Enhancing Construction Performance: A Critical Review of Performance Measurement Practices at the Project Level

Abstract

The construction industry faces significant challenges in measuring and assessing performance effectively. Conventional methods of measuring construction performance have become less effective, prompting a need to adopt non-financial performance measurements. This shift acknowledges the shortcomings of relying solely on financial performance measurement systems. As a result, there has been a substantial increase in research and focus on non-financial performance measurement systems in recent decades. This study focuses on analyzing performance measurement practices and key performance indicators (KPIs) in the construction industry, specifically at the project level. By examining 146 relevant articles, the study offers a thorough overview of various aspects of project performance. In addition to the traditional dimensions of the project management triangle (cost, time, and quality performance), the study emphasizes the importance of considering other dimensions. These include stakeholder performance, safety performance, technology utilization performance, value performance, environmental impact performance, and the application of maturity models. By incorporating these additional KPIs, a more comprehensive and holistic evaluation of project performance can be achieved. This study’s findings make notable contributions to the methodological framework of performance measurement in construction projects. By consolidating diverse research sources, the study offers valuable guidance for future research in the field of project performance. Moreover, it provides insights into selecting suitable performance measurement methods, empowering practitioners to effectively assess and manage project performance.

1. Introduction

The construction industry plays a pivotal role in the economy of each country, encompassing a wide range of associated sectors [1]. Ensuring the success of construction projects is a primary objective for project managers. However, defining and evaluating success in this context have been topics of ongoing discussion [2]. Given the industry’s significance in a nation’s development and progress, it is crucial to emphasize the achievement of project success [2]. Although stakeholders may have different perspectives on defining project success, these generally revolve around meeting project goals and expectations. Managing project performance is one approach to evaluating project success, with project performance being a subset of overall project success [3]. Over the past few decades, extensive research has been dedicated to accurately quantifying performance, leading to various stages of progress and evolution in performance measurement [4]. Measuring construction performance presents numerous challenges, as evidenced by research conducted in the UK [5] and Iraq [6]. One key challenge stems from the absence of a consistent industry-wide performance framework, impeding the assessment of project success. Furthermore, the construction sector grapples with obstacles in defining comprehensive measures due to factors such as diverse project priorities and goals, subjective assessments, the multidimensional nature of performance, data reliability issues, reliance on lagging indicators, complexities in benchmarking, project-specific influences, and a lack of consensus on standardized metrics [7,8]. Effective performance measurement remains a significant challenge in both the academic and practical domains. Scholars have explored performance measurement from perspectives ranging from control systems to learning mechanisms, particularly in the face of increasingly uncertain and volatile project contexts [9]. However, the selection of performance measurement methods is a critical yet frequently overlooked matter. The choice of measurement methods has a direct impact on the accuracy and validity of performance evaluation, thereby influencing project performance management discussions in a profound manner [10]. Performance measurement methods are crucial tools in evaluating and monitoring project performance. They guide the entire process, from input to output [11]. Various methods are commonly used for performance measurement, including the analytic hierarchy process (AHP), earned value analysis, the Delphi method, principal component analysis (PCA), regression analysis, structural equation modeling, fuzzy comprehensive evaluation, balanced scorecard, and data envelopment analysis (DEA) [6,12,13,14,15,16]. Additionally, researchers have explored alternative approaches to evaluating and modeling construction project performance. These include phase-based evaluation [17], competence assessment using prioritized factor analysis and fuzzy neural networks [18], system dynamics modeling of bidding strategies and project performance, and the use of system dynamics in managing project performance [19,20,21]. The core concept behind these evaluation methods is to objectively measure the effectiveness, efficiency, and success of a project or process. By analyzing relevant data and metrics, these methods enable informed decision-making, identification of improvement areas, and implementation of corrective actions. The ultimate aim is to enhance project performance, optimize resource allocation, mitigate risks, and achieve desired outcomes in a proactive and measurable manner.

Maturity models, such as project management maturity models (PMMMs), have been utilized in the construction industry to evaluate project management capabilities [22]. These models serve as practical tools for addressing inadequate project management performance and have been applied across different sectors, including construction [23]. Research demonstrates a positive correlation between project management maturity and project performance, especially in the domains of cost and time management [24]. To evaluate and enhance the Construction Performance Index (CPI), it is necessary to establish clear levels of performance maturity. Numerous studies within the construction industry have employed diverse maturity levels and score ranges to evaluate performance [25,26,27,28].

The project management triangle, also known as the triple constraint or iron triangle, represents the fundamental relationship between time, cost, and quality in project management. Time refers to the project’s schedule or the allotted timeframe for completion, while cost represents the financial resources allocated to the project. Quality refers to the level of excellence or adherence to specifications expected from the project’s deliverables. These three elements are interconnected, and any adjustments or changes to one element will impact the others. Balancing time, cost, and quality is crucial for successful project management, as changes in one aspect may require trade-offs in the others to maintain project integrity and achieve desired outcomes [29]. Originally, performance in project management was predominantly measured using three key indicators: cost, time, and quality. However, relying solely on these factors proved insufficient in fully capturing the intricate nature and multifaceted aspects of projects [30]. To address this limitation, project performance has evolved to encompass a broader concept, often referred to as the “iron triangle,” which includes factors such as the impact on consumers, value creation, business success, and future readiness [31]. Similarly, the evaluation of project success entails a comprehensive analysis of measurements. Unlike performance measurement, which primarily focuses on assessing specific project metrics, the evaluation of project success involves a broader examination of whether the project has achieved its overarching goals as defined by key stakeholders [32]. The recognition of the need to consider diverse criteria and perspectives for accurately assessing project success has spurred researchers to propose various KPIs that encompass multiple performance areas, leading to continuous improvements. In addition to the traditional performance indicators of cost, time, and quality, the construction industry has increasingly acknowledged the significance of integrating non-financial key performance indicators (KPIs) to evaluate project success [33,34]. Notably, researchers have emphasized the importance of safety KPIs [35,36], environmental impact KPIs [37,38], technology utilization KPIs [39,40], and stakeholder KPIs [34,41]. By incorporating these additional KPIs, it becomes possible to provide a comprehensive view of organizational effectiveness, project success, and long-term survival in a competitive market. This expanded approach considers various dimensions beyond the traditional measures of success, allowing for a more nuanced understanding of project outcomes and impacts.

In order to address the existing research gap and provide a more focused critical review, our study aims to conduct a comprehensive analysis of performance measurements, KPIs, and maturity models specifically at the project level. While previous studies have touched on related aspects, they did not specifically home in on the project level. For example, Khadim et al. [42] conducted a comprehensive review of circular economy assessment tools in the building industry, but their focus was not solely on performance measurements, and KPIs. Similarly, Moradi et al. [1] identified key performance measurement indicators up until 2020; however, they did not specify the level of application or provide a comprehensive demonstration of their findings. Also, Bhagwat and Delhi [43] conducted an analytical literature review on construction safety performance (CSP) but did not explicitly address others performance measurements. Furthermore, Rathnayake and Middleton [44] analyzed productivity at macro and micro levels, exploring industry-level trends and factors influencing productivity across sectors, without specifically focusing on project-level performance measurements. He et al. [45] conducted an analytical literature review on performance measurement methods in megaprojects, while. Xu et al. [46] focused on safety leading indicators in the construction sector. However, neither of these studies specifically focused on project-level performance measurements although analyzing performance at the project level provides valuable insights into project management practices, methodologies, and strategies. By examining project-level performance, project managers and stakeholders can identify areas for improvement, draw lessons from past experiences, and implement effective project management approaches in future projects within the same organization. This contributes to enhancing overall project delivery and performance within the construction industry. Therefore, our study aims to fill this research gap by conducting a critical review and synthesis of the existing literature on performance measurements, KPIs, and maturity models specifically at the project level. To provide a more comprehensive assessment of project performance beyond financial metrics alone, we focused on non-financial key performance indicators (KPIs) in various project management practices. This approach considers a range of factors, including stakeholder management, environmental impact, safety performance, technology utilization, and maturity models. While financial KPIs, such as cost performance, are important, they do not capture all aspects of project success. By integrating these non-financial KPIs, we aim to offer a broader perspective on project outcomes and the overall value delivered. Our study covered the period from 2010 to the first quarter of 2024, allowing us to capture the most recent developments in the field. By undertaking this comprehensive review, it provided valuable insights and contributed to a better understanding and practical application of these concepts in project management.

2. Research Methodology

The objective of this study is to systematically review research efforts that have specifically examined performance measurement practices, maturity models, and KPIs within the context of project-level conditions. Figure 1 illustrates the four primary stages of the review process: search inquiry, refinement, snowballing, and analysis. The first three stages involved identifying the pertinent literature, while the final stage concentrated on analyzing and synthesizing the articles to extract valuable insights [47].

As emphasized by Baker and Algorta [48], incorporating multiple databases in a systematic review is crucial to ensure a comprehensive collection of relevant articles. Therefore, the initial stage of our review process involved conducting a search query within two prominent databases, specifically the Web of Science (WoS) and Scopus. The query employed was “Performance measurement” OR “Key performance indicators” OR “KPI” OR “maturity model” AND “construction industry”, with a specific focus on articles exploring performance measurement practices in the construction industry at the project level. The search fields encompassed the article title, abstract, and keywords. This inquiry resulted in a significant number of research items, totaling over 23,000, as depicted in Figure 1.

The second stage of the research focused on refining the results to ensure relevance and specificity. This was achieved by utilizing the filtering capabilities of the Web of Science and Scopus databases. The research items were limited to the field of engineering while excluding other subjects. Furthermore, the results were narrowed down to include only journal articles published in the English language between the years 2010 and the first quarter of 2024, which was the period of the search. After applying these filters, a total of 791 research items remained, as illustrated in Figure 1. Specification of inclusion and exclusion criteria is a crucial step in any systematic review as it helps filter the retrieved studies and select only those that are relevant to the research objectives. In this study, we have defined clear inclusion and exclusion criteria to ensure the selection of appropriate studies. The inclusion criteria include studies that focus on performance measurement practices, specifically address the subject area of engineering, and address key performance indicators (KPIs) at the project level. Moreover, we have limited our selection to studies published in peer-reviewed journals to ensure the inclusion of rigorous and credible research. These criteria were established to ensure that the selected studies align with the research focus on performance measurement practices in the construction industry at the project level and are based on rigorous research published in reputable journals. Conversely, the exclusion criteria were set to filter out studies that may not be directly relevant to our research. We have excluded studies published in languages other than English. Additionally, studies that address performance measurement practices in industries other than construction were excluded, as the focus of this study is specifically on the construction industry. Furthermore, articles that examine performance measurement at the organizational and industry levels were not included, as the research scope is limited to the project level. Lastly, studies without available full texts were excluded, as the availability of complete research articles is essential for a comprehensive analysis and evaluation. By applying these inclusion and exclusion criteria, we aim to ensure the selection of relevant and high-quality studies that meet the specific focus and objectives of this systematic review.

