1. Introduction
In an era where technological developments and global competition play a decisive role in industries, the success of modern organizations depends on their ability to effectively manage information systems (ISs) and manage risks [1]. This is particularly true in sectors such as construction, where complex projects require careful and thoughtful planning, timely decision making, and robust risk management strategies. This text explores the critical role of ISs and risk management in ensuring project success, with a focus on the challenges facing construction projects in developing countries. Modern firms face more challenges due to the volatility, unpredictability, and ambiguity of their environment [2]. Varajão et al. [3] stated that current corporate activities depend on information systems, being crucial to a company’s productivity, efficiency, or competitiveness. Businesses differentiate themselves by improving ISs in a fast-changing commercial and technical environment. Organizations must innovate and IS project performance is crucial to their long-term success [4]. Few firms operate without using ISs to support their managerial activities. Complex organizations make it more difficult to use these technologies [5]. IS projects today can take many forms, including enterprise resource planning (ERP), customer relationship management (CRM), supply chain management (SCM), business intelligence (BI), and ERP modules. IS initiatives include custom system development, enhancement, process innovation using IT, system migration, infrastructure expansion, and consultancy [6]. Organizational IS initiatives contain implicit organizational initiatives, requiring a socio-technical concept [7]. Software development can be part of an IS project. Given the complication and heterogeneity of IS projects, it becomes necessary to understand how these initiatives are managed and evaluated, particularly in terms of their success or failure.
The difficulty and unpredictability of measuring project success [8] are now considered an issue [9]. Project stakeholders may define success differently [10]. Two elements influence project success: deliverables and project management (PM). Successful PMs focus on project scope, schedule, and budget completion. These three measures show project efficiency and efficacy. The project’s ultimate services and products indicate deliverable success [11]. Although the PM’s success and the deliverables’ success are separate, the PM’s failure could risk succession; therefore, the project and its outcomes cannot be evaluated in isolation [12,13,14]. PM success tales dominate the literature [15]. Understanding the dual nature of project success, construction and project management, paves the way for exploring why so many construction projects, particularly in software development, struggle to achieve their goals.
Software development efforts have not always been effective in recent decades [16]. Failures often hinder the software development sector [17]. The Standish Group’s publications were a significant milestone in the comprehension of ‘failure’, culminating in the first ‘Chaos Report’ in 1994 [18]. The study’s findings applied to all IS initiatives, despite its focus on software development. The idea that projects are troublesome and that failure rates have remained largely unchanged has been maintained throughout time, leading to the conclusion that this fragile scenario is unravelling [15]. Project success is associated with project risk and risk management [19]. PMI’s project management outlook emphasizes risk management [20]. The ongoing challenges in software development underscore the importance of effective risk management, which is increasingly recognized as a critical factor in project success.
According to PMI research [21], risk management strategies were the least developed among project management knowledge areas. Risk management standards vary across industries, but the core concepts are similar [20]. The sector’s risk management, according to projects, is one of the nine components of project operation, indicating a strong relationship between project performance and risk management. Risk management processes (RMPs) consist of four main steps, identifying and assessing risks, and acting and monitoring risks, and projects today are competing, recognizing the importance of great risk management through risk analysis approaches.
Most Iraqi projects suffer from delays, procrastination in completion, poor management, and exceeding the project’s set cost. There are many projects that have been halted or abandoned, or there is a change in the designs, and the biggest reason is poor planning [22]; they need information systems management to help the administration make successful, effective, and correct decisions by providing appropriate and timely information, investing the information resource in the building construction company, and controlling all generated information. Connecting all construction enterprises’ information-generating parties to the command and decision-making unit and integrating information systems management into construction projects can address challenges and facilitate project management, paving the way for more efficient and effective project implementation.
It is important to provide real-time and useful information to administrative levels for planning, organizing, controlling, and judgement, as well as the ability to exchange and share data and conversations via connections and communications within and outside an organization working on building construction projects. To store and maintain all the organizations’ information, including risk management, which is an ideal mediator for project success, procurement management, and other information, the ability to process and retrieve information in an electronic format, size, and time benefit the recipient via transmission with SDI selectivity. An examination of a company’s activities and functions should be performed using reliable information, including foundation, structure, and completion, to allow for early discovery of gaps or deviations. The capability to strategize and predict future outcomes based on analyzed options, provide alternatives in the event of plan failures, and forecast the organization’s future requirements to attain its objectives is crucial, as well as the ability to produce several correct reports quickly and in multiple formats. These all help with completing administrative tasks faster and with fewer resources. Building construction companies need high-risk management systems as well as information management systems. Therefore, this study seeks to understand how information systems contribute to improving the efficiency and effectiveness of project management by enhancing risk management processes.
The current study highlights specialized information systems, such as management information systems (MISs), construction information systems (CISs), and decision support systems (DSSs), which enhance operational procedures, minimize costs, and mitigate risks in building projects. This study investigates specialized information systems that enhance efficiency in the construction sector by optimizing project scheduling, human resources, and executive coordination. This study specifically investigates the management of diverse project risks—financial, planning, environmental, resource, and legal—and the influence of information systems on the identification, assessment, and management of these risks, in contrast to numerous studies that have explored the role of information technologies in mitigating risks or enhancing management decisions in construction projects. This study examines project risks in greater detail than many other studies and demonstrates the significance of information systems as essential instruments for risk management. Conversely, the majority of research has investigated the influence of information systems on the financial or operational aspects of projects. This study investigates the influence of these systems on various dimensions, including capacity development, information provision for decision makers, and cost reduction, while aiming to determine how information systems can concurrently impact multiple facets of project management. Furthermore, it is important to acknowledge that there are few studies that have focused on the function of information systems in the Iraqi construction sector. This study not only analyzes the general elements of information systems but also emphasizes the unique characteristics of construction projects in Iraq and the application of these systems within a specific context. This study advances earlier research by introducing a novel conceptual model and operational framework about the influence of information systems on project management success. This paper presents a paradigm that integrates information technologies with project risk management, tailored specifically for the construction sector. The research structure of this study is delineated as follows: The second section of this paper addresses the theoretical foundations and contextual background of the investigation. The third section elucidates the research methodology and the procedures undertaken to execute this study. The fourth section presents the research findings and the outcomes of statistical analyses, while the concluding section offers conclusions and recommendations.
