A Project-Based Organizational Maturity Assessment Framework for Efficient Environmental Quality Management

Abstract

This research aims to develop and validate an organizational maturity framework (OM framework) to assess an organization’s maturity and improve the operational performance of the EQM. The study adopts a multi-methods approach. Qualitative data are sourced from 18 respondents and analyzed through thematic analysis. The analysis reveals that pollution control and energy efficiency are the primary EQM concerns. The maturity assessment occurs through data from one or multiple sources, with the most preferred models being the five-phase models. Finally, maturation has diverse effects on EQM, which mirrors continuous improvement expectations. The quantitative study involved 212 respondents drawn from PBOs across the country. The data were analyzed through SEM, culminating in hypothesis testing. Three of the eight hypotheses were supported, including H4: Legal requirements have a statistically significant impact on PBO maturity (β = −0.150, p = 0.015); H5: Sustainability has a positive statistically significant impact on PBO maturity (β = 0.169, p = 0.045); and H1: the level of maturity determines efficiency in EQM (β = 0.066, p = 0.050). The rest of the variables have an inverse relationship or effects that are not statistically significant. The assessment of weightings for the determinants of PBO maturity culminates in the realization that the variables whose hypothesized relationships were confirmed received moderate priority. These findings explain why the determinants of PBO maturity only explain 8.8% of the variance in maturity, while the entire model explains only 3% of the EQM efficiency. The findings culminate in the validity of the operational instructions for improvement in the task specificity of PBO maturity for EQM performance and an improvement in the conceptualization of EQM efficiency among the PBOs.

1. Introduction

Environmental quality management (EQM) denotes the practices organizations adopt to prevent or reduce the adverse effects of their activities on the environment [1]. The importance of each component and parameter to the quality of the environment culminates in the assertion that each constituent is as essential as the total environment [2,3]. Environmental quality is an indicator of the health of the environment itself [4,5,6], for safeguarding public health and meeting stakeholder expectations amid heightened environmental awareness [7]. From a global perspective, EQM focuses on several critical areas: pollution control, energy efficiency, prevention of biodiversity loss, and the protection of natural resources. Each of these components plays a vital role in maintaining the overall health of the environment, ensuring sustainable development, and safeguarding public health [5].

Globally, pressing EQM challenges persist. For instance, recent data indicate that 99% of the world’s population is exposed to air pollution levels that exceed World Health Organization limits, with low- and middle-income countries suffering from the highest exposures [8]. Energy efficiency is another critical issue; countries with energy-intensive industries have experienced increased carbon footprints [9]. Qatar, for example, has experienced a six-fold increase in per capita CO₂ emissions since 1970 and ranks among the highest in energy consumption per capita [10]. In response, regulatory frameworks and sustainability targets are evolving rapidly. Notably, Qatar launched its National Environment and Climate Change Strategy in 2021 to bolster the environmental pillar of Qatar National Vision 2030, setting goals such as a 25% reduction in greenhouse gas emissions by 2030 and enhanced air quality monitoring [11].

The basic organizational MM comprises five levels, as shown in Figure 1 [12], which shows the sequential evolution which is represented by the maturity levels, and the criteria for measuring the level of maturity at each phase [13,14]. MMs serve a multiplicity of purposes, including acting as roadmaps for an organization to transition from one level to another [15]; a framework for prioritizing the managerial actions [16]; assessment of inputs, processes, and outputs [17]; a framework for objective and systematic improvement in organizational processes [18]; a systems-thinking approach [19]; and a convergent approach to conceptualizing processes, strategies and outcomes [20]. Maturity models provide structured frameworks to evaluate and improve processes, helping organizations transition to optimized best practices. In the context of EQM, a higher maturity level implies that an organization has well-established procedures, clear responsibilities, and a culture of continuous improvement in environmental management [21]. Despite their widespread use, challenges such as model selection, validation, and criticism of their narrow focus remain [22].

A PBO is a novel form of inter-organizational grouping designed to optimize organizational structure characteristics through reliance on knowledge from within and outside the entity [23]. PBOs are unique from other institutional infrastructures since they are organized with particular constraints from a functional and temporal perspective, intending to achieve a high-value objective central to the organization’s survival [24]. Although PBOs enjoy flexibility and innovation, their transient nature can result in inconsistencies when applying EQM practices across projects. Existing maturity models related to environmental management tend to focus on traditional functional organizations or specific domains like manufacturing. For example, recent research has proposed maturity models for green manufacturing technologies and corporate sustainability practices [25,26].

Academic studies on maturity models for environmental quality management in PBOs reveal several frameworks, but they often lack comprehensive integration of sustainability. Ormazabal et al. introduced a dynamic environmental management maturity model that emphasizes iterative improvement but is limited in cross-sector validation [27]. A critical analysis of widely used project management maturity models has been conducted to examine their strengths and weaknesses. It highlights that research by international bodies has shown that organizations with higher managerial maturity are more likely to achieve their project goals. The analysis reveals that the most advanced models are based on international project management knowledge codes. Yet, they often overlook critical structural and infrastructural factors, such as workplace conditions, necessary equipment, and adherence to professional standards and regulations. Moreover, these models typically lack robust processes for assessing the effectiveness and efficiency of best practices [28].

Current research on PBO maturity models for EQM exhibits several key gaps. There is limited research and a lack of methodological consistency, which hinders the comparability of findings. Moreover, existing PBO MMs differ fundamentally, often ignoring industry-specific challenges and the need for end-of-process practices to maintain EQM efficiency. Additionally, these models overlook the role of EQM standards, lack sufficient granularity to address firm-specific nuances, and are too inflexible to adapt to rapid technological and environmental changes.

The present study aims to develop and validate an organizational maturity assessment framework tailored to project-based organizations for effective environmental quality management. The key objectives of the research are as follows: (i) identify the critical factors that determine EQM maturity in PBOs (e.g., leadership commitment, policies, stakeholder engagement, and legal compliance); (ii) examine the impact of organizational maturity on EQM outcomes, by analyzing how different maturity levels influence the efficiency and effectiveness of environmental practices in project environments; (iii) develop and validate a practical maturity assessment model for PBOs, providing a tool to benchmark an organization’s current EQM maturity and guide improvements toward higher maturity levels. By achieving these objectives, the study will contribute to new knowledge and practice. The research extends existing maturity model literature into the domain of project-based environmental management, offering a novel framework that integrates organizational maturity concepts with EQM processes. Practically, it provides PBOs—especially those in Qatar and similar contexts—with a diagnostic instrument to assess their environmental management capabilities and a roadmap to enhance their EQM performance.