In the third stage, both backward and forward snowballing techniques were employed to ensure a comprehensive selection of articles. Forward snowballing involved reviewing the references cited within the initially identified articles. Conversely, backward snowballing involved examining articles that cited the initially identified ones. For forward snowballing, Scopus, and Web of Science (WoS) were utilized, while Google Scholar was used for backward snowballing. As a result of this process, 21 additional articles were discovered, bringing the total number of articles considered for this review to 146, as depicted in Figure 1.

The final stage of the review process involved employing a mixed review methodology approach, which encompassed both quantitative (scientometric approach) and qualitative (systematic approach) review methodologies. The utilization of this mixed review approach is widely acknowledged by numerous scholars due to its ability to mitigate biased conclusions and subjective interpretations, while simultaneously facilitating a comprehensive understanding of domain expertise and research patterns [49]. For the quantitative analysis, scientometric analyses were conducted using the VOSviewer tool, which is a platform for generating, visualizing, and investigating bibliometric networks [50]. This allowed an examination of publication trends in various performance measurement areas, authors’ keywords, publishing sources, and research analysis methods in the field of performance measurements. On the other hand, the qualitative analysis involved a systematic approach wherein the selected articles were carefully analyzed and synthesized to derive meaningful insights. This systematic approach ensured a rigorous evaluation of the articles and facilitated the identification of maturity levels, KPIs, and research gaps in the field of performance measurements.

In Figure 2, the categorization performed in this review paper includes various performance measurement practices, namely: cost performance, time performance, quality performance, stakeholder performance, safety performance, technology utilization performance, value performance, environmental impact performance, and maturity models.

3. Scientometric Analysis

3.1. Annual Publication Trend

Figure 3 illustrates the annual publication performance of research on performance measurements from 2010 to the first quarter of 2024. The graph depicts a significant growth trend, indicating substantial attention from the research community, particularly in the last five years. The publication period can be categorized into four distinct phases based on variations in the number of published articles. The first phase, spanning from 2010 to 2013, was characterized by steady development, with the annual number of published papers fluctuating by an average of seven papers each year. During this period, research on performance measurements demonstrated consistent growth. The second phase represents a period of rapid development, commencing from 2014 to 2017. The number of published papers during this period experienced a significant increase, reflecting heightened interest and engagement in the field. Subsequently, the third phase witnessed a return to a steady pace, with an average of five papers published annually between 2018 and 2019. This phase indicates a consolidation and stabilization of research efforts in performance measurements. The fourth phase marks a rapid development period starting from 2020. During this time, the publication trend experienced a remarkable increase. Notably, the years 2020 to the first quarter of 2024 collectively accounted for 41.78% of the total number of publications in the field of performance measurement research over the last 15 years. Overall, the graph reflects a growing interest in and emphasis on research related to performance measurements, with a surge in publications in recent years.

3.2. Country Co-Authorship Analysis

In Figure 4, the node size represents the number of citations received by studies from each country. The larger nodes correspond to countries such as the United States, Australia, Hong Kong, and China, indicating their significant contributions to research on performance measurement practices. The network presented in Figure 4 was generated using VOSviewer, with the analysis type set as “co-authorship” and the unit of analysis set as “countries”. Additionally, the “minimum number of documents of a country” criterion was set to “3”. A total of 13 countries met these conditions and are included in Figure 4.

The study conducted an analysis to examine the contributions of different countries and regions in the research area. Several factors were found to influence these contributions, including the countries’ knowledge base and the level of collaboration among researchers. The analysis considered 59 countries, with the number of publications from each country determining the size of their respective circles in the visualization. The study identified significant collaborations between the United Kingdom and Hong Kong, as well as between the United Kingdom and the United States of America. These partnerships showcased strong linkages and highlighted the global nature of research in the field. The analysis employed co-citation, collaboration, and research area similarities to generate clusters, which were not influenced by geopolitical differences. Overall, the findings emphasized the importance of international collaboration and knowledge sharing in advancing research in this area. The study provided valuable insights into the contributions and collaborations of different countries and regions, shedding light on the global landscape of research in the field.

3.3. Keyword Co-Occurrence Analysis

A co-occurrence network of keywords can present a visual representation of research topics in any field, offering valuable insights for researchers [51]. This network allows researchers to: (1) examine the relationships between research subjects and the methods used in past studies; (2) understand the main trends within a research field using clustering functions; and (3) identify research gaps by analyzing the frequency of specific topics addressed by different methodologies [52]. Importantly, this network analysis provides researchers with comprehensive information without the need to read entire papers, and it eliminates subjective biases often found in narrative and systematic reviews. In generating the co-occurrence network shown in Figure 5, VOSviewer utilized keywords from the titles, abstracts, and keywords of 146 included papers. Each node in the figure represents a keyword, with its size reflecting the frequency of appearance in the papers. The thickness of the curved lines connecting two keywords indicates the frequency of their co-occurrence. By applying a minimum occurrence threshold of three, 22 keywords meet the criteria and can be used to construct the network. It is led by most frequent keywords such as “performance measurements”, “key performance indicators”, “construction industry”, “maturity model”, “safety performance”, and “project performance”.

3.4. Journal Co-Citation Analysis

Table 1. Distribution of top 10 selected journals.

Journal	No of Selected Papers
Journal of Construction Engineering and Management	14
Engineering, Construction and Architectural Management	12
International Journal of Construction Management	10
Construction Management and Economics	8
Buildings	7
Journal of Management in Engineering	6
Safety Science	4
Automation in Construction	3
Energy & Buildings	3
Journal of Civil Engineering and Management	3

provides the publication count of selected articles from the top 10 prestigious journals between 2010 and 2024. Among the listed journals, seven stand out as having the highest number of publications during this period: Journal of Construction Engineering and Management, Engineering, Construction and Architectural Management, International Journal of Construction Management, Construction Management and Economics, Buildings, Journal of Management in Engineering, and Safety Science. These journals collectively published 61 papers, accounting for approximately 42% of the total articles included in the selection. Researchers and individuals interested in performance measurements and KPIs in the construction industry can consider these journals as valuable resources for publishing and gaining knowledge in the field.

3.5. Top Cited Publications

While it is commonly accepted in the scientific community that the number of citations an article receives reflects its impact on research, it is important to acknowledge that this may not always hold true, particularly for more recent publications [51]. In this section, we present the top-cited articles in performance measurements in construction research and examine their citation networks.

Table 2. Summary of top cited publications.

Rank	Ref	Publication Year	Total Citation	Key Findings
1	Choudhry [53]	2014	205	Behavior-based safety (BBS) in construction sites emphasizes the significance of conducting goal-setting sessions with workers and providing feedback through charts to enhance safety performance.
2	Meng et al. [54]	2011	116	A maturity model specifically tailored for assessing and enhancing relationships among key partners within construction supply chains. The model defines four maturity levels and provides a matrix format for evaluation, incorporating 24 assessment criteria across eight categories.
3	B. Guo and Yiu [55]	2016	108	A conceptual framework for developing leading safety indicators in the construction industry. This framework focuses on defining the purpose and attributes of these indicators. The primary functions of leading indicators are to provide informative insights about construction safety and aid in decision-making for taking remedial actions.
4	H.Ali [56]	2013	107	Development of a national benchmarking system, focusing on key performance indicators (KPIs) for measuring the performance of building construction.
5	Cambraia et al. [57]	2010	105	Proposing guidelines for identifying, analyzing, and disseminating information on near misses at construction sites. It highlights that near miss, which are more frequent than accidents and can potentially lead to accidents under different circumstances, provide valuable feedback for improving safety measures.
6	Horta et al. [58]	2010	97	Integration of data envelopment analysis (DEA) with key performance indicators (KPIs) to assess the performance of construction companies. The approach allows for the aggregation of multiple dimensions of company activity into a single performance measure, identifies efficient organizations, and suggests improvement targets for others

provides a summary of the highly cited articles focusing on the utilization of performance measurements in the construction industry. It is noteworthy that the articles have been sorted based on their total citations during the specified study period.

4. Systematic Analysis

4.1. Publication Trends in Various Performance Measurement Areas

Performance measurement research in the field of construction engineering and management (CEM) has experienced a growing trend. Publications in this area have covered a wide range of topics, spanning from theoretical advancements to practical applications.

As shown in Figure 6, among the different performance measurement dimensions analyzed, “Stakeholder performance” emerged as the most frequently cited topic, with 28 citations in the database, representing 19.18% of the total citations. Following closely behind were “Environmental performance”, “Technology performance”, and “Maturity model”, each receiving 23 citations (15.75%), 18 citations (12.33%), and 18 citations (12.33%), respectively. “Safety performance”, “Time performance” and “Quality performance” received equal attention with 13 citations each (8.90%). “Cost performance” and “Value performance” garnered 12 citations each, accounting for 8.22% of the total citations. Lastly, “Value performance” received eight citations, representing 5.48% of the total citations. This figure highlights the varying degrees of emphasis and interest given to each performance dimension within the reviewed literature from 2010 to 2024.

Figure 7 demonstrates a notable trend in recent years where researchers have shown significant interest in studying performance measurements and identifying the most suitable KPIs. The attention given to stakeholder performance highlights the acknowledgment of stakeholders’ influential role in project outcomes. Understanding and measuring stakeholder performance enables organizations to enhance collaboration, meet expectations, and ensure overall project success. Similarly, the focus on environmental performance underscores the industry’s increasing commitment to sustainability. By developing suitable performance measurements and indicators, researchers contribute to the assessment and monitoring of environmental impact, facilitating environmentally responsible practices.

The rising importance of technology in construction projects has led researchers to focus on technology performance as a key area of interest. By studying technology-related performance measurements, researchers aim to enhance project efficiency, productivity, and innovation through the effective utilization of technology. Safety performance is another critical aspect in construction projects. Researchers’ efforts to identify appropriate indicators help organizations assess and improve safety practices, leading to enhanced worker well-being and reduced risk of accidents or incidents.

4.2. Categories of Research Methods in Performance Measurements Research

Table 3. Categories of Research Methods in Performance Measurements Research.

	Types of Research Methods
	Quantitative	Qualitative	Mixed	Total
Safety performance	3	6	4	13
Value performance	3	0	5	8
Time performance	11	1	1	13
Cost performance	8	0	4	12
Technology performance	8	1	4	13
Quality performance	11	3	4	18
Environmental performance	12	6	5	23
Stakeholder performance	17	1	10	28
Maturity model	9	0	9	18
				146

presents the correlation between nine research topics and the methods employed in 146 articles from selected journals during the specified period. The analysis reveals that a majority of the studies (87.67%) utilized quantitative methods, including mixed methods, to address real-life problems through the development and utilization of optimization models and tools. However, it is important to note that qualitative methods (including mixed methods) were also employed at a significant frequency (43.8%) across all nine topics. These qualitative studies adopted a comprehensive approach by utilizing multiple qualitative approaches in a triangulation manner. Methods such as content analysis, interviews, and case studies were employed to explore the theoretical aspects underlying real-life cases. This comprehensive approach facilitated a more in-depth investigation of the topics, leading to a deeper understanding of the phenomena under study (e.g., [59,60,61]).

