The study included 252 respondents, of whom 41.3% were male and 58.7% were female. The largest proportion of respondents (56.7%) live in a large city (more than 100,000 inhabitants), 17.4% in a city (up to 100,000 inhabitants), 12.4% in a smaller city (up to 50,000 inhabitants), 7.5% in a town (up to 10,000 inhabitants), and the smallest proportion (6.0%) in a village (up to 5000 inhabitants).
With regard to the educational background of the respondents’ parents/guardians, the distribution is as follows:
4.1. Principal Component Analysis
For the purpose of explaining the shared variance of the variable set, i.e., the variability within groups of variables, factor analysis PCA was applied. PCA is based on a mathematical model in which factors are derived as standardized principal components. By analyzing the correlation matrix among the variables, and in order to assess data suitability, 20 variables were included in the analysis, representing both positive and negative opinions of the respondents.
The general factor model has the following form:
where
X—variable with a mean of zero and variance of one;
i—variable index;
F—mutually independent factors;
m—factor index;
a—factor loading;
e—specific factor associated only with the given variable.
The factor loading indicates the relative importance of each characteristic in defining the factor.
The validity of factor analysis is confirmed by meeting all the criteria: the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy was 0.750, and Bartlett’s test of sphericity reached statistical significance (Sig < 0.001). Principal Component Analysis revealed the presence of five components with eigenvalues greater than 1 (5.256, 2.052, 1.773, 1.291, and 1.224), which together accounted for 57.98% of the variance. The scree plot indicated the first break point between the third and fourth components (see graph in
Figure 3).
To determine the number of components to be retained, a parallel analysis was conducted. Based on the results of the parallel analysis and the SPSS Component Matrix table, it was concluded that a three-factor solution was more appropriate. These three components explained a total of 45.40% of the variance.
After performing the Varimax rotation, the following results were obtained (the order of variables within each component is presented according to their factor loadings within the group):
Component 1 (Group Work Competencies): Appreciation of diversity and multiculturalism; ability to apply knowledge in practice; Ethical commitment and orientation; cooperation—the ability to work in a team; Flexibility and adaptability—the ability to adapt to a new situation; communicativeness; oral and written communication in a foreign language, initiative and self-management; ability to criticize and self-criticize.
Component 2 (Cognitive and Analytical Skills): Ability to solve problems; ability to manage information—gathering and analyzing information from various sources; research and inquiry—research skills; ability to make decisions; critical thinking; basic computer skills; creativity/innovation.
Component 3 (Management Skills, including Self-Management): Ability to organize and plan; self-motivation to work; ability to learn; leadership ability—leadership and responsibility.
4.2. Results for the AHP, FAHP, and SFAHP Algorithms
In this section, the outlined algorithms, FAHP [
8,
17] with the following five degrees of optimism: pessimistic (
), semi-pessimistic (
), balanced (
), semi-optimistic (
), and optimistic (
), SFAHP, as well as the crisp AHP method, are used to compare the obtained results and sub-criteria ranking. A group of experts from the areas of social sciences, media, economics and statistics, management, and education selected groups of criteria and sub-criteria and expressed their opinion based on the meaning of linguistic measures, TFNs, and SFSs presented in
Table 2 and
Table 3. The assessments experts gave were aggregated based on the first step of the presented algorithm, securing partial consensus [
18].
Firstly, the main criteria groups were discussed. The experts agreed on the equal significance of the following groups: group work competencies (G), cognitive and analytical skills (C), and management skills (including self-management) (M) (CI = CR = 0,
= 3). In the AHP case, the weights of main criteria G, C, and M are equal to 0.33, as can be seen in
Figure 4.
The dominance of cognitive and analytical skills is especially pronounced in the case of the optimistic FAHP case, with a corresponding weight of 0.42, being 1.27 and 1.75 times higher than criteria M and G, respectively. The same quotients for 0.5 are 1.21 and 1.55, respectively, showing the decision-makers’ balanced point of view. Pessimistic point of view shows the smallest quotient numbers, while the ratios obtained for 0.25 and 0.75 are similar to the ones obtained in the balanced case. The use of spherical fuzzy sets and two different accuracy functions gives uniformly distributed values with the smallest ratios in all cases. The average values of criteria weights are as follows: = 0.38, = 0.333, and = 0.286.
