The Metaverse as a Mediating Variable in the Relationship Between Human Capital and the Quality of University Education: A Case Study
https://doi-001.org/1025/17685479293117
1_ Dr.: Ait Ahmed Lamara Mohamed University of Tissemsilt – Algeria Aitlamara1985@gmail.com
2_ Dr.: Sameur Soumia
Professor at the University of Tissemsilt (Algeria) sameur.soumia@univ-tissemsilt.dz
ORCID: https://orcid.org/0000-0002-4299-3966 3_ Dr.: Arbaoui Mohammed medm2612@gmail.com
University of Algiers 02 4_ Dr : Saridi Ahmed Université Tissemsilt
Saridi.ahmed@univ-tissemsilt.dz
Submitted: 04.05.2025. Accepted: 02.09.2025. Published: 09.12.2025
Abstract:
This study aims to analyze the role of the Metaverse as a mediating variable in the relationship between human capital and the quality of university education by assessing the level of human capital and examining the reality of adopting Metaverse technologies within the university. The study employed the descriptive method and inductive analysis to test its hypotheses. The results revealed statistically significant correlations and effects between human capital and the Metaverse, and between the Metaverse and educational quality, in addition to a direct effect of human capital on the quality of education. The study also confirmed the influential mediating role of the Metaverse in strengthening the relationship between the two variables. It recommended investing in the education and training of human capital, developing the digital infrastructure, and integrating Metaverse applications into curricula to improve the quality of university education.
Keywords: Human Capital, Metaverse, Quality of Education, Training, Mediator
Introduction
Human capital represents one of the fundamental drivers of societal development, as it embodies the total knowledge, skills, and competencies possessed by individuals and invested
across various sectors, primarily higher education. University education is the primary environment in which human capital is formed and developed to meet labor market requirements and societal needs. Therefore, the quality of university education is measured not only by curricula and courses but also by its ability to produce graduates with knowledge and practical skills that qualify them to engage professionally with high efficiency.
In recent years, digital transformations have emerged as influential factors in the educational process, with the Metaverse appearing as an innovative technological tool that provides immersive virtual environments, offering opportunities for interactive learning and practical simulation. For instance, in medical education, the Metaverse opens new horizons for training students in complex clinical scenarios realistically and safely, contributing to the enhancement of medical human capital and the development of professional skills.
Hence, studying the relationship between human capital and the quality of university education is increasingly important, focusing on the mediating role the Metaverse can play in improving educational processes and enhancing their efficiency.
Based on the above, the main research question of this study is:
To what extent does human capital contribute to improving the quality of university education, and what is the mediating role that the Metaverse can play in this relationship?
From this main question, several sub-questions arise:
- Primary Question 1: What is the level of availability of human capital, Metaverse technologies, and academic quality in the university environment under study?
- Sub-question 1.1: What is the level of availability of human capital (education, training) in the university environment under study?
- Sub-question 1.2: To what extent are Metaverse technologies employed in the educational process within the university under study?
- Sub-question 1.3: To what extent does the Metaverse contribute to the quality of university education outcomes?
- Primary Question 2: Is there a statistically significant correlation between human capital and Metaverse technologies?
- Primary Question 3: Is there a statistically significant effect between Metaverse technologies and the quality of university education?
- Primary Question 4: Does human capital have a statistically significant effect on the quality of university education?
- Primary Question 5: Does the Metaverse mediate the relationship between human capital and university education quality?
Study Hypotheses:
- Hypothesis 1: There is a statistically significant correlation between human capital and Metaverse technologies in the university environment under study.
- Hypothesis 2: Metaverse technologies have a statistically significant effect on the quality of university education.
- Hypothesis 3: Human capital has a statistically significant effect on the quality of university education.
- Hypothesis 4: Metaverse technologies mediate the relationship between human capital and the quality of university education.
Study Objectives:
- Analyze the level of human capital availability in the university environment.
- Identify the reality of adoption and use of Metaverse technologies in university education.
- Measure the impact of human capital on the quality of university education.
- Test the mediating role of the Metaverse in the relationship between human capital and university education quality.
- Provide practical recommendations for activating Metaverse technologies to enhance human capital and improve the quality of university education.
Study Significance:
The study contributes to literature on human capital and university education quality, highlighting the relatively new mediating role of the Metaverse in academic research. It provides universities with practical indicators for integrating the Metaverse into training curricula and supports academic decision-makers in improving educational quality through investment in human capital and digital technologies.
Methodology:
To analyze and address the research problem and answer the posed questions while testing the hypotheses, the study employed the deductive method using descriptive analysis to interpret relevant studies and research on human capital and Metaverse technologies in higher education. This was aimed at analyzing and deriving the theoretical and applied frameworks of the topic. The study also used the inductive method, moving from part to whole, enabling the testing and validation of the research hypotheses.