2. Literature Review
The integration of ISs into project management has emerged as a transformative force in modern organizational strategy, particularly within the construction industry, where projects are characterized by complexity, risk, and multifaceted stakeholder demands. This literature review synthesizes the current understanding of ISs, project risk management, and project management success (PMS), framing their interrelationships within a conceptual model that underpins this study. While prior research has extensively explored these domains individually, their combined dynamics—especially the mediating role of PRM in linking IS to PMS—remain underexplored, particularly in construction contexts. This section aims to provide a comprehensive examination of the state of the art, addressing the limitations of traditional frameworks and positioning this research as a bridge between theoretical insights and practical applications in Iraqi construction firms.
2.1. Information Systems in Project Management
Information systems are defined as integrated assemblies of hardware, software, and telecommunications networks designed to collect, process, store, and disseminate data, thereby supporting organizational decision making and operational efficiency [23]. In the construction sector, ISs include advanced tools such as building information modeling (BIM), ERP systems, and project management information systems (PMISs), which enable real-time data management, seamless communication, and coordination across project lifecycle phases—design, procurement, execution, and handover [24]. The exponential growth of data in contemporary organizations, fueled by technological advancements, globalization, and societal shifts, underscores the critical role of ISs in transforming raw data into actionable information, a process vital for managing the intricate workflows of construction projects [22].
The literature positions ISs as a catalyst for enhancing operational efficiency and securing competitive advantage. Raymond and Bergeron [25] demonstrated that PMISs significantly improve managerial activities, such as project planning, scheduling, resource allocation, and progress tracking, directly contributing to project success by reducing delays and cost overruns. Varajão et al. [26] further showed that IS adoption aligns with international standards like ISO 21500 and PMBOK, enhancing outcomes in information system projects by fostering structured governance and process optimization. In construction, ISs support a spectrum of functions, including cost estimation, risk tracking, procurement management, and stakeholder communication [17]. For instance, BIM facilitates 3D modeling and clash detection, preempting design errors, while ERP integrates financial and supply chain data, streamlining resource utilization [24].
However, the effectiveness of ISs hinges on organizational capacity, user proficiency, and system adaptability to project-specific needs [22]. Studies suggest that while ISs can revolutionize project management, challenges such as high implementation costs, resistance to technological change, and data security concerns often limit their full potential [11]. Despite these advances, the literature lacks a unified framework that explicitly links IS capabilities to broader project outcomes like success and risk mitigation, particularly in construction settings where contextual factors (e.g., regulatory environments, workforce skills) vary widely. This study addresses this gap by examining how ISs impact PMS directly and through the mediating influence of PRM, offering a nuanced perspective on its role in Iraqi construction firms.
2.2. Project Risk Management
Project risk management is a systematic process aimed at identifying, assessing, responding to, and monitoring risks to minimize their adverse impacts on project objectives while maximizing opportunities [20]. In construction, where projects face inherent uncertainties, such as weather variability, supply chain disruptions, labor shortages, and regulatory shifts, PRM is indispensable [17]. The Project Management Institute (PMI) classifies PRM as one of nine knowledge areas, encompassing steps like risk identification, analysis, response planning, and monitoring [20]. Despite its recognized importance, PMI research indicates that PRM remains one of the least mature areas in practice, often due to inadequate tools, inconsistent application, or a lack of integration with other project processes [20], signaling a pressing need for enhancement, particularly in high-risk sectors like construction.
A robust body of evidence links effective PRM to project success. Tahir and al Jabar [27] found that proactive risk management enhances project outcomes by addressing critical uncertainties, such as scope creep or resource shortages, before they escalate. Urbański et al. [28] demonstrated that PRM moderates the relationship between project planning and success in construction firms across Pakistan and the UK, suggesting its contextual adaptability. Reed and Angolia [29] extended this to IT projects, showing that risk identification and response planning improve both process efficiency and product performance—insights applicable to construction given the parallels in project complexity. Moreover, Kishk and Ukaga [30] emphasized that PRM levels predict project failure rates in construction, highlighting its preventive role.
However, the efficacy of PRM varies with implementation rigor and stakeholder perceptions [31]. For example, incomplete risk registers or over-reliance on qualitative assessments can undermine its benefits, while cultural attitudes toward risk (e.g., risk aversion vs. risk tolerance) shape its execution [32]. This variability positions PRM as a potential mediator in project dynamics, capable of translating data-driven insights into actionable strategies. This study leverages this capacity, hypothesizing that PRM mediates the relationship between ISs and PMS by operationalizing IS-provided data to mitigate construction-specific risks, offering a novel lens on its integrative role.
