Next Article in Journal
Mitigating Autogenous Shrinkage of Alkali-Activated Slag Mortar by Using Porous Fine Aggregates as Internal Curing Agents
Previous Article in Journal
The Negative Influence of Urban Underground Space Development on Urban Microclimate
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 16
10.3390/su14169837
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Encouraging China’s College Students to Achieve Sustainable Careers: Evidence from Structural Equation Modeling

by
Jingchao Wu
and
Teng Zhao
*
Zhejiang Academy of Higher Education, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(16), 9837; https://doi.org/10.3390/su14169837
Submission received: 17 July 2022 / Revised: 3 August 2022 / Accepted: 8 August 2022 / Published: 9 August 2022
(This article belongs to the Section Sustainable Education and Approaches)
Browse Figures
Review Reports Versions Notes

Abstract

:
China’s college graduates are experiencing extreme employment pressure, especially under the effects brought about by COVID-19. This study examines whether career-related psychological factors jointly affect college students’ career decision-making self-efficacy (CDMSE) to achieve sustainable career development. Leveraging self-administrated survey data with 703 undergraduate students, we find that career awareness, job search self-efficacy (JSSE), and career planning ability positively relate to CDMSE. We also find that career awareness positively correlates with JSSE and career planning ability. In addition, the results support that perceived career reality positively relates to JSSE and career planning ability. However, no evidence indicates the positive relationship between perceived career reality and CDMSE. Furthermore, mediation tests confirm that JSSE and career planning ability partially mediate the relationship between career awareness and CDMSE. Practical implications, limitations, and future directions are also discussed.

1. Introduction

The number of higher-education graduates in China is projected to exceed 10 million in 2022 [1]. Meanwhile, youth (aged 16–24) unemployment has increased to 18.4% [2], resulting in jobs that choose people rather than people choosing their jobs [3]. Especially under the conditions brought about by COVID-19, graduates are under extreme employment pressure, and postsecondary institutions are facing unprecedented challenges in helping students gain transferable knowledge and skills to better prepare for their future careers [4]. Both students and institutions need to discern how to sustain career success in challenging times [5]. Furthermore, a “successful” or “satisfying” career today does not always maintain the same value in the future. The increasingly complex and constantly changing career context brings about opportunities for facilitating the sustainability of careers [6].
A sustainable career means that an individual’s career experiences can cross over several social spaces through an individual agency, resulting in multiple patterns of continuity over time, thus giving meaning to the individual [7]. Since sustainable career development is crucial to personal survival and self-realization value [8], it should be a central component of career interventions and decision-making phases [9]. Researchers have studied the relationship between sustainable career development and career decision making. For example, Hauw and Greenhaus [10] showed that different career decisions could influence the sustainability of careers by influencing employees’ health and employability, where good career decisions can contribute to sustainable career development [11].
To continue, good career decision making is positively affected by career decision-making self-efficacy (CDMSE) [12]. Taylor and Betz [13] defined CDMSE as an individual’s belief that they can make successful career decisions. Therefore, a higher CDMSE is likely to be associated with better career decision making. In addition to self-efficacy, self-awareness is another influencing factor in career decision making [14]. Self-awareness includes two forms: objective self-awareness focusing on the self and subjective self-awareness focusing on external objects [14]. These two forms of self-awareness could be regarded as career awareness and perceived career reality under sustainable career development. In addition, individuals with higher job-search self-efficacy (JSSE) [15] and career planning ability [16] may be more likely to have confidence in making successful career decisions. It should be noted that these concepts originate from Western society and have been primarily examined with Western samples. However, in the Chinese context, how do these career-related psychological factors jointly affect students’ CDMSE to achieve sustainable career development? As psychological variables, these career-related variables likely influence each other [17]. Therefore, how do they relate to one another?
The present study aims to answer the two questions posed above. It contributes to the existing literature by providing a Chinese lens on students’ sustainable career development and positing practical implications based on the study’s findings, such as determining how to help students better prepare for a successful career. To our knowledge, insufficient literature jointly assesses these career-related factors from a Chinese perspective. Leveraging a self-designed survey, the Effectiveness of College Students’ Career Development Courses (ECSCDC), we examine these relationships with structural equation models.

2. Theories and Hypotheses Development

2.1. Career Decision-Making Self-Efficacy

Regarding career behavior research, CDMSE has probably received the most attention [18], especially for university-students’ career development [19,20,21]. CDMSE was designed to measure an individual’s degree of belief that they can complete necessary tasks when making career decisions [22].
CDMSE is based on two well-established psychological theories: the theory of self-efficacy and the theory of career maturity [23]. Self-efficacy theory was first presented by Bandura and viewed as an approach to the more general study of the applicability of social learning or social cognitive theory to vocational behavior [22]. Meanwhile, career maturity theory is rooted in Super’s career-pattern study and was presented by Crites [24]. To measure the degree of career maturity, Crites et al. [25] designed a career maturity inventory consisting of five subtests: self-appraisal, occupational information, goal selection, planning, and problem-solving. The CDMSE scale was developed according to this inventory [26].
Available literature shows a steady interest in the predictors of CDMSE. Most predictors are related to psychological factors, which encompass two aspects. One aspect is the attitude toward career development, such as work preference [27], career confidence [28,29], career optimism [30], thinking style, and conscientiousness [31,32], which are all related to career awareness. The other aspect is perceived support and difficulties of career development, such as familial, academic, and social support [33,34,35], career barriers, and career decision difficulties [16,35,36], which can be concluded as perceived career reality.

2.2. Career Awareness

Career awareness means one’s interests or understanding of the opportunities and requirements of various career fields [37]. It is related to three dimensions of work: the routines of work, the requisites of work, and the returns of work. Career awareness has four major aspects: knowledge, values, preferences, and self-concepts [38].
From the perspective of sustainable career development, career awareness is the starting point and forming a final career decision is the ending point [39]. Career awareness is understood as a fundamental construct of career decision making. It is seen as the basis for making choices that affect the direction of a person’s career [38]. A previous study about students’ science-related career awareness has supported that activities that support career awareness were an inherent part of supporting students in making good decisions [40]. These show the possibilities that career awareness has a positive effect on CDMSE. Thus, the following hypothesis is put forward:
Hypothesis 1:
Students with higher career awareness have higher CDMSE.

2.3. Perceived Career Reality

As career awareness mainly relates to students’ interests and understanding of careers, perceived career reality primarily focuses on their awareness of realistic career contexts. Although few studies have investigated the relationship between perceived career reality and CDMSE, previous research has discussed the effect of perceived career barriers on career decision-making processes.
Perceived career barriers refer to an individual believing that such barriers exist or may be encountered in the future [41]. Perceived career barriers are divided into internal and external types [42]. Internal barriers concern self-concept, interests, and attitudes toward work. In contrast, external barriers are defined as those difficulties related to demographic characteristics, work preparation, and the work environment. According to this definition, internal barriers can be generated for career awareness, and external barriers can be regarded as one aspect of perceived career reality. In this study, perceived career reality refers to two dimensions: positive and negative consideration of the personal real situation and the target occupation’s information.
Previous studies have demonstrated that perceived career barriers are related to CDMSE [42] and can largely influence career choice [43]. Therefore, the authors of the present study believe there is a possibility that students’ perceived career reality can influence their CDMSE. Thus, the following hypothesis is presented:
Hypothesis 2:
Students perceiving career reality more have higher CDMSE.

2.4. Job Search Self-Efficacy

Similar to CDMSE, JSSE is also derived from Bandura’s self-efficacy theory [44], and was introduced to the field of career decision making as a new concept by Taylor and Betz [13]. According to existing research, JSSE is a psychological concept that reflects one’s beliefs, feelings, confidence, and ability during the job-searching process [45,46,47,48].
Job-searching activities are closely connected with career choice, so many studies investigate the relationship between searching for a job and career decision making. Norida et al. [49] collected questionnaires from 678 undergraduate students, and the result showed a positive relationship between job-search intensity and CDMSE. Xu and Adams [50] surveyed 371 college students and found a link between JSSE and ambiguity aversion in career decision making. Therefore, it is reasonable to explore a relationship between job-search self-efficacy and CDMSE. Therefore, the following hypothesis is presented:
Hypothesis 3a:
Students with higher JSSE have higher CDMSE.
Job searching is an essential part of the sustainable career-development process. For students to think about their career plans, they must be aware of the career they want to pursue [39]. They must learn about their career interests, key competencies in the workforce, and the real career context. Accordingly, the following is hypothesized:
Hypothesis 3b:
Students with higher JSSE have higher career-planning ability.
Hypothesis 3c:
Students with higher career awareness have higher JSSE.
Hypothesis 3d:
Students perceiving career reality more have higher JSSE.

2.5. Career-Planning Ability

Generally, career planning is an ongoing process of choosing career goals and determining ways to achieve these goals [51]. Career-planning ability includes thinking about future career development, searching for career information, making preparations, and creating plans to achieve one’s career goals [52,53]. Career-planning ability is an essential concept related to career maturity. Studies have supported that high career maturity is a strong planning ability [54,55]. As career maturity is one fundamental resource of CDMSE, it is rational to investigate the relationship between career-planning ability with CDMSE. Bardick et al. [56] found that improving junior-high-students’ career-planning ability may serve to increase students’ awareness of career decision making. Diandra et al. [57] investigated undergraduates and established that a career-planning course led to higher career certainty and lower indecision. Thus, the hypothesis is put forward:
Hypothesis 4a:
Students with higher career-planning ability have higher CDMSE.
According to Hariko and Anggriana [51], there are three aspects influencing career-planning ability: (1) knowledge and understanding of oneself, (2) knowledge and understanding of the world of work, and (3) realistic reasoning about the relationship of knowledge and self-understanding with knowledge and understanding of the world of work. These three aspects are related to career awareness and perceived career reality. Thus, the following is hypothesized:
Hypothesis 4b:
Students with higher career awareness have higher career-planning ability.
Hypothesis 4c:
Students perceiving career reality more have higher career-planning ability.

3. Method

3.1. Data and Sample

Data was collected from the survey The Effectiveness of College Students’ Career Development Courses (ECSCDC). This self-designed survey was distributed to undergraduate students in a large research university on the east coast of China. The survey mainly focuses on students’ career-related outcomes, such as career awareness, perceived career reality, CDMSE, JSSE, and career-planning ability. A total of 27 survey items were designed to measure these career-related outcomes more precisely to represent these multifaceted psychological variables.
Using convenience sampling, faculty members were recruited to help distribute the survey link in November 2021 through WJX.CN, which is one of the largest online survey platforms in China. The survey participants were informed that their personally identifying information would not be presented within the study. A total of 708 responses were collected. After checking the validation of the responses and applying listwise deletion, the final analytic sample was 703 with fully complete data. Among them, 261 (37.1%) students were female and 442 (62.9%) were male.

3.2. Measures

3.2.1. Exogenous Variables

After performing exploratory factor analysis and correlation analysis (see detailed descriptions in Analytic Plan), the number of surveyed items was narrowed to measure the intended latent variables. As one of the exogenous variables in the present study, students’ career awareness was measured by three survey items: “will you prepare for a future career”, “will you be aware of the importance of career planning”, and “will you be aware of future career development”. These survey items were constructed on a 5-point Likert scale with 5 = “Strongly Agree” and 1 = “Strongly Disagree”. The mean of each item was 3.878, 3.919, and 3.855, respectively, which were all close to “Agree” (See Table 1).
Another exogenous variable was students’ perceived career reality, which mainly focused on how undergraduate students perceived realistic career contexts, such as how well they would suit their intended occupations. Two items consisted of this latent variable: “will you consider the realistic situation when you are planning your career” and “will you consider the target occupation’s information when you are planning your career”, where the mean of each item was 3.935 and 3.856, respectively.

3.2.2. Endogenous Variables

There are three endogenous variables in the structural equation model. They are comprised of several items, which were also graded on a 5-point Likert scale. First, the students’ CDMSE was measured by four items: “are you confident in your career choice making”, “are you confident in consulting field practitioners”, “are you confident in understanding your maximum value”, and “are you confident in identifying jobs and institutions relevant to your chosen career”. The mean of each item was 3.549, 3.542, 3.442, and 3.477, respectively, which ranged between “Neutral” and “Agree”. Second, JSSE was measured by two items: “will you actively collect recruitment information” and “will you use social networks to find a job opportunity”. The mean of each item was 3.374 and 3.535, respectively. Third, students’ career-planning ability was measured using three survey items: “are you able to make a career strategy”, “do you have clear career goals”, and “do you know how to achieve your career goals”. In these three items, students scored slightly above “Neutral”, with the mean of each item equaling 3.482, 3.383, and 3.415, respectively.