Key study tools included scientific references in both Arabic and foreign languages (books, journals, articles). In the applied component, a questionnaire was used, and data were analyzed using statistical models and the Social Sciences Statistical Package (SPSS).
Study Structure:
To achieve the study objectives and answer the research questions while testing the hypotheses, the research begins with an introduction and concludes with a conclusion summarizing the key findings. The main body is divided into two sections:
- Chapter 1: Conceptual Approach to Study Variables
- Chapter 2: Field Study Methodology and Applied Procedures
Section 1: Conceptual Approach to Study Variables
The individual has long been a focus of many economists and researchers, ancient and contemporary, as they considered the individual the core of any state, institution, or productive process that is difficult to replicate (Keltouma, 2018, p. 142). Attention to human capabilities began in the 17th century, e.g., in 1676 (published definitively in 1690) William Petty noted the idea of labor differences (Machlup, 1982, p. 09). The concept of human capital appeared in classical economists’ writings, especially Adam Smith (Tittembrun, 2017, p. 17), who in 1776 highlighted in The Wealth of Nations the influence of workers’ skills on productivity and output quality, arguing that wages should reflect the time, effort, and cost to acquire necessary skills (Gutherie & Petty, 2000, p. 176).
Human Capital and Its Relationship with University Education Quality
Human capital encompasses all traits related to individuals within an institution, including competencies, attitudes, skills, tacit knowledge, and worker relations (Pike, Lisa Fernstrom & Goran Roos, 2005, p. 19). It includes intangible elements such as experience, skills, and creativity, contributing to competitiveness and innovation (Bessieux-Ollier, Monique Lacroix & Elisabeth Walliser, 2006, p. 27).
The term “human capital investment” was first used by Nobel laureate Theodore Schultz, noting that human capital is a component of intellectual capital, contributing 50–90% of a company’s value, rather than material assets (Hacini & Khadra Dahou, 2018, p. 03). Becker (1962) discussed various ways to invest in human capital: vocational training, schooling, research, and knowledge acquisition (Folloni, 2010, p. 259).
Nick Bontis emphasized human capital as a source of innovation and renewal, representing an institution’s capacity to extract optimal solutions from workforce knowledge, which can be enhanced through brainstorming, process reengineering, and personal skills improvement (Nick, 1998, p. 65). Its importance in education and training contributes to institutional productivity (BLACK & Lisa M. Lynch, 1996). Malharta highlighted national knowledge assets as intangible resources with significant future growth impact (Malharta, 2003, p. 03).
A Harvard study by Gang Liu found high-level education to be the most influential factor in increasing human capital stock (Gang, 2012, p. 15). The OECD (2010) noted a strong relationship between education and health, with higher education contributing to higher individual human capital (CERI, 2010, p. 17). Modern growth theory emphasizes education and technology as central to economic growth (Michael, 2001, p. 08).
Tribus first proposed applying quality philosophy to education, suggesting that Total Quality Management adapted to higher education is essential to build human capital capable of keeping pace with a changing world. Higher education quality refers to achieving educational outcomes in knowledge, practical and scientific skills, research, innovation, infrastructure, faculty, and accreditation. Studies confirm strong statistical relationships between human capital dimensions and higher education quality.
Human capital is a cornerstone of university education quality, as faculty competency, student capabilities, and administrative skills directly influence the effectiveness of educational processes. Greater investment in knowledge, qualifications, and human skills improves academic programs, teaching methods, research, and university services, enhancing institutional competitiveness nationally and internationally.
Metaverse: Concept, Characteristics, Applications in Higher Education
Amid rapid advances in AI and augmented reality, Mark Zuckerberg announced on October 28, 2021, a new VR/AR technology called the Metaverse, renaming Facebook to Meta and introducing a new logo (Arsa & All, 2022, p. 502). Stevenson defined the Metaverse as a vast virtual environment parallel to the physical world where users interact through digital avatars (Lee & All, 2021, p. 01).
The Metaverse is a network of interconnected virtual worlds integrated with and enhancing the physical world, allowing users to represent themselves as avatars and interact (Weinberger & Daniel Gross, 2022, p. 39). Alawaad et al. defined it as an innovative communication tool capable of transforming interaction methods and introducing advanced technology into society (Alawaad, Elsir Mohamed & Ahed Musa, 2022, p. 3449).
Metin Argan et al. define it as a combination of “meta” (beyond) and “universe,” representing the convergence of virtual and augmented reality, becoming popular since 2021 (Argan, Mehpare Tokay Argan & Halime DİNÇ, 2022, p. 35).
