Introduction
Integrating humans and technology fosters innovative work styles and enhances performance (Dutta & Mishra, Reference Dutta and Mishra2024). Dutta, Srivastava and Singh (Reference Dutta, Srivastava and Singh2023) further emphasise that integrating these technological advancements into business operations is crucial for maintaining a competitive advantage. However, Castro-Casal, Neira-Fontela and Álvarez-Pérez (Reference Castro-Casal, Neira-Fontela and Álvarez-Pérez2013) highlight the struggle of developing essential knowledge and capabilities to compete with rapid technological advancements for businesses. Among these advancements, the Metaverse has evolved as a transformative technological development, changing how businesses and individuals interact (Upadhyay & Khandelwal, Reference Upadhyay and Khandelwal2022). Lim, Lee and Park (Reference Lim, Lee and Park2023) referred to the Metaverse as ‘an expansive virtual world where individuals can engage in immersive experiences and real-time interactions with others’. Metaverse offers seamless interactions between individuals and their digital counterparts with immersive technologies, artificial intelligence, cryptocurrencies, and spatial and peripheral technologies. This rapid technological transformation provides significant insights for future research in human capital development.
Augmented and virtual reality (integral Metaverse elements) can generate billions globally by 2030 (PricewaterhouseCoopers, 2019; Chris, Reference Chris2022). China and Japan have already started integrating the Metaverse into their economic strategies (Kulasooriya, Khoo, Tan & Kianchong, Reference Kulasooriya, Khoo, Tan and Kianchong2023). Indian corporations are working to establish Metaverse units, including Tech Mahindra, Tata Group, Wipro, and Infosys, actively establishing dedicated Metaverse units (Baruah, Reference Baruah2022). Notably, the Metaverse is accessible through smartphones and portable devices (Vulpen, Reference Vulpen2022), and India, being the second-largest smartphone market globally, further enhances Metaverse accessibility (Kulasooriya et al., Reference Kulasooriya, Khoo, Tan and Kianchong2023). Its revolutionary potential in gaming, entertainment, and work industries has significantly transformed communication, work styles, and workplace interaction (Lim et al., Reference Lim, Lee and Park2023; Vulpen, Reference Vulpen2022).
The influence of Metaverse spans diverse sectors, including healthcare (Shehzad, Ramtiyal, Jabeen, Mangla & Vijayvargy, Reference Shehzad, Ramtiyal, Jabeen, Mangla and Vijayvargy2024), military training (Diaz et al., Reference Díaz, Saldaña and Avila2020), and various other industries. It also extends to departmental functions including human resource (HR) management (Dwivedi et al., Reference Dwivedi, Hughes, Baabdullah, Ribeiro-Navarrete, Giannakis, Al-Debei and Wamba2022), with innovative solutions for recruitment, employee development, learning, and performance appraisal (Marabelli & Lirio, Reference Marabelli and Lirio2024). HR professionals are encouraged to leverage the Metaverse to enhance employee motivation and engagement (Lim et al., Reference Lim, Lee and Park2023).
While HR professionals may be cautious about adopting new technologies, due to their focus on people, they need to develop digital competencies to effectively navigate the Metaverse and mitigate potential risks (Zallio & Clarkson, Reference Zallio and Clarkson2022). Although the literature has identified barriers to Metaverse adoption in real-world applications (Gupta, Rathore, Biswas, Jaiswal & Singh, Reference Gupta, Rathore, Biswas, Jaiswal and Singh2024; Jung et al., Reference Jung, Cho, Han, Ahn, Gupta, Das and Dieck2024; Kumar, Shankar, Shaik, Jain & Malibari, Reference Kumar, Shankar, Shaik, Jain and Malibari2025; Mkedder & Das, Reference Mkedder and Das2024), there is a significant gap in understanding the specific competencies needed to overcome these barriers, particularly for implementing the Metaverse in human capital training and development within the service industry. Lim et al. (Reference Lim, Lee and Park2023) have integrated Metaverse in human resource development (HRD), concerning the training, organisation development, and career development and has outlined a research question, ‘What new competencies should HRD professionals develop to successfully implement the Metaverse for training?’ and the present study seeks to address this research gap. Employees should be self-motivated to learn new competencies (Mason & Brougham, Reference Mason and Brougham2024) to attain the organisational objective of technology adoption. Metaverse adoption is challenging for HRD professionals, as employees often perceive an increased workload when engaged with immersive virtual environments (Lim et al., Reference Lim, Lee and Park2023). The absence of standardised technological infrastructure is also major problem (Zhang, Shu, Luo, Yuan & Zheng, Reference Zhang, Shu, Luo, Yuan and Zheng2022). Ethical concerns further complicate Metaverse integration (Lim et al., Reference Lim, Lee and Park2023). In this context, proper utilisation of Metaverse-based training should be ensured by HRD professionals (Lim et al., Reference Lim, Lee and Park2023). Thus, for smooth Metaverse adoption and organisational readiness, HRD professionals should take a proactive role to design clear guidelines, robust support systems, and effective training methodologies tailored to immersive environments. Trainees’ performance depends on successfully implementing information and communication technologies. This study has addressed this gap with the following research questions:
RQ1: What specific competencies related to the Metaverse should human resource management professionals possess to implement training and development programmes in Metaverse environments effectively?