3.3. Analytic Plan

First, since the survey questions of the present study were derived from multiple related research articles [23,25,58,59], an exploratory factor analysis (EFA) was conducted to confirm the underlying structure among measured variables and to reduce a large number of observed variables to a smaller number of factors. The results of the EFA confirmed a five-factor model and yielded reasonable model fit indices in terms of Hu and Bentler [60], with χ2 (226) = 1102.303, CFI = 0.971, TLI = 0.955, RMSEA = 0.074, and SRMR = 0.026. Before performing correlations among 27 survey items, the Shapiro–Wilk test was conducted to ensure the normality of variables [61], showing that most of the variables were not normally distributed (see the final set of variables’ Shapiro–Wilk test results in Table 1). Thus, polychoric correlations were reported to estimate the relationships among the underlying continuous but ordered categorical scores. By integrating the EFA and correlation results, 14 items remained to become the final set of variables in the present study. Their polychoric correlations are presented in Table 2, showing that each latent factor comprised by its corresponding survey items yielded relatively high correlations.
Second, following Anderson and Gerbing [62] and Zhao et al. [63], a two-step approach was conducted using Mplus 8. The first step is to assess the adequacy of the measurement model. In terms of polychoric correlations, the estimator was diagonally weighted least squares with mean and variance adjusted (WLSMV). According to model fit indices (e.g., the comparative fit index and the Tucker–Lewis fit index) and model modification indices provided by Mplus, as well as real survey context, a step-wise measurement model of comparisons was adapted until the data suitably fit the final measurement model. The second step is to examine the adequacy of structural components of the structural regression model and to test the following relationships: (1) career awareness and JSSE, (2) career awareness and CDMSE, (3) career awareness and career-planning ability, (4) perceived career reality and JSSE, (5) perceived career reality and CDMSE, (6) perceived career reality and career-planning ability, (7) JSSE and career-planning ability, (8) JSSE and CDMSE, and (9) career-planning ability and CDMSE.
Third, to understand whether students’ JSSE and career-planning ability have mediation effects on the effects of students’ career awareness on their CDMSE, two single mediation models were derived from the final structural regression model. Their mediation effects were then separately tested using Sobel’s [64], Aroian’s [65], and Goodman’s tests [66], respectively.

4. Results

4.1. Measurement Model Results

The initial measurement model associated with the full structural model was first tested, which captured the relationships between latent factors and survey items, between errors and survey items, and the variances and covariances among errors for indicators. Considering Table 3, the model fit indices suggested a reasonable fit in terms of the recommended cutoff values by Hu and Bentler [60], with χ2 (67) = 311.228, CFI = 0.985, TLI = 0.980, RMSEA = 0.072, and SRMR = 0.021. However, the model modification indices offered by Mplus suggested correlating survey items’ errors to improve the model fitness. After considering the real meaning of the survey items, students with career goals (CPA2) were likely to know how to achieve their career goals (CPA3). The correlated error between CPA2 and CPA3 was added into the first revised measurement model, yielding a better model fit compared to the initial measurement model, with χ2 (66) = 244.984, CFI = 0.989, TLI = 0.985, RMSEA = 0.062, and SRMR = 0.019. Yet, considering individuals generally match their realistic situation (PCR1) to target occupation’s information (PCR2) when they are planning their career, and students who understand their maximum value (CDMSE3) will likely be confident in identifying jobs and institutions relevant to their chosen career (CDMSE4), as well as Mplus’ modification indices, we applied a step-wise model comparison, and additionally added the correlated errors between PCR1 and PCR2, and between CDMSE3 and CDMSE4 one by one, yielding an even better model fit with χ2 (64) = 216.837, CFI = 0.991, TLI = 0.987, RMSEA = 0.058, and SRMR = 0.018.
Step-wise chi-square difference tests were conducted to confirm further that the last model in Table 3 was statistically significantly better than the other models. The results in Table 3 demonstrate that the second model was significantly better than the initial model (Δχ2 = 66.244, p < 0.01). Similarly, it could be observed that the third model was better than the second model (Δχ2 = 17.767, p < 0.01). By parity of reasoning, we compared the third model to the last model with the correlated errors of CPA2 and CPA3, PCR1 and PCR2, and CDMSE3 and CDMSE4; the chi-square difference was Δχ2 = 10.380, p < 0.01, indicating that the last model outperformed the third model.

4.2. Structural Component Results

After the modified measurement model was adequate enough, the structural component of the full structural regression model was assessed. The structural component yielded a good model fit with χ2 (65) = 210.776, CFI = 0.991, TLI = 0.988, RMSEA = 0.041, and SRMR = 0.018. A chi-square difference test was also conducted to evaluate the fit of the structural component of the model compared to the final measurement model. The results presented in Table 3 indicate that the structural component was adequate (Δχ2 = 6.061, p < 0.05).
The final full structural regression model is presented in Figure 1. The standardized factor loadings for students’ career awareness, perceived career reality, JSSE, career-planning ability, and CDMSE ranged from 0.803 to 0.899, suggesting that the respective survey items measured these five latent factors well. For instance, for CAW2, the corresponding R-square value was 0.645, indicating that students’ career awareness explained 64.5% of the variance in CAW2. Similarly, for JSSE1, its corresponding R-square value was 0.808, meaning that students’ CDMSE explained 80.8% of the variance in JSSE1.
Focusing on the paths in the structural regression model, it was determined that one unit increasing in students’ career awareness was associated with a 0.542, 0.288, and 0.309 unit increase in students’ JSSE, CDMSE, and career-planning ability, respectively. Here, JSSE significantly positively predicted students’ career-planning ability (β = 0.451, p < 0.001) and CDMSE (β = 0.122, p < 0.05). In addition, students’ perceived career reality had significant and positive relationships with JSSE (β = 0.181, p < 0.01) and career-planning ability (β = 0.167, p < 0.01). Furthermore, students’ career-planning ability could significantly predict CDMSE, with β = 0.542, p < 0.001.

4.3. Mediation Effects of Career Awareness on Career Decision-Making Self-Efficacy

Since career awareness significantly predicted CDMSE, its mediation effects were tested via JSSE and career-planning ability, as presented in Figure 2. Significant mediational relationships were observed between career awareness and CDMSE through JSSE and career-planning ability. Specifically, JSSE partially mediated the relationship between career awareness and CDMSE with a direct effect of c = 0.293 (p < 0.001) and an indirect effect of a × b = 0.067 (p < 0.05). For career-planning ability, the indirect effect was 0.170 (p < 0.001).
A mediation test was also conducted using the tests presented by Sobel [64], Aroian [65], and Goodman [66], yielding statistically significant results, as presented in Table 4. For example, in the path via JSSE, using Sobel’s test [64], z = 2.393 (p < 0.05) was obtained, concluding that JSSE only partially mediated the effects of career awareness on CDMSE. Similar to the path via career-planning ability, a partially mediating relationship was also found (z = 4.493, p < 0.001).

5. Discussions

This study investigates the predictors of university students’ CDMSE to achieve sustainable career development. Results show that career awareness, JSSE, and career-planning ability positively relate to CDMSE (H1, H3a, H4a). We also find that career awareness positively correlates with JSSE and career-planning ability (H3c, H4b). In addition, the results support that perceived career reality positively relates to JSSE and career-planning ability (H3d, H4c). Furthermore, JSSE could significantly predict career-planning ability (H3b). However, no evidence indicates the positive relationship between perceived career reality and CDMSE (H2). Furthermore, mediation tests confirm that JSSE and career-planning ability partially mediated the relationship between career awareness and CDMSE. The main discussions are as follows.

5.1. Predictors of Career Decision-Making Self-Efficacy

The results showed that three factors could predict the CDMSE of students: career awareness, JSSE, and career-planning ability, verified in the structural regression model.
There is a significant correlation between career awareness and CDMSE, indicating that career awareness is a positive predictor of the CDMSE of students. This finding is consistent with past studies [27,28,29,30,31,32]. According to the career-awareness framework [38], a student with well-developed career awareness has the necessary career knowledge, rational occupational values, clear work preferences, and strong self-concepts. These qualities imply that the student has considered their future career a considerable amount and has sufficient information about how to evaluate and choose a career, which can raise their confidence in making a career decision, leading to higher CDMSE.
Another positive predictor of CDMSE is JSSE. The result shows a significant positive relationship between JSSE and CDMSE, which has been supported by previous studies [67]. The higher the JSSE a student has, the higher their CDMSE. As JSSE involves one’s beliefs about one’s ability to successfully perform career exploration activities [45], a student with high JSSE is willing to carry out more preparation when looking for a job due to the expectation of being successful and being able to collect more job options to choose from. This will create a more positive and proactive career decision context and lead to higher CDMSE.
Career-planning ability is also a positive predictor of CDMSE. This study found a positive effect of career-planning ability on CDMSE, which is similar to the research of Bardick et al. [56]. Betz and Luzzo [22] presented CDMSE based on Crites’ career maturity theory, in which planning ability is a key index [25]. This means that CDMSE contains requirements for students’ ability to make proper career plans. If a student has the necessary career-planning ability, the student will know how to make a career goal, decide on a career path, and achieve career development. All of these encourage the student to set career-choice principles that strengthen their beliefs and improve their self-efficacy when making career decisions.

5.2. Mediation Effects of Career Awareness on Career Decision-Making Self-Efficacy

Besides the direct effect on CDMSE, career awareness can also influence CDMSE indirectly through two different paths.
First, career awareness can enhance CDMSE by improving JSSE. Career awareness is the basis before one begins to explore career options [68], and the present study supports that career awareness is positively related to JSSE. Based on this finding, it can be predicted that when students have career awareness, their willingness, confidence, and ability to explore jobs will enforce their JSSE, positively affecting their CDMSE.
Second, career awareness can be connected with CDMSE through career-planning ability. As Magnuson and Starr demonstrated that career planning embodies the concepts of career awareness as a life skill [69], career planning ability is significantly influenced by career awareness in a positive way, which has been supported in our study. A student with well-developed career awareness will learn about the importance of career planning and know how to improve their career-planning ability based on their own features. Then, combined with the conclusion of the current study, career awareness can indicate CDMSE in an indirect way in which career-planning ability consequently plays a mediating role.
Beyond these, the present study finds that, as two mediators, JSSE and career-planning ability are significantly related. A student with higher JSSE will have better career-planning ability. It may be illustrated as a high JSSE that signifies a deeper understanding of career context and possible career options, which are the foundation of career planning and positively influence career-planning ability [51].

5.3. Effects of Perceived Career Reality on Job Search Self-Efficacy and Career Planning Ability

According to the obtained results, no evidence can support H2. CDMSE is not significantly connected with perceived career reality. However, the present study finds that perceived career reality can positively influence students’ JSSE and career-planning ability.
Perceived career reality can significantly affect JSSE. The more career reality a student perceives, the higher JSSE the student will have. This is because perceived career reality is related to a student’s perception of the real world, in which the student has to explore job opportunities. For students without work experience, one issue for them is the mismatch between imagination and reality. A student thinking that finding a job is neither too easy nor too difficult is beneficial to their job-searching process. If the students sufficiently learn about the career reality, they will establish a correct belief in their job searching efforts.
Equally, perceived career reality also positively affects career-planning ability. A student with sufficient perceived career reality can connect with the real career context. On the one hand, the student can understand their position in the career competition and to what extent they can fulfill the work requirements. On the other hand, the student comprehends what the target job will bring and how they can prepare for it. The perception of these two dimensions means the student can put themselves into a comprehensive situation while considering careers from a personal perspective. Therefore, the student can realistically make a career plan that will greatly facilitate the development of their career-planning ability.

5.4. Implications for Practice

Postsecondary education institutions are under pressure to help students who are undecided about their future paths [70]. It is necessary to improve their CDMSE to encourage them to choose a career early and adequately. This study jointly shows the positive effect of career awareness, JSSE, and career-planning ability on CDMSE. Based on the findings, some practical solutions are suggested below.
Firstly, career development courses and programs focusing on career awareness, JSSE, and career-planning ability are necessary. Previous studies have shown a positive effect of career-development courses and programs on the above three aspects [16,71,72]. As the findings indicated, students who perform well in these aspects have high CDMSE. Therefore, higher-education institutions can establish compulsory courses and relative programs for students to help them make sustainable career decisions. Such courses would be especially helpful for graduating students transitioning from school to work. In addition, the effects of family roles on students’ awareness could not be ignored [73]. Higher education institutions could also deliver these courses to students’ family in online forms to increase students’ career awareness and to better prepare for their career.
Then, considering the mediation roles of JSSE and career-planning ability, career-development courses and programs should include blended content aimed at improving students’ CDMSE. Due to limited time and resources, these courses and programs may be designed to achieve a specific purpose, such as raising career awareness, facilitating JSSE, or improving career-planning ability. However, our study finds that career awareness can, directly and indirectly, influence CDMSE through JSSE and career-planning ability. Therefore, blended and comprehensive content can promote individual aspects and enforce the interaction of all elements that affect CDMSE.
Beyond these, higher-education institutions must give students opportunities to consider themselves in the real career context they will face, promoting students’ perceived career reality. Although perceived career reality is not directly connected with CDMSE, it positively affects career ability. Some approaches can be considered. Higher-education institutions can invite mentors from companies to introduce real workplace environment and recruitment conditions, organize students to visit enterprises in different fields, and encourage students to apply for internships before looking for a job.