2. Theoretical Background and Hypothesis Development

2.1. The Concept of Environmental Quality: Focus on Qatar

Efficiency is determined through a comparison between efforts and results, with recognition of the processes through which those efforts are transformed into results [29]. Efficiency is a multifaceted concept since the units for representing the inputs and outputs tend to vary. Efficiency in environmental matters is conceptualized in a multiplicity of ways, including the technical and economic domains [30].

Qatar has the third highest ecological footprint globally due to overreliance on fossil fuels across most sectors [31]. Ref. [32] uses three ecological quality indicators in their study, including the adjusted national savings (ANSs), the per capita carbon dioxide emissions, and the energy intensity (EI). The Qatar National Vision (QNV) 2030, modeled around sustainable development goals, is fundamentally predicated on organizational maturity. The QNV 2030 is designed to transform the development of Qatar across four pillars: human, social, economic, and environmental development [33]. Public and private institutions’ involvement in achieving the QNV 2030 goals implicates the role of organizational maturity in achieving the four pillars. There are long-term goals under each of the four pillars [34,35] with integral interlinkages.

2.2. Organizational MMs

The fundamental architecture of the MM is best appreciated from the integral characteristics of the requirements for, and the key process/activity areas under each maturity phase or level [25,36]. In identifying the transition from one phase to another, an organization focuses on the following elements: specific activities [17], standards of operations [37], management of performance, and continuous improvement mechanisms [38,39]. However, as Adekunle et al. (2022) note, traditional maturity models are being re-evaluated in the context of digital transformation, which calls for more adaptive frameworks [40].

The unique nature of EQM practices influences the maturation trajectory and challenges the utility of the generic five-phase model for EQM MMs as derived from other disciplines. The researchers have thus identified novel maturation pathways, as shown hereunder. First, the three-phase EM MMs [41], whereby the changes in maturity originate from the pressure that originates from different contexts. Second, the four-phase EM MMs [42], whereby maturation occurs proactively in response to pressure from different sources. The change occurs incrementally, leading to four phases, including Stage 1 (regulation), Stage 2 (regulation and competitiveness), Stage 3 (regulation, competitiveness and visibility), and Stage 4 (regulation, competitiveness, visibility and social) [41]. Third, the generic five-phase maturation path has also assimilated into the process of assessing and modeling EQM MMs [43]. The model develops an index that captures environmental management for projects, programs, and portfolios. A recent analysis by Machado et al. further emphasizes the diversity of maturity models and the importance of tailoring these frameworks to specific industry contexts [44]. Finally, the six-phase MMs are derived from the tenets of the five-phase models, although they presume that some organizations lack any mechanisms for EQM at the start [45]. Different methodologies have been adopted for this approach, with factors such as agents involved, policies, tools, indicators, behavior over time, and causal loop diagrams, whose effect on maturity varies from Level 1 to 6.

2.3. Tools for Assessing Organizational Maturity

The assessment of maturity starts with the collection of data regarding the facets for which the model is to be developed. The type of data utilized depends on the objectives of the MM, with some models relying on qualitative data [46,47], quantitative data [43], or a mixture of the two [26]. The choice of data types is dependent on the maturity model being developed, and the characteristics of the firm. The use of data from multiple sources provides a more robust maturation path since it takes into account multiple determinants of change. The assessment approach will entail the definition of maturity states, as well as the intrinsic features of the activities under each level of maturation. While there are disparities in the process, researchers such as [40,48,49,50] concur on a five-phase methodology that is apparent from most MMs. An additional step is proposed by [46,49], who propose measures for the maintenance of the model, through improvements based on lessons learned from practice (Figure 2).

Early grid-based MMs were based on the premise that the impact of all determinants of maturation had similar effects on the outcome [51]. Over time, this perspective changed, with [46] adopting unique weights for each process or activity, in recognition of the uniqueness in the manner in which they contribute to the change within the organization. The approach is further used by [52], who advocates for the integration of the relative importance of each determinant of maturity, to develop a more practical and objective MM that is representative of reality. Subsequent research by [53] sought to generalize the weighting approach to particular functions, with leadership perceived as contributing more to maturation as compared to other functions, from the qualitative and quantitative perspective. In determining the weighting for determinants of maturity, the analytic hierarchic process (AHP) offers an objective approach that is suitable for theoretical and practical purposes [54,55,56,57]. Recently, a study applied a combined AHP-DEMATEL approach specifically for assessing digital transformation maturity in Chinese construction enterprises. Their results demonstrated that digital business applications hold the highest significance, highlighting the essential role of interconnected digital technologies with construction operations, technical skills, innovation capabilities, and infrastructure. This confirms AHP’s practical utility in objectively defining and prioritizing maturity determinants [58].

2.4. EM MMs: Context of Qatari Organizations

Research in Qatar has shown limited direct application of organizational maturity concepts at institutional, sectoral, and national levels. However, several studies have explored maturity concepts in various contexts. For instance, ref. [59] developed a maturity framework for digital diplomacy aligned with the Qatar National Vision (QNV) 2030, resulting in a four-phase model with five maturity levers ranging from low to high impact. In another study, ref. [60] examined cybersecurity maturity using the Qatar Cybersecurity Capability Maturity Model (QC2M2). This model, consisting of five levels (initiating, implementing, developing, adaptive, and agile), highlighted the role of regulatory bodies in integrating legal frameworks consistent with the Qatar National Information Assurance manual. Determinants included benchmarking and developing a national cybersecurity framework to understand, secure, expose, recover, and sustain outcomes.

A relevant case study by Prabhakaran et al. [61] investigated organizational maturity in the context of Building Information Modeling (BIM), relevant to project management and PBOs. BIM, originating from advancements in architecture, engineering, and construction to enhance built environments [62], is closely linked to improvements under the tenets of maturation. Using a mixed-methods approach, the study compared BIM maturity between the UK and Qatar, drawing qualitative insights from eight participants in each country and quantitative data from 73 companies. Findings indicated that while both countries shared determinants like ‘champions and drivers’, Qatar lagged in maturity and technical capacity. Regulatory frameworks emerged as a critical maturity determinant for Qatar, underscoring the need for benchmarking with the UK to enhance practices.

Collectively, these studies highlight the relatively novel adoption of maturity concepts in Qatar, with research emerging as recently as 2018. They underscore that while Qatari firms are beginning to conceptualize and operationalize maturity principles within institutional frameworks, the broader application remains limited, reflecting the early stages of organizational maturity development in the region.

2.5. Hypotheses Development

PBOs operate at the “edge of chaos” [63,64], balancing complex project portfolios with flexible organizational units. This complexity is compounded by the “improvement paradox” [65], where small, incremental changes drive success, while significant transformations risk destabilizing project management (PM) processes. This dynamic explains why PBO maturation trajectories differ from other institutions. Although MMs emerged in the 1980s, interest in PBO-specific models only developed around 2010. Ref. [46] introduced the Intelligent Project-Based Organization Maturity Model (IPBOMM), a five-phase model emphasizing levers such as PM, knowledge management (KM), business intelligence, governance, and competitive intelligence. Findings showed that PBOs experience non-sequential maturation, with determinants shifting across levels. It was noted that organizational maturity reflects achieving a modeled end-state of capabilities [66]. The study assigned a Level 2 maturity to organizations with structured processes but varying maturity across PM knowledge areas—some at Level 1 (e.g., PM Plan development) and others at Level 3 (e.g., cost estimation). These findings highlight the fragmented nature of PM maturity within PBOs, informing the development of the following hypothesis.

H1. 

The level of maturity determines efficiency in EQM.

Organizations utilize various resources and capabilities to advance along the maturation pathway, playing a critical role in this process [67,68]. According to the resource-based view, possessing valuable, rare, inimitable, and non-substitutable assets enables firms to achieve and sustain competitive advantages, with maturation occurring in absolute or relative terms [69]. However, resources are static, so their effectiveness in dynamic environments is limited. Hence, the dynamic capabilities theory suggests that organizations must assess, acquire, and reconfigure capabilities in response to internal and external changes [70,71]. Research consistently emphasizes the importance of flexible and instrumental resources and capabilities for organizational maturation [72,73,74]. Some studies highlight the need for general resources [75], while others stress the requirement for specific resources depending on maturity [76]. A previous study [77] underscored PBOs’ need to possess distinct resources at different maturity stages to secure temporary and sustained competitive advantages. Additionally, when maturity determinants shift across phases, the required resources must adjust accordingly [78,79]. Based on this discourse, the following hypothesis will be tested.

H2. 

Resources and capabilities have a positive statistically significant impact on PBO maturity.

Past and current studies concur that knowledge management (KM) is integral in the maturation process. KM plays an integral role in influencing awareness of what and how to change along the maturation pathway [46,80], as well as the technical and complex aspects of who participates in the process of change [81]. KM is conceptualized as the acquisition, creation, utilization, and transfer of knowledge [80], all of which contribute to the maturation process [46,82]. The transient nature of PBOs implies that critical knowledge capital is not retained at the end of a project, and the knowledge cycle differs from what is evident in other forms of organizations [80,83]. Similarly, at its core, PM is faced with challenges in KM, which is not evident from other managerial circumstances [84]. These challenges exist in particular phases of the MM [85], which focus on the creation, capturing, transfer, and reuse of knowledge. With this in mind, ref. [86] performed a study on knowledge governance (KG), intending to investigate how the configuration of the institution as a PBO influences KG mechanisms. The findings from the study highlight the close link between KG (which is part of KM) in capacity development, as well as the integral role of identifying the suitable configurations for KG to achieve the necessary outcomes. This makes it challenging for the PBO to create convergent understandings, and establish similar knowledge bases. Based on this discussion, the following hypothesis will be tested.

H3. 

Knowledge management processes have a positive statistically significant impact on PBO maturity.

There are two schools of thought on the role of legal mandates on maturity. The first posits that the contribution of these mandates is limited as a driver of maturation, based on the premise that these mandates are framed in a manner that generates a limited impetus for extensive and continuous improvement [87,88]. How they are framed, with the goal being to establish minimum standards of operation, as well as the variations across jurisdictions explains the rationale of this school of thought. The second school of thought treats these legal mandates as an integral determinant of maturation since their application to a broad range of entities within a particular market [89]. Its utility in the maturation of EQM practices is evident from the theorizations such as ‘regulatory environmentalism”. Consequently, this concept has led to the emergence of voluntary measures that are adopted by institutions as a complement and supplement to what is required under the law [90]. Based on this discourse, it is apparent that compliance guidelines have differing influences on organizational maturity. Whereas it is widely accepted that these standards drive organizations to achieve basic standards as required under the guidelines, they may not precipitate the continuous improvement that is necessary for maturation to higher levels of efficiency. As a result, the following hypothesis is developed.

H4. 

Legal requirements have a statistically significant impact on PBO maturity.

The sustainability of the processes and activities that culminate in the achievement of EQM efficiency is relevant to the maturation process [91,92,93]. Sustainability involves the creation of a dynamic equilibrium to avoid irreversible adverse effects on the carrying capacity of the environment [94]. Sustainability is conceptualized from the triple-bottom-line perspective, including the economic, social, and environmental dimensions [95]. While sustainability reorients the focus on the firm towards a more comprehensive set of outcomes, it can also act as a constraint to success [96]. The increased and novel rigor under each dimension of sustainability affects efficiency since it has implications for inputs, processes, and outcomes. Based on this discourse, the following hypothesis is developed.

H5. 

Sustainability has a positive statistically significant impact on PBO maturity.

The organizational context refers to the settings within which an integrated operational or strategic initiative is implemented [97]. The context is defined by facets such as leadership approaches, organizational factors, and organizational culture in place [73]. The effects of leadership, which vary depending on the leadership style, play a key role in the efficiency of EQM [98,99]. The culture of the firm influences PBO maturity since it has a bearing on the activities, procedures, and outcomes within the organization [100]. The set of beliefs, values, and behavioral norms predispose an organization to take certain perspectives and directions that influence the outcome of key strategic positioning. [101] and [102] found that the organizational culture was integral in the implementation and sustainability of CI initiatives, which implied that the transformation from one level of maturity to another is tied to the organizational culture. Based on this discourse, the following hypothesis is developed:

H6. 

The organizational context has a statistically significant impact on PBO maturation.

The strategic positioning of the organization influences the interaction between short- and long-term plans by the firm, as well as what activities it is involved in. The strategies adopted by PBOs, strategy is framed in the form of strategy-as-a-practice (SaP) [103], which denotes that it is a concept performed by project personnel and organizational people, rather than something that is possessed by the organization [104]. The utility of SaP arises from the reality that PBOs are concerned with success from different perspectives, including project success, PM success, and PM performance [21]. The organizational strategy influences the maturation of PBOs since it relates to how the organization operates, how it adapts to change in the environment [40], and how it morphs to exploit the opportunities in the market [105]. Ultimately, the strategy revolves around whether the company is executing tasks correctly, and whether it is focusing on the right priorities, both of which culminate in continual improvement. Based on the discourse, the following hypothesis will be tested.

H7. 

The organizational strategy has a statistically significant impact on PBO maturity.

Project characteristics significantly influence the maturity level and maturation trajectory of PBOs [44,106]. Project complexity impacts PM outcomes by introducing complex risks [107], unique success metrics [108], and requiring specialized skills and competencies [66,109], often leading to extended completion times. Complexity also affects the learning curve and continuous improvement. It was noted that, while complexity fosters a strong task focus necessary for project success, it can hinder long-term perspectives, limiting continuous improvement and maturation [109]. The impact of complexity on PBO maturity varies based on project complexity [51]. Ref. [110] highlighted that complex projects require distinct risk management approaches—either traditional (viewing risk as central to performance) or modern (considering risk effects as moderated by other factors). Based on this discourse, the following hypothesis will be tested.

