Complex Thinking as an Enabler of Scientific
Entrepreneurship: Self-Assessment in Guatemalan Higher Education
Pensamiento complejo como habilitador del
emprendimiento científico: autovaloración desde la educación superior en
Guatemala
Dr. Carlos Enrique George-Reyes. Investigador. Tecnológico de Monterrey, Universidad
Politécnica Metropolitana de Hidalgo, México
Dr. Luis Magdiel Oliva-Córdova. Profesor Titular. Investigador. Universidad San Carlos. Guatemala
Received: 2024/11/21 Revised 2024/11/27 Accepted: 2025/03/20 Online First: 2025/04/01 Published: 2025/05/01
How to cite:
George-Reyes, C.E. &
Oliva-Córdova, L.M. (2025). Pensamiento complejo como habilitador del emprendimiento
científico: autovaloración desde la educación superior en Guatemala. [Complex thinking
as an enabler of scientific entrepreneurship: self-assessment from higher
education in Guatemala]. Pixel-Bit,
Revista de Medios y Educación, 73,
Art.5. https://doi.org/10.12795/pixelbit.111533
ABSTRACT
This study analyzes the self-assessment of complex thinking
involved in the development of scientific entrepreneurial skills among students
at the Universidad of San Carlos of Guatemala, who participated in the
extracurricular workshop “Scientific Entrepreneurship with a Vision of the
Future.“ This program specializes in strengthening students‘ prototyping and
communication skills for entrepreneurship projects through an educational
platform. The intervention engaged 127 university students and was implemented
in four methodological stages: identification, design, development, and
dissemination. To measure the students‘ self-assessment of skills, the ecomplexCE
questionnaire was designed, validated, and employed to measure students‘
scientific, critical, systemic, and innovative thinking. This Likert-scale
instrument was applied before and after the workshop. The results showed an
improvement in students‘ perception of entrepreneurial skills between the
pre-test and the post-test, although the differences were not statistically
significant. It was concluded that the development of complex thinking skills
can enhance scientific entrepreneurship and that the design of training
experiences facilitated by learning platforms can positively impact students'
self-assessment of their entrepreneurial skills.
RESUMEN
Este estudio analiza la
autovaloración del pensamiento complejo enfocado en el desarrollo de
habilidades de emprendimiento científico en estudiantes de la Universidad San
Carlos de Guatemala, quienes participaron en el taller extracurricular
Emprendimiento Científico con Visión de Futuro. Este programa se especializa en
fortalecer competencias para la creación de prototipos y la comunicación de
proyectos de emprendimiento mediante una plataforma educativa. La intervención
incluyó a 127 universitarios y se estructuró en cuatro etapas metodológicas:
identificar, idear, inventar e informar. Para evaluar la autovaloración de las
habilidades, se utilizó el cuestionario ecomplexCE
con escala Likert, diseñado y validado para medir el pensamiento científico,
crítico, sistémico e innovador. Este instrumento se aplicó antes y después del
taller. Los resultados mostraron una mejora en la percepción de las habilidades
emprendedoras entre el pretest y el postest, aunque
las diferencias no fueron estadísticamente significativas. Se concluye que el
desarrollo de competencias de pensamiento complejo puede fortalecer el
emprendimiento científico y que el diseño de experiencias formativas,
acompañadas por el uso de plataformas de aprendizaje, puede influir
positivamente en la autovaloración de las habilidades emprendedoras de los
estudiantes.
KEYWORDS· PALABRAS CLAVES
Complex thinking; scientific
entrepreneurship; educational innovation; higher education
Pensamiento complejo; emprendimiento
científico; innovación educativa; educación superior
1. Introduction
Entrepreneurship requires the ability to create value
through initiative, innovation, and problem-solving (Abebe, 2023). It extends
beyond merely creating new companies to developing entrepreneurial skills in
university students, such as critical thinking, creativity, and innovation
(Wurth et al., 2022). Moreover, scientific entrepreneurship involves
applying knowledge to generate innovative solutions (Cardella et al. 2020).
In higher education, the topic of entrepreneurship is
most often associated with the business environment (Piñeiro-Chousa et al.,
2020; Surana et al., 2020). However, it is essential to note that this is a
transversal topic for all disciplines: Students must acquire the necessary
skills during their training to identify opportunities, develop innovative
projects, and solve complex problems (Virzi et al., 2015; Chepurenko et al.,
2019).
Education for entrepreneurship, as a scientific field,
is a continuously expanding area of research (Pastran, 2021). However, the
interconnection between the application of science and entrepreneurial
communities (Lansdtröm et al., 2022) is primarily limited to collaboration
among researchers rather than the training of students (Blankesteijn et al.,
2021). Therefore, it is essential to promote closer integration between
research and teaching in entrepreneurship, facilitating the transfer of
knowledge and skills from the scientific field to the classroom (Duval-Couetil
et al., 2021), which could potentially increase the innovative and
entrepreneurial capacity of students (Filser et al., 2019).
This study focused on the self-assessment of
scientific entrepreneurship skills in Guatemalan university students who
participated in a workshop mediated by a digital platform. The objective was to
evaluate the impact of the workshop on their self-assessment of entrepreneurial
skills and determine the effectiveness of the digital platform in
entrepreneurship training. The research question that guided this study was:
How does an entrepreneurship workshop mediated by a digital platform influence
the self-assessment of entrepreneurial skills of university students in
Guatemala?
2. Theoretical framework
2.1 Entrepreneurship in Higher
Education
Constant changes in professional life necessitate that
university students in training develop skills to navigate working life more
efficiently (Arevalo et al., 2022). One of these skills is related to learning
entrepreneurship and growing entrepreneurial ecosystems in universities; these
ecosystems provide students with the necessary resources to transform
innovative ideas into viable projects (Thomas et al., 2021). Additionally, they
promote collaboration among academia, industry, and government, which facilitates
access to financing and other essential support for entrepreneurial development
(Gicheva & Link, 2022).
Among the
skills that must be developed to learn entrepreneurship are the capacities,
skills, abilities, and aptitudes necessary for ventures, which can be acquired
through training and training processes (Fernández et al., 2022). These include
identifying and seizing opportunities, creativity and innovation, resource
management, and resilience after failure (Wang et al., 2023). In addition, it
is essential to develop interpersonal skills such as teamwork, effective
communication, and leadership (Harrison, 2023), which are vital to
entrepreneurial success (Bahena-Álvarez et al., 2019).
Graduates who lack these skills will face more laboral
challenges because they will be unable to adapt to an ever-changing, highly
competitive market (Boyd, 2022). The lack of entrepreneurial skills can limit
their employment opportunities and hinder their ability to grow within their
professional disciplines (de Oca Rojas et al., 2022). On the contrary, those
who possess these skills will be better equipped to create their own challenges
and make significant contributions to innovation, scientific research, and
society as a whole (Jiang & Hou, 2019).
2.2 Entrepreneurship and
development of scientific skills
Entrepreneurship and the development of scientific
skills are intrinsically linked, as both require the cultivation of creativity,
innovation, and problem-solving skills (Silva et al., 2024). Educational
programs that integrate entrepreneurship into training should promote a
science-based mindset and knowledge development (Fini et al., 2022), as this
facilitates the identification of opportunities and the application of
scientific knowledge in creating innovative solutions (Zhang et al., 2024).
Some studies have shown that entrepreneurial skills
also generate scientific training skills (Baker, 2022); thus, entrepreneurship
can be considered a universal competency with scientific traits that students
should develop during their university education (Baena-Luna et al., 2020).
This competency not only increases the employability of graduates but also
fosters greater adaptability and resilience in a changing work environment
(Cardella et al., 2021).
On the other hand, entrepreneurship training within
scientific disciplines is effective in strengthening transversal skills, such
as communication, teamwork, and leadership, which are essential for success in
both business and academia (Boyle & Dwyer, 2021). Training programs that
combine theory and practice have been successfully implemented in several
universities, yielding positive results in preparing students to address the
complex challenges that emerge in their disciplines (Diez et al., 2022).
In this way, scientific entrepreneurship fosters a
culture of interdisciplinary collaboration, allowing students to work more
effectively with experts from other disciplines to develop joint projects
(Cheng, 2022). This collaboration not only enriches the learning process but
also improves the quality and impact of research results (Azqueta et al., 2023;
Zhang, 2022). Several studies have demonstrated the importance of adopting
collaborative approaches to technology transfer and the commercialization of
scientific innovations (Muñoz & Dimov, 2023).
The integration of entrepreneurship with a scientific
approach not only benefits students and academic institutions but also
significantly impacts the economy and society in general (Cunningham &
Menter, 2021), promoting a mindset that can lead to the emergence of ideas,
prototypes, research projects, the development of innovative solutions to
social problems, and the emergence of innovative ecosystems (Niu et al., 2019).
2.3 Complex thinking: a competency that triggers scientific entrepreneurship
Complex thinking is a transversal competency that can
enrich scientific entrepreneurship (Sułkowski et al., 2020). This
macro-competency comprises the sub-competencies of critical, systemic,
scientific, and innovative thinking in educational environments (Cruz-Sandoval
et al., 2023). Its development equips students to analyze problems from
multiple perspectives, identify patterns, apply the scientific method to
validate hypotheses, and generate evidence-based solutions (Vázquez-Parra et
al., 2025). Additionally, it fosters creativity and innovation, promoting the
generation of ideas and products that address market and societal needs
(Calanchez Urribarri, 2022). In the context of scientific entrepreneurship,
these skills are crucial for transforming knowledge into viable and sustainable
proposals, thereby promoting the development of innovative solutions that
contribute to technological and social progress (López-Caudana et al., 2025).
The incorporation of complex thinking as a trigger for
entrepreneurship prepares students to approach problems holistically,
integrating knowledge from various disciplines to face challenges with a
structured yet flexible vision (Cruz-Sandoval et al., 2023). Not only does it
strengthen their training in their respective areas of study, but it also
equips them with essential skills to lead projects in a globalized,
continuously evolving environment (Farida et al., 2022). In this sense, higher
education should promote teaching strategies that foster the development of
complex thinking, providing methodological and technological tools that enable
students to develop entrepreneurial skills comprehensively (Suárez-Brito et
al., 2024). Entrepreneurial training based on complex thinking enhances the
ability of future professionals to innovate, fostering dynamic entrepreneurial
ecosystems where the interconnection between academia, industry, and society
drives the creation of applied knowledge and the development of sustainable
solutions to contemporary problems (Alvarez-Icaza et al., 2024).
3. Methodology
The study employed a quantitative research approach to
evaluate students’ self-assessments of scientific entrepreneurship skills from
the perspective of complex thinking. Its design was quasi-experimental with
pre- and post-intervention measurements but did not include a control group
(Manterola & Otzen, 2015). The choice of this design was due to the nature
of the intervention, as the workshop was offered as an open-access training
experience for all interested students, making it impossible to assign participants
to experimental and control groups randomly. Additionally, the research aimed
to minimize potential ethical biases by ensuring that all students had the
opportunity to benefit from training in scientific entrepreneurship. Although
the absence of a control group limited the possibility of establishing a direct
causal relationship between the intervention and changes in the students’
self-assessment of skills, the pre-post design made it possible to observe
trends and evaluate the perceived impact of the workshop on developing
entrepreneurial competencies (Althubaiti & Althubaiti, 2024).3.1
Participants
The study employed non-probabilistic convenience
sampling (Novielli et al., 2023; Shi & Cheung, 2024). University students
enrolled in various educational programs at the Faculty of Humanities (FH) and
the School of Secondary Education Teacher Training (EFPEM) at the San Carlos
University of Guatemala were invited to participate in an extracurricular
training experience called “Scientific Entrepreneurship with a Vision of the
Future.“ One hundred twenty-seven university students participated in April
2024. The gender composition was 79.53% women and 20.47% men. Table 1 presents
the participation percentages.
Table
1
Sample composition by educational program
|
Faculty |
Educational
Program |
n |
% |
W |
M |
|
FH |
Bachelor's in Pedagogy and
Educational Research |
37 |
29.13 |
31 |
6 |
|
Bachelor's in Pedagogy and
Curricular Planning |
22 |
17.3 |
19 |
3 |
|
|
EFPEM |
Bachelor's in Mathematics and Physics Teaching |
42 |
33.1 |
34 |
8 |
|
Secondary Education Teaching in Computing and Informatics |
26 |
20.47 |
17 |
9 |
|
|
|
Total |
127 |
100 |
101 (79.53 %) |
26 (20.47 %) |
Source: own elaboration.
3.2 Ethics
All information provided by participants was collected
with their consent (https://comiteinstitucionaletica.tec.mx/es/formatos). The
implementation was regulated and approved by the Tecnologico de Monterrey
Ethics Committee (IFE-2024-001) and supervised by the interdisciplinary
research group R4C, with technical support from the Writing Lab of the
Institute for the Future of Education at Tecnologico de Monterrey, Mexico. All
the information recovered was protected under the criteria established in the “Federal
Law on the Protection of Personal Data in Possession of Private Parties,” which
is in force in Mexico. In addition, to guarantee the participants‘
confidentiality and comply with the ethical research principles, the database
constructed was completely anonymized before its analysis, eliminating any
information that could identify the students.
3.3 Design and implementation
of the workshop
The workshop‘s instructional design had four stages: identify,
innovate, invent, and inform. It was explicitly designed to create innovative
and effective educational experiences. The workshop content was structured in
four main topics that aligned with the stages above. Each topic was linked to
specific practices designed to develop entrepreneurial skills, including
selecting literature in digital environments to identify entrepreneurship
opportunities, ideating viable projects, making low-level prototypes, and
developing effective communication skills. Table 2 shows the syllabus.
Table
2
Organization
of workshop topics
|
Instructional Design Stage |
Topic |
Skills |
Purpose |
|
Identify |
Detecting science-based entrepreneurial opportunities |
Identification of
market needs and innovation opportunities. |
Equip students with the skills to identify opportunities for scientific
entrepreneurship, recognizing areas where science can be applied to solve
real problems. |
|
Ideate |
Formulation of viable ideas that can become a science-based venture. |
Creativity to propose disruptive solutions in existing markets or create
new markets. |
Foster the ability to devise innovative solutions that leverage
scientific knowledge to create sustainable and profitable projects. |
|
Invent |
Development of scientific products and services, along with an outline of
a business model. |
Application of scientific and technological principles in the development
of products or services. Comprehension of a business model. |
Teach students how to transform ideas into tangible products or services
by efficiently utilizing technological and scientific resources. |
|
Inform |
Communication strategies to disseminate entrepreneurship projects. |
Effective communication techniques to promote scientific and
technological products. |
Train students to communicate the value of their scientific endeavors to
potential investors, customers, and partners, thereby maximizing their
chances of success. |
Each topic incorporated open educational resources,
such as documents, videos, and audio files, as well as digital tools and
artificial intelligence applications designed to generate and analyze texts. A
deliverable was requested for each topic. The workshop was organized in four
synchronous sessions and was implemented in April 2024. Figure 1 illustrates
the workshop's structure.
Figure
1
Structure and
interaction of the workshop
Source: Own
elaboration
3.4 Instrument
The employed instrument was a questionnaire called ecomplex-CE,
which aimed to measure the students‘ self-assessment of scientific
entrepreneurship skills from the perspective of complex thinking. Its
Likert-type scale had four response options: 1) strongly disagree, 2) disagree,
3) agree, and 4) strongly agree. Fifteen experts in educational sciences and
entrepreneurship had previously validated it, obtaining an overall Aiken’s V
reliability of 0.8846, which can be considered high (Merino-Soto, 2023). Table
3 shows the instrument’s specifications.
Table 3
Complex thinking dimensions and items of the ecomplex-CE instrument
|
Complex
thinking dimension |
Variables |
Items |
|
Systemic thinking |
Technical knowledge and experience in the field |
E1. I possess the necessary disciplinary knowledge
to participate in a scientific entrepreneurship project. |
|
E2. I have experience collaborating on and leading scientific
entrepreneurship projects. |
||
|
E3. My professional background enables me to make
effective contributions to scientific entrepreneurship projects. |
||
|
Technology trend analysis and market understanding |
E4. I can identify reliable information sources to analyze technological
trends relevant to entrepreneurship. |
|
|
E5. I can identify and understand related technology trends that can effectively address societal
needs. |
||
|
E6. I can select the most suitable technological trend to integrate into
my scientific entrepreneurship project from among various options. |
||
|
Scientific thinking |
Development of products/services based on technology |
E7. My experience in developing innovative services
based on science and technology enables me to make significant contributions
to entrepreneurial projects. |
|
E8. I can lead the development of products based on science and
technology |
||
|
E9. I can generate ideas for scientific
entrepreneurship that address specific challenges in the scientific and
technological fields. |
||
|
Management and protection of intellectual property |
E10. I can distinguish what can be registered as intellectual property
and what cannot. |
|
|
E11. I am familiar with the procedures for
registering intellectual property. |
||
|
E12. I can develop effective strategies to register the intellectual
property of different components of a scientific endeavor |
||
|
Critical thinking |
Agile and lean design methodologies |
E13. I can manage and complete the stages of a
scientific undertaking with short deadlines. |
|
E14. I can coordinate and synchronize team tasks to develop project
stages efficiently. |
||
|
E15. I have experience with project segmentation and
can adapt scientific ventures dynamically to meet emerging needs. |
||
|
Growth Hacking |
E16. I can design resource optimization strategies that increase user
volume, revenue, or project impact with minimal expense and effort. |
|
|
E17. I have experience in applying analytical
methodologies to examine user and market behavior data to develop effective
growth strategies. |
||
|
E18. I can look for out-of-the-ordinary solutions to the most common
challenges |
||
|
Innovative thinking |
User Experience Design |
E19. I can find innovative and unconventional
solutions to the most frequent challenges. |
|
E20. I have a background in integrating user experience principles to
develop innovative products and services in scientific entrepreneurship. |
||
|
Decision-making |
E21. I can identify and execute strategic decisions
that potentiate growth and sustainability in scientific ventures. |
|
|
E22. I can use complex problem-solving techniques to make critical
decisions for the development of scientific and technological projects. |
Cronbach's α was used to analyze the
questionnaire’s reliability, yielding an overall α of 0.9503 in the
pre-test and 0.9525 in the post-test, which indicates excellent reliability
(Luh, 2024). An internal consistency analysis was performed for the pretest.
The results yielded the Pearson correlations shown in Table 4, where a p-value
of 0.000 represents strong positive relationships; the four types of thinking
had coefficients ranging from 0.722 to 0.804. The Cronbach α values ranged
from 0.8272 to 0.8702, reflecting good internal consistency for each scale.
Table
4
Instrument reliability
analysis in the post-test
|
|
|
Systemic
Thinking |
Scientific
Thinking |
Critical
Thinking |
Innovative
Thinking |
Cronbach’s α |
|
Systemic
Thinking |
Pearson correlation |
1 |
.739** |
.735** |
.722** |
.8702 |
|
Sig. (two-tailed) |
|
.000 |
.000 |
.000 |
|
|
|
Scientific Thinking |
Pearson correlation |
.739** |
1 |
.758** |
.746** |
.8410 |
|
Sig. (two-tailed) |
.000 |
|
.000 |
.000 |
|
|
|
Critical
Thinking |
Correlación de Pearson |
.735** |
.758** |
1 |
.804** |
.8272 |
|
Sig. (two-tailed) |
.000 |
.000 |
|
.000 |
|
|
|
Innovative
Thinking |
Correlación de Pearson |
0.722** |
0.746** |
.804** |
1 |
.8600 |
|
Sig. (two-tailed) |
.000 |
.000 |
.000 |
|
|
** The correlation is
significant at the 0.01 level (two-tailed), n = 127.
Figure 2 shows the correlation coefficients with 95% confidence
intervals. Systemic and scientific thinking exhibit a high correlation (r =
.831), indicating that skills in one area are closely linked to those in the
other. Similarly, systemic and creative thinking, as well as systemic and
innovative thinking, exhibit strong correlations (r = .772 and r = .764,
respectively), indicating a significant association between these types of
thinking.
The correlations between scientific thinking and
creative and innovative thinking are also strong (r = .778 and r = .821,
respectively). The strongest correlation is between creative and innovative
thinking (r = .823), reflecting a very close association between the ability to
generate new ideas and the ability to apply them practically. The absence of
p-values for the confidence intervals reaffirms the statistical significance of
all the observed correlations.
Figure
2
Matrix
Plots for Pearson Correlation
Whether the sample distribution complied with the
parameters of normalcy was checked. Table 5 shows no extreme values for
asymmetry (greater than |2.00|) or kurtosis (between 8.00 and 20.00) (Béjar,
1952; Borroni & De Capitani, 2022). It can be inferred that the sample
follows a normal distribution. The standard deviations are relatively low in
both the pre-test and the post-test; however, in the post-test, they decrease
for each type of thinking, suggesting that the workshop may have contributed to
greater homogeneity in the participants' responses.
The asymmetry in the pretest is slightly negative; in
the posttest, it remains negative and becomes more pronounced in critical and innovative
thinking, with a value of 0.43 in both. The asymmetries close to zero suggest a
symmetrical distribution, but the presence of negative asymmetry in both tests
indicates a general trend of the scores towards the upper end of the scale. The
kurtosis in the pre-test varies, but it was close to zero or slightly negative
for most self-assessments. In the post-test, kurtosis in systemic thinking
became more negative (–0.61), while critical thinking showed a positive
kurtosis (0.10) in the post-test, indicating a leptocurtic distribution.
After verifying the normality of the sample, we
compared the total initial and final scores, as well as the scores by
dimension. To do this, various statistical tests were used: 1) the comparison
of outliers to identify if some values deviated significantly from the others
in the sample data, 2) the Student's t-test, to compare the means of two
independent groups and determine if there were statistically significant
differences between them, 3) the paired t-test, to compare the means between
the tests, and 4) the ANOVA test to analyze the differences between the two
test scores to determine if the variations between the pretest and the
post-test were greater than would be expected by chance.
Table 5
Descriptive analysis
of the pre-test and post-test
|
|
Pretest |
Post-test |
||||||
|
Mean |
SD |
Asymmetry |
Kurtosis |
Mean |
SD |
Asymmetry |
Kurtosis |
|
|
Systemic |
2.9536 |
.6495 |
–.33 |
–.10 |
3.0879 |
.5994 |
–.29 |
–.61 |
|
Scientific |
2.6890 |
.7049 |
–.11 |
–.62 |
2.8898 |
.6514 |
–.10 |
–.53 |
|
Critical |
2.9088 |
.6333 |
–.28 |
–.22 |
3.1457 |
.5480 |
–.43 |
.10 |
|
Innovative |
2.6969 |
.7558 |
–.03 |
–.62 |
2.9843 |
.7284 |
–.43 |
–.36 |
4. Analysis and results
The measurement of the difference in the means was carried
out to discover atypical data. Figure 3 shows that the results in both tests
fall within a similar range. However, they are slightly higher in the post-test
in some dimensions, which could indicate a positive effect of the workshop. The
increase in systems thinking was 0.130, which suggests a moderate improvement
in self-assessment. Scientific thinking improved by 0.203, indicating that
participants perceived themselves as having acquired an enhanced ability to
analyze trends related to scientific entrepreneurship.
Critical thinking increased by 0.236, representing a
significant improvement in participants‘ self-assessment of their ability to work
with agile and lean design methodologies. In innovative thinking, the largest
coefficient difference was 0.287, indicating progress in applying creative
ideas in a practical and effective manner.
In Figure 3, the outliers (marked in red) are
distributed throughout both tests, indicating significantly lower scores. This
suggests that those participants may have had difficulty understanding concepts
related to scientific entrepreneurship. There are no unusually high scores that
could represent areas of strength. A review of the database revealed no answers
that could pose a risk to continuing with the analysis.
To determine whether there were significant
differences between the pretest and posttest, a Student's t-test was applied to
the two samples. The results are presented in Table 6, showing a moderately
strong Pearson correlation of 0.8329, indicating a positive correlation between
the scores before and after the workshop; that is, participants who initially
reported a high self-rating tended to maintain this rating. At the same time,
those with lower assessment scores also showed a similar trend in the post-test
(Lugo-Armenta and Pino-Fan, 2022).
Figure 3
Comparison
of means between pre-test and post-test
Tukey's coefficient (Tα = 0.21) was used to evaluate
the magnitude of the differences, yielding a sample difference of 0.02. This
value indicates that the variations observed between the two measurements were
minimal and did not exceed the threshold necessary to be considered
statistically significant. Although the mean of the pretest was lower than that
of the posttest, suggesting an improvement in the participants' self-ratings,
the overall difference of 0.214, with a significance level of p = 0.05,
indicates that this increase may be due to chance rather than an actual effect
of the workshop. That is, although participants perceived a positive impact on
their entrepreneurial skills, statistical analysis does not provide sufficient
evidence to confirm that this improvement was conclusively attributable to the
intervention.
Table
6
T-Test Statistics
|
|
Pretest |
Post-test |
Sample
difference (sd) |
Tukey's Method Tα=0.22 |
|
Average |
2.822409091 |
2.843551797 |
.02 |
Si Tα<sd=Non-significant difference |
|
Variance |
.051027396 |
.04023703 |
|
|
|
Standard deviation |
0.88318 |
.8042 |
|
|
|
Pearson correlation |
.832872721 |
|
|
|
Figure 4 illustrates the paired data, indicating that
the test reveals a significant difference between the mean of the post-test
(3.0744) and the mean of the pre-test (2.1274), with a difference of 0.9470 (p
< 0.001). This result, with a 95% confidence interval and a lower standard
deviation in the paired differences, confirms that the workshop had a
statistically positive effect on the students' self-assessment. The variability
of the standard deviation in the post-test, although increased, did not compromise
the overall effectiveness of the workshop.
Figure 4
Paired
t-test between pretest and post-test
An ANOVA analysis using a fixed-effect factor was
conducted to determine whether the observed differences between the pretest and
posttest were statistically significant. The results, presented in Table 7,
demonstrate a significant impact of the workshop on the students'
self-assessment of their competency in complex thinking. In particular, the
calculated F coefficient of 129.095 was higher than the critical one (Fc =
4.06170), indicating that the variations between the measurements were not
attributable to chance. Likewise, the p-value obtained (1.35405E-33) refuted
the null hypothesis that there were no significant changes in the participants'
self-assessment.
These findings suggest that the intervention
positively impacted the students' perception of their entrepreneurial skills,
strengthening their complex thinking. Although the Student's t-test showed no
significant differences in overall means, the ANOVA analysis confirmed that the
observed variability between pre- and post-workshop measurements was
sufficiently high to suggest that the program influenced participants'
self-assessments. This reinforces the importance of implementing training
strategies that promote complex thinking in scientific entrepreneurship while
also highlighting the need for complementary studies that delve deeper into the
causal relationship between intervention and the development of these
competencies (Sisso et al., 2023).
Table
7
Simple
factor (One-way) ANOVA
|
Summary |
|
|
|
|
|
|
|
Groups |
Count |
Sum |
Average |
Variance |
|
|
|
Pre |
23 |
48.9296 |
2.127373913 |
.0405712 |
|
|
|
Post |
23 |
14.8006 |
.643504348 |
.0015239 |
|
|
|
ANOVA |
|
|
|
|
|
|
|
Source of
variation |
SS |
df |
MS |
F |
P-value |
F critical |
|
Between groups |
25.3214922 |
1 |
25.3214922 |
1203.0607 |
1.35405E-33 |
4.0617 |
|
Within the groups |
0.926092634 |
44 |
.02104756 |
|
|
|
|
Total |
26.24758483 |
45 |
|
|
|
|
In general, the results indicate an improvement in the
participants‘ perception of entrepreneurial skills between the pre-test and the
post-test, supported by an increase in the means of the dimensions evaluated.
Innovative thinking showed the highest increase, with a difference of 0.287
points, followed by critical thinking, which increased by 0.236 points.
Although these differences were not statistically significant, they reflect a
positive trend in the participants‘ self-assessment, which suggests a favorable
effect of the workshop on the development of entrepreneurial skills.
Additionally, the perception of improvement may reflect a subjective progress
in self-assessment, which is relevant to the participants‘ motivation and
willingness to undertake ventures.
These results may be related to the practical nature
of the workshop, which enabled students to apply their knowledge in creating
projects based on real-life situations, thereby strengthening their innovation
and critical thinking skills. Additionally, collaborative interactions during
the identification, ideation, invention, and information stages could have
enhanced their entrepreneurial capabilities.
5. Discussion
In this study, improvements in students‘
entrepreneurial skills were evaluated with a quantitative pretest-posttest
approach. One of the findings shows that their self-assessment of scientific
entrepreneurship skills improved significantly between the two tests. Figure 3
shows an increase in the systems thinking mean from 2.9536 to 3.0879,
suggesting a moderate improvement. According to Piñeiro-Chousa et al. (2020),
the development of entrepreneurial skills is crucial for vocational training.
This result underscores the importance of incorporating educational programs
that focus on entrepreneurial skills, highlighting the positive impact of the
workshop on students.
Additionally, critical thinking skills demonstrated a
noticeable increase in participants' self-worth. Critical thinking increased by
0.236, indicating a significant development in the ability to work with agile
and lean design methodologies. Fernández et al. (2022) state that
entrepreneurial competencies are related to skills that can be acquired through
education and training. In this sense, the improvement in critical thinking
also suggests that the workshop provided effective tools for developing essential
critical skills in scientific entrepreneurship.
In our study, the innovative thinking dimension had
the largest increase between the two tests, indicating significant progress.
The increase of 0.287 indicated an advancement in the practical application of
creative ideas, confirming that the ability for entrepreneurship arises from
the integral interactions of the students with their socialization environment
(Duval-Couetil et al., 2021). This increase in innovative thinking reflects
students' ability to generate creative solutions, evidencing the value of an
educational approach that develops complex thinking.
On the other hand, the positive correlation between
the pre-test and post-test scores was not statistically significant. Pearson's
correlation coefficient was 0.8329, and the difference measured by Student's
t-test was 0.02, which did not reach statistical significance. Although the
correlation was not statistically significant, it suggests a positive trend in
self-assessment of competencies, highlighting the importance of educational
programs focused on entrepreneurship. This finding reinforces the need to
consider the demands of the current work environment in academic training
(Boyle et al., 2021).
However, the ANOVA test results confirmed a
significant effect of the workshop on the students' self-assessment. The F
coefficient was 129.095, substantially higher than the critical Fc value of
4.06170. The p-value was practically zero (1.35405 × 10^33). This finding
supports the effectiveness of the workshop in improving complex thinking
competencies, aligning with the need to integrate digital skills in higher
education.
The nature of each statistical analysis can explain
differences in results between the two tests. The t-test exclusively compares
the means of the pretest and posttest, which may make the test less sensitive
to variations within the group if the sample is small or if there is high
individual variability in the scores. In contrast, ANOVA assesses total
variability and allows us to identify more subtle effects when considering
differences between and within groups, which increases its ability to detect significant
changes. In addition, the impact of the workshop may not be reflected uniformly
among all participants, which could dilute the significance in the
pre-test/post-test analysis but become more evident in ANOVA’s assessment of
variability. Factors such as the duration of the workshop, the initial level of
students‘ competencies, and their predisposition to self-evaluation may have
influenced these results.
However, it is worth noting that the results indicate
a greater homogeneity in the participants' responses following the workshop.
The standard deviation decreased in the post-test for each type of thinking,
suggesting greater uniformity in self-assessments. In this regard, Macías et
al. (2020) note that complex thinking necessitates a systemic and critical
approach grounded in current educational realities. Thus, the reduction in the
variability of the responses indicates that the workshop contributed to a more
uniform and consolidated understanding of the competencies, highlighting its
effectiveness.
Similarly, participants perceived their scientific
entrepreneurship skills more positively after participating in the workshop.
Scientific thinking improved by 0.203, indicating an enhanced ability to
analyze trends related to scientific entrepreneurship. This suggests that
students can develop scientific entrepreneurial skills, highlighting the
potential of educational interventions to foster entrepreneurship (Farida et
al., 2022). Another finding shows that the perception of skills in systems
thinking also improved. The average score for systems thinking increased from
2.9536 in the pre-test to 3.0879 in the post-test. This highlights that the
improvement in systems thinking reflects a better ability to integrate
scientific knowledge into entrepreneurial projects. Therefore, this study’s
findings suggest that the development of complex thinking through the workshop
influenced the participants‘ self-assessment of their entrepreneurial skills.
However, it is essential to analyze how these improvements can be applied in
real-world scientific entrepreneurship. The practical application of these
competencies is reflected in students' ability to identify opportunities in
their areas of expertise, design innovative solutions, and communicate their
ideas effectively. So, methodologies such as challenge-based learning, the use
of digital tools for project ideation, and the application of rapid prototyping
models were key to promoting critical thinking and fostering innovation. These
strategies enabled participants to develop structured approaches to solving
complex problems and, simultaneously, acquire an entrepreneurial mindset
aligned with the realities of the scientific and technological sectors.
The results underscore the importance of educational
programs that develop complex thinking for scientific entrepreneurship.
Improvements in the self-assessment of entrepreneurial skills demonstrate the
positive impact of the workshop. These findings suggest that training
experiences focused on higher skills can enhance scientific entrepreneurship,
contributing to the development of innovative and sustainable solutions.
5. Conclusions
Complex thinking is an essential catalyst in
scientific entrepreneurship, enabling innovators to unravel and address the
intricate webs of contemporary problems with creative and sustainable
solutions. This study aimed to analyze the self-assessment of complex thinking
in the development of scientific entrepreneurship skills among students at the
Universidad San Carlos de Guatemala, utilizing a workshop on scientific
entrepreneurship with a future-oriented vision. The findings show (a) an
improvement in the perception of students' entrepreneurial skills after
completing the workshop, which reflects the potential effectiveness of the
educational program, although these improvements did not reach statistical
significance, and, (b) the development of scientific entrepreneurship is
facilitated through cultivating advanced competencies, such as complex
thinking, and that using well-designed educational experiences, together with
learning platforms, can increase their self-assessment of entrepreneurial
skills.
For educational practice, these results underscore the
importance of integrating didactic strategies that promote complex thinking and
entrepreneurial self-awareness, utilizing digital resources and formative
assessments that motivate students to apply and reflect on their skills in
real-world contexts. Regarding research implications, this study encourages the
adoption of diversified analysis methods, considering broader and more varied
populations (including different student profiles, disciplines, types of
universities, and countries) and exploring different educational contexts to
deepen the understanding of how specific pedagogical interventions impact the
development of scientific entrepreneurship.
Although the results suggest an improvement in the
self-assessment of scientific entrepreneurship skills following participation
in the workshop, it is essential to note that these differences were not
statistically significant. This implies that, although participants perceived a
positive impact on their development of entrepreneurial skills, these changes
may have been influenced by subjective factors or factors external to the
program. Therefore, it is recommended that the findings be interpreted with caution
and that additional studies with more robust experimental designs and larger
sample sizes be conducted to obtain a more accurate assessment of a workshop's
impact.
A significant study limitation is the sample's size
and composition, as it focuses on a single educational institution. This may
lead to findings being influenced by specific contextual factors, such as the
students‘ academic profile, the institutional culture, and the available
resources. This makes it challenging to generalize the results to other
populations with different characteristics.
Likewise, the intervention’s short duration may not
have been long enough to generate a profound and sustained impact on the
development of entrepreneurial skills. Some effects of the workshop may only
become apparent in the medium or long term, which would not be captured in this
evaluation. Additionally, students' self-assessment, used as the primary
measure of assessment, may be subject to perceptual biases and may not
accurately reflect actual changes in their abilities. Another aspect to
consider is that the study did not account for external variables that could
have influenced the results, such as students' prior experiences in
entrepreneurship, their level of motivation, or the potential impact of other
courses or concurrent activities.
However, despite these limitations, this study can
serve as a starting point for the development of new lines of research on the
impact of educational interventions on scientific entrepreneurship. Future
studies could expand the scope by exploring the effectiveness of similar
programs in different types of enterprises and institutions. Additionally, it
would be beneficial to utilize a broader range of assessment instruments,
including both qualitative and quantitative measures, to gain a more comprehensive
understanding of the development of entrepreneurial skills. Likewise, an
expansion of the population studied would enable a more in-depth analysis of
the dynamics of scientific entrepreneurship in various educational settings,
thereby contributing to the development of more robust and generalizable
strategies for its promotion in higher education.
Author contributions
Conceptualization,
C.E.G.R.; data curation, C.E.G.R.; CEGR, and LMOC; acquisition of financing,
C.E.G.R.; research, C.E.G.R. and L.M.O.C.; methodology, C.E.G.R.; project
management, C.E.G.R. and L.M.O.C.; resources, C.E.G.R.; C.E.G.R. and L.M.O.C.;
supervision, C.E.G.R.; validation, C.E.G.R.; visualization, L.M.O.C.;
writing—preparing the original draft, CEGR; writing—proofreading and editing,
C.E.G.R. and L.M.O.C.
Funding
The authors thank Tecnologico de Monterrey for the financial support
provided through the “Challenge-Based Research Funding Program 2023,“ Project ID #IJXT070-23EG99001, entitled “Complex
Thinking Education for All (CTE4A): A Digital Hub and School for Lifelong
Learners.” The authors acknowledge the financial and technical support from the
Writing Lab, Institute for the Future of Education, Tecnologico de Monterrey,
Mexico, in the production of this work.
Data Availability Statement
The data set used in this study is available
at reasonable request to the corresponding author
Ethics approval
Not aplicable
Consent for publication
The author has consented to the publication of the results obtained by
means of the corresponding consent forms.
Conflicts of interest
The author declares that they have no conflict of interest
Rights and permissions
Open Access. This
article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution
and reproduction in any medium or format, as long as you give appropriate
credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made.
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