The internship performance of undergraduate students: Are hard or soft skills more important?

Abstract

Higher education provides students with the knowledge and skills necessary for professional development. These are applied when students make the transition from the academic world to the world of work and allow them to generate value for organizations. This study analyzes the impact that hard and soft skills have on the performance of undergraduate students in their internships. The methodology uses multiple regression techniques on data from 2010 to 2021 from a sample of 6632 students in 25 undergraduate programs at Icesi University in Colombia. The results confirm the importance of both soft and hard skills and show that soft skills explain students’ performance to a greater extent. These findings can assist universities, faculty and organizations in developing the skills necessary for students to improve their performance, build successful careers and generate value for organizations.

Keywords

Higher education soft skills hard skills internship performance professional development

Higher education should provide students with the skills necessary to successfully face their professional career challenges. Industry demands professionals who are adequately prepared for work, equipped with the knowledge and technical skills and the personal and social skills that enable organizations to generate value. Researchers label the former as hard skills and the latter as soft skills. Consequently, universities have been increasingly called upon to prepare students for the world of work and are under pressure to foster the employability of their graduates (Shivoro et al., 2017; Suleman, 2018).

In this context, organizations consider employees as constituting a fundamental factor for their success and strive to attract and hire the best individuals. Therefore, they generally prefer to hire employees who possess a good mix of hard and soft skills (Mitchell et al., 2010). Many organizations recognize that the development of hard skills alone is not enough to achieve their goals and objectives. Employers consider soft skills as also essential for success (Stewart et al., 2016), since employees are often involved in different leadership and decision-making activities, where adaptability to change, creativity and innovation, customer/user orientation and teamwork skills are required (Succi, 2019; Succi and Canovi, 2020). However, many researchers agree that there is a widening gap between the soft skills employers expect and the levels of soft skills possessed by recent university graduates (Singh and Jaykumar, 2019).

Thus, the educational goals of universities must be oriented toward the development of integral human beings, from a perspective that makes explicit and seeks to counterbalance the economic subjects and technical training on which traditional university training tends to focus. So far, universities have privileged hard skills development without considering that these skills may be meaningless when not complemented with soft skills (Robles, 2012). With this in mind, the design of the curricula and extracurricular activities of undergraduate academic programs should include the development of both hard and soft skills, which complement each other (Rotherham and Willingham, 2009).

Such training will develop in students the hard skills required for a professional, technical or academic qualification (Carter et al., 2018) within a domain of knowledge and know-how that will help them to perform certain specific tasks effectively, depending on the training discipline. Moreover, these skills can be identified, tested, and monitored easily over time (Michnick-Golinkoff and Hirsh-Pasek, 2016). Likewise, a comprehensive training approach will enable the development of soft skills, which encompass personality traits, ability to interact with others, communication, language, personal habits and interpersonal skills (Fan et al., 2017; Padhi, 2014). Soft skills further include leadership and management, communication, teamwork and collaboration, decision making, adaptability, problem-solving, empathy, persuasion, autonomy, time management, passion and optimism, critical thinking, self-motivation and self-direction and resilience (Devedzic et al., 2018; Michnick–Golinkoff and Hirsh-Pasek, 2016). Unlike technical skills, soft skills are more idiosyncratic and are not tied exclusively to a task. They are therefore transferable between jobs (Varela et al., 2013) and are difficult to define, measure and evaluate (AbuJbara and Worley, 2018).

Once the students enter the labor market – which occurs during their internship, usually in the last year of their studies – their skills are tested. It is this internship that constitutes a bridge which facilitates the transition from the academic world to the world of work (Anjum, 2020; Nunley et al., 2016) and bridges the gap between theory and practice (Zopiatis, 2007), with students being evaluated on the basis of their knowledge and how they manage to use it through their personal and social skills.

Consequently, academic internships are recognized by universities, students and enterprises as key elements in developing students, allowing them to gain experience and opening the doors to their professional development (McHugh, 2017; Silva et al., 2018). Thus, academic institutions integrate internships into their curricula (Anjum, 2020) and in some cases make them mandatory for graduation (Silva et al., 2018).