5.5. Limitations and Future Directions

Though the present study is systematically designed, four limitations are worth noting. First, career-related psychological variables may not be adequately presented by the survey items. Though our survey is carefully designed in terms of the previous literature and factor loadings in the measurement part of the structural equation modeling being reported, unobserved factors will still exist due to the complexity of psychological variables [74], which were inevitably unexamined. Future research could work toward developing additional survey items to measure these career-related psychological variables more comprehensively. Second, since we did not conduct an entirely experimental design, the present study could not draw causal inferences. However, it is still valuable for postsecondary stakeholders to understand how students’ CDMSE can be increased to help them achieve a sustainable career. Future studies could adopt quasi-experimental designs such as propensity score matching to obtain a causal relationship to a certain extent. Third, since our main purpose is to explore the relationships among these career-related social cognitive variables to make practical implications, background variables such as socioeconomic status and culture are omitted. Future research could use the propensity score technique to include these background variables as observed variables to reduce the sample selection biases to obtain more robust results. Fourth, other career-related social cognitive variables such as career knowledge [75] could be jointly assessed; however, the present survey did not collect such information. This provides another possible direction for future research.

6. Conclusions

Making sustainable career decisions is crucial in this rapidly changing world. Our results show that career awareness, JSSE, and career-planning ability can positively influence students’ CDMSE, which is essential for career decision making. In addition, the findings demonstrate that students’ perceived career reality is significantly related to JSSE and career-planning ability. Furthermore, partial mediation effects are observed from career awareness on CDMSE through both JSSE and career planning ability. These results provide information for postsecondary administrators and policymakers to design variable approaches to promote students’ CDMSE for them to ultimately achieve sustainable careers.

Author Contributions

T.Z. and J.W. conceptualized the manuscript; J.W. performed data collection; T.Z. performed the statistical analyses and interpreted the results; J.W. provided study materials and led the discussions. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Fundamental Research Funds for the Provincial Universities of Zhejiang, Hangzhou Dianzi University (GK219909299001-242), and the Research Start-up Fund, Hangzhou Dianzi University (KYS265621012, KYS265621011).

Institutional Review Board Statement

This study was conducted according to the guidelines of Hangzhou Dianzi University and approved by the Institutional Review Board of Zhejiang Academy of Higher Education.

Informed Consent Statement

Informed consent was obtained from all individuals involved in the study.

Data Availability Statement

According to data access policies, the data used to support the findings of this study are available from Zhejiang Academy of Higher Education, Hangzhou Dianzi University. Reasonable request for ECSCDC data is available through email: [email protected].

