Holistic Approach in Higher Education in Latin America to Adapt to New Social and Labor Needs: Challenges for Quality Assurance

Abstract

Higher education institutions have undergone essential transformations in recent decades, driven by legislative reforms and changes in social and economic demands. This study examines the factors that influence the continuous improvement of educational quality in Ecuador’s private higher education institutions (HEIs). A quantitative, cross-sectional, descriptive–correlational study was conducted to analyze the adaptation of HEIs to new realities, student mobility, technological development, sustainability practices, and innovation. The results indicate that technological development and environmental sustainability are key elements. There is a strong correlation between technological development and adaptation to the world of work. This suggests that integrating sustainable practices and university–industry cooperation is critical in improving the quality of higher education in Ecuador. In addition, student mobility and graduate follow-up programs are positively related to labor market adaptation and internationalization. These findings suggest the need for a holistic approach to quality assurance and provide practical guidelines for Ecuadorian HEIs to improve their performance in a rapidly changing context.

1. Introduction

In a constantly evolving educational environment, HEIs face the challenge of adapting to the changing demands of the labor market, technological trends, and students’ and society’s expectations. Higher education quality is measured by academic achievements and institutions’ ability to anticipate and respond to new realities (Camilleri, 2021).

In this context, relevant topics referenced in this article and explored by other authors are constantly updating academic programs based on skills and competencies demanded by the labor market (Römgens et al., 2019; Teixeira et al., 2016). This implies exploring the labor field and contacting the productive sector to align academic training with industry needs (Lima et al., 2021; Mascarenhas et al., 2018). Another topic is evaluating quality and student mobility, reflecting the positive effect on the results due to mobility and the role of academic exchange programs with other institutions (Amendola & Restaino, 2017). Gupta and Mahajan (2023) emphasize another approach regarding the level of participation of those involved in monitoring and promoting graduates’ employability (Gupta & Mahajan, 2023). Along with this, the practices of social responsibility and sustainability in HEIs (Hernández-Díaz et al., 2021) point to the improvement of educational quality, with relevance for the promotion of diversity and inclusion, community projects and social linkage, and the inclusion of subjects on climate change, social responsibility, and sustainable development (Filho et al., 2019).

Based on these issues, this present study explores how Ecuadorian private universities respond to these new realities, analyzing the interrelations between educational quality indicators (university–business cooperation, technological development, environmental sustainability, graduate follow-up and internationalization) and their impact on adaptation to the labor market in Ecuadorian private universities, with the potential to pave the way for a brighter future. Additionally, we propose a holistic framework for educational quality management that integrates sustainable practices, technological innovation, and multi-sectoral linkages. We specifically explore the following research questions:

• RQ1: How are indicators of educational quality (university–industry cooperation, technology, environmental sustainability, graduate follow-up, and internationalization) interrelated in Ecuadorian private universities?
• RQ2: How does investment in innovative educational technologies influence labor market adaptation, and how are these effects modulated by institutional factors (e.g., cooperation, internationalization)?

These questions guide a structural analysis (SEM-PLS) that seeks to reveal correlation patterns between technology and employability or between internationalization and university–industry cooperation. This contributes to the literature with contextualized evidence and a replicable framework for emerging educational systems.

In the Ecuadorian context, higher education institutions have undergone essential transformations in recent decades, driven by legislative reforms and changes in social and economic demands. The 2010 Organic Law of Higher Education (LOES for its Spanish acronym) and its subsequent reforms established a regulatory framework emphasizing higher education’s quality, relevance, and internationalization. However, Ecuadorian private universities face challenges in terms of financial sustainability, competition, and adaptation to the changing needs of the labor market and society.

Considering the existing literature and global trends in higher education, the following hypotheses have been considered:

H1. 

The extent of university–industry cooperation is positively associated with universities’ internationalization and technological development levels. Linking with companies and global networks improves employability and facilitates sustainable knowledge transfer (Ankrah & Omar, 2015; Etzkowitz & Leydesdorff, 2000).

H2. 

Universities with higher social engagement and environmental sustainability levels tend to have more remarkable sustainable development. Environmental and social practices are essential to develop responsible leaders, a critical aspect in education systems with a focus on SDG 4 (quality education) and SDG 13 (climate action) (UNESCO, 2021).

H3. 

Implementing innovative educational technologies and technological development is positively related to adaptation to the world of work. Investing in digital tools modernizes education and prepares students for digitally transforming industries (Bond et al., 2018).

H4. 

Graduate follow-up actions and university investment in technology positively relate to the labor market adaptation. Graduate tracking is essential to assess the relevance of educational programs (Teichler, 2018). Digital skills improve employability in post-pandemic contexts (Infante-Moro et al., 2021).

H5. 

Universities that promote technology investment tend to have a more significant commitment to sustainable development. Integrating clean technologies into universities promotes leadership in sustainability (Lozano et al., 2013). Institutions that combine innovation and sustainability have a greater social impact (Findler et al., 2019).

These hypotheses are based on the premise that educational quality cannot be understood without considering the interaction between sustainability, technology, internationalization, and cooperation with the productive sector (Leal Filho et al., 2019).

The results of this research will not only contribute to the body of knowledge on higher education but will also provide practical guidance for educational policymakers and university leaders in their pursuit of excellence and relevance in higher education. This considers the growing importance of higher education institutions in Latin America in terms of social significance, sustainability, and adaptation to labor market demands, which is considered a critical factor in educational quality.