RQ2: How can the identified Metaverse-related competencies for human resource management professionals be prioritised in emerging economies, particularly for training and development programmes?
RQ3: What causal relationships exist among the identified Metaverse-related competencies, and how do these relationships influence the effectiveness of training and development programmes in Metaverse environments?
This study aimed to identify, prioritise, and analyse the causal relationships among Metaverse-related competencies to develop effective training and development interfaces. Twenty-five competencies were identified and categorised into four key areas: technical (TC), managerial (MC), SC, and gamification (GC) categories (Table 2). The Spherical Fuzzy-Bayesian Best Worst Method (SF-BBWM), a pairwise comparison method, and Grey Influence Analysis (GINA) were applied to quantify the influence relations among the identified competencies.
Literature review
Metaverse
The term ‘Metaverse’ was first coined by Neal Stephenson in 1992 (Kim, Reference Kim2021, p. 141) and gained widespread attention in 2021 when Mark Zuckerberg, the founder of Facebook, rebranded the company as ‘Meta’ (Facebook, 2021). In simple terms, when users interact through their digital avatars, mirroring the activities and features of the physical world over a virtual environment is termed as Metaverse (Park & Kim, Reference Park and Kim2022). Defining the Metaverse remains a significant challenge due to ongoing research and its rapidly evolving nature. Despite this challenge, several prominent scholars in the field have proposed valuable definitions, as summarised in Table 1.
Table 1. Metaverse definition(s)
Metaverse for training and development
Existing literature supports Metaverse technology to enhance the employees’ competencies (Upadhyay & Khandelwal, Reference Upadhyay and Khandelwal2022). Mirdozandeh, Bagheriyan Farahabadi, Honari and Emadi (Reference Mirdozandeh, Bagheriyan Farahabadi, Honari and Emadi2025) have proposed an HRD model with competence development as a key component. In the organisational context, Metaverse could nurture the workforce to ensure organisational long-term survival and performance (Hendriks, Olt, Sturm & Moos, Reference Hendriks, Olt, Sturm and Moos2024). Lee (Reference Lee2023) recognises the Metaverse as a facilitator for HRD. Envisioned as the ultimate fusion of humans and technology, the Metaverse is an immersive and interactive virtual realm (Agarwal & Alathur, Reference Agarwal and Alathur2023; Shehzad et al., Reference Shehzad, Ramtiyal, Jabeen, Mangla and Vijayvargy2024) that offers superior and memorable learning experience compared to traditional training methodologies (Jung et al., Reference Jung, Cho, Han, Ahn, Gupta, Das and Dieck2024). These enhanced training and development experiences improve productivity and efficiency while reducing costs (Agarwal & Alathur, Reference Agarwal and Alathur2023; Shehzad et al., Reference Shehzad, Ramtiyal, Jabeen, Mangla and Vijayvargy2024). Furthermore, for making learning sessions more flexible, Ollo-López and Nuñez (Reference Ollo-López and Nuñez2024) signify the need to integrate innovative tools for combining in-person training programmes with simulated environments. In this context, the Metaverse facilitates engaging, interactive, and simulated learning experiences by alleviating the fatigue associated with lengthy training sessions in traditional approaches. These virtual environments foster collaborative learning and knowledge-sharing opportunities (El Dandachi, El Nemar & El-Chaarani, Reference El Dandachi, El Nemar and El-Chaarani2023; Jung et al., Reference Jung, Cho, Han, Ahn, Gupta, Das and Dieck2024). The Technology Acceptance Model signifies that the technology acceptance depends on its ease of use and usefulness (Davis, Reference Davis1989). The engaging environment and improved interactional frictions over the gaming Metaverse make it useful and easy to use (Jo & Lee, Reference Jo and Lee2024). Furthermore, Lim et al. (Reference Lim, Lee and Park2023) used the Technology Acceptance Model and explained that the convenient usage and relevance of the technology affect the user’s adoption intention in the organisational context. To take advantage of these virtual realm opportunities, organisations should focus on competency development, particularly at the managerial level. For the successful integration of the Metaverse, HR professionals should develop these competencies and execute them into their organisational practices (Marabelli & Lirio, Reference Marabelli and Lirio2024).