Advancements in performance evaluation have been driven by the application of mathematical models and techniques in the construction industry. These developments offer a systematic and rigorous approach to measure, assess, and improve performance based on objective criteria. The analytic hierarchy process (AHP) allows decision-makers to prioritize and evaluate criteria and sub-criteria, aiding in objective assessment and informed decision-making [62]. Regression analysis helps identify key factors impacting project performance by analyzing correlations between variables [63].

Data envelopment analysis (DEA) is a mathematical technique used to evaluate the relative efficiency of multiple entities, such as construction projects, by comparing their input–output ratios. It enables the assessment of decision-making units (DMUs) within organizations and provides a measure of efficiency when multiple inputs and outputs are involved [64]. DEA aggregates these variables into a single performance ratio, achieved by selecting appropriate weights. The DEA model determines weights that maximize the relative efficiency score for each DMU while ensuring that scores for other DMUs remain below or equal to one under similar weights. If a DMU does not attain a maximum efficiency score, it indicates that its peers are more productive, even with weights set to maximize the focal DMU’s score. This approach justifies the assigned weights economically and prevents inefficient units from claiming better scores with different weight sets. In the context of n DMUs (j = 1, …, n) that utilize m inputs $x_{ij}$ (i = 1, …, m) to produce t outputs $y_{rj}$ (r = 1, …., t), the relative efficiency of a specific DMUjo can be determined using a mathematical programming model. This model involves decision variables such as output weights ($u_r$) and input weights ($v_i$), along with a small positive number denoted as $\epsilon$.

$$maximize h_o=\frac{{\sum }_{r=1}^t{u}_r{y}_{rjo}}{{\sum }_{i=1}^m{v}_i{x}_{ijo}}$$

$$\frac{{\sum }_{r=1}^t{u}_r{y}_{rjo}}{{\sum }_{i=1}^m{v}_i{x}_{ijo}}\le 1, j=1,\dots , n$$

$$u_r\ge \epsilon ,r=1,\dots ,t$$

$$v_i\ge \epsilon ,i=1,\dots ,m$$

(1)

Model (1) is initially presented as a fractional model, but it can be transformed into a linear programming (LP) model using a straightforward approach outlined by Charnes et al. in 1978. To convert it into an input-oriented assessment, the numerator of the objective function in Model 1 is maximized, while the denominator is constrained to be equal to one. The transformation of the restriction in Model 1 into linear form is simple and becomes $\sum _r{u}_r{y}_{rj}-\sum _i{v}_i{x}_{ij}\le 0$. By implementing this transformation, the fractional model is converted into a linear programming model suitable for analysis [58].

Other models like principal component analysis (PCA), structural equation modeling (SEM), and system dynamics models contribute to analyzing complex relationships and simulating project behavior. By incorporating these mathematical developments, researchers and practitioners gain deeper insights into project performance, enabling informed decision-making and identifying improvement opportunities. In summary, the majority of the studies employed quantitative methods, while qualitative methods were also utilized frequently, allowing for a comprehensive exploration of the research topics and a richer understanding of the underlying phenomena. The main methods and models employed in these studies can be categorized as follows:

• Empirical survey [65,66,67];
• Analytic hierarchy process (AHP) [12,62];
• Delphi method [16,34,37];
• Correlation analysis [13,68];
• Data envelopment analysis (DEA) [6,64];
• Regression analysis [63,69];
• Fuzzy comprehensive evaluation [14,34];
• Markov analysis [70,71];
• Earned Value Analysis [72,73];
• System dynamics models [19,20,21];
• Structural equation modeling [74,75];
• Principal component analysis (PCA) [15,76,77];
• Econometric estimation of average response models [78];
• Multidirectional efficiency analysis (MEA) [79];
• Stochastic frontier analysis (SFA) [80,81];
• Index number approach (i.e., Tornsqvist index) [82].

4.3. Performance Measurements Practices

Measuring the performance of building projects significantly contributes to their success. It allows for the evaluation of progress, informs decision-making, promotes accountability and transparency, drives continuous improvement, facilitates benchmarking and best practices, and ensures stakeholder satisfaction. By actively monitoring and managing performance, project teams can identify areas for improvement, make timely adjustments, and deliver outcomes that align with goals and expectations. This fosters a culture of excellence, enhances project outcomes, and increases the likelihood of project success [83]. Performance measurement (PM) refers to the process of quantifying the success of past activities. Various terms, including performance measurement, performance measures, performance evaluation, key performance indicators, performance metrics, and critical success factors, have been employed in the development of PM [84].

4.3.1. Value Management Performance

Value management (VM) presents opportunities to optimize the functional value of a project while minimizing its life cycle costs. This is achieved by identifying and eliminating unnecessary expenses and ensuring that the project’s quality, reliability, and performance deliver value for money [85]. Furthermore, Noktehdan et al. [86] emphasized the role of innovation in generating value for construction projects and stakeholders. Their study highlighted how innovation can improve performance in areas such as costs, quality, time, safety, community, and environment. Similarly, Guo et al. [62] developed a strategic plan to revive the construction sector after the COVID-19 pandemic. Their plan focused on enhancing value creation, promoting sustainability, and fostering innovation within the industry. By establishing connections between performance indicators and critical success factors, their framework aimed to guide the industry towards a resilient and sustainable future amidst pandemic challenges.

In line with these perspectives, regular evaluation of VM performance is advantageous for construction projects as it guarantees the most effective execution of VM [87]. In a study by, Lin et al. [77], a comprehensive set of 18 KPIs was proposed for assessing the performance of value management (VM) in the construction industry. These KPIs were categorized into three groups based on their characteristics: predicting indicators, process-related indicators, and outcome-related indicators. By considering both objective and subjective indicators, the measurement process of VM can provide a comprehensive assessment of its performance. Additionally, Elhegazy et al. [88] conducted a study to identify and prioritize KPIs that impact the decision-making and value engineering process for selecting the structural system in multi-story buildings in Egypt. The researchers utilized the quality function deployment (QFD) technique to accomplish this objective. In addition, Madushika et al. [84] conducted a study on the KPIs for value management (VM) in the Sri Lankan construction industry, focusing specifically on the pre-construction stage. The study recommended the consideration of the identified KPIs during the implementation of VM techniques at various stages of construction to improve practices in developing countries. Also, Al-Gahtani et al. [89] identified and evaluated KPIs for measuring the performance of value management (VM) practices in the Saudi construction industry. Initially, they identified 55 KPIs and then narrowed it down to eleven indicators that were considered the most important for assessing VM performance. The research emphasized the significance of these specific KPIs in measuring VM performance and highlighted the importance of continuous performance measurement for efficient implementation of VM practices.

4.3.2. Environmental (Sustainable and Green Building) Performance

Environmental performance measurement in the construction industry is an increasingly important field of research and practice. Professionals in the sector are dedicated to mitigating the adverse environmental effects associated with building construction, both during and after the construction process [90]. Their focus lies in increasingly prioritizing the integration of green and sustainable criteria into performance measurement systems. This shift highlights the industry’s recognition of the growing significance of environmental protection and sustainability [91]. Benchmarking and establishing parameters for environmental performance indicators play a crucial role in improving the overall performance of the construction industry and informing the development of public policies [92]. For instance, Işik and Aladağ [93] conducted a study using fuzzy analytic hierarchy process (FAHP) analysis to identify measures and indicators related to sustainable performance in construction projects. Their findings revealed a prioritization of factors, with “environmental factors” ranking the highest, followed by “social factors”, “innovational factors”, “extrinsic factors”, “economic factors”, and “company performance factors”. This prioritization highlights the significance of considering environmental and social aspects performance when evaluating sustainable performance in construction projects. On the other hand, Alshuwaikhat et al. [59] developed a framework utilizing a case study methodology to identify 15 key performance indicators (best practices indicators) for smart sustainable city strategies and projects. These indicators were identified across the three crucial phases of the project lifecycle: conceptualization, planning/design, and installation/closure. This framework provides valuable insights for monitoring and enhancing the sustainability of smart sustainable city initiatives and projects throughout their lifecycle stages. Additionally, [94] proposed a range of KPIs to monitor circular construction site (CCS) processes and introduced a method to promote circularity in the construction phase. The KPIs included the Material Circularity Index (MCI), Water Circularity Index (WCI), Energy Circularity Index (ECI), Waste Circularity Index (WCI), Circularity Gap Index (CGI), and Circularity Performance Index (CPI). These indices provide a systematic framework for assessing and measuring the circularity of construction processes, facilitating the adoption of sustainable and circular practices in the construction industry. Furthermore, Geng et al. [95] highlighted the need for practical and efficient approaches to address the complexities and time-consuming nature of evaluating performance in green buildings. So, streamlined approaches are necessary to streamline the evaluation process and ensure efficient assessment of performance in sustainable construction.

Numerous investigations have been undertaken to evaluate the performance of green buildings, with each study focusing on specific indicators. For instance, Jing et al. [63] primarily investigated energy performance, while Christensen et al. [96] and Pei et al. [97] focused on evaluating indoor environmental quality (IEQ). In contrast, Altomonte and Schiavon [98] emphasized occupant satisfaction as the main aspect of evaluation. Moreover, studies such as Brown and Gorgolewski [99] and Lin et al. [100] assessed a combination of these three aspects: energy performance, IEQ, and occupant satisfaction.

To ensure practicality and cost-effectiveness compared to existing standards, it is crucial to reduce the number of indicators when measuring performance in green buildings. The advancement of communication and network technologies has significantly improved the efficiency and affordability of collecting operational data [101,102]. These developments have facilitated the process of gathering relevant data, making it more accessible and feasible. This enables the long-term collection of data for various indicators such as water usage, outdoor environmental quality, and renewable energy usage [95]. Furthermore, monitoring the indicators that influence operational outcomes, such as indoor environmental quality, building energy usage, and user satisfaction, is crucial. One example is the operational efficiency of HVAC (heating, ventilation, and air conditioning) systems, which directly impacts energy consumption [103], and user behavior significantly affects both energy consumption [104] and indoor environmental quality [105]. Addressing this gap, Li et al. [37] put forth a set of 27 KPIs specifically designed for monitoring the operational performance of green buildings. These KPIs encompass seven crucial aspects: outdoor environmental quality, HVAC system, indoor environmental quality, total resource consumption, renewable energy system, plumbing and drainage system, and user behavior. By considering and tracking these indicators, stakeholders can effectively assess and enhance the operational performance of green buildings, ensuring they meet the desired sustainability goals and deliver optimal environmental and occupant benefits.

Table 4. Example of KPIs to monitor the three dimensions of sustainability [38].

