Abstract
In the context of the connotative development of higher education, the match between what college graduates have learned and what they have used and its causes has aroused the attention of society. Human capital and family capital are two important research perspectives when analyzing what graduates learn and what they use. The study selects professional ability, general ability and allocation ability to measure graduates' human capital and analyzes their family capital from three levels: economic, cultural and social. The study verified that human capital plays a mediating role in the influence of family capital on graduates' learning-application matching. Among the factors of human capital, professional ability and allocation ability have a significant positive influence on graduates' learning-application matching while general ability has a negative influence on graduates' learning-application matching. Family economic capital and family cultural capital have a significant positive influence on graduates' learning-application matching. Based on the findings of the empirical study, we propose countermeasures for universities to improve and enhance graduates' learning-application matching.
Keywords: Private institutions, Graduates, Learning-application matching, Human capital, Family capital
1. Introduction
Since the massive expansion of higher education in 1999, the number of university graduates in China has been increasing year by year, and the employment situation has become increasingly severe. In 2001, the number of university graduates nationwide was 1.15 million; in 2011, it increased to 6.6 million; in 2021, the number of graduates is as high as 9.09 million; and in 2022, the number of graduates will exceed 10 million. The high number of graduates positively illustrates that with the development of the economy and society and the improvement of people's living standards, the number of people receiving higher education has increased significantly and reflects that the development of higher education relying on scale expansion is gradually encountering a bottleneck. How to make college graduates from “employable” to “well employed” has become an important issue for all types of colleges and universities and education authorities at all levels. In addition, the core demand of students and parents for higher education has also changed, from “having school” to “good school” and from “steady employment” to “satisfactory employment”. A “quality revolution” in the field of higher education is inevitable. In the measurement of graduates' employment quality, scholars focus on employment satisfaction, high and low salary levels, and the match between learning and application [1,2]. Among the above discussed dimensions, research on university graduates' learning-application matching has not yet become systematic, and there is more room for exploration [3].
Regarding the influencing factors of matching graduates' studied majors with their employment, scholars at home and abroad have analyzed and formed rich research results with different focuses. Some scholars have found that the influencing factors of graduate employment and major matching can be considered in two aspects, macro and micro, among which macro factors mainly include the socioeconomic environment, local industrial structure and faculty major structure; micro factors mainly include individual students and employers [4]. Some scholars divide the factors affecting the initial employment matching of senior graduates into three aspects: students' personal factors, job factors and environmental variables [5]. The economic situation of cities is an environmental variable that cannot be ignored, and large cities help to improve the degree of graduates' learning-application matching because they have a wide range of employment opportunities and lower job search costs [6]. The degree of learning-application matching of graduates is highly correlated with their majors, and the smaller the effect of wage effect and job satisfaction caused by the learning-application matching status of graduates of a certain major, the lower the degree of learning-application matching [7]. In addition, the rigidity of the higher education system and the disconnect between major settings and market demand are important factors affecting graduates' learning-application matching in China [8,9]. The factors involved in the previous studies can be grouped into a four-dimensional quadrant of “micro-macro; internal-external”. In other words, the analysis of the influencing factors of graduates' learning-application matching has not formed a common influencing factor index in a certain theoretical context. Second, not all influencing factors can be changed through educational means or human intervention, such as the social environment and economic scale of cities.
Human capital and family capital as analytical perspectives are not only supported by a well-established theoretical system [10,11] but also have a long development in empirical applications [12]. Families and individuals from socially advantaged backgrounds, with various “capital advantages,” can significantly affect employment and its matching status. Graduates from different families have significant differences in access to employment information and employment choices, which can lead to employment “differentiation” and even class entrenchment [13]. Therefore, it is important to study the factors influencing graduates' job matching in terms of their human capital and family capital, and only by considering these two important forms of capital can we draw in-depth and detailed conclusions.
In previous research, the topic of graduate employment has generally focused on public institutions, especially the former “211″ and “985″ universities, as well as the highly regarded “Double First-Class” universities. The research results on graduates from private institutions are not rich. Nowadays, as an important supplement to the form of higher education in China, private colleges and universities in China have grown rapidly, surpassing their historic numbers of schools, students, and subject areas offered, showing vitality and significant achievements. Private colleges and universities refer to higher education institutions and other educational institutions established by enterprises, institutions, and other social organizations, as well as individual citizens, using non-state education funds to cater to society. As of 2020, there were a total of 771 private ordinary universities in China, accounting for 28.16 % of the total number of ordinary universities, a year-on-year increase of 1.8 % compared to 2019. With the increase in the number of private ordinary universities, student enrollment has also continued to rise. In 2020, the number of students in private ordinary universities in China reached 7.9134 million, an increase of 0.8251 million compared to 2019, a year-on-year increase of 11.6 % [14].