Such is the importance of internships for undergraduate students that various researchers have focused on identifying the advantages they provide. Franco et al. (2019) find that internships offer students opportunities to enter the labor market. Nghia and Duyen (2019) affirm that they give students experience and allow them to make connections in professional fields. Anjum (2020) argues that internships develop the students’ social and professional skills, while Hora et al. (2020) report that learning, experience, advancement and connections are the most important internship results for students.

To shed more light on the importance of hard and soft skills in the internship performance of undergraduate students, this study explores the following research questions:

• What is the importance of hard and soft skills in the performance of undergraduate students in their internship?

• Are hard skills more important than soft skills in the performance of undergraduate students in their internship?

This study analyzes the internship performance of students at Icesi University, located in Colombia, from 2010 to 2021. The sample comprises 6632 students from 25 undergraduate programs in the Administrative and Economic Sciences, Engineering, Law and Social Sciences, Natural Sciences and Education Sciences schools.

Conceptual framework and hypothesis

The paradigm of university education has undergone a transition in recent decades. Currently, its focus has shifted toward developing skills that allow students to learn, think and communicate. Graduates are now expected to establish a set of skills that go beyond those traditionally explicit in undergraduate programs (Rao, 2014).

In this context, Icesi University tends toward a liberal education, developing and strengthening students’ autonomy – on the one hand, in terms of personal and citizen autonomy and, on the other, in terms of scientific, disciplinary and labor autonomy. Thus, its educational project moves away from the idea of the university as a place of instruction toward considering it as a place of personal and intellectual development, such that it can deliver graduates who are characterized not by professional content but by values and professional skills that transcend that content.

Accordingly, the university develops capabilities for autonomy, critical understanding, meaningful and creative communication, social and political participation and effective working with others, and the skills associated with these capabilities – such as concertation, cooperation, global-local thinking, multiple perspectives, leadership, teamwork, entrepreneurship, strategic thinking, innovation, creativity, meaningful interpersonal relationships, reflected on and valuable life purposes, lifelong individual learning, research, critical thinking, interpretation and expression (see Figure 1). These are expressed in the curricular plans of all the university’s undergraduate programs, through five lines of training: (1) Languages, (2) Citizenship, (3) Arts, Sciences, and Humanities, (4) Disciplinary Expertise, and (5) Work.

The Language training line contributes to the development of the ability to communicate meaningfully and creatively in different situations, through writing and speaking: that is, to interpret and express oneself appropriately when interacting with others, taking into account the communicative purposes and contexts in different languages, such as alphabetic (in English and Spanish), logical–mathematical, and plastic–artistic language.

The Citizenship training line has substantial implications for the capabilities of critical thinking, interpretation and expression, and social and political participation. It intends to provide skills and disposition to enable participation in society at the local, regional or global level in an active, reflective, critical and responsible manner.

The Arts, Sciences, and Humanities training line, through a group of electives in the Natural Sciences and Arts, Humanities and Social Sciences, contributes to critical thinking on social, scientific, political, aesthetic and cultural realities from different perspectives The aim is to give meaning, through dialog, reflection and contrast with life experiences, to cultural, scientific, environmental, artistic, philosophical, social, literary, political and historical legacies, among others.

The Disciplinary Expertise training line focuses on the appropriation of specific knowledge to promote scientific, conceptual, epistemic and methodological excellence based on critical thinking in each discipline. Likewise, this line of training aims to develop students’ transversal and specific skills in their academic programs to enable them to consider and solve problems specific to their disciplinary field.

Finally, the Work training line perfects the technical skills, abilities, methods, procedures and applications which allow development with greater rigor and precision competencies that the graduate will apply in the world of work. It also contributes to the development of skills associated with effectively working with others, since the associated courses lead the student to face possible work challenges and perfect their soft skills.

A similar curricular form is assumed for each of the undergraduate programs. For each semester, the courses in the different lines are interrelated, so that they contribute to that humanistic and integral approach of which we speak, to train and develop critical citizens who can respond to the demands of life in general. In addition, students have access to a wide variety of extracurricular activities which they choose according to their interests. The university designs these activities for the acquisition of knowledge and the development of soft skills. They include, for example, business fairs, contests, hackathons, volunteering, student groups, organization of events, sports, art, international exchanges and short-term trips, among others.

