University of Ciego de Ávila Máximo Gómez Báez
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ISSN: 2309-8333
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RNPS: 2411
|13(2) |2025|
This is an Open Access article under the license CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)
Estrategia y Gestión Universitaria EGU
Scientific and technological
research article
How to cite:
Esteban-Amaro, R., Estellés-
Miguel, S., Aparisi-Torrijo, S., & García-
Hurtado, D. (2025). Data science
engineering: curricular management for
responsible production and consumption.
Estrategia y Gestión Universitaria
, 13(2),
e8868.
https://doi.org/10.5281/zenodo.17186649
Received: 24/03/2025
Accepted: 29/04/2025
Published: 26/09/2025
Corresponding author:
dayanisgarcia88@gmail.com
Conflict of interest:
the authors declare
that they have no conflict of interest,
which may have influenced the results
obtained or the proposed interpretations
.
Data science engineering: curricular
management for responsible
production and consumption
Ingeniería de ciencias de datos: gestión
curricular para la producción y el
consumo responsables
Engenharia de ciências de dados: gestão
curricular para a produção e o consumo
responsáveis
Abstract
Introduction: given the rapid pace of technological change,
Data Science engineers will play a crucial role in driving
sustainable innovation. By applying data analysis, machine
learning, and artificial intelligence, they can optimize
resource use, improve energy efficiency, and develop
predictive models to help mitigate social and environmental
risks. Objective: to integrate sustainability into business
education, specifically in the context of the course
“Fundamentals of Business Organization” in the Data Science
degree at the Universitat Politècnica de València (UPV, Spain).
Method: this qualitative and constructivist research is based
on an educational action-research project in the FOE course.
It analyzes transversal competencies and applies the Pattern
Categorization Framework through practical activities,
integrating sustainability into business analysis. A final activity
is proposed that incorporates this tool into the course.
Results: a methodology is designed to introduce “Social and
Environmental Commitment” into the training of Data Science
engineers. Conclusion: students will be able to address social,
environmental, and economic challenges with ethics and
professional responsibility, guided by the Sustainable
Development Goals, particularly the goal of Responsible
Production and Consumption.
Keywords: responsible production and consumption, data
science engineering, pattern categorization framework
Resumen
Introducción: dada la rapidez de los cambios tecnológicos, los
ingenieros en Ciencia de Datos desempeñarán un papel crucial
en el impulso de la innovación sostenible. Al aplicar el análisis
de datos, el aprendizaje automático y la inteligencia artificial,
pueden optimizar el uso de recursos, mejorar la eficiencia
energética y desarrollar modelos predictivos que ayuden a
mitigar riesgos sociales y medioambientales.
Rosa Esteban-Amaro
1
Universitat Politècnica de València
https://orcid.org/0000-0001-7895-403X
roesam@upv.es
España
Sofía Estellés-Miguel
2
Universitat Politècnica de València
https://orcid.org/0000-0001-6119-373X
soesmi@omp.upv.es
España
Sofía Aparisi-Torrijo
3
Universitat Politècnica de València
https://orcid.org/0000-0003-4518-2461
soaptor@omp.upv.es
España
Dayanis García- Hurtado
4
Universidad Internacional de Valencia
https://orcid.org/0000-0001-8363-3898
dayanisgarcia88@gmail.com
España
Estrategia y Gestión Universitaria
|
ISSN
: 2309-8333
|
RNPS:
2411
13(2) | July-December |2025|
| Alonso Contreras Avila | Myrna Delfina López Noriega | Limberth Agael Peraza Pérez |
Antonia Margarita Carrillo Marín |
Objetivo:
integrar la sostenibilidad en la formación empresarial, concretamente
en el contexto de la asignatura “Fundamentos de Organización Empresarial” del
Grado en Ciencia de Datos de la Universitat Politècnica de València (UPV,
España).