In China, private universities are somewhat different from public universities that are public welfare institutions, with significant advantages and characteristics such as being “market-oriented”, “student-oriented”, with a “strong sense of adaptability”. Due to the fact that the funding for private universities mainly comes from student tuition fees, they attach great importance to student needs. Many private universities prioritize market demand and employment difficulties in the process of running schools and setting majors, facilitating the realization of a “virtuous cycle” in the subsequent enrollment process. Conducting a survey on the employment situation of graduates from private colleges and universities to understand their matching between learning and application is more representative and practical. Based on the perspectives of human capital and family capital, this study analyzes the current situation of graduate matching and its influencing mechanism and tries to further enrich the research on graduate matching in private colleges and universities. At the same time, the relevant conclusions will also provide a more comprehensive picture of China's higher education.
2. Literature review and research hypothesis
2.1. Review of important literature
2.1.1. Human capital
The concept of human capital has been enriched by theoretical and empirical studies. Theodore Schultz first introduced the concept of “human capital” in his article “Human Investment: An Economist's Perspective” [15]. Min defines human capital as the knowledge and abilities that people develop through investment in their own education and training [16]. Robert measures human capital across graduates by age, race, and profession [17]. Liu analyzes the current situation of learning-application matching, influencing factors and wage effects of college graduates from the perspective of human capital investment. The study emphasizes that through different majors, students will have different skills; some majors will provide students with more general skills, and some majors will provide students with career-specific skills [18]. With the expanding application of research, Schultz in his article “The Value of the ability to cope with imbalances” introduced the concept of allocative ability around human capital again. In an empirical study, Tian and other scholars expressed allocation ability specifically as job information gathering, communicating effective information about oneself to the employer, handling problems in recruitment assessment, and coordinating and communicating with others [19]. This study establishes a link between specific measures of human capital and the abilities of graduates. Education and training provide workers with the scientific and cultural knowledge and skills necessary for production in the sense that “education produces labor power”, which is the logical “core” of human capital theory [20]. Thus, the measurement of human capital is divided in detail into three areas: professional, general and allocation ability. The general ability, also referred to as general skills, makes students more likely to become generalists, applicable to various types of jobs. The allocation ability is defined as the competencies that graduates demonstrate in the job search process to find the “learning-application matching”.
Human capital theory is an important theoretical orientation to explain the quality of graduate employment. This theory emphasizes that the level of education, skills and work experience possessed by an individual can play a decisive role in their earnings after graduation [21]. It has been suggested that in a perfectly competitive labor market, individual human capital is proportional to work ability, and higher work ability creates higher value for the firm and higher pay for the individual [22]. The effect of human capital on the match between graduates and employers is more complex and requires consideration of specific human capital segments, which will be analyzed in detail in the hypothesis section later in the study.
2.1.2. Family capital
The concept of family capital is widely used in the educational community and has a long history. Family capital has a lifelong impact on individuals throughout their life cycle [23]. The concept of family capital is derived from social capital, and many studies have used the concept of “social capital” as a starting point for the discussion of “family capital” [24]. Li refined family background into family economic capital, family cultural capital, family social capital, and family political capital, arguing that family background can have a significant impact on students' access to educational opportunities [25]. In terms of specific measures of family capital, family capital can be divided into four major categories: social capital, cultural capital, economic capital, and political capital. Among them, social capital is further divided into intrafamily social capital (parents' expectations for their children's education, frequency of parents' checking homework, and frequency of parents' guiding their children's homework) and extrafamily social capital (parents' occupational stratification); cultural capital is measured using parents' highest education and the number of books in the family collection; economic capital is measured by family economic status; and political capital is measured by parents' political outlook [26]. In a comprehensive analysis, family social capital, family cultural capital, family economic capital, and family political capital are important indicators used by scholars to define and measure family capital in previous studies [27,28].
At present, there is no extensive research in the academic field around the influence of family capital on graduates' learning-application matching, and most scholars explore the relationship between social capital and graduates' employment quality from the perspective of social capital. In their research, Hu and other scholars proposed that social capital has both direct and indirect effects on the improvement of college students' employment quality [29]. The direct impact of social capital on graduates' employment is manifested as a direct impact on people's income returns by enhancing their individual job performance [30]. Social capital also has the potential to influence income levels by helping students acquire human capital and career status, which subsequently affects income levels, that is, exerting an indirect influence [31]. This study focuses on the impact of family capital on graduates' learning-application matching, drawing on previous research to comprehensively analyze the direct and indirect roles of family capital. The specific measurement of family capital draws on the research results of previous scholars and subdivides it into family economic capital, family cultural capital, and family social capital. Family economic capital is measured by annual per capita household income; family cultural capital is measured by parents' education; and family social capital is measured by parents' occupation.