Additionally, the curricular plan for undergraduate students includes a Professional Development Program, with the primary purpose of guiding and accompanying students in developing the skills and disposition they will need to face the challenges of the world of work. Students participate in the program in the last year of their studies. Among their activities, they attend workshops that assist them to enhance their self-knowledge and define their professional profile, undergo psycho-technical tests, understand the labor market, prepare resumes, carry out mock interviews and participate in etiquette workshops. Then their resumes are sent to organizations linked to the university. Finally, students begin their internships, which last six full-time months. Students also receive counseling from a career advisor assigned by the university who monitors progress in their work plan and supports their soft skills development.

All of the above is designed to ensure that students develop the hard and soft skills necessary for their professional life, and that the university produces high levels of employability in its graduates and builds long-term relationships with organizations.

Several studies support the benefits of this comprehensive type of training. Some argue that soft skills are as critical as hard skills for a graduate’s professional future. In this vein, Idrus et al. (2009) found that hard skills were the main reason people got a job, while soft skills were the main reason they kept it and built a successful career. Likewise, Robles (2012) states that people hired for their technical skills soon realize that they need soft skills to achieve a productive, satisfying performance and a successful career. Soft skills allow them to understand the specific characteristics of the organization and enhance their commitment to it; to act in an ethical and socially responsible manner; to work under pressure and face difficult situations; to generate new ideas; to communicate clearly and convincingly; to participate and collaborate with other people; to reach agreements that satisfy everyone; and to work with others to achieve the organization’s objectives (Succi, 2019).

Other studies provide evidence that hard skills alone are not enough to make a professional life complete, rewarding, and satisfying (Sethi, 2016), since the productivity and usefulness of hard skills derive from their combination with soft skills (Balcar, 2016). In this context, hard skills may not make sense without soft skills, due to their complementary nature (Robles, 2012). Soft skills help people manage conflicts and create inclusive relationships that improve team performance, create ideas and negotiate solutions (Succi, 2019; Succi and Canovi, 2020). Additionally, they determine how information is shared among colleagues and how executives perceive and understand their work environment (Parente et al., 2012).

The evidence reported by the studies mentioned above leads us to propose the following hypotheses:

• H1: Hard skills determine the performance of undergraduate students in their internship.

• H2: Soft skills determine the performance of undergraduate students in their internship.

The literature shows that soft skills significantly impact people’s performance and professional careers. According to Wats and Wats (2009), hard skills contribute only 15% to a person’s success, while the remaining 85% corresponds to soft skills. Furthermore, Michnick-Golinkoff and Hirsh-Pasek (2016) point out that there is growing evidence that soft skills compete with hard skills in their ability to predict employability and higher wages.

In addition, students must integrate their knowledge and soft skills to take advantage of opportunities in their early career (Baldwin et al., 2011), since employers expect new employees to demonstrate skills and attributes such as critical thinking, communication, leadership, collaboration and teamwork (Lowden et al., 2011; MacDermott and Ortiz, 2017; Pierce, 2019). These skills and attributes are required in different degrees. At the middle level, employees must have an equal degree of hard and soft skills, while at the top level a higher degree of soft skills is needed (Rao 2010). Finally, soft skills make the difference between a job well done and the ability to achieve superior performance and better results (Oliveira et al., 2016).

The findings of the studies mentioned above alead us to propose the following hypothesis:

• H3: The performance of Icesi’s undergraduate students in their internship is determined to a greater extent by soft skills than by hard skills.

Methodology

This study is carried out in the context of higher education in Colombia and analyzes the performance of Icesi students during their internship. The internship duration is at least 6 months. It is mandatory and is included in the curricula of undergraduate programs. The experience is designed to prepare students for the beginning of their professional career and their transition from the academic world to the world of work.

