Cakrawala Pendidikan
Jurnal Ilmiah Pendidikan
Vol. 44 No. 3, October 2025, pp.590-599
https://journal.uny.ac.id/index.php/cp/issue/view/2958
DOI: https://doi.org/10.21831/cp.v44i3.82747

University service quality measurement model in Peru:
Multifactorial validation
Isac Fernando Espinoza-Montes1*, Yuzara Soldevilla-Espinal1,
Martín Romeo Yurivilca-Oscanoa2
1
Universidad Nacional del Centro del Peru, Peru
2
Universidad Continental, Peru
*Corresponding Author: ifespinoza@uncp.edu.pe
ABSTRACT
The quality of university services plays a fundamental role in improving the student experience and shaping
highly competent professionals. In this context, the study aimed to develop and validate a multidimensional
model to comprehensively assess university service quality in Peru, a country facing unique educational
challenges. Using a quantitative and cross-sectional design, data were collected from 1,170 students across
20 universities through the University Service Quality Scale (CEUS), specifically designed for this research.
Exploratory and confirmatory factor analyses identified five key dimensions: Quality of Academic and
Support Processes (QuAcSuPr), Curriculum (Cu), Teaching Quality (TeQu), Library Services (LiSe), and
Educational Sustainability (EdSu). The results showed robust fit indices (IFI=0.988, CFI=0.988,
TLI=0.984, RMSEA=0.040), confirming the model's validity and applicability. The model provides a
practical framework for evaluating and improving university service quality in Peru, while also emphasizing
the importance of integrating educational sustainability as a key dimension to prepare socially responsible
graduates. Future studies could adapt the model to other contexts and explore its application in modalities
such as online learning.
Keywords: university service, quality, confirmatory factor analysis, education, sustainability
Article history
Received:
24 October 2024

Revised:
23 June 2025

Accepted:
08 September 2025

Published:
02 October 2025

Citation (APA Style): Espinoza-Montes, I. F., Soldevilla-Espinal, Y. & Yurivilca-Oscanoa, M. R. (2025).
University service quality measurement model in Peru: Multifactorial validation. Cakrawala Pendidikan:
Jurnal Ilmiah Pendidikan, 44(3), pp. 590-599. DOI: https://doi.org/10.21831/cp.v44i3.82747

