Ontology-Based Integration of Enterprise Architecture and Project Management: A Systems Thinking Approach for Project-Based Organizations in the Architecture, Engineering, and Construction Sector

Abstract

Construction projects are becoming increasingly complex due to their dynamic nature, the integration of multiple disciplines, and the need for strategic alignment between organizational processes and project management. However, traditional project management approaches often fail to address this complexity effectively. This study presents the application of IModel, a web-based semantic model grounded in systems thinking, designed to integrate enterprise architecture and project management. Through a case study conducted in a multinational AEC company, IModel was evaluated for its ability to enhance system interoperability, optimize processes, and support strategic decision-making. The methodology combined web semantic modeling with expert interviews and organizational data analysis. Findings indicate that IModel provides a comprehensive framework for knowledge management, reduces uncertainty, and improves decision-making in dynamic project environments. However, challenges related to model adoption, including the need for training in systems thinking and ontological modeling, were identified. This study contributes to the literature on innovation in construction project management, highlighting the potential of systems thinking and semantic tools to address complex problems in dynamic and evolving environments.

1. Introduction

The Architecture, Engineering, and Construction (AEC) sector is undergoing a profound transformation driven by increasing project complexity, digitalization demands, and the growing need for strategic alignment across all organizational layers. These pressures arise partly due to the fragmented and project-based nature of AEC companies, which must coordinate diverse disciplines, stakeholders, and technological systems while delivering projects under tight regulatory and financial constraints [1,2]. As organizations struggle to bridge the gap between strategy and operations, particularly in multinational environments, new tools and methods are required to enable greater coherence, adaptability, and performance.

Enterprise architecture (EA) has emerged as a promising discipline for addressing organizational complexity in recent years. EA is rooted in systems thinking and provides a structured approach to aligning business goals, processes, capabilities, and technologies [2,3]. However, while EA is widely used in IT-centric sectors, its adoption in the AEC industry remains limited. Barriers include a lack of tailored methodologies, insufficient awareness of EA’s value beyond IT, and the difficulty of integrating it with project-oriented practices typical of AEC firms [4,5].

At the same time, project management (PM) is evolving in parallel. The seventh edition of the Project Management Body of Knowledge (PMBOK^® Guide) reframes PM as a principle-based discipline supporting value delivery through interdependent capability systems [6]. This shift brings PM conceptually closer to EA, opening the door for their integration into project-based organizations (PBOs). Despite this, the literature shows that EA and PM continue to operate largely in silos, and tools that effectively unify them remain scarce [2,7,8].

A previous study introduced the IModel, an ontology-based modeling tool that integrates EA and PM perspectives into a unified, semantic framework. The IModel provides a machine-readable representation of enterprise and project knowledge that supports reasoning, alignment, and design [2]. While the theoretical foundation and structure of the IModel have been established, a critical step remained: validating its usability and value in a real-world organizational setting.

This paper addresses that gap by presenting a field-based application of the IModel in a multinational AEC company called Ontoconst. Through a consultancy engagement, the IModel was implemented to analyze the strategic alignment and operational structure of the Human Resources (HR) function, particularly its relationship with project development. The study involved collaborative architecture modeling, instantiation of EA views into an ontology, and subsequent evaluation of the model’s usefulness and quality by domain experts within the company.

The primary aim of this research is not to reiterate the design of the IModel but to test its actual performance in practice. Specifically, the paper investigates whether the IModel can (i) help uncover hidden misalignments and inefficiencies in a complex enterprise system and (ii) be understood and valued by end users outside the academic or technical modeling domain. It contributes to the empirical body of knowledge on EA and PM integration, demonstrating how a semantic approach can support strategy execution and organizational redesign in real settings.

The validation of the IModel followed a dual path. On one hand, the model was used to conduct a structured diagnosis of the HR function and its support to projects, identifying gaps, misalignments, and opportunities for redesign. On the other hand, the tool itself was assessed qualitatively by its users through structured interviews and semantic quality coding using the SEQUAL framework [9]. Results showed strong perceived value across several dimensions, particularly in integrating knowledge, facilitating analysis, and supporting communication. Despite the challenges related to the model’s complexity and the need for specialized skills, users emphasized its potential as a strategic tool to manage complexity and improve organizational understanding.

This study contributes practical evidence that supports the adoption of ontological tools like the IModel in dynamic, project-based environments. It shows that EA and PM integration can move beyond theory and provide tangible benefits for decision-making, alignment, and organizational coherence—especially in industries, like AEC, where these capabilities are most needed.

This research is guided by the following questions:

• RQ1: How can the IModel support integration between quality assurance and project management in the AEC sector?
• RQ2: What are the perceived benefits and challenges of implementing the IModel in a project-based organizational context?

By addressing these questions, the study offers both methodological and practical insights into the applicability and value of ontology-driven enterprise architecture within complex organizational environments.

The remaining document is structured as follows. Section 2 comprises the literature review presenting the state of the art about the complexity of AEC project management, an overview of EA, and its application in the AEC sector. In addition, the description of the IModel is presented. The methodology for conducting this work is presented in Section 3. Section 4 shows the results of applying the IModel in the real world. In Section 5, the discussion of the results is presented. Finally, the conclusions are presented in Section 5.

2. Literature Review

2.1. Complexity in the AEC Project Management

The AEC (Architecture, Engineering, and Construction) industry faces increasing complexity due to technological advancements, demanding client expectations, and evolving regulations [1]. Addressing this complexity requires a paradigm shift toward systems thinking and integrated management models [10]. These models must incorporate multifunctional processes and dynamic environments to manage product and project complexity effectively. The industry is also moving toward environmental sustainability, requiring companies to adapt their management practices and implement lifecycle approaches for better decision-making [11].