2.3. Project Management Success
PMS is a multidimensional construct traditionally anchored in the ‘Iron Triangle’ of cost, time, and scope [20] but increasingly expanded to encompass stakeholder satisfaction, deliverable quality, and strategic alignment [33]. In construction, achieving PMS is paramount due to the industry’s high stakes—delays, cost overruns, or quality failures can result in substantial financial losses, contractual disputes, and reputational damage [34]. The literature distinguishes between project management success (efficiency in execution, e.g., meeting deadlines) and project success (effectiveness of deliverables, e.g., client satisfaction), though these dimensions are interdependent: poor execution often jeopardizes outcomes [35].
Empirical research identifies diverse antecedents of PMS. Aga et al. [36] underscored transformational leadership and team building as critical drivers, fostering cohesion and motivation among project teams. Lindsjørn et al. [37] highlighted teamwork quality in agile environments, noting its impact on both individual and collective success. IS-specific studies further refine this picture: Rahman et al. [38] found that PMIS quality and information availability enhance PMS by improving decision making and coordination, while Engelbrecht et al. [39] linked managers’ IT competence to better project outcomes. Pimchangthong and Boonjing [40] added that organizational type and scale moderate these effects, with larger firms often leveraging ISs more effectively due to resource availability.
Despite these insights, the construction sector lacks a cohesive model integrating ISs and PRM as predictors of PMS. Success metrics remain inconsistent—some studies prioritize time and cost [41], while others emphasize stakeholder perceptions [33] or strategic benefits [35]—complicating cross-study comparisons [7]. Moreover, construction-specific challenges, such as fragmented supply chains and regulatory compliance, amplify the need for a tailored framework [21]. This research addresses these gaps by testing a structural model where ISs influence PMS directly (via efficiency gains) and indirectly (via PRM’s risk mitigation), providing a comprehensive approach to understanding success in Iraqi construction projects.
2.4. The Mediating Role of Risk Management in Linking Information Systems to Project Management Success
The interplay between ISs, project risk management, and PMS constitutes the theoretical backbone of this study, with PRM positioned as a critical mediator bridging IS capabilities and project outcomes. Risk management is a systematic process of identifying, analyzing, and responding to project risks, aiming to maximize positive events and minimize negative ones [20,26]. In construction, this process unfolds across three stages: hazard identification (e.g., spotting design flaws), risk analysis and appraisal (e.g., quantifying likelihood and impact), and risk response (e.g., mitigation or contingency planning) [42]. This study investigates how PRM mediates the relationship between IS utilization and PMS, proposing that ISs enhance project success both directly, by optimizing operational efficiency, and indirectly, by strengthening risk management practices.
ISs, encompassing tools like BIM and PMIS, provide a robust infrastructure for collecting, processing, and disseminating real-time data essential for effective risk management [43]. This enables rapid and accurate risk identification (e.g., detecting schedule conflicts via BIM), precise analysis of risk patterns (e.g., cost overrun trends via PMIS), and continuous monitoring of risk statuses (e.g., tracking mitigation efforts) [44]. For instance, ISs autonomously gather and categorize risk-related data, analyze them to reveal underlying trends, and support ongoing surveillance and management interventions [45]. By delivering timely, reliable information, ISs empower project managers to make informed decisions that reduce costs, delays, and quality issues—core components of PMS [25]. This dual influence—direct process enhancement and indirect risk mitigation via PRM—positions ISs as a linchpin in construction project success [30].
The state of the art highlights this dynamic interplay but reveals critical gaps. BIM’s 3D visualization and coordination capabilities preempt risks like design clashes [17], while PMISs accelerate decision making and oversight, reducing uncertainty [25]. Reed and Angolia [29] found that IS-supported risk identification and response planning boost project outcomes in IT contexts, a finding extensible to construction’s analogous complexity. De Bakker et al. [31] further suggest that PRM, when informed by robust data systems, amplifies success, while Varajão et al. [3] correlate IS maturity with improved outcomes, hinting at PRM’s intermediary role. In construction, where risks are multifaceted—spanning technical (e.g., structural failures), logistical (e.g., supply delays), and regulatory domains [46]—IS provides actionable insights that PRM leverages to prioritize and neutralize threats, safeguarding PMS.
Existing frameworks, such as the technology acceptance model (TAM) for IS adoption and PMI’s PMBOK for PRM processes, offer partial explanations but fall short of a holistic, construction-specific model integrating ISs, PRM, and PMS [5]. Šeduikis [47] argued that while ISs directly enhance efficiency and oversight, proficient PRM is indispensable for success by enabling managers to anticipate and mitigate unforeseen hazards—a view echoed by Walker and Lloyd-Walker [48]. This study builds on these insights, proposing a mediation model: ISs enhance PMS directly (e.g., via streamlined workflows) and indirectly (e.g., via PRM’s risk reduction). However, challenges remain—IS adoption varies due to cost and training barriers, PRM implementation lacks uniformity, and success metrics are subjective [7,49,50,51,52,53]. Despite these limitations, this consolidated framework addresses theoretical gaps, aligns with the practical realities of Iraqi construction firms, and sets a robust foundation for empirical validation in this study.
3. Research Methodology
Because the current research focuses on solving the problem caused by the lack of knowledge of the research model’s structural framework in the statistical population and explaining the concepts related to the structural model under real-world conditions, it can be classified as quantitative, practical, and correlational descriptive research [54,55,56]. A questionnaire was used to collect information for this study. The first section of the questionnaire includes demographic characteristics such as education, gender, work experience in the field of construction, workplace, and job positions. The second component consists of 79 items that measure the model’s key variables and is comprised of the research’s principal questions. Using a five-point Likert scale measurement, all elements are appraised. The procedure for distributing the questionnaire and collecting the necessary information has also been made available online. The statistical population of this study includes an unlimited number of experts and specialists who worked in Iraqi construction firms. After distributing 30 initial questionnaires and computing the variance using the technique for obtaining the sample size in an unrestricted statistical population, the projected number of snowball-selected samples was 95 individuals.