H8. 

Project characteristics have a statistically significant impact on PBO maturity.

2.6. Conceptual Model

The conceptual model provided in Figure 3 hereunder is based on the hypotheses identified above. The eight hypotheses are based on a review of past literature.

3. Methodology

The methodology will be modeled around the research onion model [111]. As a guiding framework, the research onion provides a roadmap for designing methodologies in a phased approach. Under the methodology, the researcher will first outline the most generic aspects of the research and gradually build up to the more specific techniques and processes. The study utilizes qualitative and quantitative methodological choices using the multi-methods approach. Under the mixed methods approach, qualitative research will be developed to supplement the findings from the literature review through exploratory design [112] (see Figure 4).

3.1. Study Design

The study employs a multi-method approach, integrating qualitative and quantitative methodologies to achieve comprehensive insights. Initially, an exploratory qualitative method was applied to supplement and extend findings from the literature review, highlighting existing gaps related to MMs for PBOs and their EQM efficiency.

The findings from the analysis are then used in a confirmatory design to test the eight hypotheses identified in the literature review [113]. The combination of the qualitative and quantitative methods triangulates the findings, thereby merging the exploratory and confirmatory research designs.

3.2. Population and Sampling

The qualitative population consisted of strategic-level professionals from both public and private sectors involved in national environmental management decisions. Semi-structured interviews were conducted with 18 carefully selected experts, chosen based on their roles, experience, and potential to provide deep insights into the topic under investigation.

For the quantitative study, the targeted population comprised employees from PBOs operating within the country. Random sampling was employed from an estimated 10,000 individuals to obtain a representative sample. Using a confidence level of 95% and a margin of error of 5%, a statistically robust sample size of 373 was calculated. Ultimately, 212 complete responses were obtained, indicating a 57% response rate, which is considered adequate for further analysis.

3.3. Data Collection

Semi-structured interviews were conducted to gather qualitative insights from strategic-level experts involved in EQM within PBOs in Qatar. Interviewees were strategically selected for their professional experience and knowledge of organizational maturity and EQM efficiency. Participants were approached through formal invitation letters, detailing the purpose of the study, the voluntary nature of participation, and the ethical considerations regarding confidentiality and data privacy. Each participant was informed that the interview would last approximately 30 min and that they could withdraw at any point prior to data analysis. Consent for participation and recording was obtained before commencing the interviews.

The interview questions explored participants’ roles, the use of maturity principles in PBOs, key factors influencing EQM performance, methods for assessing maturity, and the efficiency of these tools. They also addressed EQM concerns, measures to enhance efficiency, and whether maturation occurs sequentially or non-sequentially, concluding with additional insights on organizational maturity and EQM. The interview guide included open-ended questions allowing participants to articulate their views freely, ensuring the flexibility to explore unexpected insights. Interviews were audio-recorded with explicit consent, transcribed verbatim, and analyzed using thematic analysis to extract meaningful patterns and themes.

Survey Process: A structured online survey was distributed to employees working within various PBOs in Qatar to assess factors influencing organizational maturity and EQM efficiency quantitatively. The survey targeted approximately 10,000 employees, from which a random sample of 373 participants was determined based on a 95% confidence level and 5% margin of error. The survey instrument consisted of two primary sections:

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Demographic Information: Captured age, gender, academic qualifications, and tenure within the organization to contextualize responses;

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Assessment of Organizational Maturity and EQM: Participants rated statements related to organizational resources and capabilities, knowledge management, legal requirements, sustainability practices, organizational context, organizational strategy, and project characteristics on a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). Additionally, respondents provided ratings on EQM efficiency. They performed pairwise comparisons of maturity determinants using the Analytic Hierarchy Process (AHP), employing a nine-point scale (from equal importance to extreme importance).

Participants provided informed consent electronically, were assured confidentiality, and were informed about the voluntary nature of their participation and the option to withdraw. The survey took approximately 25–30 min to complete. Responses were analyzed through structural equation modeling (SEM), including exploratory and confirmatory factor analyses, to identify relationships between variables and validate the proposed conceptual model.

3.4. Data Analysis

Data for the qualitative study was sourced from 18 interviewees, through a semistructured interview process. The data were transcribed verbatim and analyzed through thematic analysis through NVIVO, using the six-phase approach [114]: getting acquainted with the data, coding, determination of initial themes, reviewing themes, defining and naming themes, and developing the report.

Data for the quantitative study was analyzed through structural equation modeling (SEM) using SPSS AMOS software 29. The analysis combined elements of both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). SEM involves testing the relationship between the observed and unobserved variables to enable a researcher to test the hypotheses developed for the study. Due to the multiplicity of variables and constructs, the researcher applied to test how the constructs load onto the defined variables in the conceptual framework [115]. EFA was conducted to transform the factor loadings into patterns that can be inspected and interpreted easily. Extraction was performed through principal components methods, with Eigenvalues of at least 1 extracted. Varimax rotation was used for the study since it maximizes the sum of the variance of the squared loadings, thereby leading to a better association of each variable and the constructs [116]. Descriptive statistics for adequacy, such as KMO and Bartlett’s test for sphericity, determine whether the null hypothesis of each variable in the correlation matrix for the population is uncorrelated [117]. To further simplify the process, the absolute values below 0.5 were suppressed in the final to ensure that the rotated component matrix provided those factors with high loadings.

Under the CFA, the latent and measured values are defined first. The preliminary measurement model is developed based on the relationships predicted under the conceptual framework. Examination of the suitability of the structural model through fit indices refers to measures for the extent to which a configuration of free and fixed parameters included in the specified model is consistent with the configuration of the variances and covariances about the observed data [118]. Fit indices for the measurement model included the following: the root mean square error of approximation (RMSEA), comparative fit index (CFI), normed fit index (NFI), degree of freedom/chi-square (x2/d.f), increment fit index (IFI), the goodness of fit index (GFI), and adjusted goodness of fit index (AGFI). Each of these indices has a cut point for acceptability, which, if not met, will imply that the model should be adjusted by changing the variables or constructs or introducing novel relationships in the model. Finally, the structural model will be specified, showing the variables fitted under the final model. The structural model will provide results of interest in the study in the following ways. First, The structural model was applied to identify the squared multiple correlations, which indicate the variance concerning a latent or measured variable explained by the model [119].

In the last phase of the analysis, the weighting of the maturity levers is assessed. The weights for the determinants of maturity are determined through AHP with a priority matrix, as shown hereunder. AHP was selected to weigh the maturity levers since it is an intuitive approach designed for multi-criteria operations with a proven track record [60]. In the first step, the hierarchy structure is created as shown hereunder, with the corresponding values (‘a11’ to ‘ann’) for the determinants of maturity based on the pairwise judgment of the respondents [116]. Second, the construction of a judgment matrix, with a scale of 1–9, whereby ‘1’ represents ‘Equal Importance’ for the two maturity levers, and ‘9’ represents ‘Extreme importance’ of the first maturity lever over the second. Third, the judgment matrix is calculated, whereby the contribution of each determinant of maturity is determined based on a priority vector (Eigenvector). Fourth, consistency checks are performed, culminating in determining the consistency ratio using the random indices created by [117]. Finally, the commensurate weights for each determinant of maturity will be calculated.

The unique methodological combination employed in this study addresses specific literature gaps and provides a robust foundation for assessing and enhancing organizational maturity for EQM. Given the novel nature of the developed framework, direct comparative benchmarking is limited; thus, findings are evaluated based on absolute analytical rigor rather than relative comparisons.

4. Results

The findings from the study are presented in this chapter. The findings from the exploratory study conducted through thematic analysis are presented first and then applied in contextualizing the CFA results.

4.1. Qualitative Study

The qualitative study seeks to answer four concerns raised about MMs for PBOs, among Qatari firms, including the absence of insulation against industry-specific challenges to maturation [120], the waning prominence of EQM practices [121,122], the need to introduce end-of-process practices [26], and recognizing the influence of standard EQM frameworks in the PBO MMs [123]. The analysis is carried out through thematic analysis, as shown hereunder.

4.1.1. Main EQM Concerns

The EQM concerns that underlie the practices of the PBOs are primarily centered on control over pollution, considering that the largest number of PBOs are involved in construction and engineering services. Interviewee 1 states that “… with pollution and management of wastes within our facility, although I think energy use is also a problem. We also focus on waste management, as part of the measures to reduce the pollution”. Energy efficiency is another prominent concern among the PBOs, with the decision influenced by internal and external concerns, including those of clients, especially among PBOs involved in IT consulting and business activities, which seek to control the carbon footprints associated with the solutions that they provide to their clients. Interviewee 6 states that “… focus on energy efficiency, to reduce the carbon footprint …”. A number of the PBOs are focused on protecting ecosystems and conserving natural resources, which mostly occur as a secondary concern to pollution control and energy efficiency. As complementary and supplementary concerns, these two concerns are perceived as measures to enhance the value propositions to clients under the PM terms. Interviewee 10, who is involved in IT consulting, states that “… protection of the environment as an umbrella goal, …”.

4.1.2. Assessment of Maturity

The findings reveal that the PBOs utilize the generic capabilities-based MM, which is developed through two main approaches in assessing maturity under EQM practices, which are indicative of the sources and types of data used in the assessment of maturity. First, a single methodology approach, whereby data are sourced from project personnel, through questionnaires and interviews (Interviewees 1, 12, and 15). Second, through a multi-phase approach, which entails two or three processes (Interviewees 10, 14, and 17). Among the PBOs that use two methodologies, data are sourced internally, from the project personnel, coupled with a review from the management through interviews. For those PBOs that utilize three processes, the input from customers/clients is used as part of the independent review of the maturation process. The prominence of the multi-phase assessment of maturity is indicative of the limited reliability of the single-sourced data from project personnel, who may not be able to provide a balanced perspective on the maturation process. The assessment of maturity can also be conceptualized from the sequential or non-sequential perspective. The findings reveal that the PBOs utilize sequential maturity modeling, whereby maturation involves change within the same set of dimensions for all levels (Interviewee 2). There are also some PBOs which utilize non-sequential modeling, whereby maturation entails the introduction of novel dimensions under each level (Interviewee 3). The prominence of sequential maturation pathways arises since the EQM concerns by the PBOs are mostly static, while those PBOs that report non-sequential maturation pathways focus on a dynamic set of EQM concerns, which vary on a project-to-project basis.

4.1.3. Effects of the Maturation Process

The change represented by maturation has several effects on the EQM efficiency of the PBOs. The most common form of transformation occurs in the forms of management facets, applied by the PBOs. Interviewee 11 cites the implementation of split planning processes with enhanced project forecasting mechanisms, all of which contribute to increased transparency and productivity. Interviewee 13 cites the amplification of risk management to anticipate and prevent issues from materializing, while Interviewee 14 cites the reduction in chaotic workflows through the elimination of random luck as a result of increased control. There is evidence that maturation influences process improvement. Interviewee 11 indicated that process improvement. The evidence herein shows that maturation leads to improvement in the performance of the PBOs. There are different perspectives on improvement in EQM performance, including the ability to deliver better services to clients and achieve transference of performance abilities. (Interviewee 8). In line with the measures to collect feedback from the clients during the assessment of maturity, it is also evident that there is improved stakeholder management among the PBOs, on account of maturation. It is also evident that the PBOs have become more efficient in the acquisition and utilization of the available resources as they transition from lower to higher levels of maturity. Those resources are integral in maturation, thereby leading to cascading effects on the institutions. Some of those resources serve specific purposes in the maturation process. For instance, improvement in information technology resources enables the PBOs to automate and digitalize their processes. As indicated by Interviewees 6 and 10, the resources also contribute to an increased ability to comply with legal mandates associated with EQM under domestic laws.

4.2. Quantitative Study

The analysis hereunder involves the development and validation of an organizational maturity framework (OM framework) to assess an organization’s maturity and improve the operational performance of the EQM.

4.2.1. Demographic Statistics

Data from 212 respondents who work among Qatari PBOs were used in the analysis. Among them, 91% of the respondents are male, with 41% aged between 36 and 45 years. 47.2% of the PBOs conceptualize EQM efficiency from a technical perspective. It is also apparent that most PBOs specialize in engineering (25.5%) and construction (24.5%), with a further 19.8% offering business consulting services.

4.2.2. Assessment of Validity

Construct validity, which refers to the extent to which the selected scales measure the values that are relevant to the analysis [118], was measured through reliability (internal consistency) Internal consistency was assessed through Cronbach’s alpha, whereby a threshold of 0.7 was adopted in line with the requirements by [115]. The assessment of construct validity is determined through the two approaches, as shown in

Table 1. Assessment of Validity.