2.1.3. Learning-application matching
Previous scholars have explored the issue of matching learning and application in multiple dimensions, and the research results have been fruitful. First, we discuss the concept of learning-application matching. The core concept of “learning-application matching” has not been uniformly defined in previous studies, and the concepts of “learning-application matching,” “employment matching,” “professional matching” and “professional mismatch” are the key words for scholars to study such issues [[32], [33], [34]]. Second, there is a discussion about the necessity of the study on the matching of studies and their applications. As an important part of the research on the employment quality of university graduates, some scholars advocate that we should broaden the caliber of majors, enhance the adaptability of majors, and pay attention to the employment rate of graduates. Some scholars also believe that we should pay attention to the analysis of graduates' learning-application matching, which is not only related to the return of students' personal human capital investment but also related to some scholars who believe that we should focus on the analysis of graduates' learning-application matching. This is not only related to students' individual human capital investment return but also related to the decision of human capital investment of the whole society and affects the sustainable economic development and social harmony and stability [35]. Finally, the factors influencing the match between learning-application are discussed. Previous studies have presented differentiated indicator choices due to different focus perspectives [36].
In past studies, the concept of “learning-application matching” was mostly described by the correspondence between “profession” and “employment”, “competence” and “job requirements”, and “knowledge and skills” and “job requirements”. Based on the research results of previous scholars, this study defines “learning-application matching” as the degree of matching between graduates' majors and their initial employment positions, which corresponds to the connotation of horizontal matching proposed by Mao and other scholars. In addition, since no long-term follow-up survey is involved, the mismatch findings can only illustrate the short-term mismatch phenomenon. In terms of the specific measurement of the learning-application matching, drawing on the practice of Xu et al. [37,38], a self-assessment method was used to investigate the “learning-application matching” status of graduates. This was done by setting questions to ask graduates about the degree of relevance between their occupations and their majors in school and classifying the degree of relevance into “very irrelevant”, “less relevant”, “average”, “more relevant”, and “very relevant”. The degree of relevance was divided into five dimensions: “very irrelevant”, “less relevant”, “average”, “more relevant” and “very relevant”, and scored from low to high (1–5).
2.2. Research hypothesis
The hypotheses involved in this study were developed based on a thorough drawing of relevant theoretical and empirical studies [39]. The study focuses on empirical aspects to explore and verify the relationship between the variables, and the hypothesis of mediating effects between the variables is also presented.
2.2.1. Matching human capital factors with graduates' learning and application
The study classifies human capital into three factors: professional ability, general ability, and allocative ability, and the empirical analysis also explores the relationship between each factor of human capital and graduates' learning-application matching from different factors. Foreign scholars have conducted much research on the impact of human capital on wages and worker mobility, with human capital acquisition focusing on on-the-job training, believing that occupational skills increase wages, and disagreeing on whether occupation-specific skills increase or decrease employee mobility [40]. General skills increase the likelihood of switching between occupations and jobs, while specific skills decrease the likelihood of career switching. In other words, the acquisition of graduates' professional ability drives students to prefer the type of job related to their field of study in their subsequent job choices. The acquisition of general ability, on the other hand, leads graduates to expand the range of employment options in subsequent career choices, and students are less likely to choose job types highly related to their majors [41]. During the development of higher education in China, there have been two discipline and subject adjustments, and in the context of the second discipline and subject adjustment, the two-way selection system of “graduates choosing their own jobs and employers accepting them on the basis of merit” has been implemented, and the “iron rice bowl” (a secure job) and “Big Pot Rice” (extreme egalitarianism) have gradually receded from history. As the “Iron rice bowl” and “Big Pot Rice” gradually withdrew from the historical stage, the position of college students in the employment market changed from “seller” to “buyer”. The phenomenon of difficult employment for graduates is related to the general environment of institutional reform. In his study, Lai proposed that college graduates should change their employment concept and pay attention to the job search process in addition to the cultivation of their professional ability under a severe employment situation [42]. Li et al. also analyzed the positive relationship between graduates' job search readiness, effort and initial job-application match through empirical research [33]. Information economics also suggests that there is information asymmetry in the labor market, where one of two sides (e.g., graduates) may have incomplete information. Graduate job seekers investing in job searches and efforts to improve their allocation ability will increase the likelihood of finding a suitable and satisfying job that matches their major [43]. Synthesizing the previous research literature, we propose the following research hypothesis:
H1
The professional ability factor in human capital positively influences the learning-application matching of graduates.
H2
The general ability factor in human capital negatively affects the learning-application matching of graduates.
H3
The allocative ability factor in human capital positively affects the graduate learning-application matching.
