recognized as a user-friendly diagnostic instrument that evaluates project alignment by monitoring team progress and identifying areas that need improvement [10]. Therefore, it is important to consider the significance of alignment in addressing these obstacles.

To address the challenges mentioned above, it is important to consider the role of alignment during project planning. The main objective of this research is to help increase infrastructure projects’ success in Saudi Arabia’s construction industry by developing a predictive model that leverages the alignment tool during the early stages of pre-project planning. This predictive model will aid infrastructure project managers in evaluating project progress, identifying projects needing corrective action, saving time and money, and offering a suitable predictive model for infrastructure projects. The model has the potential to significantly help improve infrastructure project success rates, contributing to better project management practices in Saudi’s construction industry and internationally.

2. Review of the Related Literature

The construction industry made a significant contribution to the country’s economy by creating jobs and generating income for its people. The construction industry is considered a significant asset in helping economic growth [11]. Construction projects in the KSA have increased at an unprecedented rate [12]. It is the biggest and fastest-growing construction industry in the Middle East, with investments of over USD 120 billion each year [13]. Any delay in construction projects influences the direct costs of the project. Project delays are one of the most severe and frequent issues in the Saudi construction sector [14]. Delays in construction projects often stem from slow payment delivery, coordination issues, and ineffective communication [15]. However, Alaghbari conducted a study and found that financial-related issues are among the most critical factors causing project delays [16]. Construction costs that consistently increase during the construction process can result in late payments and interruptions in cash flow for subcontractors and suppliers. This can lead to financial difficulties and ultimately, project failure. Sambasivan identified 28 reasons why projects failed and grouped them into eight categories as follows: external factors, labor and equipment issues, material problems, contractor-related issues, consultant-related issues, and client-related issues [17].

Essential to the success of these projects are the core elements of time, cost, and quality, often referred to as the “iron triangle” [18]. A project will be considered to be successful only if it is completed on time, within the budget, and it meets the user’s expectations. However, cost overruns, defined as when the final project cost exceeds the original evaluation or budget, are a significant concern [18]. Delays, in particular, contribute to substantial cost overruns and can detrimentally impact a country’s economic growth. Even though many researchers have widely investigated the reason for delays in construction projects, construction projects worldwide still suffer from delays. That is to say, the reasons for delays need much investigation to overcome different factors that can lead to them [19].

2.1. Review of Saudi Arabia’s Construction Industry

Over the last twenty years, the KSA has undergone an unparalleled number of construction projects [12], establishing itself as the leading construction hub in the Middle East. With annual investments exceeding USD 120 billion, the construction industry in the KSA is booming [13]. Saudi Arabia remains a paramount and consistently prioritized market for foreign companies [20]. According to the General Authority for Statistics (2018), the value of projects undertaken in 2018 alone amounted to SAR 208,742,604 thousand, classified by economic activity and project type [21].

Meanwhile, Mahamid’s research emphasized poor labor productivity, planning deficiencies, and scheduling issues as significant factors leading to poor project performance in the country [22]. Regarding cost implications, project delays pose significant challenges in the Saudi construction industry [14]. Zamel found that company and project characteristics largely influenced cost control in the eastern region [23].

Anaman and Amponsah conducted a comprehensive study in which they showed that most respondents estimated that construction projects typically exceed their planned duration by 10% to 30%, with over 70% of projects affected by time overruns [24]. Similar studies worldwide, such as El-Razek’s, identified causes like contractor inexperience, payment delays, and inadequate preparation contributing to project delays [15]. Studies on the KSA construction industry [8,9] have identified land acquisition and a lack of expertise as primary causes of delays. In a study conducted by Mahamid to investigate factors that lead to poor performance in construction projects in Saudi Arabia, the results revealed that poor labor productivity, improper planning, and a lack of scheduling widely contribute to poor performance [22]. A lack of planning is regarded as a critical issue that negatively affects delays in Saudi public construction projects [22].

2.2. Infrastructure Projects in the Construction Industry

Construction projects are generally categorized into four groups, namely residential, industrial, institutional, and infrastructure [25]. Infrastructure projects are crucial for the growth of both developed and developing nations’ construction sectors, as their economic progress depends on the presence of infrastructure such as highways, railways, airports, and other essential facilities. These developments are essential for a nation’s economic and social progress [26].