Variable	Item	Internal Consistency
		Cronbach’s Alpha	Factor Loadings
Resources and capabilities (RC)	PC * RC	0.973	0.954
	EE ** RC		0.931
	LB *** RC		0.966
	PR **** RC		0.942
Knowledge management (KM)	PCKM	0.944	0.959
	EEKM		0.945
	LBKM		0.897
	PRKM		0.869
Legal requirements (LR)	PCLR	0.949	0.940
	EELR		0.938
	LBLR		0.950
	PRLR		0.834
Sustainability (SUS)	PCSUS	0.876	0.921
	EESUS		0.869
	LBSUS		0.805
	PRSUS		0.820
Organizational context (OC)	PCOC	0.918	0.939
	EEOC		0.897
	LBOC		0.859
	PROC		0.876
Organizational strategy (OS)	PCOS	0.885	0.824
	EEOS		0.867
	LBOS		0.821
	PROS		0.899
Project characteristics (PC)	PCPC	0.815	0.876
	EEPC		0.745
	LBPC		0.782
	PRPC		0.800
PBO maturity	Matur1	0.962	0.932
	Matur2		0.680
	Matur3		0.929
EQM efficiency	Eff1	0.940	0.932
	Eff2		0.926
	Eff3		0.952

* PC = pollution control; ** EE = energy efficiency; *** LB = preventing loss of biodiversity; **** PR = prevention of loss of resources. The codes apply to the rest of the variables, from resources and capabilities (RC) to project characteristics).

hereunder.

• Internal consistency, based on Cronbach’s Alpha, is at least 0.7;
• Factor loadings are at least 0.7.

4.2.3. Measurement Model

The measurement model is developed through SPSS AMOS software, to reveal the relationship between the latent and measured variables that were used in the study. The initial model (left) reveals the basic relationships and the final measurement model contains the squared multiple correlations and loadings for the co-variances for the fitted model. (See Figure 5).

4.2.4. Model Fit Indices

The model fit indices included hereunder indicate how well the observed data fits the measurement and structural model. The fit indices are the product of changes from the initial measurement model to the final measurement model, which was then transformed into the structural model. As shown hereunder (

Table 2. Model fit indices.

Model Fit Index	Benchmark Value	Measurement Model	Structural Model	Source
Degree of freedom/Chi-square (/d.f)	<5.0	1.687	1.683	[124]
RMSEA	<0.08	0.066	0.046	[119]
CFI	>0.90	0.962	0.962	[125]
NFI	>0.90	0.914	0.911	[124]
GFI	>0.90	0.901	0.902	[124]
AGFI	>0.80	0.852	0.852	[125]
IFI	>0.90	0.963	0.962	[124]

), both the final measurement model and structural models met the criteria for fit indices, hence the hypothesis testing is carried out.

4.2.5. Structural Model

The structural model was developed based on the relationships identified in the literature review, to test the hypotheses identified therein. As shown in the results hereunder, three of the hypotheses were supported: the level of maturity determines efficiency in EQM (β = 0.066, p = 0.050), legal requirements (β = −0.150, p = 0.015); and sustainability (β = 0.169, CR = 2.032, p = 0.045). The rest of the hypotheses are not supported, including resources and capabilities (β = −0.047, p = 0.012); knowledge management (β = −0.132, p = 0.046); the organizational context (β = −0.017, p = 0.855); the organizational strategy (β = 0.071, p = 0.408), and project characteristics (β = 0.165, p = 0.162). The results are shown in

Table 3. Summary of hypothesis tests.

			Estimate	SE	CR	p	Supported
OM	<---	RC	−0.047	0.057	−0.824	0.012	No
OM	<---	KM	−0.132	0.075	−1.762	0.046	No
OM	<---	LR	−0.150	0.111	−1.345	0.015	Yes
OM	<---	Sus	0.169	0.083	2.032	0.045	Yes
OM	<---	OC	−0.017	0.098	−0.177	0.855	No
OM	<---	OS	0.071	0.084	0.837	0.408	No
OM	<---	PC	0.165	0.119	1.381	0.162	No
EQME	<---	OM	0.066	0.084	0.782	0.050	Yes

.

In Figure 6 hereunder, the structural model is provided. The findings show which of the measured variables related to maturation in the scope of the four EQM practices are included in the final model latent variables. The squared multiple correlations included in the model offer insight into the variance in the assessed variables that can be explained by the model.

The squared multiple correlations (

Table 4. Squared multiple correlations for the variables in the structural model.

Latent Variable	Measured Construct	Squared Multiple Correlation
Resources and capabilities	Pollution RC	95%
	EERC	87%
	LBRC	93%
Knowledge management	PCKM	92%
	EEKM	90%
	LBKM	81%
Legal requirements	PCLR	66%
	PRLR	55%
Sustainability	PCSUS	57%
	EESUS	75%
Organizational context	PCOC	93%
	EEOC	76%
	LBOC	64%
	PROC	68%
Organizational strategy	PCOS	72%
	EEOS	67%
	PROS	75%
Project characteristics	PCPC	58%
	LBPC	55%
	PRPC	61%
PBO maturity (8.8%)	Maturity 1	95%
	Maturity 2	78%
	Maturity 3	96%
EQM efficiency (3%)	Eff1	78%
	Eff2	50%
	Eff3	66%

) from the analysis for each of the measured constructs under the latent variables that are essentially the determinants of PBO maturity are provided hereunder. As shown in the table, the determinants of maturity included in the model explain 8.8% of the variance in maturity among the PBOs. Similarly, the assessed level of PBO maturity explains 3% of the EQM efficiency among the Qatari PBOs.

4.2.6. The Weighting of Maturity Levers

The weighting of maturity levers as shown in

Table 5. Weighting of determinants of maturity.

Category	Priority	Rank	(+)	(−)
Resources and capabilities	29.3%	1	10.0%	10.0%
Knowledge management	25.4%	2	16.5%	16.5%
Legal requirements	13.4%	3	8.1%	8.1%
Sustainability	13.4%	4	6.6%	6.6%
Organizational context	7.8%	5	3.0%	3.0%
Organizational strategy	6.5%	6	2.8%	2.8%
Project characteristics	4.3%	7	2.1%	2.1%

provides additional clarity about the findings above. As shown in Table 5, the resources and capabilities are assigned a 29.3% weighting on their importance in determining PBO maturity, while project characteristics rank lowest at a 4.3% weighting.

4.2.7. Validated PBO MM Framework

The proposed PBO MM (

Table 6. Proposed PMO MM.