Dimension	Examples of KPIs	Unit of Measurement
Environmental	Energy consumption	kWh
	Water consumption	m3
	Use of packed materials	%
	Use of recycled materials	%
	Waste materials production	ton
	Waste water production	m3
	Greenhouse emissions	ton
	Soil use	Qualitative
	Changes in habitat	Qualitative
Economic	Rate of return percentage of e-procurement deals	%
	Shareholders dividends	Euros
	Labor costs	Euros
	Net profit	Euros
	Gross revenue	Euros
	Subcontracting costs	Euros/%
	Rate of return	%
	Risk management processes	Qualitative
Social	Number of complaints for work environment	No.
	Costs with workers education	Euros/%
	Absenteeism rates	%
	Health benefits	Qualitative
	Ratio highest/lowest salary	%
	Job creation	No.
	Investment in human resources know-how development	Euros

presents the distribution of KPIs across three dimensions of sustainability: environmental, economic, and social, as outlined by Cruz et al. [38]. The table provides an overview of how these KPIs are categorized and allocated within each sustainability dimension. This categorization allows for a comprehensive evaluation of sustainability performance in construction projects.

The Environmental Performance Index (EPI), as defined by Heravi and Ilbeigi [106], evaluates the contractor’s performance in relation to environmental concerns and costs incurred as a result of disregarding environmental considerations during the construction portions of the project. Equation (2) illustrates the EPI:

$$\mathrm{EPI}=\frac{\left(\mathrm{SELE}\right)}{\left(\mathrm{TECP}\right)}$$

(2)

where SELE denotes the combined direct and indirect costs resulting from non-compliance with environmental protection regulations, while TECP represents the overall expenditures incurred during the construction portions of the project.

4.3.3. Stakeholder Performance

In construction projects, the performance of stakeholders, including clients, contractors, and project teams, is critical for successful execution. Each stakeholder group has specific responsibilities and contributes to the overall project performance in distinct ways. Monitoring stakeholder performance often involves using KPIs, such as budget adherence, project completion time, quality metrics, client satisfaction, and safety records. Regular evaluations, feedback mechanisms, and continuous improvement efforts are vital for enhancing stakeholder performance and achieving project success [107].

• Client Performance

Measuring construction client performance is crucial for assessing the success of construction projects. It encompasses various factors that contribute to a satisfying and effective client experience. Timely project completion, the ability to identify and address issues and deficiencies, providing sufficient employee training, and utilizing high-quality materials are key aspects that shape client performance. These factors not only ensure the successful delivery of projects but also enhance client satisfaction and overall project effectiveness [108]. By evaluating and improving client performance in these areas, construction stakeholders can foster stronger relationships, achieve better project outcomes, and establish a reputation for excellence in the industry.

In Jordan, the lack of attention to the standard of comparison used by clients to determine their satisfaction levels has been identified as a significant issue. This standard is influenced by various factors, including the clients’ characteristics, expectations, and perceptions. To address this, Rahman and Alzubi [109] developed a conceptual framework aimed at predicting client satisfaction levels based on contractor performance and service quality dimensions. By considering these factors, the framework helps to better understand and meet the specific needs and expectations of clients, ultimately enhancing their satisfaction levels. Similarly, in India, Kumaraswamy et al. [110] introduced a Construction Clients’ Charter that established essential principles for clients in areas such as procurement, design, innovation, safety, and sustainability. They also developed KPIs specifically tailored for building clients, considering design, construction, and business outcomes. These KPIs serve as valuable tools for different stakeholders at various levels, enabling industry enhancements, improving project performance, and aligning with client expectations. Also, in a study conducted by Windapo et al. [12] on South African construction projects, it was found that client knowledge has a positive and significant impact on project performance. The study emphasized the importance of clients’ understanding of project procurement systems for achieving successful outcomes. Furthermore, a study conducted by Paus Usboko and Wahyu Adi [111] analyzed contractor satisfaction with owner team performance in construction projects. The researchers aimed to identify areas of owner performance that require improvement by assessing the perspectives of contractors. Through this analysis, the study emphasizes the significance of understanding and addressing client satisfaction, as well as establishing industry standards and principles to enhance owner team performance. By focusing on these aspects, construction projects can strive for higher levels of client satisfaction and overall project success.

The Client Satisfaction Index (CSI) which measures the satisfaction of the client with the performance of the contractor, The CSI is typically calculated based on feedback obtained from client surveys or evaluations. Various surveys may incorporate questions related to several aspects, including adherence to rules, codes, and client requirements; documentation and categorization of site documents; and suggestions for quality improvement and cost reduction, etc. To determine the CSI, Heravi and Ilbeigi [106] introduced a methodology that involves assigning scores and weighting factors to each question in the questionnaire, tailored to the specific project client. The CSI is then calculated using Equation (3):

$$\mathrm{CSI}=\frac{\sum {\mathrm{a}}_{\mathrm{i}}{\mathrm{b}}_{\mathrm{i}}}{\sum 10{\mathrm{b}}_{\mathrm{i}}}$$

(3)

where (a_i) typically ranges from 0 to 5 and represents the numerical rating assigned to each element or question in the survey. On the other hand, the weighting factor (b_i) ranges from 0 to 10 and reflects the importance or significance assigned to that particular element.

ii.

Contractor Performance

Measuring the performance of construction contractors is crucial for evaluating their effectiveness and success in projects. It is important to recognize the interconnectedness of contractor and owner performance, as their interactions significantly impact overall project outcomes. Including contractor satisfaction as a metric is essential for a comprehensive evaluation of project success. By incorporating contractor satisfaction into performance assessments, a more holistic understanding of project outcomes can be achieved. This approach fosters improved collaboration, enhances project performance, and ultimately contributes to the overall success of construction projects.

Numerous studies have been conducted to explore and address this topic, recognizing its significance in the industry. For instance, in Malysia, Masrom et al. [13] addressed the oversight of subjective measures of contractor satisfaction in previous studies. They developed a contractor satisfaction model to effectively measure and predict contractor satisfaction. This model incorporates eight dimensions that contribute to the overall assessment of contractor satisfaction, including time performance, profitability, product performance, safety performance, business performance, cost performance, design performance, and relationship performance. Recognizing the crucial role of contractor satisfaction in improving the overall performance of the construction industry in Malaysia, Yunus et al. [112] evaluated the relative importance of satisfaction variables among contractors operating within the industrialized building system (IBS). The study also examined the correlation between these satisfaction factors and eight performance categories, including cost, design, product, profitability, design, business, safety, time, and relationship performance.

On the other hand, Ofori-Kuragu et al. [113] developed two tools to improve contractor performance in the Ghanaian construction industry: the contractor scorecard (ConScor) for measuring project performance and the project scoresheet (ProScor) for measuring organizational performance. ConScor allows contractors to assess and monitor their performance on specific projects, identifying areas for improvement. ProScor enables contractors to evaluate their overall organizational performance and make strategic improvements. Similarly, Tuffaha et al. [76] developed a comprehensive framework for assessing the performance of construction contractors. This framework integrates both financial and nonfinancial aspects into a single model. The researchers identified five key dimensions that contribute to the assessment: performance, actual metrics, estimated metrics, compliance, and satisfaction. By considering these dimensions, the framework provides a holistic approach to evaluating and measuring the performance of construction contractors in Saudi Arabia. Also, in Australia, Gunasekara et al. [34] addressed the limitations arising from the absence of a contractor-centric perspective and non-price measures of performance. They identified seven non-price measures of performance (MoP) that can be used to evaluate contractors’ overall performance. These measures include health and safety performance, quality performance, time performance, environmental performance, experience and track record, human resources strength, and productivity achievement.

Contractors’ interdependence is a critical factor that affects the performance of complex infrastructure projects and should be proactively managed to mitigate the associated systemic risks. Due to the lack of appropriate analysis and quantification tools to measure the contractors’ interdependence and its impacts on project KPIs, Moussa and El-Dakhakhni [114] developed conceptual framework that quantifies contractors’ interdependence using complex network theory (CNT) measures and correlates them to project KPIs. Furthermore, Kassa et al. [15] conducted a study aimed at developing a specialized performance assessment tool specifically tailored for contractor project manager performance. The assessment tool consisted of seven criteria that encompassed different aspects of project managers’ performance. These criteria included the quality of work, leadership and management skills, overall job knowledge, ability to take initiative, communication skills, ability to meet deadlines, and an overall satisfaction rating.

Table 5. Key performance indicators (KPIs) for contractor performance.

Code	Kpi	[34]	[76]	[13]	[112]	[113]	[106]	[115]	[116]
C1	Financial (business) performance	✓	✓	✓	✓	✓	✓	✓
C2	Health and safety performance	✓	✓	✓	✓	✓	✓	✓	✓
C3	Experience and track record	✓
C4	Productivity achievement	✓	✓	✓	✓	✓		✓
C5	Quality performance	✓	✓			✓		✓	✓
C6	Environmental performance	✓	✓			✓	✓	✓	✓
C7	Human resources strength	✓
C8	Cost performance		✓	✓	✓	✓	✓	✓	✓
C9	Time performance		✓	✓	✓	✓		✓	✓
C10	Relationship performance			✓	✓				✓
C11	Design performance			✓	✓
C12	Profitability		✓	✓	✓
C13	Client satisfaction		✓			✓	✓	✓	✓
C14	People		✓			✓		✓
C15	Construction cost predictability		✓
C16	Construction time predictability		✓			✓
C17	Regulatory compliance		✓
C18	End-user satisfaction		✓						✓
C19	Billing performance						✓

showcases a collection of key performance indicators (KPIs) that can be used to measure and evaluate contractor performance. These examples highlight the specific metrics and criteria that can be utilized to assess the effectiveness and efficiency of contractors in construction projects.

iii.

Project Team Performance

In construction projects, it has been found that team processes play a more significant role in team effectiveness than individual leadership characteristics. Research has indicated that team members’ satisfaction with various processes, such as communication, common goals, trust, effective utilization of team resources and problem-solving, is significantly correlated with team performance [117]. Conversely, previous research has indicated that the leadership attributes exhibited by individual team members do not exert a substantial influence on the overall performance of the team [118]. These findings suggest that promoting positive team processes and ensuring effective collaboration among team members are critical factors for achieving high team performance in construction projects [119]. Therefore, numerous researchers have focused on studying and understanding project team performance in order to enhance overall project success and outcomes.

For instance, Tennant et al. [120] conducted a study to examine the correlation between the working dynamics of site management teams and the performance of construction projects. They utilized a customized suite of KPIs designed to align with project performance criteria. Furthermore, a recent empirical study conducted by Wang et al. [67] aimed to examine the impact of team voice on project performance in the context of construction projects, with a specific focus on multi-team systems. The researchers identified clear and mutually influential impacts of positive and negative team communication on project outcomes. Furthermore, it was determined that these impacts depend on the extent of project learning and project reflexivity.

Due to the absence of consensus on how to define and measure teamwork and team effectiveness in construction projects, Adu and Opawole [121] aimed to determine the essential characteristics of effective teamwork and the difficulties encountered by teams in the construction process. The objective was to enhance the performance of projects by addressing these factors. Their research revealed that team leadership, clarity of expectations and objectives, interpersonal dynamics within the team, top management support, and open communication emerged as the top five major attributes of effective teamwork in the study area. The project construction team and project control team services are crucial functions within construction companies, demanding distinct competencies compared to other roles or tasks. Recognizing this, Lee et al. [68] developed a competency model specifically tailored for human resource development and management in construction companies. This model encompasses various aspects such as performance measurement, employment, education, and organizational ability improvement. Moreover, Bal and Bryde [41] conducted a study to explore eight KPIs that can be utilized to measure and enhance stakeholder performance and project performance, with the aim of engaging the extended project team in the construction industry. These KPIs include productivity, customer satisfaction, energy consumption, health and safety performance, earned revenue, creativity in new product development, personal knowledge, and projects completed on time and on budget.