Traditional engineering and project management practices can benefit from adopting approaches such as systems thinking [12]. This approach has gained prominence as a useful perspective in project management due to its numerous benefits [13,14]. These benefits include providing a holistic view, addressing complexity, uncovering underlying patterns and structures, breaking down silos, promoting adaptability, encouraging long-term thinking, facilitating better decision-making, fostering continuous learning, and promoting interdisciplinary collaboration [15,16].

Recent studies confirm that managing complexity in the AEC sector also involves addressing deep systemic vulnerabilities linked to design–construction interfaces in sustainable projects. Ref. [17] highlights how a lack of coordination across disciplines and fragmented decision-making in sustainable construction projects often leads to delays, cost overruns, and lower performance. Their findings emphasize the role of interface management and integrated project delivery methods in mitigating systemic design and execution gaps [17]. Additionally, ref. [18] propose the Theory of Faults (ToF) as a paradigm for managing risk in complex infrastructure systems. Their work introduces data-driven tools based on digital signal processing and fault network analysis, allowing real-time monitoring and adaptive responses to disruptions. This framework is especially relevant under aleatoric and cascading risks, where conventional models fail to capture emergent system behaviors [18]. Finally, ref. [19] stresses that external macroeconomic shocks increasingly shape the complexity of infrastructure projects. Their empirical study demonstrates how construction cost escalation under global crises, such as the COVID-19 pandemic and geopolitical tension, can severely impact planning and delivery, especially when procurement and delivery systems are not resilient. They advocate for early procurement strategies and adaptive delivery models integrating risk propagation analysis through network science [19].

Thus, a holistic approach combining systems thinking, integrated management, and sustainability considerations is crucial for managing complexity in the AEC sector.

2.2. Systems Thinking in Project Management and Construction Sector

Systems thinking has become a powerful approach in project management, particularly in complex and dynamic environments such as the construction industry [7,13]. Unlike traditional linear methods that address isolated tasks, this approach focuses on the interdependencies and feedback loops that define system behavior over time. As noted by Sterman, understanding systems as dynamic networks allows for deeper diagnosis and more effective decision-making [20]. In project settings, this perspective improves the capacity to anticipate unintended consequences, identify risks, and support adaptive planning and strategic learning [13].

The construction sector, marked by fragmented supply chains, project uniqueness, and frequent changes in regulations and resources, presents an ideal setting for systems thinking [21,22]. These characteristics often produce nonlinear dynamics and emergent challenges that traditional management tools are ill-equipped to address. Systems thinking enables a broader view that captures systemic patterns, making it a valuable asset for performance improvement across multiple project dimensions.

In recent years, various studies have demonstrated its practical value through the use of system dynamics and related tools. For example, systems thinking has been used to model the adoption of circular business models (CBMs) in the Chilean construction sector, emphasizing the importance of information flows, certification, and capacity-building as critical enablers [23]. Similarly, research on wastewater management in Victoria highlighted poor regulatory awareness and institutional fragmentation, using systems maps to reveal leverage points and propose inter-agency collaboration as a corrective mechanism [24]. Other studies have explored barriers to circularity, such as the limited use of Design for Disassembly (DfD), proposing standardized components and legal infrastructure to support material reuse [25].

Applications also extend to project governance and digital coordination. A study conducted in the UK identified governance, regulation, and communication as critical variables shaping project outcomes under complexity [21]. Moreover, integrating systems thinking with systems engineering has been proposed as a solution to the coordination challenges in BIM-based environments, supporting structured decision-making in digital project management [26].

Beyond operations, systems thinking has informed national construction policy. The Integrated Development Management Model (IDMM), grounded in this perspective, was used to assess Turkey’s five-year development plans, uncovering misalignments between goals, policies, and management levels [27].

Emerging research is also extending systems thinking into the domains of resilience and adaptability. It has proven useful in navigating disruptions like supply chain shocks and shifting regulations [28], and is increasingly seen as a strategic foundation for digital transformation, real-time decision-making, and institutional learning. Cultivating a capability-based mindset rooted in systems thinking enables organizations to manage complexity more effectively, transforming it into a competitive advantage [29].

2.3. Enterprise Architecture

Enterprise architecture (EA) is recognized as a management discipline that addresses the complexity of organizational components and is rooted in systems thinking [2,30]. It provides a strategic perspective on aligning, governing, and optimizing these elements to achieve organizational objectives [12]. The concept of “architecture” originates from the design of a building’s components, carried out by an architect, where all elements work together to achieve a goal [2,30]. In systems engineering, architecture is defined as “the fundamental concepts of a system in its environment, embodied in its elements, relationships, and principles of design and evolution” [31].

Enterprise engineering views organizations as systems designed to adapt and systematically redesign themselves, characterized as “any collection of organizations with a common set of goals and/or a single bottom line” [32]. This architectural approach designs enterprises with interconnected components working together to achieve organizational objectives [3].

Combining the concepts of architecture and enterprise, EA can be defined as “the organizational logic for business processes and IT infrastructure, reflecting the integration and standardization requirements of the company’s operating model, providing a long-term vision to build capabilities and not just meet immediate needs”. EA is built upon the stability of core business and IT elements, ensuring their preservation while promoting flexibility and adaptability. The absence of a robust architecture can hinder business excellence [33,34].

Recent research confirms that EA is not only a static documentation discipline but an evolving managerial practice. Ref. [35] argues that EA must be institutionalized through structured EA functions, organizational units responsible for facilitating and governing the use of architectural artifacts. These functions are highly contingent on organizational properties such as size, business diversity, and IT governance maturity, which must inform the optimal structuring of EA roles, responsibilities, and communication flows [35].

In parallel, ref. [36] emphasizes the importance of adaptability and agility in dynamic environments. Through a systematic literature review and bibliometric analysis, they show that existing architectural models often fail to address the rapid alignment needs of modern digital enterprises. Their proposal integrates the “building blocks” concept as a modular approach to EA, supporting flexible architectural development and seamless integration with legacy systems in volatile environments [36]. Moreover, ref. [37] highlights the need for ontological clarity in EA modeling. They propose a comprehensive ontology that defines the structural relationships among concepts such as model quality, model audience, and model objectives. This framework addresses a longstanding fragmentation in the field, where modeling practices lack consistent conceptual grounding and hinder empirical comparability across projects [37].

EA is facilitated through frameworks consisting of conventions, principles, and practices for applying architectural descriptions in specific domains [24]. A set of models known as architectural description languages (ADLs) is used to formalize EA representations [31,34]. Among the most recognized frameworks are Zachman, TOGAF, DoDAF, GERAM, and FEAF.

TOGAF, created by The Open Group, remains widely adopted due to its structured Architecture Development Method (ADM) and iterative process model [38]. ArchiMate, a modeling language also standardized by The Open Group, has gained popularity for its compatibility with TOGAF and its clear graphical notation. The integration of TOGAF and ArchiMate enables organizations to represent architectures across strategic, business, application, and technology layers coherently and dynamically [35].

Recent advances now call for a rethinking of traditional frameworks. Ref. [36] suggests that modular, adaptive architectures built on building blocks could improve organizational agility. Similarly, ref. [35] proposes that EA functions be structured according to empirical evidence rather than reference models alone, ensuring alignment with enterprise-specific contingencies. Finally, ref. [37] urges EA practitioners and researchers to adopt a well-defined ontology for EA modeling to improve model quality and usability across stakeholders.

2.4. Enterprise Architecture in the AEC Sector

The AEC sector, characterized by fragmented workflows and project-based operations, increasingly requires systemic approaches to manage complexity, align strategic objectives, and support digital modernization [4,39]. EA has emerged as a promising framework in this context, offering a holistic method to structure, coordinate, and transform organizational practices across technological and business domains [3].

EA has shown value in enhancing interoperability and collaboration within construction processes. Studies report how EA can align BIM implementations with broader enterprise processes, improving collaboration maturity and standardizing roles, guidelines, and data flows [4]. This integration supports reliable and timely decision-making by embedding technological infrastructure within the enterprise logic.

The application of EA frameworks in construction companies has also enabled the optimization of resource allocation and coordination across geographically distributed projects through distributed information systems and mathematical models [40].

Geospatial integration is another strategic extension of EA. Incorporating Geographical Information System (GIS) data into EA frameworks supports the alignment of infrastructure development with zoning regulations, environmental constraints, and site-specific factors [41]. This fusion of BIM and the GIS within EA enhances the precision of planning and execution processes.

EA further delineates boundaries between automated and human responsibilities, improving transparency and control, especially in Industry 4.0 contexts [5].

EA also enables unified cost management systems, allowing for consistent budgeting, monitoring, and benchmarking across project phases through integrated IT platforms and databases [8].

Its support for implementing Enterprise Resource Planning (ERP) systems in the construction industry is also noted. These frameworks structure cost estimation, bidding, contract management, and monitoring within centralized and interconnected systems [42].

EA has the potential to standardize fundamental business components, such as value propositions, revenue streams, key activities, and partner networks, thereby improving consistency and strategic clarity [43]. It also improves coordination between human and machine-based activities in digitally enhanced construction environments, supporting the integration of smart sensors and intelligent agents [44].

At the ecosystem level, EA has been used to map buyer–supplier relations across the infrastructure sector using multilayer network models [45]. Reference architectures for platforms like Construction Logistics Control Towers have also been developed, integrating BIM, GIS, and transport systems to support emissions reduction and real-time logistics coordination [46].

Despite these contributions, barriers still hinder EA adoption. These include limited standardization, institutional resistance to change, and a lack of regulatory support for digital integration [4,5,8]. Nonetheless, EA remains a critical enabler for aligning processes, technologies, and strategies. It supports coordinated implementation of BIM, GIS, ERP, and IoT systems, enhancing adaptability, performance, and long-term competitiveness [3]. Theoretically, EA reinforces systems thinking in construction management, while practically, it provides a roadmap for delivering smart, sustainable, and digitally integrated infrastructure.

2.5. Integration Between Project Management Domain and Enterprise Architecture: The IModel

The IModel [2] is a novel ontology-driven tool developed to model, design, and analyze project-based organizations (PBOs) using an integrated approach that combines enterprise architecture (EA) and project management (PM) principles. Conceived through the Design Science Research Method (DSRM) [47], the IModel aims to address a real-world problem: the complexity and dynamism inherent in PBOs, where tasks, structures, and roles are constantly reconfigured. This reconfiguration often leads to misalignment between organizational components such as strategy, processes, structure, and human resources. To manage this complexity, the IModel adopts a systems thinking perspective, viewing a PBO as a system composed of interdependent subsystems.

At its core, the IModel conceptually integrates the PMBOK 7th edition (Project Management Body of Knowledge) [13] and the ArchiMate 3.2 EA modeling language. On the one hand, PMBOK provides the principles, functions, and performance domains that define project management best practices. On the other hand, ArchiMate 3.2 provides an organizational taxonomy. The innovation of IModel lies in fusing these domains into a semantic web model (an ontology) that enables machine-readable representations of knowledge. Ontologies allow sophisticated reasoning and querying, helping organizations to model, reuse, and interrelate their key components across domains in a standardized, interpretable way [48].

The IModel was developed through a rigorous process based on Methontology [49], a widely used methodology for building ontologies. The process included source collection, ontology development, integration of EA and PM concepts, and expert validation. Ontological modeling was performed using Protégé [50], and the integration of the ArchiMate and PMBOK ontologies involved complex ontology matching techniques to align shared concepts such as “value”, “function”, and “project”. For example, in IModel, the PMBOK concept of “project” as a temporary endeavor is mapped to the “WorkPackage” concept in ArchiMate, enabling interoperability.

The result is a powerful and flexible artifact that can be used for various purposes, including project alignment assessment, strategic planning, organizational analysis, knowledge and resource management, risk assessment, and even system interoperability. By offering a machine-readable, structured, and integrated representation of EA and PM knowledge, IModel enables organizations—especially those operating as PBOs—to analyze better how their projects align with strategic and operational goals. It also provides the foundation for more dynamic, adaptable, and intelligent project governance systems.

The model was evaluated by 14 domain experts from both academia and industry through a structured instrument derived from the SEQUAL (Semiotic Quality) [9] framework. The results shows the IModel’s comprehensibility, alignment capabilities, and practical utility across multiple use cases. The experts noted that IModel could significantly contribute to decision-making, especially in complex environments where alignment between enterprise architecture and project execution is essential.

IModel represents an advancement in the intersection of EA and PM. It provides an integrated, ontology-based tool tailored to the needs of dynamic, project-driven organizations. Bridging gaps between PM and EA offers a unified model that enhances strategic alignment, operational efficiency, and organizational coherence—features crucial for any modern enterprise navigating digital transformation.

3. Methodology

This research seeks to applying EA in the AEC sector. In this regard, the IModel developed by Atencio et al. [2] is tested in a case study. This case study examines a construction and real estate company with a presence in Europe. For confidentiality reasons, this company will be referred to in this study as Ontoconst. As of 2022, the firm boasted a workforce of approximately 8000 European employees and reported revenues exceeding EUR 4 billion. It offers architectural, engineering, and construction solutions for intricate infrastructure ventures to public and private stakeholders [51]. Its offerings are categorized into the design and construction of both residential and commercial structures, tunneling, civil projects, foundational work, and niche solutions such as timber-based construction.

According to the Ontoconst Report, the company’s strategy aims to establish it as a top multinational in construction and real estate services. The strategy hinges on four main pillars: (i) enhancing and coordinating the company’s portfolio of specialized services; (ii) achieving profitable growth by applying best practices and operational excellence; (iii) becoming a benchmark in organizational development and talent management; and (iv) driving innovation through a focus on industrialized processes, digital transformation, BIM, and sustainable practices. Regarding the second pillar, the company’s CEO has highlighted the business process perspective as a suitable approach to achieve the goals associated with this pillar. In this regard, the challenge addressed through IModel seeks to analyze a low level of satisfaction in the HR business function from the project’s development groups. There are no existing tools and skills in the company to understand the complexity of processes, artifacts, the systems involved, and the geographical distribution of projects. Therefore, the IModel proposal and its functionalities were presented to the Quality Department (QD). The following requirement was proposed to cover using the IModel.

Answering the following questions:

• Q1: How does HR support the project development as a whole entity?
• Q2: How does HR align with the strategy?

With the following expected outcomes:

• O1: A landscape of the HR entity and projects and their interrelationships.
• O2: Architectural redesign proposals.

The established requirements are related to the following dimensions of the IModel, as presented in

Table A1. Model assessment questions.

ID	SEQUAL Quality Dimension	Question
Q1	Physical	The model and its relevant parts are available (the model has been presented in an application where its components are accessible and manipulable).
Q2	Empirical	The model is understandable.
Q3	Semantic	There is correspondence between the model and the project management domain.
Q4	Semantic	There is correspondence between the model and the enterprise/organizational domain.
Q5	Deontic	The model provides an integrated view between the organizational/enterprise and the project domains.
Q6	Deontic	The model could support analyzing and assessing the cohesion between the organizational and project components.
Q7	Deontic	The model could support assessing the alignment between organizational and project components to be analyzed. For example: analyzing the strategic alignment of business processes.
Q8	Deontic	The model provides insight into different facets of the enterprise and could be a tool for communication and shared understanding of the enterprise.
Q9	Deontic	The model could be used to perform business/project analysis, e.g., conformance analysis, gap analysis, resource analysis.
Q10	Deontic	The model could facilitate the analysis and would allow it to be performed in less time than with non-computer-processable diagrams, e.g., a flowchart.
Q11	Deontic	The model could be used for quality assurance of work processes, e.g., conformance with project management guidelines.
Q12	Deontic	The model could be deployed directly to be used for controlling, supporting, and performing work. The activation can either be manual, automatic, or interactive, e.g., developing and managing a risk breakdown structure.
Q13	Deontic	The model could be used as a tool for knowledge management, e.g., knowledge documentation, knowledge retrieval.
Q14	Deontic	The model could be used as a tool for resources management, e.g., human resources competencies, mapping/documentation, resources allocation.
Q15	Deontic	The model could be used as a collaboration tool between organizational and project actors, e.g., sharing an enterprise resources directory to be used on projects.
Q16	Deontic	The model could be used for supporting systems integration and interoperability, e.g., the integration of multiple work standards used in an organization, the integration of organizational resources available to projects, application integration (e.g., between two software programs).
Q17	Deontic	The model could be used to manage the heterogeneity and complexity of the organization and projects, e.g., identifying people and their competencies along the projects represented in the model.
Q18	Deontic	The model could be used to support risk management, e.g., developing a risk database and assessing the relationship between risks and organizational/projects components.
Q19	Deontic	The model (as a tool) has the potential to be used for modeling, analyzing, and designing a PBO.
Q20	Social	Is there correspondence between the actor’s interpretation of the model?
Q21	Syntactic	The modeling language is correctly used.

of [2], referred to as a quality assessment issue Q_i (Available in the Appendix A.):

• The mapping of organizational and the project’s components, as well as their interrelationships (Q2, Q2, Q4, Q5).
• The cohesion assessment of the mentioned components (Q6).
• The strategic alignment of these components (Q7).
• The use of the model as a communication tool and the enterprise’s understanding (Q8).
• Enabling GAP analysis (Q9).
• Computer-assisted analysis (Q10).
• Supporting complexity management (Q17).
• PBO modeling, analyzing, and design (Q19).

The following activities were performed as a consultancy to achieve the expected outcomes and analysis, as shown in Figure 1.

(1)

Developing Architecture Views: This is the starting point where architecture views are created to represent the organization’s current state (as-is). These views can be built using modeling languages like ArchiMate and help visualize the organization’s structure, roles, processes, and technologies.

(2)

Validation by Stakeholders: Once the views are developed, they must be validated with key stakeholders—such as project managers, technical leads, or clients—to ensure the representations accurately reflect the real-world system. Their feedback ensures that the models are relevant and actionable.

(3)

Instancing Ontology: This step instantiates the validated architecture views into an ontology. This involves populating a semantic model with real data and elements specific to the organization. The ontology enables structured reasoning and data analysis across EA and project management domains.

(4)

Analysis: With the instantiated ontology, stakeholders and analysts perform diagnostic analysis. This includes identifying misalignments, inefficiencies, knowledge gaps, or organizational bottlenecks, using the semantic relationships and properties captured in the ontology.

(5)

Redesign Proposals: Based on the analysis, proposals for redesign or improvement are developed. These proposals aim to enhance alignment between strategy, operations, and technology, often suggesting changes to processes, structures, or information systems.

(6)

Assessment: Finally, the impact of the proposed redesigns is evaluated through assessment. This can include measuring improvements in performance, alignment, knowledge flow, or stakeholder satisfaction. The insights gained may lead to a new iteration of the process.

Given the exploratory nature of this application and the limited number of interviewees directly involved in the model’s implementation (as shown in Section 4), a qualitative and semi-structured interview approach has been adopted. This allowed for an in-depth discussion of the perceived benefits, challenges, and alignment outcomes associated with the IModel. While the questions were broad, their responses were systematically coded and interpreted using the quality criteria (Qi) defined and validated in [2], including aspects such as strategic alignment (Q7), model comprehensibility (Q8), and support for complexity management (Q17). As such, the qualitative data collected was not standalone, but rather embedded within a validated evaluative framework, enhancing the robustness and coherence of the assessment.

4. Results

This section follows the activities structure presented in Figure 1 in the previous section.

4.1. Developing Architecture Views

Based on the phases of TOGAF [32], a set of architecture views was modeled using the ArchiMate language. These models were developed collaboratively using the Visual Paradigm Online modeler [52] through successive work sessions between the researcher and the GQD. These sessions included the analysis of the existing documentation and models of the studied company.

The modeled views were the following:

• Organization view: This model highlights a company’s internal structure, which can be depicted as nested block diagrams or traditional organizational charts. It is valuable for pinpointing competencies, authority, and responsibilities within an entity [53]. A general overview of the organization is presented in Figure A1 (see Appendix B).
• Business functions view: A business function categorizes behavior according to selected criteria, usually based on necessary business resources and competencies [53]. Figure A2 (see Appendix B) shows the business functions performed by the HR business actor.
• Stakeholders’ view: This viewpoint lets the analyst depict stakeholders, their concerns, and evaluations of these concerns regarding strengths, weaknesses, opportunities, and threats. It can also outline the primary goals addressing these concerns and assessments [53]. Figure A3 (see Appendix B) shows the main stakeholders (customers and the Ontoconst board) connected with some business drivers (customer satisfaction, employee satisfaction, and profit), constraints (project deadlines), and requirements (project quality).
• Principles view: This model enables the analyst or designer to outline principles pertinent to the current design challenge and the goals driving them. It also facilitates modeling relationships between these principles and their associated goals, noting how principles might positively or negatively affect one another. Figure A4 (see Appendix B) shows Ontoconst’s principles connected with its main courses of action.
• Business goals view: This model typically shows the drivers motivating the development of specific business goals [53]. A set of Ontoconst’s goals supporting business drivers are shown in Figure A5 (see Appendix B).
• Business process view: This model illustrates key business processes, their interrelationships, and potentially their primary steps. It typically omits detailed process flow specifics, which are the domain of business process design languages [53]. Figure A6 (see Appendix B) shows a high-level view of business processes.
• Application view: This model shows how applications support business processes and their interplay with other applications. It aids application design by pinpointing required services and business process design by detailing available services. It highlights dependencies between business processes and applications and offers insights for operational managers [53]. A summary view of Ontoconst applications is presented in Figure A7.

4.2. Validation by Stakeholders

The modeling process was conducted in 15 sessions between April 2023 and September 2023. The first session was performed in person at the Ontoconst headquarters with the GQD team. The remaining meetings were conducted online. The models were developed iteratively, shared with stakeholders to gather input and validation, and refined through each iteration to ultimately secure stakeholder acceptance.

4.3. Instancing Ontology

With the modeled ArchiMate views as an input (from activity i of this section), the IModel was instanced in Protégé. For instance, Figure A4 (see Appendix B) contains the Ontoconst principles. The concept of “principle” exists in the ArchiMate taxonomy as a “declaration of intent that outlines a universal characteristic relevant to any system within a specific architectural context” [54]. This component is also part of the IModel as a class. Therefore, the existing principles of Ontococonst are modeled as an individual of the Principle’s class, as shown in the Protégé overview presented in Figure 2.

4.4. Analysis

To examine how HR supports project execution and contributes to Ontoconst’s strategic alignment, a set of semantic queries was applied to the IModel using the ArchiMate metamodel. The results, fully detailed in Appendix B, revealed three key connections: (i) the HR_request artifact, which links the Talent Acquisition function with Project Preparation activities and is managed through the MyHR_SAP_INSPIRE application; (ii) the HR_contract artifact, which connects hiring procedures to HR’s Compensation and Benefits function; and (iii) the application-based integration of compensation systems with business functions and project-related processes. These relationships, mapped through formal SQWRL queries and visualized using OntoGraf, demonstrate the operational role of HR in enabling project delivery and resource coordination. Additionally, the model highlighted an emerging but still limited strategic alignment, reflected in a recently implemented rewards program tied to performance and financial outcomes. While these links represent progress toward architectural integration, gaps remain in terms of traceability to higher-level strategic drivers. For verification and replication purposes, the full set of queries, ontology answers, and graphical models is provided in Appendix C.

The three presented connections enabled the recognition of how HR (as a business process, business function, and business actor) supports project development. The HR_request artifact is how project managers perform different requests, such as recruitment, search and hiring of personnel, or salary review. Through an interview with Ontoconst users, the following issues were discussed with the GQD:

• There is no Service Level Agreement (SLA) that formalizes a maximum response time for the different types of requirements. Therefore, the response time performance for these requests is not controlled through a key performance indicator (KPI).
• There is no clear definition for the appropriate responses to HR requests. For instance, one response to a client’s request was the sharing of 30 curriculum vitae when the client had expected to receive a recommendation based on the required profile.
• The profile descriptions for project professionals are not uniformly corporate. Each requester from the projects defines it freely.

4.5. Redesign Proposals

The previously presented findings were analyzed with the GQD, with its manager summarizing the diagnostic as “our company is composed of disconnected elements” and “we need to rewire these elements”. This insight reflects the situation of HR, which, though it has a defined strategy and principles that guide the organization to an expected level of performance, does not materialize in HR’s support of projects. There are no formal definitions that align this business function with the strategy. Based on the perspective of Galbraith [40] and Miterev [41], the main goal of PBO design is that the organizational component must be aligned with the strategy, which also provides cohesion. In this regard, the following redesign proposals are modeled to achieve the goal.

• Proposal 1: Improving HR performance through service delivery to projects

Excellence, Collaboration, and Agility are the principles that should influence the services provided by HR to the project development. (i) Excellence. “We are committed to delivering the highest quality across our operations, services, and initiatives. By integrating time-tested approaches with forward-thinking solutions, we aim to consistently exceed the expectations of our partners, now and in the long term”. (ii) Collaboration. “Through a combination of market expertise and technical skills, we generate value for clients and stakeholders alike. Our culture of collaboration is built on mutual trust and is guided by principles of fairness, openness, and respect in every partnership”. (iii) Agility. “We swiftly identify and respond to emerging challenges and opportunities, adapting strategically and practically. As a diverse and energetic global team, we reflect agility in action—constantly innovating and pushing boundaries to deliver optimal outcomes for our clients”.

Based on these principles, a BusinessGoal component is proposed to improve business process performance, titled Business_process_performance. This definition means that the achievement of this goal may be monitored and measured.

The rule has modeled this definition, and the individual answer is presented in Figure 3.

The proposed goal is then associated with three main components—HR actor, HR business process, and HR business partner—to align them with the strategy, as shown in Figure 4.

The formalization of an HR service is proposed—HR request service—to standardize and extend it to the different PM units and countries. An SLA controls the expected performance of this service for each type of request. This SLA has been modeled as an artifact called HR request SLA. In addition, a description (called Services description) of each service is provided in the supporting system MyHR_SAP_INSPIRE, which clearly articulates the intended response for each service. Then, when the request is closed, the user provides a satisfaction valuation (HR request valuation). HR request performance is monitored through a KPI panel (HR request metrics panel) to provide information for decision-making.

Another proposal is the formalization of the existing role of HR business partner to support HR-related project needs. An HR business partner is typically an internal or external service an individual or firm provides that assists organizations in achieving their strategic objectives within HR management [55]. They are pivotal in devising and executing long-term workplace success strategies. While organizations are their primary clientele, they also cater to individuals, particularly job seekers, offering personal HR consultancy services [55].

This proposal also aligns with the PMBOK 7th guidelines, which establish that a project and organization comprises a System of Value Delivery. Several business functions are associated with projects to ensure proper project delivery [13]. These business functions (presented in Figure 5, from the IModel) should be shared by project managers and organizational actors, such as the HR business partner.

To improve the supporting activities of the HR business partner, a specialization of these business functions was associated with this actor, modeled as a group of individuals, as shown in Figure 6.

Each associated business function was formalized in the IModel through an annotation property and object property, as shown in Figure 7.

• Proposal 2. Connecting the HR business function with the strategy

Complementary with the previous proposal, the business principles should guide the HR function. In this regard, three associated principles are proposed: excellence, agility, and collaboration. The inclusion of the integrity principle is also proposed, considering the company’s relationship with its customers. Consequently, this principle is also connected with the business process performance goal (see Figure 8).

Therefore, as presented in Figure 8, business_process_performance goals are the proposed bridge connecting the HR components with the strategy.

4.6. Assessment

To evaluate the perceived value and limitations of the IModel in supporting enterprise and project integration, a qualitative assessment was conducted through a semi-structured interview with the quality director (QD) and one of the quality engineers (QEs) involved in the case study. The assessment focused on the IModel’s application after its presentation and was guided by two questions: perceived benefits and observed limitations. The responses were transcribed and coded using the SEQUAL quality dimensions framework, processed with MAXQDA 2022 v7. As detailed in Appendix C, the analysis revealed that while the IModel is positively valued for offering an integrated view of organizational and project components (Q5), enabling cross-domain analyses (Q6, Q7), and supporting strategic modeling (Q17, Q19), it also raised concerns regarding usability and internal adoption (Q2, Q8, Q9). The interviewees recognized the model’s potential to enhance decision-making and process optimization, especially through its capacity to manage complexity and heterogeneity. However, challenges such as dependency on specialized experts and lack of user autonomy were noted as barriers to its institutionalization. This dual perspective, combining operational benefits and systemic constraints, offers insight into how digital tools like the IModel can contribute to transformation in project-based organizations. The detailed coding structure, citations, and extended interview excerpts can be found in Appendix D for traceability and replication purposes.

A conceptual co-occurrence map was generated based on the qualitative analysis of expert interviews to enhance the readability and analytical clarity of the interview data (see Figure 9). The map was constructed by identifying key themes from the transcripts and extracting concept pairs that co-occurred within the same or adjacent responses. These concept pairs were then used to form a co-occurrence network graph, where each node represents a theme and edges indicate thematic linkage [56]. The size of each node reflects its degree centrality, that is, how often the concept was connected with others, while the clustering coefficient provides insight into the local cohesiveness of related themes. This visualization aims to capture the structure and density of the thematic space surrounding the IModel implementation.

Table 1. Node-level metrics from the conceptual co-occurrence map.

Concept	Degree Centrality
Service Clarity	0.20
KPI Definition	0.13
Ontological Querying	0.13
Decision-Making Support	0.13
Architecture Modeling	0.13
HR Business Partner	0.13
Strategic Support	0.13
HR Request	0.07
Process Performance	0.07
Alignment with Strategy	0.07
Values and Principles	0.07
Digital Integration	0.07
Business Roles	0.07
PMBOK Integration	0.07
Conflict Mediation	0.07
Strategy Alignment	0.07

summarizes the degree centrality and clustering coefficient of each concept identified in the interview analysis. Degree centrality indicates how frequently a theme is connected to other themes in the co-occurrence network, reflecting its prominence in the discourse [56].

The conceptual co-occurrence map (Figure 9) derived from the expert interviews reveals a modular and loosely connected thematic structure, where several clusters appear relatively isolated from one another. This configuration shows the discursive structure of the interviews.

The most central and interconnected theme is “Service Clarity”, which exhibits the highest degree of centrality. This indicates that clarity around HR services, enabled by the IModel, was repeatedly mentioned and co-occurred with other concepts such as “HR Request”, “Ontological Querying”, and “Decision-Making Support”. This cluster reflects how the model supported the formalization of HR service structures and how semantic queries helped stakeholders visualize and adjust the contribution of HR to project goals.

Another densely connected area is formed by “Ontological Querying” and “Decision-Making Support”, which also demonstrate high clustering coefficients. This suggests that these concepts were discussed in joint narratives where stakeholders highlighted the model’s analytical potential, particularly in surfacing latent knowledge and supporting more informed operational decisions.

Meanwhile, the “KPI Definition”–“Process Performance”–“Strategic Support” cluster reflects the model’s impact on performance measurement and its role in aligning local practices with broader organizational objectives. Interviewees emphasized that the IModel made it easier to identify indicators, gaps, and areas for redesign in the HR contribution to project execution.

The relatively isolated position of some clusters, such as “Digital Integration”, “Architecture Modeling” or “HR Business Partner”, or “Conflict Mediation”, is analytically meaningful. These thematic pairs represent specific, domain-focused insights that, while not heavily connected to other interview discourse themes, nonetheless reflect the IModel’s distinct contributions. For example, the discussion of digital integration points to infrastructural challenges in aligning the IModel with BIM or other operational platforms—topics not strongly tied to the strategic or procedural themes. Similarly, the notion of HR as a business partner emerged in the context of internal alignment and role definition, with limited overlap in the rest of the conversation.

5. Discussion

This study explored the implementation of the IModel, an ontology-based enterprise architecture (EA) tool, in a real-world quality management context within a project-based organization in the AEC sector. The investigation was guided by two research questions: (RQ1) How can the IModel support integration between quality assurance and project management in the AEC sector? (RQ2) What are the perceived benefits and challenges of implementing the IModel in a project-based organizational context?

In response to RQ1, the findings indicate that the IModel effectively supported integration by making organizational structures, processes, and relationships more explicit. By leveraging its multilayered ontology (aligned with frameworks such as TOGAF and ArchiMate), the model enabled cross-departmental communication, alignment of roles and responsibilities, and a clearer understanding of how strategic goals cascade into operational activities. This supports previous claims by Atencio et al. (2022) [7] and Goh (2023) [44] that EA can provide a backbone for digital coordination in fragmented environments. It also aligns with the growing call for systematized digital modeling in construction, as argued by Underwood et al. (2022) [42].

Moreover, the model helped represent abstract elements such as values, principles, and quality expectations in a structured way. This is particularly relevant in the AEC sector, where project performance is often hindered by misalignment between contractual, operational, and strategic domains. Our case confirms that enterprise modeling can serve as a mediator between formal quality systems and the informal practices through which people coordinate work—an issue widely discussed by Yun (2023) [8] and Schoonderbeek and Proper (2024) [37].

In regard to RQ2, the interviews revealed clear benefits: increased transparency, more consistent quality documentation, improved collaboration across departments, and support for knowledge reuse. The ability to run queries on the model allowed stakeholders to identify gaps in service delivery, analyze roles, and assess alignment with project goals. These functionalities resonate with findings by Kotusev et al. (2024) [35], who emphasize the value of EA when it becomes embedded as an operational function rather than a documentation activity.

These qualitative insights were further supported by a conceptual co-occurrence map derived from the interview data (see Figure 9). The map revealed several clusters of closely related concepts, such as Service Clarity, Ontological Querying, and Strategic Support, which emerged as structurally central within the discourse. This supports RQ1 by confirming that the IModel enabled multilayered integration across organizational dimensions. The relatively isolated position of other clusters, such as digital integration or HR business partner, reflects the modular nature of both the model and the implementation process, in which stakeholders addressed specific aspects relevant to their functional responsibilities. This semi-fragmented structure reinforces the adaptability of the IModel to decentralized project environments and provides further evidence in support of RQ2, indicating that perceived benefits were domain-specific but collectively aligned with broader strategic objectives.

Nonetheless, the study also identified significant challenges. First, the adoption process was hampered by resistance to formal modeling practices and a lack of prior exposure to EA tools among some participants. Second, the model’s effectiveness was constrained by the absence of digital integration with existing quality management systems. These limitations are echoed in studies by Pancote et al. (2025) [36], who underline the need for agile, modular EA solutions, and by Harmelink et al. (2025) [46], who demonstrate how EA must be supported by technological infrastructure and institutional buy-in to succeed.

Additionally, the single-case nature of this study limits its generalizability. While the IModel performed well in the context studied, further research is needed to validate its scalability across different types of AEC organizations with varying degrees of digital maturity and managerial support.

Ultimately, it can be noted that EA, when supported by formal ontologies and tailored to the operational logic of project-based environments, can provide substantial value in aligning quality assurance and project execution. It also illustrates the importance of institutional, cultural, and technical enablers for the success of such integration efforts.

Potential for Application in Other Sectors

Beyond the AEC context, the IModel, presents opportunities for broader application across other project-based and process-intensive industries. Sectors such as manufacturing, healthcare, logistics, and public infrastructure management often face challenges similar to those encountered in construction: fragmented processes, regulatory complexity, multiple stakeholder layers, and the need for alignment between operational and strategic goals. The ontology-driven structure of the IModel, particularly its layered representation of goals, processes, roles, and technologies, can be adapted to map service delivery workflows, compliance procedures, and resource planning in these domains. For example, in healthcare, the IModel, could support the integration of patient care pathways with quality protocols and digital health records. In manufacturing, it could be used to align production operations with lean management standards and strategic performance indicators. These possibilities open new avenues for future research and validation in multi-sectoral settings.

6. Conclusions

This research examined the application of the IModel, an ontology-based enterprise architecture tool, in supporting the integration of quality assurance and project management practices within a project-based organization in the AEC sector. Through a single-case study and expert interviews, we explored both the theoretical contribution of the model and its practical implications in a real-world context.

The findings indicate that the IModel provides a coherent structure for capturing enterprise logic across multiple layers—from strategic intent and business processes to supporting applications and technologies. By doing so, it enhances transparency, supports decision-making, and aligns operational practices with long-term organizational objectives. These results confirm that EA can serve not only as a planning instrument but also as a coordination and governance mechanism when embedded in day-to-day quality practices.

Interviewed experts reported that the model helped clarify responsibilities, standardize documentation, and identify systemic inefficiencies. Its ability to simulate alternative configurations through ontological queries also enabled forward-looking planning and alignment verification. These capabilities respond directly to the challenges of complexity and fragmentation identified in the literature and reinforce the value of model-based approaches in the AEC sector.

However, several limitations must be acknowledged. The study’s scope was limited to a single case within one organization, which restricts the generalizability of the findings. The qualitative methodology, while appropriate for exploratory analysis, could be complemented in future research with quantitative validation or mixed methods. Additionally, the lack of integration between the IModel and existing digital platforms such as BIM or ERP systems may have constrained its full potential.

Future research could extend this work in several directions. First, by applying the IModel in a broader set of organizations across different regions or levels of digital maturity. Second, by developing integration mechanisms that connect the IModel with operational platforms (e.g., real-time dashboards, BIM-GIS interfaces). Third, by incorporating feedback loops or simulation engines to assess dynamic performance over time. Lastly, further development of training materials and user guides could support wider adoption and institutionalization. Moreover, future work could also explore the application of the IModel in other domains such as manufacturing, healthcare, or logistics, where similar alignment challenges exist.

To further strengthen the empirical grounding of the IModel, future research should incorporate quantitative evaluation methods such as pre- and post-implementation KPIs, cost–benefit assessments, and structured user satisfaction surveys. These metrics would complement the qualitative insights and SNA presented in this study, enabling a more comprehensive understanding of the model’s impact on organizational performance, stakeholder engagement, and digital maturity progression.

While the current study focuses on a single case applied to the Human Resources function, future research will seek to extend the application of the IModel to other organizational domains, such as operations, procurement, or finance, as well as to diverse types of PBOs. This broader validation would support the model’s generalizability and its adaptability to varying structural and strategic contexts. Moreover, conducting comparative case studies may reveal how different organizational characteristics influence the effectiveness of ontology-based modeling for business alignment and transformation.

Finally, the IModel represents a pathway for operationalizing EA in complex, project-based environments. Its formal structure, practical usability, and capacity to mediate between strategy and execution make it a valuable contribution to the advancement of digital transformation in the AEC sector.