1.1. Literature Review

1.1.1. New Realities

Globalization, new technologies, changes in the organization and administration of companies, and even the effects of unexpected phenomena, such as COVID-19, have determined that HEIs have challenges and must adapt to these new realities (Chakradhar et al., 2018; Hafeez et al., 2022; Jin & Horta, 2018). In this context, quality in higher education has been the subject of extensive study and debate; some conceptualizations have been proposed, such as defining quality as purpose, excellence (Nabaho et al., 2019), transformation, or value for money (Jungblut et al., 2015). These perspectives influence how institutions approach quality improvement and adapt to new realities. In the Latin American context, Fernández (2012) highlights the importance of considering social relevance and equity in educational quality assessment processes.

It is essential to consider that every country’s common goal in this region is to educate well-rounded citizens with knowledge and skills for life. However, it is necessary to consider the intercultural context present in the region so that the conceived study programs and internationalization processes must respond to the reality of each system. In this way, the concept of interculturality is important due to its ontological and epistemological connotations. It urgently requires an equitable societal dialogue between cultures (Guilherme & Dietz, 2015).

Regarding the quality of higher education, the adaptability of HEIs to the demands of the labor market has been identified as a critical factor. It is argued (Teichler, 2018) that the employability of graduates should be a central concern in higher education and that both the perspectives of educational institutions and employers should be considered. Finally, labor market requirements vary considerably in Latin America, given that each country faces a different political, social, and economic reality. This underscores the critical importance of coordinating the actions undertaken by educational institutions and the productive system. Through this collaboration, we can effectively bridge the gap between education and the needs of the labor market.

1.1.2. Innovative Educational Practices and Educational Quality

Despite improvements, there is still a gap between the current state of teaching in universities and the rapidly evolving educational landscape, highlighting the need for further progress (Álvarez-Arregui, 2019). As Huong (2022) highlights, the challenge is innovation, and although there have been achievements in this field, there are still limitations and shortcomings (Huong, 2022). This need for innovative methods is accompanied by the challenges of capturing students’ attention and meeting diverse needs (Gayathridevi & Pushpa, 2019).

Innovative approaches, such as curriculum internationalization, virtual mobility, and international research collaborations, redefine university education (Ogden et al., 2020). These methods enrich educational quality by exposing students to diverse global perspectives and developing cross-cultural competencies crucial for professional success in multinational environments (Jones, 2013). Implementing joint degree and dual degree programs between institutions in different countries has proven particularly effective in training graduates with highly internationally competitive profiles (Marinoni & van’t Land, 2024). Moreover, integrating digital technologies into these internationalization initiatives has broadened access to global experiences, democratizing international education and preparing a more comprehensive range of students for careers in an interconnected world (Mittelmeier et al., 2021).

Regarding educational quality, it is defined as the capacity of higher education institutions (HEIs) to balance social, labor, and sustainable demands within their academic mission, aligning with frameworks such as university social responsibility and global adaptation trends (Findler et al., 2019). This holistic perspective emphasizes that quality transcends academic achievements, manifesting in institutions’ ability to adapt to labor market needs (Römgens et al., 2019), cultivating technical skills and soft skills (e.g., resilience, flexibility) to enhance graduate employability, promote sustainable practices (Leal Filho et al., 2019), embed environmental and social sustainability into curricula and institutional operations, align with the UN Sustainable Development Goals (SDGs), foster internationalization (Jones, 2013), strengthen global competencies through student mobility and cross-cultural collaboration, and drive university–industry cooperation (Mascarenhas et al., 2018). Therefore, academic training should be aligned with sector-specific demands to ensure relevance in dynamic economies.

Educational quality is not a unidimensional concept but emerges from the interaction between these variables. For example, sustainability improves institutional reputation and influences social linkage and local economic development (Hernández-Díaz et al., 2021). Similarly, internationalization implies mobility and the integration of global perspectives in curricula, strengthening transversal competencies (Ogden et al., 2020). This comprehensive vision identifies synergies that, when combined, drive educational excellence in dynamic contexts such as Ecuador (Fernández, 2012).

On the other hand, sustainability and green policies play a crucial role in developing the quality of higher education institutions (Gigauri et al., 2022). These institutions are vital in training future leaders who can contribute to the United Nations Sustainable Development Goals by integrating sustainability principles into their strategies and curricula; they can lead by example and influence social change (Žalėnienė & Pereira, 2021). Martins et al. (2014) point out that a framework integrating quality and sustainability in HEIs can further improve their performance (Martins et al., 2014).

Furthermore, the literature on educational quality has addressed curriculum design (as a mechanism to align competencies with labor demands) and institutional management (approaches to social responsibility, sustainability, and technology). However, this study prioritizes the second approach: quality management as a systemic process that integrates multiple dimensions (Römgens et al., 2019; Fernández, 2012). For example, although the curriculum may include sustainability issues, the analysis focuses on how institutional policies and operational practices (such as mobility agreements or collaboration with companies) drive quality, rather than specific pedagogical reforms.

2. Materials and Methods

A quantitative, cross-sectional, descriptive–correlational study was conducted to evaluate how Ecuadorian private universities adapt to the new realities of the global workforce and trends in sustainability and social responsibility. A positivist approach (Hair et al., 2021) was adopted, focusing on objectively measuring relationships between quantifiable variables (e.g., technological development, sustainability, employability) through a descriptive–correlational design. This approach is suitable for examining general patterns within a specific context (Ecuador) and validating a comprehensive framework through rigorous statistical techniques, such as PLS-SEM, ensuring replicability and measurement consistency. However, it is acknowledged that positivism has inherent limitations in capturing subjective or contextual dimensions, such as institutional barriers, perceptions of key actors (rectors, teachers, students), or cultural dynamics that could influence the implementation of sustainable or innovative practices.

The questionnaire was developed based on a literature review. It covered several aspects, including adaptation to the world of work, technological development, sustainability, social responsibility, and university–business cooperation. The dimensions studied are explicitly linked to the concept of educational quality adopted in this research, defined as the capacity of institutions to balance social, labor, and sustainable demands in their formative mission, as aligned with Fernández (2012). The operationalization of the items was based on a rigorous theoretical and empirical analysis process. For each criterion, key studies were identified (see

Table 1. Bibliographic sources for the creation of questions.

Criterion	Bibliographic References
Adaptation to the Labor Market	Römgens et al. (2019)
	Nghia et al. (2020)
Environmental Sustainability	Lozano et al. (2013)
	Filho et al. (2018)
Social Linkage	Benneworth and Jongbloed (2010)
	Mtawa et al. (2016)
University–Industry Cooperation	Ankrah and Omar (2015)
	Mascarenhas et al. (2018)
Sustainable development	Leal Filho et al. (2019)
	Findler et al. (2019)
Employability	Suleman (2018)
	Clarke (2018)
Technological development	Henderson et al. (2017)
	Bond et al. (2018)

) that addressed the dimensions under study.

The item related to labor market adaptability (P1–P4) is inspired by the findings of Römgens et al. (2019), who highlight that the employability of graduates depends not only on technical skills but also on transversal competencies such as resilience, flexibility, and the ability to integrate quickly into the work environment.

Items on technological development (P5–P8) are derived from the studies of Henderson et al. (2017) and Bond et al. (2018), which analyze the impact of digital technologies on academic and professional outcomes. This explicit linkage between the theoretical literature and questionnaire design ensures the instrument’s conceptual validity.

The internationalization items (P9–P12) align with studies that define internationalization as “a process that goes beyond physical mobility, integrating cultural, technological and equity perspectives” (Mittelmeier et al., 2021).

The items on employability (P13–P16) are aligned with Clarke’s (2018) human capital models and the competencies demanded by the market, according to Suleman (2018).

Environmental sustainability (P17–P20) is grounded in the studies of Lozano et al. (2013), who stress the importance of integrating eco-efficient practices (e.g., use of clean energy, emissions reduction) into university strategies to meet the Sustainable Development Goals (SDGs) and prepare responsible leaders.

Social engagement (P21–P24) follows Benneworth and Jongbloed (2010), who define university social responsibility as an essential pillar to strengthen institutional impact in communities.

Similarly, the items on sustainable development (P25–P28) are aligned with the SDGs and community practices described by Leal Filho et al. (2019) and Findler et al. (2019).

The items related to university–industry cooperation (P29–P32) are based on the methodological frameworks of Ankrah and Omar (2015) and Mascarenhas et al. (2018), which highlight the importance of strategic alliances and joint projects.

Table 1 shows the criteria and the bibliographic sources used as references for creating the questions.

A two-stage content validation process was carried out.

• Expert judgment: The expert judgment validation method was used to obtain the informed opinion of individuals with field experience and to validate the measurement instrument. In this process, professional experience, academic training, region of the country, and availability were considered. The process began with meticulously identifying candidates and comprehensive research into their professional and academic backgrounds. Their availability and willingness to participate in the study were then confirmed to ensure the reliability of their contributions. The initial questionnaire was sent to a panel of three highly experienced experts in educational management and university quality. They were selected for their extensive experience as quality assessment officers at various universities, with an average of 15 years of academic and professional experience. A structured review model was applied in which each expert individually evaluated the items’ clarity, relevance, and pertinence, providing written comments and suggestions for improvement.

During the expert judgment validation stage, we received specific written feedback and suggestions that allowed for improvements in the clarity and relevance of the items. Ambiguous formulations were modified to facilitate understanding, redundant questions were eliminated, and items were added to cover relevant aspects not initially considered. All observations were systematized in an analysis matrix, facilitating consensus-based decision-making by the research team. This collaborative approach ensured that all perspectives were considered in the decision-making process. The review with the experts constituted a single round, leading to substantive improvements to the questionnaire’s content and structure, ensuring the rigorous integration of their contributions into the final instrument.

• Pilot test: As mentioned, a pilot test was conducted with three academics from different Ecuadorian universities to ensure the understanding and functionality of the questionnaire. The heterogeneity of the experts (institutions, regions, and disciplinary areas) made it possible to adjust the clarity and relevance of the questionnaire to improve the instrument’s validity before its final application. It allowed us to verify the practical functionality and understanding of the survey, refining the final version of the instrument and improving content validity, as well as its suitability to the context of private universities in Ecuador. Subsequently, a pilot test was conducted with seven academics to assess the instrument’s comprehension and practical functionality in the target context.

An online questionnaire was designed based on a seven-point Likert scale, where one indicated “not at all relevant” and seven indicated “extremely relevant.” The questionnaire consists of 32 questions divided into eight sections. The instrument’s reliability was assessed using Cronbach’s alpha coefficient for each dimension of the questionnaire.

2.1. Sample and Data Collection

The Ecuadorian university system comprises 64 universities, including 28 private and 36 public institutions. The emphasis is placed on private universities, which possess a greater budget and operational freedom to devise innovative educational policies, in contrast to public universities. These institutions face restrictions due to state funding and the regulatory framework, which limit their ability to swiftly adapt to changes in the labor market or adopt sustainable practices. Private institutions can update curricula and establish partnerships more efficiently, making them suitable for studying educational quality and social change. A non-probabilistic convenience sampling strategy was employed, wherein all pertinent private universities were invited to participate in the study. This resulted in a response rate of 67.9% (n = 19). Academic authorities and those responsible for institutional quality units provided the data. These actors have in-depth knowledge of institutional management, quality evaluation criteria, and other variables established in this study.

The confidentiality of the information provided was guaranteed, and informed consent was obtained from the participants.

2.2. Statistical Methodology

The internal consistency of the sub-scales was evaluated by calculating Cronbach’s alpha coefficient. To validate the theoretical structure underlying the research’s criteria, a Confirmatory Factor Analysis (CFA) was performed. An initial descriptive analysis was performed for each survey criterion, calculating the mean and dispersion (standard deviation) and correlation analysis.

Considering the objectives of this paper, structural equation modeling (SEM) is presented as the most appropriate statistical technique. This approach facilitates the concurrent evaluation of relationships between observable and latent variables and the validity of the underlying constructs. Partial least squares-based structural equation modeling (PLS-SEM) was chosen due to the specific characteristics of this study: Predictive purpose: PLS-SEM is more suitable when the focus is on predicting relationships between latent variables (Hair et al., 2021) and not on strict theory validation, which aligns with the exploratory purpose of this study to identify key factors in educational quality. Small sample size: With a sample size of n = 19, PLS-SEM is more robust than Covariance-Based SEM (CB-SEM), as it does not require multivariate normal distributions or large sample sizes (Ringle et al., 2018; Hair & Alamer, 2022). Model complexity: PLS-SEM better handles models with multiple constructs and simultaneous relationships, such as the interactions between sustainability, internationalization, and labor market adaptation evaluated in this study. Furthermore, essence metrics were included to assess the model: R² (variance explained) is an indicator of the predictive power of the endogenous constructs (Hair et al., 2021); f² (effect size) measures the magnitude of the impact of the independent variables; and fit indices CFI, IFI, RMSEA, and SRMR validate the structure of the model.

3. Results

The reliability analysis results indicate that the scale used presents a high internal consistency, with a Cronbach’s alpha coefficient of 0.966. This high value means the instrument is highly reliable, suggesting that the questions included in the questionnaire consistently measure the proposed underlying constructs.

Table 2. Reliability statistics for the questions in the questionnaire.

Estimate	Cronbach’s α	Average Inter-Item Correlation
Point estimate	0.966	0.422
Lower bound (95% CI)	0.937	0.278
Upper bound (95% CI)	0.983	0.553

shows the results.

This section may be divided into sub-headings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

Moreover, the average correlation between the items was 0.422, reinforcing that the items are adequately related but not redundant. These results confirm that the questionnaire is suitable for measuring the dimensions proposed in the research and that the subsequent analyses are statistically valid based on these data.

The theoretical validation (see Table 1) for creating the questions, using Confirmatory Factor Analysis (CFA), verified that the items corresponding to each block of questions place adequate loads on the expected theoretical constructs, as shown in

Table 3. CFA results.

Factor	Relevant Items	Factor Loading (Estimated)	p-Value
Factor 1: Adaptation to the Labor Market	P2; P4	0.727; 0.761	<0.001
Factor 2: Technological Development	P5; P7	0.659; 0.572	<0.001
Factor 3: Internationalization	P9; P11	1.059 *; 1.065 *	<0.001
Factor 4: Graduate Follow-up/Employability	P13; P15	0.842; 0.942	<0.001
Factor 5: Environmental Sustainability	P17; P18	0.916; 0.916	<0.001
Factor 6: Social Linkage	P22; P23	0.736; 0.674	<0.001
Factor 7: Sustainable Development	P26; P28	0.804; 0.576	0.001
Factor 8: University–Industry Cooperation	P31; P32	0.464; 0.471	<0.007

* Note: Values > 1 are explained by the small sample size (n = 19) and are discussed in the text.

. The full values of the CFA are presented in Appendix B.

Although some factor loadings slightly exceed 1 (P9 = 1.059, P11 = 1.065, P12 = 1.038), these values reflect the inherent instability of small samples (n = 19) and do not indicate a serious error in model specification. Previous studies (Chen et al., 2001) have addressed improper solutions, showing that these values can arise as statistical artifacts in complex models, especially when there are high item correlations. To mitigate this, PLS-SEM was chosen, which is more robust for these inconsistencies in data-constrained contexts. All factor loadings were significant (p < 0.05), with high Z-values and narrow confidence intervals, and almost all questions have factor loadings above 0.4, indicating a good representation of the items within the indicators and latent factors. In this sense, based on the factor loadings of the items, connecting the results of the CFA with the critical concepts in the theoretical review and the preparation of the questionnaire, we have the following:

• Factor 1—Adaptation to the Labor Market: Defined mainly by questions P2 and P4, this factor reflects a dimension related to the competencies necessary for successful labor market insertion, highlighting technical skills and the ability to adapt to changing work environments. The high factor loadings suggest that university education emphasizes the mastery of specific skills and aspects such as flexibility, resilience, and the ability to integrate quickly into the labor market.
• Factor 2—Technological Development: Questions P5 and P7 present the highest factor loadings in this factor, suggesting a strong orientation towards managing emerging technologies and using digital tools as a fundamental part of higher education. This factor reflects the central role of technological innovation in teaching processes and preparing students to face the challenges of digital transformation in industry.
• Factor 3—Internationalization: Dominated by questions P9 and P11, this factor captures the relevance that universities give to internationalization in terms of student mobility, exposure to multicultural experiences, and international academic collaborations. The high factor loadings of these items underscore the importance of preparing students for a globalized labor market.
• Factor 4—Graduate Follow-up/Employability: This factor, with the highest loadings on questions Q13 and Q15, indicates the importance of universities in measuring and tracking the success of their graduates in the labor market. The results suggest a strong focus on employability as an indicator of the quality of academic programs.
• Factor 5—Environmental Sustainability: Defined mainly by questions P17 and P18, this factor reflects the university’s concern for instilling environmental awareness in students and preparing future professionals to make decisions aligned with sustainable development. The high factor loadings suggest that this aspect is integral to the curriculum.
• Factor 6—Social Linkage: With questions P22 and P23 showing the highest loadings, this factor highlights the universities’ commitment to social responsibility and active participation in the development of local communities. Higher education institutions aim for their students to acquire an ethical and socially responsible vision in their education.
• Factor 7—Sustainable Development: Questions P26 and P28 stand out in this factor relating to universities’ capacity to train professionals to promote sustainable development in various fields. This approach encompasses not only environmental sustainability but also economic and social sustainability.
• Factor 8—University–Industry Cooperation: Defined mainly by questions P31 and P32, this factor reflects the importance of strategic alliances between universities and the business sector. The results suggest that these collaborations are fundamental to guaranteeing that academic training is aligned with the market’s needs and facilitating students’ labor market insertion.

These results made it possible to form the composite variables corresponding to the criteria proposed in the research, which were used in the subsequent analyses. These new variables, derived from the factor loadings obtained, were used to perform descriptive statistical analysis, calculating the means and standard deviations and the Shapiro–Wilk test to evaluate the normality of each composite variable, enabling a precise description of their behavior and distributions.

Table 4. Descriptive summary and normality test.

	Adaptation to the Labor Market	Environmental Sustainability	Social Linkage	University–Industry Cooperation	Sustainable Development	Employability	Internationalization	Technological Development
Mean	4.333	3.618	4.434	4.447	4.039	4.079	3.961	4.389
Standard Deviation	0.729	0.903	0.711	0.468	0.668	0.804	1.113	0.552
Shapiro–Wilk	0.832	0.942	0.800	0.882	0.917	0.895	0.837	0.911
Shapiro–Wilk p-value	0.003	0.288	0.001	0.024	0.100	0.039	0.004	0.078

presents the descriptive statistical analysis results, which include these measures together with the relative frequencies of the response categories, providing a detailed view of the characteristics of the variables.

Table 5. Spearman’s correlation between the composite variables.

Key Correlations	Rho	p-Value
Technology → Adaptation to the Labor Market	0.742	<0.001
Technology → Employability	0.801	<0.001
Environmental Sustainability → Sustainable Development	0.742	<0.001
Internationalization → Technology	0.787	<0.001
Internationalization → University–Industry Cooperation	0.759	<0.001

presents the principal Spearman correlations between the composite variables; the complete results can be seen in Appendix B. The correlation coefficients reflect the non-parametric relationships between the criteria, highlighting those significant correlations (p < 0.05) to better interpret the constructs’ dependencies.

3.1. Strong Positive Correlations

Correlation analysis revealed significant relationships between the dimensions studied (see

Table A2. Spearman’s correlation between the composite variables.

Variable	Coefficients	Adaptation to the Labor Market	Environmental Sustainability	Social Linkage	University–Industry Cooperation	Sustainable Development	Employability	Internationalization	Technological Development
Adaptation to the Labor Market	Spearman’s Rho	-
	p-value	-
Environmental Sustainability	Spearman’s Rho	0.620	-
	p-value	0.005	-
Social Linkage	Spearman’s Rho	0.343	0.455	-
	p-value	0.151	0.050	-
University–Industry Cooperation	Spearman’s Rho	0.253	0.308	0.354	-
	p-value	0.296	0.199	0.137	-
Sustainable Development	Spearman’s Rho	0.437	0.742	0.460	0.456	-
	p-value	0.061	<0.001	0.047	0.050	-
Employability	Spearman’s Rho	0.506	0.628	0.704	0.646	0.812	-
	p-value	0.027	0.004	<0.001	0.003	<0.001	-
Internationalization	Spearman’s Rho	0.396	0.607	0.530	0.759	0.674	0.763	-
	p-value	0.093	0.006	0.020	<0.001	0.002	<0.001	-
Technological Development	Spearman’s Rho	0.742	0.653	0.582	0.697	0.623	0.801	0.787	-
	p-value	<0.001	0.002	0.009	<0.001	0.004	<0.001	<0.001	-

in Appendix B for full values). Other high correlations include Sustainable development and employability (rho = 0.812, p < 0.001) and Internationalization and employability (rho = 0.763, p < 0.001). These relationships were the basis for constructing the SEM-PLS sub-models (Figure 1), which assessed causality and mediation between variables.

3.2. Moderate Positive Correlations

The analysis of correlations reveals additional relationships with moderate significance: Environmental sustainability and social linkage: A moderate positive correlation (rho = 0.455, p = 0.050) suggests that universities prioritizing environmental practices also exhibit stronger community engagement. University–industry cooperation and sustainable development: This relationship (rho = 0.456, p = 0.050) indicates that partnerships with the productive sector contribute to broader sustainability goals. Adaptation to the labor market and employability: A significant correlation (rho = 0.506, p = 0.027) reinforces the alignment between academic updates and labor market demands. Technological development and sustainable development: This moderate correlation (rho = 0.623, p = 0.004) highlights technology’s role in advancing sustainability practices. These findings provide further evidence of interconnected dimensions influencing educational quality, though their strength is lower than the strong correlations reported in Section 3.1.

3.3. From Correlations to Structural Relationships: Rationale for SEM-PLS Modeling

The results of the correlation analysis reveal a complex and multifaceted panorama of the relationships between the different dimensions of sustainability and university social responsibility. This led us to propose the model shown in Figure 2, which shows the interrelationship between the various criteria. Based on this model, a deeper analysis was carried out using structural equation models to determine whether there is a causal relationship between the variables.

Figure 1 shows the four sub-models used for the SEM-PLS analysis.

Due to the complexity of the interrelationships proposed in the theoretical model, it was decided to divide the structural analysis into sub-models to apply SEM-PLS, considering the results of the CFA (Table 3) and the correlation matrix (Table 5). The selection of sub-models aligns with the hypotheses and theoretical framework outlined in Section 3.1 as follows: sub-model 1 (technology → labor market adaptation) directly tests H3 and H4, leveraging the strong correlation between technological development and employability (rho = 0.801, p < 0.001); sub-model 2 (university–industry cooperation → internationalization) validates H1, supported by the high correlation between these variables (rho = 0.759, p < 0.001); sub-model 3 (sustainability → social linkage) examines H2, grounded in the moderate correlation between environmental practices and community engagement (rho = 0.455, p = 0.050); sub-model 4 (technology → sustainable development) addresses H5, building on the moderate link between digital investment and sustainability (rho = 0.623, p = 0.004).

This approach ensures that each sub-model focuses on a core dimension of the holistic framework, maintaining methodological rigor despite data limitations.

To evaluate the reliability of the sub-models, Jöreskog indices were calculated. This index weighs the factor loadings and provides a more accurate estimate of internal reliability, especially in models with items of different weights. The results of this analysis are detailed in

Table 6. Jöreskog ρ reliability measurements for the sub-models.

Latent	Sub-Model 1	Sub-Model 2	Sub-Model 3	Sub-Model 4
Adaptation to the labor market	0.866
Employability	0.874
Technological development	0.820	0.818		0.827
University–industry cooperation		0.750
Internationalization		0.918
Environmental sustainability			0.896
Social Linkage			0.866
Sustainable development			0.830	0.772

.

In each sub-model, the index values are all greater than 0.7, indicating good internal consistency for these latent variables. The items measuring these constructs are closely related to each other and reliably measure an underlying concept.

Lastly,

Table 7. Measures of SEM sub-model fits.

Index	Sub-Model 1	Sub-Model 2	Sub-Model 3	Sub-Model 4
Comparative Fit Index (CFI)	0.948	0.963	0.988	0.977
Bollen Incremental Fit Index (IFI)	0.953	0.967	0.990	0.980
Bentler–Bonett Non-Normed Fit Index (NNFI)	0.924	0.945	0.982	0.982
Root Mean Square Error of Approximation (RMSEA)	0.108	0.099	0.048	0.107
Standardized Root Mean Square Error (SRMSE)	0.075	0.060	0.062	0.054
R2	0.631	0.562	0.460	0.558
f2 Effect size	0.35	0.25	0.15	0.30

Note: R² and f² follow the conventions of Hair et al. (2021) and Cohen (1988) for evaluating explained variance and effect size in PLS-SEM.

shows some indices of fit measures for evaluating the structural equation models.

The results of the SEM-PLS analysis indicate an adequate fit of the four proposed sub-models, though with nuanced considerations for sub-models 1 and 4. The comparative fit indices (CFI), Bollen incremental fit index (IFI), and Bentler–Bonett non-normalized fit index (NNFI) all exceed the benchmark value of 0.90 for all sub-models, suggesting a strong alignment between the theoretical framework and the observed data. However, the root mean square error of approximation (RMSEA) values for sub-model 1 (RMSEA = 0.108) and sub-model 4 (RMSEA = 0.107) marginally surpass the conventional threshold of 0.10. This discrepancy highlights the sensitivity of RMSEA to model complexity and small sample sizes (n = 19), where it may overestimate misspecification even in well-constructed models. Notably, the standardized root mean square residual (SRMR) remains below 0.08 (sub-model 1: 0.075; sub-model 4: 0.054), reinforcing the adequacy of the models despite the RMSEA values (Ringle et al., 2018).

For sub-models 1 and 4, these marginal RMSEA values do not invalidate the findings but highlight the need for cautious interpretation in the context of Ecuador’s private higher education sector. Studies with similar limitations (e.g., small samples, complex structures) emphasize that RMSEA should be evaluated in conjunction with other indices (CFI, SRMR) and theoretical relevance, rather than relying solely on strict cutoffs. Furthermore, the use of PLS-SEM—a method prioritizing prediction over theory validation—aligns with the study’s exploratory nature and mitigates concerns about strict adherence to RMSEA thresholds. These adjustments ensure that the conclusions remain robust within the study’s constraints, while future research with larger samples could further refine these relationships.

In addition to the fit indices, R² (variance explained) and f² (effect size) were calculated to validate the predictive ability of the sub-models. The results show the following:

Sub-model 1: R² = 0.631 for adaptation to the labor market, indicating that 63.1% of its variance is explained by the predictor variables (technology and graduate follow-up). The effect size (f² = 0.35) suggests that technology moderately impacts this relationship (H3).

Sub-model 2: R² = 0.562 for university–business cooperation, supporting that 56.2% of its variance is attributed to technology and internationalization. The effect of cooperation on technology (f² = 0.25) is moderate (H1).

Sub-model 3: R² = 0.460 for sustainable development indicates that environmental sustainability and social linkage explain 46% of its variance. The effect size (f² = 0.15) suggests that environmental sustainability has a small but significant influence (H2).

Sub-model 4: R² = 0.558 for sustainable development indicates that technology explains 55.8% of the variance. The effect (f² = 0.30) confirms a moderate relationship between technology and sustainability (H5).

These values reinforce the predictive validity of the sub-models, particularly in sub-models 1 and 4, where R² exceeds 0.5 and f² reaches moderate levels, aligning with previous studies in higher education (Findler et al., 2019).

4. Discussion

The results of this study provide valuable insights into the interrelationship between various aspects of educational quality, sustainability, and social responsibility in Ecuadorian private universities.

The correlation between technological development and adaptation to the labor market (Table 5) highlights the importance of investing in educational technology to enhance graduates’ employability. This aligns with the findings of Gayathridevi and Pushpa (2019), who emphasized the need for innovative teaching methods to meet the changing demands of the labor market. It is also reflected in the study by Bond et al. (2018), who conducted a meta-analysis on the impact of educational technology on undergraduate learning. They found that effective technology integration improves learning outcomes and develops critical skills for the modern job market.

The correlation between technological development, graduate follow-up, and employability found in this study aligns with the findings of Infante-Moro et al. (2021), who analyzed the relationship between digital skills and employability in the context of Spanish higher education. They found that technological skills are increasingly in demand by employers and that universities that effectively integrate technology into their programs significantly improve the job prospects of their graduates.

The correlation between sustainable development and environmental sustainability, as well as the positive relationship with social connectedness, suggests that universities prioritizing sustainable practices tend to have a more significant commitment to the community. This supports the arguments of Žalėnienė and Pereira (2021) on the role of HEIs in promoting sustainable development. It also aligns with the results of the study by Findler et al. (2019), who conducted a systematic review of the impacts of universities on sustainable development, finding that institutions with solid environmental sustainability practices tend to have greater community engagement and a more positive social impact.

The relationship between university–industry cooperation and sustainable development indicates that collaboration with the productive sector can foster more sustainable practices in universities. This is consistent with the triple helix approach proposed by Etzkowitz and Leydesdorff (2000), which emphasizes the importance of university–industry–government interaction for innovation and sustainable development.

Regarding the correlation between university–industry cooperation, graduate follow-up, and employability, our results are consistent with the study by Dima et al. (2018) in Romania. These authors found that university–industry collaboration improves graduate employability and fosters innovation and sustainable practices in both sectors. This result also supports the arguments of Teichler (2018), who emphasized the importance of graduate follow-up studies to improve the quality and relevance of higher education. The results suggest that such tracking informs employability and fosters greater collaboration with the business sector.

Considering the relationship between social engagement and sustainable development, the findings are similar to those of Aleixo et al. (2018), who studied implementing sustainability practices in Portuguese HEIs. They found that universities with greater community engagement tend to have a more holistic approach to sustainability, encompassing environmental, social, and economic aspects.

Regarding the relationship between student mobility, internationalization, and employability, the findings are consistent with Van Mol et al. (2021), who analyzed the impact of student mobility on employability in the European context. They found that international experience significantly improves the graduates’ employment prospects. This result suggests that Ecuadorian universities prioritizing internationalization are better positioned to prepare their students for an increasingly globalized labor market. Moreover, this finding aligns with the study by Jones (2013), who argued that internationalization in higher education not only benefits students in terms of personal development but also improves their employability by developing cross-cultural skills and a global perspective.

The results of the SEM-PLS analysis revealed an adequate fit of the four sub-models proposed to explain the relationships between social linkage, environmental sustainability, graduate follow-up and employability, labor adaptation, internationalization, university–industry linkage, technological development, and sustainable development.

• Sub-model 1: Adaptation to the world of work and investment in technology.

The CFI (0.948), IFI (0.953), and NNFI (0.924) are acceptable, indicating a good model fit. The RMSEA index (0.108) suggests a moderate fit but is complemented by an acceptable SRMR index (0.075).

• Key correlations: There is a strong correlation between adaptation to the world of work and technological development, supporting hypothesis 3 that technological investment is directly related to universities’ ability to prepare graduates for the labor market. This suggests that universities that implement innovative educational technologies better prepare their students for the labor market, fostering an optimistic outlook on the future of workforce development. In other words, technological advances improve internal educational processes and equip students with more relevant skills adapted to the evolving work environment.

Moreover, the results of sub-model 1 support hypothesis 4. The correlation between graduate follow-up and adaptation to the world of work is significant. In contrast, the relationship between investment in technology and graduate follow-up is solid and significant. This confirms that graduate tracking and technology investment are key factors in student job matching. Tracking allows universities to adjust their educational programs based on labor market outcomes, while technology investment plays a crucial role in ensuring that students acquire skills that make them competitive.

• Sub-model 2: University–industry cooperation and internationalization.

The fit indices (CFI = 0.963, IFI = 0.967, and NNFI = 0.945) are excellent. The RMSEA (0.099) and SRMR (0.060) indicate an acceptable fit, suggesting that the model is robust.

• Key correlations: A significant correlation is observed between university–industry cooperation and technological development. In addition, internationalization is strongly correlated with both university–industry cooperation and technology investment. These findings confirm hypothesis 1 that increased collaboration between universities and firms significantly impacts their technological development and internationalization programs. In practical terms, institutions that establish partnerships with the private sector improve their technological capabilities and expand their international outreach and networks.

• Sub-model 3: Sustainability and social linkage.

This sub-model presents the best fit indices (CFI = 0.988, IFI = 0.990, NNFI = 0.982, RMSEA = 0.048, and SRMR = 0.062), indicating excellent model fit.

• Key correlations: There is a strong correlation between social bonding and sustainable development and between environmental sustainability and sustainable development. These results underscore universities’ pivotal role in promoting sustainable development. By integrating ecological sustainability practices and strengthening their linkage with society, universities significantly improve their ability to promote sustainable development. The interaction between the university community and its environment and its commitment to sustainable practices drive progress toward long-term sustainability goals, inspiring others to follow suit. Sub-model 3 strongly supports hypothesis 2.

• Sub-model 4: Technology investment and sustainable development.

The fit indices (CFI = 0.977, IFI = 0.980, NNFI = 0.982, RMSEA = 0.107, and SRMR = 0.054) are indicative of a good fit.

• Key correlations: The relationship between technology investment and sustainable development is positive and significant, confirming that technology is a key enabler of sustainable development. These findings suggest that universities that invest in technology enhance their technical capabilities and demonstrate a more substantial commitment to sustainable development. It is important to note that technology investment in universities is about improving education and fostering initiatives that can positively impact institutional and social sustainability. Sub-model 4 supports hypothesis 5.

The fit indices for the four SEM-PLS sub-models show good performance, with the CFI values being especially noteworthy (above 0.94 in all cases), indicating an adequate fit. The IFI and NNFI also exceed the 0.9 threshold, reinforcing the robustness of the model. In addition, the RMSEA and SRMR values are within acceptable limits (less than 0.08 for most sub-models), suggesting that the approximation error and overall model fit are satisfactory. This implies that the evaluated models adequately represent the theoretical relationships between the latent variables, which is an essential step towards validating the overall model despite the limitation in sample size.

Finally, according to the SEM-PLS results, there is an indirect but significant relationship between internationalization and sustainable development, as well as between sustainable development and adaptation to the world of work. In the case of internationalization, this variable indirectly influences sustainable development through its effect on investment in technology. By fostering cooperation and knowledge exchange, internationalization programs impact universities’ technological capacity, promoting more sustainable practices. Similarly, sustainable development indirectly impacts adaptation to the world of work since sustainability practices promote structural changes in organizations and universities, thus preparing graduates to face a work environment with more advanced technological demands.

5. Conclusions

The results of this study underscore the importance of a holistic approach that integrates linkages with university–industry cooperation, technology, sustainability, graduate follow-up, and internationalization, responding to question 1 addressed in this study, which demonstrates that these elements are essential for enhancing the quality of education at private universities in Ecuador. Universities that balance these aspects within a framework of investment in continuous innovation and technology are better positioned to improve the employability of their graduates and adapt to the changing demands of the global labor market; these aspects respond to question 2, detailed in this present research. The resulting recommendations, such as strengthening university–industry cooperation and investing in academic mobility, can be implemented by institutions with the autonomy to reformulate their educational strategies. However, it is recognized that these practices may require adjustments in different institutional contexts, such as public universities, where resource limitations and national regulatory frameworks restrict operational flexibility.

The findings suggest that investment in technological development is crucial to improving universities’ adaptation to the world of work, that environmental sustainability practices are positively associated with better social bonding, and that university–industry cooperation is essential in promoting sustainable development in higher education institutions.

This present study contributes to the literature by offering context-specific evidence from Ecuador’s private higher education sector, emphasizing the interplay between technological innovation, sustainability practices, and labor market adaptation in enhancing institutional quality. By validating a holistic framework tailored to institutions with operational autonomy, this research provides actionable guidelines for private universities in Ecuador to align curricular and strategic initiatives with global trends while addressing local challenges. These insights enhance the scholarly understanding of quality assurance in resource-constrained environments and highlight the importance of institutional flexibility in driving educational transformation. Furthermore, the methodological application of PLS-SEM demonstrates its utility for similar studies in emerging higher education systems, offering a replicable approach for analyzing complex institutional dynamics under data limitations.

5.1. Practical Implications

Regarding implications for private universities, we highlight the importance of prioritizing investment in educational technologies (e.g., virtual learning platforms, artificial intelligence applied to teaching) to close gaps with the labor market. Furthermore, it is vital to strengthen alliances with local and international companies to design programs based on competencies demanded by industry (e.g., digital skills, sustainability). Similarly, it is crucial to integrate sustainability metrics in evaluating institutional performance and curricula, aligning with the Sustainable Development Goals (SDGs).

For governmental agencies regulating universities, it is essential to design policies that encourage collaboration between private and public universities, facilitating the transfer of best practices in sustainability and educational technology. On the other hand, promoting regulatory frameworks that recognize institutional diversity (private, public, technical) and adapting accreditation criteria to the specific capacities of each sector should be an action to consider.

Regarding implications for the private sector and international organizations, supporting university social responsibility initiatives through sponsorships for community projects or scholarships for academic mobility would be substantial. Participating in joint research networks (e.g., sustainability projects) would also be beneficial to align training with global business needs.

Universities should prioritize investment in educational technology and update their academic programs to improve the employability of their graduates. For instance, incorporating more practical, hands-on learning experiences and offering courses in high demand in the job market can significantly enhance students’ employability. Strengthening social outreach programs and community projects is vital as an integral part of sustainability strategies.

5.2. Limitations and Future Research

The response rate of 67.9% (n = 19) limits the generalizability of the findings; however, methodological strategies were adopted to mitigate this limitation: First, PLS-SEM was the most appropriate approach for this study, given its enhanced robustness when dealing with limited sample sizes and its strategic prioritization of prediction over rigorous theoretical validation. This methodological decision aligns with the study’s exploratory nature, emphasizing the identification of patterns and relationships rather than the strict validation of theoretical assumptions. Second, this was complemented with a post hoc power analysis to assess the ability to detect significant effects (Hair et al., 2021), calculating metrics such as R² (variance explained) and f² (effect size). These tools facilitate the interpretation of the practical impact of the observed relationships, even when working with limited samples.

One limitation of this study is that only private universities were included. This decision was based on exploring contexts with greater autonomy to implement innovative policies (see Section 2.1). However, future research to compare public and private universities is promising.

Another limitation is that other institutions of the Ecuadorian higher education system, such as technical and technological institutes, were not considered; a future study holds great potential. Furthermore, future studies should provide evidence from other geographic regions of developing countries.

The quantitative approach does not allow for exploring qualitative dimensions, such as the perceptions of university rectors or teachers on adopting sustainable practices, or institutional factors (e.g., leadership, organizational culture) that could explain differences in the implementation of educational technology. To overcome these limitations, it is recommended that future research adopt a mixed (quantitative–qualitative) approach, combining statistical analysis with semi-structured interviews with key stakeholders or case studies.

Given the findings on the relationship between internationalization and sustainable development, it would be interesting to explore how universities can design and implement internationalization strategies that more effectively support their sustainability objectives. Finally, the long-term impact of sustainable practices on educational quality and graduate employability could be studied.