2.2.2. Matching family capital with graduates' learning and application
There is much academic discussion about the effect of social capital on the improvement of college students' employment quality. Individuals with stronger social capital are more likely to have access to accurate and adequate employment information, which leads to increased job stability for individuals [44]. Employees who enter an employer through referrals from acquaintances receive a higher salary package [45]. Social capital related to the personal family background of college students contributes more to the quality of their employment [46]. As a derivative concept of social capital, there are relatively few research results on family capital in the field of graduate employment, especially in the field of learning-application matching. In his study, Pan mentioned that family social capital would significantly affect graduates' work area, and family economic capital and cultural capital would affect graduates' monthly salary and job nature [47]. Dong analyzed the influence mechanism of family capital of rural youth on nonfarm employment behavior and found that family social capital, cultural capital and economic capital all have significant driving effects on youth nonfarm employment behavior, which can also be understood as family capital helping young farmers achieve nonfarm employment [48]. Although there are fewer research results on family capital in the field of graduate employment, we still perceive from the findings of existing studies the positive influence of family capital on the acquisition of employability, the choice of employment opportunities and the improvement of employment quality of graduates. As an important measurement dimension of employment quality, graduates' learning-application matching is also positively influenced by family capital to some extent. Combining the relevant discussions of scholars on each dimension of family capital, the following research hypotheses are proposed:
H4
Family capital positively influences the match between graduates' learning and application.
2.2.3. The joint influence of family capital and human capital on graduates' learning-application matching
Social capital can improve the employment quality of graduates directly and indirectly by improving the human capital of graduates. Based on this, the influence of family capital on graduates' job matching can be considered both directly and indirectly. Family capital has a direct positive impact on improving graduates' learning-application matching, and their families will do their best to help graduates in the employment process [31]. In addition to the direct effect of family capital on graduates' learning-application matching, it can also influence graduates' learning-application matching through the mediating role of human capital. Family economic status affects graduates' access to human capital [49], as parents' occupation and parents' educational background will broaden graduates' social networks and help graduates achieve higher quality employment with good family background [50]. The family capital of graduates positively affects graduates' learning-application matching and can also improve graduates' learning-application matching by improving human capital accumulation. Based on the above discussion, this study proposes the following hypotheses:
H5
Graduates' family capital positively affects their human capital;
H6
Human capital plays a mediating role in the effect of graduates' family capital on the match between learning and application.
2.3. Conceptual framework
The theories involved in this study are human capital theory [51]. The study divided human capital into three influencing factors, which were measured using a scale, and the three factors were formed through factor analysis: professional, general, and allocation ability. The study further subdivided family capital into family economic capital, family cultural capital, and family social capital. The measurement of learning-application matching is based on graduates' self-assessment quiz from this study on the degree of matching their majors with their initial employment. Fig. 1 depicts the relationship between them and represents the conceptual framework of this study.
Fig. 1.
Conceptual framework.
3. Study design
3.1. Subjects
The 2022 graduates of a private undergraduate college in Guangzhou were selected as the study population, and the survey was conducted mainly by online methods such as WeChat, email, and Questionnaire Star, supplemented by subsequent offline paper questionnaire collection for students of different majors as the sample. A total of 1598 questionnaires were collected, and after data cleaning based on screening questions and missing values, 1480 valid questionnaires were finally obtained, with an effective rate of 92.6 %. Among them, the subject background of the respondents needs to be explained, because the nature of the university is economics and management, thus its students primarily study economic and management liberal arts majors. In 2022, the university started offering medical majors but there were not yet any graduates in the year this study's survey took place, so the subject background of graduates is divided into economics, management, law and education (78.4 %) and literature, history, philosophy and art (21.6 %). The specific distribution of the sample is shown in Table 1.
Table 1.
Basic information characteristics of interviewees (N = 1480).
| Variable | Frequency | Effective percentage (%) |
|---|---|---|
| Sex | ||
| Male | 367 | 24.8 |
| Female | 1113 | 75.2 |
| Admission selection method | ||
| General undergraduate | 882 | 59.6 |
| Transfer from junior college to undergraduate | 598 | 40.4 |
| Disciplinary background | ||
| Economics, management and law | 1161 | 78.4 |
| Literature, history and philosophy | 319 | 21.6 |
3.2. Variable measurement
The questionnaire was divided into two parts: basic information and measurement scale. The basic information included objective questions such as gender, admission selection method, academic background, annual per capita family income, parents' educational background, and parents' occupation. The measurement scale was based on a previous study (shown in Table 2), and a 5-point Likert scale of “1″ for very poor and “5″ for very good was used to measure the subjects.
Table 2.