Instrument used

The data were collected using a questionnaire designed by the university to evaluate students’ internship performance. The questionnaire investigated the perceptions of the direct supervisor of the student intern concerning their performance and consists of 35 items divided into five sections. The first section collected general data from the students and employers (e.g., “Mention the position held by the student”). The second section asked for the supervisor’s perceptions of the student’s hard skills. Each supervisor had to list the different activities, projects and functions the student had carried out and to evaluate them based on performance (e.g., “Describe the activities, projects and functions performed by the student”). The third section asked the supervisor about the presence of soft skills in the student and whether they were observed during the student’s performance in the organization (e.g., “The student has the ability to argue and support their own proposals”). The fourth section inquired about the student’s performance in general (e.g., “The student assumed the self-learning process on their own”). The fifth section was concerned with the supervisor’s satisfaction with the student’s performance (e.g., “Overall quality of student work”).

The reliability of the questionnaire was checked using Cronbach’s alpha coefficient. These coefficients are presented in Table 1 for each group of variables. All coefficients are greater than 0.7, and none of the confidence intervals include this value. This suggests that the questionnaire is reliable. The variables were measured on a Likert scale from 1 to 5, where 1 corresponded to “bad” (if the expectations were not met) and 5 to “excellent” (if the expectations were exceeded exceptionally).

Table 1.

Cronbach’s reliability alpha coefficient.

		Bootstrap 95% CI
Variables	Cronbach’s alpha	2.5%	97.5%
Soft skills	0.946	0.943	0.949
Hard skills	0.782	0.770	0.793
Performance 1	0.933	0.926	0.938
Performance 2	0.922	0.916	0.927
Performance 3	0.942	0.938	0.945
All	0.969	0.967	0.970

Note. Bootstrap 95% CI based on 1000 samples.

Because the data were collected from all participants at the same time using the same self-report survey questionnaire for both dependent and independent variables, confirmatory factor analysis was used to discard common method variance biases. The null hypothesis is that the correlation matrix implied or reproduced by the data and that of the specified model are statistically equal. To test this hypothesis, the root mean square error of approximation (RMSEA), the comparative fit index (CFI) and the standardized root mean square residual (SRMR) were calculated. These measurements were calculated for each model separately and are presented in Table 2. According to the RMSEAs, the three models have an acceptable fit, since the RMSEA is less than 0.1, and the confidence intervals do not contain this value. Further, all CFIs are greater than 0.95 and the SRMRs are less than 0.08. These results show that the estimated econometric models are plausible and the common method variance biases do not seem to be relevant.

Table 2.

Confirmatory factor analysis.

Dependent variable	RMSEA	RMSEA CI lower	RMSEA CI upper	CFI	SRMR
Performance 1	0.079	0.078	0.081	0.975	0.044
Performance 2	0.078	0.077	0.080	0.973	0.043
Performance 3	0.087	0.086	0.088	0.964	0.045

Abbreviations: RMSEA = root mean square error of approximation; CI = confidence intervals; CFI = comparative fit index; SRMR = standardized root mean square residual.

Sample

The data were collected from May 2010 to May 2021, with a sample size of 6632 students from 25 undergraduate programs in the Administrative and Economic Sciences, Engineering, Law and Social Sciences, Natural Sciences, Health Sciences, Sciences of Science, and Education Sciences schools (Table 3). Most of the students were enrolled in programs in the Administrative and Economic Sciences, Engineering, and Law and Social Sciences, because they are the oldest schools and have greater enrolment numbers. Undergraduate programs at Icesi are for 5 years, and, as already noted, students undertake their mandatory internship in the final year.

Table 3.

Schools and undergraduate programs analyzed.

School	Undergraduate program	Students
Administrative and economic sciences	Business (ADD)	692
	Accounting and International Finances (COF)	264
	Economic (ECO)	82
	Economic and International Business (ENI)	1036
	International Marketing and Publicity (MIP)	859
Engineering	Industrial Design (DIS)	348
	Design of Interactive Media (DMI)	266
	Biochemical Engineering (IBQ)	48
	Industrial Engineering (IND)	910
	Systems Engineering (SIS)	211
	Telematics Engineering (TEL)	135
Law and social sciences	Anthropology (ANT)	73
	Politic Sciences (CIP)	182
	Law (DER)	412
	Psychology (PSI)	522
	Sociology (SOC)	43
Natural sciences	Biology (BIO)	106
	Pharmaceutical Chemistry (QUF)	323
	Chemistry (QUI)	51
Education sciences	Foreign Languages (LEI)	34
	Arts and Technology (LAT)	7
	Primary Basic Education (LBP)	10
	Natural Sciences (LCN)	5
	Social Sciences (LCS)	4
	Spanish Literature and Language (LYL)	9

In addition, 86% of the students were aged from 19 to 24 years, and 96% of them were in cohorts from 2006 to 2017, corresponding to the period analyzed (Table 4).

