Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages

Abstract

Management in the current era is strongly supported by a range of advanced technologies that enhance organizational efficiency and effectiveness. One particularly promising technology is Virtual Reality (VR). This exploratory study investigates the perceived practicality of VR across the systems engineering project lifecycle, providing quantitative insights into how VR is valued at different stages and by various user profiles. Drawing on data from $n=56$ professionals, the study employs advanced analytical methods like GLM, CFA, SEM, CATPCA, and CHAID classification trees to extract insights from the dataset. Results reveal that the perceived utility of VR varies significantly across project stages, with the training and proposal phases receiving the highest ratings, and early planning stages such as specification and requirement definition rated the lowest. A unidimensional structure of VR practicality was validated, with strong factor loadings for stages involving prototyping and testing. Key predictors of perceived practicality included willingness to adopt VR (positive effect), familiarity with VR (mixed effects), and education level (negative effect). While traditional demographic variables had limited explanatory power, experiential and psychological factors—such as hands-on VR exposure and openness to innovation—emerged as critical determinants of perceived value. These findings offer an empirically grounded framework for the strategic implementation of VR in systems engineering. Practically, this research guides decisions on resource allocation by identifying the specific project stages (e.g., training, proposal development) and user profiles (e.g., those open to adoption, those with specific experiential backgrounds) where VR is likely to provide the most significant benefit and return on investment.

1. Introduction

Management in the current era is increasingly supported by a broad range of advanced technologies that alter the way management is conducted and the efficiency of the management process (reflected by the level of achieving the organization’s goals). This phenomenon is well-demonstrated by the most prominent and discussed example today, Artificial Intelligence (AI), which refers to computerized systems capable of perceiving their environment and taking action to achieve predefined objectives. Most contemporary AI solutions are powered by machine learning (ML), wherein algorithms are trained on large datasets to identify patterns and improve performance over time. These capabilities—once considered the exclusive domain of human cognition—position AI as a tool capable of performing tasks traditionally carried out by people. Given that management is inherently a human-centric function, the integration of AI has profound implications. Manual managerial activities are progressively being automated, and in the foreseeable future, entire managerial roles may be transformed or even replaced by intelligent systems [1].

AI is the only technology that makes centralized processes more efficient and effective [2]. This phenomenon may be demonstrated by several other notable technological developments or specific implementations: (a) Workflow management provides structured tools to oversee and coordinate sequences of actions necessary to complete specific tasks. By automating these sequences, workflow systems reduce reliance on manual processes, enhance control and transparency, and bolster the security and efficiency of organizational operations. Therefore, workflow management represents a foundational element in the broader shift toward AI-driven management systems. (b) Cloud computing, a pivotal advancement in information technology, delivers computing resources as on-demand services over the internet. This approach enables remote access to organizational data and applications from any internet-connected device, effectively removing geographical constraints. Furthermore, cloud computing promotes more efficient utilization of computational resources, leading to significant reductions in information systems (IS) costs [3]. (c) Business Intelligence (BI) is a methodology that transforms raw organizational data into insights to support informed decision-making. When combined with data visualization—often regarded as a key component of BI—these tools play a critical role in enhancing strategic decision-making at the executive level [4]. (d) E-commerce, while fundamentally a digital platform for conducting commercial transactions, extends beyond operational functions. It also encompasses strategic elements such as marketing tactics, pricing policies, and the coordination of interactions among supply chain members, thereby serving as a vital management instrument [5]. (e) Customer Relationship Management (CRM) is a strategic approach designed to manage and optimize interactions between organizations and their customers. It serves not only as a technological solution but also as a comprehensive business strategy to foster growth and improve customer satisfaction [6]. CRM systems integrate data and processes across departments to support informed decision-making and long-term relationship building. (f) Finally, Virtual Reality (VR) refers to a computer-generated graphical simulation that creates an immersive, synthetic environment. In the context of this paper, VR is examined as a promising technology with the potential to automate aspects of managerial processes, offering new dimensions of control, visualization, and engagement within organizational operations [7].

VR implementations typically employ three-dimensional (3D) displays and tracking mechanisms to recreate the real world for the user digitally. For example, when a building is designed, the entire space may be simulated, enabling a user with non-architectural skills (e.g., the ability to read and imagine charts) to practically walk through the building in the design stage and before the construction even began. To achieve this, standard hardware for VR implementation is applied, including head-mounted displays (HMDs) or multi-projection environments, with visual simulation being the primary focus, though other senses such as sound, touch, and even smell can also be engaged [8]. This dedicated hardware is required to enable complete immersion [9]; however, practical applications can be achieved even with basic consumer devices such as smartphones—such as the 3D viewing capabilities provided by Google Search using the ARCore technology [10]. Beyond the dedicated hardware, VR systems demand substantial computational power and high-bandwidth communication to function effectively. This is particularly essential for enabling real-time responsiveness—such as dynamically adjusting visual output to match head movements—thereby creating a seamless and immersive experience that closely mirrors real-world perception [11].

The virtualization of the real environment can be understood through the concept introduced by Paul Milgram and colleagues: the reality–virtuality continuum [12]. This continuum represents a seamless spectrum ranging from a completely real environment, e.g., an online close circuit camera layout, to an entirely virtual one, e.g., the abovementioned example of virtualizing the design of a building. An intermediate stage of this continuum includes Augmented Reality (AR) and augmented virtuality (actual implantation of AR in real-time). AR is defined as “an enhanced version of the real world, achieved through the use of computer-generated digital information, including visual, auditory, and other sensory elements” [13]. AR overlays digital elements onto the real world via smartphones or AR glasses, supporting, for example, healthcare and industrial training [14]. As such, VR can be viewed as a specific case within the continuum where the entire user experience is simulated and a virtual environment fully replaces the real one. While AR enables users to maintain awareness and control within the physical world, VR immerses users in a digitally constructed environment, with their presence and perception governed by the system itself. VR creates Virtual Environments (VEs), which are situated at the far end of the reality–virtuality continuum, in contrast to the entirely physical, real-world experience. Mixed Reality (MR) integrates AR and VR, enabling real-time interaction between physical and virtual objects—ideal for collaborative workspaces and advanced design applications [15].

Focusing on VR solutions, they can be classified by their level of immersion and interactivity. Non-immersive VR involves interacting with VEs through standard screens and input devices commonly used in education and architectural visualization [7]. Semi-immersive VR utilizes large projection systems to enhance presence, often found in flight simulators and museum exhibits [16]. Fully immersive VR relies on headsets and motion tracking to deliver real-time, immersive experiences, particularly in gaming and therapeutic settings [17].

Applications of VR span a wide range of disciplines, reflecting its transformative potential across sectors. In the entertainment industry, the origin of VR adoption, VR has revolutionized video gaming by offering immersive experiences. In education, it is used for training in fields such as medicine, safety, and the military, allowing learners to engage in lifelike simulations without real-world consequences. Architecture and urban design benefit from VR by creating detailed virtual models, while industrial design uses it to optimize production chains. In the business realm, VR facilitates remote collaboration through virtual meetings and simulations, expanding the possibilities for global teamwork [18]. VR has been found to offer considerable benefits in areas including construction safety management, visualization of complex processes, improved communication, real-time data acquisition, and education in construction management. It is also useful for tracking project timelines and monitoring progress [19].

Specifically, in the field of management, VR opens new opportunities for innovation and efficiency. It can be used to create immersive collaborative spaces for geographically dispersed teams, enhancing communication and decision-making. It also offers immersive employee training environments that improve skill acquisition and retention. Additionally, VR supports virtual prototyping for engineers and designers, enables real-time visualization of project progress, and contributes to risk mitigation strategies. Unlike gaming, which typically targets individual users, management applications often rely on Shared Virtual Reality (SVR)—a multi-user experience accessed over the Internet. In SVR, users interact within the same virtual environment, making it suitable for collaborative tasks and team-based project management [20,21]. For example, in large-scale construction projects—often characterized by high complexity, numerous stakeholders, and the need for client and end-user involvement—VR serves as a powerful communication tool that facilitates clearer understanding among all parties [22]. Ahmed [23] reviewed the application of AR and VR in the context of construction project management. The review highlighted that these technologies can significantly enhance the effectiveness and efficiency of core project tasks such as scheduling, progress tracking, and worker training.

One significant and centric component in management applications is project management. Hensen and Klamma [24] introduced the Visual Immersive Analytics for Project Management (VIAProMa) framework, which integrates data from existing project management tools to generate immersive 3D visualizations. This approach enhances remote collaboration by providing stakeholders with a shared virtual environment where complex project data can be explored and interpreted collectively. The emergence of the Metaverse, once considered the realm of science fiction, has further expanded the potential of such tools in project management. The Metaverse is defined as “a collective virtual shared space that encompasses both the physical and digital worlds. It is a virtual world created using a combination of VR, AR, and other technologies” [25]. According to the Institute of Project Management [25], Metaverse applications in project management may include project visualization and simulation, virtual prototyping, data-driven decision-making, and enhanced stakeholder engagement and communication.

The capabilities of VR suggest that immersive technologies are not limited to traditional industrial environments. For example, in the field of information management, Fairchild [26] demonstrated how VR can address information overload by enabling visualizations based on degrees of interest, allowing users to focus on relevant content while filtering out less pertinent data.