Key Metaverse features include:
- Persistence: Exists independently of time and space.
- Synchronicity: Participants interact with each other and digital environments simultaneously.
- Availability: All participants can log in at the same time without limits.
- Economy: Participants and companies can trade goods and services with recognized value.
- Interoperability: Users can use virtual items across different Metaverse experiences (Brik, 2022, pp. 59–60).
In higher education, students can study sciences and knowledge through highly dynamic 3D visualization, exploring new technologies and knowledge, enhancing skills, and increasing motivation for learning activities (Zidan & Saif Al-Suwaidi, 2022, p. 93). Studies, including Talan & Kalinkara, confirm its importance in enhancing knowledge and making courses more engaging (Talan & Yusuf Kalinkara, 2022, p. 333).
The Metaverse has become a strategic direction in modern university education, extending beyond technical fields to medicine (virtual surgical simulations), engineering (3D modeling), and humanities/social sciences (immersive interactive classrooms). This diversity reflects the Metaverse’s ability to provide integrated educational environments that enhance understanding, practical skills, and collaborative learning.
Medical students or surgeons can explore veins, arteries, heart valves, etc., wearing a HP Reverb G2 VR headset, interacting with patient models in a virtual operating room, learning advanced surgical techniques safely.
Figure 1: Using Metaverse Technology in Medical Sciences
Source: Article on the website https://egyptiangeographic.com/ar/news/show/690, accessed on 14/01/2025.
Chapter Two: Field Study Methodology and Its Practical Procedures
In this axis, we will address the methodological procedures, introduce the study population and sample, its model and tool, and the statistical methods used in the study.
Study Model: To clarify the dimensions of the study problem and achieve its objectives, we formed a hypothetical model reflecting the nature of the relationship between the variables. This study consists of three variables: the first variable represents human capital, the second variable represents Metaverse technology, and the third variable represents the quality of university education. The study model can be illustrated as follows:
Study Population and Sample: The study population consists of Algerian university students. The questionnaire, which is the study tool, was distributed to a simple random sample of students from the Faculty of Medicine at the University of Algiers. A total of 40 questionnaires were distributed, and 33 valid questionnaires were retrieved for statistical analysis.
Statistical Methods Used in the Study: The collected data were converted into numerical form and analyzed statistically using SPSS version 25. Appropriate statistical methods were employed to obtain significant indicators and insights relevant to the research topic, including:
- Frequencies and Percentages: To describe the sample data and its characteristics.
- Arithmetic Means: To evaluate the degree of agreement of the study sample with the questionnaire items.
- Standard Deviation: To measure the dispersion of the sample responses from the mean.
- Simple and Multiple Regression and Statistical Tests included in the regression model.
- Kenny and Baron Model, 1986: To test the mediation hypothesis and determine partial or full mediation.
Analysis and Interpretation of Statistical Processing Results: This section describes the characteristics of the study sample, presents and analyzes the sample responses for the study variables, answers the first main question, and tests the study model and hypotheses.
Description of Study Sample Characteristics: The characteristics of the study sample vary by gender, age, and role. The following table presents the distribution of sample members according to these variables:
Table 1: Distribution of Sample Members by Gender, Age, and Role
| Variable | Category | Frequency | Percentage |
| Gender | Male | 12 | 36% |
| Female | 21 | 64% | |
| Total | 33 | 100% | |
| Age | Under 25 | 3 | 9% |
| Variable | Category | Frequency | Percentage |
| 26–30 | 22 | 67% | |
| 31 and above | 8 | 24% | |
| Total | 33 | 100% | |
| Role | University Student | 26 | 79% |
| Professor or Administrative Staff | 7 | 21% | |
| Total | 33 | 100% |
Source: Prepared by the researchers based on SPSS results.
According to Table 1, the number of males is 12 (36%) while females are 21 (64%), indicating that the majority of the sample is female. The largest age group is 26–30 years (67%). Most respondents are students (79%).
Presentation and Analysis of Sample Responses for Study Variables
Analysis of the First Sub-Question of the First Main Question: What is the level of availability of human capital (education, training) in the university environment under study? Dimension One: Education
Table 2: Results of Education Dimension Items
No. Item Mean
I believe the university seeks to attract competent
Std. Dev.
Evaluation Level
01 professors and students with distinguished academic 4.23 0.59 High records
| 3.93 0.75 High 3.22 1.32 Medium 3.83 1.07 High |
The university invests in its human capital by sending them
02
for learning and internships domestically and abroad
I feel that the quality of curricula at the university
03
contributes to academic development
I consider that the teaching methods used at the university
04
help in deep understanding of scientific content
I believe that the educational resources available at the
05 university (books, platforms, digital content) effectively 3.81 0.49 High support learning
Total 3.80 0.55 High
Source: Prepared by the researchers based on SPSS results.