Leon et al. [19] implemented a team satisfaction key performance indicator (KPI) specifically designed for the construction industry in the United Kingdom. This KPI assesses team members’ satisfaction levels across four key areas: (1) the level of influence they have over their jobs, (2) their pay and working conditions, (3) the sense of achievement derived from their work, and (4) the level of respect received from their line managers/supervisors. Ratings for each area of concern are collected on a scale ranging from 1 to 10, where 1 indicates very dissatisfied and 10 indicates very satisfied. To determine the overall Team Satisfaction Performance Index (TSPI) rating, the average of the individual ratings for the four responses is calculated.

4.3.4. Schedule/Time Performance

In the realm of schedule performance, several critical factors have been identified as influential. Singapore’s public housing projects, for instance, are greatly impacted by factors such as site management, labor availability, and coordination among project parties [122]. The implementation of effective site management practices, seamless coordination, and the presence of an adequate workforce are crucial for ensuring timely project completion. Similarly, in Ethiopian public construction projects, success factors identified by Sinesilassie et al. [123] include the project manager’s competence, scope clarity, coordination, communication, and owners’ competence. Conversely, failure factors encompass conflicts, indecisiveness, unfavorable conditions, poor human resource management, and project manager ignorance. Consistently, material quality and shortages have been found to contribute to time overruns [124]. Addressing these material-related factors and ensuring effective supply chain management are key to minimizing time-related issues. Furthermore, Kapote [125] quantified project performance parameters and investigated success elements associated with partnering in Indian construction projects. In a separate study, Nwadigo et al. [126] used a dynamic Bayesian network model to examine construction time management and factors influencing construction project time. Zhao [127] studied prefabricated buildings and identified critical success factors related to time performance, including project planning, supply chain, techniques, teamwork, and external influences. Recognizing and addressing these factors can significantly improve time performance in prefabricated construction projects.

Various studies have proposed innovative approaches and methodologies for project performance monitoring, De Marco et al. [128] addressed the lack of standard methods for monitoring project schedule and cost performance in industrial buildings with repetitive structural frames. They proposed a comprehensive method that utilizes earned value metrics, performance indices, equations, curve-fitting techniques, and graphical and tabular representations. Their approach aimed to monitor progress, estimate cost and time at completion, and facilitate project performance comparisons. Similarly, Le-Hoai et al. [129] conducted a study in Vietnam to quantify the influence of factors on project time performance. Through multiple stepwise linear regressions, they identified significant variables that impact construction time performance. Their model was compared to an artificial neural network model to assess predictability. The study revealed six key characteristics that significantly influence project time performance, including subsurface site circumstances, project management works, estimating works, subcontractor competency, accuracy and completeness of design, and owner’s project financing. Examining the quantitative and qualitative dimensions of delay issues in construction projects, González et al. [130] proposed two indicators, namely reasons for noncompliance (RNC) and a delay index (DI), to measure these dimensions. Planning and subcontracts were found to be responsible for the majority of RNCs and weighted average reasons for noncompliance (WA-RNCs), making them the most prevalent and influential causes of delays. In order to address the absence of performance measurement frameworks (PMFs) for continuous evaluation of Sri Lankan construction projects, Rathnayake and Ranasinghe [66] developed methodologies that utilized indicators based on earned value management (EVM) to assess cost and time performance.

Recognizing the lack of dynamic system methods for measuring and improving project time performance in modular construction projects, a study conducted by Husin and Aulia [131] examined the various elements that influence the ability to control risks and ensure timely completion of residential high-rise building projects in modular construction. Their proposal involved the utilization of dynamic systems for risk management modelling. Additionally, Rachmawati et al. [21] considered the dynamic factors influencing work rate in construction projects and employed system dynamics to model and simulate the impact of work rate on project time and cost performance. Their study revealed that resource factors, management factors, and environmental issues played influential roles in determining work rate, which in turn affected project performance indicators. In addressing the inconsistent implementation and evaluation of collaborative scheduling (CS) practices, He et al. [132] adopted an information theory approach to assess uncertainty and information sharing between CS practices and project performance indicators. Their objective was to establish mathematical models that determined the most efficient sequence of actions to enhance CS. Furthermore, Zahoor et al. [133] conducted a comparative analysis between earned value management (EVM) and conventional progress reporting methodology (CPRM) in Pakistan. Their findings indicated that EVM outperformed CPRM in terms of accuracy, reliability, and effectiveness for measuring and forecasting project performance, mainly due to its comprehensive integration of project cost, schedule, and scope.

Table 6. Brief explanations and mathematical expressions of EVM metrics [133].

No	Terminology	Description	Formula
1	BAC (budget at completion)	The original estimated (planned) project cost	None
2	PV (planned value)	The quantity of work that should have been accomplished	PV = [BAC × % Planned Completion]
3	EV (Earned value)	The current progress achieved at any given moment	EV = [BAC × % Actual Completion]
4	AC (actual cost)	The current financial or material resources that have been expended at any given moment	AC = Cumulative money spent till date
5	SV (schedule variance)	The difference between planned and actual schedule	SV = EV − PV
6	SPI (schedule performance index)	The current status of work in relation to the planned schedule	SPI = EV/PV
7	CV (cost variance)	The difference between planned and actual cost	CV = EV − AC
8	CPI (cost performance index)	The project’s performance compared to the expenditure per unit	CPI = EV/AC
9	EAC (estimate at completion)	The projected budget, based on the current status, that is expected to be spent for the completion of the project	EAC = [(BAC)/ (Cumulative CPI)]
10	ETC (estimate to completion)	The projected additional expenditure required to complete the project, based on the existing performance	ETC = EAC − AC
11	VAC (variance at completion)	The difference between the initially projected cost and the updated estimates derived from present performance indicators	VAC = BAC − EAC

presents a comprehensive list of the corresponding methods or formulas required for their calculations for EVM [133].

4.3.5. Cost/Budget Performance

Cost performance measurements play a pivotal role for organizations in the construction industry, enabling them to assess and evaluate their performance in construction projects [134]. The evaluation of target cost contracts and guaranteed maximum price contracts in the construction sector is significantly influenced by the utilization of KPIs [135]. Overall, performance measurement in terms of cost is a significant aspect of construction project management and can help organizations improve their performance and control costs. Moreover, studies conducted by Ali and Mansor [6] and Cha and Kim [136] highlight the significance of cost performance as a crucial indicator in developing an effective performance measurement system. Their research emphasized the importance of considering cost performance as a key factor when designing a framework for measuring the success of construction projects. These studies shed light on the prioritization of indicators and reinforce the notion that cost performance holds significant influence in evaluating project performance.

In Malaysia, Waris et al. [72] applied the earned value analysis (EVA) approach on a real project and analyze the cost variances, performance indices, and forecasting indicators. The study conducted by Rahman et al. [137] delved into the extent and causes of time and cost overruns in construction projects within the southern and central regions of Peninsular Malaysia. The researchers employed a combination of questionnaire surveys and interviews with personnel involved in the construction industry to gather data and insights. Through their investigation, they aimed to shed light on the factors contributing to project delays and cost overruns in the specific geographic areas of focus. Similarly, in Pakistan, Nawaz et al. [138] identified the lack of effective measures to address factors contributing to cost overruns in construction projects. Their study revealed that cost overruns are primarily caused by human factors rather than technical or natural factors. This finding highlights the importance of improving professionalism, ethics, and communication among stakeholders within the construction industry. By addressing these human factors and promoting better collaboration and communication, the industry can mitigate cost overruns and enhance overall cost performance in construction projects.

Due to the absence of a performance measurement framework that could assist decision-making in the budgeting process for building construction, Azevedo et al. [139] developed a model using the multicriteria decision aid constructivist methodology (MCDA-C). This model allowed decision-makers to gain insight into the current situation, generate improvement actions, measure their impact, and prioritize them based on predefined goals. The MCDA-C model serves as a valuable tool to enhance the budgeting process in construction projects, enabling the production of competitive advantages while improving the quality and accuracy of budgeting. Moreover, in their study, Choi et al. [140] conducted a study that focused on a drawback of current project assessment methods. These methods presume that all project information is easily accessible once the project is finished. The cost normalization methodology presented by the researchers is specifically tailored for the purpose of phase-based performance assessment. This approach entails the evaluation of phase-level outcomes throughout the duration of current projects. The framework encompasses three key steps: currency conversion, location adjustment, and time adjustment. By considering scenarios where complete information may not be available, this framework ensures the accuracy and reliability of the assessment, providing a more robust approach to project evaluation. Additionally, Moradi et al. [73] introduced a novel earned value model (EVM) that incorporates risk analysis to enhance the accuracy and reliability of cost and time performance forecasting in projects. The proposed model addresses the challenges of uncertainty and imprecision by utilizing interval-valued fuzzy sets. Also, Rathnayake and Ranasinghe [66] developed a methodology aimed at interpreting and applying appropriate indicators to evaluate the cost and time performance of construction projects. This methodology serves as a valuable tool for continuous measurement and evaluation, ultimately helping to enhance performance levels in the Sri Lankan construction industry. By utilizing these indicators, stakeholders can effectively monitor and assess project costs and timelines, leading to improved project outcomes.

Additionally, in the same context, Du et al. [70] proposed a unique methodology known as Markovian simulation cost projection (MSCP), which integrates dynamic data with resilient cost projection methodologies. The validation of the MSCP approach was conducted by the researchers through a case study that involved a practical power plant project. They compared its performance with the traditional earned value analysis (EVA) method to enhance cost management practices and improve overall project performance. In order to model the probability distribution of cost performance indicators for each project period, the MSCP method employs a combination of Markov chain and Monte Carlo simulation techniques.

Furthermore, the singular-value decomposition feature extraction (SVDFE) method was proposed by He et al. [71] in a related study for the purpose of cost-performance prediction in building projects. The SVDFE technique outperformed other stochastic methods, such as the Markovian simulation cost projection (MSCP) method, through an actual power plant project case study. Throughout a project’s lifecycle, SVDFE efficiently captures volatile changes in cost performance, allowing for more precise estimate-at-completion (EAC) forecasts and early alerts for possible cost deviations.

Collectively, these studies highlight the importance of developing effective performance-measurement frameworks and methodologies that address specific challenges in construction projects. These frameworks and models contribute to enhanced decision-making, improved accuracy in budgeting and forecasting, and ultimately lead to better project outcomes.