This research aims to empirically examine perceptions of VR’s practicality across project lifecycle stages in systems engineering among professionals in the field. The research questions are focused on the following topics: (a) in which stages of the project lifecycle (e.g., Specification and Definition of Requirements or Testing) current VR technology should be deployed; (b) what are the perceptions towards VR adoption of each population type (e.g., openness or demographics); (c) where future VR development could address current functional technological gaps (i.e., stages were the technology is currently inefficient but further development is promising). The study employed a comprehensive, fully exploratory research design that integrated multiple analytical techniques. The raw data was collected via a questionnaire which was designed and refined for this purpose, and the analysis includes methods like General Linear Model (GLM), Confirmatory Factor Analysis (CFA), Structural Equation Models (SEM), CHAID classification tree, and Categorical Principal Components Analysis (CATPCA)—as detailed in Section 3. These analyses formed an exploratory framework that captures the nuanced and multi-dimensional nature of attitudes toward VR in systems engineering contexts. The rest of the paper is organized as follows: Section 2 screens relevant academic literature that is related to the research subject; Section 3 describes in detail the multi-stage analysis process; Section 4 describes the actual analysis of the study sample and the insights that stem from it; Section 5 binds the results to other theories and discusses the implementation of the findings, as well as limitations and further research proposals; and finally, Section 6 summarizes the insights.

2. Literature Review

As immersive technologies become increasingly integrated into engineering practice, VR has emerged as a powerful enabler throughout various stages of the systems engineering lifecycle. The following review demonstrates that VR can effectively support activities ranging from early planning and design to training, implementation, and maintenance.

2.1. VR in Systems Engineering

According to Fussell [27], VR is a three-dimensional simulation that enables users to interact with digital objects as they would in the real world. These environments can replicate actual settings or present entirely imagined worlds developed using specialized software. In recent years, VR has seen rapid advancements, with major technology providers and industry leaders, such as Facebook, Google, Microsoft, and Samsung, investing significantly in immersive imaging technologies like headsets, supporting software, and AI.

This technological progress has positioned VR as a valuable tool in systems engineering, particularly in the development of complex products like vehicles, aircraft, and ships, which often involve geographically dispersed teams. VR addresses the logistical and coordination challenges of such projects by streamlining processes like design, prototyping, and validation. It enables immersive, real-time collaboration across locations, enhancing communication and reducing travel, redesigns, and physical prototyping costs. As Fussell [27] emphasizes, VR supports more efficient decision-making and facilitates integration throughout the entire system lifecycle—from initial concept to final production.

Nevertheless, integrating VR into systems engineering requires careful attention to platform usability, project alignment, and long-term cost-effectiveness. As highlighted by Saghafian, Laumann, and Skogstad [28], deployment across domains such as software, human factors, and maintenance must be strategically planned to ensure interoperability and scalability.

2.2. VR for Design and Prototyping

One critical early application of VR is in user-centered design and ideation [29]. VR enhances user-centered design by allowing developers to step into the shoes of end-users. Avatar-based interaction enables designers to empathize with users facing disabilities, cultural barriers, or extreme environments. This perspective deepens design understanding and supports more inclusive solutions. Moreover, VR promotes creative ideation through the exploration of unconventional spatial arrangements and dynamic object combinations [30].

Following the conceptual stages, VR’s role intensifies in prototyping and technical design processes. VR significantly advances traditional Computer-Aided Design (CAD) by transforming static 2D representations into immersive 3D environments. Hsi, Linn, and Bell [31] observed that interpreting 3D structures through 2D interfaces demands considerable spatial reasoning, whereas VR simplifies this cognitive load, making models more accessible to both novices and experts. Additionally, VR enhances communication with non-technical stakeholders—such as managers and clients—by delivering more engaging and comprehensible presentations. Marks, Estevez, and Connor [32] emphasize that affordable VR solutions, including game engines, enable rapid development of interactive 3D content, surpassing the efficiency of traditional modeling approaches. Likewise, Connell and Tullberg [33] note that VR improves early-stage engineering tasks, such as preprocessing and refining Finite Element Analysis (FEA), by supporting intuitive spatial adjustments that would otherwise require advanced technical skills. A particularly effective use case is facilitating collaborative and immersive design reviews.

Design reviews, which are critical to project development, can be significantly enhanced by VR. This is achieved by an immersive three-dimensional environment that improves spatial comprehension and stakeholder engagement. For example, Wolfartsberger [34] introduced VRSMART, a VR tool that simulates CAD data and enables intuitive user interaction. Users identified more design flaws and resolved issues more efficiently than traditional screen-based methods. By immersing participants—including engineers, designers, and even non-technical stakeholders—in the design environment, VR facilitates a more intuitive understanding of complex geometries, helps detect spatial conflicts, and supports collaborative decision-making. This is further supported by Coburn, Freeman, and Salmon [35], who found that immersive VR environments improve comprehension of spatial structures, thereby enhancing the design evaluation process. Beyond design reviews, VR also broadly advances 3D data visualization capabilities, particularly in enhancing users’ ability to interpret spatial relationships and interact meaningfully with complex data [36]. While VR tools like VRSMART may not fully replace conventional review methods, they offer a powerful complementary approach that augments design accuracy, communication, and insight.

Building upon its early-stage benefits in production planning and system design, VR’s contribution extends far beyond the initial development phases. After facilitating cost-effective planning, real-time simulation, and early detection of integration issues, VR continues to add value in post-delivery stages, particularly in system support, repair, and maintenance [37,38].

2.3. VR in Maintenance and Lifecycle Support

Early work by Gomes and Zachmann [39] introduced VR tools designed specifically for maintenance and assembly training, laying the foundation for immersive procedural learning and diagnostics. This continuity between design-time simulation and real-world system upkeep highlights VR’s holistic role across the system lifecycle. Borro et al. [40] further expanded on this by developing VR-based verification systems for complex jet turbine maintenance, showcasing the technology’s ability to manage high-risk procedures precisely. More recently, Guo et al. [41] emphasized the importance of conducting maintainability analysis early in the design process, thereby closing the loop that links design considerations directly to long-term operational efficiency and reliability. In parallel, VR supports critical functions in engineering, such as risk management and hazard evaluation.

2.4. VR for Risk Management

In the realm of risk management, VR has emerged as a powerful tool for evaluating complex systems. According to Slezak, Keren, and Finseth [42], effective risk assessment demands a comprehensive understanding of system components and their interactions. As mentioned above, collaborative VR applications facilitate this process by creating immersive, shared environments where multiple stakeholders can engage in system modeling and analysis. This collective interaction deepens individual comprehension and team communication, ultimately leading to more accurate and efficient risk assessments.

2.5. VR in Education, Training and Communication

Recent literature demonstrates the growing relevance of VR implementation across diverse sectors. These applications provide further insight into how immersive environments contribute to performance, training, and collaboration in various contexts. VR has gained significant traction in both educational and industrial settings, transforming knowledge delivery, skill acquisition, and system design. A large-scale systematic review by Santilli et al. [43] compared VR-based learning to traditional instruction across 71 higher education studies. The findings demonstrated that VR environments foster improved engagement and learning outcomes, particularly in STEM and health-related disciplines, where visualization and simulation of abstract or complex systems are critical. In the realm of engineering education, Ghazali et al. [44] implemented VR laboratory modules in undergraduate electronics courses and found significant improvement in students’ comprehension and problem-solving capabilities. Similarly, Huang and Tseng [45] reviewed XR applications in applied sciences, concluding that AR is particularly useful for visualizing abstract phenomena, while VR and MR excel in simulating hazardous or complex scenarios, offering safe and experiential learning opportunities.

VR has also shown promise in industrial skills training. A comprehensive review by Radhakrishnan et al. [46] emphasized VR’s potential for autonomous and remote training in manufacturing and assembly tasks, though it highlighted design-related challenges like cybersickness and limited haptic feedback. Reinforcing this, de Giorgio et al. [47] analyzed XR applications in manufacturing and found that these technologies bridge the gap between theory and practice, offering scalable and risk-free training environments for students and professionals. Ghazali et al. [44] further support this perspective, showing that VR-enhanced labs in engineering curricula improve technical comprehension and increase student confidence and engagement. Huang and Tseng [45] further emphasized the value of XR in applied science education, especially in simulating hazardous industrial scenarios in a safe and immersive manner. Healthcare education has also embraced immersive technologies. A meta-analysis by Sung et al. [48] of 45 randomized controlled trials demonstrated that VR interventions lead to measurable improvements in clinical knowledge and procedural skills, with participants reporting higher satisfaction and confidence levels. This supports VR’s role as a supplement to conventional training, especially when logistical or ethical constraints limit real-world practice. Similarly, Strojny & Dużmańska-Misiarczyk [49] reviewed over 300 studies on the effectiveness of VR training across various professional settings. They proposed a five-point framework to improve consistency in evaluating outcomes and emphasized that VR generally leads to improved knowledge retention and skill acquisition—particularly when combined with feedback mechanisms and interactive design. Chang et al. [50] reinforce this trend, emphasizing that VR-based experiential learning models support soft-skill development and behavioral transfer. Their systematic review highlighted that VR enhances user engagement and provides safe, controlled environments for rehearsing complex interpersonal scenarios, such as conflict resolution and leadership communication. These immersive settings improved emotional regulation and increased participants’ confidence in real-world professional interactions.

While short-term learning outcomes dominate much of the literature, Stefan et al. [51] have called for more robust evaluation frameworks, citing a lack of studies measuring behavioral transfer and real-world impact. They advocated for longitudinal studies that assess whether VR training leads to sustained performance improvements on the job. VR is also making an impact in social and behavioral skill training. Chang et al. [50] reviewed 20 studies on VR-based soft skill development. They found that virtual environments were effective in teaching social interaction, safety behaviors, and professional competencies—many of which transferred to real-world behaviors. Beyond training, VR also reshapes communication and engagement practices within complex systems.

In the context of public engagement and communication, Gallego-Abellán et al. [52] demonstrated that immersive VR journalism evokes stronger empathy and emotional responses than traditional 2D media. Their findings underscore VR’s unique capacity to enhance human perspective-taking, suggesting broader implications for team collaboration, stakeholder alignment, and decision-making in complex systems engineering environments.

VR enhances user empathy through immersive simulations, supports creative design thinking [53], and improves risk management and operational safety [54].

These studies illustrate the multidimensional value of VR. From design to risk management, and from soft-skills training to stakeholder engagement, VR supports creativity, efficiency, and collaboration. De Freitas et al. [55] argue that VR not only improves decision-making but also offers novel perspectives for multidisciplinary teamwork. As implementation increases, it becomes essential to evaluate not just technical efficacy but also psychological, educational, and organizational impacts.

The present study builds on these foundations by assessing how professionals perceive the practicality of VR across various stages of the systems engineering lifecycle, identifying key contextual and personal predictors of value.

3. Methodology

3.1. Research Design and Instrument Development

This study employed a quantitative research approach to systematically explore the integration of Virtual Reality (VR) technology in enhancing systems engineering processes throughout the complete project lifecycle. This methodological approach was selected to ensure objectivity, generate measurable data, and enable statistically reliable analysis of how professionals perceive the practicality and usefulness of VR applications across various development stages.

The investigation focused on eight distinct stages within the project lifecycle: Offering and Recruiting Investors, Specification and Definition of Requirements, Concept Formulation, Design and Surveys, Development, Testing, Guidance, and Maintenance. These stages reflect the chronological progression of real-world engineering projects and provide a comprehensive structure for evaluating the perceived utility of VR throughout a project’s evolution. These eight stages were selected based on a synthesis of established systems engineering lifecycle models [56] and were validated through experts’ consultation during the instrument development phase. While other potential phases such as system deployment, operational use, or decommissioning exist in broader lifecycle frameworks, they were either conceptually included under the selected categories (e.g., operational aspects under Maintenance and onboarding under Training) or deemed less relevant for evaluating the perceived practicality of VR. The selected stages represent the most critical points where immersive technologies are expected to offer practical value across diverse engineering contexts.

To gather the required raw data, a structured questionnaire was developed. The development process began with semi-structured interviews with VR and systems engineering experts, which informed the initial version of the questionnaire. This version was then evaluated by additional domain experts, who assessed each item for clarity and relevance to the predefined research questions. Open-ended feedback and suggestions for alternative phrasings were also collected. Based on their responses and insights, a revised version of the questionnaire was constructed. This experts validation process supported the content validity of the instrument. Additionally, a pilot study was conducted to evaluate item clarity and structure, contributing to the reliability of the questionnaire prior to full-scale deployment.

The questionnaire aimed to collect detailed information in three key areas: the demographic background of participants, their professional experience, and their assessment of VR’s practicality across the identified lifecycle stages.

Demographic items included information regarding gender, age, education level, and field of specialization. To contextualize professional perspectives, respondents were also asked to report their organizational affiliation, years of experience, and current professional roles. The core of the instrument focused on measuring perceived practicality of VR during each of the eight lifecycle stages using a five-point Likert scale ranging from “not practical at all” to “highly practical”.

A pilot study was conducted with a small sample of professionals to evaluate the clarity, structure, and usability of the questionnaire. Based on the feedback received, revisions and refining were made to ensure that the wording was clear, the sequence of questions logical, and the format user-friendly. This step was essential to establish the validity and reliability of the research instrument before broad distribution.

The finalized questionnaire was administered to a purposive sample of 56 professionals who met the inclusion criteria, specifically individuals with relevant experience in systems engineering and project management and familiarity with at least several of the project lifecycle stages under investigation. While the sample size was relatively modest, the collected data was well-suited for the subsequent application of advanced statistical modeling techniques, as detailed below. They provided insight into how VR is perceived as a practical tool for supporting design, collaboration, simulation, and decision-making throughout the engineering process. Moreover, the study population, which was carefully selected, provided relatively high-quality responses compared to a crowd-sourcing sample for example. The research was approved by the institutional ethics committee for non-clinical research in humans, certification number: AU-ENG-SK-20241006.

3.2. Statistical Analysis Framework for Evaluating Perceived Practicality of VR

A range of robust statistical analysis techniques was applied to examine participants’ perceptions of the practicality of VR across different stages of the project lifecycle, as well as to explore the influence of various background and perceptual factors. The term ‘robust’ is used here to reflect the applied methodologies choices to ensure reliability under realistic data conditions—including small-to-moderate sample sizes, ordinal measurement scales, and potential violations of normality assumptions. Techniques such as General Linear Model (GLM) Repeated Measures with sphericity corrections, Structural Equation Modeling (SEM) using the Diagonally Weighted Least Squares (DWLS) estimator, Categorical Principal Components Analysis (CATPCA) with bootstrap validation, and non-parametric classification via CHAID trees were employed. Each of these methods was explicitly selected for its ability to generate stable, interpretable, and statistically sound results under challenging conditions. The analysis was divided into seven primary stages, as depicted in Figure 1 and explained below.

In the first stage, a General Linear Model (GLM) Repeated Measures analysis was conducted to evaluate how perceived practicality of VR varied across the eight predefined project lifecycle stages. This procedure allowed for within-subject comparisons across stages and included checks for sphericity and polynomial contrasts to detect patterns in stage-wise differences [57,58]. The analysis was conducted using IBM SPSS 28 [59].

In the analytical stages 2 through 4, Structural Equation Modeling (SEM) methods were applied. All SEM-related procedures—including CFA, structural modeling, and multigroup SEM—were conducted using the lavaan package (version 0.6-15) in R [60], which supports categorical SEM by accommodating ordinal variables. This was achieved through the use of the Diagonally Weighted Least Squares (DWLS) estimator [61] and NLMINB optimization [62], both suitable for modeling with ordinal indicators and complex nonlinear constraints.

Although the sample size of 56 may seem modest for SEM, the use of DWLS is particularly well-suited for small-to-moderate samples with ordinal data and has been shown to produce robust and reliable estimates under such conditions. Simulation studies like Mîndrilă [63] support the adequacy of samples as small as 50 for unidimensional models with strong loadings, provided that model complexity is not excessive. In this study, the excellent model fit indices observed (e.g., CFI > 0.95, RMSEA < 0.08) further validate the appropriateness of the SEM approach for this sample size.

Specifically, as the second stage of analysis, Confirmatory Factor Analysis (CFA) [64] was used to assess the factorial validity of a unidimensional latent variable representing perceived practicality of VR. Modification indices were reviewed to identify areas for improvement, and selected residual covariances were added to refine model fit. Following the establishment of the factor structure, the internal consistency reliability of the scale measuring the perceived practicality of VR was evaluated using Ordinal Alpha and McDonald’s Omega, which are suitable for ordinal data and provide robust reliability estimates.

In the third stage, a Structural Equation Modeling (SEM) approach [65] was applied to examine how a range of demographic and perceptual independent variables predicted the latent variable of perceived practicality of VR. The model estimation was conducted using the lavaan package in R, consistent with the CFA analysis. It employed the Diagonally Weighted Least Squares (DWLS) estimator, which is suitable for ordinal indicators, along with NLMINB optimization, for handling complex model constraints. The analysis focused on evaluating the significance of regression paths and assessing overall model fit.

In the fourth stage, a multigroup SEM approach [65] was estimated to assess how the same set of independent variables predicted perceived practicality of VR at each project lifecycle stage separately. As in the second (CFA) and third (SEM) stages, the model was implemented using the lavaan package in R. This stage-specific model enabled detailed examination of how predictors such as education, familiarity with VR, and willingness to adopt VR influenced perceptions at different points in the lifecycle. The analysis employed the Diagonally Weighted Least Squares (DWLS) estimator and NLMINB optimization—both of which were also used in the second and third stages—as they are appropriate for ordinal indicators and well suited to handle complex constraints in nonlinear models, respectively.

In stages 2, 3, and 4, SEM-related procedures were evaluated using the chi-square (χ²), the Comparative Fit Index (CFI), the Tucker–Lewis Index (TLI) [66], the Root Mean Square Error of Approximation (RMSEA) with a 90% confidence interval [66,67], and the Standardized Root Mean Square Residual (SRMR) [66].

In the fifth stage, a Categorical Principal Components Analysis (CATPCA) [59] was conducted to validate the unidimensionality of the perceived practicality of VR construct, using optimal scaling for ordinal data [68]. The model included bootstrap validation and internal consistency checks, complemented by component loadings and inter-item correlations [69]. CATPCA was performed in IBM SPSS Statistics version 28 [59].

In the sixth stage, a CHAID (Chi-squared Automatic Interaction Detection) classification tree analysis [70] was conducted using IBM SPSS Statistics version 28 [59] to identify predictors of perceived practicality of VR, using the CATPCA-derived dimension as the outcome variable. CHAID is a decision tree technique based on chi-square statistics, suitable for exploring interactions between categorical variables and identifying optimal splits. This method enabled the inclusion of nominal predictors not appropriate for SEM, such as professional domain and organizational characteristics. The model was validated through cross-validation and assessed for predictive accuracy [71].

In the seventh stage, a focused CATPCA analysis was conducted using bootstrap resampling and object scores output for selected background variables, also in IBM SPSS Statistics version 28 [59]. The purpose of this step was to identify which background characteristics were associated with meaningful variation in the latent variable of perceived practicality of VR by examining whether the 95% confidence intervals of their object scores excluded zero.

The CFA diagram (Figure 2 below) and the SEM path models (Figures 3 and 4 below) were created using IBM SPSS Amos version 28.0.0 [72], and the CHAID Classification Tree (Figure 5) was generated using IBM SPSS Statistics version 28. Starting from Section 4, only statistical abbreviations were used in place of full terms.

These seven stages form an integrated analytical framework that combines measurement validation, structural modeling, classification analysis, and individual-level exploration using bootstrap-enhanced dimensional reduction techniques.

To further support transparency and replicability, we clarify that all statistical analyses adhered to the underlying assumptions of each technique, with specific adjustments for small sample sizes and ordinal data. For instance, the use of the Diagonally Weighted Least Squares (DWLS) estimator was selected due to its robustness in small-sample, non-normally distributed ordinal data environments, as supported by simulation studies (e.g., Mîndrilă, [63]).

All the analyses were conducted using the following tools: (a) IBM SPSS Statistics 28 for GLM, CATPCA, and CHAID; (b) IBM SPSS Amos 28.0.0 for SEM visualization; and (c) the lavaan package in R (version 0.6-15) for all SEM-related models, including multigroup comparisons. The NLMINB optimization algorithm was used to handle nonlinearity in model constraints.

We also applied bootstrap validation to enhance the reliability of dimensionality reduction in CATPCA and the interpretation of object scores. Fit indices such as χ², CFI, TLI, RMSEA (90% CI), and SRMR were reported to provide comprehensive evidence of model adequacy and replicability.

The abovementioned methodological selections were grounded in best practices and documented explicitly to facilitate future replication and adaptation across similar VR-related or lifecycle-stage-based perception studies.

4. Results

4.1. Participant Demographics

The survey included 56 valid respondents, consisting of 85.7% males and 14.3% females. Regarding the age distribution, the majority of participants were in the age group of 41–50 (39.3%), followed by 31–40 (32.1%) and 20–30 (19.6%), with the remainder above 50. In terms of education, most respondents held a bachelor’s degree (58.9%), while 35.7% had obtained a master’s degree or higher. The fields of professional study were diverse, with the largest representation from Electrical Engineering (45.5%), followed by Mechanical Engineering (18.3%), and Computer Science (15.2%). Additional fields included Industrial Engineering, Aeronautics, and Economics.

Participants reported an average of 10.2 years of experience in development, projects, or systems engineering (SD = 6.8), with their tenure in their current roles averaging 4.3 years (SD = 3.5). The roles were distributed across various domains, with significant representation from Systems Engineering (27%), Project Management (21%), and Integration Engineering (17%).

Organizational affiliation was heavily skewed toward security organizations (71.4%), with a smaller proportion associated with civilian and governmental institutions. In terms of the size of the organizational domains, 42% of respondents reported being part of domains with more than 100 employees, indicating the scale of their operational environments.

This demographic profile highlights a respondent pool with substantial experience, advanced education, and representation from diverse engineering fields, reflecting the breadth and depth of expertise relevant to the study’s objectives.

4.2. Perceived Practicality of VR Across Project Stages

A GLM Repeated Measures procedure was conducted to examine perceived practicality of VR across various stages of the project lifecycle. The assessed stages included Proposal and Investor Recruitment, Specification and Requirement Definition, Concept Formation, Design and Surveys, Development, Testing, Training, and Maintenance. Perceptions were measured on an ordinal scale from 1 (very little) to 5 (very much), based on participants’ responses to the question: “To what extent do you think the use of VR would be beneficial at each stage of the project?” The analysis aimed to assess how perceptions of VR’s usefulness varied across the different lifecycle stages.

The dataset contained 56 valid responses, all with complete data for the relevant variables. No missing data was observed, ensuring the robustness of the analysis. The GLM method was used to test the differences in perceived practicality of VR across these project stages.

The descriptive statistics for each project stage are summarized in

Table 1. Perceived practicality of VR across project stages (ordered by mean).

Project Stage	Mean	SD
Training	4.00	1.04
Proposal and Investor Recruitment	3.64	1.10
Concept Formation	3.25	0.99
Development	3.20	1.12
Testing	3.18	1.19
Maintenance	3.36	1.15
Design and Surveys	3.05	1.07
Specification and Requirement Definition	2.89	1.11

, which lists the means and standard deviations for each stage. As shown, the Training stage had the highest perceived practicality (M = 4.00, SD = 1.04), followed by Proposal and Investor Recruitment (M = 3.64, SD = 1.10). In contrast, the lowest perceived utility was reported for the Specification and Requirement Definition stage (M = 2.89, SD = 1.11), suggesting that VR was seen as less practical at this early stage of the project lifecycle.

The Multivariate Test results showed a highly significant multivariate effect (Pillai’s Trace = 0.49, F = 6.78, p < 0.001), suggesting that the perceived practicality of VR differs significantly across the stages of the project lifecycle. This was confirmed by the significant results from Wilks’ Lambda, Hotelling’s Trace, and Roy’s Largest Root (p < 0.001 for all). These findings indicate that the stage of the project plays a key role in shaping perceptions of VR’s usefulness.

Mauchly’s Test of Sphericity results indicated a significant departure from sphericity (W = 0.31, χ²(27) = 59.98, p < 0.001). This test assesses whether the assumption of equal variances of the differences between all pairs of levels is met. The significance of the result indicates that the assumption of sphericity was violated. Therefore, the Greenhouse–Geisser correction was applied to adjust the degrees of freedom for the F-tests, as indicated by an epsilon value of 0.76. This adjustment ensures the robustness of the statistical results and accounts for the lack of sphericity.

The Greenhouse–Geisser (0.76) and Huynh–Feldt (0.85) epsilon values indicate the degree of correction needed due to the violation of sphericity. The results suggest that while the sphericity assumption is not fully met, the Greenhouse–Geisser correction provides an adequate adjustment for this violation.

The Tests of Within-Subjects Contrasts for the perceived practicality of VR across project stages reveal significant findings. Notably, the cubic contrast (F = 13.47, p < 0.001) demonstrates a significant effect, suggesting that the relationship between project stages and the perceived practicality of VR is not linear but rather follows a more complex pattern. Additionally, the fifth-order contrast (F = 25.75, p < 0.001) shows a very strong effect with high observed power (0.99), highlighting significant variability in the perceived usefulness of VR across different stages. The quadratic contrast (F = 8.21, p < 0.01) is also significant, supporting the idea that VR’s practicality follows a curved relationship, with its usefulness fluctuating at various stages. However, the linear effect (F = 2.65, p = 0.109) had higher-order effects (Order 4 and 6), indicating that some transitions between stages do not consistently impact the perceived practicality of VR.

These results suggest that while VR is perceived as highly practical during certain stages, such as Training and Proposal and Investor Recruitment, its perceived usefulness varies more in stages like Specification and Requirement Definition. This analysis emphasizes the need for further research to understand the factors influencing these shifts and offers valuable guidance for project managers to strategically deploy VR at the most effective stages of the project lifecycle.

Significant pairwise differences were observed between stages with high and low perceived practicality of VR. Specifically, VR was viewed as significantly more useful during the Training and Proposal and Investor Recruitment stages compared to the Specification and Requirement Definition and Development phases (p < 0.01 and p < 0.001, respectively). These results suggest that VR is perceived as significantly more useful during later and collaborative stages, whereas its utility is perceived as lower during early planning phases.

In contrast, no significant differences were found between several other pairs of stages, such as between Concept Formation and Design and Surveys or between Maintenance and Testing. These non-significant results suggest that the perceived usefulness of VR remains relatively consistent during these stages.

The profile analysis reveals that the perceived utility of VR varies significantly across different stages of the project lifecycle. Training and Proposal and Investor Recruitment are identified as stages where VR is perceived as most practical, whereas Specification and Requirement Definition is viewed as the least favorable stage for VR application. This suggests that VR is most valuable in stages involving decision-making, collaboration, or interactive processes, while its utility may be less evident in earlier, more conceptual or planning phases.

4.3. Confirmatory Factor Analysis of Perceived Practicality of VR Across Project Lifecycle Stages

CFA was conducted to validate the unifactorial structure of the latent variable perceived practicality of VR, based on seven ordinal indicators: Proposal and Investor Recruitment, Specification and Requirement Definition, Concept Formation, Design and Surveys, Development, Testing, Training, and Maintenance. These items captured participants’ perceptions of VR’s usefulness across different project stages. The analysis used the DWLS estimator with NLMINB optimization.

An analysis of item-total statistics using methods appropriate for ordinal data was conducted to evaluate the internal consistency of the eight-item scale designed to measure the perceived practicality of VR across different project lifecycle stages. This examination reveals how each item interrelates with the others and its impact on overall scale reliability, primarily using Ordinal Alpha, which is Cronbach’s Alpha based on polychoric correlations, and McDonald’s Omega.

The overall Ordinal Alpha for the eight-item scale was found to be 0.75, suggesting acceptable internal consistency. To understand each item’s contribution, two key metrics were examined: the corrected item-total correlation (representing the correlation of an item with the total score of the remaining items, based on polychoric correlations) and the Ordinal Alpha if the item were deleted. The item Proposal and Investor Recruitment showed an acceptable corrected item-total correlation of 0.37; removing this item would slightly decrease the Ordinal Alpha to 0.73. In contrast, Specification and Requirement Definition exhibited a very low corrected item-total correlation of 0.13. Notably, removing this item would increase the Ordinal Alpha to 0.78, indicating that this item does not align well with the others, and its removal enhances the scale’s internal consistency. For Concept Formation, the corrected item-total correlation was 0.38, and removing it would slightly lower the Ordinal Alpha to 0.73. The item Design and Surveys demonstrated a strong corrected item-total correlation of 0.66, and its removal would substantially decrease the Ordinal Alpha to 0.68, highlighting its significant contribution to the scale’s reliability. Similarly, Development had a strong corrected item-total correlation of 0.64, and its removal would also notably lower the Ordinal Alpha to 0.68. The item Testing showed a good corrected item-total correlation of 0.55, and removing it would decrease the Ordinal Alpha to 0.70. Training had a corrected item-total correlation of 0.43, and its removal would result in an Ordinal Alpha of 0.72. Lastly, Maintenance exhibited a corrected item-total correlation of 0.41, and if removed, the Ordinal Alpha would be 0.73.

Further assessment of reliability, aiming to validate the scale’s coherence using a different but related measure, involved calculating McDonald’s Omega. This was based on polychoric correlations for the eight items and assumed a unidimensional structure, yielding an Omega Total (ωt) of 0.76. This value also indicates acceptable reliability and is consistent with the Ordinal Alpha. The general factor loadings from this omega analysis, derived from a Schmid–Leiman solution, indicated varied contributions from the items to this general factor. Specifically, Design and Surveys (loading = 0.77) and Development (loading = 0.77) showed the highest loadings. These were followed by Testing (loading = 0.72), Maintenance (loading = 0.53), Training (loading = 0.46), Concept Formation (loading = 0.42), and Proposal and Investor Recruitment (loading = 0.38). The item Specification and Requirement Definition had a negligible loading on this general factor, as its loading was below the print threshold of 0.2, and its variance explained by the general factor (h2) was only 0.02, further supporting its misalignment with the other items. While an iterative analysis to determine McDonald’s Omega Total if each item was deleted was part of the analysis procedure, an error encountered during its execution prevented these specific values from being reported.

Additionally, McDonald’s Omega was calculated from a unidimensional Confirmatory Factor Analysis (CFA) model. This model was fit using an estimator appropriate for ordinal data. This model-based Omega for the eight-item scale was 0.76 (more precisely, 0.759). An attempt to calculate this model-based Omega iteratively, if each item were deleted, resulted in NA values for all items. This suggests that the sub-models, each with seven items, may have encountered convergence issues or other problems during the estimation or the subsequent reliability calculation.

In summary, the scale measuring the perceived practicality of VR demonstrates acceptable internal consistency, as indicated by an Ordinal Alpha of 0.75 and McDonald’s Omega Total values around 0.76. The item-total statistics for Ordinal Alpha clearly identify the item Specification and Requirement Definition as problematic for the scale’s reliability. Its very low corrected item-total correlation (0.13) and the observation that its removal increases the Ordinal Alpha to 0.78 strongly suggest that this item either measures a different aspect or introduces statistical noise. This conclusion is further corroborated by its near-zero loading on the general factor in the McDonald’s Omega analysis. Items such as Design and Surveys and Development appear to be the strongest contributors to the scale’s reliability. This is evidenced by their high corrected item-total correlations and the substantial drop in Ordinal Alpha that would occur if they were removed, as well as their high factor loadings in the omega analysis. The other items contribute adequately to the overall consistency of the scale.

The initial model fit was suboptimal (χ² = 40.02, df = 20, p = 0.005; CFI = 0.94; TLI = 0.92; RMSEA = 0.14 (90% CI [0.07, 0.20]); SRMR = 0.12). The indicator for Specification and Requirement Definition had a nonsignificant loading (b = 0.40, SE = 0.32, z = 1.24, p = 0.21; β = 0.17) and was excluded from the final model.

Model refinement was guided by Modification Indices, leading to the addition of five residual covariances between related stages. These included, for example, covariances between Proposal and Investor Recruitment and Testing (Cov = −0.29, p < 0.001) and between Maintenance and Training (Cov = 0.26, p < 0.01), capturing shared variance due to conceptual proximity.

The revised model (Figure 2) demonstrated excellent fit: χ²(9) = 4.49, p = 0.88; CFI = 0.98; TLI = 0.97; RMSEA = 0.00 (90% CI [0.00, 0.08]); SRMR = 0.05. All remaining indicators had statistically significant loadings on the latent variable perceived practicality of VR. The unstandardized factor loadings (b) ranged from 0.95 to 1.98, with standard errors (SE) ranging from 0.35 to 0.69. Corresponding z-values ranged from 2.62 to 3.17, and p-values ranged from 0.009 to 0.002. The standardized loadings (β) ranged from 0.39 to 0.82, confirming that each indicator meaningfully contributed to the unidimensional structure of perceived VR practicality.

The final set of retained stages included Proposal and Investor Recruitment, Concept Formation, Design and Surveys, Development, Testing, Training, and Maintenance. Among them, Design and Surveys, Development, and Testing showed the strongest contributions to the latent construct. This refined model confirms the unifactorial structure of perceived practicality and identifies the project stages where VR is seen as most useful, providing a solid basis for further structural analysis.

Figure 2. Unifactorial Measurement Model for the latent factor perceived practicality of VR across project lifecycle stages with estimated factor loadings and covariances (** p < 0.01, *** p < 0.001).

Figure 2. Unifactorial Measurement Model for the latent factor perceived practicality of VR across project lifecycle stages with estimated factor loadings and covariances (** p < 0.01, *** p < 0.001).

4.4. Structural Model of the Relationship Between Factors and the Perceived Practicality of VR

Following the prior adjustments and refinements, the current analysis explores the relationship between several independent variables and the latent variable perceived practicality of VR. These independent variables include age (categorized as 20–30, 31–40, 41–50, 51–60, and above 60), education level (Technician/Practical Engineer, Bachelor’s degree, and Master’s degree or higher), experience in engineering (less than a year, 1–5 years, 6–10 years, 11–20 years, and above 20 years), and tenure in the current organization (less than a year, 1–5 years, 6–10 years, 11–20 years, and above 20 years).

Additionally, domain size is considered, defined by the number of employees in the organizational unit (1–10, 11–20, 21–50, 51–100, and more than 100). The analysis also includes perceptual and organizational variables such as the extent to which the organization encourages the use of innovative technologies, the respondent’s self-rated familiarity with VR, the degree to which their work is connected to VR, and their likelihood of adopting VR solutions in systems engineering processes. All variables are measured on ordinal scales and are incorporated to capture both demographic and perceptual dimensions relevant to the perceived practicality of VR.

The diagram in Figure 3 presents the structural model, showing how each independent variable contributes to explaining the latent variable of perceived practicality of VR. The model was estimated using the DWLS method with optimization via NLMINB.

Figure 3. Structural model of the relationship between factors and the Perceived Practicality of VR (* p < 0.05, ** p < 0.01, *** p < 0.001).

Figure 3. Structural model of the relationship between factors and the Perceived Practicality of VR (* p < 0.05, ** p < 0.01, *** p < 0.001).

The model fit was acceptable: χ²(63) = 71.87, p = 0.21; CFI = 0.95; TLI = 0.98; RMSEA = 0.05 (90% CI [0.00, 0.10]); SRMR = 0.07. These results indicate a good fit of the structural model to the data.

In the structural equation model, three predictors were found to have statistically significant path coefficients in predicting the latent variable perceived practicality of VR. Education demonstrated a significant negative relationship (b = −0.36, SE = 0.17, z = −2.17, p = 0.03; β = −0.34), indicating that a higher level of education is associated with lower perceived practicality of VR. Familiarity with VR also showed a significant negative association (b = −0.32, SE = 0.13, z = −2.54, p = 0.01; β = −0.43), suggesting that greater self-reported familiarity corresponds to lower perceived practicality.

In contrast, the likelihood of adopting VR in systems engineering processes was positively associated with the perceived practicality of VR (b = 0.21, SE = 0.09, z = 2.40, p = 0.02; β = 0.39), indicating that respondents who were more willing to adopt VR reported higher levels of perceived practicality of VR. Other structural paths—including those from age, engineering experience, tenure, domain size, organizational encouragement of innovation, and work connection to VR—were not statistically significant (all p > 0.05).

These findings suggest that perceptual and motivational factors are more influential than demographic or organizational ones in shaping how individuals evaluate the practical applicability of VR in systems engineering.

4.5. Structural Model of the Relationship Between Independent Variables and Perceived Practicality of VR Across the Project Lifecycle

In the next stage, the relationship between the nine independent variables (outlined in Section 4.3) and the perceived practicality of VR at each project lifecycle stage was examined. Both the independent variables and the outcome measures were treated as ordered categorical variables. The model was estimated using the DWLS estimator with optimization via the NLMINB method. Due to the number and complexity of paths, standardized coefficients (β) are not shown in the diagram but are reported in the accompanying text. The diagram in Figure 4 visually illustrates the structural model that captures these complex interrelations across all variables and project stages.

Figure 4. Structural model of the relationship between independent variables and the perceived practicality of VR in each stage of the project lifecycle.

Figure 4. Structural model of the relationship between independent variables and the perceived practicality of VR in each stage of the project lifecycle.

The model fit was acceptable: χ²(21) = 8.13, p = 0.52; CFI = 0.97; TLI = 0.96; RMSEA = 0.07 (90% CI [0.00, 0.18]); SRMR = 0.09. These results suggest that the structural model fits the data well.

Education level was a significant negative predictor during the development stage (b = −1.14, SE = 0.37, z = −3.11, p = 0.002; β = −0.46), indicating that respondents with higher education levels tended to perceive VR as less practical in development tasks. A marginally significant negative effect was also found in the training stage (b = −0.49, SE = 0.29, z = −1.70, p = 0.089; β = −0.27), suggesting that this trend may extend beyond development and warrants further attention.

Familiarity with VR was positively associated with perceived practicality in multiple stages. It was significant in the design and surveys stage (b = 0.63, SE = 0.27, z = 2.35, p = 0.019; β = 0.42), the development stage (b = 0.46, SE = 0.21, z = 2.21, p = 0.027; β = 0.27), and the testing stage (b = 0.81, SE = 0.29, z = 2.82, p = 0.005; β = 0.54), suggesting that greater familiarity enhances perceptions of VR usefulness in these contexts.

Willingness to adopt VR was another strong positive predictor. It showed significant associations in the design and surveys stage (b = 0.44, SE = 0.16, z = 2.80, p = 0.005; β = 0.40) and the development stage (b = 0.68, SE = 0.18, z = 3.75, p < 0.001; β = 0.53).

Age (b values ranging from −0.19 to 0.20) and engineering experience (b values ranging from −0.14 to 0.11) were not significant in any stage. Similarly, tenure, domain size, and organizational encouragement of innovation showed no statistically significant effects on the perceived practicality of VR across the lifecycle stages.

These results highlight that education level, familiarity with VR, and willingness to adopt are the most influential factors shaping perceptions of VR’s practicality, with effects varying by project lifecycle stage. Notably, higher education predicted reduced practicality during development (and marginally also during training), while openness and experience with VR predicted increased practicality, particularly during the design, development, and testing stages.

4.6. Predicting Perceived Practicality of VR Using a CHAID Classification Tree Model

As presented in Section 4.2, the latent variable perceived practicality of VR was constructed and validated through CFA. In the current section, it was also extracted as a single dimension using CATPCA. This analysis was based on seven ordinal variables, each corresponding to a different stage in the project lifecycle: Proposal and Investor Recruitment, Concept Formation, Design and Surveys, Development, Testing, Training, and Maintenance. Each item measured the extent to which respondents perceived VR to be practical or useful at that specific stage (ranging from “Very little” = 1 to “Very much” = 5). The CATPCA results supported the unification of these indicators into one coherent latent construct, providing the foundation for further validation through CFA.

As part of the CATPCA procedure, several alternative factorial structures—including two-factor, three-factor, and four-factor models—were also examined. However, these models demonstrated poor fit indices, further confirming that the unifactorial model most accurately represents the underlying structure of the data and is therefore adopted in the present study.

The results of the CATPCA further confirmed the validity of the latent construct perceived practicality of VR as a single dimension. A one-dimensional solution was specified using spline ordinal transformations for each of the seven project lifecycle stages, and the iterative process converged after 15 steps, meeting the convergence criteria.

The final solution yielded an eigenvalue of 3.28, accounting for 46.79 percent of the total variance. The internal consistency of the dimension was high, with a Cronbach’s alpha of 0.81. To assess the stability of this solution, a balanced bootstrap procedure with 1000 samples was conducted.

The bootstrap results were consistent with the initial analysis. The average eigenvalue was 3.47, with a 95% confidence interval ranging from 2.84 to 4.15, explaining, on average, 49.58% of the variance. The bootstrap estimate of Cronbach’s alpha was also high at 0.83, with a 95% confidence interval between 0.76 and 0.89, reinforcing the internal reliability of the construct.

The component loadings for each stage were strong, ranging from 0.51 to 0.78. Specifically, the Testing stage showed the highest contribution with a loading of 0.78, followed by Design and Surveys at 0.78, Maintenance at 0.75, Development at 0.69, and Training at 0.66. Concept Formation loaded at 0.57, and Proposal and Investor Recruitment at 0.51. All variables exceeded the conventional threshold of 0.50, indicating meaningful contributions to the latent factor. The bootstrapped confidence intervals for these loadings demonstrated consistency and statistical robustness, with most lower bounds above 0.50—for example, 0.59 for Testing, 0.60 for Design and Surveys, and 0.51 for Maintenance.

Correlations among the transformed variables indicated moderate to strong associations, particularly among stages such as Testing, Design and Surveys, and Maintenance, suggesting a cohesive grouping of stages where VR is perceived as most practical. In contrast, the Proposal and Investor Recruitment stage, while still above the loading threshold, exhibited weaker correlations with other stages, supporting its relatively lower centrality in the construct. This observation aligns with the subsequent CFA decision to consider model refinement by evaluating the contribution of each stage based on significance and strength of loading. In conclusion, the CATPCA findings validate the unifactorial structure of the latent variable perceived practicality of VR.

In addition to the SEM, a CHAID classification tree analysis was conducted to further investigate the predictors of the perceived practicality of VR, using the CATPCA-derived outcome variable. The tree included all ordinal variables previously tested in the SEM, encompassing demographic characteristics such as age, education level, years of engineering experience, tenure in the current organization, and organizational unit size. It also incorporated a range of attitude-based measures reflecting participants’ perspectives, experiences, and openness toward VR adoption.

Crucially, the CHAID model enabled the inclusion of nominal variables that could not be accommodated in the SEM due to estimation limitations. These additional variables reflected participants’ professional and organizational contexts—such as sex, academic background, organizational role, technical domain, and organization type—as well as their initial exposure to VR and their belief in its potential to enhance systems engineering, integration, and testing processes. This expanded set of predictors allowed for a more comprehensive and inclusive exploration of the factors influencing perceptions of VR’s practicality. The CHAID decision tree is depicted in Figure 5.

The classification tree was cross-validated using 10-fold cross-validation and reached a maximum depth of three, resulting in 12 nodes, when 7 of them were terminal. Despite the wide range of variables considered, only four predictors were selected as significant in the final CHAID model: willingness to adopt VR, familiarity with the technology, education level, and age. Other predictors—including demographic characteristics (such as sex), professional roles, organizational context, and other attitudinal measures—were not selected by the algorithm as effective splitting variables, suggesting that their influence may be more indirect or context-dependent.

Willingness to adopt VR was the most decisive predictor, forming the initial split in the tree. Participants with lower willingness (≤4.0) comprised the majority of the sample (94.6%) and had lower perceived practicality scores (mean = −0.10), whereas those reporting high willingness (>4.0) demonstrated significantly more favorable evaluations (mean = 1.69). This result underscores the importance of openness to technological adoption in shaping perceptions of technological utility.

Figure 5. CHAID classification tree predicting perceived practicality of VR.

Figure 5. CHAID classification tree predicting perceived practicality of VR.

Within the low-willingness group, familiarity with VR further differentiated responses. Participants with limited familiarity (≤3.0) reported more negative views (mean = −0.34), while those with greater familiarity (>3.0) expressed mildly positive attitudes (mean = 0.16). A deeper split within the low-familiarity group revealed an interaction effect: participants with the lowest willingness (≤3.0) exhibited the most negative evaluations (mean = −0.69), while those slightly more open (>3.0) reported more neutral perceptions (mean = 0.16).

Among participants with high willingness, age emerged as a meaningful factor. The most favorable evaluations came from older individuals (above 50, mean = 2.19), followed by younger respondents (mean = 1.96), and then middle-aged participants (mean = 0.92), suggesting a potential U-shaped relationship between age and optimism toward VR.

Education level further distinguished responses among highly familiar participants. Those with a technician or practical engineer background reported the highest levels of perceived practicality (mean = 3.03), while those with bachelor’s or master’s degrees gave more neutral assessments (mean = 0.05). This pattern aligns with the SEM results, which showed that higher education levels were associated with lower perceived practicality, particularly during the development stages.

The gain summary indicates that the nodes with the highest predicted practicality scores were Node 10 (3.03), Node 7 (2.19), and Node 5 (1.96), each representing small but distinct subgroups. In contrast, Node 8 had the lowest predicted mean (−0.69), highlighting a group characterized by both low willingness and limited familiarity. Most participants (44.6%) were grouped in Node 11, with a predicted mean of 0.05, reflecting a general tendency toward neutral perceptions.

The model’s resubstituting error was 0.53 (SE = 0.12), while the 10-fold cross-validation error was higher at 1.49 (SE = 0.33), suggesting some degree of overfitting but still a reasonable level of generalizability given the modest sample size.

In summary, the CHAID analysis highlights the central role of individual-level psychological factors—especially willingness to adopt VR and familiarity with the technology—in shaping perceptions of its practical utility. Education and age also contributed meaningfully, whereas sex, organizational characteristics, and professional roles did not emerge as key drivers in this model.

4.7. Complementary CATPCA Analysis of Background Variables Influencing Perceived Practicality of VR

As presented in Section 4.2, the latent variable of perceived practicality of VR was constructed and validated through Confirmatory Factor Analysis (CFA). In Section 4.6, this construct was extracted as a single dimension using Categorical Principal Component Analysis (CATPCA).

The CHAID model included a wide range of demographic, experiential, and attitudinal variables. However, only four variables emerged as statistically significant predictors: likelihood of adopting VR in one’s professional role, familiarity with VR, education level, and age. These variables successfully distinguished between participants with higher and lower levels of perceived practicality of VR. Other variables—such as academic degree, organization type, years of engineering experience, activity area, tenure in the organization, organizational size, support for innovation, and current role—were not retained in the final CHAID model. This outcome suggests that these variables did not contribute significantly to predicting perceived practicality of VR across the overall sample.

To further explore the potential relevance of the variables not retained in the CHAID model, a complementary CATPCA analysis was conducted. This analysis employed bootstrap resampling with a specific focus on object scores and their 95% confidence intervals. The objective was to assess whether any of these supplementary background variables were significantly associated with the latent dimension of perceived practicality of VR, even if they did not meet the CHAID model’s criteria for global significance.

The CATPCA bootstrap results revealed that for a subset of participants, the confidence intervals around their object scores did not include zero. This indicates that their perceived practicality of VR was significantly shaped by their individual demographic or experiential profiles. Although these variables may not have emerged as significant across the entire sample, they appear to play an important role in explaining individual differences. These findings underscore the value of combining exploratory methods such as CATPCA with classification models to capture patterns that may not be uniformly distributed across all respondents.

The results showed that participants with academic backgrounds in hardware and engineering disciplines tended to report higher levels of perceived practicality of VR. Similarly, non-military participants and those with over 20 years of engineering experience exhibited elevated object scores, suggesting a positive relationship between long-term technical exposure and the perceived practicality of VR. Participants working in hardware or mechanical domains also showed higher practicality scores.

Long organizational tenure—particularly more than 20 years—was associated with increased perceived practicality of VR, possibly due to a deeper understanding of internal processes. Organizational size also mattered; individuals in medium and large organizations (21 or more employees) tended to perceive VR as more practical. Supportive organizational cultures that encourage innovation were linked to higher object scores. Participants who first experienced VR in a professional or academic setting also rated the technology as more practical.

Leadership and management roles were associated with higher perceived practicality of VR, suggesting that those in strategic positions are more likely to recognize the broader potential of the technology. The strongest association was found among individuals whose current work involves direct interaction with VR, reinforcing the influence of hands-on experience on the perceived practicality of VR.

In this context, positive object scores reflect a positive relationship with perceived practicality of VR, while negative scores indicate a weaker sense of its usefulness. These insights complement the CHAID findings by highlighting specific background factors that contribute meaningfully to how certain individuals perceive VR, even when those variables do not emerge as generalizable predictors in classification models.

Based on these results, three participant profiles can be identified: (a) technically educated individuals in hardware and engineering fields who have extensive professional experience and are currently engaged in hands-on VR-related work; (b) experienced professionals in innovation-supportive, mid-to-large organizations, often in leadership or managerial roles; and (c) those who were first exposed to VR in academic or professional contexts and have had a long organizational tenure. These profiles help illustrate how background characteristics can shape individual perspectives on the practicality of VR, even if such variables are not universally predictive.

4.8. Summary of Findings

The results presented across Section 4.1, Section 4.2, Section 4.3, Section 4.4, Section 4.5 and Section 4.6 provide a comprehensive view of how VR is perceived in systems engineering across different stages of the project lifecycle and what factors shape this perception. A profile analysis using GLM Repeated Measures revealed statistically significant variation in the perceived practicality of VR between lifecycle stages (Pillai’s Trace = 0.49, F = 6.78, p < 0.001), with Training (M = 4.00) and Proposal and Investor Recruitment (M = 3.64) rated highest, while Specification and Requirement Definition (M = 2.89) was rated lowest in perceived practicality. Confirmatory Factor Analysis (CFA) validated a unifactorial structure for the construct “perceived practicality of VR,” excluding one weakly loading stage (Specification), and yielding excellent model fit (χ²(9) = 4.49, p = 0.88; RMSEA = 0.00).

Structural equation modeling (SEM) identified three significant predictors of the perceived practicality of VR: education (β = −0.34, p = 0.03), familiarity with VR (β = −0.43, p = 0.01), and willingness to adopt VR (β = 0.39, p = 0.02). Interestingly, both education and familiarity exhibited a negative effect, while willingness to adopt was positively associated with the perceived practicality of VR. When these predictors were examined across lifecycle stages, education significantly reduced perceived practicality during development (β = −0.46, p = 0.002), whereas familiarity and adoption willingness enhanced perceived practicality during design, development, and testing stages (all β > 0.40, p < 0.03). The CHAID decision tree model further emphasized the role of willingness to adopt as the primary node split, followed by familiarity, education, and age. Participants with high willingness and familiarity consistently rated VR as more practical. For instance, technicians/practical engineers rated practicality highest (M = 3.03), while those with master’s degrees reported more neutral evaluations (M = 0.05). Additional exploratory CATPCA analysis revealed that participants with long engineering experience, hardware-based backgrounds, leadership roles, and initial exposure to VR in professional settings tended to report higher practicality scores—despite these variables not emerging as significant in SEM or CHAID.

In summary,

Table 2. Summary of key findings and relevant papers across analytical methods and project lifecycle stages.

Analysis Type	Key Findings	Relevant Papers
GLM Profile Analysis	Perceived practicality of VR is highest during the Training and Proposal stages and lowest during the Specification stage.	[43,48]
CFA	Participants’ responses indicate that perceived practicality of VR is based on one clear idea, and it is especially strong during the Design, Development, and Testing stages.	[31,34,35]
SEM (Latent Variable)	Perceived practicality of VR is lower among participants with higher education levels and greater familiarity with VR and higher among those with greater willingness to adopt the technology.	[73,74]
SEM (By Lifecycle Stage)	In the Development stage, participants with higher education levels perceive VR as less practical, whereas in the Design and Testing stages, participants who are more familiar with or willing to adopt VR perceive it as more practical.	[34,35,44,47]
CHAID Tree	Perceived practicality of VR is highest among participants who are open to adoption, moderately familiar with the technology, have technical (rather than academic) education, and are either younger or older but not middle-aged.	[43,48,73]
CATPCA (Background Profiles)	Perceived practicality of VR is higher among participants in engineering roles, those with extensive professional experience, individuals in leadership positions, and those with direct hands-on experience using VR.	[50,52,55]

presents the key findings across methods and stages, consolidating insights into a coherent comparison.

Together, these findings suggest that although VR is broadly perceived as practical during later, interactive stages of engineering projects, individual attitudes—particularly openness to adoption and prior experience—are the most consistent drivers of positive evaluations. Demographic variables such as education and age exert more nuanced, and sometimes paradoxical, effects, underscoring the importance of tailoring VR implementation strategies to users’ specific contexts and readiness levels.

5. Discussion

This study examines the perceived practicality of Virtual Reality (VR) across various stages of the systems engineering lifecycle, highlighting that its perceived value varies depending on both the nature of each project phase and individual-level characteristics. The participants reported significant differences in VR utility across lifecycle stages, with training and proposal and investor recruitment rated as the most practical contexts. In contrast, the specification and requirement definition phase received the lowest ratings—likely due to its abstract and text-heavy nature, which is less supported by VR’s immersive strengths. From a practical standpoint, these findings strongly suggest that organizations should prioritize VR investment and deployment for stages where its immersive and interactive capabilities offer clear advantages, such as in creating engaging training simulations or compelling client presentations during proposal development. Conversely, for early abstract planning stages like specification and requirement definition, where VR was perceived as less useful, alternative digital tools or more traditional methods might currently be more resource-efficient, or new types of VR applications tailored for such abstract tasks may need to be developed.

These findings are consistent with prior studies. For example, Santilli et al. [43] emphasized the value of VR in training environments that rely on experiential learning and spatial understanding, while Sung et al. [48] demonstrated how VR enhances stakeholder engagement and persuasive communication during proposal stages. Both studies reinforce the current results by showing that VR delivers the greatest benefit in phases where interactivity, visualization, and user immersion play a central role.

These stage-specific perceptions can also be interpreted through the lens of the Kano model [75], which categorizes user expectations into required, performance-related, and unexpected “delighter” attributes. Applying this model to the current findings, VR was observed as a “delighter” in the training and proposal phases—where users do not necessarily expect for immersive technologies but find them highly engaging and valuable when present. Practically, this means that introducing VR in these stages can significantly enhance user satisfaction and perceived value beyond basic expectations. For the specification phase, where VR may fail to meet basic expectations, attempting to force-fit current VR solutions could be counterproductive. Instead, efforts could focus on developing VR tools specifically designed to augment abstract conceptualization or accepting that VR’s primary strengths lie elsewhere in the lifecycle. Conversely, according to the current study results, in the specification phase, VR may fail to meet even the basic expectations of clarity and usability, resulting in negative perceptions, especially among experienced or highly educated participants.

In the design, development, and testing stages, where VR is expected to support tasks such as visualization, prototyping, and interaction, its perceived practicality behaves like a performance attribute: the more it delivers, the more satisfaction it generates, particularly among technically trained or hands-on users. These patterns were reinforced by a confirmatory factor analysis, which revealed a coherent, unidimensional structure of perceived VR practicality across stages—excluding the specification phase, which showed a weak statistical contribution. This suggests that VR utility is conceptualized as a consistent factor, particularly salient in the design, development, and testing stages, where immersive visualization, prototyping, and simulation are most applicable.

At the individual level, psychological and experiential factors were found to be key drivers of perception. Openness to adopting VR emerged as the strongest positive predictor of perceived utility. The practical implication for organizations is profound: fostering a culture that encourages and supports innovation adoption may be more critical for successful VR integration than focusing solely on technical training or the inherent features of the VR technology itself. Strategies could include creating internal champions for VR, showcasing successful pilot projects, and providing safe spaces for experimentation without fear of failure. This proactive approach to managing adoption is likely to yield higher perceived practicality and, ultimately, better utilization of VR resources. These findings support established theories by Rogers [73] on innovation diffusion, in which “innovativeness” was outlined as a critical trait influencing the rate of adoption of new technologies. He posits that individuals who are more open to changes and willing to adopt innovations earlier perceive greater benefits, especially when the relative advantage of the technology is not yet universally recognized. This theoretical framework directly aligns with the current finding that openness to adopting VR significantly predicts higher ratings of its practicality across project stages.

Conversely, higher education levels and greater familiarity with VR were associated with more critical evaluations of the technology. Practically, this suggests that implementation strategies should not assume that more educated or technically familiar individuals will automatically be strong advocates for VR. Instead, organizations might need to engage these groups with more sophisticated use cases, demonstrate clear, evidence-based benefits that address their potentially higher expectations or concerns, and ensure that the VR tools are genuinely user-friendly and solve specific, complex problems relevant to their expertise. Simply providing access to VR may not suffice; a targeted value proposition is needed for these discerning users. These critical perceptions among highly educated or VR-experienced participants may stem not from resistance to innovation but rather from a more informed and discerning perspective grounded in deeper engagement with the technology in practical settings.

These findings are consistent with broader models of technology acceptance, such as the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). According to Davis [74], TAM emphasizes perceived usefulness and perceived ease of use as primary factors influencing user acceptance. In the context of VR, perceived usefulness is directly tied to whether users believe that immersive technology improves their performance in specific tasks such as design visualization or stakeholder engagement. The current study’s identification of openness to adoption as a strong predictor resonates with TAM’s construct of behavioral intention, which is shaped by user attitudes.

Moreover, the negative relationship between education/familiarity and VR utility may reflect a situation where experienced users perceive lower “ease of use” or encounter a mismatch between VR capabilities and their professional needs. This aligns with the UTAUT model, which aims to explain information technology acceptance by users, where effort expectancy is one of the influencing factors [76]. Indeed, recent empirical research has confirmed that UTAUT provides a practical framework for investigating individual acceptance and use of VR tools and that factors such as performance expectancy and effort expectancy significantly influence user behavior [77]. Their study further concludes that acceptance is a prerequisite for realizing VR’s full educational potential, reinforcing the idea that user openness is a necessary condition for successful implementation.

These insights hold important implications for the successful integration of VR in engineering and organizational contexts. Operationally, this means that given that perceived usefulness and openness to innovation are central to acceptance, organizations should prioritize early engagement strategies that foster user curiosity, reduce perceived effort, and highlight task-specific benefits. This is particularly relevant for professionals in mid-career or managerial positions who may approach new technologies with a more analytical or risk-aware mindset. This can be achieved by practical steps such as demonstrating measurable value through small-scale pilot implementations, showcasing success stories from peer institutions, or offering hands-on workshops that may help bridge the gap between technical familiarity and practical endorsement. As Ustun et al. [77] emphasized, acceptance is not automatic, even among technically skilled users, but must be “cultivated through meaningful exposure, usability-oriented design, and alignment with user needs and values” (p. 1068). Customizing communication and training for specific professional roles such as engineers, decision-makers, and operators can enhance inclusivity and long-term adoption of VR throughout the system’s lifecycle.

At the same time, the study highlights current limitations in the perceived VR utility, particularly during early project stages. The specification and requirement definition phase was consistently rated low, likely due to the limited availability of VR tools suited to abstract planning tasks. Although some scholars have proposed that VR could support ideation and early-stage modeling [55], most existing tools are optimized for mid- and late-stage activities. This highlights a gap in the application of VR in conceptual and documentation-heavy contexts, presenting clear opportunities for future development. For VR developers and researchers, this signals a practical market need for innovative VR solutions that can effectively support abstract thinking, collaborative document creation, or the visualization of complex requirements in immersive ways, moving beyond current VR strengths in spatial simulation and training.

The identified participant profiles (see Section 4.7) offer a practical lens for segmenting potential VR users within an organization. For instance, technically educated individuals with hands-on VR experience might be targeted as early adopters and internal champions, while experienced professionals in innovation-supportive organizations could be engaged for strategic input on VR deployment, guiding tailored implementation efforts.

Several limitations must be acknowledged. The relatively small and specialized sample (n = 56) may limit generalizability to broader engineering populations; however, as explained in Section 3, the statistical approach applied addresses this issue and provides reliable results. The study’s population is also skewed towards the security sector. This factor acts both ways: on the one hand, it also limits the generalization of the results, but on the other hand, the security sector is a ‘heavy user’ of VR-based tools. Therefore, the population is highly relevant to the research question. Additionally, the study relied on self-reported perceptions rather than observed behaviors, introducing potential biases. The cross-sectional design also precluded tracking changes in perception over time, limiting insights into how attitudes towards VR may evolve in response to technological advances or organizational shifts. Factors such as organizational culture or specific VR tool experience were not systematically measured, even though they likely influenced participant attitudes. Furthermore, this study was explicitly designed as an exploratory, quantitatively oriented investigation, aiming to identify preliminary patterns and stage-specific variations in perceived VR utility within a relatively structured sample. While it offers valuable insights into users’ perceptions of VR across the systems engineering lifecycle, the reliance on self-reported data represents a key limitation. Such perceptions may not fully align with actual usage patterns, behavioral engagement, or real-world implementation outcomes.

The identified perceptual patterns provide a valuable foundation for guiding organizations in prioritizing VR investments in stages where its practicality is perceived as highest—particularly Training, Proposal, and Design. In addition, fostering openness to adoption and providing hands-on exposure can enhance perceived value and support smoother implementation. The results also highlight the importance of adapting VR strategies to different user profiles, considering factors such as education level and experience. A phased implementation approach, starting with stages where VR is most valued, may optimize both impact and user acceptance.

These practical insights are grounded in the study’s broader contribution to the literature, which provides a systematic, empirically grounded evaluation of VR’s perceived practicality across the full project lifecycle. It introduces a validated measurement tool for assessing how engineers perceive the contribution and practicality of VR across different project stages and demonstrates how key psychological and experiential factors—notably willingness to adopt and familiarity shape these perceptions more strongly than demographic or organizational variables.

Although this study offers important contributions to the literature, further empirical work is needed to strengthen the evidence base and refine practical guidance for VR adoption. Integrating complementary empirical data, such as behavioral observations and longitudinal case studies in operational settings, would provide a more comprehensive understanding of VR’s real-world effectiveness and adoption dynamics.

Additionally, incorporating qualitative methods such as interviews or focus groups could help uncover the underlying factors shaping users’ attitudes toward VR. This mixed-methods approach would enrich interpretation and yield a more nuanced understanding of the user experience.

Future studies should address these limitations through longitudinal designs that examine how perceptions evolve over time and whether VR integration leads to measurable project benefits. Expanding the sample to encompass a broader range of industries, geographic regions, and organizational contexts will further strengthen the external validity of the findings. In addition, it would be valuable to explore how specific training programs or organizational interventions may actively shape these evolving perceptions and support more effective adoption.

Moreover, researchers may examine how VR can be adapted to support early-stage project tasks and identify strategies that encourage adoption across diverse professional profiles. As VR capabilities continue to evolve, it is also important to assess its potential added value in later lifecycle phases, such as long-term system monitoring or decommissioning, which were not the focus of the current study.

Building on these directions, future investigations should delve deeper into the differentiated needs and expectations of specific user groups within systems engineering. While the present study highlights key predictors such as openness to adoption, education level, and prior familiarity, it does not fully capture the task-specific requirements of varied professional roles, including engineers, project managers, designers, and technical operators. Understanding these nuances through subgroup analysis, qualitative interviews, or persona-based modelling could support the development of more tailored and effective implementation strategies. Such customization may ultimately increase adoption rates and amplify the long-term impact of immersive technologies across the engineering project lifecycle.

In addition, to further support effective implementation, organizations and developers could benefit from applying the Kano model mentioned above as a strategic framework to differentiate between basic expectations, performance needs, and “delighter” features of VR across lifecycle stages.

By mapping user expectations and satisfaction responses, the model can help identify which phases present the greatest opportunity for value-adding experiences and which require improvements to avoid dissatisfaction. Integrating such models into VR planning and evaluation processes could enhance alignment with stakeholder priorities and maximize user acceptance.

This exploratory study sheds light on the adoption of VR across the systems engineering project lifecycle. The findings may be beneficial both when making the principal decision whether to implement the technology (at each stage of the project) and when considering the approach to assimilate the technology.

6. Conclusions

This exploratory study evaluated perceptions of Virtual Reality (VR) across various project lifecycle stages and demonstrated their diverse range. The results indicated that the perceived practicality of VR is highest during the training and proposal stages and lowest during the specification stage. The perceived practicality of VR is lower among participants with higher education levels and greater familiarity with VR and higher among those with greater willingness and openness to adopt the technology. Generally, the perceived practicality of VR is higher among participants in engineering roles, those with extensive professional experience, individuals in leadership positions, and those with direct hands-on experience using VR. These multi-dimensional findings not only shed light on current perceptions but also provide practical guidance for implementing VR technologies more efficiently and effectively within systems engineering organizations. The results directly inform strategic decisions regarding when to deploy VR considering the stages when the technology is most efficient (prioritizing training and proposal stages), for whom specific adoption strategies might be most effective (considering user willingness, education, and familiarity), and where future VR development could address current technological gaps (e.g., inefficiency in early-stage conceptual tasks). Ultimately, understanding these nuances is key to maximizing the return on investment and the transformative potential of VR in the project lifecycle.