Study variables and specific measurements.
| Variable Type | Variable Name | Method of measurement |
|---|---|---|
| Dependent variable | learning-application matching | From Xu et al. [37], divide them into 5 dimensions: “very irrelevant”, “less relevant”, “general”, “relatively relevant” and “very relevant”. Values of 1–5 from low to high |
| Independent variable | Family capital | From Wang [49], assign 1–5 to the annual income of family from low to high. |
| From Li [50], assign 1–5 values to the education level of parents from low to high. | ||
| From Li [50], the occupation of father/mother is assigned 1–5 from low to high according to professional prestige. | ||
| Intermediary variable | Human capital | From Huang [31] and Liu [18] to extract professional specific skills (professional ability), including 8 items in total |
| From Huang [31], Liu [18] and Wang [38] to extract general vocational skills (general ability), including 10 items in total | ||
| From the research and allocation ability of Tian [19] with 7 items in total | ||
| Control variable | Sex | 0 for women and 1 for men |
| Admission selection method | The value of undergraduate is 0, and the value of upgrading from junior college to undergraduate is 1. | |
| Disciplinary background | The value of literature, history, philosophy and arts is 0, and the value of economic management, law and education is 1. |
The relevant research literature suggests that there is a need to pay attention to possible endogeneity problems in the study of human and family capital and graduates' learning-application matching. The so-called data endogeneity problem refers to the fact that certain variables affect the error term of the regression model, making the regression model not have causal inference and subsequently not meet the hypothesis that the least squares method holds [52]. Since graduates with more family capital and human capital accumulation have stronger initiative in employment selection, the study is prone to self-selection bias. Solutions to the problem of self-selection bias include experiments or natural experiments, modeling the selection process, incorporating more control variables and instrumental variables [53]. Since randomized or natural experimental methods are often costly or even infeasible and modeling the selection process and finding instrumental variables are difficult, this study dealt with self-selection bias by incorporating the necessary control variables, including gender, admission selection method, and disciplinary background. to address the problem of self-selection bias.
3.3. Analysis methods
In this study, the data obtained were analyzed using software such as SPSS 19.0. First, descriptive statistical analysis was performed, followed by exploratory factor analysis, reliability testing, structural equation modeling, and robustness testing as analytical methods to test the theoretical hypotheses.
4. Data analysis and hypothesis testing
4.1. Exploratory factor analysis
In this study, an exploratory factor analysis of human capital variables was conducted using SPSS 19.0, and the results are shown in Table 3. The questionnaire involved a total of 25 questions, using a 5-point Likert scale, with a minimum score of 1 and a maximum score of 5, ranging from “very poor”, “poor”, “fair”, “better”, and “very good”. After performing the operations related to factor analysis, the statistical results showed that the KMO statistic was 0.946, which was greater than 0.9, indicating the existence of common factors among the variables, and according to Wu [54], the human capital scale designed in this study was suitable for factor analysis. Bartlett's spherical hypothesis statistic approximated the chi-square value of 3423.065, with 210 degrees of freedom, and the significance level was less than 0.01, which further supports the existence of common factors among the 25 question items of the human capital scale and its suitability for factor analysis. Based on the above analysis, the human capital variable was forced into three factors by using the principal component method with maximum variance orthogonal rotation. Three rounds of exploratory factor analysis were conducted on the 25 questions, three questions were deleted successively, and 21 questions were finally retained to be grouped into three influential factors.
Table 3.
Human capital factor analysis.
| Professional ability | Allocation ability | General ability | |
|---|---|---|---|
| Q2.1.6 My interest in this major | 0.810 | ||
| Q2.1.7 My emotional tendency to the major | 0.799 | ||
| Q2.1.8 My learning attitude towards this major | 0.774 | ||
| Q2.1.1 My cognition of employment prospect of the major | 0.764 | ||
| Q2.1.5 My knowledge of the professional background | 0.718 | ||
| Q2.1.2 My mastery of professional knowledge | 0.708 | ||
| Q2.1.3 My professional practice skills | 0.708 | ||
| Q2.3.7 My ability to learn from others' successful experiences in job search | 0.762 | ||
| Q2.3.6 My ability to deal with problems in recruitment assessment | 0.756 | ||
| Q2.3.1 My job search ability | 0.728 | ||
| Q2.3.4 My ability to transmit my own valid information to the employer | 0.718 | ||
| Q2.3.2 My resource utilization in job searches | 0.702 | ||
| Q2.3.3 My resume production ability | 0.677 | ||
| Q2.3.5 My ability to gather information during job searches | 0.668 | ||
| Q2.2.7 My data statistics ability | 0.816 | ||
| Q2.2.2 My computer application capability | 0.738 | ||
| Q2.2.3 My ability to express words | 0.683 | ||
| Q2.2.9 My innovation ability | 0.662 | ||
| Q2.2.6 My organization and coordination ability | 0.652 | ||
| Q2.2.8 My ability to find and solve problems | 0.652 | ||
| Q2.2.4 My understanding and induction | 0.616 | ||
| Explainable variance (%) | 26.152 | 25.932 | 22.625 |
| Cumulative explained variance (%) | 26.152 | 52.084 | 74.709 |
In the exploratory factor analysis, the variance explained by the first factor was 26.152 %, which did not reach half of the total variance explained (74.709 %), and there was no single factor that explained most of the variance, indicating that there was no obvious homogeneous variance problem in this study. The total variance explained by the three factors is 74.709 %, which indicates that the three factors extracted can explain human capital well, and the human capital scale in this paper can represent the human capital status of graduates more scientifically. In addition, this study also conducted multiple cointegration analysis on all variables entering the model, and the results found that the variance inflation factor (VIF) of all variables was less than 4, which met the test criteria, so all heavy cointegration problems were excluded.