Table 4.

Students’ ages and cohorts.

Age
19–21	8%
22–24	78%
25–30	13%
More than 30	0.7%
Cohort
1996-1 to 2005-2	3%
2006-1 to 2017-2	96%
2018-1 to 2020-2	1%

In total, 6632 valid surveys were collected, since the supervisor carried out the evaluation and the student’s career advisor received it personally and confirmed that it had been completed. The university processes this information on one of its platforms.

Variables

The dependent variable is the performance, and two groups are defined. The first includes the evaluation of the supervisor of the student’s performance in the specific activities they carried out, and the second pertains to the general evaluation of the student’s performance. The independent variables are the soft skills (including leadership, teamwork, reflected on and valuable life purposes, lifelong individual learning, critical thinking, interpretation and expression) and the hard skills (in the activities, projects and functions the students undertook during their internship) listed by the supervisors in order of importance. Finally, the control variables are the undergraduate program, the cohort and the age of the student.

Data analysis

The database contains different groups of ordinal variables (with values from 1 to 5) on soft skills (1 group), hard skills (1 group) and performance (2 groups, and a third group resulting from combining the first two). Each group is summarized in a continuous variable through principal components analysis (PCA).

Based on the principal components, a set of variables is exchanged for a few (in this case, only one) that contain as much information as possible from this set. The resulting variable is a linear combination of the set of observed variables

x_{p c a} = a_{1}^{*} x_{1} + a_{2}^{*} x_{2} + \dots + a_{k}^{*} x_{k}

x_{p c a} = {a^{*}}^{'} x

such that

v a r (x_{p c a}) = \max_{a_{1}, a_{2}, . . ., a_{k}} v a r ({a^{*}}^{'} X)

where

X

is a matrix composed of the variables

x_{1}, x_{2}, \dots, x_{k}

and

{a^{*}}^{'}

is a transposed vector containing the weights

a_{1}, a_{2}, \dots, a_{k}

. In other words,

x_{p c a}

will contain as much information as possible from the set

X

when its variance is maximum. It can be shown that

v a r (a^{'} X) = a^{'} Σ a

with

Σ

equal to the matrix of variances and covariances of the variables that make up

X

Σ

is usually standardized to contain ones on the diagonal and correlations in the other cells.

In most applications, $Σ$ contains Pearson correlations. However, these correlations make sense when the variables are continuous; they are not helpful with ordinal variables. To solve this problem, polychoric correlations are used. These correlations were designed for ordinal variables. The first step in finding them is to assume that the observed values of one variable, $x_{i}$ are generated by a latent variable, $x_{i}^{*}$

x_{i} = 0 \Leftrightarrow x_{i}^{*} \leq a_{1}

x_{i} = c \Leftrightarrow a_{c} < x_{i}^{*} \leq a_{c + 1}

c \in {2,3,4,5}

x_{i} = 5 \Leftrightarrow x_{i}^{*} > a_{5}

Additionally, it is assumed that latent variables, $x_{i}^{*}$ are distributed according to a joint normal distribution function

x^{*} = (\begin{array}{c} x_{1}^{*} \\ ⋮ \\ x_{6}^{*} \end{array}) \sim N (μ, Σ)

where

μ

is the vector of means, in this way, the cumulative distribution function is given by

Φ (t_{1}, \dots, t_{6}; μ, Σ) = \int_{- \infty}^{t_{1}} \dots \int_{- \infty}^{t_{6}} [\frac{1}{| Σ | {(2 π)}^{3}} \exp [- \frac{1}{2} {(x^{*} - μ)}^{'} Σ^{- 1} (x^{*} - μ)]] d x_{1} \dots d x_{6}

where

t_{1}, \dots, t_{6}

are dummy integration variables. From this function is found the likelihood of the observable sample,

L (μ, Σ)

; that is, the ordinal variables. Then, the function is maximized in the parameters

μ

and

Σ

. The elements of

\hat{Σ},

off-diagonal, are known as the polychoric correlations estimated between the ordinal variables,

x_{i}

. The polychoric correlation between two ordinal variables is equivalent to the Pearson correlation between the associated latent variables. Once

Σ

is estimated, the PCA analysis is applied to summarize each group in a single continuous variable.

Regression analysis

Once each group of ordinal variables is summarized in a continuous variable, they are related in a regression as follows

D E S E M_{h i} = α + β_{1} S O F T_{i} + β_{2} H A R D_{i} + \sum λ_{j} θ_{j} + ϵ_{i}

$D E S E M_{h i}$ represents one of three performance indices; $S O F T_{i}$ represent soft skills indices, $H A R D_{i}$ represents hard skills index; $θ_{j}$ represents a group of fixed effects to control for systematic differences in the undergraduate program, cohort, and age; and $ϵ_{i}$ the random disturbance term, which represents the other factors that affect the level of performance and are not observed. The parameters of interest, $β_{1} a n d$ $β_{2,}$ indicate the direction and magnitude of the effects of the soft and hard skills on performance. To compare these magnitudes, the regression model is modified as follows

D E S E M_{h i}^{*} = β_{1}^{*} S O F T_{i}^{*} + β_{2}^{*} H A R D_{i}^{*} + \sum λ_{j}^{*} θ_{j} + ϵ_{i}^{*}

The asterisks indicate that the variables of the model have been standardized; that is, it is true that

x_{i}^{*} = \frac{x - μ_{x}}{σ_{x}}

$μ_{x}$ and $σ_{x}$ represent the mean and standard deviation of the variable $x$ , respectively. The new coefficients, $β_{1}^{*}$ and $β_{2}^{*}$ are the standardized coefficients and allow the comparison of the effects of each variable of the model. Since each variable is standardized, it is possible to find the one that has the greatest effect on the performance indices.

Results

Table 5 presents a descriptive data analysis of the main variables of the econometric model. The variables have mean 0 and variance 1 because they were standardized. In addition, it is highlighted that all of them have asymmetric distributions to the left. This indicates that most of the individuals are concentrated in the upper part of the distributions; that is, most of the individuals have a high performance – measured by the three performance variables – and a high rating in hard and soft skills. Additionally, all distributions have a kurtosis close to three, indicating that there is no significant proportion of outliers.

Table 5.

Descriptive data analysis.

Variable	S. Deviation	Skewness	Kurtosis
Performance 1	1	−0.56	2.73
Performance 2	1	−0.55	2.76
Performance 3	1	−0.47	2.46
Soft	1	−0.53	2.65
Hard	1	−0.36	2.45

Figure 2 shows the correlations between the performance indices and soft and hard skills. All correlations are positive and have a high magnitude, and suggest a positive relationship between the soft and hard skills, and the performance and feedback between the soft and hard skills. It should be noted that the degree of soft-performance association is higher than the hard-performance association.

Table 6 shows the performance regressions with the soft and hard skills without controlling for the fixed effects (FE) of the undergraduate program, cohort and age. Regardless of the type of performance, the soft and hard skills have a positive and significant effect on each of them. The high magnitudes of the determination coefficients of the regressions with ranges from 65% to 79% are worth emphasizing. They show that the soft and hard skills explain a considerable proportion of student performance. However, the magnitude of the coefficient of the soft skills is greater than for hard skills, indicating that soft skills are more important than hard skills in determining student performance.

Table 6.

Performance and soft–hard skills.

	(1)	(2)	(3)
	Performance 1	Performance 2	Performance 3
Soft	0.663***	0.677***	0.707***
Soft	(0.011)	(0.009)	(0.008)
Hard	0.183***	0.252***	0.233***
Hard	(0.011)	(0.009)	(0.008)
N	6632	6632	6632
R2 adjusted	0.650	0.774	0.797
FE	—	—	—

Note. Standard errors in parentheses. *p < .10, **p < .05, ***p < .01.

Due to the high correlation between hard and soft skills, it is necessary to verify collinearity problems between the variables. Table 7 shows the variance inflator factors (VIF). With values less than 10, there is no multicollinearity problem.

Table 7.

Variance inflator factor.

Variable	VIF	1/VIF
Soft	2.23	0.448212
Hard	2.23	0.449059

Abbreviation: VIF = Variance inflator factor.

Table 8 shows the regressions with the undergraduate program, cohort and age as control variables. Again, the results are almost identical; that is, the positive influence of soft and hard skills on performance, and the greater importance of soft skills. Such results are robust to the inclusion of controls.

Table 8.

Performance and soft–hard with control variables.

	(1)	(2)	(3)
	Performance 1	Performance 2	Performance 3
Soft	0.662***	0.676***	0.707***
Soft	(0.011)	(0.009)	(0.008)
Hard	0.178***	0.249***	0.229***
Hard	(0.011)	(0.009)	(0.008)
Constant	−0.000	0.000	0.000
Constant	(0.007)	(0.006)	(0.006)
N	6625	6625	6625
R² adjusted	0.653	0.775	0.799
FE	Program	Program	Program
	Cohort	Cohort	Cohort
	Age	Age	Age

Note. Standard errors in parentheses. *p < .10, **p < .05, ***p < .01.

Since the sample comprises students from different undergraduate programs who undertook the internship at different periods, it is necessary to verify whether the results are robust. For this, the regressions presented in Table 6 were carried out, first, for each period, and then for each undergraduate program (only for undergraduate programs with more than 30 students) separately. The estimated coefficients and their confidence intervals are presented in Figures 3 and 4.

Figure 3 shows that although the coefficients are not constant over time, they are positive in most cases. In addition, the order in the magnitude of the effects is always maintained – first the soft skills and then the hard skills – which confirms that soft skills influence performance more than hard skills, regardless of the period analyzed. Finally, identical conclusions are obtained when analyzing the graph of the coefficients by undergraduate program (Figure 4).

Furthermore, the confidence intervals per year were intercepted only in 2010 (for performance variables 1 and 2). Therefore, from 2011 to 2021 there is always a significant difference between the effects of the variables soft and hard skills. This shows that the difference is significant; that is, in statistical terms, the effect of soft skills on the different performance measures is greater than that of hard skills. When the regressions are run by undergraduate program, the result is similar. In most cases, the effect of soft skills is statistically greater than that of hard skills (there are some exceptions).

Finally, the longitudinal effects on the perception of internship performance are presented in Table 9, which shows the coefficients of soft and hard skills for the three performance variables. The most relevant conclusion is that the effect of soft skills is always bigger than that of hard skills. Also, the effect of soft skills tends to increase over time, while the effect of hard skills tends to decline. This corroborates the greater importance of soft skills compared to hard skills and highlights the efforts of Icesi University to develop both hard and soft skills in its students. To achieve this over the years, the university has established a learning assurance system that allows it to detect gaps in the achievement of student learning objectives and to generate actions to close them.

Table 9.

Longitudinal data analysis.

Year	Variable	Performance	Performance	Performance	Variable	Performance	Performance	Performance
Year	Variable	Coef 1	Coef 2	Coef 3	Variable	Coef 1	Coef 2	Coef 3
2010	Soft	0.540	0.540	0.540	Hard	0.287	0.287	0.287
2011		0.648	0.648	0.648		0.126	0.126	0.126
2012		0.545	0.545	0.545		0.260	0.260	0.260
2013		0.600	0.600	0.600		0.250	0.250	0.250
2014		0.553	0.553	0.553		0.261	0.261	0.261
2015		0.626	0.626	0.626		0.190	0.190	0.190
2016		0.655	0.655	0.655		0.222	0.222	0.222
2017		0.699	0.699	0.699		0.145	0.145	0.145
2018		0.721	0.721	0.721		0.131	0.131	0.131
2019		0.755	0.755	0.755		0.120	0.120	0.120
2020		0.642	0.642	0.642		0.210	0.210	0.210
2021		0.835	0.835	0.835		0.046	0.046	0.046

Discussion

The results in Table 6 confirm hypotheses 1 and 2 and show the relevance of both hard and soft skills in the performance of internship students. While hard skills represent knowledge and professional skills, soft skills allow the transfer of this knowledge effectively and help people to manage conflicts, create inclusive relationships, persuade and influence customers, negotiate with business partners and communicate with peers and superiors (Robles, 2012; Succi, 2019; Succi and Canovi, 2020). Soft skills also determine how executives perceive and understand their work environment (Parente et al., 2012).

Traditional education privileges the development of hard skills through curricula focused on knowledge and professional skills. In contrast, the results of this study suggest that hard skills alone are not enough to achieve optimal performance (Sethi, 2016) because their productivity and usefulness derive from their combination with soft skills (Balcar, 2016).

Table 6 also shows that soft skills have the greatest influence on the performance of internship students. These results confirm H3, following other studies that have found that a person’s success in their professional career is determined to a greater extent by soft skills (Wats and Wats, 2009) because it is these abilities that predict employability and higher wages (Michnick-Golinkoff and Hirsh-Pasek, 2016).

Therefore, soft skills are expected by employers due to their positive impact on performance and results (Baldwin et al., 2011; Lowden et al., 2011; Oliveira et al., 2016; Pierce, 2019), and they gain in importance as the job level increases. Thus, at the top level, a high degree of soft skills is necessary (Rao, 2010). Furthermore, organizations are demanding soft skills such as problem-solving, critical thinking, communication, collaboration, teamwork and leadership (MacDermott and Ortiz, 2017; Pierce, 2019) when they seek people who can transcend knowing-doing and privilege knowing-being to build a better workplace and make the organization more productive and successful.

When regressions are run on the undergraduate programs, there are some exceptions to the effect of soft skills on the different performance measures: IBQ and LEI for the first performance measure; IBQ, LEI, QUI and SOC for the second measure; and LEI for the third (see Table 3 for definitions of acronyms). In these cases, the effects of soft and hard skills are statistically equal. This could be explained by the fact that in these disciplines students do not achieve greater development of their soft skills, or the supervisors do not give greater importance to these skills in their evaluation.

Finally, the study shows how, over the years, the perception of supervisors has changed regarding the importance of both hard and soft skills in students’ performance during their internships. In addition, it is evident that the effect of soft skills is always greater than that of hard skills. These results indicate the important role universities play in the training of students and in preparing them for the world of work in a way that responds to the needs of organizations.

This study shows not only that the Icesi University students had developed both hard and soft skills, but also that enterprises pay much attention to soft skills development (Stewart et al., 2016; Succi, 2019; Succi and Canovi, 2020) and evaluate students according to how much they use these skills during their time in the organization.

Limitations and implications for future research

Although this study’s overall sample size was large as compared to other studies, there are still some limitations, since it includes students from only one university. Other studies comparing the different educational projects of various universities would be very valuable, considering that universities have different learning objectives and graduation competencies for their students. Additionally, the study does not differentiate the results by company size. It is possible that the performance of the student and the satisfaction of the employer vary among large and small companies due to the differences in their structure and management and in the availability of resources. Research on student performance by company size could provide valuable insights. Finally, this study does not include a comparison of the results by school and academic program, which could be valuable due to the differences that exist among disciplines.

Conclusions

The results suggest the importance of both soft skills and hard skills in an undergraduate student’s performance during an internship. It also provides evidence that soft skills have greater importance in explaining that performance.

Such evidence is helpful in developing a better understanding of employers’ perceptions concerning the importance of integrated training in higher education (that is, training in both hard and soft skills). Universities might use these results to review their educational projects and design programs that include the development of hard and soft skills through curricular and extracurricular activities. In this context, dialog between academia and employers will improve universities’ understanding of the needs of industry. Furthermore, it will contribute to the effectiveness of students’ training and will thus respond to labor market demands, heightening their chances of early employment when they have completed their internship.

Although the study offers valuable findings, future research might include the perceptions of students and universities about integrated training in hard and soft skills and how it can be achieved in higher education. Additionally, it would be valuable to understand how organizations can contribute to the strengthening and further development of students’ soft skills and their transition from the academic world to the world of work.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mónica Franco-Ángel

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