The information systems questionnaire for this study was originally formulated based on theoretical frameworks and prior research, comprising 33 questions across 6 dimensions: Operating Efficiency, Cost Reduction, Providing Information to Decision Makers, Customer Service, Capacity Development, and Communication. The Delphi approach was employed to analyze the experts’ perspectives on the identified factors. To this end, the researcher solicited the opinions of 17 experts regarding the specified indicators and dimensions using a five-point Likert scale measurement. Following the aggregation of expert opinions, only items with an average rating of 3 or above were chosen. This strategy is one of the criteria proposed by Fink et al. [45] for achieving consensus among members regarding the selection or elimination of certain items in the Delphi method. Ultimately, following two Delphi rounds and achieving a Kendall agreement coefficient of 0.83, it was concluded that experts identify 27 items across four dimensions (Operating Efficiency, Cost Reduction, Providing Information to Decision Makers, and Capacity Development) as relevant for the information systems questionnaire.
The project management success questionnaire, as established by Minarro-Viseras et al. [44], comprises 27 questions. Furthermore, the project risk management questionnaire was developed and assembled based on Ahbab’s [42] risk management questionnaire. This questionnaire had a single component; nevertheless, the researcher classified the 25 questions into six categories (finance, planning, management, environment, resources, and legal). The categorization is based on scholarly literature.
The validity and dependability of the surveys were then evaluated. Several responders validated the surveys’ superficial validity. The content validity was confirmed by the assessments performed by several academic and industrial experts. Because the project management success questionnaire is standard and has been utilized numerous times, there was no need for a confirmatory factor analysis type of construct validity. Instead, confirmatory factor analysis was only used to determine the construct validity of the information systems and project risk management questionnaires in SmartPLS 4.0 software. Figure 1 displays the factor loadings associated with the various items of information systems, whereas Figure 2 displays the factor loadings related to the variable items of project risk management. Each item’s factor loadings greater than or equal to 0.5 suggest a solid structure. According to Hair Jr. et al. [57], validation of the measurement model’s suitability with a factor that loads greater than 0.5 requires keeping items with a load greater than 0.5 in the model. Items with factor loadings less than 0.5 are removed from the model and are not used in the process of analysis. Cronbach’s alpha coefficient was used to estimate the reliability of questionnaires for the information systems questionnaire as 0.916, project management success as 0.960, and project risk management as 0.927. Then, descriptive statistics were used to analyze the demographic variables of the research, and inferential statistics were used to analyze the data using a structural equation model, simple regression, and multiple regression using a step-by-step procedure in SPSS and Smart PLS software. Table 1 provides a summary of the demographics of the respondents, who were mostly site engineers (34.7%) and project managers (25.3%), with 51.6% working in the government sector and 38.9% in contracting firms; all had expertise with PM. In total, 80% of respondents are male, and 45.2% have more than ten years of experience. Finally, the respondents reported holding graduate or postgraduate degrees.
4. Results and Discussion
4.1. Descriptive Analysis of the Main Research Variables
The results in Table 2 demonstrate that the relevance level (p-Value) is less than 5% (p0.05), and since the absolute value of the calculated t is greater than the t (1.96), at a confidence level of 95%, it can be stated that the mean and standard deviation of information systems are 4.105 and 0.627, and since the mean of information systems is greater than the mean of (3), it can be stated that information systems are at a favorable level. Also, the mean and standard deviation of project management success are 3.5801 and 0.559, and because the mean of project management success is greater than the mean of (3), it can be said that project management success is at a favorable level. Furthermore, the mean and standard deviation of project risk management are 3.7821 and 0.648, and because the mean of project risk management is greater than the mean of (3), it can be said that project risk management is at a favorable level.
4.2. Measurement Model
First, the measurement model is analyzed to establish the dependability (internal consistency) and validity (convergent and discriminant validity) of the constructs and measuring instruments. The constructions’ dependability was evaluated using Cronbach’s alpha, the composite dependability of each construct, and the factor load of each item [58]. One of the approaches for determining reliability is Cronbach’s alpha, for which a value of 0.7 or higher is acceptable [57]. The values obtained for this index in Table 3 suggest adequate measuring tool dependability. Another method for assessing the dependability of study constructs is the composite dependability of the constructs, for which a level of 0.7 or higher is acceptable. The results obtained for this index in Table 4 demonstrate the adequate dependability of the measurement instruments. In addition, the Average Variance Extracted (AVE) index is employed to assess convergent validity. The average extracted variance should be at least 0.5 [57]. Researchers such as Bagozzi and Yi [59] likewise affirm a value greater than 0.4, indicating that the construct in question explains 40% to 50% of the variation in its indicators. These results are also provided in Table 3, which illustrates the well-explained variance of the measurement instruments.
Each item’s factor loadings that are equal to or greater than 0.5 indicate a good construct. Figure 3 depicts the factor loadings associated with the research variables. According to Hair Jr. et al. [57], the adequacy of the measurement model with a factor load greater than 0.5 is confirmed because items with factor loads less than 0.5 are removed from the model and are not employed in the process of analysis.
To evaluate the discriminant validity of the constructs, the root mean square of the extracted variance of each variable must be larger than its association with other variables [58]. This implies that the correlation between each latent variable and its indicators (observed variables) must be higher than its correlation with other variables. In Table 4, the average square root of the extracted variance is provided at the end of each row (values of the principal diameter of the matrix). The values of each variable are greater than their respective correlations with other variables, which validates the acceptable variance of the measuring methods.
4.3. Structural Model
As shown in Table 5, initially, the Pearson correlation coefficient was calculated to examine the existence of a significant relationship between the primary variables in this study. Next, variance-based structural equation modeling was modeled using the SmartPLS software employed to examine the existence of a significant relationship between this study’s primary variables.
Path coefficients (β) and the t statistic are used to study the relationship between variables in PLS. If t is outside the range of ±1.96, the path coefficient is substantial at the level of 0.05, and if t is outside the range of ±2.58, the path coefficient is meaningful at the level of 0.01.
4.4. The Main Hypothesis
The use of information systems influences the success of project management through the mediating variable of project risk management.
Regarding the mediating analysis of project risk management in the influence of information systems on the success of project management, using the well-known bootstrapping method, first, information systems’ direct and indirect channels for the independent variable, the dependent variable (project management success), and the mediating variable (project risk management) are analyzed according to Figure 4, and the outcomes are presented in Table 6.
Based on the findings presented in Table 6, the full effect between information system variables and project management success is significant in the absence of a mediator, and the indirect effect between information systems and project management success is significant with the presence of a mediator, and the direct effect between information systems and project management success is significant with the presence of a mediator. Project risk management partially mediates the effect between information system variables and project management success, in the sense that a portion of the effect of the predictor variable (information systems) on the outcome variable (project management success) is mediated by the project risk management variable. In addition, the Sobel test and Z-value were utilized to confirm the importance of the risk management mediation variable in this study. According to the calculations, the Z-Value of the Sobel test equals 3768, which, since it is greater than 1.96, indicates that the influence of the mediating variable of project risk management on the effect of information systems on the success of project management is statistically significant at the 95% confidence level.
4.5. Sub-Hypotheses of the Research
The use of information systems affects project management’s success.
The results obtained from Table 7 show that the p-value was found to be zero, and the use of information systems has a 15.8% ability to predict the success of project management.
As shown in Table 8, the T-value for the variable of using information systems is 4.324%, and the degree of significance is 0.001, indicating that the success of project management can predict the use of information systems with a 95% level of confidence; the regression line equation with nonstandard coefficients is provided below.
Project management success = 2.086 + 0.364 use of information systems
The use of information systems has an impact on project risk management.
The results in Table 9 reveal that the p-value is zero and that the utilization of information systems has a 19.7% ability to anticipate project risk management.
As shown in Table 10, the T-value for the variable of using information systems is 4.909, and the significance level is 0.001, indicating that project risk management can predict the use of information systems with a 95% level of confidence, and the regression line equation with nonstandard coefficients is as follows:
Project risk management = 1.859 + 0.469 use of information systems
Project risk management affects the success of project management.
The results obtained from Table 11 show the p-value was found to be 0.007 and project risk management has a 6.6% ability to predict the success of project management.
As shown in Table 12, the T-value for the variable project risk management is 2.766, and the significance level is 0.001. Therefore, the success of project management can predict project risk management with a 95% level of confidence, and the regression line equation with nonstandard coefficients is as follows:
Project management success = 2.682 + 0.237 project risk management
The dimensions of information systems can predict the success of project management.
The results obtained from Table 13 show that the p-value is zero, and Capacity Development in the first step has a 16.3% ability to predict project management success.
As seen in Table 14, the T-value is 4.393, and the significance level is 0.001 in the first step of the stepwise regression of Capacity Development at a confidence level of 95%, so it can significantly predict the variable of project management success, and the equation of its regression line with nonstandard coefficients is as follows:
Project management success = 2.375 + 0.292 Capacity Development
The dimensions of information systems can predict project risk management.
The outcomes obtained from Table 15 show that the p-value is zero, and Capacity Development in the first step has a 20.3% ability to predict project risk management.
As seen in Table 16, in the first step of the stepwise regression of Capacity Development with the T-value of 4.987 and the significance level of 0.001 at the confidence level of 95%, they can markedly predict the project risk management variable and the equation of its regression line with nonstandard coefficients is as follows:
Project risk management = 2.232 + 0.375 Capacity Development
5. Discussion of Analytical Results
The results of this study, employing a descriptive–correlational methodology with a structural equation modeling approach, revealed that information system variables have a direct effect on project management success in the absence of a mediator, an indirect effect with a mediator, and a direct effect with a mediator. Management of project risk has a partial effect. Information system utilization is 15.8% connected with project management success. Utilization of information systems predicts project risk management by 19.7%. Risk management predicts the success of project management by 6.6%. The regression results indicated that among the dimensions of information systems, the Capacity Development dimension has the ability to predict the success of project management and project risk management.
This study’s findings regarding the influence of information systems on project success align with the literature [25,38,39,40,43], which underscored the significance of PMIS in enhancing project success. This study utilized information systems to enhance project success. Conversely, this aligns with the findings of Varajão et al. [26], who underscored the significance of employing standardized project management methodologies (ISO/PMBOK) and the necessity for proficient project management to enhance the outcomes of information systems. The findings about the influence of risk management on project success align with those of Tahir and al Jabar [27] and Reed and Angolia [29], indicating that risk management directly impacts project outcomes. This study revealed that risk management significantly mediates the association between information systems and project success. This comparison indicates that in both studies, risk management is recognized as a crucial element in mitigating issues and enhancing project success. Azeez and Yaakub [22] asserted that information quality, user satisfaction, and net benefits are directly correlated with organizational success. This study discovered that information systems can substantially enhance project success by delivering precise and timely information to project managers. This conversation underscores the significance of information quality in determining project success. This study diverges from Kishk and Ukaga [30], who asserted that risk management can forecast project failure, by concentrating primarily on project success and the utilization of information systems to mitigate risks and enhance project outcomes. Consequently, the focus of this study is predominantly on problem prevention and success augmentation rather than failure prediction. The research conducted by Urbański et al. [28] explicitly highlights the influence of project design on project success. This study primarily emphasizes the influence of information technology and risk management on project success, without directly addressing elements of project design.
The findings of this study underscore the critical role of information technology and project risk management in ascertaining the success of construction projects in Iraq. The construction sector in Iraq encounters numerous problems stemming from its unique attributes, including volatile economic and political conditions, infrastructural obstacles, and a demand for trained labor. In this context, the utilization of sophisticated technologies, particularly information systems, is crucial for enhancing project implementation procedures, mitigating risks, and augmenting the likelihood of project success. This study’s results unequivocally show that information systems directly influence the success of project management within the Iraqi construction sector. This industry, confronted with constraints including resource delivery delays, financial difficulties, and inadequate coordination among contractors and various teams, particularly in intricate and large-scale projects, can leverage information technologies in its endeavors. The implementation of these systems can enhance decision-making processes, improve accuracy, and minimize human errors at various stages of the project. In the Iraqi construction sector, characterized by unstable conditions and considerable risks, information systems can furnish project managers with critical data and insights consistently and promptly. This enables project managers to make decisions more swiftly and precisely and to effectively utilize constrained resources. Information systems can function as a mechanism for elucidating and documenting project information, thereby mitigating issues associated with planning, implementation delays, and cost escalations. The principal finding of the current study is that project risk management, as a mediating variable, significantly enhances the association between information systems and project success. In the intricate and dynamic landscape of the Iraqi construction sector, where projects frequently encounter risks including economic volatility, political turmoil, supply chain disruptions, and safety concerns, risk management is acknowledged as an essential determinant of project success. In this context, information systems can gather, analyze, and deliver risk-related data to project managers, enabling them to detect hazards and implement suitable preventive measures. Effective risk management in Iraqi building projects, often impacted by financial and political issues, can mitigate costs and save delays. This management is especially crucial in extensive projects necessitating coordination among multiple contractors, consultants, and various stakeholders. Information systems are particularly adept at detecting risks in the first phases of a project and assessing their impact on the execution process and budget. Therefore, they enhance risk management and, as a result, increase project success. The current study shows that project risk management serves as a partial mediator in amplifying the influence of information systems on project success, both directly and through its effects on management and implementation processes. The utilization of information systems alone cannot ensure project success; it must be concurrently integrated with robust and effective risk management. This methodology mitigates delays, regulates expenses, and averts possible problems in projects. This study’s findings underscore that information systems can effectively serve a predictive function in project success. Utilizing these tools enables project managers to examine current trends and model various situations, facilitating the anticipation of future hazards and risks. This predictive capability allows project managers to enhance decision making concerning resource distribution, task scheduling, and management tactics. In the Iraqi construction sector, frequently confronted with several crises and problems, forecasting risks and project success constitutes a competitive advantage. In projects necessitating substantial investments and meticulous scheduling, the capacity to accurately forecast and mitigate risks significantly enhances the probability of success. This study shows that employing information systems to enhance project risk management can substantially elevate project success within the Iraqi construction sector. Conversely, risk management serves as a crucial intermediary in this process, significantly mitigating potential risks and enhancing the likelihood of project success. This study underscores the necessity of concurrently employing these two aspects and posits that to attain enhanced performance in construction projects in Iraq, project managers must optimally utilize information systems and successfully apply risk management techniques.
6. Conclusions
Information is one of the most vital organizational capitals since it influences all physical facilities and environmental decision making. Information can affect the competitive dynamics of a business and offer a competitive advantage. Additionally, successful information system firms can modify the dynamics of industry competition and gain an advantage through innovation. As a result of the usefulness of the project management information system in implementing projects faster, less expensively, and with higher quality, the general expectations of project managers increased. Organizations employ information systems to boost their production and profitability; as a result, they can more easily adjust to environmental changes and receive current information. In addition, managers can estimate project costs, profitability, and budgets using information systems. Common purposes for information systems include the implementation of activities, the management of commodities and materials, the collection and classification of financial and non-financial information, and the preservation of information. Information systems can assist managers with decision making, project planning, organization, and control. Information systems, knowledge, and learning are the best means of gaining a permanent competitive advantage. It is unacceptable for employees to passively retain crucial organizational knowledge, and the rapid development of business changes, relocation, and decreasing educational standards make it hard to gain insight, comprehensive information, and consistent information. Therefore, the knowledge and information that employees are exposed to must infiltrate all levels of the organization for everyone to attain, develop, or complete it. Due to rapid technological improvements, cyclical markets, and globalization, organizations may face financial and non-financial risks. In other words, the risk rises if complexity is not well managed, as rising complexity also increases the likelihood of change. Using modern information systems, the information stored without benefit for old projects or the information available to those with expertise must be utilized and easily accessed to avoid and anticipate risks and create an information base for the risks that may occur during the project’s lifetime. Risk management is the management of the intrinsically complex relationship between return on capital and risk. This is accomplished using information systems and the management of knowledge hidden inside these controls, which is advantageous to the organization’s stakeholders. Effective risk management tactics enable you to identify project dangers, weaknesses, and opportunities, as well as the project’s strengths. By preparing for unanticipated events, competent risk management helps the project manager to respond effectively if something goes wrong during the project’s execution. To guarantee successful project management, the project manager must determine how to handle potential risks so he can recognize, mitigate, or eliminate difficulties as necessary. Risk management is essential to the success of projects because the achievement of project objectives depends on planning and preparedness, in addition to the outcomes and evaluations that contribute to the project’s advancement and attainment of its strategic objectives. Thus, risk management and information systems play complementary roles in the success of construction projects. Considering the findings of this study and its significance in enhancing risk management processes and the efficacy of construction projects, it is imperative that subsequent research investigates the limitations of this study and explores opportunities for further advancement in the application of information systems and risk management across various industries and nations, particularly in intricate and dynamic environments like Iraq. The primary drawback of this study is the sample size, comprising 95 construction experts currently working in Iraq. This sampling has yielded useful insights within its specific geographical context, although it may have limitations in extrapolating the results to other countries in the region or different industries. Conversely, the distinct economic, cultural, and political conditions of Iraq may possess distinctive attributes that influence the utilization of project management information systems and risk management differently than in other nations. For instance, in numerous other nations, the information technology infrastructure and management systems may markedly differ from those in Iraq, and these disparities can directly influence research outcomes; consequently, generalizing the findings to other geographical areas and industries should be approached with caution and necessitates additional comparative research across various domains. The absence of coverage for various types of information systems across different projects constitutes another limitation that may impact the comprehensiveness of the results. In light of the aforementioned, the subsequent research recommendations are proposed for future studies: Expanding this research to further countries in the Middle East or to nations with analogous attributes in industrial development and construction project infrastructure [60]. Analyzing construction projects across many sectors (such as energy and transportation) can help enhance the comprehension of information systems and risk management applications in diverse contexts [61]. Analyzing cultural and social impacts on information systems and risk management in construction projects helps enhance comprehension of the difficulties and potential within this domain [62]. This study concentrated on management information systems (MISs), computer information systems (CISs), and decision support systems (DSSs). Subsequent study may categorize various information systems (e.g., project management information systems, financial information systems, human resource information systems, etc.) and investigate the distinct impacts of each of them on project success and risk management [63].
Conceptualization, N.S.S.T.; methodology, N.S.S.T. and M.G.; formal analysis, M.G.; investigation, H.S. and D.J.E.; data curation, H.S. and D.J.E.; writing—original draft preparation, N.S.S.T. and M.G.; writing—review and editing, H.S. and D.J.E.; visualization, M.G.; project administration H.S. All authors have read and agreed to the published version of the manuscript.
The raw data supporting the conclusions of this article will be made available by the authors on request.
The authors declare no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 Factor loadings of information system factors in confirmatory factor analysis.
Figure 2 Factor loadings of project risk management factors in confirmatory factors.
Figure 3 Conceptual model of research using effect coefficient (factor loading).
Figure 4 Significance checks of the coefficients of the conceptual model of the research using T-value values.
Profile of demographics respondents.
| Frequency | Percent | |
|---|---|---|
| Gender | ||
| Male | 76 | 80.0 |
| Female | 19 | 20.0 |
| Education | ||
| BA | 59 | 62.1 |
| Master’s | 34 | 35.8 |
| PhD | 2 | 2.1 |
| Experience | ||
| 1–5 years | 34 | 35.8 |
| 6–10 years old | 18 | 18.9 |
| 11–15 years old | 12 | 12.6 |
| 16–20 years | 17 | 17.9 |
| More than 20 years | 14 | 14.7 |
| Place of Activity | ||
| Contractor or contracting company | 37 | 38.9 |
| Consulting office | 3 | 3.2 |
| Client or beneficiary | 1 | 1.1 |
| Architectural and design company | 3 | 3.2 |
| Government department | 49 | 51.6 |
| Other | 2 | 2.1 |
| Current position | ||
| Project manager | 24 | 25.3 |
| Site engineer | 33 | 34.7 |
| Office engineer | 14 | 14.7 |
| Chief engineer | 6 | 6.3 |
| Resident engineer | 15 | 15.8 |
| Other | 3 | 3.2 |
Sample T-test for checking the variables.
| Variable | N | Mean | Std. Deviation | t | df | Sig. | Mean Difference | 95% Confidence Interval of the Difference | |
|---|---|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||||
| Information systems | 95 | 4.1026 | 0.62796 | 17.114 | 94 | 0.001 | 1.10263 | 0.9747 | 1.2306 |
| Project management success | 95 | 3.5801 | 0.55903 | 10.114 | 94 | 0.001 | 0.58012 | 0.4662 | 0.6940 |
| Project risk management | 95 | 3.7821 | 0.64893 | 11.747 | 94 | 0.001 | 0.78211 | 0.6499 | 0.9143 |
Reliability and validity test of measurement tool.
| Fit Indices | Cronbach’s Alpha | Composite Reliability | Average Variance Extracted (AVE) |
|---|---|---|---|
| Information Systems | 0.943 | 0.949 | 0.475 |
| Project Management Success | 0.907 | 0.920 | 0.405 |
| Project Risk Management | 0.947 | 0.953 | 0.502 |
Correlation matrix of research variables and the square root of AVE.
| Fornell–Larcker Criterion | Project Management Success | Information Systems | Project Risk Management |
|---|---|---|---|
| Project Management Success | 0.637 | ||
| Information Systems | 0.573 | 0.689 | |
| Project Risk Management | 0.446 | 0.460 | 0.709 |
Pearson correlation between the main research variables.
| Information Systems | Project Management Success | Project Risk | ||
|---|---|---|---|---|
| Information Systems | Pearson Correlation | 1 | 0.409 ** | 0.454 ** |
| Sig. (2-tailed) | 0.001 | 0.001 | ||
| N | 95 | 95 | 95 | |
| Project management success | Pearson Correlation | 0.409 ** | 1 | 0.276 ** |
| Sig. (2-tailed) | 0.000 | 0.007 | ||
| N | 95 | 95 | 95 | |
| Project risk management | Pearson Correlation | 0.454 ** | 0.276 ** | 1 |
| Sig. (2-tailed) | 0.000 | 0.007 | ||
| N | 95 | 95 | 95 | |
** Correlation is significant at the 0.01 level (2-tailed).
Path coefficient table and significance.
| Analyze | Original Sample (O) | Sample Mean (M) | Standard Deviation (STDEV) | T | p Values | |
|---|---|---|---|---|---|---|
| Specific Indirect Effects | Information Systems -> Project Risk Management -> Project Management Success | 0.201 | 0.198 | 0.037 | 5.435 | 0.001 |
| Path Coefficients | Information Systems -> | 0.672 | 0.673 | 0.056 | 11.968 | 0.001 |
| Information Systems -> | 0.460 | 0.461 | 0.094 | 4.917 | 0.001 | |
| Project Risk Management -> | 0.437 | 0.437 | 0.074 | 5.900 | 0.001 | |
| Total Effects | Information Systems -> | 0.873 | 0.871 | 0.047 | 18.597 | 0.001 |
Summary of the regression model of the first sub-hypothesis.
| Model | R | R Square | Adjusted | Std. Error of the Estimate | Durbin–Watson | F | Sig. |
|---|---|---|---|---|---|---|---|
| 1 | 0.409 | 0.167 | 0.158 | 0.51284 | 2.193 | 18.693 | 0.001 |
Regression model coefficients of the first sub-hypothesis.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 2.086 | 0.350 | 5.967 | 0.001 | |
| Information Systems | 0.364 | 0.084 | 0.409 | 4.324 | 0.001 | |
Summary of the regression model of the second sub-hypothesis.
| Model | R | R Square | Adjusted | Std. Error of the Estimate | Durbin–Watson | F | Sig. |
|---|---|---|---|---|---|---|---|
| 1 | 0.454 | 0.206 | 0.197 | 0.58142 | 2.220 | 24.098 | 0.001 |
Regression model coefficients of the second sub-hypothesis.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 1.859 | 0.396 | 4.690 | 0.001 | |
| Information Systems | 0.469 | 0.095 | 0.454 | 4.909 | 0.001 | |
Summary of the regression model of the third sub-hypothesis.
| Model | R | R Square | Adjusted | Std. Error of the Estimate | Durbin–Watson | F | Sig. |
|---|---|---|---|---|---|---|---|
| 1 | 0.276 | 0.076 | 0.066 | 0.54025 | 1.968 | 7.650 | 0.007 |
Regression model coefficients of the third sub-hypothesis.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 2.682 | 0.329 | 8.140 | 0.001 | |
| Project risk management | 0.237 | 0.086 | 0.276 | 2.766 | 0.007 | |
Summary of the stepwise regression model of the fourth sub-hypothesis.
| Model | R | R Square | Adjusted | Std. Error of the Estimate | Durbin–Watson | F | Sig. |
|---|---|---|---|---|---|---|---|
| 1 | 0.415 a | 0.172 | 0.163 | 0.51146 | 2.151 | 19.300 | 0.001 |
a. Predictors: (Constant), Capacity Development.
Stepwise regression model coefficients of the fourth sub-hypothesis.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 2.375 | 0.279 | 8.501 | 0.001 | |
| Capacity Development | 0.292 | 0.066 | 0.415 | 4.393 | 0.001 | |
Summary of the stepwise regression model of the fifth sub-hypothesis.
| Model | R | R Square | Adjusted | Std. Error of the Estimate | Durbin–Watson | F | Sig. |
|---|---|---|---|---|---|---|---|
| 1 | 0.459 a | 0.211 | 0.203 | 0.57950 | 2.136 | 24.872 | 0.001 |
a. Predictors: (Constant), Capacity Development.
Coefficients of the stepwise regression model of the fifth sub-hypothesis.
| Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. | ||
|---|---|---|---|---|---|---|
| B | Std. Error | Beta | ||||
| 1 | (Constant) | 2.232 | 0.317 | 7.051 | 0.001 | |
| Capacity Development | 0.375 | 0.075 | 0.459 | 4.987 | 0.001 | |
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Details
; Sarvari Hadi 3
; Edwards, David J 4
1 Department of Civil Engineering, Isf.C., Islamic Azad University, Isfahan 81595-39998, Iran
2 Department of Management, Isf.C., Islamic Azad University, Isfahan 81595-39998, Iran
3 The Infrastructure Futures Research Group, College of the Built Environment, City Centre Campus, Birmingham City University, Millennium Point, Birmingham B4 7XG, UK
4 The Infrastructure Futures Research Group, College of the Built Environment, City Centre Campus, Birmingham City University, Millennium Point, Birmingham B4 7XG, UK, Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa