Hindrances to the Utilisation of the Metaverse for Net-Zero Buildings in South Africa

Abstract

Net-zero buildings (NZBs) are a key component of sustainable development in the architecture, engineering, and construction (AEC) industry, offering a path to mitigating environmental impacts. The Metaverse, as an emerging digital technology, has the potential to enhance NZB optimisation by facilitating design simulation, stakeholder collaboration, and real-time performance assessment. However, its integration into the AEC sector faces several obstacles. This study investigates the hindrances to Metaverse adoption for NZBs within South Africa’s AEC sector, a region striving to meet sustainability goals amid technological and infrastructural constraints. A quantitative research design was employed, utilising a structured questionnaire to gather data from registered and practising built environment professionals. Out of 163 distributed questionnaires, 121 valid responses were analysed using descriptive statistics and exploratory factor analysis. The findings categorise the hindrances into five key clusters: (1) Metaverse adoption barriers, (2) integration challenges, (3) technological limitations, (4) acceptance-related concerns, and (5) human- and skill-based obstacles. This study concludes that despite the Metaverse’s transformative potential for advancing NZBs, overcoming these barriers requires strategic interventions from industry professionals and policymakers. This research provides actionable insights to facilitate the effective integration of Metaverse technologies into sustainable construction practices, ensuring a more seamless transition towards digitalised NZB implementation.

1. Introduction

To date, it is widely acknowledged that the architecture, engineering, and construction (AEC) sector remains one of the most significant sectors of the global economies [1]. The influence of the sector on the developmental processes towards an improved economy in different countries is a testament to its significance in nations such as Singapore [2], Hong Kong [3], China [4], United Kingdom [5], Turkmenistan [6], India [7], Ghana [8], and Nigeria [9]. The sector contributes a great percentage to the gross domestic product of developing countries, thereby signifying its potential to contribute to national development [10]. Not only does the sector play a vital role in the infrastructural development of a nation, but it also positively impacts the socioeconomic status of the people. Furthermore, the AEC sector has been able to cater to both skilled and unskilled categories of workers while providing various forms of employment opportunities for different classes of citizens. While the key role played by the sector cannot be overemphasised, it is important to note that the sector is adjudged as the major source of various factors that adversely impact human and natural environments.

Typical examples of the negative impacts of the AEC sector on the environment include air and noise pollution, water consumption, waste generation, dust and gas emissions, and, notably, energy-related greenhouse gas emissions [11]. According to Lima et al. [12], 45–65% of waste deposited in landfills and 35% of global carbon dioxide emissions are traceable to the processes and operations of the sector. The AEC sector significantly impacts air quality, leading to various health issues, through the emission of greenhouse gases (GHGs), such as carbon dioxide (CO₂), and volatile organic compounds (VOCs). Additionally, the AEC industry exhibits characteristics such as excessive resource consumption and depletion of natural resources, which are driven by an increasing demand for raw materials. Furthermore, Glimskär and Lundberg [13] highlight the prevalence of accidents and work-related injuries, particularly musculoskeletal disorders (MSDs), within the sector. The architecture, engineering, and construction (AEC) sector is widely recognised as one of the most energy-intensive industries, consuming significant amounts of energy throughout the lifecycle of buildings and infrastructures: from material production and construction to operation, maintenance, and demolition [14]. This high energy demand, coupled with the sector’s substantial carbon emissions and resource consumption, has tarnished its image, positioning it as a major contributor to environmental degradation and climate change [15]. In response, there has been a global push towards sustainable construction solutions and practices aimed at minimising the negative impacts of the AEC sector [16]. Among these solutions, green buildings (GBs), sustainable buildings (SBs), and net-zero buildings (NZBs) or net-zero-energy buildings (NZEBs) have emerged as critical pathways for achieving sustainability in the built environment [17,18].

The Metaverse, an emerging digital ecosystem, represents a transformative technology with the potential to revolutionise the AEC sector [15]. In the context of NZBs, the Metaverse offers unique capabilities for optimising design, construction, and operational processes. Similarly, the Metaverse can enhance collaboration among multidisciplinary teams, streamline project management, and support post-occupancy evaluations, ensuring that NZB goals are met throughout the building lifecycle [15,18].

Despite its potential, the adoption of Metaverse technologies in the AEC sector remains limited, particularly in developing regions such as South Africa. South Africa presents a unique case study due to its dual challenges of a high energy demand and limited access to advanced technologies. These challenges highlight the need for targeted research to explore the feasibility and potential of Metaverse technologies in advancing NZBs within the South African context. Therefore, this study seeks to assess the barriers to the use of the Metaverse to achieve net-zero buildings (NZBs) in the South African AEC sector.

2. Sustainable Construction and Net-Zero Buildings

The architecture, engineering, and construction (AEC) sector is a significant consumer of global resources, including water, land, energy, and materials, throughout the lifecycle of buildings and infrastructure: from manufacturing and construction to operation, maintenance, and demolition [19]. In response to the growing awareness of sustainability challenges, the United Nations introduced the Sustainable Development Goals (SDGs), which have become a global framework for addressing environmental, social, and economic issues. Within the AEC sector, this has led to the emergence of sustainable construction (SC) as a pathway to achieving SD by integrating environmental stewardship, socioeconomic equity, and cultural preservation [20]. Among the various SC concepts, net-zero buildings (NZBs) or net-zero-energy Buildings (NZEBs) have gained prominence as a critical strategy for reducing the sector’s environmental footprint and advancing global sustainability goals.

NZBs are part of a broader family of sustainable building concepts, including passive homes [21], passive houses [22], low-energy buildings [23], energy-plus buildings [24], zero-carbon buildings [25], net-zero-carbon buildings [25], carbon-neutral buildings [25], zero-emission buildings [25], low-embodied-carbon buildings [26], and low-embodied-energy buildings [27]. These concepts share a common objective of minimising energy consumption and carbon emissions, albeit through different approaches. For instance, passive homes focus on maximising energy efficiency through design and insulation, while NZBs aim to balance energy consumption with renewable energy generation. Despite their varying methodologies, these concepts collectively contribute to the SDGs, particularly Goal 7 (Affordable and Clean Energy) and Goal 13 (Climate Action), by reducing the sector’s reliance on fossil fuels and mitigating greenhouse gas emissions.

The high energy demand of the AEC sector, driven by construction material production, transportation, and building operations such as heating, ventilation, and air conditioning (HVAC), underscores the importance of NZBs in achieving a carbon-neutral future [28]. By integrating renewable energy systems, energy-efficient designs, and sustainable materials, NZBs can significantly reduce the sector’s energy consumption and carbon footprint. However, the effectiveness of NZBs in contributing to SDGs depends on several factors, including regional energy policies, technological advancements, and the availability of renewable energy resources. For example, in regions with limited access to renewable energy, the feasibility of achieving net-zero-energy buildings may be constrained, necessitating alternative strategies such as low-energy or passive designs.

While NZBs are widely regarded as a key solution for sustainability, it is essential to critically examine their assumptions and limitations. One common assumption is that NZBs inherently contribute to SDGs by reducing operational energy use. However, this overlooks the embodied energy and carbon associated with construction materials and processes, which can account for a significant portion of a building’s lifecycle impact [26]. To address this, stakeholders must adopt a holistic approach that considers both operational and embodied energy, ensuring that NZBs deliver genuine sustainability benefits. Additionally, the socioeconomic implications of NZBs, such as affordability and accessibility, must be carefully evaluated to ensure that these solutions are inclusive and equitable. For instance, the high upfront costs of NZB technologies may pose barriers to adoption in developing regions, exacerbating existing inequalities in access to sustainable housing.

There are several definitions of NZBs found in the literature; these all point towards the same known objective of the concept, which is low energy use. According to Wells et al. [29], the notion of NZBs encapsulates structures characterised by several key features, achieving a balance between energy generation and consumption, significantly minimising energy demands, and ultimately ensuring that energy costs are either zero or result in net-zero GHG emissions. Voss et al. [30] define an NZB as a highly energy-efficient structure that meets their annual energy needs through on-site energy generation in combination with the public energy grid to offset the building’s overall energy consumption and feeds back to the grid any excess clean energy produced on-site. These are buildings with a low energy use and better energy performance compared to the conventional ones [31]. Additionally, NZBs are defined as buildings equipped to generate ample renewable energy on-site to meet their energy needs [32]. Based on their different modes of energy generation and usage, NZBs are classified by Laustsen [31] and Ahmed et al. [33] into four widely known models, namely Net-Zero-Cost Energy Buildings (NZ-cost-EBs), Net-Zero Source Energy Buildings (NZ-source-EBs), Net-Zero-Emissions Buildings (NZ-EBs), and Net-Zero Site Energy Buildings (NZ-site-EBs). As further explained by Ahmed et al. [33], NZ-cost-EBs have zero energy utility bills, NZ-source-EBs produce one unit of energy for every unit consumed on-site and quantify the energy generation at the source, NZ-EBs generate emission-free energy at a level equal to or greater than the emission-producing energy they consume, while NZ-site-EBs generate one unit of energy for every unit consumed exclusively on the site itself with no consideration for the source of the energy. As portrayed by Wu and Skye [34], NZBs require an effective and efficient energy infrastructure, renewable energy sources, and energy efficiency measures. Hence, the various processes involved in achieving effective and efficient energy management, which is a signature of NZBs, make technological integration non-negotiable.

3. Industry 4.0 Technology Influx: The Metaverse

The fourth industrial revolution (4IR) is characterised by the fusion of digital, physical, and biological systems, driving transformative advancements across industries, including the architecture, engineering, and construction (AEC) sector [25]. This era is marked by the proliferation of technologies such as artificial intelligence (AI), augmented reality (AR), virtual reality (VR), automation and robotics, and the internet of things (IoT), which are reshaping traditional workflows and enabling new levels of efficiency and innovation [28]. Vaidya et al. [35] identify nine pillars underpinning Industry 4.0, including big data and analytics, autonomous robots, simulation, system integration, IoT, cybersecurity, cyber–physical systems, cloud computing, additive manufacturing, and AR. Among these, the Metaverse has emerged as a groundbreaking technology, offering a virtual shared space for interaction, creation, and engagement with digital assets and environments.

The Metaverse, often associated with Web 3.0, cryptocurrencies, non-fungible tokens (NFTs), and blockchain, represents a paradigm shift in how digital and physical worlds converge [36,37]. Defined by Qiu et al. [38] as a comprehensively immersive parallel digital reality, the Metaverse enables users to interact at an unprecedented scale. It provides a three-dimensional virtual space where users, represented by avatars, can socialise, collaborate, telecommute, and engage in various activities [39]. While the Metaverse has shown immense potential across fields such as education, healthcare, and tourism, its application in the AEC sector, particularly in advancing net-zero buildings (NZBs), remains underexplored despite its transformative possibilities.

In the field of education, educators and learners can utilise the Metaverse to translate themselves into a simulated digital sphere with the capacity to reconfigure their access time and points to information, definitions of space, and sensory inputs [40]. In the healthcare sector, the Metaverse also holds immense benefits for fitness and wellness, healthcare facilities, healthcare supply chains, medical marketing, medical education and training, telemedicine, and telehealth, among others [41]. The COVID-19 pandemic has also birthed what is called Metaverse tourism, which further exposes the boundless possibilities that exist in this novel and growing technological concept [42,43]. Tourists can experience Game Reserves and Safari in a three-dimensional (3D) immersive virtual world due to the limitations posed by distance and other restrictions during the pandemic. It has also been proffered that the Metaverse concept aligns with that of Web 4.0 in the context of Industry 5.0, a virtual space where users and entities can collaborate in a human-centric manner to create personalised values [44].

In the context of NZBs, the Metaverse offers unique opportunities to enhance sustainability and efficiency throughout the building lifecycle. For instance, during the design phase, stakeholders can use immersive VR and AR environments to simulate energy performance, optimise building orientation, and test renewable energy integration strategies [45,46]. These simulations enable real-time adjustments to design parameters, ensuring that NZB goals are met before construction begins. The study of Moon and Kong [47] also highlighted one of the numerous benefits of Metaverse adoption for firefight safety education for increased prevention of safety accidents. This further alludes to the potential of Metaverse in enhancing the health and safety (H&S) aspects of building and infrastructural projects. Similarly, the Metaverse can facilitate collaborative planning and coordination among multidisciplinary teams, reducing the errors and inefficiencies that often arise in traditional workflows [48]. During the construction phase, the Metaverse can support real-time progress tracking, enabling stakeholders to monitor energy-efficient construction practices and ensure compliance with sustainability standards [49]. Furthermore, the Metaverse can enhance post-occupancy evaluations by providing immersive tools for monitoring energy consumption and occupant behaviour, enabling continuous optimisation of NZB performance [50].

Despite these promising applications, the integration of the Metaverse into NZB projects faces significant practical challenges. One major barrier is the high cost of advanced hardware and software required for immersive simulations, which may be prohibitive for smaller firms or projects in developing regions like South Africa [45]. Additionally, the lack of standardised protocols for data exchange and interoperability between Metaverse platforms and existing AEC tools hinders seamless integration [51]. Data security and privacy concerns also pose significant challenges, particularly given the sensitive nature of project data and the potential for cyber threats [35]. Furthermore, resistance to change among professionals, coupled with a lack of digital literacy and technical expertise, slows the adoption of Metaverse technologies in the AEC sector.

The implementation of the Metaverse in NZB projects also raises ethical and social considerations. For example, the reliance on AI-driven tools for design optimisation and decision-making may lead to concerns about job displacement and the erosion of human expertise. Additionally, the digital divide between developed and developing regions could exacerbate inequalities in access to Metaverse-enabled tools and technologies, limiting their global impact on sustainability goals. Addressing these challenges requires a multifaceted approach, including investments in digital infrastructure, workforce training, and the development of ethical guidelines and governance frameworks.

Factors Affecting the Use of the Metaverse

Different literature globally and across disciplines have established the benefits of the Metaverse, especially in the AEC sector. However, the adoption and implementation of this technological concept are yet to be maximised due to several impeding factors. These barriers are aligned with the users, professionals, clients, stakeholders, technological components, or a combination of all. The summarised factors hindering the use of the Metaverse, as drawn from the literature, are presented in

Table 1. Factors affecting the use of the Metaverse.

S/N	Hindrances	Authors
1	Narrow viewing angles, sensitive afterimage effects, and environmental influences	Choi et al. [52]
2	Privacy concerns, dispute issues, security concerns, poor social acceptability, and the complexity of the virtual economy involved	Sepasgozar et al. [49]
3	Potentially high cost of technology, high financial investment requirement, client lack of comprehension, lack of interoperability across Metaverses, limited multi-user access, poor broadband connectivity, unrealistic and complex sensory experience, and lack of technical skills	Contarino [53]
4	Concern over the physical and psychological health of users, potentially high cost of training of users, time constraints, lack of awareness, lack of understanding, inadequate incentive policies, inadequate policy planning, and potentially high cost of equipment	Yeung [54]
5	Possible environmental pollution, legal barriers, possible digital addiction, mental health issues, complex data management, challenging network infrastructure, ethical issues, security and privacy concerns, and interoperability issues	Trivedi et al. [55]
6	Non-adoption intention, distrust, technostress, psychological risk, security risk, technological dependence risk, infrastructure risk, social risk, and performance risk	Kumar et al. [56]
7	Complexity of use, poor user perception, slow technology adoption rate, and organisational factors	Nguyen et al. [57]
8	Lack of policies and regulations, lack of planning and management, lack of financial support, small market size, poor sociocultural background, unstable social environment, technology infrastructure issues, poor human resources, privacy, security, and ethical issues, cybersecurity threat, governance issues, resistance to change, lack of awareness, limited access, lack of skilled talents, lack of theoretical foundations, and low public acceptance	Chen et al. [58]
9	Technological failure, poor technology use, user hesitation, low perception of benefits by users, lack of requisite knowledge, lack of expertise, complexity in implementation, lack of collaboration, uncertainty of benefits, lack of commitment, rigid organisational structures, low awareness level, paucity of users, limited understanding of the technology, integration challenges, lack of standardisation and governance, challenging information-sharing mediums, and technological limitations	Bag et al. [59]
10	Multi-sensory communications challenges, complex and ultra-massive connectivity issues, issues associated with device proliferation, challenges on decentralised services requirements, and ultra-high data and internet connectivity issues	Zawish et al. [60]
11	Standards and ethical concerns, interoperability issues, potentially high cost of technology, computational and predictability issues, information security concerns, communication and delay issues, data privacy concerns, and cybersecurity risks	Chengoden et al. [61]
12	Possible technology addiction, high cost of technology, difficulty in use, lack of preference for the technology, possible loss of privacy, potential technology-related health issues, and poor stakeholder acceptance	Adetayo et al. [62]
13	Obsolete management technologies, data security concerns, possible malfunctioning of devices, lack of professional expertise, complexities in digital technology integration, digital modelling and simulation complexities, lack of emotional intelligence in virtual characters, rigid organisational structure and culture, lack of technical staff, economic viability concerns, lag in policies, poor societal acceptance, and lack of supporting laws and regulations	Chen and Ruan [63]

. These generic variables constituted the fundamentals of the questionnaire survey administered to relevant, practising, and duly registered AEC professionals. While the identified variables are drawn from different disciplines, as evident in the literature section, it is important to note that this study is specifically focused on identifying the barriers to adopting and implementing the Metaverse to optimise NZBs. Hence, the identified variables in Table 1 will be filtered and streamlined to the field of focus of this study.

4. Research Methodology

This study employed a quantitative approach to investigate the hindrances to utilising the Metaverse for net-zero buildings in the South African AEC industry. A structured questionnaire survey targeted engineers (civil, structural, mechanical, and electrical), construction managers, project managers, and health and safety officers operating within the South African AEC sector. Due to logistical constraints, the survey was administered electronically, reaching out to respondents through their respective professional organisations and LinkedIn profiles. The questionnaire, developed based on an extensive review of existing literature, facilitated the ranking of variables by respondents according to their knowledge and opinions. The questionnaire was first subjected to ethical clearance approval from the institution of the researchers, after which a pilot study was conducted with four academic experts and six industry professionals to ensure validity. Based on data from relevant professional bodies, the sample size comprised 163 construction professionals selected using a random sampling technique from the pool of 3515 professionals. Given the large population of respondents, this sampling technique allows every respondent to have a chance of being selected to be part of this study. The sample size was deduced using the Yamane formula with a 95% confidence level and 7.5% tolerance value. A total of 121 responses were received, yielding a response rate of 74.2%, which was deemed acceptable for online surveys following established standards. Following data collection, cleaning and screening were carried out to ensure the data could be adopted for analysis, with all 121 responses qualifying for analysis. Descriptive and exploratory factor analysis techniques were employed to analyse the retrieved data using SPSS version 22 software. Descriptive analysis provided insights into the demographic characteristics of the respondents, while exploratory factor analysis (EFA) helped identify underlying relationships and patterns among variables. This was adopted for this study because EFA allows researchers to investigate the theoretical foundations of a phenomenon and reduce the data to a smaller set of summarised variables. To ensure the reliability and validity of the data collection instrument, theoretical and empirical assessments were conducted. Cronbach’s alpha coefficient, which measures the internal consistency of a set of scales to indicate how closely related the items are as a group, was employed to assess the internal consistency and reliability of the questionnaire. A Cronbach’s alpha value of 0.811 was obtained for hindrances to utilising the Metaverse for net-zero building projects, indicating the high reliability and validity of the data collected.

5. Findings

5.1. Demographic Information of Respondents

Regarding professional qualifications, the most common role is quantity surveyor, comprising nearly one-third (32.9%) of the respondents. Civil, structural, mechanical, and electrical engineers represent 28% of the group, making it the second most frequent qualification. Construction managers account for 22% of the group, followed by project managers at 13.4%. Health and safety officers have the least common role, accounting for only 3.7% of the group. Concerning their industry experience, most respondents (63.4%) have between one and five years of experience. Those with 6-to-10 years of experience make up 31.7%, while a smaller fraction, 4.9%, have 11-to-15 years of experience. Regarding educational qualifications, nearly half of the respondents (47.6%) hold a Bachelor’s degree. Diplomas are also quite common, held by 28% of the respondents. Honours degrees are held by 18.3% of the respondents, whereas only a small number have achieved a Master’s degree (2.4%) or have a Competency Certificate (1.2%). A Matric Certificate (grade 12) is held by 2.4% of the individuals. The employment sector of the respondents is fairly divided, with a slight preference for the private sector, where 47.6% are employed, followed closely by the public sector with 41.5%. A small group of 10.9% works across both private and public sectors. Concerning the respondents’ project involvement, the largest group of respondents (30.5%) is involved in one-to-two construction projects. Those working on three-to-four projects represent 32.9%, and a similar proportion (22%) are engaged in five-to-six projects. Only 2.4% are involved in seven-to-eight projects, and 12.2% are working on more than eight projects. Based on the profile of the respondents as presented in this section, it can be deduced that the respondents adopted for this study possess adequate knowledge and experience to provide insight into the aim of this study.

5.2. Barriers to the Use of the Metaverse for Net-Zero Buildings

5.2.1. Descriptive Analysis Result

From the analysis of respondents’ opinions of barriers to adopting and using the Metaverse for NZBs,

Table 2. Descriptive statistics for barriers to Metaverse utilisation for NZBs.

Barriers	Mean	Std. Deviation	Rank
Limited access to Metaverse technologies	3.96	0.736	1
Poor electricity supply	3.90	0.893	2
Lack of demonstrated success of the Metaverse	3.88	0.759	3
Lack of education and training	3.85	0.744	4
Uncertainty about return on investment	3.84	0.936	5
Lack of technical experts	3.83	0.831	6
Poor internet connectivity	3.82	0.867	7
Low technological readiness	3.82	0.845	7
Lack of skilled personnel	3.78	0.766	9
Resistance to change	3.76	0.988	10
Low awareness	3.76	0.839	10
Lack of stakeholder collaboration	3.74	0.775	12
Technology interoperability challenges	3.73	0.930	13
Lack of standards and frameworks	3.72	0.792	14
Data security concerns	3.72	0.746	14
Unsupportive government legislations	3.68	0.821	16
High implementation cost	3.68	0.995	16
Data privacy concerns	3.66	0.984	18
Risk management issues	3.65	0.880	19
Knowledge transfer issues	3.65	0.770	20
Regulatory and legal issues	3.63	0.988	21
Low market demand	3.63	0.748	21
Data ownership and right issues	3.60	0.954	23
Inadequate collaborative tools	3.57	0.731	24
Negative public perception	3.45	0.846	25
High energy consumption	3.38	0.954	26

revealed the mean item score ranking and the standard deviation. The respondents ranked the estimation techniques using a five-point Likert scale where 1 = Strongly Disagree (SD); 2 = Disagree (D); 3 = Neutral (N); 4 = Agree (A); and 5 = Strongly Agree (SA). The most significant barrier identified is “Limited access to Metaverse technologies”, with a highest mean score of 3.96 and a standard deviation of 0.736, indicating moderate agreement and relatively low variability in responses. “Poor electricity supply” is the second most notable barrier, with a mean score of 3.90 and a higher standard deviation of 0.893, which implies a slightly broader range of opinions on this issue. The third-ranked barrier is the “Lack of demonstrated success of Metaverse”, with a mean score of 3.88 and a standard deviation of 0.759, suggesting that evidence of successful Metaverse applications in sustainable construction is an important consideration for the industry. The bottom of the list shows barriers with lower mean scores, such as “Negative public perception” and “High energy consumption”, with means of 3.45 and 3.38, respectively. The relatively high standard deviations for these items, especially “High energy consumption” (0.954), suggest a greater diversity in how respondents view these issues’ importance or impact.

5.2.2. Exploratory Factor Analysis Result

All 26 identified barriers to adopting and using the Metaverse for NZBs were subjected to exploratory factor analysis (EFA). To initiate the exploratory factor analysis (EFA), an initial assessment of data suitability was conducted by scrutinising the correlation matrix to identify coefficients of 0.3 and above, indicative of appropriateness for factor analysis, as demonstrated in

Table 3. KMO and Bartlett tests and the pattern matrix ^a for barriers to Metaverse utilisation for NZBs.

		Component
	1	2	3	4	5
Poor internet connectivity	0.884
Poor electricity supply	0.793
Lack of education and training	0.742
Lack of stakeholder collaboration	0.633
Lack of skilled personnel	0.469
Uncertainty about return on investment	0.427
Lack of demonstrated success of the Metaverse	0.382
Unsupportive government legislations	0.349
Regulatory and legal issues		−0.800
Risk management issues		−0.784
Negative public perception		−0.778
Inadequate collaborative tools		−0.668
Data ownership and right issues		−0.623
Knowledge transfer issues		−0.468
Lack of technical experts			−0.729
Low technological readiness			−0.709
Lack of standards and frameworks			−0.677
Limited access to Metaverse technologies			−0.452
Data security concerns				−0.828
Data privacy concerns				−0.755
Low market demand				−0.301
High implementation cost					0.787
Resistance to change					0.700
High energy consumption					0.678
Low awareness					0.519
Technology interoperability challenges
Kaiser–Meyer–Olkin measure of sampling adequacy					0.861
Bartlett’s test of sphericity	Approx. Chi-Square				1564.839
	Df				325
	Sig.				0.000

Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalisation. a. Rotation converged in 20 iterations.

. The application of the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was employed to assess the adequacy of the data distribution for proceeding with EFA. A KMO value below 0.5 is deemed unacceptable, while a value exceeding 0.6 is considered suitable for EFA [64]. A KMO value of 0.861 was recorded, surpassing the acceptable threshold of 0.6. Furthermore, Bartlett’s test of sphericity indicated statistical significance in the variables, with a value of (0.000) indicating significance below the conventional threshold of 0.050, thereby rendering them amenable to factor analysis. Based on the eigenvalues of the extracted result, the first five components collectively explain 69.093% of the variance within the variables. Hence, applying direct oblimin rotation yielded the pattern matrix, as depicted in Table 3, elucidating the allocation of variables within each cluster corresponding to the five components collectively identified in the total variance explained.

Table 3 further presents the pattern matrix, delineating the grouping of the twenty-six (26) variables sourced from the literature into five distinct clusters. These clusters are interpreted based on the discerned intrinsic relationships among the variables within each cluster.

• Cluster A comprises seven (7) variables, as detailed in Table 3. These variables encompass “Poor internet connectivity” (88.4%), “Poor electricity supply” (79.3%), “Lack of education and training” (74.2%), “Lack of stakeholder collaboration” (63.3%), “Lack of skilled personnel” (46.9%), “Uncertainty about return on investment” (42.7%), “Lack of demonstrated success of Metaverse” (38.2%), and “Unsupportive government legislations” (34.9%). These variables collectively denote challenges encountered within the Metaverse ecosystem. Consequently, this cluster aptly signifies “Challenges in Metaverse Adoption”.
• Cluster B comprises six (6) variables, as outlined in Table 3. These variables are “Regulatory and legal issues” (−80.0%), “Risk management issues” (−78.4%), “Negative public perception” (−77.8%), “Inadequate collaborative tools” (−66.8%), “Data ownership and right issues” (−62.3%), and “Knowledge transfer issues” (−46.8%). The negative values denote the inverse relationship of each variable within the cluster. Together, these variables highlight challenges prevalent within the context of Metaverse implementation. Therefore, this cluster is appropriately labelled “Challenges in Metaverse Integration”.
• Cluster C comprises four (4) variables, as presented in Table 3. These variables include “Lack of technical experts” (−72.9%), “Low technological readiness” (−70.9%), “Lack of standards and frameworks” (−67.7%), and “Limited access to Metaverse technologies” (−45.2%). These negative values denote the inverse correlation of each variable within the cluster. Collectively, these variables underscore challenges pertinent to the Metaverse landscape. Hence, this cluster is aptly labelled “Technological Barriers in Metaverse Adoption”.
• Cluster D entails three (3) variables, as outlined in Table 3. These variables include “Data Security Concerns” (−82.8%), “Data privacy concerns” (−75.5%), and “Low market demand” (−30.1%). The negative values signify the inverse relationship of each variable within the cluster. These variables collectively highlight critical issues within the Metaverse landscape. Therefore, this cluster is appropriately labelled “Challenges in Metaverse Acceptance”.
• Cluster E comprises four (4) variables, as depicted in Table 3. These variables encompass “High implementation cost” (78.7%), “Resistance to change” (70.0%), “High energy consumption” (67.8%), and “Low awareness” (51.9%). Collectively, these variables underscore significant challenges within the Metaverse domain. Therefore, this cluster is appropriately labelled “Human Obstacles to Metaverse Adoption”.

A reliability assessment was conducted on the variable clusters, confirming the validity of the variables within their respective clusters. Specifically, “Challenges in Metaverse Adoption” exhibited an alpha value of 0.823, “Challenges in Metaverse Integration” showed an alpha value of 0.810, “Technological Barriers in Metaverse Adoption” demonstrated an alpha value of 0.784, “Challenges in Metaverse Acceptance” displayed an alpha value of 0.789, and “Human Obstacles to Metaverse Adoption” demonstrated an alpha value of 0.871.

6. Discussion of Findings

At the heart of the hindrances identified in this study lies the issue of limited access to Metaverse technologies, which is underscored as the most significant hindrance with the highest mean score. This reflects a broader concern over the accessibility of cutting-edge technologies necessary for Metaverse applications. Research by Trivedi et al. [55] on smart cities and Bilotti et al. [65] on the educational sector highlights the critical role of enabling technologies and infrastructure in facilitating wider Metaverse adoption. The barrier of poor electricity supply is closely followed, indicative of the infrastructural challenges that can impair the effective deployment of Metaverse technologies. Chengoden et al. [61] similarly note the importance of reliable power and connectivity in healthcare, emphasising the foundational role of infrastructure in digital transformation.

Scepticism regarding the proven effectiveness of Metaverse technologies emerges as another significant barrier. The necessity for clear evidence of success to overcome scepticism and build confidence among potential adopters is paralleled in the supply chain management research by Bag et al. [59], highlighting the universal need for successful case studies in advocating for Metaverse adoption. Concerns over high energy consumption reflect the environmental and operational cost considerations associated with Metaverse technologies. Musamih et al. [66] and Park and Kim [67] discuss the sustainability aspects and the imperative for efficient energy management solutions in healthcare and other domains, underscoring the environmental implications of Metaverse applications.

The barriers related to technological readiness, the lack of skilled personnel, and resistance to change are further echoed in the studies by AlDhanhani et al. [68] and Rospigliosi [69]. These studies underline the importance of skill development and managing organisational culture to embrace Metaverse technologies, highlighting the human factors in technological adoption. Security and privacy concerns are also prominent among the barriers to Metaverse adoption. Trivedi et al. [55] and Xu et al. [70] stress the need for robust security frameworks to protect users in Metaverse platforms, pointing to the criticality of trust and safety in digital environments.

The exploratory factor analysis carried out on the hindrances impeding the effective utilisation of the Metaverse for net-zero buildings produced five-factor clusters, which are explained below:

6.1. Cluster A—Challenges in Metaverse Adoption

The challenges grouped under Cluster A reflect a combination of infrastructural inadequacies and socioeconomic constraints, revealing the complexities of broadening Metaverse adoption in the AEC sector. These barriers manifest in practical scenarios such as limited access to high-speed internet and advanced hardware, which are critical for seamless Metaverse operations. For instance, in South Africa uneven internet infrastructure and high costs of cutting-edge technology create significant hurdles for small- and medium-sized enterprises (SMEs) aiming to adopt Metaverse tools. Additionally, socioeconomic disparities exacerbate these challenges as resource-constrained firms struggle to invest in the necessary technological upgrades.

The implications of these challenges are profound. Without addressing infrastructural gaps, the AEC sector risks being unable to leverage the Metaverse’s capabilities for NZB optimisation, such as real-time collaboration, immersive simulations, and data-driven decision making. This could further widen the gap between developed and developing regions in achieving sustainability goals. To mitigate these barriers, stakeholders must prioritise investments in digital infrastructure, particularly in underserved areas. Public–private partnerships could play a pivotal role in subsidising the costs of advanced hardware and software for SMEs. Furthermore, government policies to improve internet accessibility and affordability would be essential for enabling Metaverse adoption.

The ethical and technical concerns raised by Rospigliosi [69] and Wang et al. [71] further complicate the adoption landscape. For example, reliance on artificial intelligence and deep learning algorithms in Metaverse environments raises questions about data privacy, security, and algorithmic bias. These issues are particularly relevant in educational and professional settings, where sensitive data are often shared. Industry leaders must establish robust ethical guidelines and security protocols to address these concerns. Collaborative efforts between technology developers and regulatory bodies could ensure that Metaverse platforms adhere to global standards for data protection and ethical AI use.

From a technical perspective, integrating diverse technologies and ensuring user engagement remain significant challenges, as highlighted by Shi et al. [72]. For instance, the heterogeneity of Metaverse platforms often leads to compatibility issues, making it difficult for AEC professionals to integrate these tools into their workflows seamlessly. A unified approach, as suggested by Park and Kim [67], could involve developing interoperable standards and frameworks that facilitate smoother integration. Additionally, user engagement can be enhanced through targeted training programmes and awareness campaigns, ensuring professionals have the skills to navigate Metaverse environments effectively.

6.2. Cluster B—Challenges in Metaverse Integration

The challenges grouped under Cluster B highlight the complexities of integrating Metaverse technologies into existing systems and workflows, particularly in the AEC sector. These challenges encompass regulatory uncertainties, public acceptance issues, and privacy and data rights concerns. For instance, the lack of standardised regulations for Metaverse applications creates ambiguity for AEC professionals, who must navigate varying legal frameworks across regions. This is particularly relevant in South Africa, where the regulatory environment for emerging technologies is still evolving. Additionally, public scepticism about the Metaverse, fuelled by concerns over data privacy and security, poses a significant barrier to its widespread adoption. These issues are exacerbated by the sector’s reliance on sensitive project data, which require robust safeguards to prevent breaches and misuse.

The implications of these challenges are far-reaching. Without clear regulatory frameworks and public trust, integrating Metaverse technologies into NZB optimisation processes could be delayed or even derailed. For example, the inability to ensure data privacy and security could deter stakeholders from adopting Metaverse tools for collaborative design and simulation, limiting their ability to achieve sustainability goals. To address these barriers, policymakers and industry leaders must collaborate to develop comprehensive regulatory guidelines that address the unique challenges of Metaverse integration. These guidelines should prioritise data protection, user privacy, and ethical AI use, ensuring compliance with global standards such as the General Data Protection Regulation (GDPR). Public awareness campaigns could also build trust and demystify the Metaverse for AEC professionals and end-users.

The findings of Wang et al. [73] and Said [74] further underscore the importance of addressing universal access and governance issues in Metaverse integration. For instance, disparities in access to Metaverse technologies could exclude smaller firms and marginalised communities from participating in NZB projects, perpetuating inequalities in the AEC sector. To mitigate this, stakeholders could explore innovative financing models, such as subscription-based services or shared resource pools, to make Metaverse tools more accessible. Additionally, governance frameworks should be established to ensure equitable access and prevent monopolistic practices by technology providers. These frameworks could be developed through multi-stakeholder collaborations involving governments, industry bodies, and civil society organisations.

From a technical perspective, the integration of artificial intelligence (AI) and the need for secure, privacy-preserving systems, as highlighted by Rawat and Alami [75], remain critical challenges. For example, AI-driven Metaverse applications often require vast amounts of data to function effectively, raising concerns about data ownership and consent. To address these issues, developers could adopt privacy-by-design principles, embedding data protection measures into the core architecture of Metaverse platforms. Blockchain technology could also be leveraged to enhance transparency and security, enabling users to maintain control over their data while participating in Metaverse environments.

6.3. Cluster C—Technological Barriers in Metaverse Adoption

The challenges grouped under Cluster C highlight the technological barriers that hinder the adoption of Metaverse technologies in the AEC sector. These barriers stem from the reliance on advanced technologies such as virtual reality (VR), augmented reality (AR), and blockchain, which require a high degree of technical expertise and infrastructure readiness. For instance, the integration of VR and AR into NZB optimisation processes demands sophisticated hardware and software, which may be beyond the reach of many firms, particularly in developing regions like South Africa. Additionally, the lack of a skilled workforce capable of developing and managing these technologies exacerbates the problem, creating a significant gap between technological potential and practical implementation.

The implications of these barriers are profound. Without addressing the shortage of technical expertise and infrastructure, the AEC sector risks being unable to fully leverage the Metaverse’s capabilities for NZB optimisation. For example, the inability to deploy VR and AR tools for immersive design simulations could limit the sector’s ability to identify and address sustainability challenges early in the project lifecycle. To mitigate these barriers, stakeholders must prioritise investments in technological infrastructure and workforce development. This could involve partnerships with educational institutions to create specialised training programmes focused on Metaverse technologies. Additionally, government incentives and funding schemes could encourage firms to adopt advanced tools and technologies, bridging the gap between current capabilities and future needs.

The research by Amirulloh and Mulqi [76] underscores the importance of cultivating a skilled workforce and advancing technological infrastructure to overcome these barriers. For example, the development of VR and AR applications for NZB optimisation requires not only technical expertise but also a deep understanding of sustainability principles. To address this, industry leaders could collaborate with universities and research institutions to develop interdisciplinary curricula that combine technical skills with sustainability knowledge. Furthermore, the establishment of innovation hubs and technology incubators could provide a platform for knowledge sharing and collaboration, fostering a culture of continuous learning and innovation.

The findings of Chengoden et al. [61] further highlight the need for standardisation and robust frameworks to ensure the interoperability and accessibility of Metaverse technologies. For instance, the lack of standardised protocols for data exchange between different Metaverse platforms could hinder seamless integration, limiting the sector’s ability to achieve NZB goals. To address this, stakeholders could advocate for the development of industry-wide standards and frameworks that promote interoperability. Collaborative efforts between technology providers, industry bodies, and regulatory authorities could ensure that these standards are aligned with global best practices, enabling smoother integration and adoption.

6.4. Cluster D—Challenges in Metaverse Acceptance

The challenges grouped under Cluster D focus on the barriers to user acceptance of Metaverse technologies, particularly in the AEC sector. These barriers are deeply rooted in concerns over data security, privacy, and the perceived ease of use and usefulness of the technology. For instance, the study by Aburbeian et al. [77] highlights how data security and privacy concerns directly impact users’ willingness to adopt the Metaverse, as professionals in the AEC sector often handle sensitive project data that require robust safeguards. In South Africa, where digital literacy and trust in emerging technologies vary widely, these concerns are particularly pronounced, creating a significant hurdle for widespread acceptance.

The implications of these challenges are critical. Without addressing data security and privacy concerns, the AEC sector risks low adoption rates for Metaverse tools, limiting their potential to optimise NZB projects. For example, if professionals perceive Metaverse platforms as insecure or difficult to use, they may resist adopting these tools for collaborative design, simulation, or project management. To mitigate these barriers, stakeholders must prioritise the development of secure, user-friendly platforms that align with the needs and expectations of AEC professionals. This could involve implementing end-to-end encryption, multi-factor authentication, and transparent data usage policies to build trust. Additionally, user training programmes and intuitive interface designs could enhance the perceived ease of use, making the technology more accessible to a broader audience.

The findings of Vilar et al. [78] further emphasise the importance of aligning market demand with the offerings of the Metaverse. For instance, while there is a high level of interest in using the Metaverse for educational purposes, its successful deployment in the AEC sector will depend on addressing specific industry needs, such as real-time collaboration, immersive simulations, and data integration. To achieve this, developers must engage with AEC professionals to co-create solutions that address real-world challenges. Pilot projects and case studies could demonstrate the practical benefits of the Metaverse, helping to build confidence and acceptance among potential users.

The research by Riva et al. [79] delves into the ethical and social challenges of adapting to the Metaverse, including concerns about the erosion of physical and social reality. These challenges are particularly relevant in the AEC sector, where face-to-face interactions and hands-on experiences have traditionally played a central role. For example, over-reliance on virtual environments could lead to a disconnect between professionals and the physical spaces they design, potentially compromising the quality and sustainability of NZB projects. To address these concerns, stakeholders must strike a balance between virtual and physical interactions, ensuring that the Metaverse complements rather than replaces traditional practices. Ethical guidelines and best practices could help navigate these complexities, fostering a responsible and sustainable approach to Metaverse adoption.

6.5. Cluster E—Human Obstacles to Metaverse Adoption

The challenges grouped under Cluster E focus on the human-centric barriers to Metaverse adoption, which include resistance to change, ethical dilemmas, and societal concerns. These obstacles are deeply rooted in the psychological and cultural dimensions of technology adoption, making them particularly complex to address. For instance, the study by Wang et al. [71] implicitly highlights the broader spectrum of challenges, such as high costs and societal resistance, that could hinder the seamless adoption of the Metaverse. In the context of South Africa’s AEC sector, these challenges are exacerbated by varying levels of digital literacy and a general scepticism towards emerging technologies, particularly among older professionals who may be less familiar with immersive digital environments.

The implications of these human obstacles are significant. Without addressing resistance to change and ethical concerns, the AEC sector risks slow or uneven adoption of Metaverse technologies, limiting their potential to optimise NZB projects. For example, professionals who are hesitant to embrace new tools may continue to rely on traditional methods, missing out on the efficiency and sustainability benefits offered by the Metaverse. To mitigate these barriers, stakeholders must prioritise awareness campaigns and capacity-building initiatives that demonstrate the practical benefits of the Metaverse. Hands-on workshops, pilot projects, and success stories from early adopters could help demystify the technology and build confidence among sceptics. Additionally, involving end-users in the design and implementation of Metaverse solutions could foster a sense of ownership and reduce resistance.

The research by Rospigliosi [69] underscores the complexity of human obstacles, particularly in the context of ethical dilemmas posed by artificial intelligence and immersive technologies. For instance, the increasing reliance on AI-driven tools in the Metaverse raises concerns about job displacement, algorithmic bias, and the erosion of human agency. These concerns are particularly relevant in the AEC sector, where human expertise and judgment play a critical role in design and decision-making processes. To address these issues, industry leaders must establish clear ethical guidelines and governance frameworks that ensure the responsible use of Metaverse technologies. Transparent communication about the role of AI and its limitations could also help build trust and alleviate fears among professionals.

The findings of Riva et al. [79] further emphasise the importance of addressing the digital divide and ethical concerns in shaping the future of the Metaverse. For example, disparities in access to technology and digital skills could exclude certain groups from participating in Metaverse-enabled projects, perpetuating inequalities in the AEC sector. To bridge this gap, stakeholders could explore inclusive strategies such as subsidised access to Metaverse tools, targeted training programmes for underrepresented groups, and community-driven initiatives that promote digital literacy. Additionally, efforts to preserve social realities and human connections in the Metaverse could help mitigate concerns about the erosion of physical and social interactions. For instance, hybrid models that combine virtual and face-to-face collaboration could ensure that the human element remains central to AEC workflows.

7. Conclusions and Recommendations

This study extensively explored the barriers to adopting Metaverse technologies for net-zero buildings, identifying limited access to these technologies, infrastructural deficiencies, and the lack of proven applications as the most significant hindrances. Insights from diverse construction-related professionals highlighted critical technological and human-centric challenges grouped into five distinct clusters through exploratory factor analysis. These clusters emphasised technological readiness, integration difficulties, data security, and human obstacles like resistance to change. The findings underscore the necessity for improved technological infrastructure, robust educational frameworks, and comprehensive regulatory guidelines to foster broader acceptance and effective deployment of Metaverse applications. Addressing these challenges is paramount for leveraging the Metaverse’s transformative potential in advancing sustainable construction practices, necessitating targeted strategic interventions across sectors to overcome the identified barriers and facilitate the integration of Metaverse technologies.

To overcome the identified barriers and fully harness the transformative potential of the Metaverse for net-zero building (NZB) optimisation, this study recommends a structured and phased implementation framework, which is essential. This framework should address immediate needs while laying the groundwork for sustainable, long-term adoption. In the short term, stakeholders should prioritise initiatives that build foundational capabilities and address critical gaps. Investment in technical training and education is paramount, with a focus on elevating construction professionals’ digital literacy and technical competencies. Tailored training programmes, workshops, and certifications should be developed to equip professionals with the skills needed to navigate and leverage Metaverse technologies effectively. Concurrently, awareness campaigns should be launched to demystify the Metaverse and highlight its potential benefits for NZB projects. These campaigns could include case studies, pilot projects, and success stories from early adopters to build confidence and reduce resistance to change. Additionally, stakeholders should establish task forces to identify and address immediate data security and privacy concerns, ensuring that Metaverse platforms adhere to global standards such as GDPR.

In the medium term, efforts should shift towards fostering collaboration and developing industry standards. Promoting synergies between academia, industry practitioners, and government entities is crucial to facilitating the sharing of insights, best practices, and innovative solutions. Collaborative platforms, such as industry forums and innovation hubs, could serve as knowledge exchange and co-creation spaces. Simultaneously, stakeholders should work together to establish standardised guidelines for implementing Metaverse technologies in NZB projects. These guidelines should address interoperability, data exchange protocols, and ethical considerations, ensuring consistency and streamlined processes across the sector. Furthermore, public–private partnerships should be leveraged to fund the development of accessible and affordable Metaverse tools, particularly for small- and medium-sized enterprises (SMEs) in developing regions like South Africa. In the long term, the focus should be on scaling adoption and ensuring sustainable integration. Stakeholders must prioritise the development of robust governance frameworks that address ethical dilemmas, algorithmic bias, and the digital divide. These frameworks should be designed to promote equitable access to Metaverse technologies, ensuring that all stakeholders, regardless of size or location, can participate in NZB projects. Additionally, efforts should be made to integrate Metaverse tools into broader sustainability initiatives, aligning their use with global climate goals. This could involve embedding Metaverse-enabled simulations and data analytics into national and international sustainability frameworks. Finally, continuous monitoring and evaluation mechanisms should be established to assess the impact of Metaverse adoption on NZB optimisation and identify areas for improvement.

To measure the success of these initiatives, key performance indicators (KPIs) should be established. In the short term, KPIs could include the number of professionals trained, the percentage of firms adopting Metaverse tools, and the level of user satisfaction with data security measures. In the medium term, KPIs could focus on the development and adoption of industry standards, the number of collaborative projects initiated, and the reduction in implementation costs for SMEs. In the long term, KPIs should assess the impact of Metaverse adoption on NZB outcomes, such as energy efficiency, carbon emission reduction, and overall project sustainability. Regular reporting and feedback loops should be implemented to ensure that these KPIs are tracked and used to refine strategies over time. By adopting this structured implementation framework, stakeholders can systematically address the barriers to Metaverse adoption and unlock its full potential for advancing net-zero buildings. This approach ensures that short-term actions build momentum, medium-term strategies foster collaboration and standardisation, and long-term objectives drive sustainable and equitable integration.

This study was limited to the Gauteng province of South Africa due to time and cost constraints. Also, a dearth of studies on this research area limits the development of a conceptual framework grounded in applicable theories. Hence, future research should focus on incorporating other provinces of South Africa to compare with the findings of this study. Also, future studies can focus on developing targeted strategies to address these challenges, mainly through education and policy adjustments, to facilitate the seamless integration of Metaverse technologies into everyday construction practices.

8. Practical Implications

The findings of this study offer valuable practical implications for stakeholders within the South African AEC industry and can be adapted in other developing nations. By leveraging the insights garnered, construction professionals and relevant stakeholders can make informed decisions, strategically plan for adopting Metaverse technologies, and effectively manage risks associated with their implementation. Capacity-building initiatives to enhance digital literacy and technical skills can empower the workforce to utilise Metaverse solutions proficiently. Policymakers and industry entities can use these insights to develop policies, regulations, and standards that promote the ethical and efficient use of Metaverse technologies. Collaboration and knowledge sharing among stakeholders are essential for accelerating the adoption and implementation of Metaverse solutions and driving collective progress towards sustainability goals. Overall, embracing the opportunities presented by the Metaverse can pave the way for a more sustainable, efficient, and resilient AEC sector in South Africa and other developing economies. By providing the characteristics of the defined clusters, relevant stakeholders and policymakers in the sector will be able to improve the potential of the Metaverse for NZBs and a sustainable AEC sector in South Africa and the rest of the world. Also, this study was deemed necessary to provide an understanding of the best strategies to deploy in maximising the potential benefits of technologies such as the Metaverse for achieving NZBs.