The present study has addressed the research gap of Lim et al. (Reference Lim, Lee and Park2023) by identifying 25 Metaverse-related competencies with a mixed-method approach, drawing insights from related literature (secondary source) and experts’ opinions (primary source) to implement Metaverse-based training and development in the service industry.
Categorisation of Metaverse competencies
The identified Metaverse-related competencies were categorised into four key areas: TC, MC, SC, and GC based on experts’ opinions.
Technical competencies: Acquiring a strong foundation in diverse technological domains is essential for optimal usability and user engagement in the Metaverse (Gupta et al., Reference Gupta, Rathore, Biswas, Jaiswal and Singh2024). A comprehensive literature review underscores the significance of the following TC for implementing the Metaverse for training and development (refer to Table 2).
Table 2. Metaverse competencies
Managerial competencies: Robust MC are required for effectively utilising Metaverse as a training and development platform (Marabelli & Lirio, Reference Marabelli and Lirio2024). The existing literature highlights several critical MC, such as product management, ethical and social responsibility, legal framework, fair pricing, authorisation, and computing resources management (see Table 2).
Stimulating competencies: The literature emphasised the importance of user safety requirements for adopting Metaverse technology (Mkedder & Das, Reference Mkedder and Das2024). Accordingly, the study identifies the following as key incentivising factors: content loss, technical support, unstable connections, privacy and security, and cheating (see Table 2).
Gamification competencies: The literature highlights the importance of GC in enhancing learning experiences (Flavián et al., Reference Flavián, Ibáñez-Sánchez, Orús and Barta2024; Jo & Lee, Reference Jo and Lee2024). Several studies have demonstrated that incorporating enjoyable elements into the Metaverse enhances user-friendliness and engagement (Shardeo, Sarkar, Mir & Kaushik, Reference Shardeo, Sarkar, Mir and Kaushik2024). The study identifies several critical gamification attributes, such as playability, social interaction, nudge experiences, control, flow experiences, alternate reality experiences, and hedonic experiences (see Table 2).
Research methods
This study has proposed a mixed-method decision-making model, and the methodological flowchart is presented in Figure. 1.
Figure 1. Methodological flowchart.
CSF and selection of experts
The Critical Success Factor (CSF) theory was first introduced by Christine V. Bullen and John F. Rockart in 1981. This theory emphasises identifying and focusing on critical areas that significantly influence organisational success. Once identified, these vital areas can dramatically enhance an organisation’s overall performance and success. To ensure optimal performance, managers must develop a deep understanding of these critical success factors. This study employs the Critical Success Factor theory to identify and analyze the essential competencies for implementing a Metaverse-based training and development platform. By understanding and addressing these critical factors, managers can allocate resources effectively and achieve desired organisational outcomes.