4.2. Reliability test
Table 4 reports the results of the reliability test of the human capital scale. The results show that the Cronbach's alpha values of all three factors are greater than 0.9, indicating that the scale has good internal consistency. The CR values of all the factors were also greater than 0.9, indicating that the combined reliability was also good. In addition, the AVE values of all the factors were greater than 0.8, which again verified the good discriminant validity among the factors.
Table 4.
Analysis results of reliability and validity of human capital scale.
| Variable | Number of items | Mean value | Standard deviation | Cronbach's alpha | CR | AVE |
|---|---|---|---|---|---|---|
| Professional ability | 7 | 3.614 | 1.321 | 0.945 | 0.913 | 0.862 |
| Allocation ability | 7 | 3.523 | 1.235 | 0.947 | 0.932 | 0.811 |
| General ability | 7 | 3.421 | 1.286 | 0.932 | 0.922 | 0.801 |
4.3. Hypothesis testing results
4.3.1. Path analysis results
The results of the path analysis are shown in Table 5, and Model 1 shows the effects of control variables on the dependent variable of learning-application matching. The results show that the learning-application matching status of graduates whose admission selection method is general undergraduate is better than that of graduates transferring from junior college to undergraduate, and the learning-application matching status of graduates with a discipline background of economics, management, law and education is better than that of graduates with a literature, history, philosophy and art background.
Table 5.
Path analysis results.
| Variable | Model 1 |
Model 2 |
Model 3 |
Model 4 |
|---|---|---|---|---|
| learning-application matching | learning-application matching | Mean value of human capital | learning-application matching | |
| Sex | 0.098 (0.051) | 0.089 (0.042) | 0.077 (0.041) | 0.061 (0.031) |
| Admission selection method | 0.241*** (0.021) | 0.161*** (0.019) | 0.154*** (0.017) | 0.062*** (0.020) |
| Disciplinary background | 0.424*** (0.058) | 0.326*** (0.052) | 0.234*** (0.041) | 0.186*** (0.051) |
| Family economic capital | 0.420***(0.022) | 0.786*** (0.023) | 0.312***(0.125) | |
| Family cultural capital | 0.360** (0.034) | 0.675*** (0.021) | 0.224***(0.011) | |
| Family social capital | −0.069 (0.033) | 0.542*** (0.322) | −0.119 (0.202) | |
| Professional ability | 0.632*** (0.043) | |||
| General ability | −0.021*(0.342) | |||
| Allocation ability | 0.236***(0.127) | |||
| Constant | 0.535***(0.163) | 0.398***(0.128) | 0.765***(0.172) | 0.032*** (0.009) |
| R2 | 0.094 | 0.142 | 0.450 | 0.691 |
Note: standard error in brackets; * for P < 0.05, ** for P < 0.01, *** for P < 0.001.
Model 2 reports the effects of family economic capital, family cultural capital, and family social capital on learning-application matching. The results indicate that both family economic capital and family culture capital show a significant positive effect on graduates' learning-application matching (β1 = 0.420, p < 0.001; β2 = 0.360, p < 0.01), and overall, graduates' family capital positively affects their learning-application matching, which is supported by H4.
As shown in Model 3, graduates' family capital shows a significant positive effect on the mean value of their human capital (β1 = 0.786, p < 0.001; β2 = 0.675, p < 0.001; β3 = 0.542, p < 0.001), indicating that higher graduates' family capital is more beneficial to the accumulation of human capital, and H5 is supported.
Model 4 reports the regression results of the independent and mediating variables on the dependent variable. A closer look at the effects of the human capital factors on graduates' learning-application matching revealed that professional ability (β = 0.632, p < 0.001) and allocative ability (β = 0.236, p < 0.001) had a significant positive effect on graduates' learning-application matching, while general ability had a negative effect on graduates' learning-application matching (β = −0.021, p < 0.05), and the study Hypotheses H1, H2 and H3 were supported.