Acknowledgments

The authors would like to thank all the participants of the survey of ECSCDC.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. The Class of 2022 College Graduates Will Break 10 Million for the First Time. Available online: http://www.moe.gov.cn/jyb_xwfb/s5147/202111/t20211122_581508.html (accessed on 13 July 2022).
  2. The National Economy Is Recovering in May. Available online: http://www.stats.gov.cn/xxgk/sjfb/zxfb2020/202206/t20220615_1858284.html (accessed on 13 July 2022).
  3. Asuquo, P.N.; Inaja, A.E. Fostering sustainable career development and employability among young people in the changing world of work: Employers’ perspective. Procedia-Soc. Behav. Sci. 2013, 84, 1492–1499. [Google Scholar] [CrossRef] [Green Version]
  4. Ciarocco, N.J. Traditional and new approaches to career preparation through coursework. Teach. Psychol. 2018, 45, 32–40. [Google Scholar] [CrossRef] [Green Version]
  5. Zhou, W.; Pan, Z.; Jin, Q.; Feng, Y. Impact of self-perceived employability on sustainable career development in times of covid-19: Two mediating paths. Sustainability 2022, 14, 3753. [Google Scholar] [CrossRef]
  6. De Vos, A.; Dujardin, J.-M.; Gielens, T.; Meyers, C. Conceptual framework for sustainable careers. In Developing Sustainable Careers across the Lifespan: European Social Fund Network on ‘Career and Age (Age, Generations, Experience); De Vos, A., Dujardin, J.-M., Gielens, T., Meyers, C., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 9–28. [Google Scholar] [CrossRef]
  7. Van der Heijden, B.I.J.M.; De Vos, A.; De Vos, A.; van der Heijden, B.I.J.M. Handbook of Research on Sustainable Careers; Chapter 1: Sustainable Careers: Introductory Chapter; Edward Elgar Publishing: Cheltenham, UK, 2015. [Google Scholar] [CrossRef] [Green Version]
  8. Kiani, A.; Liu, J.; Ghani, U.; Popelnukha, A. Impact of future time perspective on entrepreneurial career intention for individual sustainable career development: The roles of learning orientation and entrepreneurial passion. Sustainability 2020, 12, 3864. [Google Scholar] [CrossRef]
  9. Lent, R.W.; Brown, S.D. Career decision making, fast and slow: Toward an integrative model of intervention for sustainable career choice. J. Vocat. Behav. 2020, 120, 103448. [Google Scholar] [CrossRef]
  10. Hauw, S.D.; Greenhaus, J.H. Building a sustainable career: The role of work-home balance in career decision making. Acad. Manag. Proc. 2014, 2014, 13367. [Google Scholar] [CrossRef]
  11. Park, I.-J.; Gu, M.; Hai, S. How can personality enhance sustainable career management? The mediation effects of future time perspective in career decisions. Sustainability 2020, 12, 1167. [Google Scholar] [CrossRef] [Green Version]
  12. Luzzo, D.A. Value of career-decision-making self-efficacy in predicting career-decision-making attitudes and skills. J. Couns. Psychol. 1993, 40, 194–199. [Google Scholar] [CrossRef]
  13. Taylor, K.M.; Betz, N.E. Applications of self-efficacy theory to the understanding and treatment of career indecision. J. Vocat. Behav. 1983, 22, 63–81. [Google Scholar] [CrossRef]
  14. Özek, H.; Ferraris, A. The role of self-awareness and self-efficacy on career decision-making: An innovative perspective. In Innovation and Capacity Building: Cross-Disciplinary Management Theories for Practical Applications; Vrontis, D., Weber, Y., Thrassou, A., Shams, S., Tsoukatos, E., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 207–229. [Google Scholar] [CrossRef]
  15. Taylor, K.M.; Popma, J. An examination of the relationships among career decision-making self-efficacy, career salience, locus of control, and vocational indecision. J. Vocat. Behav. 1990, 37, 17–31. [Google Scholar] [CrossRef]
  16. Reese, R.J.; Miller, C.D. Effects of a university career development course on career decision-making self-efficacy. J. Career Assess. 2006, 14, 252–266. [Google Scholar] [CrossRef]
  17. Creed, P.A.; Patton, W.; Prideaux, L.-A. Predicting change over time in career planning and career exploration for high school students. J. Adolesc. 2007, 30, 377–392. [Google Scholar] [CrossRef]
  18. Betz, N.E.; Hammond, M.S.; Multon, K.D. Reliability and validity of five-level response continua for the career decision self-efficacy scale. J. Career Assess. 2005, 13, 131–149. [Google Scholar] [CrossRef]
  19. Chung, Y.B. Career decision-making self-efficacy and career commitment: Gender and ethnic differences among college students. J. Career Dev. 2002, 28, 277–284. [Google Scholar] [CrossRef]
  20. Foltz, B.M.; Luzzo, D.A. Increasing the career decision-making self-efficacy of nontraditional college students. J. Coll. Couns. 1998, 1, 35–44. [Google Scholar] [CrossRef]
  21. Wang, J.-L.; Zhang, D.-J.; Shao, J.-J. Group training on the improvement of college students’ career decision-making self-efficacy. Health 2010, 2, 551. [Google Scholar] [CrossRef] [Green Version]
  22. Betz, N.E.; Luzzo, D.A. Career assessment and the career decision-making self-efficacy scale. J. Career Assess. 1996, 4, 413–428. [Google Scholar] [CrossRef]
  23. Miguel, J.P.; Silva, J.T.; Prieto, G. Career decision self-efficacy scale—Short form: A rasch analysis of the portuguese version. J. Vocat. Behav. 2013, 82, 116–123. [Google Scholar] [CrossRef]
  24. Crites, J.O. Career maturity. NCME Meas. Educ. 1973, 4, 1–8. Available online: https://eric.ed.gov/?id=ED077976 (accessed on 13 July 2022).
  25. Crites, J.O.; Savickas, M.L. Revision of the career maturity inventory. J. Career Assess. 1996, 4, 131–138. [Google Scholar] [CrossRef]
  26. Betz, N.E.; Klein, K.L.; Taylor, K.M. Evaluation of a short form of the career decision-making self-efficacy scale. J. Career Assess. 1996, 4, 47–57. [Google Scholar] [CrossRef]
  27. Gianakos, I. Predictors of career decision-making self-efficacy. J. Career Assess. 2001, 9, 101–114. [Google Scholar] [CrossRef]
  28. Paulsen, A.M.; Betz, N.E. Basic confidence predictors of career decision-making self-efficacy. Career Dev. Q. 2004, 52, 354–362. [Google Scholar] [CrossRef]
  29. Hargrove, B.K.; Creagh, M.G.; Burgess, B.L. Family interaction patterns as predictors of vocational identity and career decision-making self-efficacy. J. Vocat. Behav. 2002, 61, 185–201. [Google Scholar] [CrossRef] [Green Version]
  30. Garcia, P.R.J.M.; Restubog, S.L.D.; Bordia, P.; Bordia, S.; Roxas, R.E.O. Career optimism: The roles of contextual support and career decision-making self-efficacy. J. Vocat. Behav. 2015, 88, 10–18. [Google Scholar] [CrossRef]
  31. Fan, J. The role of thinking styles in career decision-making self-efficacy among university students. Think. Ski. Creat. 2016, 20, 63–73. [Google Scholar] [CrossRef]
  32. Bullock-Yowell, E.; Andrews, L.; Buzzetta, M.E. Explaining career decision-making self-efficacy: Personality, cognitions, and cultural mistrust. Career Dev. Q. 2011, 59, 400–411. [Google Scholar] [CrossRef]
  33. Xing, X.; Rojewski, J.W. Family influences on career decision-making self-efficacy of Chinese secondary vocational students. New Waves-Educ. Res. Dev. J. 2018, 21, 48–67. Available online: https://files.eric.ed.gov/fulltext/EJ1211290.pdf (accessed on 13 July 2022).
  34. Burns, G.N.; Jasinski, D.; Dunn, S.; Fletcher, D. Academic Support Services and Career Decision-Making Self-Efficacy in Student Athletes; Wiley Online Library: Hoboken, NJ, USA, 2013. [Google Scholar] [CrossRef]
  35. Quimby, J.L.; O’Brien, K.M. Predictors of student and career decision-making self-efficacy among nontraditional college women. Career Dev. Q. 2004, 52, 323–339. [Google Scholar] [CrossRef]
  36. Brown, C.; Reedy, D.; Fountain, J.; Johnson, A.; Dichiser, T. Battered women’s career decision-making self-efficacy: Further insights and contributing factors. J. Career Assess. 2000, 8, 251–265. [Google Scholar] [CrossRef]
  37. Braverman, M.T.; Young, J.C.; King, N.J.; Paterson, C.A.; Weisskirch, R.S. Career awareness and part-time work examined in lives of high school seniors. Calif. Agric. 2002, 56, 55–60. [Google Scholar] [CrossRef]
  38. Wise, R.; Charner, I.; Randour, M.L. A conceptual framework for career awareness in career decision-making. Couns. Psychol. 1976, 6, 47–53. [Google Scholar] [CrossRef]
  39. Deng, X.; Zeng, H.; Liang, M.; Qiu, J. Relations between different career-development profiles, academic self-efficacy and academic motivation in adolescents. Educ. Psychol. 2022, 42, 259–274. [Google Scholar] [CrossRef]
  40. Kotkas, T.; Holbrook, J.; Rannikmäe, M. Exploring students’ science-related career awareness changes through concept maps. Educ. Sci. 2021, 11, 157. [Google Scholar] [CrossRef]
  41. Albert, K.A.; Luzzo, D.A. The role of perceived barriers in career development: A social cognitive perspective. J. Couns. Dev. 1999, 77, 431–436. [Google Scholar] [CrossRef]
  42. Patton, W.; Creed, P.A.; Watson, M. Perceived work related and non-work related barriers in the career development of australian and south african adolescents. Aust. J. Psychol. 2003, 55, 74–82. [Google Scholar] [CrossRef]
  43. Watts, L.L.; Frame, M.C.; Moffett, R.G.; Van Hein, J.L.; Hein, M. The relationship between gender, perceived career barriers, and occupational aspirations. J. Appl. Soc. Psychol. 2015, 45, 10–22. [Google Scholar] [CrossRef]
  44. Strauser, D.R.; Berven, N.L. Construction and field testing of the job seeking self-efficacy scale. Rehabil. Couns. Bull. 2006, 49, 207–218. [Google Scholar] [CrossRef]
  45. Solberg, V.S. Assessing career search self-efficacy: Construct evidence and developmental antecedents. J. Career Assess. 1998, 6, 181–193. [Google Scholar] [CrossRef]
  46. Maddy, L.M., III; Cannon, J.G.; Lichtenberger, E.J. The effects of social support on self-esteem, self-efficacy, and job search efficacy in the unemployed. J. Employ. Couns. 2015, 52, 87–95. [Google Scholar] [CrossRef]
  47. Tolentino, L.R.; Sibunruang, H.; Garcia, P.R.J.M. The role of self-monitoring and academic effort in students’ career adaptability and job search self-efficacy. J. Career Assess. 2019, 27, 726–740. [Google Scholar] [CrossRef] [Green Version]
  48. Teye-Kwadjo, E. The job-search self-efficacy (jsse) scale: An item response theory investigation. Int. J. Appl. Posit. Psychol. 2021, 6, 301–314. [Google Scholar] [CrossRef]
  49. Norida, A.; Tajudin, N.M.; Kalthom, H.; Jano, Z.; Sharif, M.; Shahrulanuar, M. Model of self-esteem, job-search intensity and career decision-making self-efficacy for undergraduate students. In Proceedings of the International Conference on Science, Technology and Social Sciences (ICSTSS 2012), Pahang, Malaysia, 28–30 October 2012; Springer: Berlin/Heidelberg, Germany, 2014. [Google Scholar] [CrossRef]
  50. Xu, H.; Adams, P. Ambiguity aversion in career decision-making: Its longitudinal prediction for college career outcomes. J. Couns. Psychol. 2020, 67, 232. [Google Scholar] [CrossRef]
  51. Hariko, R.; Anggriana, T.M. Reviewing the role of families in student career planning. Konselor 2019, 8, 6–11. [Google Scholar] [CrossRef]
  52. Talib, J.A.; Salleh, A.; Amat, S.; Ghavifekr, S.; Ariff, A.M. Effect of career education module on career development of community college students. Int. J. Educ. Vocat. Guid. 2015, 15, 37–55. [Google Scholar] [CrossRef]
  53. Neureiter, M.; Traut-Mattausch, E. Two sides of the career resources coin: Career adaptability resources and the impostor phenomenon. J. Vocat. Behav. 2017, 98, 56–69. [Google Scholar] [CrossRef] [Green Version]
  54. Chen, S.; Zhou, K. Career planning decision-making of college students based on cognitive science. NeuroQuantology 2018, 16, 487–493. [Google Scholar] [CrossRef]
  55. Zhang, R.; Song, Y. Relationship between employees’ career maturity and career planning of edge computing and cloud collaboration from the perspective of organizational behavior. PLoS ONE 2021, 16, e0257582. [Google Scholar] [CrossRef]
  56. Bardick, A.D.; Bernes, K.B.; Magnusson, K.C.; Witko, K.D. Junior high career planning: What students want. Can. J. Couns. Psychother. 2004, 38, 104–117. Available online: https://cjc-rcc.ucalgary.ca/article/view/58732 (accessed on 13 July 2022).
  57. Prescod, D.; Gilfillan, B.; Belser, C.; Orndorff, R.; Ishler, M. Career decision-making for undergraduates enrolled in career planning courses. Coll. Q. 2019, 22, n2. Available online: https://files.eric.ed.gov/fulltext/EJ1221402.pdf (accessed on 13 July 2022).
  58. Solberg, V.S.; Good, G.E.; Nord, D.; Holm, C.; Hohner, R.; Zima, N.; Heffernan, M.; Malen, A. Assessing career search expectations: Development and validation of the career search efficacy scale. J. Career Assess. 1994, 2, 111–123. [Google Scholar] [CrossRef]
  59. Savickas, M.L.; Porfeli, E.J. Career adapt-abilities scale: Construction, reliability, and measurement equivalence across 13 countries. J. Vocat. Behav. 2012, 80, 661–673. [Google Scholar] [CrossRef]
  60. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Modeling: A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  61. Hanusz, Z.; Tarasińska, J. Normalization of the kolmogorov–smirnov and shapiro–wilk tests of normality. Biom. Lett. 2015, 52, 85–93. [Google Scholar] [CrossRef] [Green Version]
  62. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411–423. [Google Scholar] [CrossRef]
  63. Zhao, T.; Zhang, Y.; Wu, C.; Su, Q. Will anti-epidemic campus signals affect college students’ preparedness in the post-covid-19 era? Int. J. Env. Res. Public Health 2021, 18, 9276. [Google Scholar] [CrossRef]
  64. Sobel, M.E. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol. 1982, 13, 290–312. [Google Scholar] [CrossRef]
  65. Aroian, L.A. The probability function of the product of two normally distributed variables. Ann. Math. Stat. 1947, 18, 265–271. [Google Scholar] [CrossRef]
  66. Goodman, L.A. On the exact variance of products. J. Am. Stat. Assoc. 1960, 55, 708–713. [Google Scholar] [CrossRef]