A two-step approach was adopted to identify and prioritise critical Metaverse-related competencies. In the first step, 25 Metaverse-related competencies were identified through literature and validated by experts with competency categorisation. Employing snowball and judgmental sampling techniques, a panel of industry experts, based on their extensive experience (over 10 years) and understanding of the Metaverse, was selected. To check their Metaverse understanding, two screening questions were asked: ‘Are you aware of Metaverse?’ and ‘Have you utilised the Metaverse platform?’. Favourable responses were received from 21 industry experts (refer to Table 3). In the second step, these experts were invited to prioritise the identified competencies based on their relevance and importance for successful Metaverse implementation in training and development. Recognising India’s potential as a global leader in Metaverse technology, as highlighted by Meta’s CEO at Fuel for India 2021, this study focused on Indian experts from the service industry. Given India’s young and digitally savvy population, the country is ready to become an essential player in Metaverse adoption by 2030. The researchers utilised the SF-BBWM and GINA to prioritise and examine the interrelationships among critical Metaverse-related competencies.
Table 3. Details of the expert’s profile
Spherical Fuzzy-Bayesian Best Worst Method
The Best-Worst Method (BWM) is a decision-making technique to evaluate multiple criteria. In this method, decision-makers identify the most and least favourable criteria, and their relative weights are calculated with fewer pairwise comparisons, with more consistent results than other multi-criteria decision-making techniques like Analytic Hierarchy Process (Rezaei, Reference Rezaei2015, Reference Rezaei2016). A Bayesian approach is used to aggregate the diverse opinions of multiple decision-makers. The Credal ranking measures agreement among decision makers towards the preferability of one criterion over another through confidence level with a threshold of 0.5 (Mohammadi & Rezaei, Reference Mohammadi and Rezaei2020). Spherical fuzzy sets have been incorporated into the BBWM to address the inherent uncertainty and imprecision in decision-making (Kutlu Gündoğdu & Kahrama, Reference Kutlu Gündoğdu and Kahraman2019). This method accounts for potential outliers and delivers a more robust estimation of overall preferences. The studies of Mohammadi and Rezaei (Reference Mohammadi and Rezaei2020), Jafarzadeh Ghoushchi, Bonab and Ghiaci (Reference Jafarzadeh Ghoushchi, Bonab and Ghiaci2023), and Gandhi, Kant, Thakkar and Shankar (Reference Gandhi, Kant, Thakkar and Shankar2024) were followed for analysis (Appendix 1).
Grey influence analysis
This approach utilises the input–output model proposed by Liu and Forrest (Reference Liu and Forrest2010) and grey theory (Julong, Reference Julong1989) to analyse the causal relationship among the identified factors with its four key coefficients – direct influence coefficient, complete influence coefficient, grey responsibility, and influence coefficient are computed through a straightforward 11-step process (Rajesh, Reference Rajesh2024). No limit on response count and aggregation-based analysis without the risks of data losses makes GINA preferable over other causal modelling techniques (i.e., ISM/DEMATEL) (Rajesh et al., Reference Rajesh2024). To ensure the validity and reliability of GINA, the incomplete and biased responses should be manually cleaned (Rajesh, Reference Rajesh2024).
This method is implemented to understand the influential relationship among these competencies. The result enables managers to implement control actions where necessary. The studies of Rajesh (Reference Rajesh2024) were followed for analysis (Appendix 2).
Data analysis and results
Spherical Fuzzy-Bayesian Best Worst Method
The SF-BBWM was employed to calculate the combined weights for the categories and subcategories of the identified competencies. Data were collected in linguistic terms from experts who were asked to identify and compare the best and worst criteria. Each expert assigned a value of ‘1’ to both the selected best and worst criteria. The collected data were then converted into Spherical Fuzzy Numbers, and the prioritisation function was established. Finally, the aggregated and local weights for the four main categories and their respective subcategories were calculated.
The four major categories’ total weight and subcategories should be one, as illustrated in Table 4. The global weights for each competency are calculated by multiplying the local weights by the weight of their respective primary categories (e.g., the global weight of T1 is 0.278 × 0.217). Experts assigned global rankings to each competency based on their priorities. These credal rankings are represented in the diagram (refer to Figure 2), highlighting the GC’s subcategories. Specifically, G1, with a weight of 0.259, dominates the category, demonstrating confidence level of 0.63 over G2 (0.201), confidence level of 0.96 over G6 (0.167), and 1.0 over G4 (0.130), G5 (0.128), G3 (0.089), and G7 (0.073), respectively. This is interpreted as 63% agreement among experts about the superiority of G1 over G2, as 96% agreement among experts about the superiority of G1 over G6, and as 100% agreement among experts about the superiority of G1 over G3, G4, G5, and G7.