Método:
esta investigación cualitativa y constructivista se basa en una
investigación-acción educativa en la asignatura FOE. Se analizan competencias
transversales y se aplica el Marco de Categorización de Patrones mediante
actividades prácticas, integrando la sostenibilidad en el análisis empresarial. Se
propone una actividad final que incorpora esta herramienta en la asignatura.
Resultados:
se diseña una metodología que introduce el “Compromiso Social y
Ambiental” en la formación de ingenieros en Ciencia de Datos.
Conclusión:
los
estudiantes pueden abordar los retos sociales, ambientales y económicos con
ética y responsabilidad profesional, guiados por los Objetivos de Desarrollo
Sostenible, en particular, el de Producción y Consumo Responsables.
Palabras clave:
producción y consumo responsables, ingeniería ciencias de datos,
marco de categorización de patrones
Resumo
Introdução: dada a rapidez das mudanças tecnológicas, os engenheiros em Ciência
de Dados desempenharão um papel crucial no impulso à inovação sustentável. Ao
aplicar análise de dados, aprendizado de máquina e inteligência artificial, eles
podem otimizar o uso de recursos, melhorar a eficiência energética e desenvolver
modelos preditivos que ajudem a mitigar riscos sociais e ambientais. Objetivo:
integrar a sustentabilidade na formação empresarial, especificamente no contexto
da disciplina “Fundamentos de Organização Empresarial” do curso de Ciência de
Dados da Universitat Politècnica de València (UPV, Espanha). Método: esta
pesquisa qualitativa e construtivista baseia-se em uma investigação-ação
educacional na disciplina FOE. São analisadas competências transversais e
aplicado o Marco de Categorização de Padrões por meio de atividades práticas,
integrando a sustentabilidade na análise empresarial. Propõe-se uma atividade
final que incorpora essa ferramenta à disciplina. Resultados: é elaborada uma
metodologia que introduz o “Compromisso Social e Ambiental” na formação de
engenheiros em Ciência de Dados. Conclusão: os estudantes poderão enfrentar
desafios sociais, ambientais e econômicos com ética e responsabilidade
profissional, orientados pelos Objetivos de Desenvolvimento Sustentável, em
especial o de Produção e Consumo Responsáveis.
Palavras-chave:
produção e consumo responsáveis, engenharia de ciência de
dados, estrutura de categorização de padrões
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Introduction
In the current context, universities are called upon to play a crucial role in
training professionals capable of addressing global challenges such as climate change
and social inequality (Bracho Fuenmayor, 2022; Raimo et al., 2024; Díaz-Romero et
al., 2025). The Universitat Politècnica de València (UPV, Spain) has taken on this
challenge by integrating the Sustainable Development Goals (SDGs) into its strategic
plan, promoting an education that not only technically prepares students but also
instills a social and environmental commitment (Boni et al., 2019). In this regard,
engineering education is transitioning toward a competency-based approach that
better responds to current needs (Malhotra et al., 2023). Furthermore, the
incorporation of critical topics such as safety in Industry 4.0 necessitates updating
the curriculum to address these challenges (Qian et al., 2023). Thus, the course
“Fundamentals of Business Organization” (FBO) in the Data Science Degree has
become a key space for integrating sustainability into the training of future
engineers, where human-AI collaboration can contribute to competency-based
curriculum development (Padovano & Cardamone, 2024).
The Data Science Degree at UPV, launched in the 2021/2022 academic year,
prepares students to lead data analysis projects across various fields, including
industrial process improvement, risk analysis, product design, and decision-making
within organizations.
Currently, data forms the foundation of our understanding of the world; from
vehicle movements to temperature monitoring in hospitals, every process requires
data for its operation (Ahadov et al., 2019; Bonfield et al., 2020; Bellucci et al.,
2022). The objective of the Data Science Degree is to train professionals capable of
generating knowledge from data. By learning to design data collection processes in
diverse areas, including industry, graduates will be equipped to lead data analysis
projects aimed at optimizing industrial processes. Moreover, acquiring skills to
process, analyze, and integrate data from multiple sources will enable engineers to
extract valuable information and effectively communicate strategies for informed
decision-making.
During the first semester of the first year of the Data Science degree at UPV,
students take the course “Fundamentals of Business Organization” (FBO), offered by
the Department of Business Organization (DBO) at UPV. This course is positioned at
the beginning of the degree and provides students with a foundation for their entry
into the job market. Understanding the basic principles of business management is
useful for guiding their future careers, as well as delving into the relationship
between business management and data science, particularly focusing on aspects
related to the management of business information systems and data processing for
improvement in direction, management, and business communication.
The FBO course introduces fundamental concepts related to business
organization and economics, addressing the functions of Organizational
Management. It covers key topics such as business theory, organizational structure,
socioeconomic environment analysis, strategic management, human capital
management, as well as market models and competitiveness. Additionally, it
examines various functional areas, including financing and investment systems,
production and operations management, and marketing and commercialization
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systems.
However, technical competence or business knowledge alone is insufficient
for a successful transition to the rapidly evolving and volatile job market (Ahmad et
al., 2022; Bizami et al., 2023; Bahroun et al., 2023). Companies increasingly
emphasize that graduates often struggle to apply the knowledge acquired in real-
world scenarios, arguing that higher education institutions (HEIs) remain focused on
theoretical teaching rather than applied learning and the development of functional
skills (Direito & Freitas, 2024; García-Hurtado et al., 2024). Beyond technical
experience, transversal skills such as adaptability, teamwork, and effective
communication are fundamental to complement data science competencies and
pave the way for long-term professional success (Škare et al., 2022; Villazon
Montalvan et al., 2024). Indeed, a recent survey conducted at UPV (UPV, 2024)
reinforces this perspective, revealing that even recent graduates acknowledge the
importance of transversal skills and often feel unprepared in these areas.
On the other hand, the current climate emergency and other sustainability
crises threaten the well-being and development opportunities of future generations.
Collective human activities have altered Earth's ecosystems to the point where our
very survival is at risk (Curren, 2009). Therefore, it is essential to learn how to do
things differently; to develop the knowledge, skills, values, and attitudes necessary
for making informed decisions and taking both individual and collective actions in
response to local, national, and global urgencies.
It is at the intersection of technical, business, and transversal competencies
that the skill of “Social and Environmental Commitment” is proposed for
incorporation. This implies acting with ethics and professional responsibility in the
face of social, environmental, and economic challenges, guided by democratic
principles, fundamental values, and the Sustainable Development Goals (SDGs). To
foster this skill, a learning process centered on the active participation of students,
in collaboration with their professors and peers, is proposed (Alarifi et al., 2016;
Henri et al., 2017; Gómez-Ríos et al., 2023). Through this approach, students are
encouraged to take an active role as agents of social change by utilizing the Pattern
Categorization Framework. This framework, developed by the authors (Esteban-
Amaro et al., 2024), is considered a key tool for facilitating the transformation of
engineering research ideas into real-world applications, with a focus on
sustainability and the circular economy.
In this study, the cross-disciplinary competencies currently developed in the
FBO course will be initially analyzed. Subsequently, the Pattern Categorization
Framework will be explored as a tool to promote sustainability and the circular
economy. The objective is to integrate sustainability into business education,
specifically within the context of the “Fundamentals of Business Organization”
course of the Data Science degree at the Universitat Politècnica de València (UPV,
Spain).
Methods and materials
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The research adopted an educational action-research methodology (López
Velázquez et al., 2020; Latorre, 2005) with a qualitative and constructivist approach
(Fierro et al., 2000), applied in the FBO course of the Data Science degree at UPV.
This methodology enhances educational processes through cycles of planning,
action, observation, and reflection. Through group activities such as PESTEL, SWOT,
and Value Chain analysis, competencies such as innovation, teamwork,
communication, and social and environmental commitment are developed.
An activity based on the Pattern Categorization Framework (Esteban-Amaro
et al., 2024) was included to address Sustainable Development Goal 12 and integrate
business sustainability, promoting reflective and experiential learning. The program
develops five cross-disciplinary competencies, with FBO particularly promoting
innovation, teamwork, and communication. These competencies were addressed
through creative problem-solving, collaboration, and presentations tailored to the
audience.
During the semester, students analyzed a real company using tools such as
PESTEL (Kotler, 1967), Porter’s Five Forces (Porter, 1989; Bell & Rochford, 2016),
Value Chain (Porter et al., 1985), SWOT (Learned et al., 1969; Bell & Rochford, 2016;
Puyt et al., 2023), BCG Matrix (Hambrick et al., 1982), and Porter’s Strategies, while
also evaluating organizational structure, information systems, finance, and
marketing (Font, 2012).
FBO provides an environment conducive for future engineers to develop
competencies linked to sustainability (Johnston, 2016; Sánchez-Carracedo et al.,
2020). In contrast to the linear model of the value chain (Porter, 1985), the circular
economy model (Ellen MacArthur Foundation, 2015) is introduced, proposing the
funnel metaphor as a more realistic and nuanced vision of sustainability.
Figure 1
Funnel Metaphor
Source: Authors' own elaboration.
Students are presented with the categorization of patterns as a continuous
process toward circularity, rather than a fixed state that can be achieved without
the use of materials or energy. This perspective aligns with a dynamic and
evolutionary approach to sustainability. To illustrate this concept, the three-step
configuration of the funnel metaphor is adopted (see Figure 2), which highlights the
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progressive nature of circularity and the interconnected strategies necessary for its
implementation. This approach has been termed the Pattern Categorization
Framework.
Figure 2
Pattern Categorization Framework
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Source: Authors' own elaboration.
Note. (1) optimization, (2) preservation, and (3) circularity of resources.
Pattern (1) Optimization seeks to enhance resource efficiency by reducing
material consumption and utilizing renewable biomaterials. An efficient value chain
is promoted to reduce emissions and waste, applying strategies such as eco-
efficiency, lean management, cleaner production, and zero waste. Industrial
symbiosis allows waste to be converted into useful inputs, strengthening supply
chains by reducing dependence on new materials. Technologies like eco-design and
eco-efficiency facilitate more sustainable production, with success measured by
resource use efficiency.
Pattern (2) Preservation focuses on extending product lifetimes to increase
economic value and reduce the need for new resources. Strategies such as repair,
reuse, redistribution, and remanufacturing promote durable products and reduce
total cost of ownership, thereby improving quality of life (Ellen MacArthur
Foundation, 2015). This approach transforms the relationship with customers,
prioritizing functionality over ownership, which positions sustainability as a key
factor for reputation and marketing (Accenture Strategy, 2014; Rosa et al., 2019).
Technologies such as modular and standardized design support this transition.
Pattern (3) Circularity aims to minimize value loss by reintegrating waste
into new production chains. Strategies such as recycling, upcycling, and downcycling
require a systemic approach and reverse logistics networks (Ellen MacArthur
Foundation, 2015). The creation of circular networks at both local and global levels
is promoted, supported by technologies for traceability, transparency, design for
disassembly, and recycling. The availability of reliable data is essential to foster
collaboration among stakeholders and achieve more ambitious circular value chains
(Hassiotis, 2020).
Results and discussion
The primary outcome of this work is the implementation of an educational
activity based on the application of the Pattern Categorization Framework, aimed
at identifying and exploring existing opportunities within a company to promote the
Sustainable Development Goals (SDGs), particularly Goal 12: Responsible Production
and Consumption. This activity yields learning outcomes that include the proposal
and design of sustainable initiatives applied to real business contexts. Figure 3
presents a diagram illustrating the development of this activity.
Figure 3
Development of a new activity to promote SDG “Responsible Production and
Consumption”
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Source: Authors' own elaboration.
The activity begins with a theoretical session addressing the fundamental
concepts of sustainability, circular economy, and the Pattern Categorization
Framework, complementing the previously scheduled practices in the course.
Subsequently, students are asked to represent and describe the funnel metaphor as
applied to the selected companies. To do this, they create a map of the flow of
material resources, energy, and labor throughout the value chain, illustrating how
products traverse the various stages of the life cycle: from production, sales, and
distribution to use and, finally, end-of-life management when waste exits the
system.
In the next phase, students must design initiatives aligned with the three
sustainability patterns discussed in the course: resource optimization, product value
preservation, and interconnection of multiple funnels. Resource optimization
focuses on reducing dependence on non-renewable materials, improving efficiency,
and minimizing waste. Students identify ways for the company to utilize its resources
more efficiently, increasing productivity and decreasing environmental impact.
On the other hand, product value preservation aims to slow the flow of
resources by extending product lifetimes through strategies such as reuse, repair,
remanufacturing, and refurbishment. Students analyze how the company can
implement practices that prolong the life of its products, thereby reducing the need
to manufacture new goods. Finally, the interconnection of multiple funnels
emphasizes the creation of collaborative networks in which resources are recycled
and reused within the same or across various value chains, thus strengthening
circularity. Students investigate how the company can integrate into these
collaborative networks to optimize resource use and close the product life cycle.
Each group must develop three initiatives for each category, presenting
them in a structured format that utilizes the attributes defined in the conceptual
card for each pattern. These attributes allow for the precise capture of the essential
characteristics of each action, such as value generation, initiative objectives, the
approach adopted, and the technologies required for implementation.
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This framework provides students with a clear and systematic structure to
design and evaluate proposals that promote sustainability and circularity in the
business realm. The initiatives designed are presented to the other groups, fostering
an exchange of ideas and a comparative analysis of the different proposals. This
dynamic creates a collaborative learning environment that stimulates critical
reflection on the various ways to apply the theoretical concepts discussed in the
course.
Regarding course organization, it currently consists of nine sessions, two of
which are dedicated to team presentations. A restructuring of the schedule is
proposed to distribute the sessions as follows: four sessions for the previously
scheduled activities and three for the new pattern analysis activity. Of these last
three, one session will be devoted to introducing the basic concepts of circular
economy and sustainability, while the other two will allow groups to develop and
analyze their initiatives. Of the two sessions dedicated to presentations, one will be
reserved for showcasing the previously scheduled activities, and the final session will
be exclusively dedicated to presenting the results of this new activity.
The implementation of this methodology reinforces the fundamental role
that universities play in training professionals capable of addressing global
challenges such as climate change and social inequality. In line with the strategic
commitment of the Universitat Politècnica de València (UPV) to integrate the SDGs
into its institutional plan, this initiative in the FBO course aims to promote explicit
social and environmental commitment among future Data Science engineers. This
also responds to the growing demand in the labor market for professionals who, in
addition to technical competencies, possess cross-disciplinary skills and a strong
sense of social responsibility.
The incorporation of the Pattern Categorization Framework, developed by
Esteban-Amaro et al. (2024), represents a significant contribution, as it provides a
structured tool that facilitates the practical application of engineering research
ideas to real business contexts, with a focus on sustainability and the circular
economy. Unlike traditional linear models of the value chain, this activity introduces
the circular economy model from the Ellen MacArthur Foundation (2015) and the
funnel metaphor, providing a progressive view of circularity and interconnected
strategies (optimization, preservation, and circularity).
This approach enables students to understand how to reduce dependence on
non-renewable resources, extend product lifetimes, and reintegrate waste into new
production chains (Hassiotis, 2020). Furthermore, it highlights the importance of
having reliable data to promote collaboration and achieve more ambitious circular
value chains, an area where training in Data Science is essential.
This proposal also addresses frequent criticism of higher education
institutions for their excessive emphasis on theoretical teaching at the expense of
applied learning and the development of functional skills (Direito & Freitas, 2024).
By focusing on practical group activities such as PESTEL analysis, SWOT, Value Chain,
and now the application of the Pattern Categorization Framework, the FBO course
fosters key cross-disciplinary competencies, such as innovation, teamwork,
communication, and social and environmental commitment.
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The relevance of these “soft” skills is repeatedly emphasized by companies,
which perceive a gap in their application by recent graduates. By integrating Social
and Environmental Commitment as an essential competency, guided by the SDGs,
students are prepared to act with ethics and professional responsibility in the face
of current social, environmental, and economic challenges.
Although the study represents a valuable educational intervention, it is
essential to recognize its limitations, such as its initial application in a single course
and the absence of empirical data on its pedagogical impact. However, the
methodological design based on educational action research promotes continuous
improvement of educational processes and fosters reflective and experiential
learning.
Future research could expand this framework to other courses and
educational levels, as well as conduct comparative studies between different
universities to assess its versatility and cross-cutting impact on sustainability
education. Additionally, it will be crucial to carry out systematic evaluations of the
impact of the Pattern Categorization Framework in relation to SDG 12, considering
various organizational and cultural contexts, to consolidate and validate these
findings.
Conclusions
Data science engineers play a key role in promoting sustainable innovation
by using data analysis to optimize decision-making. By analyzing sustainability
education in the Data Science degree at UPV and its alignment with institutional
strategic objectives, it is believed that strengthening sustainability-oriented learning
can contribute to a more ethical professional practice prepared for future
challenges.
This study proposes an educational intervention in the course “Fundamentals
of Business Organization” by incorporating a practical activity that integrates
sustainability into business analysis. This initiative aims to complement students'
social skills with the development of social and environmental commitment,
fostering an ethical approach to social, economic, and ecological challenges, guided
by fundamental values and the Sustainable Development Goals (SDGs), particularly
SDG 12: Responsible Production and Consumption. This objective is addressed
through the Pattern Categorization Framework presented in this work.
Preliminary results indicate that this framework provides a structured tool
that facilitates the practical application of student ideas in real contexts, enhancing
their understanding of the impact of sustainability on quality of life, productivity,
and economic, social, and environmental development. This approach contributes
to training professionals capable of promoting responsible production and
consumption practices in organizational settings.
UPV is actively leading the transition toward more sustainable education,
aligning its programs with the SDGs and promoting innovative methodologies.
Initiatives like the Pattern Categorization Framework not only enhance technical
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training but also raise students' social and environmental awareness, solidifying the
university's role as a transformative agent.
Among the main limitations of the study is its application in a single course,
which restricts its reach to other curricular contexts. Additionally, the lack of
empirical data on the implementation prevents a full assessment of its pedagogical
impact.
For future research, it is recommended to apply this methodological
framework in other courses and educational levels to evaluate its versatility and
cross-cutting reach in sustainability education. Furthermore, comparative studies
with other universities that have adopted similar practices should be conducted to
identify success factors, institutional barriers, and opportunities for improvement.
Finally, it is essential to systematically assess the impact of the Pattern
Categorization Framework as an educational tool, particularly regarding SDG 12, in
various organizational and cultural contexts.
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| Rosa Esteban-Amaro | Sofía Estellés-Miguel | Sofía Aparisi-Torrijo |
Dayanis García- Hurtado |
About the main author
Rosa Esteban
-Amaro: s
he holds a PhD in Business Administration and Management
from the Polytechnic University of Valencia. Her research focuses mainly on
sustainability and the circular economy of value chains. She is a professor in the
Department of Business Organization, where she teaches business management
courses in the Data Science Enginee
ring degree program.
Declaration of author responsibility
Rosa Esteban
-Amaro 1: Conceptualization, Data curation, Formal analysis,
Research, Methodology, Resources, Software, Supervision, Validation/Verification,
Visualization, Writing/original draft and Writing, review and editing.
Sofía Estellés
-Miguel 2: Methodology, Supervision, review and editing.
Sofía Aparisi
-Torrijo 3:
Data curation, Writing/original draft and Writing, review and
editing
.
Dayanis García
- Hurtado 4: Methodology, Supervision, Writing/
original draft and
Writing, review and editing.
Financing:
Special Acknowledgments:
