Predicting effects of career adaptability and educational identity on the career decision-making of Chinese higher vocational students

Abstract

The present study aimed to explore factors affecting Chinese higher vocational students’ career decision-making. A sample (N = 983) was surveyed with a questionnaire. The results showed that somewhat more than half of the students (57.4%) decided to apply for a bachelor’s degree whereas the rest decided to work (22.4%) or undecided (20.2%). Academic performance, grade, gender, study major, and career adaptability were shown to predict decision-making. By contrast, educational identity did not predict participants’ career decision-making. These findings imply that career education should be based on students’ choices for future development.

Introduction

Career decision-making is the most important decision or choice among the essential transitions that people make during their lifetime and has significant long-term implications for individuals’ lifestyles, emotional welfare, and economic and social status, as well as their sense of personal productivity and contribution to society (Gati et al., 2019). Although individuals at different stages of life place focus on career choice (e.g., Gati & Asher, 2001; Super, 1980), they still face difficulties (e.g., Amir et al., 2008; Osipow, 1999; Tinsley, 1992). Career decision-making describes the process of choosing work via educational pathways (Arthur & Nunes, 2014; Lent & Brown, 2013). In recent years, the process and content of career decision-making have been shown to be key issues related to career development. Whereas process concerns how people conduct career decision-making, the content aspect concerns are related to multiple elements or factors (e.g., socially, culturally, or psychologically) affect individuals in their career decision-making (Kulcsar et al., 2020; Lent & Brown, 2020). Savickas et al. (2009) proposed Life Design Approach (LDA) model for career intervention. It endorses five presuppositions about people and their work lives: contextual possibilities, dynamic processes, non-linear progression, multiple perspectives, and personal patterns, which are witnessed as some smart skills that influence career choice or expectations. The present study focused on the content aspect of career decision-making in order to answer practical questions as to why higher vocational students make different choices in a Chinese cultural context.

In China, vocational education system consists of two stages. The lower level is called secondary vocational education, recruiting students who just complete 9-year compulsive education, while the higher level is called higher vocational education, recruiting students who attend college entrance examination. According to Chinese governmental statistics, Chinese higher education is consisted of 1240 institutions offering degree and 1468 higher vocational colleges (MOE, 2020). That means, students have two options after they finish high school study according to their academic achievement of the nationwide College Entrance Examination. Students with high scores are enrolled into universities while students with low scores go to vocational colleges. Students in American community colleges can apply for further study at universities after achieving associate degree, and the same is true with Chinese higher vocational students who have the opportunity to pursue a bachelor degree.

Educational context is a key lens to predict the career decision. Over the last few decades, a large body of research has focused on educational trajectories (Hayward & Hoelscher, 2011; Holm et al., 2013; Lent & Brown, 2020). American scholars studied outcomes of vocational study and students’ access to and success in higher education (Boesel & McFarland, 1994; Dare, 2006). Although an increasing number of studies have been conducted on Chinese students’ decision-making about their higher education (Fang & Wang, 2014; Lee & Morrish, 2012; Liu et al., 2013), the majority of these studies were concerned with choices about overseas education. Research that focuses on Chinese domestic higher education is limited, with the exception of Lai et al.’s (2014) and To et al.’s (2014) studies. Hung et al. (2000) surveyed the educational intentions of secondary students in Shenzhen, the first and largest Special Economic Zone (SEZ) in China. The authors showed that 90% of secondary students opted to continue into higher education on completion of their senior secondary education. After controlling for the effects of the students’ gender, family background, and ability, the results showed further that students’ expected rate of applying for higher education had a positive effect on their intention to pursue higher education. In order to clarify the subject of “decision-making” in educational context, the current study defines it as the choices related to that students prefer when they graduate. Based on the extant literature, we hypothesize that:

H₁

Demographic variables (gender, major, grade, academic performance, social economic status) predict Chinese vocational college students’ career decision-making.

Some studies (e.g., Amir et al., 2008; Jing et al., 2021; Lai et al., 2014; Lee & Morrish, 2012; Liu et al., 2013) have shown that adaptability and identity also play a significant role on adolescents’ future decision-making. Buss (2020) examined how students in a professional practice doctoral program were developing professional identities as educational leaders, educational researchers, and working researchers. The results showed that possible selves “connections to current views of self” and “congruency with overall identity” were predictive of leadership and researcher identity roles, respectively. Compared with identity, career adaptability as a psychological resource promoting career tasks more actively influences or predicts career decision-making (e.g., Dostanić et al., 2021). Levin and Lipshits-Braziler (2021) investigated the conceptual similarity and empirical overlap between the Career Adapt-Abilities Scale (CAAS) and the Career Decision-Making Adaptability (CDA) indicator. The results showed that CAAS subscale “control” and the CDA subscale “procrastination” and “speed of making a final decision” were significant predictors of decisional difficulty and distress. In China, higher vocational education students have been shown to account for more than half of all students in higher education (MOE, 2020). The latest survey showed that increasingly, higher vocational graduates tend to apply for a bachelor’s degree (Mycos, 2021). On the one hand, COVID-19 decreases the demand for employees which impacts the career decision-making of college graduates. On the other hand, knowledgeable employees are urgently needed in emerging high-tech industries, resulting in the increasing popularity of graduates with higher degrees. Therefore, a key issue for students is whether to work or pursue bachelor’s degree. The aim of the present study was to examine the effect of career adaptability and educational identity on career decision-making among Chinese higher vocational students.

Career adaptability

Super and Knasel (1981) defined adaptability in a vocational context according to how individuals cope with and adjust to changes in the world of work. Other authors regarded career adaptability according to the individual ability of adults to adjust to transformations of their working environment (Fugate et al., 2004; Hall, 2004; Rottinghaus et al., 2005). Definitions of career adaptability were developed further on the basis of career construction theory which refers to the resources of and personal readiness shown by an individual when they deal with the predictable versus unpredictable tasks of career development, occupational transition, and personal trauma (Savickas, 2013). Multiple perspectives needed to be depicted, such as career decision-making tendencies (Gati & Levin, 2012), coping capacities (Rottinghaus et al., 2012), and self-regulation strategies (Creed et al., 2009). Accordingly, Savickas and Porfeli (2012) developed the Career Adapt-Abilities Scale (CAAS) in order to operationalize career adaptability in four dimensions: Concern, control, curiosity, and confidence. Concern refers to planning, awareness of educational and career choices, and preparation for an individual’s future; Control stands for activities related to decisions, responsibility, and self-reliance in order to meet future goals; Curiosity refers to behaviors such as exploring, investigating, observing, and being curious about possibilities; Confidence represents positive attempts to solve problems, improve skill and ability, and overcome obstacles.

We investigated research on these topics and found that in career decision or choice, elements influencing adaptability and academic performance were highlighted. For example, Dostanić et al. (2021) investigated associations between decision-making styles, career decision self-efficacy, and career adaptability among high school students. The authors’ findings showed that career decision self-efficacy mediated a positive relationship between rational and intuitive styles of decision-making as well as a negative relationship between the dependent style of decision-making and career adaptability. The rational style had a direct effect only on career adaptability. Ashari et al. (2019) examined associations between career interest, knowledge, adaptability, and maturity. Career interest, knowledge, and adaptability were found to be positively related to “intention to choose” scale, whereas career maturity was not significantly related to career choice in predicting career. Avram et al. (2019) tested the relationship between career adaptability and academic performance as well as the mediating role of career adaptability in the relationship between personality and academic performance. The results showed that career adaptability was positively related to academic performance. Career adaptability is also conceived as the key factor to influence vocational education and identity (Brown & Bimrose, 2018; Kirchknopf, 2020). Extensive research based on the CAAS dimensions has explored their associations with other adaptability measures (e.g., Xu, 2020) and testifies to the measure’s construct validity (Rudolph et al., 2017a, 2017b). All the research above implied that career adaptability covered occupational, vocational, educational, psychological and social elements for individuals to make a successful decision for future development. Therefore, the following hypothesis is presented:

H₂

Career adaptability predicts Chinese higher vocational students’ career decision-making.

Educational identity

Educational identity derives from these items as the individual pursues academic goals (Negru-Subtirica & Pop, 2018). To date, most studies of identity have been based on Marcia’s (1966) theoretical frameworks, revealing that identity formation concerns a combination of the processes of exploration and commitment, where there are four identity statuses (achievement, foreclosure, moratorium, and diffusion). Some researchers (e.g., Crocetti et al., 2008; Luyckx et al., 2006) explored further to build up multi-facet models for detailed and extended process studies. A dynamic formation-of-identity lifespan model proposed by Crocetti et al. (2008) and Crocetti (2017) contains three factors: (1) Commitment refers to the lasting choices made in different stages of life and the self-confidence that individuals build up accordingly, (2) In-depth exploration represents the extent of further exploration of the commitments they have enacted. (e.g., focusing on a specific domain, searching for additional information, finding new problems, and talking with others about their commitments), (3) Reconsideration of commitment involves comparing present commitments with possible alternative commitments because the current ones make them doubtful and wishing to change. These three factors are combined to form the measurement tool (i.e., Utrecht-Management of Identity Commitments Scale) that classifies the different stages of identity status (Crocetti et al., 2011; Meeus et al., 2010). According to Negru-Subtirica et al. (2018), each identity status is characterized by different levels of commitment, in-depth exploration, and reconsideration of commitment, as following: (1) achievement status indicates high levels of commitment and in-depth exploration, and low levels of reconsideration of commitment, (2) Early closure status indicates high levels of commitment and low levels of in-depth exploration and reconsideration of commitment, (3) Moratorium status indicates low levels of commitment, moderate levels of in-depth exploration, and high levels of reconsideration of commitment, (4) Searching moratorium status indicates high levels of all identity processes, capturing the adaptive facet of moratorium, (5) and Diffusion status indicates low scores on all identity processes.

A large number of studies have been carried out, based on the theoretical framework of educational identity outlined above, on identity status, identity process, and elements influencing identity in educational context. Campbell et al. (2019) revealed that diffusion and moratorium statuses were associated with negative emotional formations such as depression and anxiety whereas the achievement and foreclosure statuses were associated with the more positive emotions. McKay et al.’s (2021) study showed that the identity statuses of achievement and early closure status were related to the most adaptive appraisals and achievement emotions whereas the opposite was true of the moratorium and searching moratorium statuses. Further research on identity process revealed that personal and social adjustment were positively correlated with commitment whereas anxiety, depression, and behavioral disorders were negatively correlated with commitment (Crocetti et al., 2008, 2011; Luyckx et al., 2006). Academic performance is another key variable as regards educational identity. Pop et al. (2016) argued that the educational identity processes of commitment and reconsideration of commitment were predicted by academic performance. Moreover, authors have found that education identity was related to vocational identity and psychological well-being (Negru-Subtirica & Pop, 2018). Thus, the third hypothesis is formulated:

H₃

Educational identity predicts Chinese higher vocational students’ career decision-making.

In China, there is limited research on educational identity. Gao et al. (2014) examined the predictive effects of self-esteem and well-being on educational identity. Their results showed that commitment and in-depth exploration positively predicted self-esteem and well-being whereas reconsideration negatively predicted well-being. The research on career adaptability is more prevalent. Chen (2021) explored the influence of family cohesion on career adaptability for vocational students as well as the mechanism of proactive personality (e.g., being adaptive to surroundings) and psychological capital (e.g., self-efficacy, resilience, optimism). Their findings showed that the variables psychological capital, family cohesion, and proactive personality were positively correlated with career adaptability. Further, proactive personality and psychological capital played a significant chain mediating role between family cohesion and career adaptability.

Methods

Participants

The participants were 983 vocational students from a vocational College in the Shandong province of China. This vocational college is among the upper levels of the total 1483 Chinese vocational colleges with respect to its size, academic quality, and career education. After 3 years of education, higher vocational students earn associate degrees and either choose paid work or continue their education by pursuing a higher degree in another university. The students follow a 2 + 1 model for learning, that is, they take two years to complete regular school course learning in order to acquire basic skills then they undertake an off-campus internship over one year. In our study, due to the availability of participants according to their schedule, students in the first and second year were assigned the paper version of the questionnaire, in contrast to an online version of the questionnaire for the third-year participants. All of the participants were enrolled voluntarily and informed about the purpose of study, the data confidentiality, and their right to withdraw from the study at any time. The missing data were checked and the outliers were deleted. As Table 1 shows (males 43.1%, females 56.9%; first-year 34.4%, second-year 27.8%, third-year 37.8%), participants are from the following majors: Mechatronics Engineering (15.8%), Business English (17.1%), Electronic Information (19.1%), Project Cost Management (22%), and Early Childhood Education (26%). Their family incomes varied: 47.6% for below 400,000 yuan, 15% for 40,000 to 60,000 yuan, 20% for 60,000 to 100,000 yuan, 8.5% for 100,000 to 140,000 yuan, and 8.1% for over 140,000 yuan.

Table 1.

Frequency description of participants

	Frequency	Percent
Gender
M	424	43.1
F	559	56.9
Grade
1st	338	34.4
2nd	273	27.8
3rd	372	37.8
Family income (RMB)
Below 40,000	468	47.6
40,000–60,000	154	15.7
60,000–100,000	197	20.0
100,000–140,000	84	8.5
Above 140,000	80	8.1
Major
Mechatronics Engineering	155	15.8
Business English	168	17.1
Electronic information	188	19.1
Project cost management	216	22.0
Early childhood education	256	26.0
Academic performance
Poor	27	2.7
Below average	67	6.8
Average	534	54.3
Above average	230	23.4
Excellent	125	12.7
Decision
Work	220	22.4
Get a degree	564	57.4
Not decide	199	20.2
Total	983	100

Measures

Utrecht-management of identity commitments scale

The Utrecht-Management of Identity Commitments Scale (U-MICS; Crocetti et al., 2008) was adopted for the present study. The U-MICS consists of 13 items with the following response options: 1—completely untrue, 2—untrue, 3—sometimes true/sometimes not, 4—true, and 5—completely true. Items assess three factors: commitment (5 items; e.g., ‘‘My education gives me security in life’’), in-depth exploration (5 items; e.g., ‘‘I try to find out a lot about my education’’), and reconsideration of commitment (3 items; e.g., ‘‘I often think it would be better to try to find a different education’’). In order to avoid misunderstanding of items caused by different language and cultural contexts, we revised some items to ensure that the items were relevant to our study and that participants understood the items. For instance, the item of ‘‘My education gives me security in life’’ was revised to “My higher vocational education gives me security in life”. Negru-Subtirica and Pop (2018) reported internal consistency reliabilities of 0.70, 0.67, and 0.94 in three dimensions of commitment, in-depth exploration and reconsideration of commitment. In the present study, three items are deleted because of the low loadings (below 0.4). The rest of 10 items are extracted in 3 dimensions and the Cronbach’s Alpha reliability is shown in Table 2.

Table 2.

Cronbach’s α reliability of educational identity and career adaptability

Scale	Subscale	Item number	α	M	SD
Educational identity	Commitment	4	0.899	3.43	0.868
	Exploration	4	0.861	3.53	0.772
	Reconsideration	2	0.730	3.12	0.961
Career adaptability	Concern	4	0.831	3.86	0.712
	Control	5	0.916	3.95	0.740
	Confidence	6	0.939	3.93	0.696

Career adapt-abilities scale

The Career Adapt-Abilities Scale (CAAS; Savickas & Porfeli, 2012) was used to measure career adaptability for Chinese higher vocational students. This 24-item scale consists of four dimensions: concern, control, curiosity, and confidence, each of which has 6 items. Participants responded to each item based on a 5-point Likert scale ranging from 1 (completely untrue) to 5 (completely true). Savickas and Porfeli (2012) reported internal consistency reliabilities of 0.85, 0.89, 0.85, and 0.91 for concern, control, curiosity, and confidence, respectively. Rudolph et al.’s (2017b) study meta-analytically supported the convergent and criterion validity of the CAAS. Higher total scores indicate higher career adaptability. After factor extraction was undertaken in the current study, 15 items remained to form 3 dimensions, without curiosity dimension; the results were acceptable, as is shown in Table 2.

Variables and modeling

The career intentions after post-secondary vocational education was regarded as a dependent variable. As Hung et al. (2000) showed that in China, to work or continue to apply for higher education are the main trajectories for high school graduates. Participants responded as follows to the question: what is your future plan after graduation: (1) to work; (2) to apply for a bachelor’s degree; (3) have not decided yet. Independent variables included both subjective and objective dimensions. The subjective variables covered both educational identity and career adaptability with 6 dimensions: commitment, exploration, reconsideration, concern, control, and confidence. Objective variables contained 5 demographic factors: gender, grade, major, family income, and perceived academic performance. Since the dependent variable was categorical, a multiple logistic regression model was adopted for the present study. The basic form of equations sees Appendix.

Data analysis

SPSS 23.0 was used for data analysis. Descriptive statistics were calculated before hypotheses testing. After factor analysis, factors were extracted as explanatory variables. They are commitment, exploration, reconsideration, concern, control, and confidence. Adopting stepwise backward regression method, a multi-classification logistic model was testified to a good degree of fitness. All explanatory variables were first input into the model for significance testing. The least significant variable was then eliminated, and next the regression equation was re-fitted until only the remaining variables were retained in the equation.

Results

Over half of the participants (57.4%) chose the option of applying for a bachelor’s degree (see Table 3) after graduation, and the proportions of participants who chose to work (22.4%) or who were undecided (20.2%) was close. Among the respondents who chose to work were comparatively high proportions of males (23.1%) and third-year students (34.9%), as well as students who had a poor academic performance (37%), a low family income (23.9%), and majored in early childhood education (27%). Among the respondents who chose to apply for a bachelor’s degree were comparatively high proportions of females (63.3%), as well as students who had an above average academic performance (66.5%), had an average family income (65%), were first-year (63.6%), or majored in business English (69.6%). Undecided respondents contained comparatively high proportions of males (27.4%) and second-year students (26.7%), as well as students who had a high family income (28.8%), majored in electronics and information (31.4%) and had a below-average academic performance (32.8%).

Table 3.

Distributions of career decision-making results

	To work	Apply B. D	Undecided
Gender
M	98 (23.1%)	210 (49.5)	116 (27.4%)
F	122 (21.8%)	354 (63.3%)	83 (14.8%)
Grade
1st	41 (12.1%)	215 (63.6%)	82 (24.3%)
2nd	49 (17.9%)	151 (55.3%)	73 (26.7%)
3rd	130 (34.9%)	198 (53.2%)	44 (11.8%)
Family income (RMB)
Below 40 thousand	112 (23.9%)	259 (55.3%)	97 (20.7%)
40–60 thousand	32 (20.8%)	88 (57.1%)	34 (22.1%)
60–100 thousand	41 (20.8%)	128 (65%)	28 (14.2%)
100–140 thousand	15 (17.9%)	52 (61.9%)	17 (20.2%)
Above 140 thousand	20 (25%)	37 (46.3%)	23 (28.8%)
Major
Mechatronics Engineering	35 (22.6%)	76 (49%)	44 (28.4%)
Business English	32 (19%)	117 (69.6%)	19 (11.3%)
Electronic information	47 (25%)	82 (43.6%)	59 (31.4%)
Project cost management	37 (17.1%)	145 (67.1%)	34 (15.7%)
Early childhood education	69 (27%)	144 (56.3%)	43 (16.8%)
Academic performance
Poor	10 (37%)	11 (40.7)	6 (22.2%)
Below average	19 (28.4%)	26 (38.8%)	22 (32.8%)
Average	125 (23.4%)	310 (58.1%)	99 (18.5%)
Above average	42 (18.3%)	153 (66.5%)	35 (15.2%)
Excellent	24 (19.2%)	64 (51.2%)	37 (29.6%)
Total	220 (22.4%)	564 (57.4%)	199 (20.2%)

The results showed that explanatory variables such as gender (X₁), grade (X₂), major (X₃), academic performance (X₅), concern (X₉), and confidence (X₁₁) were included in the model and had a significant impact on the explained variables. However, explanatory variables such as family income (X)₄, commitment (X₆), exploration (X₇), reconsideration (X₈), and control (X₁₀) were eliminated, and their effect on the explained variables was not significant. Therefore, the analysis that followed was based on the regression results of the significant model (P = 0.000). The model explained 19.2% (Nagelkerke R²) of the variance and correctly classified 64.8% of cases.

Regression on choosing to work

As shown in Table 3, 22.4% of total participants chose to work. The results from the regression analysis showed that (see Table 4), compared with participants who were undecided as to what to do after graduation, participants who chose to work were in the high level (OR 1.58) of confidence. Compared with third-year participants, first-year (OR 0.183) and second-year (OR 0.233) participants were less likely to choose to work. Compared with academically excellent participants, academically poor (OR 4.522) and average (OR 2.524) participants were more likely to choose to work.

Table 4.

Regression of choosing to work after graduation

Predictors	B	S. E	Wald	OR	95% CI
Confidence	.411	.195	4.434*	1.508	1.029	2.212
Concern	− .081	.182	.196	.922	.645	1.319
[Gender = 1] Male	− .199	.303	.429	.820	.452	1.486
[Grade = 1] 1st year	− 1.699	.288	34.757***	.183	.104	.322
[Grade = 2] 2nd year	− 1.457	.281	26.946***	.233	.134	.404
[Major = 1] Mechatronics Engineering	.248	.430	.333	1.282	.551	2.980
[Major = 2] Business English	.467	.376	1.543	1.595	.763	3.334
[Major = 3] Electronic Information	− .050	.378	.018	.951	.453	1.997
[Major = 4] Project Cost Management	− .017	.373	.002	.984	.474	2.041
[Academic = 1] Poor	1.509	.618	5.971*	4.522	1.348	15.172
[Academic = 2] Below average	.427	.438	.951	1.532	.650	3.612
[Academic = 3] Average	.926	.317	8.513*	2.524	1.355	4.703
[Academic = 4] Above average	.538	.366	2.155	1.712	.835	3.510

OR means the odds ratio compared with ‘‘not decide’’. Gender reference group = Female; Grade reference group = 3rd year students; Major reference group = Early childhood education; Academic performance reference group = Excellent

CI confidence interval

*p = .05; ***p = .001

Regression on choosing to apply for a bachelor’s degree

Also as shown in Table 3, nearly 57.4% of all participants chose to apply for a bachelor degree. The results from a second regression analysis provided further evidence (as shown in Table 5). Compared with participants who were undecided as to what to do after graduation, those who chose to apply a for a bachelor’s degree were in the high level (OR 1.458) of concern. Compared with female participants, male participants were less likely (OR 0.520) to choose to apply for a bachelor’s degree. Compared with third-year participants, second year (OR 0.543) participants were less likely to choose to apply for a bachelor’s degree. Compared with participants who majored in early childhood education, those who majored in business English and project cost management were more likely to continue to apply for a bachelor’s degree after completing post-secondary vocational education. Compared with academically excellent participants, average (OR 1.864) and above average (OR 2.221) academic participants were more likely to choose to continue to apply for a bachelor’s degree after completing post-secondary vocational education.

Table 5.

Regression of choosing to apply a bachelor degree after graduation

Predictors	B	S. E	Wald	OR	95% CI
Confidence	.055	.162	.115	1.057	.769	1.452
Concern	.377	.153	6.073*	1.458	1.080	1.968
[Gender = 1] Male	− .655	.250	6.837***	.520	.318	.849
[Grade = 1] 1st year	− .249	.241	1.064	.780	.486	1.251
[Grade = 2] 2nd year	− .611	.247	6.107*	.543	.335	.881
[Major = 1] Mechatronics Engineering	.286	.359	.635	1.332	.659	2.692
[Major = 2] Business English	.895	.320	7.806*	2.447	1.306	4.585
[Major = 3] Electronic Information	− .164	.320	.263	.849	.454	1.588
[Major = 4] Project Cost Management	.748	.309	5.873*	2.114	1.154	3.872
[Academic = 1] Poor	.244	.572	.183	1.277	.416	3.915
[Academic = 2] Below average	− .481	.374	1.661	.618	.297	1.285
[Academic = 3] Average	.623	.251	6.131*	1.864	1.139	3.051
[Academic = 4] Above average	.798	.292	7.465*	2.221	1.253	3.936

OR means the odds ratio compared with ‘‘not decide’’. Gender reference group = Female; Grade reference group = 3rd year students; Major reference group = Early childhood education; Academic performance reference group = Excellent

CI confidence interval

*p = .05; ***p = .001

Discussion

The present study investigated how career adaptability and educational identity influence vocational students’ career decision-making. The results showed that more than half of the higher vocational students tested decided to go to university to continue their study, in accordance with previous findings (e.g., Liu & Morgan, 2016). The result indicated that second year students less intend to continue their education, may be explained as follows: in their first year, students have less urgent needs to decide, their decisions may institution depended; in their second year, if accepted career guidance or intervention, they find more options available that may have affected their future positively; and only when they enter the internship stage in their third year, they realize that a higher level of education can lead to better career outcomes. The decision-making of education trajectory reflects that dependent decision-making styles (e.g., institutive, rational) develops if career guidance intervention and internship happen.

Pursuing higher education has become a trend for Chinese students in recent years, a trend which is influenced in current society by Confucian culture (Bao & Haas, 2009; Fong, 2004; Liu & Lu, 2011) as well as intensive competition for work. Firstly, the quota for Chinese vocational students for entry into undergraduate institutions has increased significantly in recent years. Mycos’ (2021) report showed at the end of 2021 that 20% of vocational students have the opportunity to enter universities to study for a bachelor’s degree, compared with that rate of only 15% in 2017. Secondly, in China, graduates who undertake a higher level of education can more easily find work, as Confucian classic work Analects stated that “a man in service who is superior should study; a man who is superior in study should serve” (Eno, 2015; p. 106). Yue and Qiu (2022) found that “double-first initiative” project (i.e., the university list published by Chinese authorities on late September 2018 for developing a number of world-class universities and disciplines by the end of 2050) university graduate students had twice as much opportunity of gaining employment compared with higher vocational students. Thirdly, the Covid-19 has produced negative effects on the employment of college graduates: the hindered job interviews, the decreased rate of employment, the increased employment pressure, and the pessimistic future economic expectations. In that case, continuing to study is one leverage to prepare for a better job in the future.

Shen’s (2016) findings that Chinese vocational students tend to come from mostly low social status families are echoed in the present study which showed that 47.6% of the participants’ family annual incomes were below 40 thousand RMB. The current study indicates that factors such as gender, grade, major, and academic performance predict decision-making of apply for a bachelor’s degree (H₁). Our study indicates female students are more likely to choose education whereas males choose to work which is contradictory to the result of Bolat and Odacı (2017) in Turkish context. Yue and Qiu (2022) disclosed that the rate of Chinese male college graduates to receive interview notices is 42% higher than females, and female with better performance and higher education level were subject to serious discrimination. Another intrigued result in our study shows that third-year male students with low performance and low family income intend to work directly after post-secondary education. This reflects the current demand of a large number of male workers in China’s labor market. That indicates that they have a lot of job opportunities, which do not require much academic skills. For these male students, it is an easy way to make their life.

As to subjective factors, our results suggest that confidence predicts decisions to work whereas concern predicts decisions to apply for a bachelor’s degree (H₂). Levin and Lipshits-Braziler (2021) indicated that “confidence” correlated with “information gathering” was negligible to career decisional difficulty. Whereas “concern” was regarded as a predicator of career indecision. These results raise questions about the relationship between education and career. The differences found in our study in participants’ decisions about their future suggest that education identity has not yet become an endogenous factor affecting career-decision. Most of the decisions observed in our study were due to exogenous factors such as economic pressures and employment barriers. The choices of third-year participants to work may be may due to their not having either advantages in competition for employment or the opportunity to further their studies; and they have no other options to enter the labor market. Career education instead of knowledge education should be a predicator of decision-making. As Zhu et al. (2021) stated, decision difficulties come from a lack of readiness and career intervention. Gu et al. (2020) indicated that career intervention course had a positive impact on reducing students’ difficulties of making career decisions. Proactive, systematic, multilevel, and structured interventions over longer periods of time would likely help youth develop their career decision-making skills. Based on literature above, we may infer that although Chinese higher vocational students choose to further learning or work directly, they still face difficulty or indecision in the process of decision-making.

Gender is an essential factor affecting career versus work decisions, for example, employment disadvantages that females face lead to an extension of their time in education. When graduates continue their studies because of concerns about career barriers, education is less likely to bring them well-being, and the undertaking of diplomas reflects exigency rather than bringing about growth and self-realization. When graduates choose to work, confidence is a key predicting factor, indicating that self-affirmation is the basis for career development. Confidence should be derived from the achievement of academic goals as well as from the increased number of possibilities brought about by diversified education in its practical forms. This reflect a utilitarian rather than idealistic approach to higher learning. Unfortunately, this appears to be the trend around the globe as young people make decisions based on practical factors as opposed to self-actualization when making decisions after post-secondary education.

The results showed that educational identity did not predict career decision (H₃). Conversely, career adaptability was shown to prospectively affect students’ decisions (H₂). Therefore, it can be concluded that educational identity associates with personality traits (Klimstra et al., 2012) whereas career adaptability is reflected in the prospective effect. Students who were in the median range of academic performance were more willing to choose to continue their studies than the students who were academically excellent because students in Confucian culture heritage consider academic achievement as the job seeking leverage instead of solving learning problems (Choi & Kim, 2013). Yue and Qiu (2022) disclosed that the employment rate for graduates of teachers’ majors, science, and engineering is higher than that of graduates of the humanities and applied majors. Thus, in order to get a better job, students in the “lesser” majors may need to apply to study in a bachelor’s degree for the sake of their future development, confirming that concern predicts education decisions.

Implications and limitations

The findings from the present study are of great significance for policy makers and practical application in Chinese higher vocational education. Firstly, policy makers are suggested to take consideration of up-down policy for vocational college students who intend to pursue further study for bachelor degree. Like National Qualification Frameworks in the United Kingdom, Chinese National Credit Bank of Vocational Education (CNCBVE) is a platform to record and credit the process of lifelong learning. However, it has not yet provided interfaces for vocational education to baccalaureate degree education. The platform should be fully utilized to issue flexible, suitable and plentiful courses to satisfy diversified demands from vocational students.

Second, administrators in vocational colleges can classify curricula into practical courses for students who tend to work after graduation, and applied and theoretical courses for those with academic pursuit to improve the practicality of the courses and break through barriers to college students career development. As most of Chinese higher vocational colleges are school-based, and thus students are lack of real working experience which deters them to make a rational decision, they are badly in need of urgent guidance for lifelong career development. Most career education is not regular curricula (Farenga & Quinlan, 2016), and therefore relevant vocational courses should be offered with high quality and sufficient quantity.

Third, career counselors in higher vocational colleges need to be trained to obtain qualification for career guidance for students. Most of the counselors are part-time teachers who focus on making students employed with ideal salary regardless of guidance for college students’ lifelong development. Besides, teacher-centered approach is adopted in career courses, and results into the lack of interest of vocational students and the decrease of their practicality. Actually, career counselors should be enrolled with the duty to instruct college students to design future career through improving their interest in career courses and cultivating their career goals. Chinese career counselors may learn from some western theories. For example, a new positive framework for career management which offers two axes: Purposeful Identitarian Awareness (Di Fabio, 2014) from reflexivity to self-attunement and the Positive Self & Relational Management (PS & RM) model (Di Fabio, 2016). Chinese higher vocational institutions can build up a platform to provide online consulting service to clients who are more convenient to accept guidance and intervention. Thus, with the efficient assistance, higher vocational colleges students are able to make suitable and confident decisions by constantly self-reflection and self-appraisal.

Because the present study only focuses on three ways of students’ decisions with quantitative analysis, it did not lead respondents to demonstrate the complexity of their decision-making. For instance, they might either have too many ideas or have no ideas for selecting undecided choice. Future research should explore qualitatively in order to have in-depth understanding of these complexities. The samples are selected in one institution and future research may be conducted by collecting data from different vocational colleges in different regions.

Conclusion

The current study showed that more than half of the Chinese vocational students (57.4%) decided to apply for a bachelor’s degree whereas the rest decided to work (22.4%) or were undecided (20.2%). Academic performance, grade, gender, study major, and career adaptability were shown to predict decision-making. Among career adaptability sub-dimensions, concern led Chinese vocational students to apply for a bachelor’s degree whereas confidence led to work. By contrast, educational identity and family income did not predict these students’ career decision-making. These findings imply that career education should be based on vocational students’ choices and that more attention needs to be paid to the psychological and social predictors of individual development.

Appendix

$$\text{In}\left(\frac{\text{p}\left(\text{y}\le \left(\text{j}\right|\text{x}\right)}{1-\text{P}\left(\text{y}\le \left(\text{j}\right|\text{x}\right)}\right)={\mathrm{\mu }}_{\text{j}}-\left(\mathrm{\alpha }+\sum _{\text{i}=1}^{\text{k}}{\mathrm{\beta }}_{\text{i}}{\text{x}}_{\text{i}}\right)$$ 1

$$\text{P}\left(\text{y}\le \left(\text{j}\right|\text{x}\right)=\frac{{\text{e}}^{{\mathrm{\mu }}_{\text{j}}-\left(\mathrm{\alpha }+\sum _{\text{i}=1}^{\text{k}}{\mathrm{\beta }}_{\text{i}}{\text{x}}_{\text{i}}\right)}}{1+{\text{e}}^{{\mathrm{\mu }}_{\text{j}}-\left(\mathrm{\alpha }+\sum _{\text{i}=1}^k{\mathrm{\beta }}_{\text{i}}{\text{x}}_{\text{i}}\right)}}$$ 2

Note: y is the explanatory variable, representing the decisions of career and education, xi represents the explanatory variable, i is the sequence of factor that affects the decisions, α is the intercept term, and β is the partial regression coefficient of the logistic regression model, indicating the explanatory variation. The direction and extent of the influence of quantity xi on decisions, and μ1, μ2, … μj are the demarcation points.

Funding

This study supported by Shandong Association of Higer Education. The project's name is ‘Research of Principal Leadership Model based on Professional Standard for Chinese Higher Vocational Colleges.’

Declarations

Conflict of interest

The authors declare no competing interests.