4.3.6. Quality Performance

The quality of a construction project is determined by several criteria. Structural integrity ensures compliance with standards and regulations for a safe and stable structure. Compliance with building codes and legal requirements ensures safety and environmental standards are met. Functionality and performance assess whether the building meets operational needs. Aesthetics and design excellence contribute to visual appeal and integration with surroundings. Material choice and workmanship impact durability. Sustainability considers energy efficiency and eco-friendly practices. Durability and maintenance focus on long-term reliability. User comfort and safety prioritize health, safety, and accessibility. Cost control and value for money optimize expenses. Stakeholder satisfaction measures meeting expectations and preferences [141,142]. By considering these criteria, building projects can aim for high-quality outcomes prioritizing safety, functionality, design excellence, sustainability, and stakeholder satisfaction. Despite the construction industry receiving less attention in quality research compared to the more advanced manufacturing sector, Delgado-Hernandez and Aspinwal [143] developed a framework for integrating quality into construction projects. This framework offers a structured approach to quality improvement and can be adapted to different company sizes and project types. Additionally, Leong et al. [144] addressed the lack of studies on the relationship between quality management systems (QMSs) and project performance in construction. They measured the effectiveness of QMS maintenance and practices, finding significant impacts on client satisfaction and time variance. However, other performance indicators such as cost variance, cost performance index, and safety-related factors showed weak or no significant relationship with QMS variables. In a study by Wanberg et al. [145], the relationship between safety and quality in construction was explored. The research revealed a strong positive correlation between rework and injuries, as well as between the number of defects and first aid rates. Strategies promoting both safety and quality, such as preplanning and leadership at the workface, were identified. The findings emphasized the importance of addressing rework, demolition, schedule pressure, and unstable work processes to enhance safety and quality in construction projects.

In a comprehensive exploration of factors impacting the quality performance of construction projects, various studies have shed light on critical parameters and key indicators. Shaikh and Darade [146] identified these parameters at the planning stage, while also providing testing procedures, an integrated checklist, and a rearranged project quality plan (PQP) to ensure activity quality. Similarly, Shaikh, and Darade [147] focused on maintaining activity quality by presenting correct testing procedures, an integrated checklist for external quality audits, and an updated PQP with contents and key indicators. Building on this research, Dilipbhai and Somabhai [148] identified both success and adverse factors significantly influencing quality performance in construction projects in Pakistan. They emphasized the importance of continuous improvement, joint working, communication, technical expertise, ISO certification, and effective procurement units for achieving good quality performance.

Examining the critical factors impacting quality performance in government-financed construction projects in Tanzania, Raphael and Phillip [149] highlighted the significance of project financing processes, contractor experience, project technology, equipment availability, procurement systems, and project manager knowledge and skills. Sheikh et al. [150] focused on the construction phase of building projects in Pakistan, identifying key factors influencing process quality and recommending measures for improvement, including the development of national building codes, promoting teamwork, rigorous contractor selection, vigilant supervision, and management commitment to quality improvement.

Jose and Ambili [151] delved into critical factors affecting quality performance in construction projects, emphasizing the positive impact of project manager coordination and rapport with owner representatives, decision-making authority of the project manager’s team, and overall commitment of all project stakeholders. On the other hand, Buba and Tanko [152] examined the impact of leadership styles on quality performance in public projects in Nigeria, highlighting the directing and coaching leadership styles as influential in achieving high aesthetic and functional quality.

Lastly, Neyestani and Juanzon [153] developed a framework for assessing the effects of total quality management (TQM) implementation on organizational performance in the construction industry. Their research emphasized the significance of KPIs related to customer satisfaction, market share, quality cost, profitability, employee satisfaction, and sales growth for evaluating TQM implementation at both project and enterprise levels. Collectively, these studies provide valuable insights into the multifaceted aspects of quality performance in construction projects.

According to the research by Heravi and Ilbeigi [106], a quality performance index is included as one of the indices in their model. This index, referred to as $QP{I}_{Product}$, quantifies the quality of the final project product. It is defined using Equation (4) as follows:

$$QP{I}_{Product}=\frac{SEL{Q}_{Prd}}{TECP}$$

(4)

where $SEL{Q}_{Prd}$ represents the sum of direct and indirect expenses incurred due to the lack of quality in the final project product. On the other hand, TECP represents the total expenses associated with the construction phases of the project.

4.3.7. Safety Performance

The construction industry has encountered significant challenges concerning safety performance. Research studies have revealed a concerning rise in workplace fatalities over the past decade, placing financial burdens on project stakeholders [154]. To address this issue, there is a need for proactive approaches in measuring safety performance. One approach is the use of leading indicators, which can provide insights into potential hazards and help in accident prediction and prevention [155]. Various components have been identified for measuring safety and health management performance in the construction industry, including safety regulations, safety culture, leadership, performance measurement, risk assessment, safety planning, safety inspection, and compliance [156]. Moreover, the introduction of a leading indicator-based jobsite safety inspection method has proven effective in measuring safety performance within the construction industry. This method has led to notable enhancements in safe behavior and improved working conditions [36,53]. In a related context, Yiu and Chan [157] identified a lack of connection between project characteristics and safety performance in construction projects. To bridge this gap, they established a potential linkage between project characteristics and safety performance, considering the perspectives of key stakeholders such as clients, contractors, and consultants. The study emphasized the importance of various project characteristics, including senior management commitment, a clear understanding of work activities, effective planning and execution, and proper housekeeping. These factors were found to facilitate project efficiency and contribute to long-term safety performance. By recognizing and prioritizing these project characteristics, stakeholders can enhance safety practices and create a safer working environment for construction projects.

Most studies focus on lagging indicators, such as accident statistics, which fail to reflect the overall safety scenario and proactive measures [155]. So, Guo and Yiu [55] proposed a conceptual framework for developing leading safety indicators for the construction industry that can clarify the nature of the indicators in terms of definition, purpose, and attributes that can reflect and monitor the safety conditions and facilitate proactive safety management. Also, Guo et al. [158] developed a theoretical model that conceptualizes the safety level of a construction project. This model enables the design of leading indicators and encompasses a set of leading indicators specifically designed to measure the safety state, safety practices, and pressures within a construction project. Additionally, Bhagwat et al. [36] developed a practical and viable leading indicator-based jobsite safety inspection (JSI) method for construction projects. The researchers validated the effectiveness of this method through a comprehensive case study. The results demonstrated that the JSI method is capable of identifying safety issues and providing early warnings of potential accidents, enabling proactive measures to be taken to address them. The implementation of the JSI method can significantly contribute to improving safety performance on construction sites by facilitating timely interventions and mitigating potential risks. Moreover, Rajaprasad and Mukkamala [64] evaluated the safety performance of construction project sites and compared four machine learning (ML) classifiers to develop a predictive model using data envelopment analysis (DEA). In a related context of safety performance prediction, Abbasianjahromi and Aghakarimi [159] devised a framework utilizing machine learning techniques to forecast and enhance safety performance in construction projects. The framework developed by the researchers serves as a valuable tool for project managers and practitioners, enabling them to gain insights into their safety performance and make informed decisions to improve it.

In a study conducted by Mahmoud et al. [160], the focus was on establishing KPIs to evaluate and enhance the safety performance of building developers in Nigeria. The research identified a comprehensive set of 137 KPIs, categorized into nine distinct groups. These categories covered various aspects of safety management, including design, planning, and procurement; construction safety policy; management effort and support; communication and maintenance of effective safety behavior; rewards and sanctions for project stakeholders; safety training and enlightenment; administration of safety processes; investigation and reporting of accidents; and construction safety personnel. The development and implementation of these KPIs provides a framework for assessing safety performance and promoting continuous improvement in the Nigerian construction industry. Ultimately, this contributes to creating safer working environments for both workers and stakeholders. And due to the lack of empirical evidence on how complexity and resilience influence safety performance in construction projects, Peñaloza et al. [35] introduced and evaluated a framework based on resilience engineering (RE) to assess safety performance measurement systems (SPMSs) in construction projects. The framework combines two established tools, the resilience assessment grid (RAG) and the technical, organizational, and environmental (TOE) framework, to determine the alignment of SPMSs with RE principles and their ability to address the complexity attributes of construction projects using design science research (DSR) methodology. Similarly, Engler Bridi et al. [65] employed design science research (DSR) methodology to propose a method for evaluating the implementation of safety management best practices in construction sites. This method incorporated systems thinking as a theoretical lens, which offered unique characteristics. Contrasting with traditional surveys, the proposed method provided a more reliable and comprehensive assessment by considering the context, interactions, emergence, and diversity of perspectives in safety management.

Table 7. Key performance indicators (KPIs) for safety performance.

Code	Kpi	[35]	[53]	[36]	[161]	[162]	[163]	[158]
S1	Lost time injury rate	✓
S2	Non-conformities indicator	✓
S3	Accident frequency rate	✓				✓	✓
S4	Incidents reported effectiveness	✓				✓	✓
S5	Housekeeping (workplace cleanliness evaluation)	✓	✓
S6	Personal protective equipment (PPE): availability and usage.		✓
S7	Safety measures for elevated work (Access to heights)		✓
S8	Construction tools, plant, and equipment safety		✓	✓
S9	Scaffolding: safety standards for scaffolding		✓
S10	Safety training (knowledge)			✓			✓	✓
S11	Safety initiatives			✓			✓
S12	Working at height safety			✓
S13	Compliance with electrical regulations safety			✓
S14	Management commitment			✓		✓	✓	✓
S15	Fire safety: prevention and response to fires			✓
S16	Emergency preparedness			✓
S17	Healthy and safe site condition			✓				✓
S18	Toolbox meetings: safety meeting frequency and effectiveness				✓	✓
S19	Safety observations (climate)				✓			✓
S20	Pre-brief meetings/pre-start meetings				✓	✓	✓
S21	Site surveillance inspections carried out				✓
S22	Occupational health and safety audits				✓		✓
S23	Penalties/infringements				✓
S24	Safety compliances				✓	✓
S25	Hazards closed out: timely hazard closure tracking				✓			✓
S26	Inspections carried out				✓
S27	Hazards reported				✓
S28	Drug tests				✓	✓
S29	Safe work method statements/job safety analysis documents reviewed and amended				✓
S30	Alcohol tests				✓
S31	Site inductions				✓
S32	Employee involvement and empowerment and social support						✓	✓
S33	Safety motivation						✓	✓

showcases a collection of key performance indicators (KPIs) that can be used to measure and evaluate safety performance.

The Safety Performance Index (SFI), as depicted in Equation (5), takes into account safety concerns in the final outcome of an executive project. Several studies have explored safety performance using various methods. However, Heravi and Ilbeigi [106] established the SFI by considering two potential consequences that result exclusively from a lack of safety observance. The SFI is calculated as the sum of two sub-indices.

$$SFI=x_1\times SF{I}_c+x_2\times SF{I}_s$$

(5)

$${\displaystyle \sum }{x}_i=1$$

where $SF{I}_c$ is the safety performance index of the project outcome in terms of expenses as calculated in Equation (6). And $SF{I}_s$ the safety performance index of the project outcome in terms of scheduling as calculated in Equation (7).

$$SF{I}_c=\frac{ELSO}{TECP}$$

(6)

where ELSO represents the expenses incurred due to damages resulting from inadequate safety observance, while TECP denotes the total expenses associated with the construction phases of the project.

$$SF{I}_s=\frac{IDWH}{TIWH}$$

(7)

where IDWH represents the number of individuals who experienced reduced working hours or dismissal due to safety non-compliance, while TIWH represents the total number of individuals’ working hours during the construction phase.