	Maturity Levers	EQM Focus	Initial	Emergent	Structured	Aligned	Optimized
Primary factors	Legal requirements	Pollution control protection of resources
	Sustainability	Pollution control energy efficiency
High priority	Resources and capabilities	Pollution control energy efficiency protect loss of biodiversity
Viable options	Organizational strategy	Pollution control energy efficiency Protection of resources
	Project characteristics	Pollution control protect loss of biodiversity Protection of resources
Potential determinants	Knowledge management	Pollution control energy efficiency Protect loss of biodiversity
	Organizational context	Pollution control energy efficiency Protect loss of biodiversity Protection of resources

) considers the following conditions concerning the maturity levels of the PBOs and the effects of that maturity on EQM performance. The creation adopts the six-phase approach included in Figure 2. First, the preferred maturation pathway from the interviews and survey data is the five-phase capabilities-based model. Second, the variables whose hypothesized relationships were supported in the analysis are prioritized under the model, owing to the specificity of their effects on the PBO Maturity. Third, the weightings for the maturity determinants provide a secondary criterion for prioritization of the levers, specifically for PBOS seeking to enhance PBO maturity. Based on the hypothesis tests above, only two hypotheses relating to the determinants of maturity among the PBOs are supported. The results reveal that PBO maturity has a statistically significant positive effect on EQM efficiency. To discern the fundamental reasons for this circumstance, it is imperative to consider the disparity in the weighting of the determinants of maturity and how they influence the maturation process and, ultimately, the EQM performance. Sustainability, which weighs 13.4%, is the only variable with a statistically significant positive effect on PBO maturity. As a result, the maturity framework contains qualifying criteria for the seven variables, guiding the PBOs in selecting the most suitable areas to focus on.

5. Discussion

The first research question sought to determine the drivers/factors of PBO maturity. The review of the literature, as well as the development of the measurement model, revealed that seven factors influence PBO maturity from the perspective of EQM. These factors include resources and capabilities, knowledge management, legal requirements, sustainability, organizational context, strategy, and project characteristics. The study demonstrated and validated a framework for the effects of the maturity of PBOs on EQM efficiency, thereby capturing the interdependence and multifaceted nature of EQM efficiency. While PBO maturity has a statistically significant impact on EQM efficiency, the model only explains 8.8% of the PBO maturity, while that maturity explains 3% of EQM efficiency. From these findings, it is apparent that the concepts of maturity are still novel among Qatari PBOs, as concluded in earlier studies [63,64,65]. Similarly, despite the extensive theorization of maturity modeling for PBOs, it is common for organizations to report low levels of maturity, as evidenced in [19,44,70]. The results of this study resonate with recent insights by Kanaan et al., who analyzed the impact of open innovation on the performance of entrepreneurial firms in Jordan, highlighting that knowledge management significantly mediates this relationship, while stakeholder relationships serve as a crucial moderator. These findings suggest that, alongside focusing on maturity, emphasizing stakeholder engagement and knowledge management could also significantly enhance organizational performance and efficiency [126].

Under the second research question, only two of these factors affect PBO maturity from the perspective of the EQM performance, as predicted under the model. First, the sustainability of the process has a positive statistically significant impact on the maturity of PBOs. The findings align with theorizations by [96,127,128], which deliver the impetus for PBOs to transform their operations in a manner that can be maintained into the foreseeable future. Through sustainability, which is conceptualized from the economic, social, and environmental perspective, PBOs have linked their short-term and long-term goals while also considering the interests of multiple entities directly influenced by the maturation process. Moreover, recent research on entrepreneurial firms in Pakistan further underscores the critical role of entrepreneurial alertness and networking in enhancing organizational success. This study found that entrepreneurial alertness significantly drives business success, and its positive impact is magnified considerably through effective entrepreneurial networking. This finding aligns with the notion that robust networking capacities boost entrepreneurial success and enhance firms’ sustainability, particularly by supporting them during periods of crisis. Therefore, fostering entrepreneurial alertness and robust networking strategies could be critical considerations for enhancing the maturity and overall performance of project-based organizations [129].

Second, the effects of legal requirements on PBO maturity and EQM efficiency are highlighted under the two schools of thought presented in the literature review. On one hand, as indicated by [87,88], legal requirements limit maturation since they only achieve basic standards among PBOs. On the contrary, as predicted by [77], the mandates can stimulate regulatory environmentalism among the PBOs. The statistically significant yet negative results reveal that PBOs in Qatar have not responded positively to the environmental mandates, limiting the effects of outcomes such as regulatory environmentalism. The potentiation of a ‘race to the bottom’ is evident from the inverse relationship. The interviews provided limited evidence regarding the extent to which existing laws have influenced the propensity of PBOs to achieve improvements in EQM efficiency. Insights from Jordanian SMEs suggest that when firms proactively adopt green innovation, their performance significantly improves, mainly when technological turbulence is considered a moderating factor. This implies that while regulatory frameworks alone might not effectively drive environmental responsiveness, the active adoption of innovative green practices can positively enhance organizational performance. Thus, policymakers should enforce environmental regulations and actively encourage green innovation, leveraging stakeholder engagement and technological adaptation to foster effective knowledge management and ultimately achieve sustained EQM efficiency [130].

The statistically significant yet negative relationship between the two variables in the model provides a unique insight into the current state of affairs among the Qatari PBOs. First, the literature review predicted a positive statistically significant relationship between PBO maturation and the possession of resources and capabilities [72,73,74]. Under the theorization, possessing the right resources and capabilities enables an organization to acquire the necessary inputs and implement the right processes to transition from lower to higher levels of maturity [69]. The negative relationship from the findings can be explained by the lack of the right resources and capabilities among the PBOs to ensure that the level of maturity contributes to EQM efficiency.

A similar scenario exists concerning the effects of KM and legal requirements. The literature review predicts a scenario whereby PBOs face challenges in transforming KM capabilities to maturation within the organization, primarily due to the transient nature of the projects handled by the PBOs [83,88]. The interview data reveal that only a limited number of the PBOs utilize the capabilities due to maturity to influence the operations and outcomes among their clients. The resultant gap is magnified by the global nature of the effects of failure to achieve EQM goals [5]. As a result, the PBOs failed to utilize the KM processes to achieve transference of the benefits of maturation fully.

The rest of the variables, including the organizational context and strategy, have statistically insignificant effects on PBO maturity. Background information from [24] reveals that PBOs operate at two levels: the organizational and the project levels. The disparity in the organizational arrangements, as evidenced by the demographic information and the diversity of projects that the PBOs are involved in, explains why the organizational context is found to have no discernible effect on maturation and, ultimately, EQM efficiency. A similar perspective is evident concerning the organizational strategy. A case study by Schneckenberg and Aql on a leadership development program for Qatari executives highlights how targeted leadership interventions considering local cultural and linguistic contexts can effectively enhance organizational maturity through transformative learning. Such insights suggest that integrating context-specific leadership programs could potentially mitigate the barriers to maturity observed in this study.