4.3.2. Mediation effects and robustness analysis
Currently, the common method used to test the mediating effect is the stepwise test regression coefficient method, but the test power of this method is low, and it is difficult to test for a significant mediating effect when the mediating effect is weak [55]. This study used Mplus7.4 software to construct a structural equation model to estimate the mediating effect.
Table 6 reports the results of the tests of mediating effects. The results show that the coefficients of the mediating effects of human capital are significantly positive in the effects of family economic capital, family cultural capital and family social capital on graduates' learning-application matching. Moreover, the path coefficients of family economic capital and family cultural capital are significant in Model 4, and the path coefficients of family social capital are not significant, so human capital plays a partially mediating role in the effects of family economic capital and family cultural capital on graduates' learning-application matching and a fully mediating role in the effects of family social capital on graduates' learning-application matching. H6 is supported.
Table 6.
Test results of mediation effect.
| Path | Coefficient of effect | Standard error | Bootstrap 95 % Confidence Interval |
|
|---|---|---|---|---|
| Lower limit | Upper limit | |||
| Family economic capital→ learning-application matching | 0.453*** | 0.021 | 0.432 | 0.527 |
| Family cultural capital→ learning-application matching | 0.089*** | 0.013 | 0.071 | 0.116 |
| Family social capital→learning-application matching | 0.067*** | 0.003 | 0.053 | 0.077 |
Note: the number of bootstrapping resampling is 10000; * for P < 0.05, ** for P < 0.01, *** for P < 0.001.
In addition, to test the stability of the mediating effect, this study uses the bootstrap method to resample 10,000 random samples from the sample to estimate the confidence interval of the coefficient of the mediating effect. The results show that the mediating effect of the three types of paths remains significantly positive, and the 95 % confidence interval does not contain 0. Therefore, the mediating effect of human capital is significant and robust.
5. Conclusion and insights
The results of the hypothesis tests were all supported, and based on the results, this study makes the following discussion and recommendations.
5.1. Results and discussion
First, this study classifies graduates' family capital into family economic capital, family cultural capital and family social capital. It is pointed out in the literature that based on the analysis of social capital theory, social capital will have a profound impact on graduates' employment, i.e., differences in students' family capital will cause differences in employability and eventually affect students' employment choices, resulting in “employment differentiation” or even “class entrenchment”. This study investigates the relationship between graduates' family capital and their employment choices. This study investigates the relationship between graduates' family capital and their human capital status and learning-application matching, i.e., whether different family capital statuses affect graduates' human capital and learning-application matching. The results of the empirical analysis show that family economic capital and family cultural capital will have a positive impact on graduates' learning-application matching, and this finding reflects that private college graduates' family economic background and parents' cultural background will play a role in promoting their learning-application matching in the labor force employment market with high competitive pressure, and parents will provide some help in terms of economy and experience. In addition, family capital can significantly improve the human capital status of graduates and help students develop various abilities required for employment. It is worthwhile to conduct an in-depth study on how to improve the learning-application matching status of graduates from both family capital and human capital in the future.
In this study, the number of graduates from economic, management, legal and educational fields is high, and the employment direction matching with such majors includes state institutions or career establishment. For a long time, Chinese parents regarded the “iron rice bowl” and “Big Pot Rice” as synonymous with decency and stability, and the “institutional” jobs represented by civil servants and teachers were favored by most families. Parents of students with superior family resources will do their best to help their children find jobs that match their studies.
Second, human capital plays a mediating role in the effect of family capital on graduates' learning-application matching; that is, family capital influences college students' learning-application matching by changing their human capital status. From the test results of the mediating effect, the mediating effect of human capital exists and is robust among family economic capital, family cultural capital and family social capital. Education is one of the important ways to acquire human capital; therefore, universities should pay attention to the accumulation of students' human capital in the process of teaching and learning and improve the level of students' human capital from the setting of majors, talent training programs, and the integration of industry and education to finally achieve the purpose of improving graduates' learning-application matching.
Although family capital is, to some extent, difficult to change in the short term through external intervention, it is clear from this study that the effect of family capital on graduates' employment (learning-application matching) occurs through human capital status, thus providing a focus point for the introduction of educational tools to improve the employment quality.
Third, human capital has a significant and stable effect on graduates' learning-application matching. In the study, professional, general, and allocation ability are selected to measure graduates' human capital. The data analysis shows that professional and allocation ability have a significant positive effect on graduates' learning-application matching, and general ability have a negative effect on graduates' learning-application matching. This finding is broadly consistent with the finding in Liu's study that general skills (general abilities) increase the likelihood of graduates switching to fields unrelated to their studies, while career-specific skills (professional ability) decrease this likelihood [18]. In terms of allocation ability, the findings of the empirical study are also consistent with the research hypothesis described in Li's study that the greater the graduate's effort in job search, the higher the probability of a learning-application matching [33].