Figure 2. Credal ranking of gamification competencies.
Table 4. Assignment of final weights and rankings under SF-BBWM
Grey influence analysis
A survey was conducted among industry experts to analyse the interrelationships among the four competency categories – TC, MC, SC, and GC – and their constituent competencies. Experts were consulted to rank the influence of each competency on others using a 1–5 scale, providing valuable insights for further analysis. After collecting and cleaning the expert responses, the data were converted into a grey scale to facilitate analysis. Three models – critical, ideal, and typical – were developed to represent the competencies’ minimum, maximum, and average influence relationships.
To quantify these relationships, direct and complete influence coefficients were computed. Additionally, grey influence, responsibility, and necessity coefficients were determined to evaluate the impact and significance of each competency. This comprehensive analysis provides a significant understanding of the complex dynamics of the Metaverse competency framework. This highlights the overall significance of each factor, indicating whether it influences or is influenced by other factors. Finally, the total influence was calculated to identify the top 20% of factors (based on the Pareto principle) with the highest influence. For instance, the results indicate that TC (0.574) exert the highest influence on other categories, followed by GC (0.514), SC (0.487), and MC (0.425), as shown in Table 5 (four categories of competencies) and Figure 3.
Figure 3. Grey responsibility, influence, and total coefficients four Metaverse competency categories.
Table 5. Grey responsibility, influence, and important coefficients
By applying the top 20% rule (Pareto principle), the analysis revealed that scripting and 3D modelling (TC), ethical and social responsibility and computing resources (MC), privacy and security and content loss (SC), and playability and hedonic experiences (GC) are the most influential competencies within their respective competency categories.
Discussion
Organisations spend capital on training and development programmes (Saeed, Ali & Ashfaq, Reference Saeed, Ali and Ashfaq2024) as HRD has become significant for improving organisational performance (Malik, Budhwar & Kazmi, Reference Malik, Budhwar and Kazmi2023). HRD professionals could use the Metaverse for providing immersive learning opportunities (Lim et al., Reference Lim, Lee and Park2023) to strengthen the organisational competitive position (Saeed et al., Reference Saeed, Ali and Ashfaq2024). This study has used a mixed-method approach to identify 25 Metaverse competencies for developing Metaverse-based training and development platform. Based on expert opinions, these 25 Metaverse competencies were categorised into four broad groups: TC, MC, SC, and GC.
The authors utilised the SF-BBWM to prioritise the categorised Metaverse competencies. As shown in Table 4, TC (0.278) and GC (0.259) are prioritised as the most critical competencies for developing Metaverse-based training and development platforms, consistent with past studies (Flavián et al., Reference Flavián, Ibáñez-Sánchez, Orús and Barta2024; Gupta et al., Reference Gupta, Rathore, Biswas, Jaiswal and Singh2024). A positive perception of employees for Metaverse-based training depends upon its user-friendly interface (Saeed et al., Reference Saeed, Ali and Ashfaq2024), as perception is influenced by users’ interaction with a particular technology (Taheri, D’Haese, Fiems & Azadi, Reference Taheri, D’Haese, Fiems and Azadi2022). Within TC, T1 (Scripting) (0.060) is identified as an essential skill for interacting with and instructing in the Metaverse, followed by T3 (Ability to create web 3.0 infrastructure) (0.044), which facilitates a transparent and direct interaction interface. These competencies are followed by T2 (0.039), T4 (0.038), T6 (0.035), T7 (0.030), and T5 (0.030).