Table 2 shows that Item 01 ranks first with a mean of 4.23 and a standard deviation of 0.59, reflecting the university’s effort to attract competent staff and students. The university invests in human capital through learning and internship opportunities domestically and abroad. Teaching methods help deepen understanding of scientific content, though the evaluation of the curriculum quality is medium, possibly due to a lack of curriculum updates.
Dimension Two: Training
Table 3: Results of Training Dimension Items
Std. Evaluation
| Dev. Level 3.84 0.70 High 3.50 0.89 High 3.72 0.67 High |
No. Item Mean
The university provides training programs for students and
06
staff to enhance their knowledge
The university utilizes external expertise to train and
07
develop its human capital
The university administration provides continuous and
08
updated training programs for students and professors
The duration and content of training programs align with
09 the requirements of students and professors at different 3.11 1.74 Medium levels
I believe practical training helps link theory with
10 4.16 0.52 High
professional reality
I believe training directly contributes to improving the
11 3.74 1.25 High
quality of university education
Total 3.67 0.68 High
Source: Prepared by the researchers based on SPSS results.
Table 3 indicates that most items received a high evaluation, reflecting the university’s efforts to develop human capital through continuous, comprehensive training programs for students and staff. The university also relies on external expertise to ensure training quality, linking theory to practice and improving educational quality. However, respondents felt that the duration and content of training programs do not fully meet the needs of students and faculty at different levels, especially medical students who require intensive, practical training aligned with their studies.
Analysis of the Second Sub-Question of the First Main Question: To what extent are Metaverse technologies utilized in the educational process at the university under study?
Table 4: Results of Items on the Extent of Metaverse Technology Utilization
Std. Evaluation
| Dev. Level 4.08 0.49 High 2.58 1.24 Low 3.09 1.23 Medium 3.93 0.64 High |
No. Item Mean
Faculty members are sufficiently aware of the importance
12
of employing the Metaverse in university education
The university administration provides training programs
13
on using the Metaverse in teaching
The university provides digital equipment supporting
14
Metaverse technology utilization
I believe that mastery of academic content enhances my
15
experience within Metaverse educational environments
I think the practical skills I acquire make my Metaverse
- experience more effective, allowing repeated practice 3.89 0.54 High without time or space constraints
Total 3.51 0.74 High
The table shows that respondents believe familiarity with academic content improves their experience in Metaverse learning environments, and acquired skills make the experience more effective, with opportunities for repeated practice. Faculty members are aware of the Metaverse’s importance in education, but training programs and digital equipment to support its use are lacking.
Analysis of the Third Sub-Question of the First Main Question: To what extent does Metaverse technology contribute to the quality of university education outcomes?
Table 5: Results of Items on the Contribution of Metaverse Technology to Education Quality
No. Item Mean
The Metaverse provides an interactive learning
Std. Dev.
Evaluation Level
- environment that enhances academic content and learning 4.12 0.54 High quality
No. Item Mean
I believe using the Metaverse helps convey complex
Std. Dev.
Evaluation Level
18
concepts more clearly than traditional teaching
Using the Metaverse enhances active participation and
4.09 0.37 High
19 collaboration among students, making learning more 4.14 0.55 High engaging
The Metaverse helps bridge the digital skills gap among
20 3.99 0.64 High
university students
The Metaverse offers opportunities for practical training
21 3.70 1.02 High
and realistic simulations without physical presence
I believe the Metaverse allows better collaborative
22 learning between students and professors than traditional 3.78 0.61 High methods
I believe the Metaverse helps convey complex concepts
23 4.02 0.58 High
more easily and realistically
I believe the Metaverse enables learning experiences not
24 3.89 0.59 High
achievable in traditional classrooms
I feel that using the Metaverse contributes to improving
25 3.83 0.56 High
assessment and exams through realistic simulations
I believe the Metaverse makes the learning process more
26 interactive, enhancing my understanding of university 3.87 0.88 High content
Total 3.94 0.71 High
All items in this dimension received a high evaluation (3.70–4.14), indicating that students recognize the Metaverse’s role in providing an interactive learning environment, clarifying complex concepts, enhancing collaboration, and enabling practical learning experiences not achievable in traditional classrooms. It also contributes to digital skill development and more effective assessment methods, positioning the Metaverse as an innovative tool for improving university education quality.
Analysis of the First Main Question: Main Question 1: What is the level of availability of human capital, Metaverse technologies, and academic quality in the university under study?
Table 6: Means and Standard Deviations of Study Variables Variable Mean Std. Dev. Level
| Human Capital | 3.74 | 0.66 | High |
| Metaverse Technology | 3.51 | 0.54 | High |
| University Academic Quality | 3.94 | 0.71 | High |
Source: Prepared by the researchers based on SPSS results.
The table shows that the highest mean is 3.94 (high) for university academic quality, followed by human capital (mean = 3.74, high), and Metaverse technology (mean = 3.51, high). Thus, the levels of study variables in the universities under study are high. Students and staff are aware of the importance of the Metaverse in enhancing the educational process, but the university has not yet officially implemented this technology nor provided the necessary infrastructure.
Table (7): Correlation between Human Capital Education and Metaverse Technologies Variable Correlation Coefficient
Human Capital Education 0.691** Metaverse Technologies —
Source: Prepared by the researchers based on SPSS results.
From the table above, given that the Sig value equals 0.000, which is less than the significance level of 0.01, and the Spearman correlation coefficient is 0.69, we observe a positive relationship; that is, the higher the level of human capital education, the greater its ability to effectively employ metaverse technologies in the educational process.
Second sub-hypothesis of the first main hypothesis: There is a statistically significant correlation between human capital training and metaverse technologies in the university environment under study.
Table No. (8): The Correlational Relationship Between Human Capital Training and Metaverse Technologies
Variable Correlation Coefficient
Human Capital Training 0.658** Metaverse Technologies
Source: Prepared by the researchers based on SPSS results. Statistically significant at less than 0.01
Through the above table, considering that the Sig value equals 0.000, which is less than the significance level of 0.01, and Spearman’s correlation coefficient estimated at 0.65, we observe a positive relationship, meaning that the more investment in human capital training and the development of its technical skills, the greater its ability to deal with metaverse educational environments and employ them to achieve interactive learning.
There is a statistically significant correlational relationship between human capital and metaverse technologies in the university environment under study.
Table No. (9): The Correlational Relationship Between Human Capital and Metaverse Technologies
Variable Correlation Coefficient
Human Capital 0.796** Metaverse Technologies
Source: Prepared by the researchers based on SPSS results. Statistically significant at less
than 0.01
Through the above table, considering that the Sig value equals 0.000, which is less than the significance level of 0.01, and Spearman’s correlation coefficient estimated at 0.79, we observe a positive relationship, meaning that increased investment in human capital through education and training facilitates the adoption of metaverse technologies and enhances the ability to use them in improving the educational process.
There is a statistically significant effect between metaverse technologies and the quality of university education.
Table No. (10): Correlation and Determination Coefficients for the Higher Education Quality Model (1)
F Value
Significance
Correlation R
Adjusted R Standard Error of
Level
Coefficient
Square
Square
Estimate
223.026 0.000 0.743 0.552 0.549 0.37666
Source: Prepared by the researchers based on SPSS results.
Table No. (11): Simple Regression Model of the Effect of Metaverse Technology on University Education Quality
| Regression Coefficient Std. Error Standardized Coefficient Calculated t Significance Level Statistical Significance 1.231 0.177 7.452 0.000 Significant 0.624 0.042 0.743 15.911 0.000 Significant |
Variable
Constant
Metaverse Tech.
Source: Prepared by the researchers based on SPSS results.
Through the results of the previous two tables, the second main hypothesis is confirmed as follows:
- The regression coefficient equals 0.62 with a positive sign, indicating a direct relationship between metaverse technology and the quality of university education, meaning that the more these technologies are employed, the more the quality of educational outcomes improves.
- The correlation coefficient equals 0.74, which indicates a direct relationship between metaverse technologies and the quality of higher education, and the strength of this relationship is 0.74, significant at the 5% level.
- The t-test value equals 15.91, significant at less than 5%, which confirms the presence of a relationship between metaverse technologies and the quality of university education.
- The F-test value equals 223.02, significant at the 5% level, meaning that the independent variable (metaverse technologies) is suitable for predicting the dependent variable (quality of university education).
- R² equals 0.55, meaning that the independent variable (metaverse technologies) explains 55% of the variation in the dependent variable (quality of university education), while 45% is due to other variables that were not part of the study.
Through the statistical analysis results above, the second main hypothesis is accepted: there is a statistically significant effect between metaverse technologies and the quality of university education.
The regression equation in this model is: Y = 1.23 + 0.62Z
There is a statistically significant effect between human capital and the quality of university education.
Before testing this hypothesis and its sub-hypotheses, variables and regression assumptions must first be determined.
Dependent variable: Quality of university education (Y) Independent variables: Represented in the dimensions of human capital investment: X1: Human capital education X2: Human capital training
The hypotheses will be tested first to answer the third main hypothesis after confirming the significance of the model as a whole. The appropriate mathematical formula for estimating the model using multiple linear regression is (Kewan, 2015, p. 35):
Y = B0 + B1X1 + B2X2 + EI
After conducting the partial significance test for the model, the t-test for the model variables indicated that all independent variables are statistically significant, meaning that the significance level was less than 0.05. Therefore, the model as a whole is accepted. A set of procedures was also performed to ensure that the data meet regression assumptions.
First Sub-Hypothesis of the Third Main Hypothesis:
There is a statistically significant effect between human capital education and the quality of university education.
Table No. (12): Correlation and Determination Coefficients for University Education Quality Model (2)
F Value
Significance
Correlation
R Adjusted R Standard
Level
Coefficient
Square
Square
Error
182.408 0.000 0.675 0.455 0.451 0.38428
Source: Prepared by the researchers based on SPSS results.
Table No. (13): Simple Regression Model of the Effect of Human Capital Education on University Education Quality
Variable
Constant
Human Cap. Edu.
Regression Coefficient
Std. Error
Beta
Calculated t
Significance
Statistical Significance
| 1.620 0.152 10.889 0.000 Significant 0.528 0.038 0.675 13.539 0.000 Significant |
Source: Prepared by the researchers based on SPSS results.
Through the results of the previous two tables, the first sub-hypothesis of the third main hypothesis is confirmed as follows:
- The regression coefficient equals 0.52 with a positive sign, indicating a direct relationship between the process of human capital education and the quality of university education, meaning that for every one-unit increase in human capital education, the level of university education quality increases by 0.52 units (direct change).
- The correlation coefficient equals 0.67, which indicates a direct relationship between human capital education and the quality of university education, and the strength of this relationship is 0.67, significant at the 5% level.
- The t-test value equals 13.53, significant at less than 5%, confirming the presence of a relationship between human capital education and the quality of university education.
- The F-test value equals 182.408, significant at the 5% level, meaning that the independent variable (human capital education) is suitable for predicting the dependent variable (quality of university education).
- R² equals 0.45, meaning that the independent variable (human capital education) explains 45% of the variation in the dependent variable (quality of university education), while 55% is due to other variables.
Through the statistical analysis results above, the first sub-hypothesis of the third main hypothesis is accepted: there is a statistically significant effect between human capital education and the quality of university education.
Second Sub-Hypothesis of the Third Main Hypothesis:
There is a statistically significant effect between human capital training and the quality of university education.
Table No. (14): Correlation and Determination Coefficients for University Education Quality Model (3)
F Value
Significance
Correlation
R Adjusted R Standard
Level
Coefficient
Square
Square
Error
192.155 0.000 0.704 0.495 0.492 0.38976
Source: Prepared by the researchers based on SPSS results.
Table No. (15): Simple Regression Model of the Effect of Human Capital Training on the Quality of University Education
| Coefficient Error Coefficient t Level Significance Constant 1.856 0.133 13.905 0.000 Significant Human Cap. 0.534 0.038 0.704 13.862 0.000 Significant Training |
Variable
Regression
Std.
Standardized
Calculated Significance
Statistical
Source: Prepared by the researchers based on SPSS results.
Through the results of the previous two tables, the second sub-hypothesis of the third main hypothesis is confirmed as follows:
- The regression coefficient equals 0.53 with a positive sign, indicating a direct relationship between human capital training and the quality of university education, meaning that for every one-unit increase in human capital training, the level of university education quality increases by 0.53 units (direct change).
- The correlation coefficient equals 0.70, which indicates a direct relationship between human capital training and the quality of university education, and the strength of this relationship is 0.70, significant at the 5% level.
- The t-test value equals 13.86, significant at less than 5%, confirming the presence of a relationship between human capital training and the quality of university education.
- The F-test value equals 192.155, significant at the 5% level, meaning that the independent variable (human capital training) is suitable for predicting the dependent variable (quality of university education).
- R² equals 0.49, meaning that the independent variable (human capital training) explains 49% of the variation in the dependent variable (quality of university education), while 51% is due to other variables.
Through the statistical analysis results above, the second sub-hypothesis of the third main hypothesis is accepted: there is a statistically significant effect between human capital training and the quality of university education.
There is a statistically significant effect between human capital and the quality of university education.
Table No. (16): Correlation Coefficient, R Square, Adjusted R Square, and Standard Error of Estimate Between the Dependent Variable and Independent Variables
Model R R² Adjusted R² Standard Error of Estimate
1 0.784a 0.614 0.610 0.34840
- Predictors: Constant, education, training
Source: Prepared by the researchers based on SPSS results.
Through the above table, we observe that the correlation coefficient between the dependent variable (quality of university education) and the independent variables (education and training) is 0.78, and R Square equals 0.61, while the adjusted R Square equals 0.61, with a standard error of 0.34. This means that the independent variables explain 61% of the variation in the quality of university education, and the remaining 39% is due to other variables not included in the study.
Table No. (17): ANOVA Results for Testing the Significance of the Regression
First Sub-Hypothesis of the First Main Hypothesis: There is a statistically significant correlation between human capital education and Metaverse technologies in the university under study.
Model Sum of Squares DDL Mean Squares F Sig.
Regression 28.251 2 9.302 88.437 0.000b
Residual 18.854 30 0.108
Total 47.105 32
- Dependent Variable: Quality of University Education
- Predictors: (Constant), Education, Training Source: Prepared by the researchers based on SPSS results.
From the table above, we note that the value of F equals 88.43 with a statistically significant value of 0.000; therefore, there is a relationship between the quality of university education and the independent variables, which means that the regression is significant and not equal to zero. Table (18): Correlation and Determination Coefficients for the University Education Quality Model
Model
Unstandardized Coefficients (B)
Standard Error
Standardized Coefficients (Beta)
T Sig.
Constant 1.343 0.141 — 9.566 0.000
Model
| 0.114 0.051 0.246 2.254 0.025 0.255 0.049 0.416 5.208 0.000 |
Human Capital Education
Human Capital Training
Unstandardized Coefficients (B)
Standard Error
Standardized Coefficients (Beta)
T Sig.
Source: Prepared by the researchers based on SPSS results.
From Table (18), we observe that all regression coefficients are statistically significant at less than 0.05; therefore, there is a statistically significant effect, and all variables contribute to the regression equation of this model.
We also note that human capital training is the most contributing variable, with a Beta value of
0.41 and a significance value of 0.000, followed by human capital education with a Beta value of 0.24 and a significance value of 0.025. Therefore, they contribute to explaining the variance in the dependent variable.
The regression equation in this model is:
Y = 1.343 + 0.114X₁ + 0.255X₂
Based on the above statistical analysis, the third main hypothesis, which states that there is a statistically significant effect between human capital and the quality of university education, is accepted.
Metaverse technologies mediate the relationship between human capital and the quality of university education.
To test the validity of this hypothesis, the Baron & Kenny (1986) model was used, which is considered the first model to examine the mediator variable and is widely used in many studies. Baron & Kenny indicated that establishing mediation requires the following conditions:
- The independent variable must affect the mediator in the first equation.
- The independent variable must affect the dependent variable in the second equation.
- The mediator must affect the dependent variable in the third equation.
If these conditions are met in the expected direction, then the effect of the independent variable on the dependent variable should be lower in the third equation than in the second. Perfect mediation occurs if the independent variable has no effect when the mediator is controlled.
Applying these conditions produced the following findings:
- The independent variable (human capital) affects the mediator (metaverse technology) in equation one: 0.77, statistically significant.
- The independent variable affects the dependent variable (quality of university education) in equation two when controlling for the metaverse variable: 0.64, statistically significant.
- The mediator (metaverse technology) affects the dependent variable in equation three when included in the regression equation: 0.28, statistically significant.
The three conditions are met, noting that the direct effect of the independent variable (human capital) on the dependent variable (quality of university education) equals 0.48, statistically significant, and lower than equation two (0.64). Full mediation did not occur because the direct effect did not disappear when controlling for the mediator.
Thus, metaverse technology represents partial mediation, as its effect is partial.
(As text representation since diagrams cannot be drawn exactly)
- Equation 1: 0.77 – Effect of independent variable on mediator
- Equation 2: 0.64 – Effect of independent variable on dependent variable without mediator
- Equation 3: 0.28 – Effect of mediator on dependent variable
- Equation 3 (Direct Effect): 0.48 – Effect of independent variable on dependent variable with mediator
It is evident from the model that:
- Human capital has a significant effect on metaverse technology.
- Metaverse technology has a significant effect on the quality of university education.
- Human capital has a direct significant effect on the quality of university education. Thus, there is partial mediation.
The results indicate that activating metaverse technologies is an essential component in enhancing the effect of human capital on the quality of university education. The analysis showed that the metaverse acts as an effective mediator that deepens the relationship between the two variables, making investment in digital skills and infrastructure necessary for using these technologies.
The study confirms that integrating metaverse environments into curricula and teaching methods improves the educational experience and enhances efficiency. The results call for institutional strategies that support this transformation and encourage further research into metaverse applications in university education.
- There is a statistically significant correlation between human capital and metaverse technology.
- There is a statistically significant effect between metaverse technology and the quality of university education.
- There is a statistically significant effect between human capital and the quality of university education.
- Metaverse technology partially mediates the relationship between human capital and the quality of university education.
- Strengthen digital capacities of human capital through specialized training for faculty and students on metaverse technologies.
- Develop the technological infrastructure in universities to support metaverse environments.
- Integrate metaverse applications into curricula to improve interaction and experiential learning.
- Encourage innovative teaching using virtual and augmented reality.
- Establish long-term institutional strategies for digital transformation.
- Conduct periodic assessments to measure the effectiveness of metaverse technology in education.
References:
- Alawaad, H., Elsir Mohamed, & Ahed Musa. (2022). Metaverse: Imagine a spatial medium for your messages according to classical communication models. Central European Management Journal, 30(03), 3449.
- Argan, M., Mehpare Tokay Argan, & Halime DİNÇ. (2022). Take me to other universes! Metaverse event experience of users. Journal of Internet Applications and Management, 13(01), 35.
- Arsa, U. W., & ALL. (2022). Branding in Metaverse: Why big brand advertising in Metaverse. The 4th Open Society Conference OSC (p. 502). Indonesia: Faculty of Law, Social and Political Sciences, Universitas Terbuka.
- Bessieux-Ollier, C., Monique Lacroix, & Elisabeth Walliser. (2006). Human Capital: accounting approach versus managerial approach. Revue International sur le Travail et la Société, 04(02), 27.
- Black, S. E., & Lisa M. Lynch. (1996). Human capital investments and productivity. The American Economic Review, 86(02), 266.
- Ceri, C. F. (2010). Social capital, human capital, and health: What is the evidence. London: OECD.
- Djellal, S., & Aicha Cherfaoui. (2021). The impact of human capital on the quality of higher education: Case study University of Akli Mohand Oulhadj Bouira. Les Agregats des Connaissances, 07(02), 140.
- Folloni, G. (2010). Human capital measurement: A survey. Journal of Economic Surveys, 24(02), 259.
- Gang, L. (2012). Measuring human capital – An OECD project. 2nd World KLEMS Conference (p. 15). Cambridge: Harvard University.
- Gutherie, J., & Petty, R. (2000). Intellectual capital literature review: Measurement, reporting, and management. Journal of Intellectual Capital, 11(2), 176.
- Hacini, I., & Khadra Dahou. (2018). Effect of human capital management on firm performance via balanced scorecard. Management and Economics Review, 03(01), 03. 12- Keltouma, T. B. (2018). Human capital in the economic institution between past and present – A comparative study. Journal of Business & Management, 06(04), 142.
13- Kewan, J. N. (2015). Estimation of multivariate multiple linear regression models and applications. Nablus, Palestine: Faculty of Graduate Studies, An-Najah National University. 14- Lee, L.-H., & ALL. (2021). All one needs to know about Metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda. Journal of LaTeX Class Files, 14(08), 01.
- Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1177.
- Machlup, F. (1982). Lecture on theory of human capital (1st ed.). Islamabad, Pakistan: Institute of Development Economics.
- Malharta, Y. (2003). Measuring knowledge assets of a nation: Knowledge system for development. United Nations Advisory Meeting of the Department of Economic and Social Affairs (p. 03). New York: Division for Public Administration and Development Management. 18- Michael, P. (2001). National education policy constructions of the “knowledge economy”: Towards a critique. Journal of Educational Enquiry, 02(01), 08.
- Nick, B. (1998). Intellectual capital: An exploratory study that develops measures and models. Journal of Management Decision, 36(02), 65.
- Pike, S., Lisa Fernstrom, & Goran Roos. (2005). Managing intellectual capital in practice. Oxford: An imprint of Elsevier Ltd.
- Talan, T., & Yusuf Kalinkara. (2022). Students’ opinions about the educational use of the Metaverse. International Journal of Technology in Education and Science, 6(2), 333. 22- Tittembrun, J. (2017). Human capital theory and evidence in light of socio-economic structuralism. Poland: World Scientific News.
- Weinberger, M., & Daniel Gross. (2022). A Metaverse maturity model. Global Journal of Computer Science and Technology: H Information & Technology, 22(02), 39.
- Ashraf Mohamed Zidan, & Saif Al-Suwaidi. (2022). The post-traditional world: Metaverse
(2nd ed.). Turkey: Dar Al-Asala Publishing.
- Ayman Mohamed Ibrahim Breik. (2022). Metaverse applications and their relation to the future of digital journalism: A foresight study during the next two decades 2022–2042. Egyptian Journal of Media Research, 78, 59–60.