4.3.8. Innovation and Technology Performance

Innovation and technology performance in the construction industry encompasses the degree to which innovative technologies and processes are effectively and efficiently adopted, implemented, and utilized to enhance construction projects and overall industry performance [164]. The integration of technology, such as building information modelling (BIM) and computer vision, has significantly enhanced construction performance measurement practices. By combining lean principles with BIM technology, construction projects have seen increased efficiency, visualization, better building processes, performance, risk mitigation, and cost reduction [5,165]. Additionally, the adoption of digital technologies, like computer vision in construction project management (CV-CPM), has shown promise in addressing challenges related to data collection, analysis, and project monitoring, leading to improved decision-making and competitiveness in the industry [166,167]. By continuously monitoring and benchmarking technology performance indicators, industry stakeholders can identify areas for improvement, establish best practices, and promote the adoption of innovative technologies and practices throughout the construction sector.

For instance, Ramos-Hurtado et al. [39] proposed a methodology for developing and evaluating an augmented reality (AR) application in the context of safety applications. Specifically, their study focused on an AR application for inspecting collective protective equipment (CPE). The researchers also identified KPIs to measure the functionality and usability of the AR application. Also, Ratajczak et al. [60] developed AR4C (augmented reality for construction), an augmented reality (AR) application that aimed to optimize construction project performance. This prototype integrated building information modeling (BIM) and a location-based management system (LBMS). AR4C facilitated seamless data exchange between BIM software and the unity environment, incorporating LBMS into BIM software and the AR4C application. This integration enabled the visualization of 3D models and information in augmented reality, providing a valuable tool for enhancing construction project performance and supporting informed decision-making processes in the industry.

Due to the lack of theoretical frameworks to explain and measure the innovation diffusion process of 4D BIM, Gledson and Greenwood [168] measured the rate of adoption and the time lag between awareness and first use of 4D BIM innovation among construction planning practitioners using descriptive statistics, inferential statistics, and relative importance indices. Additionally, Shin et al. [40] examined the utilization of BIM in the development stage of railway infrastructure projects. A comparative analysis approach was utilized to evaluate the cost-effectiveness, labor hours, and differences in the number of workers by comparing projects of similar scope. The assessment centered on situations in which construction firms autonomously employed BIM and instances where professional BIM consulting services were engaged. The study sought to offer insights on the efficacy and advantages of employing BIM in the construction sector, particularly in the realm of railway infrastructure projects. On the other hand, in healthcare projects, Choi et al. [169] established functional requirements of a BIM-based benchmarking tool for healthcare projects, which can streamline the data collection, validation, analysis, and reporting processes, and reduce the time and effort involved in benchmarking practice. Moreover, Bapat et al. [170] and Bapat et al. [171] identified KPIs related to integrated project delivery (IPD) and BIM performance for mega construction projects. They utilized the factor comparison method (FCM) and fuzzy decision-making trial and evaluation laboratory (DEMATEL) approach to analyze and evaluate these KPIs. Also, Khanzadi et al. [172] conducted a study in Iran to identify and prioritize BIM applications based on KPIs in the construction stage of project life cycles. They explored the capabilities of BIM in enhancing construction project performance and productivity, ranking the applications according to their importance and contribution to KPIs such as time, safety, cost, quality, and sustainability using a qualitative stage (Delphi) and a quantitative stage (Fuzzy-AHP).

Recognizing the limited focus on technological innovation capabilities in the link between information platform usage and project performance, Yang and Huang [75] conducted a study to validate a model that explores the relationships among these factors in the context of capital facility projects. Their findings revealed that the utilization of a cloud platform serves as an effective IT tool for supporting project management in the construction industry. Additionally, Amusan et al. [69] developed a site logic regression model to assess the effectiveness of site management. The model incorporated key variables such as informatics, innovation, and performance, highlighting their interrelationships and influences.

Table 8. Key performance indicators (KPIs) for technology utilization performance.

Technology		Augmented Reality (AR)	Building Information Modeling (BIM)	BIM and IPD Adoption	BIM, AR, and Lean Construction
Code	Kpi	[39]	[40]	[170]	[60]
T1	Time to complete the inspection	✓
T2	Usability	✓
T3	Weight of the app	✓
T4	Cost	✓
T5	Hardware	✓
T6	Software	✓
T7	Operating system (OS)	✓
T8	4D simulation of construction information		✓
T9	5D simulation of cost estimation		✓
T10	BIM coordination in construction projects		✓
T11	Organizational human resources		✓
T12	BIM investment costs		✓
T13	Creating BIM models from 2D drawings		✓
T14	BIM training		✓
T15	BIM implementation overview		✓
T16	Team and personnel		✓
T17	Financial aspects		✓
T18	Impact assessment		✓
T19	Enhancing collaborative decisions by adoption of IPD and BIM			✓
T20	Project stakeholder’s early involvement in IPD and BIM adopted projects			✓
T21	Awareness and benefits of IPD and BIM for infrastructure projects			✓
T22	Mandatory implementation of BIM and IPD by government authorities			✓
T23	Improvement in productivity through use of IPD and BIM			✓
T24	Effect on overall life cycle cost of project by use of IPD and BIM			✓
T25	Improved design flexibility by utilizing IPD and BIM			✓
T26	Accessibility and accuracy of information by BIM			✓
T27	Speed of construction and delivery after IPD and BIM adoption			✓
T28	Progress monitoring efficiency for infrastructure projects through IPD and BIM			✓
T29	Resource optimization by collaboration of IPD and BIM			✓
T30	Minimized amount of rework by integrating IPD and BIM			✓
T31	Client’s satisfaction by implementing BIM and IPD			✓
T32	Facilitating access to real-time data by IPD and BIM			✓
T33	Minimizing claims and disputes through implementation of IPD and BIM			✓
T34	Interoperability and compatibility of data by IPD and BIM			✓
T35	Performance ability ratio (PAR)				✓
T36	Current progress (CP)				✓
T37	Percent plan completed (PPC)				✓
T38	Reason for non-completion (RNC)				✓
T39	Delay indicator (DI)				✓
T40	Extra effort (EE)				✓
T41	Quality gate (QG)				✓
T42	Construction errors (CE)				✓
T43	Extra costs (EC)				✓

showcases a collection of key performance indicators (KPIs) that can be used to measure and evaluate technology utilization performance.

4.3.9. Maturity Levels of Performance in the Construction Sector

Maturity models are valuable tools for assessing the success of building projects. They enable performance benchmarking against industry standards, track project progress, identify areas for improvement, and promote process refinement [173]. Maturity models also aid in proactive risk management, foster a culture of continuous improvement, facilitate stakeholder alignment, and support knowledge transfer [174]. By leveraging maturity models, project teams can effectively evaluate and enhance the success of building projects through structured frameworks that provide guidance and promote excellence at various maturity levels. Various international standard models focus on different aspects of construction performance. For example, the supplier quality management system (SQMS) model concentrates on the quality management system of external stakeholders, ensuring their adherence to quality standards. The service quality (SERVQUAL) model, on the other hand, is used to assess and improve client satisfaction within construction projects. Similarly, the performance of internal stakeholders involved in project management is evaluated using the project management maturity model (ProMMM) and the construction performance assessment system created by the Construction Industry Institute (CII 10-10). In order to evaluate the effectiveness of external stakeholders and promote operational efficiency inside the supply chain, the supply chain maturity model (SCMM) is utilized. Lastly, the building research establishment environmental assessment method (BREEAM) focuses specifically on evaluating the environmental performance and sustainability aspects of construction projects. In contrast, the Malcolm Baldrige National Quality Award (MBNQA) model is used for measuring overall quality performance [175]. Each standard model proposes different frameworks for assessing and categorizing construction performance maturity, utilizing assigned scores. The SQMS model divides a total of 1000 points into three different levels, with level 2 having a maximum score of 500 points. The ProMMM, SERVQUAL, and CII 10-10 models assess maturity based on four levels, each with a score range of 250 points. The SCMM evenly allocates 1000 points across five levels, with each level receiving a score of 200 points [27]. The BREEAM approach categorizes 1000 points into six levels, spanning from unallocated (0–299 points) to exceptional (850–1000 points). The MBNQA categorizes a total of 1000 points into six distinct maturity levels. Levels 1 and 6 are assigned a score range of 100 points each, while the remaining four levels are assigned a score range of 200 points each. These models provide varying score ranges and levels to measure and evaluate construction performance maturity [176].

To evaluate and enhance the Construction Performance Index (CPI), it is vital to establish different levels of performance maturity. Various studies in the construction field utilize diverse maturity levels and score ranges to assess performance. Considering the specific maturity levels and corresponding score ranges used in each construction-related study is crucial for accurate evaluation and improvement of the CPI. Furthermore, Willis and Rankin [177] conducted a study where they established a connection between construction industry maturity and performance. They employed the construction industry macro maturity model to investigate this relationship. The study’s findings indicate that higher maturity within the construction industry leads to more efficient implementation of regulations and management practices, resulting in improved project performance. Therefore, aligning with the specific maturity levels and score ranges used in research studies and recognizing the impact of industry maturity on performance can further enhance the evaluation and improvement of the CPI. Also, Htoo et al. [23] investigated the impact of time, quality, and cost management maturity on the performance of building construction projects in Myanmar. The researchers also aimed to compare maturity levels between public and private organizations. Using a five-level maturity model, they discovered a positive correlation between project management maturity and project performance. Specifically, higher levels of maturity in cost and time management were found to be associated with improved project performance. These findings emphasize the importance of industry maturity in achieving successful outcomes and emphasize the necessity for ongoing development and progress within the construction sector. These findings serve as a motivation for researchers to establish maturity levels of performance in various applications within the construction industry.

For instance, Mohamed and Chinda [25] employed a framework comprising five levels to evaluate the development of safety culture. Each level was assigned a score-range of 200 points. The score ranges, along with the estimated construction safety culture (CSC) index, are used to identify the precise level of CSC maturity. Additionally, Trinh and Feng [16] used a five-level capacity maturity model to evaluate the resilience of safety culture. This model allowed them to assess the level of resilient safety culture within a range of “1 = Pathological” to “5 = Generative”. Moreover, Musonda et al. [26] discovered a noteworthy association between an organization’s leadership, procedures, involvement, communication, commitment, and competence (referred to as LIP +3C) and both the safety culture maturity model (SCMM) and behavior. Consequently, the study suggested a consolidated scale, derived from three models, for evaluating the extent of safety culture, as presented in

Table 9. Synthesized health and safety model and proposed measures for maturity [26].