INTRODUCTION
Evaluating and improving service quality in higher education is essential for advancing
academic standards and fostering holistic student development. Universities are expected to create
environments that not only provide high-quality education but also support the personal, social,
and professional growth of their students. In Peru, the last few decades have seen significant
transformations in higher education, including shifts in educational offerings, the adoption of
quality-focused policies, and responses to external challenges. These transformations are part of
a global trend, where higher education institutions (HEIs) increasingly focus on enhancing service
quality to remain competitive and relevant in an evolving educational landscape. The enactment
of University Law N° 30220 in 2014 was a turning point for Peruvian higher education. This law
emphasised the importance of accreditation and licensing, introducing a national framework for
ensuring educational quality. Through the National System of Evaluation, Accreditation, and
Certification of Educational Quality (SINEACE), the law required institutions to meet rigorous
standards and emphasized the need to establish quality assurance systems that are sustainable and
adaptable, enabling continuous improvement. The goal was to create a more accountable system
where universities were responsible for consistently maintaining and enhancing the quality of
their services (Rodríguez & Montoro, 2013). As part of this initiative, the National
Superintendence of University Education (SUNEDU) was established to oversee the licensing
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and accreditation of universities. By 2019, SUNEDU had granted licenses to 94 universities and
graduate schools, including 46 public institutions, signalling considerable progress in ensuring a
baseline of quality in the sector (Superintendencia Nacional de Educación Superior, 2020).
However, the rapid expansion of private universities, especially between 1995 and 2014,
introduced new challenges regarding quality and relevance. The increase in the number of private
institutions met growing demand but also led to inconsistencies in educational quality, as many
new universities lacked the infrastructure, faculty, and resources needed to provide a high
standard of education. This expansion raised concerns about the ability of HEIs to deliver relevant
and effective services aligned with academic, professional, and societal needs. Moreover, as the
higher education market became more competitive, there was a growing need for Peruvian
universities to adopt comprehensive quality assurance systems that address the diverse
expectations of students, faculty, and society.
Furthermore, the COVID-19 pandemic brought additional challenges to the higher
education sector, necessitating an abrupt shift to remote learning. This shift exposed several
vulnerabilities in the system, particularly related to infrastructure and digital readiness. For many
students, connectivity issues, limited access to digital devices, and a lack of preparedness for
virtual learning created significant barriers to academic success. These challenges resulted in
increased dropout rates and interruptions in students' academic progress, posing a threat to
educational continuity and equity (Rojas, 2020). In response, the Ministry of Education
implemented oversight measures to support institutions in adapting to remote learning and
maintaining quality under unprecedented circumstances. This situation underscored the need for
a quality assessment model that accounts for the realities of digital and remote learning while
recognising the varied needs of students.
In this evolving educational landscape, it is essential to have a model tailored to the unique
characteristics of Peruvian universities, which differ in terms of resources, mission, and student
demographics. Existing tools for assessing service quality in higher education often fall short of
capturing the holistic view required in the Peruvian context. Most notably, they lack an emphasis
on educational sustainability, a critical component for fostering professionals who are not only
skilled but also capable of contributing to sustainable development. Educational sustainability
refers to the ability of universities to provide an equitable learning environment while embedding
principles of social responsibility, environmental stewardship, and adaptability to future
challenges. By including sustainability as a core aspect of service quality, universities can ensure
they are developing graduates equipped to address the social and environmental issues facing
modern societies. Higher education service quality is a complex, multidimensional construct that
has been explored through various theoretical and empirical lenses. For instance, Zineldin et al.
(2011) introduced the "5Qs" model, identifying five core dimensions of service quality: object
quality (educational outcomes in terms of skills and knowledge), process quality (how services
are delivered), infrastructure quality (availability of resources), interaction quality (quality of
social and communicative exchanges), and environment quality (the educational setting). This
model highlights the diverse facets of service quality that contribute to the overall student
experience and satisfaction. Similarly, Abbas (2020) developed the HEISQUAL approach, which
views service quality as an integrated system encompassing both technical and operational
aspects. Key elements include faculty qualifications, institutional infrastructure, and the
development of student competencies, with a strong emphasis on evaluation from the student’s
perspective. These multidimensional frameworks illustrate the need for comprehensive quality
models that address the full range of attributes impacting service quality in higher education,
particularly in culturally unique and economically diverse contexts like Peru’s.
Likewise, although there is progress in research on the quality of university service,
important gaps persist in the literature that justify the creation of a measurement model for the
Peruvian context. A particularly neglected aspect is educational sustainability, which is vital to
promoting the formation of responsible citizens. The lack of measurement instruments limits the
carrying out of systematic and comparable evaluations between universities, limiting the ability
to generate substantial and sustainable improvements in the University System.

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Building on these perspectives, this study aims to develop and validate a multifactorial
model of university service quality specifically adapted to the Peruvian context. This model not
only assesses traditional elements of service quality, such as curriculum design and faculty
performance, but also incorporates support processes, library resources, and educational
sustainability, components that are increasingly relevant to students and society. The primary
objective is to establish a rigorous and comprehensive tool that enables Peruvian universities to
systematically evaluate service quality, thereby fostering informed decision-making in
institutional policies and practices. Such a tool will help universities identify strengths and areas
for improvement, align their offerings with national and international standards, and enhance their
overall contribution to society.

METHOD
This study employed a descriptive, quantitative approach to develop and validate a model
for assessing university service quality in Peru. The research design centred on exploratory and
confirmatory factor analyses (EFA and CFA) to identify and confirm the core dimensions and
attributes that characterise service quality in Peruvian higher education institutions. The
descriptive methodology enabled the systematic capture of student perceptions across multiple
service dimensions, laying the groundwork for a model that could be applied across varied
institutional contexts.
The study’s final sample included 1,170 Peruvian university students, reflecting 92.3% of
the initial 1,300 respondents. The sample was drawn from 21 universities, encompassing both
public and private institutions, to ensure a diverse and representative cross-section of Peru’s
higher education landscape. Exclusion criteria applied to students in the early stages of their
academic programmes (up to the seventh semester), as their limited exposure to university
services might not accurately reflect the full spectrum of service quality, thus potentially skewing
results.
After rigorous data cleaning to remove incomplete or atypical responses, the final sample
maintained a balanced representation with 438 male (37%) and 732 female (63%) participants.
Institutional representation included students from private (60%) and public (40%) universities,
ensuring a varied perspective on service quality. To ensure the robustness and generalisability of
the findings, the sample size exceeded the recommendation by Kline (2011) of 18 cases per
estimated parameter (n = 65), reinforcing the validity of the proposed model. Participation was
voluntary and anonymous, supporting both data integrity and reliability.
Data was collected using the University Service Quality Scale (CEUS), an instrument
specifically developed for this study. The final CEUS instrument comprises 15 items measuring
five core dimensions of service quality: Quality of Academic and Support Processes (QuAcSuPr),
Curriculum (Cu), Teaching Quality (TeQu), Library Services (LiSe), and Educational
Sustainability (EdSu) (Table 1).
The CEUS was developed following a systematic scale construction approach (Muniz,
2018). The process began with a pool of 70 items generated from a literature review. This pool
was refined through expert judgment and pilot testing to ensure content validity and contextual
relevance. Iterative refinement resulted in a final set of streamlined indicators that accurately
represent the constructs of interest.
Data was collected online between February and July 2021 using a convenience sampling
method. The data analysis followed a two-step statistical procedure. First, an Exploratory Factor
Analysis (EFA) was conducted using SPSS 26 to identify the underlying factor structure.
Subsequently, a Confirmatory Factor Analysis (CFA) was performed using AMOS to validate the
measurement model. Model fit was assessed using standard indices: RMSEA, CFI, TLI, and IFI.
The reliability and convergent validity of the constructs were evaluated using Composite
Reliability (CR) and Average Variance Extracted (AVE).

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Table 1. Conceptual model of university service quality with latent variables and indicators
Latent Variables
Curriculum (Cu)

Teaching Quality
(TeQu)

Library Services
(LiSe)

Educational
Sustainability
(EdSu)

Quality of
Academic and
Support Processes
(QuAcSuPr)

Conceptualisation
“An official document that plans
and organises the contents and
actions to be addressed in the
educational process, aiming to
meet social demands; it
demonstrates the relevance of
contents and activities to
society’s needs” (De La Cruz et
al., 2022, p. 1503).
“A creative process through
which those who teach and those
who learn interact with an object
of knowledge, revealing its own
logic of construction and
mutually transforming each
other” (Morán, 2004, p. 4).
Library services focus on
efficiently providing access to
information through effective
resource management and user
training to meet informational
needs (Merlo Vega, 2000).
The institution’s capacity to
provide an accessible and
equitable learning environment
while fostering sustainable
development through education,
integrating sustainability
principles in teaching, research,
and management (García-Sanchis
et al., 2015; Perez et al., 2009).
Actions that support teaching and
learning, including curriculum
design, student admission and
evaluation, faculty management,
and administrative support,
fostering a conducive learning
environment (Cubaque Mendoza
et al., 2014).

Indicators
pe1: The curriculum develops research
competencies required in the occupational
market, pe2: The curriculum is suitable for the
occupational market, pe3: The curriculum is
adapted to social demand, pe4: The curriculum
improves students' competencies, pe7: The
curriculum contains elective, specific and
specialized subjects appropriate to achieve
vocational training.
do1: Teachers have appropriate academic
profiles; do2: Teachers have relevant
professional experience; do4: Teachers possess
required communication skills; do5: Teaching
methods support competence acquisition; do6:
Adequate academic guidance.
sb1: Access to updated books and journals; sb4:
Suitable reading and study spaces; sb5: Library
hours accommodate students; sb6: Adequate
librarian support.
sed3: Socially responsible management of
environmental resources; sed4: Social
responsibility in academic management; sed5:
Social responsibility in knowledge production;
sed6: Human development through social
responsibility.

di2: University meets student needs; se3:
University web service is precise, quick, and
dynamic; cp2: High-quality face-to-face
instruction; cp3: High-quality learning
evaluation; cp4: High-quality online instruction.

FINDINGS AND DISCUSSION
Findings
Exploratory Factor Analysis (EFA)
The EFA performed in this study commenced with rigorous evaluation criteria to ensure
data suitability for factor analysis. Sample adequacy was confirmed by the Kaiser-Meyer-Olkin
(KMO) measure, yielding a value of 0.937, which indicates a highly acceptable sample adequacy
for factor analysis procedures. Additionally, Bartlett’s test of sphericity, yielding a Chi-square
value of 3821.656 with 231 degrees of freedom (Sig. = 0.000), further confirmed the data's
suitability. This significance level demonstrates that the correlations between variables were
sufficiently large for EFA, supporting the appropriateness of the data for factorial analysis. Based
on Kaiser’s criterion, which suggests retaining factors with eigenvalues greater than 1, four factors
were identified. Additionally, a fifth factor with an eigenvalue of 0.745 was included, justified by
both its theoretical relevance and contribution to the cumulative explained variance. Altogether,
these five factors accounted for 54.531%, 7.993%, 6.998%, 4.643%, and 3.387% of the total
variance, respectively, collectively explaining a substantial proportion of the overall variance in
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service quality perceptions. The EFA identified five key components representing distinct
dimensions within university service quality: Quality of Academic and Support Processes
(QuAcSuPr), Teaching Quality (TeQu), Library Services (LiSe), Curriculum (Cu), and
Educational Sustainability (EdSu).
Table 2. Rotated components matrix for university service quality dimensions
ITEM
54. University’s learning assessment process is high
quality
55. Online learning process is high quality
53. In-person learning process is high quality
48. University’s web service is precise, quick, and
dynamic
41. University understands students’ needs
11. Teachers have relevant professional experience
14. Teaching methods are adequate for competence
acquisition
10. Teachers have the appropriate academic profile
13. Teachers possess required communication skills
15. Academic guidance provided by teachers is
adequate
20. Library hours are suitable for students
19. Library has suitable spaces for reading and other
academic activities
21. Library staff cooperation is adequate
16. Library has up-to-date books and journals
3. Curriculum aligns with social demands
2. Curriculum is suitable for the job market
1. Curriculum develops research skills needed in the
job market
4. Curriculum enhances student competencies
31.University manages its involvement in sustainable
human development responsibly
28. University manages environmental resources
responsibly
29. University manages academic training with social
responsibility
30. University manages knowledge production with
social responsibility

QuAcSuPr
0.843

TeQu
0.242

LiSe
0.092

Cu
0.197

EdSu
0.173

0.819
0.805
0.75

0.162
0.345
0.193

0.114
0.089
0.366

0.224
0.255
0.114

0.258
0.23
0.153

0.621
0.288
0.273

0.271
0.764
0.711

0.249
0.16
0.275

0.216
0.236
0.242

0.38
0.137
0.236

0.298
0.269
0.075

0.689
0.643
0.532

0.318
0.282
0.424

0.236
0.262
0.356

0.166
0.152
0.341

0.179
0.219

0.234
0.26

0.839
0.771

0.148
0.198

0.113
0.211

0.076
0.336
0.249
0.185
0.121

0.164
0.458
0.186
0.308
0.081

0.735
0.613
0.103
0.174
0.254

0.171
0.175
0.792
0.769
0.759

0.348
0.075
0.01
0.078
0.258

0.22
0.314

0.319
0.163

0.106
0.241

0.735
0.227

0.213
0.815

0.423

0.241

0.298

0.061

0.697

0.534

0.229

0.252

0.26

0.577

0.457

0.39

0.251

0.247

0.562

Note: Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalisation;
Rotation converged in 7 iterations.

The rotated component matrix, shown in Table 2, details the factor loadings for each item
across these dimensions. Notably, items within the Quality of Academic and Support Processes
dimension displayed high loadings, such as 0.843 for “University’s learning assessment process
is high quality” and 0.819 for “Online learning process is high quality,” underscoring the
importance of robust assessment and digital infrastructure. The Teaching Quality dimension
showed strong associations with items like “Teachers have relevant professional experience”
(loading of 0.764) and “Teaching methods are adequate for competence acquisition” (0.711),
indicating that instructional expertise and effective pedagogy are critical to perceived service
quality. The Library Services dimension, marked by items like “Library hours are suitable for
students” (0.839) and “Library has up-to-date books and journals” (0.613), emphasizes the need
for accessible and resourceful library services. The Curriculum dimension was similarly
characterized by significant loadings on items such as “Curriculum aligns with social demands”
(0.792) and “Curriculum develops research skills needed in the job market” (0.759), reflecting
the necessity of a curriculum that meets both societal expectations and market requirements.
Finally, Educational Sustainability emerged as a vital dimension, with loadings of 0.815 for
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“University manages its involvement in sustainable human development responsibly” and 0.697
for “University manages environmental resources responsibly,” underscoring the university's role
in fostering sustainable practices and social responsibility.
Confirmatory Factor Analysis (CFA)
Following the EFA, multiple CFA were conducted to verify the fit of the factor solutions.
Five key dimensions influencing service quality perception were identified: Quality of Academic
and Support Processes (QuAcSuPr), Curriculum (Cu), Teaching Quality (TeQu), Library Services
(LiSe), and Educational Sustainability (EdSu). The results confirm the proposed factor structure,
providing a robust tool for evaluating higher education service quality in Peru. The conceptual
model presented evaluates the quality of university service in Peru through five latent variables:
curriculum (Cu), quality of teaching (TeQu), library services (LiSe), educational sustainability
(EdSu) and quality of processes, academic and support (QuAcSuPr).
Each latent variable is represented by a set of indicators measured through factor loadings,
which indicate the strength of the relationship between the indicator and the corresponding
construct: Curriculum (Cu), evaluates the relevance of the study plan in the face of social demands
and of the labor market through three indicators (pe2, pe3, pe4), all with factor loadings equal to
0.78; Teaching quality (TeQu) focuses on critical aspects such as the academic and professional
profile of teachers, teaching methods and academic orientation based on four indicators (d1, d4,
d5, d6) with factor loadings between 0.76 and 0.83; library services (LiSe), measures the quality
of access to bibliographic resources, infrastructure and user service, its indicators (sb4, sb5, sb6)
present factor loadings between 0.86 and 0.89, indicating a high level of relevance for the
construct; educational sustainability (EdSu), addresses the integration of social responsibility and
sustainable development in institutional management through three indicators (sed4, sed5, sed6)
with factor loadings that range between 0.81 and 0.88; quality of academic and support processes
(QuAcSuPr), analyzes the key processes that support teaching and learning, such as the quality
of face-to-face and online instruction with two indicators (cp2, cp3) with factor loadings of 0.90
and 0.86, which shows a strong relationship with the construct. The model also reflects the
correlations between the latent variables, which range from 0.57 to 0.77, indicating moderate to
high relationships. These connections suggest a significant interaction between the constructs,
especially between QuAcSuPr and LiSe (r = 0.77) and between TeQu and EdSu (r = 0.72).
Model fit evaluation
The model fit was assessed using IFI, CFI, TLI, NFI, and RMSEA indices, based on criteria
from Hu and Bentler (1998) and Kline (2011). The incremental indices, IFI (0.988), CFI (0.988),
TLI (0.984), and NFI (0.982), all exceed the 0.95 threshold, indicating a significantly better fit
than a null model and explaining a substantial portion of the observed variance. The RMSEA
value of 0.040 further supports a good model fit, being below the 0.05 threshold recommended
by Hu and Bentler (1998) and Nikkhah et al. (2018). The results from the fit indices indicate an
excellent incremental fit, with the RMSEA confirming acceptable model fit. These findings
underscore the validity and robustness of the model for the context analyzed. Figure 1 illustrates
the measurement model’s structure, featuring five latent variables associated with multiple
observed indicators, as identified through EFA and CFA.
Model parameter evaluation
CFA validated the proposed factor structure and the measurement quality of the latent
constructs as shown in Table 3. Standardised factor loadings, representing the correlation between
each item and its corresponding latent factor, were all above 0.70, indicating strong relationships
between items and their constructs (Hair et al., 2010). This result suggests high reliability and
convergence of items within each factor. CR values for each factor exceeded the 0.70 benchmark,
confirming internal convergence among items within each factor (Fornell & Larcker, 1981).
Additionally, the AVE for each factor surpassed the 0.60 threshold, supporting discriminant
validity among the latent constructs.

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Figure 1. Path Diagram of the University Service Quality Measurement Model in Peru
Note. The covariance between e15 and e16 was included due to the complementary relationship between items sed4
(academic training) and sed5 (production of knowledge), which justifies the residual relationship. The inclusion of
this covariance contributed to improving the overall fit of the model

Table 3. Factor loadings and AVE of latent variables
Latent Variables
Curriculum (Cu)

Teaching Quality (TeQu)

Library Services (LiSe)

Educational Sustainability (EdSu)

Quality of Academic and Support
Processes (QuAcSuPr)

Observed
Variables
p2
p3
p4
AVE - Cu
d1
d4
d5
d6
AVE - TeQu
sb4
sb5
sb6
AVE - LiSe
sed4
sed5
sed6
AVE - EdSu
cp2
cp3
AVE - QuAcPr

Factor
Loadings
0.784
0.781
0.784
0.613
0.762
0.823
0.827
0.832
0.659
0.866
0.857
0.887
0.757
0.81
0.875
0.877
0.730
0.905
0.862
0.781

Variance
(Squared Loadings)
0.615
0.610
0.615
0.581
0.677
0.684
0.692
0.750
0.734
0.787
0.656
0.766
0.769
0.819
0.743

Internal consistency of the validated model
Once the factorial structure of the model was validated through EFA and CFA, the internal
consistency of the identified dimensions was assessed. The results showed a McDonald’s Omega
(Ω) of 0.932 and a Cronbach’s Alpha (α) of 0.931 for the complete scale, indicating excellent
internal consistency. These indices far exceed the 0.70 threshold recommended for reliable
instruments (Kline, 2011) and demonstrate that the items in the model consistently measure the
proposed dimensions. These findings reinforce the validity of the model and its capacity to
evaluate university service quality accurately and reliably in the Peruvian context.
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Discussion
The results of this study underscore the importance of a multifactorial approach to
evaluating service quality in Peruvian universities. Through exploratory and confirmatory factor
analyses, this validated model identifies five essential dimensions, Quality of Academic and
Support Processes (QuAcSuPr), Curriculum (Cu), Teaching Quality (TeQu), Library Services
(LiSe), and Educational Sustainability (EdSu). These dimensions provide a nuanced view of
university service quality and represent a valuable tool for future research and institutional
evaluations. This multifaceted model captures the complexity of service quality in higher
education, aligning with diverse needs and expectations, as recommended by quality models like
SERVQUAL (Sahin et al., 2023; Yusof et al., 2012).
Curriculum and Teaching Quality emerged as core components influencing educational
service quality. A well-designed curriculum aligned with industry demands and societal needs
enhances student satisfaction, loyalty, and academic performance (Chang et al., 2017). This
finding aligns with Morán Oviedo's (2004) observation that the interaction between teachers and
students, facilitated by a relevant curriculum, is fundamental for developing academic and
professional competencies. Likewise, De La Cruz et al. (2022) and Yusof et al. (2011) highlight
the need for curricula to equip students with market-relevant skills, ensuring that higher education
institutions (HEIs) remain responsive to labour demands. Furthermore, teaching quality directly
impacts the overall student experience, reinforcing the need for faculty who are both qualified
and capable of engaging students effectively (Yusof et al., 2012). The inclusion of dimensions
like reliability and assurance in the SERVQUAL model also reinforces the importance of teacher
competence and instructional consistency in educational service quality (Sahin et al., 2023; Alam
& Mezbah-ul-Islam, 2021). This integrated approach contributes to comprehensive curriculum
development, improving educational outcomes and aligning institutional objectives with student
expectations.
Library Services play a critical role in supporting academic quality by providing students
with essential learning resources. The significance of library services aligns with research
indicating that high-quality library support positively impacts student performance and
satisfaction (Alam & Mezbah-ul-Islam, 2021; Hossain & Ahmed, 2013). The adapted
SERVQUAL model has been instrumental in evaluating library quality, focusing on dimensions
such as tangibility, reliability, and responsiveness, which help libraries identify areas for
improvement and maintain service standards (Hossain & Ahmed, 2013). Specific tools like the
Service Performance Control Matrix (SPCM) have been effective in helping academic libraries
pinpoint critical areas for development, ensuring that they meet the evolving needs of students
and faculty alike (Hossain & Ahmed, 2013). The role of libraries extends beyond providing
physical resources, serving as hubs for digital information access, which is increasingly relevant
in the context of remote learning and technological advancements in higher education (Razi-urRahim, 2017).
Educational Sustainability emerged as an equally vital, though often underestimated,
dimension in assessing university service quality. Educational sustainability encompasses the
responsible management of institutional resources and infrastructure to support long-term
educational objectives (Sahin et al., 2023). This study's focus on sustainability aligns with the
work of García-Sanchis et al. (2015), who argue that universities should not only prioritise
academic goals but also contribute to sustainable development, fostering socially responsible
citizenship among students. By integrating sustainability into quality assessments, universities
can ensure that they are cultivating professionals capable of addressing global social and
environmental challenges. The use of the PESTE (political, economic, social, technological, and
environmental) framework in assessing sustainability adds further depth, offering a holistic view
of the factors influencing HEIs (Sahin et al., 2023). This comprehensive approach to educational
sustainability supports institutional resilience, adaptability, and societal relevance, which are
crucial for long-term success in a rapidly changing educational landscape.
Quality of Academic and Support Processes was identified as a critical dimension
encompassing instructional, administrative, and infrastructural aspects essential for creating an
effective educational environment. Academic and administrative support processes contribute
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directly to student satisfaction and academic success, which are key components of perceived
service quality (Chang et al., 2017). This aligns with the findings of Cubaque et al. (2014), who
emphasise the importance of support processes in educational quality. Effective administrative
support enhances the student experience by ensuring efficient resource management, timely
responses to student needs, and the provision of essential learning facilities. Models that integrate
academic and support services, such as the PESTE-SERVQUAL framework, offer valuable
insights into how these elements contribute to overall satisfaction and institutional loyalty (Sahin
et al., 2023). This study’s validated model also benefits from high model fit indices (IFI = 0.988,
CFI = 0.988, TLI = 0.984, NFI = 0.982, RMSEA = 0.040), confirming its robustness and
alignment with established criteria (Hu & Bentler, 1998; Kline, 2011). Compared to previous
studies, this model provides a more comprehensive and contextually relevant perspective, tailored
to the Peruvian higher education sector and addressing the specific needs and expectations of its
student population. The robustness of these fit indices supports the model's potential applicability
across various educational contexts, enhancing its value as a reliable tool for assessing service
quality in universities globally (Sahin et al., 2023; Abbas, 2020).

CONCLUSION
This study developed and validated a multidimensional model for assessing university
service quality in the Peruvian context. The validated model identifies five essential dimensions:
Quality of Academic and Support Processes (QuAcSuPr), Curriculum (Cu), Teaching Quality
(TeQu), Library Services (LiSe), and Educational Sustainability (EdSu). Its inclusion of
Educational Sustainability represents a significant contribution, highlighting the role of
universities in preparing socially responsible graduates. The model serves not only as a robust
diagnostic tool for institutions to identify improvement areas and allocate resources strategically
but also as a mechanism for sector-wide benchmarking. Consequently, it enables data-driven
decision-making to enhance student experience and institutional competitiveness. Future studies
are encouraged to explore the model's applicability in other educational settings and modalities,
as well as its long-term impact on student retention and success.
ACKNOWLEDGMENT
The authors would like to express their sincere gratitude to the National University of
Central Peru for the support and facilities provided to carry out this research. The valuable
resources and academic environment offered by the institution were instrumental in achieving the
objectives of this study.

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