5.2. Research insights
Compared with the topic of the employment difficulties of college graduates, the problem of “learning-application matching” does not appear to be prominent or acute, but this kind of problem is related to the employment quality of graduates in the context of the connotative development of higher education and is a side reflection of the employment difficulties of graduates.
First, government departments should create a good employment environment and build a good information bridge between the labor market, private institutions and individual graduates. Employment is an issue of people's livelihood, and graduates' “education to job” is an important measure to test the quality of higher education personnel training. In China, compared with graduates from public colleges and universities, graduates from private colleges and universities are at a relative disadvantage in the employment process and suffer from different degrees of discrimination in the employment market. A fair and good employment environment is a prerequisite to ensure that graduates from private undergraduate colleges and universities can find a job that relates to their field of study, and the creation of this environment requires active guidance and appropriate intervention from the government. Many problems of mismatch between field of learning and application are related to information asymmetry. Government departments have the strength to speed up the construction of employment information network platforms, build dynamic information communication channels for the market, universities and graduates, effectively help graduates of private colleges and universities solve employment problems and help graduates improve the level of matching between learning and application.
Second, based on their own characteristics and market demand, private colleges and universities should provide assistance to graduates in their teaching and practice activities. In addition to the employment rate of graduates, the employment quality index should also be considered. Private colleges and universities should conduct dynamic surveys on employment quality indices such as the matching of graduates' learning and application to test their talent cultivation results and realize the virtuous cycle of enrollment and employment. Currently, the teaching characteristics of private undergraduate colleges and universities are increasingly focused on theory and systematics, showing convergence with public colleges and universities, that obviously weakens their own characteristics and competitive advantages. Private undergraduate colleges and universities should adjust their professional training programs in accordance with market changes, change the rigid teaching mode, integrate teaching forms and methods that meet the characteristics of the times, guide students to form a clear understanding of their majors and encourage them to perform career planning as early as possible. The private institutions should focus on cultivating complex talents with “theory + skills”, strengthening communication and cooperation with employers, establishing a long-term and solid cooperation platform between schools and enterprises, providing graduates with internship and apprenticeship opportunities related to their majors, encouraging graduates to continuously improve their professionalism and abilities during the internship process, and laying a good foundation for graduates to successfully realize the match between learning and application.
Finally, students should pay attention to the accumulation of their own human capital and to the transformation of family capital into human capital that is conducive to the realization of their own learning-application matching. Everyone's family environment and personal conditions have various differences, and graduates should base their job search in light of their own situation. In addition to salary, workplace, career prospects and other factors, graduates should also consider career value, study match and other factors. College students should have a clear career planning path in the process of choosing their majors and learning professional courses and make good use of schools, teachers, peers and parents to obtain employment information to achieve a rational choice of professional learning and career search. For majors with lower employment thresholds, graduates should pay more attention to professional course learning, obtaining internship opportunities related to their majors, resume writing, job application skills and other professional abilities and allocation abilities, and make use of their own ability advantages to enhance their professional field employment competitiveness.
5.3. Research limitations and future research perspectives
Despite the attempt to comprehensively explain the influence mechanism of graduates' learning-application matching and some meaningful investigation and analysis, there is still some room for improvement in this study. First, the data collection of the study adopts the self-reporting method of questionnaire survey, and some graduates may have insufficient self-understanding, which may lead to bias in the data, and multiple methods of data collection can be tried in future studies, such as using the interview method to explore some of these issues in depth. Second, in terms of sample diversity, there may be homogeneity within a university, so future studies could try to collect data from Guangzhou city, Guangdong province or even the whole country for comparative analysis. Third, this study mainly explored the mediating effect of family capital on graduates' learning-application matching but did not further explore the influence of corresponding moderating variables. Future studies could try to investigate the moderating effect of graduates' awareness of independent matching or other environmental factors on the overall mechanism to deepen the research findings.
Data availability
Data will be made available on request.
Funding
This study received support from 2020 Guangdong Province university youth innovation talent project (No.2020WQNCX118); a grant from the Guangzhou Huashang College (No.2021HSXK10).
Ethical approval
Ethical approval was obtained from the Ethics Committee of the Guangzhou Huashang College, China (No.2020WQNCX118). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.
Informed consent
Informed consent was obtained from all participants.
CRediT authorship contribution statement
Huixia Liu: Writing – review & editing, Writing – original draft, Funding acquisition, Formal analysis, Data curation, Conceptualization. Zhenjie Liao: Writing – review & editing, Writing – original draft, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data will be made available on request.