Under the third research question, the relative weights of the drivers of PBO maturity are computed. The results reveal that resources and capabilities are perceived as the key priority, with a score of 29.3%, followed by KM at 25.4%. These findings contradict what [48,49] concluded concerning leadership (which is under organizational context) as being of the highest priority. Project characteristics are viewed as being the least priority determinant of PBO maturity at 4.3%, thereby contradicting what [114,115,116] posited. However, the findings reveal that the prominence of resources and capabilities, as well as KM capabilities, potentiate the ability of PBOs to overcome the complexities that are predicted to affect maturation under project characteristics adversely. The findings on the relative weights reveal that variables whose hypothesized relationships are confirmed in the analysis have ranked third and fourth in the weighting list. A significant contribution of this study is identifying and empirically validating the precise priority ranking of maturity drivers within the unique operational environment of Qatari PBOs, providing clear managerial direction and strategic implications previously unestablished in the regional literature.

6. Conclusions

This research aimed to develop and validate an organizational maturity framework (OM framework) to assess an organization’s maturity and improve the operational performance of the EQM. Data from 18 interviewees were used to complement the theorizations from the literature review, which culminated in the identification of seven determinants of maturity for PBOs concerning EQM practices. The model was validated through survey data from 212 respondents and analyzed through SEM. The conclusions based on the findings are as follows.

Under the first objective, the findings reveal that the drivers of maturity identified from the literature review are relevant to the PBO maturity level. However, only two of the hypothesized relationships (legal requirements and sustainability) are supported, with the determinants of PBO maturity explaining a small proportion of the maturity level, and consequently, the PBO maturity accounts minimally for EQM efficiency. Interview findings highlighted that EQM concerns primarily centered on pollution control and energy efficiency, with organizations emphasizing sequential maturity pathways. Maturation was observed to enhance management processes, improve stakeholder engagement, optimize resource utilization, and boost regulatory compliance through better use of technology.

Under the second objective, the weighting of maturity determinants revealed significant variations in their perceived importance. Survey results showed that although resources and capabilities and knowledge management were highly valued by respondents, they did not significantly impact EQM outcomes, revealing a critical disconnect between perceived importance and actual effectiveness. The limited effect of PBO maturity on EQM efficiency can be attributed to the lower prioritization of the variables that statistically influenced maturity. Thus, management teams face a dilemma regarding prioritizing factors to achieve optimal maturity and EQM performance.

Under the third objective, a maturity framework is developed, taking into account the subconstructs under the seven variables that were found to influence PBO maturity. The framework is based on the capability framework, with five levels of maturation, and is sequential. It is designed for use with data sourced from project personnel, who are considered to be most informed about EQM practices about the project and organization. The model offers a comprehensive approach through which the four EQM concerns can be addressed.

Under the fourth objective, operational recommendations from the framework underscore the need to reorient the conceptualization of EQM efficiency among PBOs. The results reveal disparities in defining EQM efficiency, including variations in applying technical, material, and functional efficiency. The interviews and surveys indicated variations in EQM targets, specifically in pollution control, energy efficiency, biodiversity protection, and natural resource preservation. These complexities contribute to how maturation process improvements fail to translate fully into EQM efficiency outcomes. There is thus a critical need for convergence in conceptualizing EQM efficiency and better aligning maturation improvements with EQM outcomes, allowing PBOs to effectively translate their developed capabilities into measurable environmental performance results.

7. Implications

7.1. Methodological Implications

The proposed OM framework provides a structured tool for assessing maturity in PBOs, specifically targeting EQM efficiency. It acts as a clear roadmap, detailing processes, practices, and characteristics necessary for organizations to transition effectively from lower to higher maturity levels. This structured roadmap helps management visualize the cause-and-effect relationships clearly, increasing buy-in and simplifying the implementation process. The framework uniquely segments environmental interventions into distinct areas, allowing for targeted actions rather than generalized approaches, thus reducing uncertainties during EQM strategy execution [17,110].

7.2. Theoretical Implications

Theoretically, this study enhances the maturity-model literature by synthesizing maturity-modeling theories with the resource-based view and systems-thinking approaches. It introduces an innovative conceptualization of EQM efficiency by breaking down environmental quality concerns into four components, facilitating more precise analyses of maturity impacts. Additionally, highlighting specific variables such as resources, capabilities, knowledge management, and legal mandates reveals previously unexamined relationships. For instance, the identified negative impact of regulatory mandates diverges from existing literature, providing novel context-specific insights into how regulatory frameworks might inadvertently constrain organizational maturity [18,80].

7.3. Managerial Implications

From a managerial perspective, the proposed OM framework serves as a strategic tool that can guide Qatari PBOs toward more effective EQM efficiency. Managers can leverage this maturity framework as a structured diagnostic roadmap, clearly indicating necessary processes, practices, and behaviors to achieve higher maturity levels. The approach provides actionable strategic insights, highlighting the prioritization of resources and capabilities and knowledge management, crucial for informed managerial decision-making. Furthermore, by facilitating clear benchmarking opportunities and fostering cross-organizational learning, this framework enables managers to identify and adopt best practices relevant to their organization’s EQM objectives [16,46].

7.4. Policy Implications

Under the fourth research question, findings illustrate how drivers of organizational maturity can be integrated strategically to enhance EQM efficiency. The maturity drivers identified include two categorized as primary factors due to confirmed hypothesized relationships, one as a priority factor for high weighting, two as viable options with positive impacts, and two as potential determinants due to high correlation values despite unconfirmed hypotheses. Additionally, critical adjustments to existing legal requirements are necessary to avoid the potential “race to the bottom” effect [89]. Policymakers should incentivize PBOs to adopt comprehensive EQM practices addressing Qatar’s primary environmental concerns—pollution control, energy efficiency, natural resource protection, and biodiversity preservation. Similar policy adjustments are also needed for variables like organizational strategy (positive but statistically insignificant) and organizational context and project complexity (negative insignificant impacts). Moreover, reconceptualizing EQM efficiency is essential due to its low explained variance (3%) at current maturity levels. Consistent with findings from prior studies [46,66], maturity progression is non-linear, with significant improvements often emerging at higher maturity levels (4–5). Consequently, aligning maturity capabilities strategically with EQM outcomes requires explicitly established theoretical linkages to ensure a successful transition from maturity to practical EQM efficiency.

8. Future Directions

Future research should deepen understanding of PBO maturity and its impact on EQM. Incorporating industry specialization as a moderating or mediating variable and utilizing multi-group structural equation modeling (SEM) would clarify sector-specific maturity pathways and EQM outcomes. Refining EQM efficiency measurement through standardized, sector-relevant metrics and employing longitudinal methods can better capture temporal relationships between maturity enhancements and environmental impacts.

Additionally, future frameworks should integrate external drivers like evolving regulations, technological advancements, and economic shifts, examining their influence on EQM practices and maturity progression. Investigating emerging technologies—such as BIM, IoT, AI, and blockchain—can highlight their roles in improving compliance monitoring, decision-making, and EQM efficiency.