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Article

Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era

by
Joanna Rosak-Szyrocka
1,* and
Sunil Tiwari
2,*
1
Faculty of Management, Czestochowa University of Technology, 42-200 Czestochowa, Poland
2
Department of Tourism Studies, School of Business Studies, Central University of Kerala, Periye 671316, India
*
Authors to whom correspondence should be addressed.
Sustainability 2023, 15(23), 16167; https://doi.org/10.3390/su152316167
Submission received: 12 October 2023 / Revised: 13 November 2023 / Accepted: 17 November 2023 / Published: 21 November 2023
(This article belongs to the Section Sustainable Education and Approaches)
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Abstract

:
Sustainability has a significant role in the reputation and status of a higher education institution. Universities have a part in “forming the values of society” through educating the present and future generations of decision makers. Universities can help with economic and sustainable development (SD) in several ways, including mediating social conflicts and educating the public on scientific and technological issues. This study aimed to evaluate the students’ agreement level (as a latent construct) to examine the causal relationship between predictors (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0), mediating (Education and Community Awareness) and outcome (Sustainable Development) variables, the multivariate statistical method; as a result, Partial Least Square Structural Equation Modeling (PLS-SEM) was performed. In order to characterize potential links, a dependency model in the form of structural equations was built based on the classification of the questions. The correlations between the various parameters were then confirmed using statistical techniques. The authors used SEM structural equations, which enabled them to ascertain the relationships between the overlapping parts in the subsequent step to identify potential connections between the survey’s questions. Research has shown that education and sustainable development have a close connection. And it is especially important to stay alert and pick up information throughout the study. This study provides new information on sustainable development in modern Universities 4.0 and Society 5.0. This study adds empirical evidence of factors that influence the sustainability of universities as a driver of innovation and economic growth. This study also provides practical implications for the development of universities’ competitiveness.

1. Introduction

In the period of Society 5.0, universities exist. According to certain theories, Society 5.0 is the social stage that comes after the hunting society (Society 1.0), the agricultural society (Society 2.0), the industrial society (Society 3.0), and the information society (Society 4.0). In particular, the stage known as “Society 5.0” is one in which innovation in science and technology is the primary driver of change. The terms “ultra-smart society” and “Society 5.0” are often used interchangeably [1,2]. The global context of education has given educational policies and practices a new emphasis in recent years. The practice of teaching development in education and education for sustainable development has made this “global character” of current education evident in educational discourse and policy [3,4].
A society that can respond to its various needs in a finely tuned manner that enables all people to receive high-quality services, transcending differences like age, gender, region, and language, and that allows people to live vibrantly and comfortably is referred to as an ultra-smart society [5]. An environment where everyone can be a service provider, delivering finely tuned and personalized services, or the concept of humans living in peace with robots leads to an increase in quality of life and are all characteristics of an ultra-smart society [1]. Moreover, the systems that meet the following criteria were conceived as the technical pre-requirements: many items need to be connected through networks, they need to be highly systematized, and numerous diverse systems need to be linked and coordinated [6,7]. Quality of life could be significantly improved in this future ultra-smart society since everything is linked through IoT technology, and all technologies are merged. A smart society is an advanced civilization after the industrial, informational, and agricultural societies. Its primary carrier is the digital data processing system [8,9,10]. Society 5.0 wants everyone to actively participate in increasing the adoption of digital technology across a wide range of processes [11]. An interdisciplinary field of study in education for sustainable development [12].
Many articles discuss the issue of sustainable development, but from the perspective of implementing its tasks and the readiness and awareness of students in relation to sustainable development [13,14,15,16,17,18]. This article fills the gap by assessing students’ levels of agreement (latent construct) with regard to examining the causal relationship between predictors (Skills and activities, Behavioral dissemination, Society 5.0, and Industry 4.0) and mediating (Education and Community Awareness) and outcome (Sustainable Development) variables. Partial Least Square Structural Equation Modeling (PLS-SEM), a multivariate statistical method, was used. Moreover, the link between Education, Community Awareness, Sustainable Development, Skills, Behavior dissemination, encouraging sustainable development, and the interaction between universities and society are also examined in this manuscript, which is novel in its work. This study aimed to evaluate the students’ agreement level (latent construct) for examining the causal relationship between predictors (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0) and mediating (Education and Community Awareness) and outcome (Sustainable Development) variables. The experiment made use of the CAWI questionnaire. The correlations between the various characteristics were then confirmed using statistical techniques. The authors then used SEM structural equations, which enabled them to ascertain the relationships between the overlapping components and look for probable connections between the survey’s questions.

2. Literature Review

Development that fulfills the needs of the present without compromising the ability of future generations to satisfy their own is the definition of sustainable development (SD) (WCED, 1987). In September 2015, 17 Sustainable Development Goals (SDGs) were established, placing education at the center of the plan to advance sustainable development. At that time, all members of the United Nations agreed that a more sustainable world was necessary [19]. Universities have a crucial role in tackling the world’s biggest problems and attaining the Sustainable Development Goals of the 2030 Agenda for sustainable development since they aim to produce and distribute knowledge. They have seen how crucial it is to include the Sustainable Development Goals and the 2030 Agenda in their plans, both for the sake of society and for what appears to be the institution’s advantage. The goal of the Sustainable Development Goals is to guarantee that all people, including women and men, have equitable access to affordable, high-quality technical, vocational, and postsecondary education, including higher education, by the year 2030.
Higher education is crucial to accomplish the more general goals of sustainable development, which is a goal in and of itself [20,21]. Higher education is the new model of education that “enables learners to reflect via multicultural, global, and future-oriented views, on their responsibility for the complex impacts of decision-making and conduct” [22]. This “new learning” method evaluates the potential for a sustainable future in an open-minded, contemplative, and participatory manner. As a result, academics have described higher education for sustainable development as both a “topic” in the curriculum and a kind of “transformative learning” that aims to bring about social change [23]. Equipping primary and secondary school teachers with the information, abilities, and attitudes necessary to educate for sustainability successfully falls to higher education institutions. University courses across disciplines serve as tools to educate higher education students on how to adopt sustainable attitudes and behaviors into their daily lives [24,25,26,27]. There are several ramifications for international attempts to identify “sustainable solutions” from the role universities play in knowledge production [28]. Because of these characteristics, it may be appropriate to consider HESD as a hybrid and large area of education for sustainable development [29,30]. Institutions of higher education play a crucial role in altering society in the direction of a more sustainable future [31]. In order to instill sustainable awareness in various behaviors, higher education institutions might be considered the drivers of advancement. These organizations see it as crucial to investigate, test, develop, and communicate the principles of sustainable development [32]. Increasing student understanding of the Sustainable Development Goals is essential for both their implementation and future breakthroughs since it may serve as a source of motivation and inspiration for young scientists looking for answers [33]. Universities must include ethical research and innovation in all their research endeavors, encourage study on issues related to the Sustainable Development Goals, encourage social entrepreneurs, and encourage capacity development and science for and with society [34]. All actions within the university should be directed toward sustainable goals, including smart campuses, women’s empowerment, innovative methodologies, and awareness-raising human rights campaigns. University governance structures should be in accordance with the principles of sustainable development [35]. In addition, universities should collaborate with other institutions to create collaborative research groups or courses on the issues relevant to increasing the capacity of building and sustainable development. Universities can also promote inter-stakeholder conversations and activities.
In light of this, all three pillars, namely ecological, financial, and social factors, embody a foundation for all-encompassing sustainable development [36]. Through their contribution to the development of people, the creation of knowledge, and innovation, universities play a crucial role in the Sustainable Development Goals as a catalyst for fulfilling the whole set of objectives.
Education can and must help create a new vision for global sustainability in development (UNESCO, 2015). In addition to being an essential component of sustainable development, education is crucial for its promotion [6]. One of the United Nations’ Sustainable Development Goals is focused on students acquiring the information and abilities needed to advance sustainable development [37]. Education is the driving force behind fostering sustainability since it is a vital instrument for communication and the basis of the “sustainable attitude” [38]. Higher education institutions (HEIs) may be essential to advancing sustainable development (SD) in an era of global climate action. In the development of sustainability-related concerns, higher education institutions are seen as “changing agents” and “catalysts” [15,39,40,41,42]. For three reasons, higher education institutions must implement education for sustainability.
  • Projects created by teaching and research centers that combine sustainability principles across disciplines can improve sustainability;
  • Outreach initiatives can assist in influencing broader perspectives through different teaching methods;
  • An institutional culture of sustainability increases awareness among university staff and the local and global communities (for instance, by promoting biodiversity, lowering greenhouse gas emissions, using energy more effectively, and minimizing the ecological impact).
Only a few of the more complex demands that HEIs must simultaneously fulfill are those of massification, globalization, marketization, and digitalization [43]. Science and technology developments that mainly support the economic sector’s growth demands are given significant attention in Society 5.0 [37]. The importance of higher education institutions in fulfilling the Sustainable Development Goals has been recognized by several writers [44,45,46,47].
The words “Society 5.0” and “Industry 4.0” are utilized to denote significant shifts in technology and society, specifically in the context of the Fourth Industrial Revolution. Although these concepts are separate, they are interconnected and have a common objective of utilizing sophisticated technologies to improve society and industries. The concept of Industry 4.0 encompasses a range of cutting-edge technologies, including the Internet of Things (IoT), artificial intelligence (AI), big data, robotics, and sophisticated manufacturing techniques. The primary objective of Industry 4.0 is to enhance operational efficiency, minimize expenditure, and enhance the overall productivity of manufacturing and industrial operations. The aforementioned phenomenon carries significant ramifications for occupational positions, necessitating a labor force that is equipped with proficiencies in technology and data analytics. Society 5.0 expands upon the technological advancements of Industry 4.0, encompassing a wider range of societal dimensions outside the confines of industry. These aforementioned technologies encompass artificial intelligence (AI), the Internet of Things (IoT), big data analytics, robotics, and various other cutting-edge technological advancements. Society 5.0 proposes a future societal framework whereby technology is used to effectively tackle prevailing social challenges, augment healthcare provisions, optimize educational systems, and promote holistic welfare. The significance of prioritizing a human-centered approach to the integration of technology is underscored. In brief, Industry 4.0 primarily concerns the utilization of advanced technologies to revolutionize manufacturing and industry. By contrast, Society 5.0 expands upon this notion by envisioning a more comprehensive societal transformation, wherein technology is employed to tackle challenges across diverse sectors, with a particular emphasis on enhancing the welfare of individuals. These aforementioned notions are mutually reinforcing, collectively contributing to the overarching objective of a future characterized by technical advancement and a focus on human well-being.

3. Materials and Method

The present study was conducted under two standardized steps; first, the questionnaire was designed and tested. Second, the collection and estimation of data were conducted. During the research, the CAWI questionnaire was used. This study aims to determine how college students rate certain sustainable development pillars and university activities. The link between education, community awareness, sustainable development, skills, behavior dissemination, encouraging sustainable development, and the interaction between universities and society are also examined in the essay. Before the start of this study, we established a minimal sample size and a method of communicating with the respondents. The survey was made available to participants online due to the diversity of countries included. The study was conducted from April 2021 to June 2022. Three hundred and two respondents participated in the survey, and their demographic details are provided in Table 1. Moreover, the collected data were analyzed via PLS-SEM. Partial Least Squares (PLS) is a statistical method that is commonly used in multivariate analysis. It is particularly useful when dealing with datasets that include a large amount of Structural Equation Modeling (SEM) and is a statistical technique used to analyze complex relationships between observed and latent variables. It is a powerful tool in social sciences and psychology, to model intricate interactions between latent variables inside a given framework. Partial Least Squares Structural Equation Modeling (PLS-SEM) has gained significant popularity in various academic domains, including business, social sciences and other disciplines. This approach is often utilized by academics seeking to comprehend and study intricate interactions among variables. Partial Least Squares Structural Equation Modeling (PLS-SEM) enables researchers to effectively create models of latent variables, which are theoretical entities that cannot be directly seen but can be inferred from observable variables. This approach proves to be particularly advantageous when addressing abstract ideas that are not amenable to straightforward quantification. Partial Least Squares Structural Equation Modeling (PLS-SEM) is frequently employed in the context of predictive modeling, with an emphasis on forecasting the values of endogenous variables by leveraging the stated relationships within the model. Additionally, it serves as a tool for experimental endeavors, enabling researchers to investigate intricate linkages within the dataset. It is noteworthy that PLS-SEM and covariance-based SEM (CB-SEM) represent distinct methodologies within the field of Structural Equation Modeling. Researchers may opt for either methodology depending on the characteristics of their data and the specific goals of their research. Partial Least Squares Structural Equation Modeling (PLS-SEM) is frequently used in scenarios when the primary objective is prediction, and the model exhibits greater complexity or is based on fewer sample sets.
Following this study, conclusions were drawn after analyzing the findings. Twenty-one questions made up the study questionnaire. Based on the categorization of questions, a dependency model in the form of structural equations was developed to define the possible relationships (Figure 1), and the following hypotheses were proposed:
H1 and H2:
Skills and activities positively influence the education system and community awareness of higher institutes.
H3 and H4:
Behavioral dissemination positively influences the education system and community awareness.
H5 and H6:
Society 5.0 positively influences the education system and community awareness.
H7 and H8:
Industry 4.0 positively influences the education system and community awareness.
H9 and H10:
Education positively influences community awareness and sustainable development.
H11: 
Community awareness positively influences sustainable development.

4. Data Estimation and Results

To evaluate the students’ agreement level (latent construct) to examine the causal relationship between predictors (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0), mediating (Education and Community Awareness) and outcome (Sustainable Development) variables, and the multivariate statistical method, Partial et al. Equation Modeling (PLS-SEM) was performed. Partial Least Square PLS-SEM [48,49] and covariance-based [50] methods are two important SEM frameworks, as suggested by [51]. Moreover, PLS-SEM is the most effective framework for establishing the metric relationships between latent, predictor, mediating, and outcome variables [51]. At small sample sizes (100 to 300), it is a prominent measurable force with non-normal distributions at the NPC (Normal Probability Curve) and works as a “causal-predictive” model. This is recommended by several researchers and academicians [51,52,53]. The present research work is ideally suited for using the PLS-SEM model; therefore, we utilized this model through a smart PLS application. Under two phases, the estimation of data through PLS-SEM was performed; the fitness and usability of the model were checked under the first phase and in the second phase, and the implications of the model (PLS-SEM) are presented [54,55]. Under the determined research variables, the current paper employed PLS-SEM to examine variance-based Structural Equation Modeling [56,57,58]. With the help of the outer weights of the model and factor loading, the indicator of the reliability of each data construct was measured. Factors loading all selected items confirmed the indicator reliability to be above the threshold value, i.e., 0.7 [59]. However, a factor loading value less than 0.7 caused a drop in a few items related to different constructs (predictor, mediating, and outcome) and cleared the way to perform composite reliability [51], and the average variance was extracted (AVE) without any hindrance. Furthermore, Table 2 presents the reliability and validity statistics of each construct, including predictor (Skills and activities, Behavioral dissemination, Society 5.0, and Industry 4.0), mediating (Education and Community Awareness) and outcome (Sustainable Development) variables under different estimators. Additionally, each indicator’s results confirm the reliability and validity of all research variables. Therefore, it is observed that designed constructs are qualified and suitable to measure the sustainable development of students in premier higher education institutions.
The contribution of each indicator or test item toward the total constructs and each construct in the total tool must be defined and determined in PLS-SEM [60]. In this context, Table 2 presents the outer loadings of each construct (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0, Education, Community Awareness, and Sustainable Development) under the respective loadings of the original sample’s mean, S.D., and t-ratio. The respective outer loading of each construct is collectively constituted through the loading of each item under it. Furthermore, the significance level of the outer loading of each variable is confirmed by the p-value, which is significant at the 0.01 level of significance for all constructs in this work (please see Table 3). Based on the classical model theory, convergent and discriminant validity and reliability frameworks were investigated under the reflective model [61]. In this sense, to ensure the selected model’s internal reliability, the association between the linkage of items with their latent constructs was measured through a reflective measurement framework. Moreover, the heterotrait–monotrait ratio of correlation (HTMT) [62], discriminant validity (square root of AVE) [59], and construct validity, i.e., convergent (through AVE), were also examined and documented [51,60]. The Extent of construct differentiation from other constructs” and the “Extent of high correlation between theoretically identical constructs are measured under discriminant and convergent validity, respectively. All reliability and validity indicator values were greater than the threshold, as shown in Table 3, and descriptive statistics were also reliable. The threshold value of CR was 0.70 [59,63], and AVE was 0.50 [59]. It is inferred from Table 3 that the values of AVE and CR are above the thresholds [59]. With the help of discriminant validity, different purposes, and indicators of theoretical constructs were measured [51]. However, discriminant validity was measured by taking the square root of AVE, which ought to be greater than the correlation to corresponding factors [59]. With the help of the aforementioned estimations, it was confirmed that each indicator, factor, and variable was valid and reliable to perform PLS-SEM and measure sustainable development.
In addition, a new framework of discriminant validity was measured and confirmed by the Heterotrait–monotrait ratio of correlation (HTMT), and its threshold value of HTMT was under 0.85 [64,65] or <0.90 [62,66,67]. The same was observed in the current study. Moreover, before establishing structural modeling via PLS-SEM and testing the proposed relationships, it was also essential to examine accuracy and uniformity. Among the selected indicators, to establish appropriate relationships and avoid any problem of collinearity, the VIF (variance inflation factor) is a good indicator [60]. In this context, VIF must be less than five [51]. It is more appropriate and significant if it is less than or close to three [60] hence, to avoid any such problem with existing collinearity, it is recommended to maintain a VIF value of less than five for each indicator and test item. However, in the case of the present work, the VIF value was less than the threshold, as reported in Table 4, and did not support any formative constructs [58].

Structural Model (PLS-SEM)

Figure 2 reports the output of PLS-SEM in the form of the explanatory power of selected constructs, predictors (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0), mediating (Education and Community Awareness), and outcome variables (Sustainable Development). The model explains the variance in each predictor, mediating, and outcome variable due to each indicator, predictor variable, and mediating variable, respectively. The variances of each predictor variable, including Skills and activities, Behavioral dissemination, Society 5.0, and Industry 4.0 in Education (mediating variables) were 0.920, 0.769, 0.783, and 0.703, respectively. In a percentage term (R2), these contributions are 84.64%, 59.13%, 61.30%, and 49.42%, respectively. This signifies that Skills and activities have the highest influence and impact on education, i.e., 84.64%, followed by Society 5.0, Behavioral dissemination, and Industry 4.0. Likewise, the variances of each predictor variable, including Skills and activities, Behavioral dissemination, Society 5.0, and Industry 4.0 in community awareness (mediating variable), were 0.632, 0.629, 0.540, and 0.573, respectively. In a percentage term (R2), these contributions are 39.94%, 39.56%, 29.16%, and 32.83%, respectively. This signifies that Skills and activities have the strongest influence and impact on community awareness, i.e., 39.94%, followed by Behavioral dissemination, Industry 4.0., and Society 5.0. Moreover, the mediating variable education is associated with community awareness (another mediating variable) with 0.844 variance, which shares 71.23% in overall community awareness. Lastly, education plays a very high and significant role in sustainable development, with a 0.932 variance and 86.86% sharing. By contrast, community development is also a detrimental component of sustainable development and has a 54.61% contribution. Based on the aforementioned PLS-SEM output in Figure 2, this model comprehensively investigated all the relationships between the predictor (Skills and activities, Behavioral dissemination, Society 5.0 and Industry 4.0), mediating (Education and Community Awareness), and outcome (Sustainable Development) variables and all the proposed alternative hypotheses were tested and achieved a 0.01 level of significance.

5. Discussion and Conclusions

The transformation of society toward sustainable development and the adjustments to how people interact with the environment is necessary to meet the Sustainable Development Goals [68]. Without universities, it is unlikely that the Sustainable Development Goals can be met [68]. Universities are crucial in tackling the world’s biggest problems and attaining Sustainable Development Goals since they aim to develop and spread knowledge. To reform society and deal with these difficulties, leadership, education, and research are crucial [69]. The research results and their analysis showed a significant association between education and sustainable development. There is a significant association between community awareness and sustainable development. There is also a significant association between skill and behavior dissemination. There is not a significant association between promoting sustainable development and University 4.0. There is a significant association between Society 5.0 and participation in activities by universities for sustainable development. Research has shown that education and sustainable development have a close connection. The data analysis showed that aspects of sustainable development are a very important issue for students in all analyzed countries. When choosing a college or university, students are most interested in whether or not the school is involved in activities that help the environment. The issues covered in teaching have the biggest share associated with social justice, health, and well-being and incorporate social responsibility/business ethics. Universities should take specific steps to promote knowledge of sustainable development, including giving students a chance to participate in meaningful work, taking adequate measures to limit adverse effects on the environment and society, scheduling frequent gatherings of students and teachers to raise awareness of sustainable development, analyzing technical processes in terms of sustainable development, and improving the efficiency of the educational environment. Climate change is one of today’s most important issues, and institutions must take action to lessen its impact. Respondents are worried about the impacts of climate change and agree that governments worldwide should take all necessary measures to combat it. Moreover, students believe that climate change will badly impact the population and way of life, and students vote for a government that takes more effort to combat the issue. Additionally, students do not vote for a government that takes greater effort to combat climate change, and students believe that climate change harms the population or way of life. The next generation of sustainability leaders must be developed by higher education institutions, who must also spearhead significant global, regional, and local initiatives and play a critical role in accomplishing the SDGs goals [70]. Beliefs, attitudes, skills, and behavior changes may be affected by education, especially if sustainable development challenges are widely and well understood. In order to attain sustainability, all levels of society—regardless of their circumstances or locations—need to shift their perspectives on education for sustainable development (2016). Planning various sustainable development activities that may raise education awareness for sustainable development is possible within the school community, particularly among teachers and students who execute sustainable school education throughout the country. Teachers significantly impact pupils via all their acts and behaviors, serving as change agents.
Education must be purposeful and address sustainable development; universities can perform this by incorporating sustainability into their curricula and teaching strategies, developing the necessary competencies and skills, fostering humanistic values, assessing students’ sustainability knowledge, creating courses that teach global awareness, and providing online and lifelong learning opportunities [6,71]. Students may, therefore, serve as advocates for the greatest environmental programs [17]. The growth of more sustainable communities depends heavily on higher education. Degrees in education are especially relevant in this situation since the education of teachers, who will become future educators, is a potent force for societal change [72]. The higher education industry significantly influences students’ habits and contributes to a thriving society as a transformative agent [17]. Institutions of higher learning may act as a link between various stakeholder groups. Forming future professionals and applying knowledge and ideas are unique responsibilities of higher education institutions [17].
In terms of the advancement of sustainability, universities have evolved into crucial players [73,74]. In order to achieve this goal and focus on providing high-quality education, universities must change their roles, organizational structures, and leadership styles to meet the new demands of sustainability [75,76]. If they achieve this, their commitment and social responsibility level can be sufficient to meet these demands. This study aims to describe the features of University 4.0, assess the components of the university’s Society 5.0, and assess the degree to which universities are active in sustainable development. Our research and analysis show that University 4.0 should take action for sustainable development, and the aspects of sustainable development are a priority. The analysis of the results showed that for Society 5.0, the actions of University 4.0 on sustainable development are important, and Society 5.0 is also involved in these actions. In the case of education, it has been shown that it is important to raise public awareness about sustainable development. University 4.0 should raise awareness of sustainable development among students. Using the C-Pearson coefficient, researchers found a strong link between students’ knowledge and sustainable development. They also found a strong link between shared skills and knowledge, how people act, and social awareness and sustainable development. The sustainable mentality motivates us to leave behind the silos of old management disciplines by emphasizing management ethics, entrepreneurship, environmental studies, systems thinking, and self-awareness [77,78]. When promoting sustainable development (SD) at a time of global climate action, higher education institutions (HEIs) might play a crucial role. The demands of massification, globalization, marketization, and digitization are only a few of the more complicated areas that HEIs must address at the same time [2,43]. The intrinsic complexity of SD, which requires systemic change rather than just adaptation [79,80], is probably one of the reasons it has not permeated mainstream academics and university administration. In other words, addressing the SD problem head-on could result in more conflicts and impasses and, thus, greater complexity [81]. Bauer et al. [82] argued that HEIs should embrace SD as a whole-institution strategy to promote transformational behaviors at all levels. According to the authors [43,83], institutions should strive to increase students’ ability to handle complexity and ambiguous situations and lean toward a more integrated viewpoint. For society to flourish sustainably, universities are essential institutions. This is shown by organizations like the University Leaders for a Sustainable Future group, which was founded in 2015, as well as how crucial it is for institutions to collaborate in this area, creating networks and clusters like sustainable campuses or macro-campuses [1,84,85]. Creating so-called sustainable thinking, responsibility, and social commitment is also noteworthy. The relevant integration of sustainable ideas in university study programs is also important [86,87]. Together, they develop a multidisciplinary understanding of these three areas (sustainable growth and climate, economy, and society), recognizing that they are always a part of society and that partnerships, communication, and accountability must be established with other specialized institutions, considering the protection of human rights. In the future, we aim to focus our research on teachers and the skills they need to help students become aware of and develop skills for sustainable development.

Author Contributions

Conceptualization, J.R.-S. methodology, S.T. formal analysis, J.R.-S. and S.T.; investigation, J.R.-S. and S.T.; resources, J.R.-S.; data curation, J.R.-S. and S.T.; writing—original draft preparation; J.R.-S. and S.T.; Supervision and project administration, J.R.-S. and S.T.; funding acquisition, J.R.-S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Rosak-Szyrocka, J.; Żywiołek, J.; Shahbaz, M. Quality Management, Value Creation and the Digital Economy; Routledge Advances in Production and Operations Management; Routledge: London, UK, 2024. [Google Scholar]
  2. Rosak-Szyrocka, J.; Apostu, S.A.; Turi, J.A.; Tanveer, A. University 4.0 Sustainable Development in the Way of Society 5.0. Sustainability 2022, 14, 16043. [Google Scholar] [CrossRef]
  3. O’flaherty, J.; Liddy, M. The impact of development education and education for sustainable development interventions: A synthesis of the research. Environ. Educ. Res. 2018, 24, 1031–1049. [Google Scholar] [CrossRef]
  4. Sujith, A.V.L.N.; Qureshi, N.I.; Dornadula, V.H.R.; Rath, A.; Prakash, K.B.; Singh, S.K. A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM). J. Food Qual. 2022, 2022, 6382839. [Google Scholar] [CrossRef]
  5. Tricahyono, D. Strengthening Awareness of Post-Identity as Asset for Society 5.0 through Reflective Learning. Pancasila J. Keindonesiaan 2022, 2, 237–245. [Google Scholar] [CrossRef]
  6. Rieckmann, M. Education for Sustainable Development Goals: Learning Objectives; UNESCO: Paris, France, 2017. [Google Scholar]
  7. Babulak, E. Third Millennium Smart Cyberspace. ICSES 2019, 3, 1–2. [Google Scholar]
  8. Chen, X.; Tang, X.; Xu, X. Digital technology-driven smart society governance mechanism and practice exploration. Front. Eng. Manag. 2022, 10, 319–338. [Google Scholar] [CrossRef]
  9. Pratama, R.A.; Saputra, M.A.; Pratiwi, I.M.; Lestari, N.I. Student Teachers’s Readiness to Face Society 5.0 Challenges: Are They Ready to Teach with Competencies Needed? In Proceedings of the Universitas Lampung International Conference on Social Sciences (ULICoSS 2021), Bandar Lampung, Indonesia, 30–31 August 2021; pp. 470–476. [Google Scholar]
  10. Apdillah, D.; Panjaitan, K.; Stefanny, N.T.P.; Surbakti, F.A. The Global Competition in the Digital Society 5.0 Era: The Challenges of the Younger Generation. J. Humanit. Soc. Sci. Bus. 2022, 1, 75–80. [Google Scholar] [CrossRef]
  11. Da Costa Tavares, M.C.; Azevedo, D.C.; Maria, G. Society 5.0 as a Contribution to the Sustainable Development Report; Springer: Singapore, 2021; pp. 49–63. [Google Scholar]
  12. Akturk, C.; Talan, T.; Cerasi, C.C. Education 4.0 and University 4.0 from Society 5.0 Perspective. In Proceedings of the 12th International Conference on Advanced Computer Information Technologies (ACIT), Kapitula, Slovakia, 9–11 June 2022; pp. 577–582. [Google Scholar]
  13. Adams, R.; Martin, S.; Boom, K. University culture and sustainability: Designing and implementing an enabling framework. J. Clean. Prod. 2018, 171, 434–445. [Google Scholar] [CrossRef]
  14. Stough, T.; Ceulemans, K.; Lambrechts, W.; Cappuyns, V. Assessing sustainability in higher education curricula: A critical reflection on validity issues. J. Clean. Prod. 2018, 172, 4456–4466. [Google Scholar] [CrossRef]
  15. Fehlner, W. Educating for Sustainability: The Crucial Role of the Tertiary Sector. J. Sustain. Dev. 2019, 12, 18. [Google Scholar] [CrossRef]
  16. Chankseliani, M.; McCowan, T. Higher education and the Sustainable Development Goals. High. Educ. 2021, 81, 1–8. [Google Scholar] [CrossRef]
  17. Žalėnienė, I.; Pereira, P. Higher Education for Sustainability: A Global Perspective. Geogr. Sustain. 2021, 2, 99–106. [Google Scholar] [CrossRef]
  18. Samuelsson, I.P.; Park, E. How to Educate Children for Sustainable Learning and for a Sustainable World. Int. J. Early Child. 2017, 49, 273–285. [Google Scholar] [CrossRef]
  19. Annan-Diab, F.; Molinari, C. Interdisciplinarity: Practical approach to advancing education for sustainability and for the Sustainable Development Goals. Int. J. Manag. Educ. 2017, 15, 73–83. [Google Scholar] [CrossRef]
  20. McCowan, T. Universities and the post-2015 development agenda: An analytical framework. High. Educ. 2016, 72, 505–523. [Google Scholar] [CrossRef]
  21. Ekamilasari, E.; Pursitasari, I.D. Students’ Critical Thinking Skills and Sustainability Awareness in Science Learning for Implementation Education for Sustainable Development. Indones. J. Multidiscip. Res. 2021, 1, 121–124. [Google Scholar] [CrossRef]
  22. Adomßent, M.; Fischer, D.; Godemann, J.; Herzig, C.; Otte, I.; Rieckmann, M.; Timm, J. Emerging areas in research on higher education for sustainable development—Management education, sustainable consumption and perspectives from Central and Eastern Europe. J. Clean. Prod. 2014, 62, 1–7. [Google Scholar] [CrossRef]
  23. Sipos, Y.; Battisti, B.; Grimm, K. Achieving transformative sustainability learning: Engaging head, hands and heart. Int. J. Sustain. High. Educ. 2008, 9, 68–86. [Google Scholar] [CrossRef]
  24. Rungfamai, K. State, university, and society: Higher educational development and university functions in shaping modern Thailand. High. Educ. 2019, 78, 149–164. [Google Scholar] [CrossRef]
  25. Nagy, K.; Hajrizi, E.; Palkovics, L. Responsible Innovation in Support of Society 5.0—Aspects of Audit and Control. IFAC—PapersOnLine 2020, 53, 17469–17474. [Google Scholar] [CrossRef]
  26. Hallinger, P.; Chatpinyakoop, C. A Bibliometric Review of Research on Higher Education for Sustainable Development, 1998–2018. Sustainability 2019, 11, 2401. [Google Scholar] [CrossRef]
  27. Lozano, R.; Lozano, F.J.; Mulder, K.; Huisingh, D.; Waas, T. Advancing Higher Education for Sustainable Development: International insights and critical reflections. J. Clean. Prod. 2013, 48, 3–9. [Google Scholar] [CrossRef]
  28. Martens, P. Sustainability: Science or fiction? Sustain. Sci. Pract. Policy 2006, 2, 36–41. [Google Scholar] [CrossRef]
  29. Cifci, I.; Kahraman, O.C.; Tiwari, S.; Rasoolimanesh, S.M. Demystifying meal-sharing experiences through a combination of PLS-SEM and fsQCA. J. Hosp. Mark. Manag. 2023, 32, 843–869. [Google Scholar] [CrossRef]
  30. Gupta, S.K.; Tiwari, S.; Hassan, A.; Gupta, P. Moderating Effect of Technologies into Behavioral Intensions of Tourists toward Use of Mobile Wallets for Digital Payments: TAM Model Perspective. Int. J. Hosp. Tour. Syst. 2023, 16, 43–57. [Google Scholar]
  31. Berchin, I.I.; Dutra, A.R.d.A.; Guerra, J.B.S.O.d.A. How do higher education institutions promote sustainable development? A literature review. Sustain. Dev. 2021, 29, 1204–1222. [Google Scholar] [CrossRef]
  32. Elmassah, S.; Biltagy, M.; Gamal, D. Framing the role of higher education in sustainable development: A case study analysis. Int. J. Sustain. High. Educ. 2022, 23, 320–355. [Google Scholar] [CrossRef]
  33. Manolis, E.N.; Manoli, E.N. Raising awareness of the Sustainable Development Goals through Ecological Projects in Higher Education. J. Clean. Prod. 2021, 279, 123614. [Google Scholar] [CrossRef]
  34. Owen, R.; Macnaghten, P.; Stilgoe, J. Responsible research and innovation: From science in society to science for society, with society. In Emerging Technologies: Ethics, Law and Governance, Library of Essays on the Ethics of Emerging Technologies, 1st ed.; Marchant, G.E., Wallach, W., Eds.; Taylor & Francis Group: Abingdon, UK, 2017; pp. 117–126. [Google Scholar]
  35. Hoover, E.; Harder, M.K. What lies beneath the surface? The hidden complexities of organizational change for sustainability in higher education. J. Clean. Prod. 2015, 106, 175–188. [Google Scholar] [CrossRef]
  36. Tien, N.H.; Thi, D.; Mai, A.; Luong, A.; Ngoc, N.M.; Duc, L.D.M.; Dat, N. Sustainable Development of Higher Education: A Case of Business Universities in Vietnam. J. Hunan Univ. Nat. Sci. 2021, 47, 41–56. [Google Scholar]
  37. Hikmat, H. The Readiness of Education in Indonesia in Facing the Society Era 5.0. J. Basicedu 2022, 6, 2953–2961. [Google Scholar] [CrossRef]
  38. Zengin, Y.; Naktiyok, S.; Kaygın, E.; Kavak, O.; Topçuoğlu, E. An Investigation upon Industry 4.0 and Society 5.0 within the Context of Sustainable Development Goals. Sustainability 2021, 13, 2682. [Google Scholar] [CrossRef]
  39. Findler, F.; Schönherr, N.; Lozano, R.; Reider, D.; Martinuzzi, A. The impacts of higher education institutions on sustainable development. Int. J. Sustain. High. Educ. 2019, 20, 23–38. [Google Scholar] [CrossRef]
  40. Littledyke, M.; Manolas, E.; Littledyke, R.A. A systems approach to education for sustainability in higher education. Int. J. Sustain. High. Educ. 2013, 14, 367–383. [Google Scholar] [CrossRef]
  41. Leal Filho, W.; Raath, S.; Lazzarini, B.; Vargas, V.R.; de Souza, L.; Anholon, R.; Quelhas, O.L.G.; Haddad, R.; Klavins, M.; Orlovic, V.L. The role of transformation in learning and education for sustainability. J. Clean. Prod. 2018, 199, 286–295. [Google Scholar] [CrossRef]
  42. Shields, R. The sustainability of international higher education: Student mobility and global climate change. J. Clean. Prod. 2019, 217, 594–602. [Google Scholar] [CrossRef]
  43. Giesenbauer, B.; Müller-Christ, G. University 4.0: Promoting the Transformation of Higher Education Institutions toward Sustainable Development. Sustainability 2020, 12, 3371. [Google Scholar] [CrossRef]
  44. Caiado, R.G.G.; Filho, W.L.; Quelhas, O.L.G.; Nascimento, D.L.d.M.; Ávila, L.V. A literature-based review on potentials and constraints in the implementation of the sustainable development goals. J. Clean. Prod. 2018, 198, 1276–1288. [Google Scholar] [CrossRef]
  45. Boni, A.; Lopez-Fogues, A.; Walker, M. Higher education and the post-2015 agenda: A contribution from the human development approach. J. Glob. Ethics 2016, 12, 17–28. [Google Scholar] [CrossRef]
  46. Owens, T.L. Higher education in the sustainable development goals framework. Eur. J. Educ. 2017, 52, 414–420. [Google Scholar] [CrossRef]
  47. Fissi, S.; Romolini, A.; Gori, E.; Contri, M. The path toward a sustainable green university: The case of the University of Florence. J. Clean. Prod. 2021, 279, 123655. [Google Scholar] [CrossRef]
  48. Lohmöller, J.B. Latent Variable Path Modeling with Partial Least Squares. In Physica; Springer Science & Business Media: Berlin/Heidelberg, Germany, 1989; pp. 199–226. [Google Scholar] [CrossRef]
  49. Chin, W.W. Commentary: Issues and opinions on structural equation modelling. JSTOR 1998, 22, vii–xvi. [Google Scholar]
  50. Jöreskog, K.G.; Sörbom, D. LISREL 8: Structural Equation Modelling with the SIMPLIS Command Language; Scientific Software International: Skokie, IL, USA, 1993. [Google Scholar]
  51. Hair, J.F., Jr.; Sarstedt, M.; Ringle, C.M.; Gudergan, S.P. Advanced Issues in Partial Least Squares Structural Equation Modelling; SAGE Publications: New York, NY, USA, 2017. [Google Scholar]
  52. Peng, D.X.; Lai, F. Using partial least squares in operations management research: A practical guideline and summary of past research. J. Oper. Manag. 2012, 30, 467–480. [Google Scholar] [CrossRef]
  53. Kahraman, O.C.; Cifci, I.; Tiwari, S. Residents’ entrepreneurship motives, attitude, and behavioral intention toward the meal-sharing economy. J. Hosp. Mark. Manag. 2023, 32, 317–339. [Google Scholar] [CrossRef]
  54. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  55. Ringle, C.M.; Sarstedt, M.; Straub, D.W. Editor’s comments: A critical look at the use of PLS-SEM in “MIS Quarterly”. MIS Q. 2012, 36, iii–xiv. [Google Scholar] [CrossRef]
  56. Arora, N.; Dhiman, N. Influence of psychological capital on turnover intentions: Empirical evidence from Indian paramedics. Int. J. Work. Innov. 2020, 2, 247. [Google Scholar] [CrossRef]
  57. Dhiman, N.; Arora, N.; Dogra, N.; Gupta, A. Consumer adoption of smartphone fitness apps: An extended UTAUT2 perspective. J. Indian Bus. Res. 2020, 12, 363–388. [Google Scholar] [CrossRef]
  58. Saura, J.R.; Dwivedi, Y.K.; Palacios-Marqués, D. Editorial: Online User Behavior and User-Generated Content. Front. Psychol. 2022, 13, 895467. [Google Scholar] [CrossRef]
  59. Fornell, C.; Larckera, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statistics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  60. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
  61. Freeze, R.; Raschke, R.L. An assessment of formative and reflective constructs in IS research. In Proceedings of the Fifteenth European Conference on Information Systems, ECIS 2007, St. Gallen, Switzerland, 7–9 June 2007. [Google Scholar]
  62. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
  63. Churchill, G.A., Jr. A Paradigm for Developing Better Measures of Marketing Constructs. J. Mark. Res. 1979, 16, 64–73. [Google Scholar] [CrossRef]
  64. Clark, L.A.; Watson, D.; Reynolds, S. Diagnosis and Classification of Psychopathology: Challenges to the Current System and Future Directions. Annu. Rev. Psychol. 1995, 46, 121–153. [Google Scholar] [CrossRef]
  65. Kline, R.B. Convergence of Structural Equation Modeling and Multilevel Modeling. In The SAGE Handbook of Innovation in Social Research Methods; SAGE Publications: New York, NY, USA, 2011; pp. 562–589. [Google Scholar]
  66. Gold, A.H.; Malhotra, A.; Segars, A.H. Knowledge Management: An Organizational Capabilities Perspective. J. Manag. Inf. Syst. 2001, 18, 185–214. [Google Scholar] [CrossRef]
  67. Teo, A.C.; Tan, G.W.H.; Ooi, K.B.; Lin, B. Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. Int. J. Mob. Commun. 2015, 13, 478. [Google Scholar] [CrossRef]
  68. Cámara, E.; Fernández, I.; Castillo-Eguskitza, N. A Holistic Approach to Integrate and Evaluate Sustainable Development in Higher Education. The Case Study of the University of the Basque Country. Sustainability 2021, 13, 392. [Google Scholar] [CrossRef]
  69. Yuan, X.; Yu, L.; Wu, H. Awareness of Sustainable Development Goals among Students from a Chinese Senior High School. Educ. Sci. 2021, 11, 458. [Google Scholar] [CrossRef]
  70. Franco, I.; Saito, O.; Vaughter, P.; Whereat, J.; Kanie, N.; Takemoto, K. Higher education for sustainable development: Actioning the global goals in policy, curriculum and practice. Sustain. Sci. 2019, 14, 1621–1642. [Google Scholar] [CrossRef]
  71. Tilbury, D. Another world is desirable: A global rebooting of higher education for sustainable development. In The Sustainable University: Progress and Prospects; Routledge Studies in Sustainable Development; Sterling, S., Maxey, L., Luna, H., Eds.; Taylor and Francis Group: Abingdon, UK, 2013; pp. 97–112. [Google Scholar]
  72. Sánchez-Carracedo, F.; Moreno-Pino, F.M.; Romero-Portillo, D.; Sureda, B. Education for Sustainable Development in Spanish University Education Degrees. Sustainability 2021, 13, 1467. [Google Scholar] [CrossRef]
  73. Schiller, D.; Radinger-Peer, V. Introduction: The Role of Universities in Regional Transitions towards Sustainability. Sustainability 2021, 13, 7940. [Google Scholar] [CrossRef]
  74. Burmann, C.; García, F.; Guijarro, F.; Oliver, J. Ranking the Performance of Universities: The Role of Sustainability. Sustainability 2021, 13, 13286. [Google Scholar] [CrossRef]
  75. Filho, W.L.; Kovaleva, M.; Gomes, B.F.; Fudjumdjum, H.; Emblen-Perry, K.; Platje, J.; Tuladhar, L.; Vasconcelos, C.R.; LeVasseur, T.J.; Minhas, A.; et al. Sustainability practices at private universities: A state-of-the-art assessment. Int. J. Sustain. Dev. World Ecol. 2021, 28, 402–416. [Google Scholar] [CrossRef]
  76. Pongiglione, F. The need for a priority structure for the Sustainable Development Goals. J. Glob. Ethic 2015, 11, 37–42. [Google Scholar] [CrossRef]
  77. Vogt, M.; Weber, C. The Role of Universities in a Sustainable Society. Why Value-Free Research is Neither Possible nor Desirable. Sustainability 2020, 12, 2811. [Google Scholar] [CrossRef]
  78. Kassel, K.; Rimanoczy, I.; Mitchell, S.F. The Sustainable Mindset: Connecting Being, Thinking, and Doing in Management Education. Acad. Manag. 2016, 2016, 16659. [Google Scholar] [CrossRef]
  79. Sanchez, D.O.M. Sustainable Development Challenges and Risks of Industry 4.0: A literature review. In Proceedings of the 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, 17–21 June 2019; pp. 1–6. [Google Scholar] [CrossRef]
  80. Moore, J.; Pagani, F.; Quayle, M.; Robinson, J.; Sawada, B.; Spiegelman, G.; Van Wynsberghe, R. Recreating the university from within. Int. J. Sustain. High. Educ. 2005, 6, 65–80. [Google Scholar] [CrossRef]
  81. Lattu, A.; Cai, Y. Tensions in the Sustainability of Higher Education—The Case of Finnish Universities. Sustainability 2020, 12, 1941. [Google Scholar] [CrossRef]
  82. Bauer, M.; Bormann, I.; Kummer, B.; Niedlich, S.; Rieckmann, M. Sustainability Governance at Universities: Using a Governance Equalizer as a Research Heuristic. High. Educ. Policy 2018, 31, 491–511. [Google Scholar] [CrossRef]
  83. Zamora-Polo, F.; Sánchez-Martín, J.; Corrales-Serrano, M.; Espejo-Antúnez, L. What Do University Students Know about Sustainable Development Goals? A Realistic Approach to the Reception of this UN Program Amongst the Youth Population. Sustainability 2019, 11, 3533. [Google Scholar] [CrossRef]
  84. Shiel, C.; Filho, W.L.; Paço, A.D.; Brandli, L. Evaluating the engagement of universities in capacity building for sustainable development in local communities. Eval. Program Plan. 2016, 54, 123–134. [Google Scholar] [CrossRef]
  85. Prieto-Jiménez, E.; López-Catalán, L.; López-Catalán, B.; Domínguez-Fernández, G. Sustainable Development Goals and Education: A Bibliometric Mapping Analysis. Sustainability 2021, 13, 2126. [Google Scholar] [CrossRef]
  86. Al-Kumaim, N.H.; Alhazmi, A.K.; Mohammed, F.; Gazem, N.A.; Shabbir, M.S.; Fazea, Y. Exploring the Impact of the COVID-19 Pandemic on University Students’ Learning Life: An Integrated Conceptual Motivational Model for Sustainable and Healthy Online Learning. Sustainability 2021, 13, 2546. [Google Scholar] [CrossRef]
  87. Miquelajauregui, Y.; Bojórquez-Tapia, L.A.; Eakin, H.; Gómez-Priego, P.; Pedroza-Páez, D. Challenges and opportunities for universities in building adaptive capacities for sustainability: Lessons from Mexico, Central America and the Caribbean. Clim. Policy 2022, 22, 637–651. [Google Scholar] [CrossRef]
Sustainability 15 16167 g001
Figure 1. Proposed model.
Figure 1. Proposed model.
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Figure 2. The PLS-SEM output.
Figure 2. The PLS-SEM output.
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Table 1. Demographic profile of students.
Table 1. Demographic profile of students.
Demographic VariableFrequencyPercentage
Age
Below 25 years8528.14%
26–309130.13%
31–355417.88%
Above 35 years7223.84%
Gender
Male15451%
Female14849%
Nature of Institute
Public19664.90%
Private10635.09%
Nationality
China4615.23%
Austria030.99%
France051.65%
Germany196.29%
India061.98%
Japan020.66%
Pakistan15551.32%
Portugal030.99%
Spain227.28%
Turkey4113.57%
Educational Qualification
Under Graduation5618.54%
Post Graduation18561.26%
M. Phil185.96%
Ph.D.4314.24%
Field of Study
Economics4514.90%
Environment8026.49%
Informatic051.65%
Management15651.65%
Tourism165.29%
Source: Primary data.
Table 2. Reliability and validity.
Table 2. Reliability and validity.
ConstructsMeanSDVIF
Skills and activities
α = 0.732
ρ = 0.820
CR = 0.702
AVE = 0.735		3.530.741.84
Behavioral dissemination
α = 0.821
ρ = 0.734
CR = 0.873
AVE = 0.640		3.730.801.75
Society 5.0
α = 0.834
ρ = 0.732
CR = 0.722
AVE = 0.739		3.850.791.83
Industry 4.0
α = 0.845
ρ = 0.730
CR = 0.743
AVE = 0.841		3.840.861.54
Education
α = 0.890
ρ = 0.733
CR = 0.845
AVE = 0831		3.830.791.83
Community Awareness
α = 0.898
ρ = 0.734
CR = 0.820
AVE = 0.773		3.730.7931.63
Sustainable Development
α = 0.749
ρ = 0.792
CR = 0.840
AVE = 0.731		3.600.861.43
Source: Primary data.
Table 3. Outer loadings.
Table 3. Outer loadings.
ConstructsOriginal
Sample		MeanS.D.t-Ratiop-Value
SA0.8380.9490.04538.6320.000 **
BD0.8200.8900.06552.5830.000 **
S5.00.8020.9340.09840.5930.000 **
I4.00.7380.7490.03454.9210.000 **
ED0.8400.7090.09339.7430.000 **
CA0.8300.8900.04526.8720.000 **
SD0.9890.9320.05427.8430.000 **
Source: Primary data. ** = significance at 0.01 level.
Table 4. Discriminant validity (square root of AVE).
Table 4. Discriminant validity (square root of AVE).
ConstructsSABDS5.0I4.0EDCASD
SA0.832
BD0.7980.844
S5.00.7400.7640.895
I4.00.8980.7590.7400.748
ED0.7340.8400.7940.7050.894
CA0.7040.7430.7010.8340.7840.749
SD0.8430.7030.8300.7040.8040.8440.750
Heterotrait–monotrait ratio of correlation (HTMT)
SASABDS5.0I4.0EDCASD
BD0.884
S5.00.7640.844
I4.00.8500.7300.784
ED0.7630.7850.7560.745
CA0.8220.7330.8330.8500.733
SD0.7860.7050.8400.7440.7500.877
0.8540.7400.7750.7740.7840.7530.704
Source: Primary data.
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Rosak-Szyrocka, J.; Tiwari, S. Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era. Sustainability 2023, 15, 16167. https://doi.org/10.3390/su152316167

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Rosak-Szyrocka J, Tiwari S. Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era. Sustainability. 2023; 15(23):16167. https://doi.org/10.3390/su152316167

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Rosak-Szyrocka, Joanna, and Sunil Tiwari. 2023. "Structural Equation Modeling (SEM) to Test Sustainable Development in University 4.0 in the Ultra-Smart Society Era" Sustainability 15, no. 23: 16167. https://doi.org/10.3390/su152316167

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