Psychometric properties and factor structure of a motivation scale for higher education students to graduate and stimulate their entrepreneurship

Abstract

The purpose of this research work is to provide a measurement instrument, through the validation of a proposed scale, to determine the relevant factors that affect the motivation of university students, and that can be used as anticipatory indicators of personal entrepreneurship to achieve educational and work success. To carry it out, exploratory and confirmatory factor analyses have been carried out, based on the principal components’ method, which have been validated through the usual model fit measures in the literature, considering an analysis of reliability and reliability of the measurement instrument. To obtain this purpose, a sample of the university population was selected, through a simple random sampling, considering heterogeneity of courses, subjects, areas, and teaching modalities, of 596 individuals, with a higher percentage of women compared to men, as can be seen from the total number of enrolled in university degrees in the Spanish education system. The scale proved to have good psychometric properties, obtaining good internal consistency and validity. Among the main findings, we can highlight several dimensions for motivation, for instance, emotional self-management and adversity management; and learning strategies, such as: active self-management of study material, study management and self-management of effort, among others. In conclusion, a scale has been validated to determine which dimensions should be considered to promote student motivation as a method of personal entrepreneurship, and which can be used by educational authorities to propose extracurricular training that affects the improvement of students' competence, both in academic and emotional management. The dataset was analyzed using exploratory and confirmatory factor analysis.

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https://doi.org/10.1007/s11365-024-00948-8
1 3
Psychometric properties andfactor structure
ofamotivation scale forhigher education students
tograduate andstimulate their entrepreneurship
ElisaI.Villena‑Martínez1 · JuanJoséRienda‑Gómez1 ·
DoloresLucíaSutil‑Martín2 · FernandoE.García‑Muiña3
Accepted: 22 January 2024
© The Author(s) 2024
Abstract
The purpose of this research work is to provide a measurement instrument, through
the validation of a proposed scale, to determine the relevant factors that affect the
motivation of university students, and that can be used as anticipatory indicators of
personal entrepreneurship to achieve educational and work success. To carry it out,
exploratory and confirmatory factor analyses have been carried out, based on the
principal components’ method, which have been validated through the usual model
fit measures in the literature, considering an analysis of reliability and reliability
of the measurement instrument. To obtain this purpose, a sample of the university
population was selected, through a simple random sampling, considering heteroge-
neity of courses, subjects, areas, and teaching modalities, of 596 individuals, with a
higher percentage of women compared to men, as can be seen from the total number
of enrolled in university degrees in the Spanish education system. The scale proved
to have good psychometric properties, obtaining good internal consistency and
validity. Among the main findings, we can highlight several dimensions for motiva-
tion, for instance, emotional self-management and adversity management; and learn-
ing strategies, such as: active self-management of study material, study manage-
ment and self-management of effort, among others. In conclusion, a scale has been
validated to determine which dimensions should be considered to promote student
motivation as a method of personal entrepreneurship, and which can be used by edu-
cational authorities to propose extracurricular training that affects the improvement
of students’ competence, both in academic and emotional management. The dataset
was analyzed using exploratory and confirmatory factor analysis.
Keywords Motivation· Learning strategies· Exploratory and confirmatory factor
analysis· Internal validity and consistency· Reliability· Higher education·
Entrepreneurship
Presented at 2nd BENI Conference 2023, held online on 29th and 30th June 2023
Extended author information available on the last page of the article
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Introduction
The effectiveness and efficiency of the use of public resources is a topic of
great current academic interest (Jimenez, 2019). Considering university educa-
tion, both in Spain and in most continental European countries, higher education
institutions have a public character, funded mainly by states, through citizens’
taxes, and with a modest share of training costs by students, around 20% of the
real cost (Oroval & Escardíbul, 2011; Sempere & Calatayud, 2022; Oroval &
Escardíbul, 2011). In this context, early dropout and the excessive number of
years that university students take to graduate only add to the waste of states’
financial resources.
According to the "Análisis del abandono de los estudiantes de Grado en las uni-
versidades presenciales en España" (translation into “Analysis of the dropout rate
of undergraduate students in face-to-face universities in Spain”) (Mellizo-Soto,
2022), 11% of students leave the university system without completing their stud-
ies, and 6% do so during the first year. Among the underlying factors and vari-
ables that can be identified in the study, we can highlight those that affect the stu-
dent level, such as academic performance; those at degree level, exogenous to the
students, such as tuition fees and family income; and, to a lesser extent, those at
university level. It can be observed that academic performance in the first year is
intrinsically related to the permanence in studies. Moreover, the socio-economic
level of families is correlated with dropout when academic results are not good
(Herbaut, 2020; Troiano etal., 2021).
The adaptation of university study programs according to the Bologna Plan,
where the structures of teaching in terms of training and assessment must be car-
ried out through the acquisition of competencies, skills, and abilities, meant a
paradigm shift in higher education, focusing learning on the student and his or
her ability to adapt to his or her environment (Montero Curiel, 2010). These new
university programs incorporated a wide range of competencies and skills for the
training of future professionals, as final goals, but they forgot to incorporate skills
and tools to enhance academic success during their training. (Domenech etal.,
2019) show how self-efficacy in education and emotional competence are vari-
ables of relevance for academic success, skills that are not fostered in higher edu-
cation. Adequate management, promotion and training of socio-emotional skills
leads to an increase in students’ motivation to meet their achievement indicators
(Villena Martínez etal.,2023).
Motivation and an adequate learning strategy are the basis for entrepreneurial
university students to graduate from their studies, joining the labor market
and, in this way, returning to society through their contribution to the public
system the investment made in their training, fulfilling one of SDG4: Social-
emotional learning goals "The student is able, through participatory methods,
to motivate and empower others to demand and use educational opportunities",
and Cognitive Learning Objectives "The learner conceives education as a public
good, a common good, a fundamental human right and a basis for ensuring that
other rights are fulfilled". The success rate in graduation is a good indicator of
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the satisfactory use of public resources. As for the suitability rate in Spain, this
stands at 38.4%, indicating the percentage of students who finish their studies
within the established theoretical time, with an average duration of 4.9 years, and
rising in Engineering degrees to 5.5 years (MU1,2022).
According to the Oposita Test portal and the Netquest Studies Service (Oposita
Test, 2023), 74% of people residing in Spain between the ages of 18 and 55 consider
"that being a civil servant allows you to have a better quality of life", and 51% are
considering taking the exam to be. These results show that there are few economic
incentives on the part of public administrations, little personal motivation of
students to undertake, due to the insufficient promotion of entrepreneurial talent in
educational institutions, and the risk aversion of individuals. Kan and Tsai (2006)
find that the degree of risk aversion has a negative impact on the decision to become
self-employed. That less risk averse individuals becomes entrepreneurs. Despite the
existence of a growing number of studies that relate risk aversion to entrepreneurship
with personality traits (extroversion, introversion, neuroticism, etc.) (Sahinidis
etal.,2020; Tsaknis etal.,2022), few have addressed the study that entrepreneurial
competence can be trained to achieve higher levels of entrepreneurial talent (Fairlie
& Holleran, 2012). Other studies have shown that individuals’ risk aversion to
entrepreneurship is a mediating variable for the total effect of personality traits on
entrepreneurial intent (Ahmed etal., 2022).