However, these projects face challenges as well in meeting deadlines due to time limits. Multiple research studies have examined these challenges; different researchers have found different reasons varying across countries based on economic, political, and social factors. According to [27], one crucial reason for this delay is the decentralized nature of the planning and decision-making process. This approach leads to delays and creates conflicts because of the involvement of multiple stakeholders, including government agencies, private companies, and local communities, as each one of them has its priorities and interests.

Regarding Saudi Arabia, the involvement of stakeholders, especially government authorities, sets infrastructure projects apart from other types of construction. According to [28], government-driven infrastructure projects focus on the nation’s development and connectivity. On the other hand, other construction projects in Saudi Arabia have more diverse ownership and funding sources, primarily coming from the private sector, such as real estate developers and commercial investors. These projects focus on meeting market demands and generating profits. A study by [12] focused on infrastructure projects in Saudi Arabia, highlighting factors such as poor risk management, budget overruns, poor communication management, scheduling delays, poor estimation practices, cash flow difficulties, design discrepancies, a lack of efficient change management, inadequate project structure, and a lack of teamwork as critical contributors to infrastructure project failure. According to a study by [29], a survey of 160 project managers of infrastructure projects in Saudi Arabia resulted in a ranked list of causes of cost overrun. The top ten causes were market conditions, design changes, the practice of assigning a contract to the lowest bidder, delays, design errors, a lack of contractor and consultant planning before the project, poor coordination with government agencies and parallel contracts, inconsistent management strategies, poor client–staff communication, and stakeholders’ lack of participation during the conceptual phase.

Understanding the importance of infrastructure projects and the specific challenges that they are facing to succeed emphasizes the need for improving infrastructure project management practices to overcome these obstacles. Various techniques have been adopted for this purpose, such as pre-project planning and pre-planning methods, which will be discussed in the next section.

2.3. Pre-Project Planning and Pre-Planning Methods

Planning serves as a roadmap to guide us toward our desired destination. It involves breaking down project processes into measurable and identifiable activities. Effective planning is essential in the management of construction projects [30]. This phase provides essential information for implementation, such as drawings, specifications, and tender documents. During the planning phase, the project owner is responsible for choosing the project area, providing funding, making decisions related to project development, and appointing project planners and consultants [31]. The project team should also develop an integrated schedule for coordinating design, procurement, and construction activities [32]. Although project planning may require a significant time investment upfront, it ultimately saves time in the long run. On the other hand, inadequate planning can lead to project delays, resulting in both time and financial losses [33].

Industry practitioners have long recognized the importance of pre-project planning in the construction industry because of its effect on cost reduction, saving time, and project success [34]. Pre-project planning is described by the CII [35] as “the process of developing sufficient strategic information for owners to address risks and commit resources to maximize the chance for a successful project”. Pre-project planning encompasses all the processes involved in developing the project. It is a crucial step in achieving project goals and provides essential information necessary for the project’s success. Various project planning tools can be utilized to organize and execute project planning operation, allowing project teams to focus on critical issues related to project objectives [36]. Pre-project planning practices may vary across different industries and organizations [37]. Construction organizations should strive to achieve optimal efficiency. The construction industry’s complexity can lead to a lack of project alignment. Many researchers agree that effective pre-project planning can enhance project performance. During this phase, specific tools need to be developed to improve performance measurement and monitoring [36], thus enhancing the pre-project planning process. In essence, project planning tools are utilized to enhance project planning [38].

Different tools are available to help with organizing and completing project planning. These tools enable teams to concentrate on a project and coordinate their efforts toward the same goals. They convert predetermined strategies into concrete actions to serve the project’s objectives [39]. Several tools can enhance the efficiency of pre-project planning, including the Project Definition Rating Index (PDRI), front-end planning (FEP), and alignment [40,41]. Many practitioners have said that the behavioral parts of project management are harder to handle than the quantitative ones. Thus, during the pre-project planning phases, alignment ensures that different experts are included and allowed to contribute their knowledge to the project’s objectives. Critical knowledge may only be discovered with proper alignment. Studies by [42,43] have emphasized the importance of alignment between business strategies and project management systems and confirmed that it is among the most important components of project management that help achieve the objectives of the project. Alignment ensures stakeholder harmony, reduces conflicts, and enhances project deliverables, thus aligning with business objectives.

Organizational performance models are conceptual frameworks that communicate how an organization operates. These models can help set a context for change, demystify organizational concepts, and focus attention on the essential fundamentals and relationships of a process or theory [44]. Several organizational performance models focus on organizational alignment, but little information is available on what leads to establishing shared goals for the cross-functional team and how effective teams can ensure commitment to those goals [45,46].

Alignment exists in three dimensions in the project environment. The top–bottom alignment within an organization is the first dimension (vertical) (executive, business, project, and functional). The second dimension (horizontal) consists of cross-organizational coordination via organizations of functional groups (business, project management, operation, and others). The third dimension (longitudinal) involves the alignment of the objectives throughout the project’s life cycle (business planning, pre-project planning, project execution, and facility operation). The alignment thermometer is a self-check tool used during the early planning phase of a project. Each member of the planning team gets a thermometer to fill out and then returns to the person in charge of the survey [10]. Keeping track of scores over time can help with evaluating progress and discussing trends with teammates [10]. Alignment is regarded as an ongoing process for enhancing project deliverables and facilitates the ongoing assessment and discussion of alignment trends within project teams, contributing to overall project success. Previous studies conducted by the CII have shown that the likelihood of a project’s success increases when more effort is devoted to planning before the project begins [10]. Two crucial aspects of this planning phase are clearly defining the project’s scope and ensuring that the project team’s goals are aligned with the business needs of the facility. Skipping this stage will result in delays, increased costs, and project modifications later. Ten essential issues that impact team alignment during pre-project planning introduced by the CII have been investigated and ranked [10]. The issues are divided into five categories, namely culture, the execution process, information, the tools of project planning, and barriers. Project teams will be more united and have a better chance of success if they focus on these issues during the early planning stages.

According to previous research, there are no definitive solutions to deal with infrastructure projects’ delays and poor communication and planning. In addition, there is potential to consider the significance of alignment to address these obstacles and ensure infrastructure projects’ success. Therefore, this study aims to develop a predictive model for infrastructure projects using an alignment tool. The model will be used to forecast project success and proactively address potential causes of delay. The study will examine current alignment practices in infrastructure construction projects, analyze construction project success rates with input from industry specialists, and construct a forecasting model based on the alignment tool for infrastructure projects. This predictive model will assist infrastructure project managers in evaluating project progress, identifying projects needing corrective action, saving time and money, and providing a suitable predictive model for infrastructure projects.

3. Methodology

3.1. Research Design

The research methodology process is divided into several phases based on the design order. The results of each phase serve as input for the succeeding phases. The research began with an investigation of previous studies to understand current practices and methods used to improve infrastructure project planning and select the best pre-project planning tool (alignment tool) for creating an organizational performance model in the early stages of pre-project planning for infrastructure projects. Following this, a questionnaire was prepared, considering the ten essential issues that affect team alignment during pre-project planning to build a predictive model. The participants were asked to choose one of the projects they worked on and then determine the level of agreement to alignment issues and rate the project’s overall success. Our goal was to gather expert opinions as the basis of our analysis. The study utilized descriptive and inferential analysis techniques, with a primary focus on assessing the success rates of infrastructure projects. This led to the development of a predictive model driven by the alignment tool for infrastructure projects. This research used multiple linear regression techniques during the model’s development and obtained validation and reliability outputs for the model. Figure 1 shows the research process.

3.2. Data Collection

A questionnaire was used to collect data for this study. The questionnaire aimed to evaluate the success of infrastructure projects and rank alignment issue statements for developing a forecasting model based on the initial infrastructure project alignment tool. The survey was designed with direct, concise questions to prevent misunderstandings and reduce response time. Incomplete surveys were excluded and only completed ones were collected.

The questionnaire survey was created to gather information from professionals involved in constructing two main infrastructure projects in Saudi Arabia, which were roads and pipelines. These projects were chosen due to their large scale, significant financial investment, and crucial role in societal and economic development. The questionnaire was divided into two sections, namely demographics and an alignment assessment. The demographics section consists of five questions, while the alignment section addresses evaluating the success of infrastructure projects and ranking alignment issues. This study used the ten critical alignment issues highlighted by the CII [10] during pre-project planning as a tool to measure infrastructure projects’ success. The alignment issues were presented as statements to evaluate their influence on infrastructural project success. The ten alignment issue statements are presented in

Table 1. The alignment issue statements [10].

#	Alignment Issue Statements *
Issue-1	Project leadership is defined, effective, and accountable.
Issue-2	Stakeholders are appropriately represented on the project team.
Issue-3	The priority between cost, schedule, and required project features is clear.
Issue-4	Communication within the team and with stakeholders is open and effective.
Issue-5	Team meetings are timely and productive.
Issue-6	The pre-project planning process includes sufficient funding and an appropriate schedule and scope to meet objectives.
Issue-7	The team culture fosters trust, honesty, and shared values.
Issue-8	The reward and recognition system promotes meeting project objectives.
Issue-9	Teamwork and team-building programs are effective.
Issue-10	Planning tools (e.g., checklists, simulations, and workflow diagrams) are effectively used.

* Based on the ten critical alignment issues emphasized by the CII during pre-project planning.

.

The second technique used in the data collection was the Likert scale. The Likert scale, created by Rensis Likert in 1932, is a commonly used research tool [47]. In this research, the Likert scale was employed to gather respondents’ evaluations (agreement level) of the alignment issues for one of the projects they worked on using a ten-point scale and rate the project’s success using a five-point rating system.

The Likert scale chosen for assessing the agreement level in this research was a ten-point scale. On this scale, one signifies “strongly disagree” and ten means “strongly agree” with the alignment issue statements, as shown in

Table 2. Agreement level.

	Strongly Disagree								Strongly Agree
Alignment Issues	1	2	3	4	5	6	7	8	9	10

. The overall success rating of the selected project used a five-point scale (successful, on the way to success, late, unsurpassed, and failing). This careful design ensures a comprehensive understanding of the participants’ perspectives and allows for a robust analysis of alignment issues within infrastructure project management [48].

After designing the survey, a pilot study was conducted with three experts for approval. They tested its content validity, removed irrelevant terms, and made changes to enhance its effectiveness in measuring the study parameters and ensuring the tools’ validity. An online survey was built using the SurveyMonkey platform and distributed through an online service. The study chose this technique because it allows for an improved reach to a wider audience in different locations.

3.3. Study Population and Sample Selection

The study targeted individuals registered as engineers and project managers with the Saudi Council of Engineers (SCE), particularly those engaged in infrastructure and transportation projects. All the participants were from different positions, parties, and sectors and had different experiences. Based on a recent statistical report by the SCE regarding the workforce, the study population was estimated [49]. The sample size was determined to be 66, utilizing a 10% margin of error and a 90% confidence level.

The researchers randomly selected practitioners and then used the snowballing technique [50] to increase the sample size for the research. This involved asking the selected practitioners to invite other interested individuals to participate via a link to a shareable survey. The snowballing process helped to attract more specialized and engaged participants in the infrastructure project management, resulting in a greater variety of responses from a wider range of locations. To reduce bias in the snowball sampling, the first practitioners were randomly selected to make sure diversity and inclusion criteria were met. However, it is important to note that using the snowballing technique increased the possibility of including participants whose experience and education might not fully align with the research requirements. To address this, the collected surveys were filtered based on specific population criteria. Responses from participants who didn’t meet these criteria were excluded from the study.

3.4. Data Analysis

The study involved two main processes for statistical analysis, namely descriptive and inferential statistical analyses. These two methods are commonly used in research to extract opinions about managerial behavior and are considered the most appropriate scientific method for studying humanitarian issues [51]. The data were analyzed using IBM SPSS Statistics (version 27.0) [52]. The first section describes the participants’ demographics and studies the ranking of the critical alignment issues from the perspective of the study participants. The second section is devoted to building and validating the predictive model and providing instructions on how to use it. Descriptive statistics were utilized to investigate the demographic characteristics of the participants in terms of frequencies and percentages and to study the critical alignment issues by calculating the mean and standard deviation of the ranking of critical alignment issues from the perspective of study participants.

3.5. Model Development and Evaluation

The last step in the research process was the development of a model. The data analysis results were used to build a predictive model. The main goal of this study is to create a predictive model to assess the success of infrastructure projects based on the alignment tool used in the early stages of pre-project planning. The multiple linear regression technique was employed in developing the model. The multiple linear regression method used in model development is a set of statistical processes for estimating the relationships between a dependent (response) variable and one or more independent variables (predictors) and finding the best model to fit the data using backward, forward, and stepwise multiple linear regression methods [53]. Finally, cross-validation was used to evaluate the model’s reliability and capability to predict the success rate of infrastructure projects in their early stages.

3.6. Regression Analysis

Simple linear regression is a model with a single predictor x that relates to a response y, forming a straight line. This simple linear regression model can be represented as [49]

$$y={\beta }_0+{\beta }_{1}x+\epsilon$$

(1)

where the intercept ${\beta }_0$ and the slope ${\beta }_1$ are unknown constants and $\epsilon$ represents the random error component.

In multiple linear regression, more than one regressor variable is involved. Generally, the response variable y may be related to k regressors, $x_1,x_2,\dots ,x_k,$ [49] so that

$$y={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\dots +{\beta }_k{x}_k+\epsilon$$

(2)

The method of least squares is used to estimate ${\beta }_0,{\beta }_1, {\beta }_2, \dots ,{\beta }_k$. That is, this study estimated ${\beta }_i$’s to find the lowest total of the squared differences between the straight line and the observations [53].

To assess whether the model can accurately predict the success rate of infrastructure projects, inferential statistics such as R-squared, ANOVA, statistical hypothesis testing of the significance of regression, the F-test, t-test, p-value, and residuals were used as measures in this study. The R-squared value measures the percentage of variation in the response variable that a linear model explains. The ANOVA test is used to analyze differences between group means. Statistical hypothesis testing uses statistics to determine the probability that a given hypothesis is true. The F-test is used to test the significance of the model, whereas the t-test is used to check the significance of individual regression coefficients. The p-value is the estimated probability of rejecting the null hypothesis of a study question. Residuals are the disparities between the observed and predicted values of the dependent variable.

3.7. Model Reliability

Model reliability is the measure of how consistently a method accurately assesses something. When the same method is applied to the same sample under consistent conditions, it should produce consistent and predictable results. If it fails to do so, the method of measurement could be considered unreliable. There are four primary types of reliability, each of which can be evaluated by comparing different sets of results generated by the same method [54]. One of the most prevalent types is inter-rater reliability, which evaluates the extent of agreement among two or more raters, observers, coders, or examiners. It specifically deals with consistency in the application of a rating system. Inter-rater reliability can be assessed using various statistical measures such as a percentage agreement, kappa, product–moment correlation, and the intraclass correlation coefficient. Higher inter-rater reliability values indicate a stronger concurrence between two examiners, while lower inter-rater reliability values signify a weaker agreement between them [55].

4. Results and Discussion

4.1. Questionnaire Results

4.1.1. Data and Demographic Information of Respondents

A total of 200 questionnaires were sent to the respondents, out of which 110 were completed and returned, resulting in a 55% response rate. The collected data were coded and entered into SPSS. Upon initial examination, 33 records were found to have missing values in several variables, leading to their deletion. A further consistency check was performed to identify and eliminate any errors made during the completion of the questionnaires, resulting in the deletion of 11 additional records due to inconsistent responses. Eventually, 66 respondents constituted the final sample for our study. Most participants were either project managers or engineers and almost half possessed ten years of experience and had worked on more than five projects. The participants primarily worked on road projects, engaging with various stakeholders such as owners, contractors, and consultants. These findings suggest that the respondents’ demographic characteristics were inclusively represented, thus enhancing the credibility of the study results.

Out of the 66 survey participants, 31 (47%) were project managers, 32 (48.5%) were engineers, and only three (4.5%) worked in other positions. In terms of years of experience, almost half of the participants (45.5%) had more than ten years of experience, while around 20% had 2–5 years or 5–10 years of experience. Only 10.6% of the participants had two years of experience or less. Regarding the number of projects participants worked on, almost 40% had worked on more than ten projects, a third had worked on three to ten projects, and about 25% had worked on three projects or less. Regarding sectors, 41 (62%) of the participants were working on road projects, while 13.6% had experience in infrastructure and 13.6% had experience in oil and gas. The remaining 10.6% were involved in commercial buildings or healthcare facilities. When asked about the parties involved in their projects, most participants mentioned they worked with the owner (39.4%) or contractor (33.3%) of the project.

4.1.2. The Alignment Issues’ Ranking

In the final section of the study questionnaire, participants were asked to rank the ten alignment issues considered in the current study from most important to least important. The mean and standard deviation (SD) of the participants’ rankings were then calculated.

Table 3. Overall alignment issues’ ranking.

Alignment Issues	Mean	SD
Issue-1	2.70 (1)	2.17
Issue-2	3.58 (2)	2.74
Issue-3	4.17 (3)	2.62
Issue-4	4.32 (4)	2.15
Issue-5	5.41 (5)	1.85
Issue-6	5.5 (6)	2.47
Issue-7	6.29 (7)	1.78
Issue-8	7.44 (8)	2.11
Issue-9	7.77 (9)	2.35
Issue-10	7.83 (10)	2.69

shows the mean and standard deviation of alignment issue rankings for all participants. The mean rankings for alignment issues range from 2.7 to 7.83. Based on the mean ranking for each alignment issue, the results show that “Project leadership” (2.70) is the most important issue, while “Planning tools” (7.83) is the least important, with a rank mean very close to “Teamwork and team building programs are effective” (7.77).

4.2. Predictive Model Based on Alignment Tool Development

The primary purpose of this part of the data analysis is to build a predictive model based on an alignment tool to predict the success rate of infrastructure projects in their early stages. The second part of the study questionnaire was devoted to collecting the data to build such a model. The participants were asked to choose one of the projects they worked on and then determine the level of agreement to alignment issues using a ten-point scale and rate the success of that project using a five-point scale. Inferential statistics such as the F-test, ANOVA, and all the methods discussed in the Data Analysis Section were employed to develop such a model.

In inferential statistics, multiple linear regression is a statistical method used to estimate the relationships between a dependent (response) variable and one or more independent variables (predictors). The general model can be formulated as follows:

$$y={\beta }_0+{\beta }_1{x}_1+{\beta }_2{x}_2+\dots +{\beta }_{10}{x}_{10}+\epsilon$$

where $y$ is the rate of success, $x_i, i=\mathrm{1,2},\dots ,10$ are the levels of agreement to the ten alignment issues, and $\epsilon$ is the error term.

To develop the best fitted model, this study used backward, forward, and stepwise multiple linear regression methods, and the three methods yielded the same model. Based on the regression analysis, the following reduced model was postulated as a prediction tool:

$$y=1.733+0.154x_1+0.192x_2+0.188x_3+0.235x_4$$

where $y$ is the rate of success; $x_1$ is the level of agreement to alignment issue (3), “The priority between cost, schedule, and required project features are clear”; $x_2$ is the level of agreement to alignment issue (7), “The pre-project planning process includes sufficient funding, schedule, and scope to meet objectives”; $x_3$ is the level of agreement to alignment issue (9), “Teamwork and team building programs are effective”; and $x_4$ is the level of agreement to alignment issue (10), “Planning tools (e.g., checklists, simulations, and workflow diagrams) are effectively used”.

The result will then emerge to determine the project’s status according to the following classification represented in Figure 2:

• y < 2: Failing;
• 2 < y ≤ 4: Unsurpassed;
• 4 < y ≤ 6: Late;
• 6 < y ≤ 8: On way to success;
• 8 < y ≤ 10: Successful.

4.2.1. Model Evaluation

The model was assessed to determine its ability to accurately predict the success rate. The R-squared value of the model is 0.724, indicating that the independent variables explain 72.4% of the variance in the success rate. The adjusted R-squared is 0.703, which is slightly lower than the R-squared value. Another method to evaluate the model’s predictive ability is by examining the F-ratio. A high F-ratio or a p-value less than 0.001 indicates that the model makes predictions well. Based on these statistics, the model can predict the success rate significantly. The statistical measures of the prediction model are displayed in

Table 4. The statistical measures for the proposed prediction model.

R-Squared	Adjusted R-Squared	F	p-Value
0.724	0.703	34.81	<0.001

.

The standardized betas are interpreted in a similar way to correlation coefficients and are directly comparable, making them a better measure to provide insight into the importance of the different predictors.

Table 5. Coefficients for the multiple regression model.

Model	Unstandardized Coefficients		Standardized Coefficients	t	p-Value
	B	Std. Error	Beta
(Constant)	1.733	0.564		3.075	0.003
3-The priority between cost, schedule, and required project features is clear.	0.154	0.077	0.184	2.008	0.05
7-The pre-project planning process includes sufficient funding and an appropriate schedule and scope to meet objectives.	0.192	0.073	0.268	2.63	0.011
9-Teamwork and team-building programs are effective.	0.188	0.084	0.228	2.228	0.03
10-Planning tools (e.g., checklists, simulations, and workflow diagrams) are effectively used.	0.235	0.081	0.332	2.909	0.005

presents the multiple linear regression model results, where the four predictors are statistically significant (p < 0.05) and have positive standardized betas, which indicates a positive effect of the predictors (alignments 3, 7, 9, and 10) on the dependent variable success rate. From the results of standardized betas, this study also concluded that alignment 10 had the most substantial impact on the success rate, followed by alignments 7 and 9. Finally, alignment 3 had the least effect on the success rate.

The regression analysis results yield a model with only four alignment issues of the ten issues considered in the current study. The results established that from the perspective of the study participants, project success depends significantly on having clear priorities of cost, scheduling, and required project features (issue 3); the pre-project planning process (issue 7); effective teamwork and team-building programs (issue 9); and planning tools (issue 10).

4.2.2. Model Validation

To validate the linear regression model results, this study used the cross-validation method by randomly splitting the responses of the 66 participants into two data sets, namely a fitting set and a validation set. First, this study used 58 responses (approximately 88% of the total responses) to fit the multiple regression model. Then, the fitted model results were validated using the remaining responses, as shown in

Table 6. Results of validation.

Case	Success Rate		Absolute Error	Error Percentage *	Actual vs. Prediction
	Actual	Prediction
1	5	4.76	0.24	4.8%
2	7	6.57	0.43	6.2%
3	7	7.38	0.38	5.4%
4	8	7.85	0.15	1.9%
5	8	7.69	0.31	3.9%
6	9	9.27	0.27	3.0%
7	9	8.62	0.38	4.2%
8	10	9.42	0.58	5.8%

* $\mathrm{E}\mathrm{r}\mathrm{r}\mathrm{o}\mathrm{r}\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{c}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{g}\mathrm{e}={\displaystyle \frac{|\mathrm{A}\mathrm{c}\mathrm{t}\mathrm{u}\mathrm{a}\mathrm{l}-\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}|}{\mathrm{A}\mathrm{c}\mathrm{t}\mathrm{u}\mathrm{a}\mathrm{l}}}\times 100.$

.

The measure used to validate the fitted model is the mean square error (MSE), which tells you how close our success rate prediction model is to the actual success rate, and it is calculated as follows:

$$MSE={\displaystyle \frac{1}{n}}\sum _{i=1}^n{\left(y_i-\widehat{y_i}\right)}^2$$

where $n$ is the size of the validation set, $y_i$ is the actual success rate, and $\widehat{y_i}$ is the predicted success rate.

Table 6 shows the results of using the fitted model to the validation set (eight responses, approximately 12% of the total responses) and testing its prediction power. The results establish that our model’s prediction error percentage varies from 1.9% to 6.2% with an MSE of 0.13. These results show that the model accurately estimates infrastructure project success at an early stage, reaching a prediction accuracy of 94%.

4.3. How to Use the Model

The alignment model for infrastructure is a tool used to assess how well project participants are aligned and their ability to work together to achieve clearly defined project goals. It is designed to quickly identify any disagreements that need more discussion and ensure that the team is working together effectively. The alignment model should be used regularly during early project planning and as the project progresses and new team members join. By evaluating and improving performance on the ten critical alignment issues, the model helps measure the project’s progress. It provides guidance on how to assess if the project team is aligned and what actions to take based on the results. The alignment model is a self-evaluation survey for use during the pre-project planning phase. The questionnaire consists of 10 simple statements. The basic process of using the model is as follows:

• Decide on the survey’s timing and how often it will be conducted.
• Participants will read ten alignment issue statements and rate their agreement on a scale of one to ten.
• Specify the participants and identify which employees will complete the questionnaire.
• Choose the method for distributing and collecting the questionnaire.
• Collect the completed survey responses.
• Review the results with the team to understand the current alignment and plan a path forward.
• Develop and implement a plan to address any identified issues.
• Plot the average score for each statement in the following equation:

$$y=1.733+0.154x_3+0.192x_7+0.188x_9+0.235x_{10}$$

After that, the outcome will be known and the project’s status will be understood according to the prediction model thermometer.

5. Conclusions

The construction sector is a crucial industry for any country’s economy but faces challenges like budget overruns, project delays, and conflicts among involved parties. One of the major issues is completing projects on time, which is a problem globally and is particularly evident in Saudi Arabia. To address these challenges, alignment during project planning is crucial. It is essential to consider the significance of alignment to overcome these obstacles. This study aims to create a predictive model using the alignment tool during pre-project planning for infrastructure projects in Saudi Arabia to improve project success rates and substantially contribute to the construction industry.

The research methodology process involves several phases that provide input for succeeding phases. It began with investigating previous studies to understand current practices and methods for improving infrastructure project planning and selecting the best pre-project planning tool. Infrastructure projects in the Saudi construction industry are used as a case study. A questionnaire was prepared to address essential alignment issues during pre-project planning and expert opinions were gathered. The study utilized descriptive and inferential analysis techniques to assess the success rates of infrastructure projects, leading to the development of a predictive model based on the alignment tool. Multiple linear regression techniques were used during the model’s development, and validation and reliability outputs were obtained using the SPSS tool.

The mean rankings for alignment issues range from 2.7 to 7.83. Based on the mean ranking for each alignment issue, the results show that “Project leadership” and “Stakeholders are appropriately represented on the project team” are the most important issues, while “Planning tools” is the least important with a rank mean very close to “Teamwork and team-building programs are effective”. The ranking results, with minor variances, agree with the findings of [10]. “Project leadership” now precedes “appropriate stakeholders’ representation,” and “structured and resource pre-project planning process” comes before “trust, honesty, and shared values” for infrastructure projects. Additionally, this study found that the developed predictive model reached a 94% accuracy rate in forecasting the project’s success in the early stages based on the alignment tool, highlighting its effectiveness. This research is significant in creating a predictive model applicable to infrastructure projects, enhancing project management practices by enabling project teams to evaluate project progress, identify projects in need of corrective action, and ultimately improve project performance, leading to cost and time savings in future infrastructure projects.

The study’s limitations may include the data collection process, as various agencies may initiate infrastructure projects, affecting the data’s reliability. Another significant limitation is the small sample size and the restricted data collection process, which focused solely on infrastructure projects in Saudi Arabia carried out in the last two years. Additionally, the study focuses on infrastructure projects without considering the impact of delivery methods, digital means, and other characteristics of infrastructure projects on the model results. The model is effective for road and pipeline projects, but its applicability to other types of infrastructure is uncertain due to variations in project dynamics, stakeholder interactions, and risk factors.

Although promising, the current model requires further improvement through research and validation processes to achieve better results. Future research could build upon this foundation to make the model more widely applicable by testing it on various project types in Saudi Arabia, thereby establishing it as a standard for project evaluation and planning. Furthermore, updating the current model might involve changing the questionnaire format to a more objective five-point scale to increase participant agreement.