Among GC, G1 (Playability) (0.054) is identified as the most significant competency, as it sustains employees’ interest and alleviates the monotony of learning sessions with positive psychology (Thomas et al., Reference Thomas, Baral, Crocco and Mohanan2023). Additionally, the Metaverse enables advanced and collaborative learning through social interaction (G2) (0.052), overcoming geographical barriers (Jung et al., Reference Jung, Cho, Han, Ahn, Gupta, Das and Dieck2024). These competencies are followed by G6 (0.043), G4 (0.033), G5 (0.033), G3 (0.023), and G7 (0.019). Alternate reality experiences (G6) create a fictional real world and thus address the limitations of navigation for Metaverse users (Malik et al., Reference Malik, Budhwar and Kazmi2023). HRD professionals could leverage it to develop a controlled environment for practising the learned skills. The deep sense of nudge, enjoyment, and entertainment with nudge experience (G3), flow experience (G5), and hedonic experiences (G7) competencies addresses the limitation of frustration and disengagement over the Metaverse. SC fosters a sense of belonging among human capital and enhances users’ inclination to adopt the Metaverse. S4 (Privacy and Security) (0.063) significantly influences Metaverse adoption. These findings align with the studies by Mkedder and Das, Reference Mkedder and Das2024, Gupta et al. (Reference Gupta, Rathore, Biswas, Jaiswal and Singh2024). The second most significant competency, S1 (content loss) (0.061), impacts the long-term usability of the Metaverse, followed by S2 (0.046), S5 (0.039), and S3 (0.030).
MC are essential for delivering a high-quality, standardised Metaverse-based training and development platform (Marabelli & Lirio, Reference Marabelli and Lirio2024). Among these, M2 (Ethical and Social Responsibility) (0.051) facilitates the management of ethical and societal norms, while M1 (Product Management) (0.045) addresses the need to manage interdisciplinary expert teams. These competencies are followed by M4 (0.035), M3 (0.051), M5 (0.029), and M6 (0.028).
To analyse the interdependencies among Metaverse competency categories, causal relationships were examined using GINA. The results indicate that SC and MC are highly interdependent with TC (0.574) and GC (0.514). Furthermore, scripting (T1) (0.342), ethical and social responsibility (M2) (0.409), privacy and security (S4) (0.473), and playability (G1) (0.362) are identified as the most influential competencies within their respective categories.
Overall, the findings highlight the importance of TC and GC for successfully adopting the Metaverse for training and development in the service industry.
Conclusion and further enhancements
The study recommends the Metaverse platform for providing enhanced human capital training and development opportunities through its immersive features. The authors investigated Metaverse competencies for successfully developing Metaverse-based learning platforms, thereby addressing the research gap identified by Lim et al. (Reference Lim, Lee and Park2023). A mixed-method approach was employed to identify these competencies, which were then analysed using the SF-BBWM and GINA approach. The findings emphasise the importance of TC for ensuring efficient Metaverse functionality and GC for creating engaging and interactive learning experiences.
Additionally, scripting, playability, privacy and security, and ethical and social responsibility competencies are critical Metaverse competencies. Furthermore, this study contributes significantly by offering an in-depth understanding of Metaverse competencies and their implications for academicians and policymakers.
Theoretical contribution
This study has contributed significant theoretical implications for advancing the Metaverse as a training and development platform in the service industry.
First, an in-depth literature review on Metaverse adoption highlights several adoption barriers (Gupta et al., Reference Gupta, Rathore, Biswas, Jaiswal and Singh2024; Mkedder & Das, Reference Mkedder and Das2024). Lim et al. (Reference Lim, Lee and Park2023) have emphasised the need for competency development to successfully implement the Metaverse platform for training and development. To the best of our knowledge, no study has investigated Metaverse competencies – TC, MC, SC, and GC by incorporating interdisciplinary research to address adoption barriers, particularly for its implementation in training and development within the service industry.
Second, it is not feasible to address all competencies simultaneously due to the significant resource investments required. Therefore, this study prioritises technical and gamification as the most critical competency categories, along with privacy and security, scripting, playability, and ethical and social responsibility competencies, as the most crucial Metaverse competencies for developing mitigation strategies against Metaverse adoption barriers on a priority basis. Future researchers could extend this work by developing theoretical frameworks and empirical testing.
Third, the interconnections among Metaverse competencies suggest that developing a specific competency creates a ripple effect on other competencies. For example, developing TC influences MC. The study highlights significant causal effects of scripting and 3D modelling (TC), playability and hedonic experiences (GC), privacy and security and content loss (SC), and ethical and social responsibility and computing resources (MC), on the remaining competencies within their respective categories. Future studies can leverage these interrelationships to develop practical solutions for facilitating Metaverse-oriented training and development programmes.
Practical implications
This study provides significant managerial implications for academicians, industry practitioners, and policymakers.
First, the Metaverse, no longer restricted to science fiction, has emerged as an integral component of many organisations and a key strategic asset for various sectors. Its adoption has significantly transformed service-based organisations. This study identifies 25 critical Metaverse competencies for leveraging this technology in training and development. These competencies, which range from technical skills to gamification techniques, have been emphasised in recent research. These competencies are crucial for managing and orchestrating the resources needed to implement Metaverse-based solutions while adhering to relevant guidelines and regulations. Service industry managers can integrate these competencies to effectively govern the Metaverse and related knowledge domains, thereby driving the implementation and utilisation of Metaverse solutions within their organisations.
Second, experts have emphasised the importance of competencies in product management, legal frameworks, and ethical considerations. The study focuses on the need for a holistic approach to develop Metaverse competency, with ethics as a core component. Organisations can establish robust governance practices by equipping HR professionals with relevant Metaverse competencies. Improved Metaverse governance can strengthen mechanisms for data sharing and data availability. This facilitates the deployment of Metaverse solutions while ensuring user safety, transparency, and accountability.
Third, experts recommend that policymakers in service firms exercise greater caution regarding privacy and security, scripting, playability, and ethical and social responsibilities. Such caution is essential for fostering continuous and active learning engagements, ensuring seamless interactions with the Metaverse implementation, adopting a user-friendly learning approach, and adhering to ethical and societal norms when creating training and development platforms for HRs.
Fourth, competencies related to augmented Reality/virtual reality technologies, ethical and social responsibility, and legal frameworks would promote the advantages of the Metaverse in the service industry. This knowledge can support the development of effective strategies by leveraging collective expertise and experience. HR professionals recommend developing augmented Reality/virtual reality technology competencies to create a collaborative environment where stakeholders can share best practices and experiences. Additionally, creating web3 infrastructure can facilitate the preparation for Metaverse adoption. This can enhance data infrastructure, interoperability, digital skills development, and regulatory mapping for adopting the Metaverse in service industry settings.
Limitations and future directions
This study has a few limitations, providing scope for future research.
First, this study has addressed Metaverse adoption barriers in the Indian service industry. Future research could validate these identified and examined Metaverse competencies across other industries and countries. Additionally, these exploratory findings could be further investigated using experimental methods.
Second, despite the Metaverse being in its nascent stage, the existing literature highlights crucial competencies for implementing Metaverse-based training and development programmes. Future researchers may conduct longitudinal studies for exploring additional Metaverse competencies developed over time and their potential implications.
Third, the weights derived from SF-BBWM could be integrated with TOPSIS or VIKOR to extend this study (Rezaei, Reference Rezaei2015). Additionally, future researchers could employ Interpretive Structural Modeling (ISM) or Decision-Making Trial and Evaluation Laboratory (DEMATEL) to develop structural models or digraphs, as the GINA methodology does not support this (Rajesh, Reference Rajesh2024).
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/jmo.2025.10019.
Funding statement
The authors declare that they have not received any funding to conduct this study.
Conflicts of interest
The authors declare that they have no conflict of interest.
Swati Garg is a research scholar at the Department of Business Administration, National Institute of Technology, Kurukshetra, India. Her research area of interest includes Human Resource Management, Competency Mapping, Digital competencies, Training and development, etc.
Chandra Sekhar is an assistant professor at the Department of Business Administration, NIT Kurukshetra. His research interests include strategic HRM, Playful work design, Employee time theft behaviour, AI Adoption in HR, human capital, HR flexibility, Career Management, organisational behaviour, and strength-based leadership. He teaches subjects like Organisational Behaviour, Human Resource Management Analytics, Compensation Management, Performance Management and Appraisal.
Deepak Kumar is an associate lecturer in the Department of Computer Science and Information Technology at La Trobe University. He holds an integrated postgraduate degree in Information Technology (B.Tech) and Finance (MBA) from the Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, India. Deepak completed his PhD jointly at the Indian Institute of Technology Kanpur and La Trobe University, Melbourne, focusing on ‘Blockchain-based Decentralized Financing for Small and Medium Enterprises’. His research interests include Blockchain Technology, Artificial Intelligence, Entrepreneurship, SME Finance, Entrepreneurial Finance, Fintech, and SMEs.
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