Entrepreneurship can be interpreted from several perspectives (Diandra & Azmy,
2020). Entrepreneurship as a discipline (Croci Cassidy, 2016). Entrepreneurship
as economic development (Hessels & Naudé, 2019). Entrepreneurship as a search
for opportunities (He et al., 2020). Entrepreneurship as a skill and competence
associated with talent (Nururly etal.,2018). Entrepreneurship as education to trans-
form society (Ratten & Usmanij, 2020). Entrepreneurship as a skill and personal
competence (Kyguolienė & Švipas, 2019). In our research, we address the study of
motivation, through the development and validation of a questionnaire, as a socio-
emotional skill that allows us to address the positive management of adversity and
emotional management, as basic traits to foster talent and entrepreneurial spirit,
aligned with (Kyguolienė & Švipas, 2019) personal skills.
The main goal of this research is to propose and validate a new motivation sur-
vey, with the questions adapted to learning strategies and motivation to remain in
university studies, as well as the inclusion of some additional questions about their
learning tools on the study of the psychometric properties of the scale proposed in
(Zurita Ortega etal.,2019). To achieve the objective of validation of the proposed
scale, exploratory factor analysis and confirmatory factor analysis techniques have
been used, as well as measures of consistency and internal validity (Gill etal., 2022;
Martínez-Líbano etal., 2022; Vucaj, 2022). Considering the relationship between
the design of questionnaires in the educational field and their validation using factor
analysis tools, the study by Schreiber etal. (2006) (in Moguerza etal., 2017) should
be highlighted.
1 MU: Ministry of Universities.
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The main contribution of this research is to obtain a scale of measurement that
allows higher education professionals to determine which most relevant aspects
should be enhanced to students to achieve success in the completion of studies, and
to offer an assessment of the socio-emotional skills of students that allows them to
carry out training activities for emotional management and adversity management.
Key aspects to promote talent and entrepreneurship. In addition, the results of the
scale scale allow educational institutions to address the training of talent through
motivation by increasing activities conducive to the management of emotions and
improving achievement indicators. Curricular and extracurricular training that
enhances students’ socio-emotional skills to improve their motivation will result in a
decrease in dropout rates and an increase in success rates.
This paper is organized as follows: "Literature review" section presents the lit-
erature review; "Methodology and Materials" section presents Methodology and
Materials, including the description of the sample, the sampling procedure, a brief
description of the instrument structure and, finally, the analysis techniques used;
"Results" section describes the implementation of the methodology and the most
salient results. "Discussion and conclusions." section presents discussion, limita-
tions and future research and the main conclusions.
Literature review
Personal entrepreneurial skills andeducation
It has been proven in the literature that entrepreneurs are made, not born (Paul Dana,
2001). Becoming an entrepreneur is a training process that begins, in many cases,
at university. The creation of new academic study programs has incorporated the
promotion and training of some of the skills for entrepreneurship (Bauman & Lucy,
2021), such as creativity, problem-solving, and risk management, but competen-
cies on emotional management (Aly etal., 2021; Al-Tekreeti etal., 2024), or adver-
sity management (Shepherd & Williams, 2020; Osiyevskyy etal., 2023) have not
been incorporated. Academic programs focus on three types of skills (Gieure etal.,
2020): technical skills, such as oral and written communication and organization;
business management, as decision-making and marketing skills; and, personal skills,
such as risk management and tenacity, However, the programs do not incorporate
skills to foster entrepreneurship and personal development.
(Depositario etal. (2011) developed a questionnaire, called PEC (Personal Entre-
preneurial Competence), to measure these competencies. Alusen (2016) conducted
research on personal entrepreneurship competencies among CEOs of companies.
(Reyes etal., 2018) conducted research on personal entrepreneurship competencies
in students. Entrepreneurial competence, defined by (Driessen & Zwart, 2006), con-
sists of knowledge, motivation, ability and personal characteristics. Alusen (2016)
defined personal entrepreneurial competencies as the set of qualities and personal-
ity traits that make individuals more or less likely to become entrepreneurs, or at
least predict their intention to become entrepreneurs. One of the most used classi-
fications to classify personal entrepreneurial competencies are those developed by
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Management System International (MSI) in 1989 (in (Kyguolienė & Švipas, 2019)):
opportunity seeking, persistence, commitment to work contract, risk-taking, demand
for efficiency and quality, goal seeking, information seeking, systematic planning
and monitoring, persuasion and networking, self-confidence. These competencies
are listed in (Depositario etal., 2011).
Entrepreneurship education can be defined as the process of practical application
of knowledge, attitudes, skills, and competencies, not only of starting a new
business, but fostering a learning environment that promotes personality traits
and entrepreneurial behaviors, such as becoming a creative and independent
thinker, taking risks, and taking responsibility (Gautam & Singh, 2015). Ndofirepi
(2020) has found evidence on relationship between entrepreneurial education and
entrepreneurial intentions through psychological traits, as risk-taking, and need
for achievement. (Saif & Ghania, 2020) show the relationship between the need
for achievement for entrepreneurship and motivation for achievement. In this way,
the main contribution of this research work is to propose a scale as an instrument
whose purpose is to determine the factors that affect the motivation for achievement
to finish university studies, and that serves as an indicator to establish the need for
achievement in entrepreneurship.
Motivation andlearning strategies
Academic success in higher educatcades at the end of these studies are the most
important goals that university students set themselves in order to be able to
integrate into the labor market in the best conditions. Identifying these factors
that affect academic achievement has motivated much of the research in educa-
tional psychology (Mega etal., 2014). Most research has focused on the role that
motivation, learning strategies, and emotional competence have on learning and
academic performance (Pekrun etal., 2002, 2011). Most of this research has been
approached from different analysis techniques, correlation analysis (Ravyse etal.,
2017), qualitative analysis (Pekrun etal., 2002), experimental classroom approaches
(Kramarski et al., 2002), structural equation models (Tokan & Imakulata, 2019;
Hayat etal., 2020).
In the literature we can find different theories about motivation that could be
applied in the learning process: intrinsic and extrinsic motivation theory (Ryan
& Deci, 2000); self-determination theory (Ryan & Deci, 2020), the ARCS model
(Keller, 1987), social cognitive theory (Bandura, 1989) and expectancy theory (Van
Eerde & Thierry, 1996). Currently, the most accepted theories in the literature are
based on the consideration of motivation as a set of intrinsic and extrinsic factors,
and the theory of self-determination, as a broader concept, which emanates from
the previous theory, and which includes personality traits, autonomy of individuals,
their psychological wellness, and all issues of direct relevance to educational
settings. Intrinsic factors are related to the cognitive and affective structure of the
student. Regarding extrinsic factors, they refer to the structure of teachers and the
performance of their educational work (Buzdar et al., 2017; Sánchez & Vargas,
2016; Sivrikaya, 2019).
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Regarding learning strategies, (Weinstein etal.,2000) define them as "the differ-
ent combinations of activities students use while learning, with greater variability
over time or as any behaviors that facilitate the acquisition, understanding or later
transfer of knowledge and skills". Pintrich etal. (1991) grouped these learning strat-
egies into three basic aspects: cognitive, resource management and metacognitive
strategies, and into 9 strategies: rehearsal, elaboration, organization, critical thinking,
metacognitive self-regulation, time and study environment, effort regulation, peer
learning and help seeking, proposing, and validating an instrumental scale of meas-
urement called MSLQ (Motivation and Learning Strategies Questionnaire). Subse-
quent research has linked the concepts of motivation and learning strategies by ana-
lyzing the different indicators that determine academic achievement (Loyens etal.,
2008). Through meta-analysis research, Credé and Phillips (2011) determine the
main learning strategies detected in these studies, such as self-efficacy, effort man-
agement, study management and self-regulation. At present, most research are based
on the scale of scores collected according to the MSLQ scale (Pintrich etal., 1991)
(see Rashid and Rana, (2019)). Student self-regulation, as a characteristic of quality
learning, will be decisive in ensuring high academic performance. Numerous stud-
ies confirm that self-regulation is a very relevant factor in current learning theories
(Panadero, 2017; Zimmerman, 2015). Furthermore, others affirm that intrinsic
motivation is one of the essential elements for improving academic performance
at university (Buzdar etal., 2017); Theobald, 2021)). Nevertheless, other research
has shown relationships between different teaching–learning strategies and student
motivation at the university stage in different educational environments (Cayubit,
2022; Lugosi & Uribe, 2022; Michailidis etal., 2022).
Moreover, to obtain evidence on the relationships between learning strategies
and student motivation, at present, there are many instruments and tools available
based on scales validated in different contexts and applied to different educational
stages. Thus, the EDAOM (Inventory on Learning Styles and Motivational Orien-
tation) is available (Castañeda & Ortega, 2004). As it has been mentioned before,
Pintrich et al. (1991) developed and validated a scale called Motivational Strate-
gies for Learning Questionnaire (MSLQ) of 81 items. Subsequently, this question-
naire was reduced and validated to a new 40-item scale (Pintrich etal., 1993), called
MSLQ-SF. This scale has been translated and validated internationally, in Spain
(Roces etal., 1995), in China (Rao & Sachs, 1999) and in many other countries.
In Spain, Zurita Ortega et al. (2019) carried out a validation of the questionnaire
MSLQ_SF adapted to university students, obtaining good psychometric indicators,
but detecting some variations over the original questionnaire in terms of motivation
and learning strategies factors.
Validity andreliability forpsychometric instruments
Validity and reliability relate to the interpretation of scores from psychometric
instruments in educational research (Cook & Beckman, 2006). Methods for assess-
ing the validity of results from psychometric instruments derive from theories of
psychology and educational assessment (Messick, 1989). Validity refers to “the
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degree to which evidence and theory support the interpretations of test scores by
the proposed uses of tests” (AERA/APA/NCME, 1999; Borsboom et al., 2004).
Validity is not a property of the instrument, but of the instrument’s scores and their
interpretations and the inference (Cook & Beckman, 2006). Messick, (1989) identifies
five sources of evidence to support validity: content, response process, internal structure,
relations to other variables, and consequences. Content evidence "involves assessing the
relationship between a test’s content and the construct it is intended to measure” (AERA/
APA/NCME, 1999). Response Process: “reviewing the actions and thought processes
of test takers can help the fit between the construct and the performance” (AERA/APA/
NCME, 1999). Internal Structure: reliability and factor analysis are evidence for the
internal structure (Floyd & Widaman, 1995; Sellbom & Tellegen, 2019; Shrestha, 2021).
Relations to other variables: the aim is to correlate the scores obtained with the instru-
ment with another similar instrument that has already been validated. Consequences: the
aim is to assess the intended or unintentional shortcomings of the proposed instrument,
as well as the source of their possible invalidation (Abeele etal., 2020).
Reliability is a necessary condition, but not sufficient (Sürücü & Maslakci,
2020). It refers to the consistency of scores from one assessment to another (AERA/
APA/NCME, 1999). An instrument that does not yield reliable scores does not
permit valid interpretations (Cook & Beckman, 2006). There are different ways to
measure reliability. For internal consistency, the Cronbach’s alpha can be applied
(Cronbach, 1951). For agreement inter-rater reliability, Phi coefficient, weighted
Kappa coefficient or Kendall’s taus, can be computed (Nunnally & Bernstein, 1994).
For temporal stability, a test–retest reliability can be worked out (Noble etal., 2021).
Scores measuring a single construct would correlate highly. If internal consistency
is low, scores are measuring more than one construct (Cook & Beckman, 2006).
Examples of how psychometric properties should be instrumentalized and studied
for the validation of a scale can be seen in Moguerza etal. (2017), Moret-Tatay etal.
(2015) and Zurita Ortega etal. (2019).
Methodology andmaterials
Materials
Instruments
The questionnaire is an adapted version of MLSQ-SF to consider dimensions
as knowledge, planification, study management, time management, emotional
management, perseverance, and adversity management. It consisted of 41 ques-
tions (Table1), divided into 2 dimensions and 7 subscales: learning strategies
and intrinsic motivation. The first dimension includes aspects of study organiza-
tion and planning, active self-management of study, effort and understanding of
materials. The second dimension includes subscales of emotional management
and managing adversity. The complete list of items considered can be found in
Table1. All items were rated on a Likert scale from 1 to 10, where 1 means never
and 10 means always.
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Table 1 List of items
Source: Authors
Code Items
P1 I try to adjust my study methods to meet the subject requirements in the degree
P2 I make sure to keep up with weekly course readings and assignments
P3 When I do a midterm, I compare myself with my teammates
P4 As I read the materials, I try to relate the material to the ones I already know
P5 When studying the readings for the subjects, I highlight the material to organize my ideas
P6 I try to look for support in evidence when a reading, theory or conclusion is presented in class
P7 When I’m confused about something I’m reading, I read it again and try to clear it up
P8 I generally prefer to go to a place where I can concentrate on my study
P9 I make an effort to do academic work well even though I don’t like it
P10 I prefer class materials that pique my curiosity, even if it’s hard to learn
P11 I think that the materials of the subjects are useful for acquiring knowledge
P12 When I take an exam, I think about the consequences of making a mistake
P13 When studying for the subject, I summarize the main ideas of the readings and the class
P14 When I study for a subject, I review the readings and my notes, finding the most important ideas
P15 Every time I tackle a topic, I try to think about why I should learn it
P16 I generally perform adequately in the topics of the subjects
P17 I often check the order of the material of the subjects before studying
P18 When I study for classes, I set goals to direct my activities during each period of study
P19 I find it most satisfying to understand all the contents of this degree
P20 I rarely find an hour to review my notes or readings before an exam
P21 I feel very restless when I take an exam
P22 I try to understand the materials in the classes by making connections between what I have learned and
the readings
P23 When I study, I review my notes
P24 I relate my ideas to what I’m learning in the subjects
P25 When studying for a subject, I try to determine which concepts I don’t understand well
P26 It’s hard for me to fit into a study schedule
P27 I make an effort to work on the course materials even though they are boring
P28 It’s important for me to understand the content of the subjects
P29 I feel like my heart races when I take an exam"
P30 I try to apply what I have learned in each subject in other class activities such as presentations or debates
P31 Whenever there is a statement or conclusion, I think of alternatives
P32 I question myself to make sure I understood what I’ve been studying in class
P33 Whether it’s at home or at university, I have a fixed place to study
P34 In a class I like, I prefer material that challenges me to learn new things
P35 I am interested in the areas to which the subjects of the degree belong
P36 I develop my own ideas from the subject materials
P37 If the subject materials are difficult to understand, I look for alternatives
P38 I properly manage the study time for the subjects
P39 When the work of the subject is difficult, I only do the easiest
P40 If I take messy notes in class, I make sure to tidy them up later
P41 When I study, I make an outline, diagram, mind map or similar, for the important concepts
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(a) F1: Active self-management of study material. Items P4, P6, P10, P15, P16, P22,
P24, P25, P28, P30, P31, P32, P34, P36 and P37.
(b) F2: Organization of material. Items P1, P2, P17, P18, P26, P38, P40.
(c) F3: Study management. Items P5, P13, P14, P23, P41.
(d) F4: Self-management of effort. Items P7, P8, P9, P27, P33.
(e) F5: Understanding of study content. Items P11, P19, P35.
(f) F6: Emotional self-management. Items P21 and P29.
(g) F7: Adversity management. Items P20 and P39.
Procedure
The data for the study were obtained through simple random sampling, in the dif-
ferent classes, courses and degree courses, guided by the classroom teachers, with
a duration of 10 min to fill in an online questionnaire, via a QR code, from their
mobile phones. The students were provided with a random code, to maintain ano-
nymity in their answers, and not coinciding with others, through an algorithm of
numbers. In the questionnaire, students were provided with informed consent and an
information sheet about the study, which explained the characteristics and purpose
of the study. For acceptance, they simply ticked the appropriate box. The study was
approved by the university’s Research Ethics Committee.
Participants
The analysis focuses on a significant sample of 596 students enrolled in dif-
ferent degrees from a variety of subject areas at the Spanish public university
Universidad Rey Juan Carlos. The degrees they are studying correspond to the
area of Social Sciences (Economics, Business, Marketing, Education, Politics),
the Arts and Humanities (History, Language and Literature) and Legal Sciences
and International Relations. The courses in which the students are enrolled
range from first to fifth year, the majority being first and second year. Some
questionnaires were excluded from the analysis because they were incomplete.
All descriptive results are displayed at Table2. Concerning the variable Sex, a
30% of individulas were male and 70% female… In addition, a 44% do have a
scholarship to study, and as a result, 56% of families or themselves must finance
their university studies.. A 57% of students do not work to finance their stud-
ies.. We can also observe the household income distribution. Most of students,
63%, chose the degree course they are taking as their first option, and 22% as
their second option. This information is relevant because it is directly related to
the intrinsic motivation of the students to continue their studies. Table3 shows
that 31% of students do not have a scholarship and do not work, so the main
funders of studies are families; In addition, 26% do have a scholarship and are
not working. Only 1.34% of the students surveyed work full-time and have also a
scholarship. 17% work part-time and have scholarships. If we focus on the con-
tingency table (Table4) between having a scholarship and the sex of the student,
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we observe that 34.51% of women have a scholarship, compared to 9.42% of
men. This difference is reduced, between the two sexes, when individuals do not
have a scholarship.
Data analysis
Data were analyzed using JASP 0.17.2.1. software for both exploratory factor
analysis (EFA) (Moguerza et al., 2017) and confirmatory factor analysis (CFA)
(Moguerza etal., 2017). To carry out the EFA analysis, certain preliminary tests
were carried out, multivariate normality, linearity and correlation between variables
(Tabachnick & Fidell, 1989). An oblimin rotation was performed to determine the
factor loadings, accepting those factors with an eigenvalue greater than 1 (Corner,
2009). The number of factors was determined through hypothesis testing and
Table 2 Descriptive analysis
Source: Authors
Variables Categories Frequency Percent
Sex Male 181 30.369
Female 413 69.295
Other 1 0.168
Missing 1 0.168
Get a college shcolarship No 334 56.040
Yes 261 43.792
Missing 1 0.168
Do you work? Yes, full time 23 3.859
Yes, part time 234 39.262
No 338 56.711
Missing 1 0.168
Household Income From 0 to 12.450€ 63 10.570
From 12.451 € to 20.200 € 134 22.483
From 20.201 to 35.200 € 174 29.195
From 35.201 € to 60.000 € 136 22.819
From 60.001 € to 300.000 € 40 6.711
More than 300.000€ 2 0.336
Missing 47 7.886
In what position did you choose
your current studies? First option 370 62.081
Second option 130 21.812
Third option 51 8.557
Fourth or greater option 40 6.711
Missing 5 0.839
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1 3
also using Horn’s parallel analysis (Horn, 1965; Lloret-Segura et al., 2014). To
determine the internal consistency of the scale, Cronbach’s Alpha, homogeneity
items, KMO index and Barlett’s sphericity test (Kaiser, 1974) were used.
After the EFA analysis, a confirmatory factor analysis was carried out to deter-
mine the goodness of fit of the data, which is essential to establish the validity of
the scale. To confirm the adequacy of the model, different fit indices were used;
the chi-square
𝜒2
 statistic (la Du & Tanaka, 1989); the goodness-of-fit index
(GFI) whose reference value is 0.90 to consider the model acceptable (Hu &
Bentler, 1999); the square root of the mean square residues (RMSR), based on the
residuals, where if the value is close to 0, the better the fit, and whose reference
value is 0. 08 (Jöreskog & Sörbom, 1979); within the incremental fit indices, the
comparative fit index (CFI), normed fit index (IFI), all of them between 0 and 1,
and whose reference value is 0.9 (Bentler, 1990); and finally, within parsimony
adjustment indices, the error of the root mean square approximation (RMSEA)
of the RMSR. In this case, the smaller, and closer to 0, the better (Steiger, 2000).
Table 3 Contingency table work and college scholarship
Source: Authors
Contingency Tables
Do you work?
College
Schoalrship Yes, at full time Yes, at part time No Total
No Count 15 135 183 333
% of total 2.525% 22.727% 30.808% 56.061%
Yes Count 8 99 154 261
% of total 1.347% 16.667% 25.926% 43.939%
Total Count 23 234 337 594
% of total 3.872% 39.394% 56.734% 100%
Table 4 Contingency table
college scholarship and sex Contingency Tables
Sex
College
Scholarship Male Female Other Total
No Count 124 208 1 333
% of total 20.875% 35.017% 0.168% 56.061%
Yes Count 56 205 0 261
% of total 9.428% 34.512% 0.000% 43.939%
Total Count 180 413 1 594
% of total 30.303% 69.529% 0.168% 10%
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1 3
Table 5 Means, standard deviations, skewness and Kurtosis of items
Mean Std. Deviation Skewness Std. Error of
Skewness Kurtosis Std. Error of
Kurtosis
P1 7.471 1.927 -0.951 0.1 1.316 0.2
P2 5.995 2.271 -0.501 0.1 -0.158 0.2
P3 5.598 2.758 -0.347 0.1 -0.735 0.2
P4 7.502 1.859 -0.768 0.1 0.734 0.2
P5 7.626 2.26 -1.12 0.1 1.245 0.2
P6 6.008 2.275 -0.547 0.1 0.074 0.2
P7 8.686 1.425 -1.103 0.1 1.058 0.2
P8 8.527 2.047 -1.984 0.1 4.493 0.2
P9 8.696 1.472 -1.284 0.1 1.893 0.2
P10 8.012 1.818 -1.023 0.1 1.522 0.2
P11 7.014 1.878 -0.761 0.101 1.418 0.201
P12 8.097 2.078 -1.492 0.1 2.45 0.2
P13 7.704 2.141 -1.105 0.1 1.263 0.2
P14 8.352 1.686 -1.409 0.1 3.196 0.2
P15 6.128 2.488 -0.549 0.1 -0.166 0.2
P16 7.178 1.489 -0.4 0.1 0.271 0.2
P17 7.535 2.125 -1.12 0.1 1.33 0.2
P18 6.582 2.252 -0.695 0.1 0.355 0.2
P19 7.451 2.026 -0.881 0.1 0.851 0.2
P20 4.432 2.97 0.209 0.1 -1.001 0.2
P21 6.449 2.835 -0.6 0.1 -0.523 0.2
P22 7.407 1.914 -0.937 0.1 1.51 0.2
P23 8.921 1.516 -2.288 0.1 7.902 0.2
P24 7.934 1.658 -0.987 0.1 2.039 0.2
P25 7.912 1.627 -1.017 0.1 2.191 0.2
P26 4.881 2.968 0.034 0.1 -1.1 0.2
P27 7.624 1.731 -0.957 0.1 1.872 0.2
P28 8.352 1.62 -1.066 0.1 1.28 0.2
P29 6.63 2.971 -0.733 0.1 -0.45 0.2
P30 7.417 1.995 -0.857 0.1 0.77 0.2
P31 6.615 2.087 -0.479 0.1 0.076 0.201
P32 6.884 2.09 -0.538 0.1 0.035 0.2
P33 8.606 2.007 -2.006 0.1 4.631 0.2
P34 7.417 2.09 -0.858 0.1 0.771 0.2
P35 7.637 1.976 -1.008 0.1 1.021 0.201
P36 7.123 1.894 -0.604 0.1 0.63 0.201
P37 7.653 1.837 -1.093 0.1 1.9 0.2
P38 6.446 2.284 -0.748 0.1 0.38 0.201
P39 4.284 2.826 0.027 0.101 -1.132 0.201
P40 7.813 2.474 -1.261 0.1 1.058 0.2
P41 6.526 3.009 -0.686 0.1 -0.515 0.2
Source: Authors
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Results
Internal consistency
The Cronbach’s Alpha of the proposed scale is α = .892 (α = .938 in Zurita Ortega
etal., 2019), with a total explained variance of 51.26.%, in seven factors. As for
Cronbach’s Alpha, if any item of the scale is eliminated, it takes values between
.885 and .91. ANOVA tests with Friedman’s test and Hotelling’s t-squared indicate
that the multivariate means are statistically different (Carey etal., 2022; Göktuna
etal., 2022). Table5 presents a descriptive analysis of the scales, together with
their skewness and kurtosis, where the values are within the appropriate ranges.
It shows the descriptive statistics of the values measured according to the derived
scores for each of the questions in the questionnaire. We can observe the aver-
age values collected, the standard deviation, as well as the measures of skewness
and kurtosis, to verify the normality of the values of the distribution. In terms of
skewness, we can point out that most of the questions have some symmetry to the
left; regarding kurtosis, we observed that the distribution is leptokurtic in most of
the items. These results indicate a certain concentration of the values measured
around the mean values of the variables.
Exploratory andconformitory factor analysis
In relation to the validation of the Exploratory Factor Analysis (EFA), the
Barlett’s test of sphericity was p < .001, with a Chi-squared value of 8888.89, and
a Kaiser–Meyer–Olkin index (KMO) of .92. According to Table6, the pvalue for
Bartlett’s test is smaller than 0.05 significance level. That fact shows us factor analysis
is appropriate for reducing dimensions and obtaining constructs. The Chi-squared
Test shows us a similar result. A KMO index greater than 0.8 indicates that EFA
is suitable for the analysis. Once the suitability of applying principal component
analysis through Bartlett and Chi-squared contrasts has been verified, a factor
analysis is performed. To improve the orthogonality of the estimated factors, an
Table 6 Bartlett’s test
Source: Authors
Bartlett’s Test
𝜒2
df p
8885.895 820.000 < 0.001
Table 7 Chi-squared test
Source: Authors
Chi-squared Test
Value df p
Model 1015.186 554 < 0.001
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Table 8 Rotate factor loading of
dimensions
Source: Authors
F1 F2 F3 F4 F5 F6 F7
P1 0.13 0.582 0.274 0.122 0.198 0.109 -0.115
P2 0.208 0.694 0.214 -0.063 0.11 0.147 -0.046
P3 -0.022 0.005 -0.022 0.172 -0.001 0.102 0.339
P4 0.528 0.089 0.305 -0.081 0.055 -0.026 -0.226
P5 0.138 0.347 0.479 0.115 0.082 0.084 -0.136
P6 0.528 0.323 0.137 -0.071 -0.052 0.069 0.046
P7 0.311 -0.067 0.212 0.549 0.076 -0.081 -0.217
P8 0.025 0.229 0.083 0.404 0.286 -0.006 -0.106
P9 0.189 0.261 0.16 0.526 0.027 0.117 -0.305
P10 0.452 -0.097 0.142 0.248 0.186 -0.127 -0.171
P11 0.261 0.145 0.051 0.079 0.628 -0.004 -0.082
P12 0.115 -0.021 0.035 0.564 0.178 0.294 0.177
P13 0.223 0.145 0.738 0.061 -0.032 0.032 0.026
P14 0.147 0.197 0.657 0.328 0.127 0.002 -0.1
P15 0.499 0.141 0.128 0.016 0.03 0.058 0.216
P16 0.474 0.275 0.099 0.104 0.134 -0.291 -0.056
P17 0.204 0.432 0.093 0.313 0.079 -0.03 -0.016
P18 0.365 0.574 0.051 0.121 0.16 0.104 0.086
P19 0.219 0.17 0.122 0.062 0.741 0.127 0.085
P20 0.106 -0.214 -0.069 -0.004 -0.263 0.19 0.477
P21 0.001 0.036 0.058 0.066 0.022 0.907 0.083
P22 0.556 0.095 0.417 0.079 0.168 0.106 -0.096
P23 0.137 0.18 0.542 0.33 0.155 0.012 -0.09
P24 0.594 0.08 0.27 0.226 0.198 -0.006 -0.088
P25 0.486 0.122 0.309 0.408 0.119 0.028 -0.094
P26 0.107 -0.609 -0.026 -0.138 -0.186 0.156 0.2
P27 0.276 0.339 0.293 0.38 0.135 0.266 -0.194
P28 0.422 0.1 0.278 0.296 0.406 0.083 -0.151
P29 0.014 0.078 0.046 0.067 0.018 0.897 0.057
P30 0.602 0.068 0.223 0.091 0.325 0.034 -0.104
P31 0.698 0.05 0.016 -0.044 0.066 -0.069 0.16
P32 0.696 0.08 0.078 0.149 0.15 0.138 0.107
P33 0.001 0.245 0.048 0.557 -0.018 -0.021 0.034
P34 0.59 0.072 0.027 0.086 0.097 -0.023 -0.185
P35 0.237 0.245 0.06 0.084 0.647 -0.071 -0.001
P36 0.607 0.256 0.033 0.157 0.241 -0.015 0.057
P37 0.485 0.225 0.199 0.33 -0.028 0.094 0.077
P38 0.223 0.705 0.202 0.093 0.167 -0.056 0.061
P39 -0.017 -0.018 0.029 -0.114 0.079 0.031 0.741
P40 0.084 0.532 0.308 0.335 0.003 0.126 0.003
P41 0.171 0.185 0.586 -0.071 0.107 0.013 0.262
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1 3
oblimin rotation is performed (Luo etal., 2019). To consider the number of factors,
all those whose associated eigenvalue is greater than 1 are considered (Moguerza
etal., 2017). In addition, a parallel analysis is performed to determine the number of
significant factors (Horn, 1965) (Table7).
The EFA has confirmed the existence of 7 main factors, whose factor loadings
are shown in Table8, according to criteria set. The variables have been associated
with each of the dimensions, factors, according to the criterion of having the highest
factor load in a significant way, and not distributed among the rest of the dimen-
sions. In those cases where the factor load could not be assigned a certain dimen-
sion, because it was shared between several factors, the item for the validation of the
scale was not considered.
In a CFA incremental fit indices are those indices that evaluate the improvement
of the proposed model in relation to a base model (McNeish etal., 2018; Jordan-
Muiños, 2021). CFI (Comparative fit index, the GFI (Goodness of fit index) and
TLI (Tucker-Lewis index) are examples of these fit indices. If CFI gives a value
greater or equal to .95, the model is said to fit the sample (Lai, 2021). For GFI, a
cut-off point greater than .89 is recommended in a sample of 100 cases, while in
larger samples, a cut-off greater than .93 is recommended (Cho etal., 2020). Xia
and Yang (2019) recommend a cut-off point for TLI greater than .90. When the
RMSEA (Root Mean Squared Error of Approximation) gives a value less than or
equal to .06, the model is an adequate fit for the sample (Lai, 2021). For SRMR
(Standardized Root Mean square Residual), a cut-off point less than .09 is rec-
ommended in a sample of 100 cases or less, while for a sample greater than 100
cases, a cut-off point of .08 or less is recommended (Cho etal., 2020). Another
indicator we can consider evaluating the fit of the sample to the proposed model
is the chi-square (χ2); if its value is statistically significant (e.g., p < .05), the fit
of the model is poor compared to the sample. Rigdon (1996) has shown “CFI is
problematic because of its baseline model because CFI seems to be appropriate
in more exploratory contexts, whereas RMSEA is appropriate in more confirma-
tory contexts”. On the other hand, CFI does have an established parsimony adjust-
ment, although the adjustment included in RMSEA may be inadequate. Otherwise,
Table 9 Fit indices EFA
Source: Authors
Fit indices EFA
RMSEA RMSEA 90% confidence SRMR TLI CFI
0.037 0.034—0.041 0.026 0.915 0.943
Table 10 Fit indices CFA
Source: Authors
Fit indices EFA
RMSEA RMSEA 90% confidence GFI SRMR TLI CFI
0.056 0.053 – 0.059 0.978 0.056 0.929 0.844
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1 3
Table 11 Chi-square test
The estimator is ML. Source: Authors
Model
𝜒2
df p
Baseline model 8367.371 703
Factor model 1842.839 644 < 0.001
Table 12 Parameter Estimates. Factor loadings
Source: Authors
Factor Indicator Estimate Std. Error z-value pStd. Est. (all)
F1 P4 1.053 0.073 14.39 < 0.001 0.567
P6 1.214 0.090 13.423 < 0.001 0.534
P10 0.894 0.073 12.170 < 0.001 0.492
P15 1.126 0.101 11.119 < 0.001 0.453
P16 0.820 0.059 13.899 < 0.001 0.551
P22 1.352 0.071 19.133 < 0.001 0.708
P25 1.044 0.062 16.832 < 0.001 0.643
P28 1.033 0.062 16.643 < 0.001 0.638
P30 1.378 0.074 18.522 < 0.001 0.691
P31 1.138 0.083 13.706 < 0.001 0.546
P32 1.400 0.079 17.770 < 0.001 0.671
P34 1.112 0.083 13.393 < 0.001 0.533
P36 1.201 0.073 16.459 < 0.001 0.634
P37 1.020 0.073 14.050 < 0.001 0.556
F2 P1 1.341 0.074 18.128 < 0.001 0.697
P2 1.542 0.088 17.484 < 0.001 0.679
P17 1.100 0.087 12.591 < 0.001 0.518
P18 1.395 0.090 15.498 < 0.001 0.619
P26 -1.300 0.126 -10.333 < 0.001 -0.438
P38 1.532 0.089 17.246 < 0.001 0.672
P40 1.490 0.100 14.928 < 0.001 0.602
F3 P5 1.279 0.093 13.816 < 0.001 0.566
P13 1.398 0.086 16.324 < 0.001 0.654
P14 1.289 0.064 20.036 < 0.001 0.766
P23 0.933 0.061 15.327 < 0.001 0.616
P41 1.259 0.129 9.727 < 0.001 0.419
F4 P7 0.742 0.062 12.041 < 0.001 0.521
P8 0.942 0.088 10.716 < 0.001 0.460
P9 0.937 0.061 15.340 < 0.001 0.636
P27 1.195 0.069 17.422 < 0.001 0.691
P33 0.718 0.088 8.138 < 0.001 0.358
F5 P11 1.074 0.081 13.200 < 0.001 0.573
P19 1.409 0.087 16.204 < 0.001 0.695
P35 1.265 0.085 14.824 < 0.001 0.639
F6 P21 2.713 0.156 17.381 < 0.001 0.957
P29 2.497 0.155 16.136 < 0.001 0.841
F7 P20 1.555 0.251 6.205 < 0.001 0.524
P39 1.144 0.198 5.764 < 0.001 0.405
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1 3
(p ≥ .05), the model is considered to fit the sample adequately (Walker & Smith,
2017). However, given that the chi-square fit statistic is affected by large samples,
the ratio of the chi-square statistic to the respective degrees of freedom (χ2 /df) is
preferred (Wheaton etal., 1977). The chi-square statistic, with large sample sizes, it
will most probably remain statistically significant.
As it can be seen in Table9, the goodness-of-fit measures for the explora-
tory factor analysis would be within the desirable values, as can be seen in the
literature, and would provide indicators that the adjusted model has a good fit
to the sample values. In Tables10 and 11, we can see different values of the
incremental adjustment indices for the confirmatory factor analysis model. In this
case, regarding the Confirmatory Factor Analysis (CFA), the following results
were obtained (Tables10 and 11), all goodness-of-fit measures are within the
ranges recommended by other studies, except for the values of the CFI and for
χ2, which would be slightly lower than those recommended in the literature. In
any case, according to Ridgon (1996), in confirmatory factor analysis contexts,
the RMSEA is a more appropriate goodness-of-fit measure. In addition, the χ2
index has certain limitations when the samples are very large, tending to reject
the null hypothesis of a good fit between the factorial model and that provided
by the sample values (Wheaton etal., 1977).If we focus on the estimates of the
parameters associated with each of the items that are part of the identified dimen-
sion, we can see that, in each of them, the effects of the variables are positive
and statistically significant, except in the case of question P26 "It’s hard for me
to fit into a study schedule". This indicates that, when the results of the survey
need to be scaled, this question is reversed, and should be recoded appropriately
(Table12). In most cases, the estimated parameter takes a value of 1 or higher,
indicating that the individual effect of that question on the associated dimension,
on average, provides a greater value on the construct (Table13).
If we now focus our attention on the reliability of each of the constructs, using
the w and Cronbach’s alpha coefficients, we can observe that most of them obtain
acceptable values, according to the literature, except in the case of the F7 factor,
where the coefficient to measure reliability would indicate that additional variables
would be needed to improve the information provided by the construct. (Table14).
Table 13 Reliabilitycoefficients
Source: Authors
Reliability Coefficient ω Coefficient α
F1 0.870 0.877
F2 0.681 0.558
F3 0.700 0.700
F4 0.639 0.652
F5 0.678 0.665
F6 0.892 0.892
F7 0.357 0.350
Total 0.874 0.888
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1 3
However, the total reliability of the proposed instrument would obtain very adequate
values, according to those provided in the literature.
Concerning information provided by Table14, where the correlations between the
different dimensions are displayed, we can see that they take values close to 0, and
would indicate that there is little, although significant in some cases, or no relationship
between the constructs. This property is desirable to ensure that there is orthogonality
between constructs, and in this way, to guarantee that the variables associated with each
of them do not provide information that can be associated with more than one of them.
Validity
In order to test the internal validity of the proposed scale, actions have been planned to
carry it out at the content, criterion and construct levels. For content validity, the scale
proposal was submitted to the judgement of several experts, teachers and pedagogues, in
which some modifications and adaptations of the initial questions were made according
to their expressed criteria. Regarding criterion validity, no similar scale has been used
for data collection, although the properties obtained can be checked by the goodness
of fit of the model. Pintrich etal. (1993) obtained model goodness-of-fit measures, in
the standardized solution, of GFI = .77, AGFI = .73,
𝜒2
⁄df = 3.49 and RMR = .07 for the
motivation scale, and model goodness-of-fit measures, in the standardized solution, of
GFI = .78, AGFI = .75,
𝜒2
⁄df = 2.26 and RMR = .08 for the cognitive learning strategies
scale. Zurita Ortega etal. (2019) do not conclude their work by performing a confirma-
tory factor analysis, so no goodness-of-fit indicators of the estimated model are avail-
able. For construct validity, as mentioned in the previous section, exploratory and con-
firmatory factor analyses were carried out. The results have not been confornted with
other available instruments, since they have been considered to propose different indica-
tor proposals than those established, the orientation of the questions has been modified
to adapt them to the needs of the study, and they have been defined in an alternative
way to collect complementary information on other aspects. This confrontation, once the
instrument has been validated, will be proposed as a future line of research.
Table 14 Correlation matrix
Source: Authors
**p < 0.01
F1 F2 F3 F4 F5 F6 F7
F1 1
F2 -.131** 1
F3 0.061 -0.077 1
F4 .114** -.201** -0.008 1
F5 -.297** .286** 0.082 -.248** 1
F6 .226** -0.069 0.080 0.074 -.178** 1
F7 -.350** .322** -0.028 -.234** .336** -.177** 1
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1 3
Discussion andconclusions
Discussion
Numerous studies have verified the importance of students’ intrinsic motivation,
together with their learning strategies as indicators of achievement to get success,
both in the completion of studies and entrepreneurial capacity (Inzunza etal.,2018;
Zurita Ortega et al.,2019). A version of the extended questionnaire by (Pintrich,
1991) with a gender perspective, in Spain, has been validated by (Ramírez etal.,
2022). To establish relationships between students’ intrinsic motivation and different
learning strategies, a scale based on 41 questions was developed based on the vali-
dated MSLQ-SF questionnaire developed by Pintrich etal. (1993), translated into
several languages, and validated in different countries. The present research work
aims to verify the psychometric properties of this proposal to study the motivational
factors of university students from different university fields with the aim of staying
in the degree and finishing successfully, and that it can be used as a leading indicator
for the personal skills required for entrepreneurship, considering common and rele-
vant aspects, such as the management of emotions and the management of adversity.
The results obtained in this research are satisfactory in terms of internal consist-
ency, with a Cronbach’s Alpha of .892. In addition, better fit indicators are obtained
than those provided by Pintrich etal. (1993) (see "Validity" section) according to
the standards of goodness of models today. Through exploratory and confirmatory
factor analysis, 7 final factors have been obtained (an eighth factor corresponding
to a question of the questionnaire, P3, was eliminated) and two main dimensions;
learning management strategies and intrinsic motivation associated with emotional
self-management and adversity management. The main measures of the model seem
to indicate that the model is valid and reliable for estimating motivation and learning
strategies as part of a theoretical model based on structural equations. The implica-
tions derived from the intrinsic relationship of learning strategies and intrinsic moti-
vation can be found in several previous studies (Inzunza etal., 2018) among others.
In the EFA analysis, items with factor loadings above 0.1 were considered,
and the choice of the number of factors was determined by a parallel analysis
(Horn, 1965). The KMO and Barlett’s sphericity test instruments have dem-
onstrated the adequacy of the analysis. In the CFA analysis, all the parameters
associated with the items of the EFA model questions were found to be sta-
tistically significant for each of the factors they predict (p < .001). As for the
relationship between the estimated latent factors, a positive relationship has
been observed between the estimated parameters associated with each of them,
except with Factor 7 (Adversity management) whose relationship is negative;
and the estimated relationship between Factor 6 (Emotional self-management)
with Factor 7, both intrinsic motivation factors. The overall results indicate
that appropriate learning strategies have positive effects on each other, and lead
to an improvement in emotional self-management and a reduction in adverse
situations. These results, although similar to other studies mentioned above,
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1 3
are structured differently from them, emphasizing organizational motives of
time and resources, understanding of materials and self-management of effort.
The findings of this research are relevant because it focuses on student engage-
ment for success, highlighting good organization of materials and time (Peck
etal., 2018), efficient effort management (Anthonysamy etal., 2020; Schunk
& DiBenedetto 2020) and understanding of materials (Esra & Sevilen,2021)
through other learning mechanisms, such as tutorials, as other authors have
stated (Effeney etal., 2013).
Theoretical contribution
The adapted version of the questionnaire proposed by (Pintrich etal., 1993), in
its short version, MLSQ-SF, and which forms the central part of this research,
has obtained good psychometric results, obtaining a shorter modality, with dif-
ferent constructs from the original, since the questions were directed towards a
global learning strategy to achieve success in the completion of the studies. and
aspects more related to intrinsic motivation derived from emotional management
and adversity management were included. These skills have been identified in
the literature as relevant, as influencing students’ intrinsic motivation and as
an indicator of intentionality towards entrepreneurship. This adapted version is
structured in two blocks, learning strategies towards achievement, from which
five factors are derived, and management of intrinsic motivation, related to emo-
tions and the management of adversity. The identified constructs are partially
in agreement with those determined by Cardozo (2008), Martínez and Galán
(2000) and Roces etal. (1995), but they incorporate aspects of emotional man-
agement as determining variables in intrinsic motivation. In this research, spe-
cial emphasis has not been placed on academic performance, but on the need for
achievement for the success of the being graduated, together with the value as an
anticipatory sign of personal competence towards entrepreneurship. Therefore,
the validation of this instrument provides a useful tool to determine what actions
can be derived, by the different educational agents involved, to promote motiva-
tion towards achievement and capacity for entrepreneurial intention.
Other implications
The main implications of the result of this research can be seen reflected in the
frequent use that educators, policymakers, and society in general can make to
determine the strategies that must be articulated to achieve adequate motivation
and stimulation of university students. With regard to teachers, the instrument will
make it possible to obtain information on the shortcomings of students and mod-
ulate correction mechanisms to help them achieve their goals and achievements.
These actions, carried out in advance and adapted to the personal circumstances
of the students, will help to obtain better results of satisfaction and encouragement
to achieve their own objectives. As for the implications that may be relevant for
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1898
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1 3
policymakers, it is a matter of developing actions at a global level, through extra-
curricular training, that promotes and trains socio-emotional skills together with
cognitive competencies, to achieve academic success. Policies of training, aware-
ness, guidance and psychological support, once the individual needs of students
have been determined, will result in an improvement in their professional skills,
assertiveness, improved management of emotions, preparation for risk and uncer-
tainty, and will increase their capacity for creativity and entrepreneurial compe-
tence. Finally, with respect to society, considering that education is subsidized by
the state, through public resources from the collection of taxes, any dropout rate
can be considered as an embezzlement of public funds. This instrument can be
used by public administrations to analyse the deficiencies of the system, determine
which aspects may represent an opportunity for improvement, with the ultimate
objective of using public resources efficiently and effectively.
Limitation andfuture research
This research has certain limitations. Some of the questions adapted from the instru-
ment, despite having been validated by a set of experts, have not been adequately
understood by the students, having been confused with another intentionality. The
sample has been chosen through certain areas of knowledge, mainly in Social Sci-
ences, Humanities and Law, not having obtained data on experimental and engi-
neering degrees where the structure could be different. One of the factors obtained
has limited internal consistency, which suggests that, to provide broad validity, the
inclusion of more questions related or more explicitly related to the management of
adversity should be considered.
As for future lines of research, the intention is to extend the analysis to other
areas of knowledge, to extend the questions related to those dimensions whose con-
sistency was not high, to include other types of questions with a language that can
be more understandable for students, to eliminate or reword those questions that
have not been relevant in the validation of the scale. It is planned to develop a ques-
tionnaire, in which variables related to the intrinsic motivation of individuals and
aligned with personal capacities towards entrepreneurship are incorporated, to deter-
mine the anticipated correlation of one instrument against the other.
Conclusions
This paper attempts to validate the psychometric properties of a scale based on
the MSLQ-SF, with its adaptation to Spain by Roces etal. (1995), which has been
implemented in a sample of the Spanish university population of the Universidad
Rey Juan Carlos, in different fields of knowledge. This scale tries to determine
relevant factors for an adequate intrinsic motivation of students to graduate, based
on appropriate learning strategies, that can be used as an indicator that approximates
their intention about entrepreneurial skill. The validity of the instrument has been
verified through different goodness-of-fit measures, obtaining good properties.
International Entrepreneurship and Management Journal (2024) 20:1879–1906 1899
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1 3
The dimensions obtained and the estimated relationships between them offer us
a framework from which universities can complement formal academic training
with tools for time management, effort, understanding of materials, emotional self-
management, and management of adversity in terms of anxiety, providing tools
for improving motivation as a link with personal entrepreneurship.The results in
the validation of the scale have partially differed from those obtained by similar
studies, therefore, its scope of application must be subject to the conditions and
circumstances of the environment in which the data have been collected. This fact,
even if it remains somewhat general, will have to be contrasted with more research
that addresses it, in order to extrapolate the results to a general level. This scale is
expected to be complemented by the development of a new instrument that collects
information on the relationship between motivation and personal entrepreneurial
competence, with which several elements are shared, and with which it is possible
to extrapolate the causal relationships between socio-emotional skills of intrinsic
motivation and entrepreneurial intention in a more general field.
Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
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. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permis-
sion directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
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Authors and Affiliations
ElisaI.Villena‑Martínez1 · JuanJoséRienda‑Gómez1 ·
DoloresLucíaSutil‑Martín2 · FernandoE.García‑Muiña3
* Juan José Rienda-Gómez
juanjose.rienda@urjc.es
Elisa I. Villena-Martínez
elisa.villena@urjc.es
Dolores Lucía Sutil-Martín
doloreslucia.sutil@urjc.es
Fernando E. García-Muiña
fernando.muina@urjc.es
1 Rey Juan Carlos University, Financial Economics andAccounting, Madrid, Spain
2 Rey Juan Carlos University, Business Economics, Madrid, Spain
3 Rey Juan Carlos University, Business Organization, Madrid, Spain
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