The ranking of sub-criteria is managed in the same manner as the ranking of the main criteria. The fuzzy comparison matrix of sub-criteria belonging to group G is presented in
Table 4. This matrix is consistent, with CI = 0.051 and CR = 0.042, calculated as follows:
In all cases, the sub-criterion G1, named appreciation of diversity and multiculturalism, ranked first, with the weights 0.462, 0.416, 0.385, and 0.371 in the AHP and three basic cases of the FAHP algorithm. It is followed by the ability to apply knowledge in practice, having 1.84 times smaller weight than G1 (AHP). In the case of the SFAHP, those ratios are 1.2 and 1.23, using AC1 and AC2, respectively. The sub-criteria G4 and G5 have the same weight equal to 0.062 in the AHP case, while also being in the fourth and fifth positions; their values ascend from the pessimistic to the optimistic point of view. In the SFAHP, the sub-criterion G4 has a slightly higher weight, between 4% and 6%. At the end of the group work competencies group lies the sub-criterion communicativeness, being approximately 15.3, 12.6, 2.4, and 2.9 times less important than the highest ranked sub-criterion G1 (AHP, balanced FAHP, and SFAHP).
Out of six sub-criteria, there are two pairs of equal rankings in the AHP case in the group cognitive and analytical skills. In the highest rank, with the corresponding weight of 0.337, are ability to solve problems, ability to manage information, and, at the end, critical thinking and basic computer skills, with a corresponding weight of 0.039. Using fuzzy algorithms, no two sub-criteria are ranked the same. The C1 still has the highest ranking, with a weight less than in the AHP, with a similar situation in the case of sub-criteria C2. Middle part of the ladder is taken by sub-criteria C3 and C4, with a little bit higher weights than in the AHP case: 0.188, 0.195, and 0.198 are the weights of sub-criteria in the pessimistic, balanced, and optimistic FAHP, while the corresponding weights for C4 are 0.093, 0.106, and 0.111. Applying the SFAHP, the leading sub-criteria have average weights of 0.242 and 0.239, while the average weights for the sub-criteria at the bottom, C5 and C6, are equal to 0.117 and 0.108. The corresponding TFNs for the sub-criteria are as follows: (0.145, 0.316, 0.713), (0.140, 0.316, 0.671), (0.076, 0.193, 0.461), (0.032, 0.102, 0.266), (0.023, 0.037, 0.120), and (0.023, 0.037, 0.120). The matching consistent comparison fuzzy matrix and weights can be seen in
Table 5 and
Figure 5.
Management skills (including self-management) group consists of four sub-criteria. The highly influential ability to organize and plan, with a weight of 0.575, is twice as important as the sub-criterion self-motivation to work and more than eight times as important as the sub-criterion ability to learn and leadership ability, all in the AHP case. The weights in the FAHP case are given in
Figure 6, where one can observe that ratios M1/M2 and M1/M4 in the optimistic case are equal to 1.529 and 8.784. In the balanced case, the weights of all sub-criteria are equal:
= 0.52,
= 0.334,
= 0.084, and
= 0.062.
Using spherical fuzzy sets (0.708, 0.299, 0.266), (0.603, 0.405, 0.316), (0.403, 0.579, 0.366), and (0.372, 0.612, 0.365), one can obtain that in the SFAHP case, averaged weights of sub-criteria are, respectively, equal to 0.334, 0.286, 0.198, and 0.183.
The final ranking of sub-criteria is presented in
Figure 7, and their weights in the case of AHP, five degrees of optimism in FAHP, and SFAHP are given in
Table 6. It can be observed that the sub-criteria from group M, namely M1, is still ranked highest; however, the remaining sub-criteria from the management skills group, M2, M3, and M4, ranked fifth, tenth, and eleventh, respectively. Furthermore, the highest-ranked sub-criteria from group work competencies, namely G1, corresponding to appreciation of diversity and multiculturalism, ranked second overall is 1.84 times higher than G2 and 3.47 times higher than G3 for the AHP case, and 1.45 and 2.44 higher than G2 and G3 in the case of FAHP, respectively (averaged over all points of view). When applying the SFAHP, previously described relations are equal to 1.96 and 1.96 with the first defuzzification method and 1.23 and 1.27 using the second way of defuzzification. The first two sub-criteria from the group cognitive and analytical skills, C1 and C2, are placed third and fourth in all eight different rankings, showing the importance of solving problems and analyzing the information gathered. Ethical commitment and research skills are in the middle of the ladder, being 4.32 and 6.85 times (AHP) and 4.081 and 4.051 times (balanced FAHP) less important than G1. The smallest values in the ranking procedure, being of smaller importance, are determined by the sub-criteria basic computer skills and communicativeness.
The Surface Fuzzy AHP [
19], using surface instead of sphere to ensure a higher degree of freedom to a decision-maker, could also be applied in similar situations. The operation with geodesic mappings and tensor calculus [
20,
21] might be adopted to obtain stronger results.
During the sub-criteria ranging process, eight different variants were used, possibly leading to inconsistencies. To estimate and analyze ranking similarities applying the AHP, the FAHP (five different situations), and the SFAHP (two different situations) to all influencing sub-criteria, as well as to assess the accuracy and validity of the proposed model, the Spearman rank correlation coefficient [
22] is applied as follows:
where
represents the number of elements in ranking, and
and
stand for the
element in the rankings used for comparison.
Applying Equation (18), it can be observed that min{
} = 0.93529, as can be seen in
Figure 8, yielding that all rankings have high similarity [
23]. The lowest value of the coefficient
for SFAHP is obtained when it is compared with optimistic and pessimistic FAHP and AHP, while the value
is reached when comparing AHP and FAHP (
).
The classical crisp AHP and FAHP methods were applied. In all cases, from pessimistic to optimistic (FAHP) and two variants of SFAHP, the most influential sub-criteria was ability to organize and plan. Differences can be seen in the second ranking sub-criteria: favoring appreciation of diversity and multiculturalism (AHP and pessimistic FAHP), ability to solve problems (majority of FAHP cases), and self-motivation to work (SFAHP). A similar situation holds for sub-criteria placed third, fourth, and fifth, while in the middle of the ladder stand ethical commitment and orientation and ability to make decisions. Sub-criteria of least importance for AHP, FAHP, and SFAHP are basic computer skills and communicativeness. Considering the ranking of criteria in the AHP compared to all five rankings in the FAHP, it can be concluded that there are no significant differences, with complete overlap with pessimistic FAHP. Semi-pessimistic and semi-optimistic FAHP differ only in four ranking places, while FAHP () and FAHP () have just two differences, yielding to the coefficient . The ranking results between two SFAHP methods, favoring sub-criteria from the management (and self-management) skills group, differ in six ranking places, obtaining approximately 99% similarity ranking.
The competencies within Component 2 (the most significant in terms of importance), namely ability to solve problems and ability to manage information—gathering and analyzing information from various sources—ranked the highest. These findings suggest that young people are capable of identifying challenges, handling unforeseen situations, selectively approaching information, recognizing what is relevant, and applying this knowledge in decision-making. This is of key importance for employers, as it allows them to rely on their employees.
Among the highest-ranked competencies in Component 1, appreciation of diversity and multiculturalism and ability to apply knowledge in practice indicate that young people in Serbia are oriented toward diverse (including international) labor markets. They value diversity, which ultimately enhances their tolerance and their ability to work in international and multicultural teams and companies. Moreover, the capacity to transfer knowledge into practice strengthens their self-confidence and work efficiency, thereby fostering business development and growth.
Within Component 3, the best-ranked competencies were ability to organize and plan and self-motivation to work. these suggest that young people who are able to organize and plan their activities are more likely to achieve goals within set deadlines and tasks. Similarly, those who are self-motivated often maintain stronger focus in reaching their objectives, which leads to greater job satisfaction and personal success and consequently contributes to the overall success of the company, an aspect equally valuable to employers.
4.3. Readiness for the Labor Market and the Employment Process—Challenges
The respondents expressed the view that the family has a very strong influence on the development of competencies. The modes for all items (variables) were either six or seven, while the mean scores ranged from 5.33 (leadership ability—leadership and responsibility) to 5.89 (basic computer skills). For all variables, the median values were within the interval of five to six. The variables demonstrated relatively low variability (coefficients of variation ranged from 19.61% to 30.61%) and strong negative skewness, with the exception of creativity/innovation, which exhibited slight negative skewness (Skewness = −0.219).
When considering the competencies with the highest factor loadings from each of the three previously examined components—ability to solve problems, appreciation of diversity and multiculturalism, and ability to organize and plan—the Mann–Whitney U test revealed a statistically significant difference only in the assessment of ability to organize and plan between male (
Me = 2.0,
n = 109) and female respondents (
Me = 3.0,
n = 85),
U = 3572.50, z = −2.811,
p = 0.005. The effect size is as follows:
i.e., it can be said that the impact is small [
24]. The variable shows a higher mean rank for male respondents (indicating a better assessment of ability to organize and plan).
Respondents whose fathers attained the level of magister or master’s degree and doctor of science (Ph.D.) awarded the highest score for the development of the ability to solve problems in 29.6% and 42.9% of cases, respectively. Similarly, the largest proportion of respondents whose mothers had achieved the level of the doctor of science (Ph.D.) (40%) assigned the maximum score (7) for the development of ability to solve problems.
Regarding the development of the competency of appreciation of diversity and multiculturalism, mothers with higher levels of education exert a greater influence. Specifically, 50% and 40% of respondents whose mothers had attained a magister or master’s degree or a doctor of science (Ph.D.), respectively, awarded the highest score (7) for the development of this competency.
Although young people assessed that the family has a significant influence on the development of competencies, it can be concluded that fathers exert a somewhat greater influence than mothers (no differences were found between fathers’ and mothers’ educational levels). The Kruskal–Wallis test revealed a statistically significant difference in the evaluation of ability to solve problems across six groups of fathers categorized by educational level (Gp1, n = 11: elementary school; Gp2, n = 87: secondary school, 3 or 4 years; Gp3, n = 21: college, 3 years of study; Gp4, n = 45: faculty, 4 years of study; Gp5, n = 27: magister or master’s degree, 5 years of study; Gp6, n = 7: doctor of science—Ph.D.), χ2(5, n = 163) = 12.276, p = 0.015. The highest mean ranks were recorded for fathers with a magister or master’s degree and a doctor of science (Ph.D.), indicating a stronger influence on the development of the competency ability to solve problems.
It is particularly interesting to note that the Kruskal–Wallis test revealed a statistically significant difference in the assessment of ability to solve problems across five groups of respondents categorized by their permanent or temporary place of residence (Gp1, n = 12: village, up to 5000 inhabitants; Gp2, n = 15: town, up to 10,000 inhabitants; Gp3, n = 25: smaller city, up to 50,000 inhabitants; Gp4, n = 34: city, up to 100,000 inhabitants; Gp5, n = 112: large city, more than 100,000 inhabitants), χ2(4, n = 198) = 10.379, p = 0.035. The highest mean ranks were observed among respondents whose fathers had attained a magister or master’s degree (5 years of study) and a doctor of science (Ph.D.), suggesting a stronger influence on the development of the competency ability to solve problems.
As for the reasons behind insufficient preparedness for the labor market, the respondents identified the following, in order:
Unfair practices in the job market (bribery, corruption, and nepotism);
Personal irresponsibility (lack of interest and laziness);
Lack of awareness of job market demands;
Development and education strategies of the Republic of Serbia;
The country’s poverty;
None of the above/I consider myself prepared for the labor market;
An uninspiring social and family environment.
From the previously outlined sequence, it can be observed that young people also demonstrate the characteristic of self-criticism, as they identify personal irresponsibility (lack of interest, laziness) as the second most important reason for their insufficient readiness for the labor market.
After graduation, the most frequently cited reason for difficulties in gaining employment is insufficient knowledge of young people about employment opportunities (31.2%). An equal proportion of respondents (23.7% each) reported a lack of adequate employment programs and a lack of competence with regard to the requirements of employers, while 21.5% identified an insufficient number of jobs in the labor market as the key barrier.
The chi-square test of independence revealed a statistically significant association between the perceived reasons for difficulties in finding employment after graduation and gender, χ
2(3,
n = 186) = 10.511,
p = 0.015. The value of Cramer’s V was 0.238, indicating a medium effect size [
25]. For female respondents, the greatest barriers were an insufficient number of jobs in the labor market and a lack of adequate employment programs, whereas for male respondents, the main obstacles were insufficient knowledge of young people about employment opportunities and a lack of competence with regard to the requirements of employers.
4.4. Media Shaping of Key Competencies for the Labor Market
The media, as a mirror of society, are not the primary source of responsibility for the lack of adequate competencies required by the contemporary labor market. Educational institutions, particularly universities, should take the initiative in more rapidly and comprehensively adapting curricula to labor market demands, acknowledging the changing context and the implementation of new technologies, strongly reinforced by the application of artificial intelligence. In this regard, the media industry should be considered one of the most dynamic and demanding sectors, given that the daily use of AI in media strongly influences media business activities. Consequently, curricular changes in the education of media professionals are imperative [
26]. Newly acquired competencies and/or the upgrading of existing ones may contribute to strengthening competitiveness and employability while also accelerating career development and mobility among those already employed.
The increasingly pervasive and influential media discourse and information channels exert a strong impact on lifelong learning, requiring individuals to make continuous investments of available resources in acquiring new knowledge and skills related to the functions of both traditional and digital media, as well as to modes of media consumption. Precisely through the lens of these needs emerges the necessity of information and media literacy, achieved through the transfer of knowledge and skills to a broad audience. To minimize potential risks, the media industry is under dual scrutiny: that of state regulation and that of established systems of self-regulation [
27].
The development of competencies is not a matter of a single moment but represents a dynamic process in which required competencies are continuously redefined in relation to the labor market. Supporting this claim is a long-term trend that illustrates decision-making regarding job location, where companies prioritize the availability of qualified labor and rank it first, with 74% of respondents identifying this factor as crucial. Labor costs (64%) occupy the second position, while all other determinants—such as the flexibility of labor legislation or the effects of industrial capacity concentration in a single location—are considered less relevant. Indicators from the World Economic Forum [
28] unambiguously suggest that workforce transformation is accelerating, while the opportunity for proactive management of these changes is closing, serving as a clear signal to governments and workers that proactive planning and action within the framework of the new architecture of the global labor market are imperative.
Some studies indicate that students identify mass media as the most important source of career guidance, despite the fact that they most frequently perceive them as manipulative, while significantly fewer recognize their informative and educational functions. Mass media indirectly shapes students’ aspirations by reinforcing values such as social recognition, mobility, and identity. Although the influence of media culture is substantial, the media have not assumed a dominant role in the career orientation process, particularly when compared with mechanisms of self-evaluation and the influence of the family [
29].
When examining media channels that contribute to the development of key competencies for positioning in the labor market, the majority of respondents identified social networks as the main contributors (69.5%), followed by television (23.2%), printed media (5.8%), and radio (1.5%).
Among social networks, those with the highest reported contribution to the competency development for the labor market were as follows:
YouTube, with a mean score of 5.53, a median of 6, and a mode of 7, the variable exhibits a very strong negative skewness (Skewness = −1.081) and relatively low variability (CV = 27.88%).
Instagram, with a mean score of 5.11, a median of 5, and a mode of 7, the variable shows strong negative skewness (Skewness = −0.743) and relatively low to moderate variability (CV = 34.85%).
LinkedIn, with a mean score of 4.83, a median of 5, and a mode of 7, the variable indicates very strong negative skewness (Skewness = −0.563) and relatively low variability (CV = 38.98%).
Social networks with the highest contribution to the development of competencies required in the labor market (LinkedIn, Instagram, and YouTube) display (see
Table 7) a significant direct linear interrelationship, with correlation coefficients falling within the interval
.