Level	Synthesized Key Indicators	Maturity Level	Mean Value
1	No care culture	Pathological	1
	Level of awareness is low
2	Individuals are blamed for incidents	Reactive	2
	Reactive response
	Safety action is seen after an accident or incident
3	Management discusses safety	Calculative	3
	Workers are not involved in planning for safety
	Effort to comply with regulations
4	Workers are not involved in planning for safety	Proactive	4
	Management monitors H&S
	There is communication about H&S
5	Safety is how work is completed	Generative	5

. The study identified and named five progressive levels of safety culture, including SCMM-1 (pathological), SCMM-2 (reactive), SCMM-3 (calculative), SCMM-4 (proactive), and SCMM-5 (generative).

On the other hand, Hu and Wu [178] highlighted the low maturity level of risk management (RM) within construction enterprises. They emphasized the need for implementing a comprehensive, structured, and practical RM system to enhance this maturity level. Their findings underline the significance of developing the maturity level performance of risk practices within the construction industry to effectively mitigate potential risks. Similarly, Wibowo and Taufik [28] evaluated the risk management maturity level of construction public client organizations. They employed a framework consisting of four maturity levels, with each level assigned an equal score of 25 points. In the context of construction supply chain relationships, Meng et al. [54] developed a maturity model specifically tailored for evaluating these relationships. The model consists of four levels of maturity, providing a framework to assess the current state of relationships between companies and identify areas that can be improved. This model serves as a valuable tool for enhancing collaboration, communication, and coordination within the construction supply chain.

Furthermore, Soewin and Chinda [175] developed a dynamic model of the CPI to evaluate and enhance performance maturity levels in the construction industry over an extended period. The model incorporates six levels of construction performance maturity, and the total score of 1000 points is divided among these six levels. Additionally, Jawad and Ledwith [14] presented a measurement model for evaluating the success of project control systems (PCSs). Their study introduced the PCS maturity model (PCSMM), which consists of five levels to assess the maturity of PCS based on specific capabilities. This model enables organizations to assess their project control capabilities and identify areas for improvement.

5. Discussion and Future Recommendations

While the reviewed studies offer valuable insights, there are significant research gaps that require attention in the field of construction performance measurements. Figure 8 illustrates the research gaps and future directions, highlighting the need to address these gaps to develop comprehensive and effective models, frameworks, and tools for project performance evaluation and management.

5.1. Enhancing Objectivity and Robustness in Performance Measurement

Subjectivity and Uncertainty in KPI Selection: The subjectivity and uncertainty involved in selecting key performance indicators (KPIs) can impact the validity and reliability of results. To address this, future research should focus on mitigating subjectivity and enhancing objectivity through the use of standardized measurement tools and protocols. This will ensure a more robust KPI selection process and reduce potential bias and inconsistency in data collection.

Causal Relationships in Performance Evaluation: Current methods may not capture the causal relationships among KPIs, limiting the accuracy of performance evaluation. To overcome this limitation, future research should integrate the system dynamics method with other approaches, such as expert systems and simulations. By adopting a hybrid approach, researchers can gain a more comprehensive understanding of project dynamics and capture the complex interactions among KPIs. This integration will improve decision-making and enhance the accuracy of performance predictions.

5.2. Adapting to the Evolving Construction Landscape

Evolving Nature of Construction: The construction industry is dynamic and continuously evolving, driven by factors like digital transformation. Future research should acknowledge the changing landscape and address emerging trends and challenges. This includes monitoring, updating, and adapting models, methods, and findings to ensure their relevance in the evolving construction industry. By staying current with industry developments, researchers can provide valuable insights and contribute to the effective management of construction projects.

Contextual Factors: Consideration of contextual factors, such as project complexity, client involvement, and the external environment, is crucial for a comprehensive understanding of performance measurement. Future studies should explore the influence of these factors on various aspects, including competency models, performance measurement, team working, and project outcomes. By accounting for contextual factors, researchers can provide more accurate and context-specific performance evaluations.

5.3. Overcoming Measurement Limitations

Measurement Limitations: Accurately measuring certain aspects, such as contractor performance, building condition, motivation, and project outcomes, poses challenges. Additionally, the forecasting process in this study was restricted to examining the impact of performance indices on each other using documented project information, without explicitly accounting for unexpected factors such as severe weather conditions or force majeure circumstances. To enhance future research in this field, it is crucial to explore innovative measurement approaches and incorporate multiple indicators to achieve a more comprehensive assessment. By addressing these measurement limitations, researchers can enhance the accuracy and reliability of performance measurements. Furthermore, the development and advancement of performance evaluation methods are expected to continue playing a crucial role in various fields and industries. As technology evolves and new challenges arise, researchers and practitioners will increasingly focus on refining existing methods and exploring innovative approaches to effectively assess and improve performance. This ongoing development is essential to keep pace with the changing demands of modern systems, processes, and work environments. In this dynamic landscape, it is important to consider emerging technologies such as artificial intelligence (AI) and machine learning (ML). These technologies have the potential to revolutionize performance evaluation by analyzing vast amounts of data, identifying patterns, and providing valuable insights. Integrating AI and ML techniques into performance evaluation processes can unlock new possibilities for understanding and optimizing performance metrics, leading to more efficient and effective outcomes.

5.4. Enhancing Performance Measurement in Megaprojects: Addressing Uncertainty and Validation

Megaprojects, which are defined by their longer duration and complex nature, inherently involve uncertainty when it comes to measuring performance. However, a thorough examination of the literature indicates that there is a widespread tendency to heavily rely on questionnaires and interviews as the primary means of gathering data, while overlooking the potential value of other sources of data, such as project information and papers. Furthermore, there is a limited amount of research that specifically examines the uncertainty associated with performance measurement when choosing data processing methods. In order to tackle this problem, researchers should employ a scientific methodology when it comes to performance measuring techniques, with the goal of improving the dependability of the outcomes. The vigorous implementation of archive analysis can enhance other techniques of data collection, hence minimizing the impact of personal subjectivity from experts or interviewees. Fuzzy set theory is a useful tool for properly managing uncertainty in performance monitoring. Furthermore, it is necessary to enhance the level of parameterization in measuring methodologies used in the majority of investigations.

When conducting a comprehensive evaluation of project performance, it is crucial to consider several extra aspects for a holistic assessment. These include stakeholder satisfaction to gauge their contentment, sustainability, and environmental impact to assess resource efficiency and adherence to green practices, innovation, and technology adoption to gauge a project’s forward-thinking approach, risk management to evaluate the ability to identify and mitigate risks, collaboration and communication effectiveness, health and safety measures, social impact on the community, and financial performance. By considering these aspects, project stakeholders can obtain a well-rounded understanding of the project’s performance, identify areas for improvement, and make informed decisions for future success.

6. Conclusions

Key performance indicators (KPIs) are essential for measuring the execution of building projects. They align projects with goals, evaluate performance objectively, detect issues early, support informed decision-making, drive continuous improvement, establish accountability, enable benchmarking against industry standards, and facilitate organizational learning. By leveraging KPIs effectively, project teams can ensure goal alignment, make data-driven decisions, identify areas for improvement, and ultimately enhance project success and deliver high-quality outcomes. The analysis of performance measurement articles in project management reveals significant trends and areas of focus. Incorporating new key performance indicators, particularly non-financial ones, is crucial for obtaining a comprehensive evaluation of project success. While performance measurement focuses on specific project metrics, evaluating project success requires a broader examination of goal attainment as defined by stakeholders. Researchers have proposed diverse KPIs that encompass multiple performance areas to accurately assess project success. By emphasizing comprehensive evaluation metrics, continuous improvements in project assessment and management are facilitated. Adopting a systematic review approach allows researchers and practitioners to stay updated on evolving KPIs, ensuring a holistic understanding of project success.

One notable trend from this study is the emergence of stakeholder performance as the most discussed topic, which underscores the recognition of stakeholders’ influential role in project outcomes. Understanding and measuring stakeholder performance are crucial for fostering collaboration and ensuring project success. Another area of focus is environmental performance, which garners significant attention in the literature. This emphasis reflects the industry’s growing commitment to sustainability and the need for suitable measurements to assess and monitor environmental impact. Researchers recognize the importance of integrating environmental considerations into project management practices and seek to develop robust metrics to assess environmental performance. Furthermore, the increasing importance of technology in construction projects drives researchers to explore technology performance as a key area of interest. By studying technology-related measurements, they aim to enhance project efficiency, productivity, and innovation through effective technology utilization. This focus on technology performance aligns with the industry’s pursuit of digitalization and the adoption of advanced technologies to improve project outcomes. Additionally, safety performance is another critical aspect that receives considerable attention in the literature. Efforts are concentrated on identifying appropriate indicators to assess and improve safety practices, ultimately promoting worker well-being and reducing the risk of accidents or incidents. Researchers recognize that a safe working environment is essential for project success and strive to develop effective safety performance measurements and practices. It is evident that there has been a surge of research and attention dedicated to non-financial performance measurement systems over the past few decades. This acceptance and recognition reflect the growing realization that financial metrics alone cannot provide a comprehensive understanding of organizational performance. Non-financial measures, such as stakeholder performance, safety performance, environmental impact, value performance and technology performance have gained prominence as crucial indicators of projects success.

Quantitative research methods, primarily rooted in the literature, particularly optimization models and tools, are predominantly used to address real-life problems in the field. These methods encompass empirical surveys, the analytic hierarchy process (AHP), the Delphi method, correlation analysis, data envelopment analysis (DEA), regression analysis, fuzzy comprehensive evaluation, Markov analysis, earned value analysis, system dynamics models, structural equation modeling, and principal component analysis (PCA). These quantitative approaches provide valuable insights for performance measurement, supporting informed decision-making and effective project management. On the other hand, qualitative methods, including content analyses, interviews, and case studies, are frequently employed alongside quantitative approaches, forming a comprehensive investigation strategy. This synergistic approach enables a deeper understanding of the theoretical aspects underlying real-life cases, contributing to a holistic exploration of performance measurement topics. This work makes a dual contribution that holds significance for both academia and industry practice. Firstly, it aims to enhance the understanding of interdependencies that arise among project performance practices, thereby shedding light on their modeling. By doing so, it addresses a critical aspect of project management research. Secondly, it strives to offer a more comprehensive and holistic assessment of project performance by incorporating both financial and non-financial indicators at the project level. This approach provides a broader perspective on project performance evaluation. Furthermore, through this research, it identifies existing research gaps that require further attention and improvement in future studies. To advance the field of construction performance measurement, it is crucial to address these significant research gaps and future directions. These include enhancing objectivity and robustness in measurement through standardized tools and protocols, capturing causal relationships among key performance indicators (KPIs) using integrated approaches such as expert systems and simulations, adapting to the evolving construction landscape by monitoring and updating models and methods, considering contextual factors for accurate performance evaluations, overcoming measurement limitations through innovative approaches and multiple indicators, and addressing uncertainty and validation challenges in megaproject performance measurement through scientific methods and data exploration. By addressing these areas, researchers can improve the reliability and effectiveness of performance measurement in construction projects.

The limitations of this study include a focus solely on the construction industry, which has led to the exclusion of valuable and informative articles on general methods of project performance evaluation. To address this limitation, future research could consider expanding the scope to encompass a broader range of industries.