  67. Brown, C.; Darden, E.E.; Shelton, M.L.; Dipoto, M.C. Career exploration and self-efficacy of high school students: Are there urban/suburban differences? J. Career Assess. 1999, 7, 227–237. [Google Scholar] [CrossRef]
  68. Nasir, R.; Lin, L.S. The relationship between self-concept and career awareness amongst students. Asian Soc. Sci. 2013, 9, 193. [Google Scholar] [CrossRef] [Green Version]
  69. Magnuson, C.S.; Starr, M.F. How early is too early to begin life career planning? The importance of the elementary school years. J. Career Dev. 2000, 27, 89–101. [Google Scholar] [CrossRef]
  70. Reese, R.J.; Miller, C.D. Using outcome to improve a career development course: Closing the scientist-practitioner gap. J. Career Assess. 2010, 18, 207–219. [Google Scholar] [CrossRef]
  71. Hansen, J.M.; Jackson, A.P.; Pedersen, T.R. Career development courses and educational outcomes: Do career courses make a difference? J. Career Dev. 2017, 44, 209–223. [Google Scholar] [CrossRef]
  72. Ware, M.E. Evaluating a career development course: A two year study. Teach. Psychol. 1981, 8, 67–71. [Google Scholar] [CrossRef]
  73. Zhao, T.; Su, Q.; Hu, X. The relationships between family characteristics and undergraduate students’ COVID-19 responses: A cross-sectional study in China. Front. Public Health 2022, 10, 873696. [Google Scholar] [CrossRef]
  74. Zhao, T.; Perez-Felkner, L. Perceived abilities or academic interests? Longitudinal high school science and mathematics effects on postsecondary stem outcomes by gender and race. Int. J. STEM Educ. 2022, 9, 42. [Google Scholar] [CrossRef]
  75. Babenko, O.; Alves, C.B.; Bahry, L.M. Using structural equation modeling to investigate students’ career awareness in science. Can. J. New Sch. Educ. 2012, 4, 1. Available online: https://cdm.ucalgary.ca/index.php/cjnse/article/view/30508 (accessed on 13 July 2022).
Sustainability 14 09837 g001 550
Figure 1. Final full structural model (standardized results). Note: The statistically insignificant path (H2) was omitted from the figure. * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 1. Final full structural model (standardized results). Note: The statistically insignificant path (H2) was omitted from the figure. * p < 0.05, ** p < 0.01, *** p < 0.001.
Sustainability 14 09837 g001
Sustainability 14 09837 g002 550
Figure 2. A portion of the complete structural model of career awareness’ mediation effect on career decision-making self-efficacy.
Figure 2. A portion of the complete structural model of career awareness’ mediation effect on career decision-making self-efficacy.
Sustainability 14 09837 g002
Table
Table 1. Descriptive statistics for variables of interest.
Table 1. Descriptive statistics for variables of interest.
Analytic Sample (N = 703)
Primary Variables of InterestsMeanSDMinMaxSkewnessKurtosisPr
(Shapiro-Wilk)
Exogenous Variables
Career Awareness (CAW)
1. Prepare for future career3.8780.81415−0.4383.170.000 ***
2. Aware of the importance of career planning3.9190.82615−0.3812.8610.000 ***
3. Aware of future career development3.8550.81215−0.3843.1040.000 ***
Perceived Career Reality (PCR)
1. Consider personal real situation3.9350.77315−0.5923.8490.000 ***
2. Consider the target occupation’s information3.8560.72515−0.3153.4830.000 ***
Endogenous Variables
Career Decision-making Self-efficacy (CDMSE)
1. Confidence in career choices3.5490.87115−0.1572.6460.069
2. Confidence in consulting practitioners3.5420.88415−0.272.9980.044 *
3. Confidence in your values3.4420.89315−0.0132.7230.161
4. Confidence in identifying jobs and institutions3.4770.86715−0.192.9170.204
Job Search Self-efficacy (JSSE)
1. Collect recruitment information3.3740.96715−0.2942.9180.157
2. Use social networks3.5350.95115−0.4783.0230.001 **
Career-Planning Ability (CPA)
1. Career strategy3.4820.85715−0.1352.9220.172
2. Career goal3.3830.95215−0.3713.0530.014 *
3. Career achievement plan3.4150.88215−0.1652.9860.387
Note: * p < 0.05, ** p < 0.01, *** p < 0.001; SD = standard deviation.
Table
Table 2. Polychoric correlation coefficients between categorical variables for variables of interest.
Table 2. Polychoric correlation coefficients between categorical variables for variables of interest.
1234567891011121314
1. CAW1-
2. CAW20.663-
3. CAW30.6670.676-
4. CDMSE10.540 0.491 0.530 -
5. CDMSE20.517 0.553 0.570 0.709-
6. CDMSE30.533 0.480 0.517 0.6630.715-
7. CDMSE40.514 0.465 0.545 0.6450.6500.727-
8. JSSE10.515 0.460 0.507 0.502 0.516 0.550 0.577 -
9. JSSE20.492 0.449 0.439 0.505 0.485 0.495 0.494 0.730-
10. PCR10.529 0.582 0.559 0.517 0.457 0.433 0.455 0.443 0.461 -
11. PCR20.535 0.538 0.569 0.503 0.513 0.434 0.479 0.445 0.451 0.752-
12. CPA10.505 0.502 0.507 0.613 0.571 0.577 0.598 0.622 0.528 0.504 0.581 -
13. CPA20.513 0.545 0.559 0.595 0.555 0.637 0.584 0.590 0.501 0.511 0.516 0.723-
14. CPA30.491 0.467 0.541 0.568 0.550 0.632 0.608 0.575 0.476 0.452 0.499 0.7080.823-
Note: Polychoric correlations are reported; relative high correlations are marked as bold values.
Table
Table 3. The model fit indices for the measurement models and structural components.
Table 3. The model fit indices for the measurement models and structural components.
CFITLIRMSEASRMRχ2dfΔχ2Δdf
Measurement Models
Initial0.985 0.980 0.072 0.021 311.22867
CPA2 with CPA30.989 0.985 0.062 0.019 244.9846666.244 ***1
PCR1 with PCR20.990 0.986 0.060 0.019 227.2176517.767 ***1
CDMSE3 with CDMSE40.991 0.987 0.058 0.018 216.8376410.380 **1
Structural Component
Full structural model0.9910.9880.0410.018210.776656.061 *1
Note: * p < 0.05, ** p < 0.01, *** p < 0.001. CFI = comparative fit index; TLI = Tucker–Lewis index; RMSEA = root mean square error of approximation; SRMR = standardized root means square residual.
Table
Table 4. Mediation effects of career awareness on career decision-making self-efficacy (unstandardized).
Table 4. Mediation effects of career awareness on career decision-making self-efficacy (unstandardized).
EffectMediation Test
DirectIndirectTotalSobelAroianGoodman
CAW → JSSE → CDMSE0.293 ***0.067 *0.360 2.393 *2.379 *2.408 *
CAW → CPA → CDMSE0.293 ***0.170 ***0.463 4.493 ***4.477 ***4.510 ***
Note: * p < 0.05, *** p < 0.001.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Wu, J.; Zhao, T. Encouraging China’s College Students to Achieve Sustainable Careers: Evidence from Structural Equation Modeling. Sustainability 2022, 14, 9837. https://doi.org/10.3390/su14169837

AMA Style

Wu J, Zhao T. Encouraging China’s College Students to Achieve Sustainable Careers: Evidence from Structural Equation Modeling. Sustainability. 2022; 14(16):9837. https://doi.org/10.3390/su14169837

Chicago/Turabian Style

Wu, Jingchao, and Teng Zhao. 2022. "Encouraging China’s College Students to Achieve Sustainable Careers: Evidence from Structural Equation Modeling" Sustainability 14, no. 16: 9837. https://doi.org/10.3390/su14169837

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop